diff --git "a/3800.jsonl" "b/3800.jsonl"
new file mode 100644--- /dev/null
+++ "b/3800.jsonl"
@@ -0,0 +1,590 @@
+{"seq_id":"631771400","text":"from pymongo import MongoClient\n\nfrom flask import Flask, render_template, jsonify, request\n\napp = Flask(__name__)\n\nclient = MongoClient('localhost', 27017)\ndb = client.seoul_matjip\n\n\n# HTML 화면 보여주기\n@app.route('/')\ndef home():\n    return render_template('index.html')\n\n\n# API 역할을 하는 부분\n@app.route('/api/restaurant_list', methods=['GET'])\ndef show_matjips():\n    # 1. db에서 mystar 목록 전체를 검색합니다. ID는 제외하고 like 가 많은 순으로 정렬합니다.\n    matjips = list(db.restaurants.find({}, {'_id':False}).sort('like',-1))\n    print(matjips)\n    # 참고) find({},{'_id':False}), sort()를 활용하면 굿!\n\n    # 2. 성공하면 success 메시지와 함께 stars_list 목록을 클라이언트에 전달합니다.\n    return jsonify({'result': 'success', 'msg': 'list 연결되었습니다!', 'list': matjips})\n\n\n@app.route('/api/restaurant_like', methods=['POST'])\ndef like_matjips():\n    # 1. 클라이언트가 전달한 name_give를 name_receive 변수에 넣습니다.\n    name = request.form[\"name\"]\n\n    # 2. mystar 목록에서 find_one으로 name이 name_receive와 일치하는 star를 찾습니다.\n    current_like = db.restaurants.find_one({'name':name})[\"like\"]\n    # 3. star의 like 에 1을 더해준 new_like 변수를 만듭니다.\n    new_like = current_like + 1\n    # 4. mystar 목록에서 name이 name_receive인 문서의 like 를 new_like로 변경합니다.\n    # 참고: '$set' 활용하기!\n\n    db.restaurants.update_one({'name': name}, {'$set':{'like': new_like}})\n    # 5. 성공하면 success 메시지를 반환합니다.\n    return jsonify({'result': 'success', 'msg': name+'좋아'})\n\n\n@app.route('/api/delete', methods=['POST'])\ndef delete_star():\n    # 1. 클라이언트가 전달한 name_give를 name_receive 변수에 넣습니다.\n    name = request.form[\"name\"]\n    # 2. mystar 목록에서 delete_one으로 name이 name_receive와 일치하는 star를 제거합니다.\n    db.restaurants.delete_one({'name':name})\n    # 3. 성공하면 success 메시지를 반환합니다.\n    return jsonify({'result': 'success', 'msg': name+'삭제~'})\n\n\nif __name__ == '__main__':\n    app.run('0.0.0.0', port=5000, debug=True)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"537385002","text":"import tensorflow as tf\nimport numpy as np\nimport hyperchamber as hc\nimport inspect\n\nfrom hypergan.trainers.base_trainer import BaseTrainer\n\nTINY = 1e-12\n\nclass DepthTrainer(BaseTrainer):\n    \"\"\" Runs an optimizer multiple times and combines the output into a mixture. \"\"\"\n    def create(self):\n        self.hist = [0 for i in range(2)]\n        config = self.config\n        self.global_step = tf.train.get_global_step()\n        self.mix_threshold_reached = False\n        decay_function = config.decay_function\n        variables = self.gan.d_vars() + self.gan.g_vars()\n        self.ema = [ tf.Variable(_v) for _v in variables ]\n        self.store_v = [ _v.assign(_v2) for _v,_v2 in zip(self.ema, variables) ]\n        self.combine = [ _v.assign(config.decay *_ema + (1.-config.decay)*_new) for _v, _ema, _new in zip(variables, self.ema, variables)]\n        self._delegate = self.gan.create_component(config.trainer, d_vars=self.d_vars, g_vars=self.g_vars, loss=self.loss)\n        self._delegate.create()\n        self.slot_vars_g = self._delegate.slot_vars_g\n        self.slot_vars_d = self._delegate.slot_vars_g\n\n    def required(self):\n        return \"\".split()\n\n    def _step(self, feed_dict):\n        gan = self.gan\n        sess = gan.session\n        config = self.config\n        loss = self.loss \n\n        gan.session.run(self.store_v)\n        for i in range(config.depth or 2):\n            self._delegate.step(feed_dict)\n        gan.session.run(self.combine)\n\n","sub_path":"hypergan/trainers/depth_trainer.py","file_name":"depth_trainer.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"451221872","text":"#!/usr/bin/env python\nfrom __future__ import division, print_function, absolute_import\n\nimport os\n# import sys\n# import subprocess\nimport glob\n\n\ndef configuration(parent_package='', top_path=None):\n    from numpy.distutils.misc_util import Configuration\n\n    config = Configuration()\n    config.add_data_dir('tests')\n\n    # check for a config file in ..\n    cfg_headers = []\n    try:\n        with open('crappy.cfg', 'r') as fcfg:\n            newsource = fcfg.readline().strip()\n            #newsource = os.path.join('..', newsource)\n        if os.path.isfile(newsource):\n            cfg_headers += [newsource]\n    except IOError as e:\n        print(\"I/O error({%d}): %s\" % (e.errno, e.strerror))\n\n    base_headers = [h for h in glob.glob('base/*.h')]\n    template_headers = [h for h in glob.glob('templates/example.h')]\n    template_headers += cfg_headers\n    depends = base_headers + template_headers\n\n    sources = []\n    sources += [s.replace('.h', '.cxx') for s in template_headers]\n    sources += [os.path.join('base', 'crappy.cxx')]\n    sources += [os.path.join('templates', 'initmodule.cxx')]\n\n    import generate_functions\n    generate_functions.main(template_headers, '')\n\n    config.add_extension('crappy',\n                         define_macros=[('__STDC_FORMAT_MACROS', 1)],\n                         depends=depends,\n                         include_dirs=['base', 'templates'],\n                         sources=sources)\n    return config\n\nif __name__ == '__main__':\n    from numpy.distutils.core import setup\n    setup(**configuration(top_path='').todict())\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"230885953","text":"# Plotting and reporting Bayes Factor given idata, var name, prior distribution and reference value\nimport logging\n\nfrom scipy.stats import gaussian_kde\n\nfrom ..data.utils import extract\nfrom .plot_utils import get_plotting_function\n\n_log = logging.getLogger(__name__)\n\n\ndef plot_bf(\n    idata,\n    var_name,\n    prior=None,\n    ref_val=0,\n    xlim=None,\n    colors=(\"C0\", \"C1\"),\n    figsize=None,\n    textsize=None,\n    hist_kwargs=None,\n    plot_kwargs=None,\n    ax=None,\n    backend=None,\n    backend_kwargs=None,\n    show=None,\n):\n    \"\"\"\n    Bayes Factor approximated as the Savage-Dickey density ratio.\n\n    The Bayes factor is estimated by comparing a model (H1) against a model in which the\n    parameter of interest has been restricted to be a point-null (H0). This computation\n    assumes the models are nested and thus H0 is a special case of H1.\n\n\n\n    Parameters\n    -----------\n    idata : obj\n        a :class:`arviz.InferenceData` object\n    var_name : str\n        Name of variable we want to test.\n    prior : numpy.array, optional\n        In case we want to use different prior, for example for sensitivity analysis.\n    ref_val : int\n        Point-null for Bayes factor estimation. Defaults to 0.\n    xlim :  tuple, optional\n        Set the x limits, which might be used for visualization purposes.\n    colors : tuple\n        Tuple of valid Matplotlib colors. First element for the prior, second for the posterior.\n        Defaults to ('C0', 'C1').\n    figsize : tuple\n        Figure size. If None it will be defined automatically.\n    textsize: float\n        Text size scaling factor for labels, titles and lines. If None it will be autoscaled based\n        on figsize.\n    plot_kwargs : dicts, optional\n        Additional keywords passed to :func:`matplotlib.pyplot.plot`\n    hist_kwargs : dicts, optional\n        Additional keywords passed to :func:`arviz.plot_dist`. Only works for discrete variables\n    ax : axes, optional\n        :class:`matplotlib.axes.Axes` or :class:`bokeh.plotting.Figure`.\n    backend : str, optional\n        Select plotting backend {\"matplotlib\",\"bokeh\"}. Default \"matplotlib\".\n    backend_kwargs : bool, optional\n        These are kwargs specific to the backend being used, passed to\n        :func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`.\n        For additional documentation check the plotting method of the backend.\n    show : bool, optional\n        Call backend show function.\n\n    Returns\n    -------\n    dict : A dictionary with BF10 (Bayes Factor 10 (H1/H0 ratio), and BF01 (H0/H1 ratio).\n    axes : matplotlib axes or bokeh figures\n\n\n    Examples\n    --------\n    Moderate evidence indicating that the parameter \"a\" is different from zero\n\n    .. plot::\n        :context: close-figs\n\n        >>> import numpy as np\n        >>> import arviz as az\n        >>> idata = az.from_dict(posterior={\"a\":np.random.normal(1, 0.5, 5000)},\n        ...     prior={\"a\":np.random.normal(0, 1, 5000)})\n        >>> az.plot_bf(idata, var_name=\"a\", ref_val=0)\n    \"\"\"\n    posterior = extract(idata, var_names=var_name)\n\n    if ref_val > posterior.max() or ref_val < posterior.min():\n        _log.warning(\n            \"The reference value is outside of the posterior. \"\n            \"This translate into infinite support for H1, which is most likely an overstatement.\"\n        )\n\n    if posterior.ndim > 1:\n        _log.warning(\"Posterior distribution has {posterior.ndim} dimensions\")\n\n    if prior is None:\n        prior = extract(idata, var_names=var_name, group=\"prior\")\n\n    if xlim is None:\n        xlim = (prior.min(), prior.max())\n\n    if posterior.dtype.kind == \"f\":\n        posterior_pdf = gaussian_kde(posterior)\n        prior_pdf = gaussian_kde(prior)\n\n        posterior_at_ref_val = posterior_pdf(ref_val)\n        prior_at_ref_val = prior_pdf(ref_val)\n\n    elif posterior.dtype.kind == \"i\":\n        prior_pdf = None\n        posterior_pdf = None\n        posterior_at_ref_val = (posterior == ref_val).mean()\n        prior_at_ref_val = (prior == ref_val).mean()\n\n    bf_10 = prior_at_ref_val / posterior_at_ref_val\n    bf_01 = 1 / bf_10\n\n    bfplot_kwargs = dict(\n        ax=ax,\n        bf_10=bf_10.item(),\n        bf_01=bf_01.item(),\n        xlim=xlim,\n        prior=prior,\n        posterior=posterior,\n        prior_pdf=prior_pdf,\n        posterior_pdf=posterior_pdf,\n        ref_val=ref_val,\n        prior_at_ref_val=prior_at_ref_val,\n        posterior_at_ref_val=posterior_at_ref_val,\n        var_name=var_name,\n        colors=colors,\n        figsize=figsize,\n        textsize=textsize,\n        hist_kwargs=hist_kwargs,\n        plot_kwargs=plot_kwargs,\n        backend_kwargs=backend_kwargs,\n        show=show,\n    )\n\n    plot = get_plotting_function(\"plot_bf\", \"bfplot\", backend)\n    axes = plot(**bfplot_kwargs)\n    return {\"BF10\": bf_10, \"BF01\": bf_01}, axes\n","sub_path":"arviz/plots/bfplot.py","file_name":"bfplot.py","file_ext":"py","file_size_in_byte":4833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"116550240","text":"import climate\nimport sys\n\nimport capmetro\n\n\ndef write(st):\n    print(','.join((\n        st.retrieved.isoformat(),\n        str(st.timestamp),\n        str(st.vehicle_id),\n        str(st.trip_id),\n        str(st.route_id),\n        str(st.direction_id),\n        str(st.start_date),\n        str(st.start_time),\n        str(st.latitude),\n        str(st.longitude),\n        str(st.speed),\n    )))\n\n\n@climate.annotate(\n    root='load protobuf files from this directory',\n    before=('only process files before YYYY-MM-DDTHH:MM:SS', 'option'),\n    after=('only process files after YYYY-MM-DDTHH:MM:SS', 'option'),\n)\ndef main(root, before=None, after=None):\n    print('retrieved,timestamp,vehicle_id,trip_id,route_id,direction_id,'\n          'start_date,start_time,latitude,longitude,speed')\n    capmetro.process(write, root, after, before)\n\n\nif __name__ == '__main__':\n    climate.call(main, stream=sys.stderr)\n","sub_path":"pb-to-csv.py","file_name":"pb-to-csv.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"557867085","text":"# -*- coding:utf-8 -*-\n\nfrom PyQt4 import QtCore, QtGui, uic\nfrom PyQt4.Qt import QObject, QFileDialog, QMessageBox, QByteArray, QBuffer,\\\n    QFont, QHeaderView, QCheckBox\nimport sys\nfrom PyQt4.QtGui import QStandardItemModel, QStandardItem\n\nif __name__ == '__main__':\n    app=QtGui.QApplication(sys.argv)\n    window=uic.loadUi(\"/home/arama/Repo/TestTable/form.ui\")\n    tm=QStandardItemModel(5,3)\n    tm.setHeaderData(1, QtCore.Qt.Horizontal, \"bb\")\n    \n    si=QStandardItem(\"text\")\n    #si.setTextAlignment(QtCore.Qt.AlignCenter)\n    \n    si2=QStandardItem()\n    si2.setCheckable(True)\n    si2.setEditable(False)\n    si2.setTextAlignment(QtCore.Qt.AlignCenter)\n    si3=QCheckBox()\n    headList=['a','b','c']\n    headers=[]\n    f=QFont('Lucida',20, QtGui.QFont.Bold)\n    si.setFont(f)\n    hw=window.tableView.horizontalHeader()\n    #hw.setResizeMode( QHeaderView.Fixed)\n    tm.setItem(0,0,si)\n    tm.setItem(0,1,si2)\n    window.tableView.setModel(tm)\n    window.tableView.setColumnWidth(1,40)\n    window.tableView.horizontalHeader().setFont(f)\n    window.tableView.verticalHeader().hide()\n    \n    \n\n    window.show()\n\n    sys.exit(app.exec_())\n    \n    \n    ","sub_path":"t.py","file_name":"t.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"309014896","text":"__author__ = 'lionel'\nimport xlrd\n#zhab sb\n\nlocation = '/Users/lionel/Desktop/ExcelTest.xlsx'\ndata = xlrd.open_workbook(location)\ntable = data.sheets()[0]\nnrows = table.nrows\nncols = table.ncols\nprint('line number =', nrows)\n\n\ndef getExcelData(type):\n    if (type == 'pie'):\n        pieChart()\n    if (type == 'barH'):\n        None\n    if (type == 'bar'):\n        None\n\n    dataList = [[0 for col in range(2)] for row in range(nrows)]\n    for i in range(nrows):\n        dataList[i] = table.row_values(i)\n\n    return dataList\n\n\ndef pieChart():\n    return None\n\n\ndef barChart():\n    unmodifiedList = getExcelData('bar')\n    labelList = ['I have no idea how to creat an empty list']\n    valueList = [0]\n    del labelList[0]\n    del valueList[0]\n    for i in range(len(unmodifiedList)-1):\n        labelList.append(unmodifiedList[i][0])\n    for i in range(len(unmodifiedList)-1):\n        valueList.append(unmodifiedList[i][1])\n    print(valueList)\n\nbarChart()","sub_path":"ExcelData.py","file_name":"ExcelData.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"378006136","text":"# ------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# ------------------------------------------------------------------------------\nimport atexit\nimport json\nimport os\n\nimport msal\nfrom azdata.cli.commands.arc.azure.models.spn import Spn\nfrom azdata.cli.commands.arc.common_util import get_config_file\nfrom azdata.cli.core.configuration import Configuration\nfrom azdata.cli.core.deploy import display\nfrom azdata.cli.core.prompt import prompt_for_input\nfrom azdata.cli.core.logging import get_logger\n\nfrom . import constants as azure_constants\n\nlog = get_logger(__name__)\n\n# #############################################################################\n# -- AAD related functions --\n# #############################################################################\n\ndef acquire_token(scopes):\n    \"\"\"\n    Obtains AAD bearer token for given scope\n    \"\"\"\n    spn = _check_for_spn()\n\n    token = None\n    while not token:\n        try:\n            token = _get_token_using_msal(spn, scopes)\n        except BaseException:\n            display('Service principal \"{}\" failed to authenticate with Azure. Please try again\\n'.format(spn.client_id))\n            \n        # If we did not get a token, prompt for spn\n        if not token:\n            spn = _prompt_for_spn()\n\n    _store_spn_in_env(spn)\n    return token\n\n\ndef _check_for_spn():\n    \"\"\"\n    Checks if service principal is stored in the environment\n    or contained in config_file. Returns spn if found, prompts\n    user for spn otherwise\n    \"\"\"\n    spn = Spn()\n\n    # Check if spn in environment\n    get_spn_from_env = True\n    for spn_env in azure_constants.SPN_ENV_KEYS.values():\n        if spn_env not in os.environ or not os.environ[spn_env]:\n            get_spn_from_env = False\n            break\n    if get_spn_from_env:\n        spn.authority = os.environ[azure_constants.SPN_ENV_KEYS[\"authority\"]]\n        spn.tenant_id = os.environ[azure_constants.SPN_ENV_KEYS[\"tenant_id\"]]\n        spn.client_id = os.environ[azure_constants.SPN_ENV_KEYS[\"client_id\"]]\n        spn.client_secret = os.environ[azure_constants.SPN_ENV_KEYS[\"client_secret\"]]\n        return spn\n    else:\n        # Prompt the user\n        return _prompt_for_spn()\n\n\ndef _store_spn_in_env(spn: Spn):\n    \"\"\"\n    Stores service principal object in environment\n    :param spn: Service Principal object\n    \"\"\"\n    if spn.authority and spn.client_id and spn.tenant_id and spn.client_secret:\n        os.environ[azure_constants.SPN_ENV_KEYS[\"authority\"]] = spn.authority\n        os.environ[azure_constants.SPN_ENV_KEYS[\"tenant_id\"]] = spn.tenant_id\n        os.environ[azure_constants.SPN_ENV_KEYS[\"client_id\"]] = spn.client_id\n        os.environ[azure_constants.SPN_ENV_KEYS[\"client_secret\"]] = spn.client_secret\n    else:\n        missing_keys = []\n        for spn_env in azure_constants.SPN_ENV_KEYS.values():\n            if spn_env not in os.environ or not os.environ[spn_env]:\n                missing_keys.append(spn_env)\n        raise ValueError(\"The following service principal values are missing: {}\".format(\", \".join(missing_keys)))\n\n\ndef _prompt_for_spn():\n    \"\"\"\n    Prompts the user to enter the service principal info\n    \"\"\"\n    spn = Spn()\n\n    display('Please provide the service principal information for upload:')\n    # public cloud we can use the default login url\n    spn.authority = azure_constants.PUBLIC_CLOUD_LOGIN_URL\n    spn.tenant_id = prompt_for_input('Service principal tenant id: ')\n    spn.client_id = prompt_for_input('Service principal client id: ')\n    spn.client_secret = prompt_for_input('Service principal secret: ')\n\n    return spn\n\n\ndef _get_token_using_msal(spn, scopes):\n    \"\"\"\n    Uses the MSAL library to obtain AAD auth token for \n    the given service principal and scope(s)\n\n    :param spn: service principal object\n    :param scopes: list containing scopes requested to \n    access a protected API\n    \n    :return: auth token string, None if auth fails\n    \"\"\"\n    cache = msal.SerializableTokenCache()\n    cache_file = _get_cache_file()\n    if os.path.exists(cache_file):\n        cache.deserialize(open(cache_file, \"r\").read())\n    atexit.register(\n        lambda: open(cache_file, \"w\").write(cache.serialize())\n        if cache.has_state_changed\n        else None\n    )\n\n    app = msal.ConfidentialClientApplication(\n        spn.client_id,\n        spn.client_secret,\n        azure_constants.AAD_LOGIN_URL + spn.tenant_id,\n        token_cache=cache,\n    )\n\n    # First look up a token from cache, since we are looking for token for the current app, NOT for an end user.\n    # Notice we give account parameter as None.\n    result = app.acquire_token_silent(scopes, account=None)\n    if result:\n        log.info(\"Get AAD token from cache.\")\n    else:\n        log.info(\"No suitable token exists in cache. Get a new one from AAD.\")\n        result = app.acquire_token_for_client(scopes)\n    if \"access_token\" in result:\n        return result[\"access_token\"]\n    else:\n        log.error(\"Failed to get access token from AAD with the following error\")\n        log.error('Error: \"{}\"'.format(result.get(\"error\")))\n        log.error('Error description: \"{}\"'.format(result.get(\"error_description\")))\n        log.error('Correlation Id: \"{}\"'.format(result.get(\"correlation_id\")))\n\n\ndef _get_cache_file():\n    \"\"\"\n    Get token cache file\n    \"\"\"\n    cli_name = Configuration().CLI_NAME\n    config_dir = os.path.expanduser(Configuration().CLI_CONFIG_DIR)\n    cache_file = os.path.join(\n        config_dir, \"{}-{}\".format(cli_name, azure_constants.AAD_PROFILE_FILENAME)\n    )\n    if not os.path.exists(cache_file):\n        log.info(\"Create AAD token cache file.\")\n        f = open(cache_file, \"w+\")\n        f.close()\n    return cache_file","sub_path":"azdata_cli_arc-20.3.7-py2.py3-none-any/azdata/cli/commands/arc/azure/ad_auth_util.py","file_name":"ad_auth_util.py","file_ext":"py","file_size_in_byte":5782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"408125700","text":"\n# -*- coding:utf-8 -*-\nfrom Tvplay import MovieInfoModel\nimport re\nimport requests\nclass Tvteleplay:\n    def __init__(self,data):\n        self.__info = MovielnfoModel()\n        self.__data_module(data)\n    def __data_module(self,data):\n        # 获取名称\n        pattern = \"<span property=\\\"v:itemreviewed\\\">(.*)<span class=\\\"year\\\">\"\n        ret = re.search(pattern, data, re.S)\n        self.__info.set_name(ret.group(1).split(\"<\")[0])\n\n        # 导演\n        pattern = '导演</span>: <span class=\\'attrs\\'>(.*)<span class=\\'pl\\'>编剧'\n        ret = re.search(pattern, data, re.S)\n        pattern = \"(.*)</a\"\n        ret = re.search(pattern, ret.group(1), re.S)\n        self.__info.set_dirrector(ret.group().split(\">\")[1][:-3])\n\n        # 主演\n        pattern = '主演</span>(.*)<span class=\"pl\">类型'\n        ret = re.search(pattern, data, re.S)\n        # ret=ret.group(1)\n        str = \"\"\n        for i in ret.group(1).split(\"</a>\")[:-1]:\n            i = i.split(\"\\\">\")[1]\n            str += i + \"/\"\n        self.__info.set_author(str)\n\n        # 评分\n        pattern = \"<div class=\\\"rating_logo ll\\\">豆瓣评分</div>(.*)人评价\"\n        ret = re.search(pattern, data, re.S)\n        pattern = \"property=\\\"v:average\\\">(.*)</strong>\"\n        ret = re.search(pattern, ret.group(), re.S)\n        self.__info.set_actor(ret.group(1))\n\n        # 简介\n        pattern = \"<div class=\\\"indent\\\" id=\\\"link-report\\\">(.*)<div id=\\\"celebrities\\\" class=\\\"celebrities related-celebrities\\\">\"\n        ret = re.search(pattern, data, re.S)\n        pattern = 'class=\"\">(.*)</span>'\n        ret = re.search(pattern, ret.group(), re.S)\n        self.__info.set_score(ret.group(1))\n    @property\n    def get(self):\n        return self.__info\n\n# # 名称，导演，主演，评分，简介\n# ret = requests.get(\"https://movie.douban.com/subject/26849758/?tag=热门&from=gaia_video\")\n# data = ret.content.decode(\"utf-8\")\n# # print(data)\n","sub_path":"爬虫/TVteleplay.py","file_name":"TVteleplay.py","file_ext":"py","file_size_in_byte":1939,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"243077627","text":"# -*- coding: utf-8 -*-\n\"\"\"\nTencent is pleased to support the open source community by making BK-BASE 蓝鲸基础平台 available.\n\nCopyright (C) 2021 THL A29 Limited, a Tencent company.  All rights reserved.\n\nBK-BASE 蓝鲸基础平台 is licensed under the MIT License.\n\nLicense for BK-BASE 蓝鲸基础平台:\n--------------------------------------------------------------------\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\ndocumentation files (the \"Software\"), to deal in the Software without restriction, including without limitation\nthe rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,\nand to permit persons to whom the Software is furnished to do so, subject to the following conditions:\nThe above copyright notice and this permission notice shall be included in all copies or substantial\nportions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT\nLIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN\nNO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,\nWHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\"\"\"\n\nfrom copy import deepcopy\n\nfrom common.bklanguage import BkLanguage, bktranslates\nfrom django.utils.functional import Promise\nfrom django.utils.translation import ugettext as _\nfrom django.utils.translation import ungettext_lazy\nfrom django.utils.translation.trans_real import translation\n\n\ndef translate_based_on_language_config(msg):\n    \"\"\"\n    基于翻译对���表的国际化\n    @param msg:\n    @return:\n    \"\"\"\n    language = BkLanguage.current_language()\n    if language in [\"zh-cn\"]:\n        return bktranslates(msg)\n    return msg\n\n\nclass Bilingual(object):\n    \"\"\"\n    双语模块\n    \"\"\"\n\n    def __init__(self, zh, en=None):\n        \"\"\"\n        @param {String} zh 传入的中文，未翻译内容\n        @param {String} [en] 传入的英文，已翻译内容\n        \"\"\"\n        if en is None:\n            en = self.do_translate(zh)\n        self.zh = zh.replace(\"\\n\", \" \")\n        self.en = en.replace(\"\\n\", \" \")\n\n    def __unicode__(self):\n        return _(self.zh)\n\n    def __str__(self):\n        return _(self.zh)\n\n    def format(self, *args, **kwargs):\n        \"\"\"\n        对Bilingual进行格式化，kwargs 参数支持双语对象\n        \"\"\"\n        kwargs_zh = deepcopy(kwargs)\n        kwargs_en = kwargs\n        for _k, _v in list(kwargs.items()):\n            # 兼容懒翻译的情况\n            if isinstance(_v, Promise):\n                try:\n                    # 获取懒翻译前的中文\n                    kwargs_zh.update({_k: _v._proxy____args[0]})\n                except (AttributeError, KeyError):\n                    pass\n            # 兼容格式化占位内容也为双语的情况\n            elif isinstance(_v, Bilingual):\n                kwargs_zh.update({_k: _v.zh})\n                kwargs_en.update({_k: _v.en})\n        self.zh = self.zh.format(*args, **kwargs_zh)\n        self.en = self.en.format(*args, **kwargs_en)\n        return self\n\n    @staticmethod\n    def do_translate(msg, language=\"en\"):\n        \"\"\"\n        根据已有国际化文件，把中文转成英文（默认）\n        @param {String} msg 中文字符串\n        @param {String} language 语言\n        \"\"\"\n        eol_message = msg.replace(str(\"\\r\\n\"), str(\"\\n\")).replace(str(\"\\r\"), str(\"\\n\"))\n        translation_object = translation(language)\n        return translation_object.gettext(eol_message)\n\n    @classmethod\n    def from_lazy(cls, lazy_obj):\n        \"\"\"\n        将一个懒翻译对象转化为双语对象\n        \"\"\"\n        # 获取懒翻译前的中文\n        return cls(lazy_obj._proxy____args[0])\n\n\nEXTRA_TRANSLATION = [\n    ungettext_lazy(\n        \"Ensure this value has at most %(limit_value)d character (it has %(show_value)d).\",\n        \"Ensure this value has at most %(limit_value)d characters (it has %(show_value)d).\",\n        \"limit_value\",\n    ),\n    ungettext_lazy(\n        \"Ensure this value has at least %(limit_value)d character (it has %(show_value)d).\",\n        \"Ensure this value has at least %(limit_value)d characters (it has %(show_value)d).\",\n        \"limit_value\",\n    ),\n]\n","sub_path":"src/api/dataflow/flow/utils/language.py","file_name":"language.py","file_ext":"py","file_size_in_byte":4450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"39577623","text":"#Calculando la media aritmética\n#En matemáticas, la media aritmética de un conjunto de números es el valor\n#que se obtiene a partir de la suma de varios números dividido entre el\n#número de sumandos.\n\n#En este ejercicio deberás programar un algoritmo encargado de calcular 5 medias\n#aritméticas, a partir de 4 números dados.\n\n#Si te fijas en los casos de prueba, cada línea de las entradas se corresponde con\n#la misma línea de las salidas, es decir, la primera línea de la entrada (cuyos \n#valores son 2, 4, 6 y 8) se corresponde con la primera línea de la salida (cuyo\n#valor es 5), y así sucesivamente.\n\n# Entradas y salidas\n#Para cada línea de las entradas aparecen 4 números naturales.\n#Las salidas deberán ser números reales.\n\n#Casos de prueba\n#Entrada            Salida\n#2 4 6 8        |   5\n#8 5 20 7       |   10\n#12 6 18 4      |   10\n\ni = 0\nx = 0\nlista = [4,0,6,0]\nnumero2 = 0\n\nwhile x < 2:\n    while i < 4:\n        valor = int(input(\"Ingresa el valor\"))\n        lista[i] = valor\n        i = i + 1\n\n    for numero in lista:\n        numero2 = numero + numero2 \n\n    resultado = numero2 / 4\n    print(float(resultado))\n    x = x + 1\n    i = 0\n    resultado = 0\n    numero = 0\n    numero2 = 0\n    \n\n","sub_path":"mediaAritmetica.py","file_name":"mediaAritmetica.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"143967241","text":"from math import *\nimport numpy\nimport numpy as np\nimport matplotlib . pyplot as plt\nfrom decimal import *\nimport random\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cm\nfrom matplotlib.ticker import LinearLocator, FormatStrFormatter\nimport time\nfrom matplotlib import rcParams\n\n#diag zero check #default is no which is 1/switch to zero is it is\ndiagZero=1\n#diag zero check #default is no which is 1/switch to zero is it is\ndiagDom=1\n\nip_name = input(\"Please enter full input filename including extension: \")\n\nif ip_name == \"\":\n\n    ip_name = \"nas_Sor.in\"\n\n    print(ip_name, \" selected as input filename\")\n\nelse:\n\n    print(\"Default \",ip_name, \" selected as input filename\")\n\n \n\n# User prompted for output filename. If no value entered default nas_Sor.out selected.    \n\nop_name = input(\"Please enter full output filename including extension: \")\n\nif op_name == \"\":\n\n    op_name = \"nas_Sor.out\"\n\n    print(op_name, \" selected as output filename\")\n\nelse:\n\n    print(\"Default \",op_name, \" selected as output filename\")\n\n##\n\n\n## Reading in file and storing as matrix \n\nwith open(ip_name,'r') as infile:\n\n    n = int(infile.readline())\n\n    A = np.zeros((n,n))\n\n    b = np.zeros((n,1))\n\n    mat_temp = infile.readlines()\n\n    \n\n    for row in range(n+1):\n\n        if row != n:\n\n            for col in range(n):\n\n                # Now have this reading in one element at a time. Easy to pass into the CSR storage format\n\n                A[row,col] = float(mat_temp[row].strip().split(\"\\t\")[col])\n\n        else:\n\n            # b read in as a column vector            \n\n            b[0:,0] = mat_temp[row].strip().split(\"\\t\")\n\n \n\nprint(\"File as matrix ---------\")    \n\nprint(A)\n\nprint(b)\n\n##\n\n\n## FUNCTION DEFINITION\n# This of course returning CSR with convention that first element is 0 rather than 1. Python implementation\ndef csr_store(B,diagZero,diagDom):\n    # Obtain size of matrix. Cycling through each row    \n    val = []\n    col = []\n    rowStart = [] \n    \n      \n    for i in range(B.shape[0]):\n        \n        \n        # Set this to zero at start of each new row. Used to populate rowStart vector        \n        startCheck = 0\n        \n        # Iterating through each column\n        for j in range(B.shape[0]):\n            #diagonally zero check\n            if (i==j) and B[i,j] == 0:\n                diagZero=0\n            #diagonally dominant check  \n            if (i==j) and (B[i,j]-(sum(B[i,0:len(B)])-B[i,j])<=0) and (B[i,j]-(sum(B[0:len(B),j])-B[i,j])<=0):\n                diagDom=0\n            \n            # Ignore zero entries\n            if B[i,j] != 0:\n                \n                # When first entry in a row reached iterate check value\n                startCheck += 1\n                # Appending relevant values to val and col vectors\n                val.append(B[i,j])\n                col.append(j)\n                \n                # Here if startCheck is 1 then new rowStart value needs to be appended\n                if startCheck == 1:\n                    rowStart.append(len(val)-1)\n                    \n    # Finally append the last value to row start, namely the length of the val vector\n    rowStart.append(len(val))\n    # Output is returned as a dictionary that can be referenced by the various different key values    \n    return{'val':val, 'col':col, 'rowStart':rowStart,'diagZero':diagZero,'diagDom':diagDom}\n    \n\n\n                  \n# run the sparse sor\ncsr = csr_store(A,diagZero,diagDom)\nprint(csr)\n    ","sub_path":"matrixCheck.py","file_name":"matrixCheck.py","file_ext":"py","file_size_in_byte":3463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"315501147","text":"from django.shortcuts import render\r\nfrom rest_framework import viewsets\r\nfrom rest_framework import mixins\r\nfrom .models import UserFav,UserLeavingMessage,UserAddress\r\nfrom rest_framework.permissions import IsAuthenticated\r\nfrom utils.permissions import IsOwnerOrReadOnly\r\n\r\nfrom rest_framework_jwt.authentication import  JSONWebTokenAuthentication\r\nfrom rest_framework.authentication import SessionAuthentication\r\nfrom .serializers import UserFavSerializer,UserFavDetailSerializer,LeavingMessageSerializer,AddressSerializer\r\n\r\n\r\n\r\nclass UserFavViewset(mixins.CreateModelMixin,mixins.ListModelMixin,mixins.DestroyModelMixin,viewsets.GenericViewSet):\r\n    \"\"\"\r\n    list:\r\n        获取用户收藏列表\r\n    retrieve:\r\n        判断某个商品是否已经收藏\r\n    create:\r\n        收藏商品\r\n    \"\"\"\r\n    permission_classes = (IsAuthenticated,IsOwnerOrReadOnly)\r\n    authentication_classes = (JSONWebTokenAuthentication,SessionAuthentication)\r\n    lookup_field = \"goods_id\"\r\n    def get_queryset(self):\r\n        return UserFav.objects.filter(user=self.request.user)\r\n\r\n    def get_serializer_class(self):\r\n        if self.action == \"list\":\r\n            return UserFavDetailSerializer\r\n        elif self.action == \"create\":\r\n            return UserFavSerializer\r\n\r\n        return UserFavSerializer\r\n\r\n\r\n\r\nclass LeavingMessageViewset(mixins.ListModelMixin,mixins.DestroyModelMixin,mixins.CreateModelMixin,viewsets.GenericViewSet):\r\n    \"\"\"\r\n    list:\r\n        获取留言\r\n    create:\r\n        添加留言\r\n    delete:\r\n        删除留言功能\r\n    \"\"\"\r\n\r\n    permission_classes = (IsAuthenticated,IsOwnerOrReadOnly)\r\n    authentication_classes = (JSONWebTokenAuthentication,SessionAuthentication)\r\n\r\n    serializer_class = LeavingMessageSerializer\r\n    def get_queryset(self):\r\n        return UserLeavingMessage.objects.filter(user=self.request.user)\r\n\r\n\r\n#ModelViewSet 可以实现增删改查的所有功能\r\nclass AddressViewset(viewsets.ModelViewSet):\r\n    \"\"\"\r\n    收货地址管理\r\n    list:\r\n        获取收货地址\r\n    create:\r\n        添加收货地址\r\n    update:\r\n        修改收货地址\r\n    delete:\r\n        删除收货地址\r\n    \"\"\"\r\n    permission_classes = (IsAuthenticated,IsOwnerOrReadOnly)\r\n    authentication_classes = (JSONWebTokenAuthentication,SessionAuthentication)\r\n\r\n    serializer_class = AddressSerializer\r\n    def get_queryset(self):\r\n        return UserAddress.objects.filter(user=self.request.user)\r\n\r\n\r\n","sub_path":"apps/user_operation/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"322417528","text":"import sys\nsys.path.append('utils')\nimport os\nimport argparse\nfrom tqdm import tqdm\nimport json\nfrom tensorboardX import SummaryWriter\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nfrom dataset.spot import DailyDataset, get_loader, TOTAL_STD\nfrom utils.tool import clip_grads, to_gpu\nfrom utils.decompose_model import TDec_RNN_v1 as Model\n\n\ndef get_args():\n    parser = argparse.ArgumentParser(description='Experimental setting.')\n    parser.add_argument('--epochs', default=50, type=int,)\n    parser.add_argument('--batch_size', default=64, type=int,)\n    parser.add_argument('--N', default=2000, type=int,)\n    parser.add_argument('--W', default=21, type=int,)\n    parser.add_argument('--input_dim', default=24, type=int,)\n    parser.add_argument('--output_dim', default=24, type=int,)\n    parser.add_argument('--hidden_size', default=24, type=int,)\n    parser.add_argument('--cell', default='gru', type=str,)\n    parser.add_argument('--hard_gate', default=0, type=bool,)\n    parser.add_argument('--res_penalty', default=0.01, type=float,)\n    parser.add_argument('--seasonal_penalty', default=3e-6, type=float,)\n    parser.add_argument('--smooth_penalty', default=0.01, type=float,)\n    parser.add_argument('--flag', default='test_aplha_30', type=str,)\n    args = parser.parse_args()\n    return args\n\n\nif __name__ == '__main__':\n\n    args = get_args()\n    # import pdb\n    # pdb.set_trace()\n\n    if args.flag is None:\n        writer = SummaryWriter(log_dir=None)\n        writer_path = list(writer.all_writers.keys())[0]\n    else:\n        # e.g. 'runs/gru/hidden64'\n        writer_path = os.path.join('runs/decompose', args.flag)\n        writer = SummaryWriter(log_dir=writer_path)\n\n    with open(os.path.join(writer_path, 'config.json'), 'w') as f:\n        f.write(json.dumps(vars(args), indent=4))\n\n    dataset = DailyDataset(N=args.N, W=args.W)\n    loader = get_loader(dataset, batch_size=args.batch_size, shuffle=True, num_workers=2)\n\n    TEST_LENGTH = 182\n\n    _, trainY = loader.dataset.get_io('2011-06-30', '2015-12-31')\n    _, testY = loader.dataset.get_io('2011-06-30', '2016-06-30')\n\n    with torch.no_grad():\n        train_period_input = to_gpu(trainY)\n        train_period_output = to_gpu(trainY) * TOTAL_STD\n        test_period_input = to_gpu(testY)\n        test_period_output = to_gpu(testY[-TEST_LENGTH:]) * TOTAL_STD\n\n    model = to_gpu(Model(input_dim=args.input_dim, output_dim=args.output_dim,\n                         hidden_size=args.hidden_size, cell=args.cell, hard_gate=args.hard_gate))\n\n    criterion = nn.MSELoss()\n    metric = nn.L1Loss()\n    regularization = nn.MSELoss()\n    optimizer = optim.RMSprop(model.parameters(), lr=0.001)  # slow down\n\n    epochs = args.epochs\n    global_step = 0\n    for epoch in tqdm(range(epochs)):\n        train_period_forecast = model.forecast(train_period_input)[0] * TOTAL_STD\n        train_mse = criterion(train_period_forecast, train_period_output)**.5\n        train_mae = metric(train_period_forecast, train_period_output)\n\n        test_period_forecast = model.forecast(test_period_input)[0][-TEST_LENGTH:] * TOTAL_STD\n        test_mse = criterion(test_period_forecast, test_period_output)**.5\n        test_mae = metric(test_period_forecast, test_period_output)\n\n        writer.add_scalar('train_mse', train_mse.data, global_step=epoch)\n        writer.add_scalar('train_mae', train_mae.data, global_step=epoch)\n        writer.add_scalar('test_mse', test_mse.data, global_step=epoch)\n        writer.add_scalar('test_mae', test_mae.data, global_step=epoch)\n\n        for _, (_, y, item) in enumerate(loader):\n            x = to_gpu(y)\n\n            optimizer.zero_grad()\n\n            # out = model(x)\n            out, res_loss, seasonal_loss, smooth_loss = model(x)\n\n            loss = criterion(out, x) + args.res_penalty * res_loss + args.seasonal_penalty * \\\n                seasonal_loss + args.smooth_penalty * smooth_loss\n            writer.add_scalar('train_loss', loss.data, global_step=global_step)\n            writer.add_scalar('res_loss', res_loss.data, global_step=global_step)\n            writer.add_scalar('seasonal_loss', seasonal_loss.data, global_step=global_step)\n            writer.add_scalar('smooth_loss', smooth_loss.data, global_step=global_step)\n            loss.backward(retain_graph=True)\n            clip_grads(model)\n            optimizer.step()\n            global_step += 1\n\n        torch.save(model.state_dict(),\n                   os.path.join(writer_path, \"snapshots%s.pth\" % epoch))\n\n    writer.export_scalars_to_json(os.path.join(writer_path, \"history.json\"))\n","sub_path":"NSW/experiment_decom.py","file_name":"experiment_decom.py","file_ext":"py","file_size_in_byte":4576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"428169901","text":"from Siding.async_siding import Siding\nimport sys\nimport zerorpc\n\nPORT = \"4242\"\n\n\ndef server():\n  addr = 'tcp://127.0.0.1:' + PORT\n  s = zerorpc.Server(Siding())\n  s.bind(addr)\n  print('start running on {}'.format(addr))\n  s.run()\n\ndef main():\n server()\n\nif __name__ == '__main__':\n  main()","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"494603695","text":"import datetime\nimport time\nimport pandas as pd\n\nCITY_DATA = { 'chicago': 'chicago.csv',\n              'nyc':'new_york_city.csv',\n              'new york':'new_york_city.csv',\n              'new york city': 'new_york_city.csv',\n              'washington': 'washington.csv' }\n              #reading the city bikeshare data\n\n\ndef get_filters():\n    \"\"\"\n    Asks user to specify a city, month, and day to analyze.\n    Returns:\n        (str) city - name of the city to analyze\n        (str) month - name of the month to filter by, or \"all\" to apply no month filter\n        (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n    \"\"\"\n\n    print(\"-\"*50)\n    print('\\nHello! Let\\'s explore some US bikeshare data!')\n\n    city = input('''Would you like to look at the data for Chicago, New York or Washington from the US Bikeshare data?\\n''')\n    city = city.lower()\n    city = city.replace(\" \", \"\")\n    # Taking the user input for city (chicago, new york city, washington)\n    while city not in ['chicago','newyorkcity','newyork','nyc','washington'] :\n    \tcity = input(\"You have entered an invalid input.\\nCheck the correct spelling of the city and enter here \")\n    \tcity = city.lower()\n    \tcity = city.replace(\" \", \"\")\n\n    # Taking the user input for month of the year starting from January to June\n    month = input(\"\\nSince we only have the bikesharing data starting from the month of January till the June,\\nEnter a month from 'January to June' in case you want to filter by a specific month or enter 'all' to apply no filter\\n\")\n    month = month.lower()\n    while month not in ['january','february','march','april','may','june','all']:\n    \tmonth = input(\"\\nYou have entered an invalid input.\\nCheck the correct spelling of the month and enter month only from 'January to June' or 'all' :  \")\n    \tmonth = month.lower()\n\n    # Taking the user input for the day of the week\n    day = input(\"\\nWould you want us to filter the data by any particular day of the week?\\nIf yes, enter the day you want to filter your data by and if you do not want to filter the data by day, enter 'all' :\\n\")\n    day = day.lower()\n    while  day.lower() not in ['sunday','monday','tuesday','wednesday','thursday','friday','saturday','all']:\n    \tday = input(\"\\nYou have entered an invalid input.\\nCheck the correct spelling of the day and enter here :  \")\n    \tday = day.lower()\n    print('-'*50)\n    return city, month, day\n\n\ndef load_data(city, month, day):\n    \"\"\"\n    Loads data for the specified city and filters by month and day if applicable.\n    Args:\n        (str) city - name of the city to analyze\n        (str) month - name of the month to filter by, or \"all\" to apply no month filter\n        (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n    Returns:\n        df - Pandas DataFrame containing city data filtered by month and day\n    \"\"\"\n    #load data into pandas dataframe\n    df = pd.read_csv(CITY_DATA[city])\n\n    #converts the Start Time column to datetime\n    df['Start Time'] = pd.to_datetime(df['Start Time'])\n\n    # extracts month ,day of week and hour from Start Time to create new columns\n    df['month'] = df['Start Time'].dt.month\n    df['day_of_week'] = df['Start Time'].dt.weekday_name\n    df['hour'] = df['Start Time'].dt.hour\n\n    # filters by month if user asks to\n    if month != 'all':\n        # use the index of the months list to get the corresponding int\n        months = ['january', 'february', 'march', 'april', 'may', 'june']\n        month = months.index(month) + 1\n\n        # filters by month to create the new dataframe\n        df = df[df['month'] == month]\n\n    # filters by day of week if user asks to\n    if day != 'all':\n        # filter by day of week to create the new dataframe\n        df = df[df['day_of_week'] == day.title()]\n    return df\n\ndef convert_seconds(sec):\n\t\"\"\"Converts seconds into days,hours,minutes and seconds\n\tArgs:\n\t\t (int)sec: seconds in float\n\tReturns:\n\t\t\tdays,hours,miuntes,seconds\n\t\"\"\"\n\t#computes days,hours and minutes\n\td = datetime.timedelta(seconds=sec)\n\tminutes = d.seconds//60\n\thour = minutes//60\n\tminutes=minutes%60\n\thour=hour%60\n\tseconds = d.seconds\n\tseconds = seconds%60\n\treturn d.days,hour,minutes,seconds\n\n\ndef time_stats(df,month,day):\n    \"\"\"Displays statistics on the most frequent times of travel.\"\"\"\n\n    print('\\nCalculating The Most common Time of Travel.\\n')\n    start_time = time.time()\n\n    # displays the most common month\n    popular_month = df['month'].mode()[0]\n    months=[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\"]\n    if month == \"all\" :\n    \tprint(\"The most popular month is : \",months[popular_month-1])\n    else:\n    \tprint(\"The data has been filtered by the month of {}\".format(month))\n\n    # displays the most common day of week\n    popular_day = df['day_of_week'].mode()[0]\n    if day == \"all\" :\n    \tprint(\"The most popular day of the week is : \",popular_day)\n    else :\n    \tprint(\"The data has been filtered by the day of {}\".format(day))\n    # displays the most common start hour\n    popular_hour = df['hour'].mode()[0]\n    print(\"The most popular hour is : \",popular_hour)\n\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*50)\n\n\ndef station_stats(df):\n    \"\"\"Displays statistics on the most popular stations and trip.\"\"\"\n\n    print('\\nCalculating the in & out stations and the route with the highest number of subscribers.\\n')\n    start_time = time.time()\n\n    # displays most commonly used start station\n    popular_start_station = df['Start Station'].mode()[0]\n    print(\"The start station with the maximum influx is the :\",popular_start_station)\n\n    # displays most commonly used end station\n    popular_end_station = df['End Station'].mode()[0]\n    print(\"The end station with the maximum outflow is the :\",popular_end_station)\n\n    # displays most frequent round trip\n    df['Station'] = df['Start Station'].str.cat(df['End Station'],sep=\"  --  \")\n    popular_trip=df['Station'].mode()[0]\n    print(\"The route with maximum number of subscribers is :\",popular_trip)\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*50)\n\n\ndef trip_duration_stats(df):\n    \"\"\"Displays statistics on the total and average trip duration.\"\"\"\n\n    print('\\nCalculating Trip Duration.\\n')\n    start_time = time.time()\n\n    # Displays the total travel time\n    total_travel_time = df['Trip Duration'].sum()\n    days,hours,minutes,seconds=convert_seconds(int(total_travel_time))\n    print(\"The total duration of travel of all the users is : {} days, {} hours, {} minutes, {}seconds \".format(days,hours,minutes,seconds))\n\n    # Displays the mean travel time\n    mean_travel_time = df['Trip Duration'].mean()\n    days,hours,minutes,seconds=convert_seconds(int(mean_travel_time))\n    print(\"The mean duration of travel of all the users is : {} days, {} hours, {} minutes, {}seconds \".format(days,hours,minutes,seconds))\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*50)\n\n\ndef user_stats(df,city):\n    \"\"\"Displays statistics on bikeshare users.\"\"\"\n\n    print('\\nCalculating User Statistics.\\n')\n    start_time = time.time()\n\n    # Displays the counts of user types\n    user_types = df['User Type'].value_counts()\n    print(\"The type of user and their respective counts are \")\n    print(user_types)\n\n    if city != \"washington\" :\n\n    \t# Displays the count of gender\n    \tgender_count = df['Gender'].value_counts()\n    \tprint(\"\\n\\nThe Genders\\' of users and their respective counts are \")\n    \tprint(gender_count)\n\n    \t# Displays the earliest, most recent, and most common year of births of the Users\n    \toldest_birth = df['Birth Year'].min()\n    \trecent_birth = df['Birth Year'].max()\n    \tcommon_birth = df['Birth Year'].mode()[0]\n    \toldest_birth , recent_birth , common_birth = int(oldest_birth),int(recent_birth),int(common_birth)\n    \tprint(\"\\n\\nThe year of birth of the Oldest user is : {} \\nThe year of birth of the youngest user is : {} \\nThe most common year of birth of the users is : {}  \".format(oldest_birth,recent_birth,common_birth))\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*50)\n\n\ndef main():\n\n    while True:\n        city, month, day = get_filters()\n        df = load_data(city, month, day)\n\n        time_stats(df,month,day)\n        station_stats(df)\n        trip_duration_stats(df)\n        user_stats(df,city)\n\n        restart = input('\\nIf you\\'d like to start all over again, please enter yes else enter a no.\\n')\n        if restart.lower() != 'yes':\n            break\n\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"bikeshare.py","file_name":"bikeshare.py","file_ext":"py","file_size_in_byte":8602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"373470288","text":"import tkinter as tk  # python 3\nfrom tkinter import font  as tkfont  # python 3\n\nfrom tkinter import ttk\n# import Tkinter as tk     # python 2\n# import tkFont as tkfont  # python 2\n\n\"\"\"\nfrom :\nhttps://stackoverflow.com/questions/32864610/understanding-parent-and-controller-in-tkinter-init\n\nRoughly speaking, the original code1 attempted to use a pseudo-MVC (model, view and controller) architecture. \nThough without the \"model\" part -- there was just a \"view\" (some frames) and a \"controller\" (the main application). \nHence, the reference to a controller object. The original code was actually written to show how to \"stack\" frames, \nso it's MVC implementation is very shallow and under-documented since that wasn't the point of the example.\n\nTo answer your specific questions:\n\nself represents the current object. This is a common first parameter for any method of a class. \nAs you suggested, it's similar to Java's this.\n\nparent represents a widget to act as the parent of the current object. \nAll widgets in tkinter except the root window require a parent (sometimes also called a master)\n\ncontroller represents some other object that is designed to act as a common point of interaction for several pages of widgets. \nIt is an attempt to decouple the pages. That is to say, each page doesn't need to know about the other pages. \nIf it wants to interact with another page, such as causing it to be visible, it can ask the controller to make it visible.\n\nYou asked \"There is a function already defined called show_frame, \nbut why is the controller being used to call this function?\" Notice that show_frame is defined in a separate class, \nin this case the main program class. It is not defined in the other classes. For the other classes to be able to call it, \nthey must call it on an instance of the main class. That instance is named controller in the context of these other classes. \n\"\"\"\n\"\"\"\nDate : 10/08/2020\nCode modified or rather added to have menus to main window for additional functionality. This can be commented out if \nnot required. But certainly do useful when you want to use 'save', 'save as' and exit commands etc. \n\nPravin Padale\nD Sign design\n\"\"\"\n\nfont_list = {0: 'Arial 11', 1: 'Arial 11 bold', 2: 'Arial 16 bold',\n             3: 'Arial 26 bold', 4: 'Verdana 11', 5: 'Verdana 11 bold',\n             6: 'Verdana 16 bold', 7: 'Verdana 26 bold', 8: 'Tahoma 11',\n             9: 'Tahoma 11 bold', 10: 'Tahoma 16 bold', 11: 'Tahoma 26 bold'}\n\n\nclass TemplateApp(tk.Tk):\n    def __init__(self, *args, **kwargs):\n        tk.Tk.__init__(self, *args, **kwargs)\n\n        self.title_font = tkfont.Font(family='Helvetica', size=18, weight=\"bold\", slant=\"italic\")\n\n        # Main Page title on the main frame.\n        main_title = tk.Label(self, text=\"App Heading\", font=font_list[3])\n        main_title.pack(side=\"top\", fill=\"x\")\n\n        def donothing():\n            pass\n\n        # the container is where we'll stack a bunch of frames\n        # on top of each other, then the one we want visible\n        # will be raised above the others\n        container = tk.Frame(self)\n        container.pack(side=\"top\", fill=\"both\", expand=True)\n        container.grid_rowconfigure(0, weight=1)\n        container.grid_columnconfigure(0, weight=1)\n\n        # Definitions of menubar ====================================================\n        menubar = tk.Menu(self)\n        filemenu = tk.Menu(menubar, tearoff=0)\n        filemenu.add_command(label=\"New\", command=donothing)\n        filemenu.add_command(label=\"Open\", command=donothing)\n        filemenu.add_command(label=\"Save\", command=donothing)\n        filemenu.add_command(label=\"Save as...\", command=donothing)\n        filemenu.add_command(label=\"Close\", command=donothing)\n        filemenu.add_separator()\n        filemenu.add_command(label=\"Exit\", command=self.quit)\n        menubar.add_cascade(label=\"File\", menu=filemenu)\n\n        editmenu = tk.Menu(menubar, tearoff=0)\n        editmenu.add_command(label=\"Undo\", command=donothing)\n        editmenu.add_separator()\n        editmenu.add_command(label=\"Cut\", command=donothing)\n        editmenu.add_command(label=\"Copy\", command=donothing)\n        editmenu.add_command(label=\"Paste\", command=donothing)\n        editmenu.add_command(label=\"Delete\", command=donothing)\n        editmenu.add_command(label=\"Select All\", command=donothing)\n        menubar.add_cascade(label=\"Edit\", menu=editmenu)\n\n        helpmenu = tk.Menu(menubar, tearoff=0)\n        helpmenu.add_command(label=\"Deactivate License\", command=donothing)\n        helpmenu.add_command(label=\"About...\", command=donothing)\n        menubar.add_cascade(label=\"Help\", menu=helpmenu)\n\n        self.config(menu=menubar)\n\n        # End of menubar ==============================================================\n\n        self.frames = {}\n        for F in (StartPage, PageOne, PageTwo):\n            page_name = F.__name__\n            frame = F(parent=container, controller=self)\n            self.frames[page_name] = frame\n\n            # put all of the pages in the same location;\n            # the one on the top of the stacking order\n            # will be the one that is visible.\n            frame.grid(row=0, column=0, sticky=\"nsew\")\n\n        self.show_frame(\"StartPage\")\n\n    def show_frame(self, page_name):\n        '''Show a frame for the given page name'''\n        frame = self.frames[page_name]\n        frame.tkraise()\n\n\nclass StartPage(tk.Frame):\n\n    def __init__(self, parent, controller):\n        tk.Frame.__init__(self, parent)\n        self.controller = controller\n        label = tk.Label(self, text=\"This is the start page\", font=controller.title_font)\n        label.pack(side=\"top\", fill=\"x\", pady=10)\n\n        button1 = tk.Button(self, text=\"Go to Page One\",\n                            command=lambda: controller.show_frame(\"PageOne\"))\n        button2 = tk.Button(self, text=\"Go to Page Two\",\n                            command=lambda: controller.show_frame(\"PageTwo\"))\n        button1.pack()\n        button2.pack()\n\n\nclass PageOne(tk.Frame):\n\n    def __init__(self, parent, controller):\n        tk.Frame.__init__(self, parent)\n        self.controller = controller\n        label = tk.Label(self, text=\"This is page 1\", font=controller.title_font)\n        label.pack(side=\"top\", fill=\"x\", pady=10)\n        button = tk.Button(self, text=\"Go to the start page\",\n                           command=lambda: controller.show_frame(\"StartPage\"))\n        button.pack()\n\n\nclass PageTwo(tk.Frame):\n\n    def __init__(self, parent, controller):\n        tk.Frame.__init__(self, parent)\n        self.controller = controller\n        label = tk.Label(self, text=\"This is page 2\", font=controller.title_font)\n        label.pack(side=\"top\", fill=\"x\", pady=10)\n        button = tk.Button(self, text=\"Go to the start page\",\n                           command=lambda: controller.show_frame(\"StartPage\"))\n        button.pack()\n\n\nif __name__ == \"__main__\":\n    app = TemplateApp()\n    app.title(\"Your App Name\")\n    app.state(\"zoomed\")\n    app.mainloop()\n","sub_path":"MultiFrameAppTemplate.py","file_name":"MultiFrameAppTemplate.py","file_ext":"py","file_size_in_byte":7010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"524138345","text":"import warnings\nimport numpy as np\n\nfrom ml_forest.core.elements.frame_base import Frame\nfrom ml_forest.core.elements.label_base import Label\nfrom ml_forest.core.elements.ltrans_base import LTransform\n\n\nclass LFlow(object):\n    def label_encoding_transform(self, frame, lab_fed, l_transform):\n        \"\"\"\n\n        :param frame: Frame object\n        :param lab_fed: Label object\n        :param l_transform: LTransform object\n        :return:\n        \"\"\"\n        if not isinstance(frame, Frame) or not isinstance(lab_fed, Label) or not isinstance(l_transform, LTransform):\n            raise TypeError(\"Something wrong.\")\n        new_label_values = l_transform.encode_whole(lab_fed.values)\n\n        return new_label_values, l_transform\n\n\nclass FFlow(object):\n    def supervised_fit_transform(self, frame, lst_fed, f_transform, label):\n        \"\"\"\n\n        :param frame:\n        :param lst_fed:\n        :param f_transform:\n        :param label:\n        :return:\n        \"\"\"\n        l_values, fed_values, prevstage = self.f_collect_components(lst_fed, label)\n        work_layer = frame.depth - prevstage\n\n        if work_layer == 0:\n            new_feature_values, model_collection, stage = self.last_layer_supervised_train(\n                frame, work_layer, fed_values, l_values, f_transform\n            )\n            # raise NotImplementedError(\"Not implemented yet. Need to be more careful.\")\n        else:\n            if f_transform.tuning:\n                new_feature_values, model_collection, stage = self.out_sample_train_with_tuning(\n                    frame, work_layer, fed_values, l_values, f_transform\n                )\n            else:\n                new_feature_values, model_collection, stage = self.out_sample_train(\n                    frame, work_layer, fed_values, l_values, f_transform\n                )\n\n        # f_transform documenting\n        f_transform.record_models(model_collection)\n\n        return new_feature_values, f_transform, stage\n\n    def unsupervised_fit_transform(self, lst_fed, f_transform):\n        l_values, fed_values, prevstage = self.f_collect_components(lst_fed, None)\n        new_feature_values, model_collection = self.in_sample_train(fed_values, f_transform)\n        stage = prevstage\n\n        # f_transform documenting\n        f_transform.record_models(model_collection)\n\n        return new_feature_values, f_transform, stage\n\n    @staticmethod\n    def f_collect_components(lst_fed, label):\n        if label:\n            l_values = label.values\n        else:\n            l_values = None\n\n        if len(lst_fed) == 1:\n            fed_values = lst_fed[0].values\n        else:\n            fed_values = np.concatenate(list(map(lambda x: x.values, lst_fed)), axis=1)\n\n        prevstage = max(map(lambda x: x.stage, lst_fed))\n\n        return l_values, fed_values, prevstage\n\n    def in_sample_train(self, fed, f_transform):\n        model, values = f_transform.fit_whole(fed)\n        models = {(0,): model}\n\n        return values, models\n\n    def last_layer_supervised_train(self, frame, work_layer, fed_values, l_values, f_transform):\n        model, values = f_transform.fit_singleton(fed_values, l_values, fed_values)\n        models = {(0,): model}\n\n        prevstage = frame.depth - work_layer\n        stage = prevstage + 1\n\n        return values, models, stage\n\n    @staticmethod\n    def out_sample_train(frame, work_layer, fed_values, l_values, f_transform):\n        lst_test_keys, lst_train_keys = frame.get_train_test_key_pairs(work_layer)\n\n        values = []\n        models = []\n        for i in range(len(lst_test_keys)):\n            test_key = lst_test_keys[i]\n            test_idx = frame.get_single_fold(test_key)\n\n            train_key_pack = lst_train_keys[i]\n            train_idx = []\n            for key in train_key_pack:\n                train_idx.extend(frame.get_single_fold(key))\n\n            x_train = fed_values[train_idx, :]\n            y_train = l_values[train_idx, :]\n            x_test = fed_values[test_idx, :]\n\n            model, tmp = f_transform.fit_singleton(x_train, y_train, x_test)\n            models.append((test_key, model))\n            if len(tmp.shape) == 1:\n                tmp = tmp.reshape((-1, 1))\n            values.append(tmp)\n\n        values = np.concatenate(values, axis=0)\n        prevstage = frame.depth - work_layer\n        stage = prevstage + 1  # TODO: should probably change to f_transform.rise\n\n        return values, dict(models), stage\n\n    @staticmethod\n    def out_sample_train_with_tuning(frame, work_layer, fed_values, l_values, f_transform):\n        lst_test_keys, lst_train_keys = frame.get_train_test_key_pairs(work_layer)\n        if min(map(len, lst_train_keys)) < 2:\n            raise ValueError(\"Training portion has less than 2 folds, can't train with validation.\")\n\n        values = []\n        models = []\n        for i in range(len(lst_test_keys)):\n            test_key = lst_test_keys[i]\n            test_idx = frame.get_single_fold(test_key)\n\n            train_key_pack = lst_train_keys[i]\n            validation_key = train_key_pack[-1]\n            validation_idx = frame.get_single_fold(validation_key)\n\n            train_key_pack = train_key_pack[:-1]\n            train_idx = []\n            for key in train_key_pack:\n                train_idx.extend(frame.get_single_fold(key))\n\n            x_train = fed_values[train_idx, :]\n            y_train = l_values[train_idx, :]\n            x_validation = fed_values[validation_idx, :]\n            y_validation = l_values[validation_idx, :]\n            x_test = fed_values[test_idx, :]\n\n            model, tmp = f_transform.fit_singleton(x_train, y_train, x_validation, y_validation, x_test)\n            models.append((test_key, model))\n            if len(tmp.shape) == 1:\n                tmp = tmp.reshape((-1, 1))\n            values.append(tmp)\n\n        values = np.concatenate(values, axis=0)\n        prevstage = frame.depth - work_layer\n        stage = prevstage + 1\n\n        return values, dict(models), stage\n","sub_path":"ml_forest/core/constructions/mini_pipe.py","file_name":"mini_pipe.py","file_ext":"py","file_size_in_byte":5975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"89665271","text":"'''\n\nThis file is to assess the validity of columns in the files provided, by using DataFrame\nCurrently, evaluate the validity of email and phone_number\n\n'''\n\nimport pandas as pd\nimport io\nimport re\n\n# input field\ninput_file = 'matrix_output1.csv'\noutput_file = 'matrix_assessed_data_output.csv'\n\n\ndef assess_columns_using_dataframe(file):\n\n    if isinstance(file, io.TextIOWrapper):\n        # extract the name of the file\n        fileName = file.name\n    elif isinstance(file, str):\n        fileName = file\n    print(\"this is the file:\", fileName)\n    # read the csv output file\n    data = pd.read_csv(fileName)\n    print(data)\n\n    # remove the first column(\"unique_id\") when analyzing the columns\n    data = data.drop(data.columns[0], axis=1)\n\n    # to see the type of all the columns in the file\n    print(data.dtypes)\n\n    data_framed = pd.DataFrame(data)\n    # transfer the type of postcode\n    # however, it seems different countries have different postcode rules, cannot transfer to numeric format easily\n    # data_framed['postcode'] = pd.to_numeric(data_framed['postcode'])\n    data_framed['dob'] = pd.to_datetime(data_framed['dob'])\n    # print(data_framed['first_name'])\n    # data_framed['phone_1'] = pd.to_numeric(data_framed['phone_1'])\n    # data_framed['phone_2'] = pd.to_numeric(data_framed['phone_2'])\n    # data_framed['phone_3'] = pd.to_numeric(data_framed['phone_3'])\n    print(data_framed.describe(include = 'all'))\n\n#  use regular expression to evaluate the validity of each content in the field\ndef assess_columns_using_dataframe_and_reg(file):\n\n    # fieldnames\n    fieldsname = ['unique_id','first_name','last_name','address_line','suburb','city','postcode','country','dob','email','phone_number']\n\n    if isinstance(file, io.TextIOWrapper):\n        # extract the name of the file\n        fileName = file.name\n    elif isinstance(file, str):\n        fileName = file\n    print(\"this is the file:\", fileName)\n    # read the csv output file\n    data = pd.read_csv(fileName)\n\n    # remove the first column(\"unique_id\") when analyzing the columns\n    data = data.drop(data.columns[0], axis=1)\n\n    # transfer data to dataframe format\n    data_framed = pd.DataFrame(data)\n\n    # transfer data type\n    data_framed['dob'] = pd.to_datetime(data_framed['dob'])\n\n    # to see the type of all the columns in the file\n    print(data.dtypes)\n\n    # see the general situation of data columns\n    print(data_framed.describe(include = 'all'))\n\n    # extract all the email contents from column and save to a dictionary\n    # use regex to find the valid contents' format\n    email_records = data_framed['email'].to_dict()\n    em_reg = \"[^@]+@[^@]+\"\n    calculate_validity_of_single_column(email_records,em_reg,\"email\")\n\n\n    # new zealand's phone number formats\n    # phone_reg = \"([34679]{1}(\\d){7}$)|(^2(\\d){7}$)|(^2(\\d){8}$)|(^2(\\d){9}$)|(^8[367]{1}(\\d){6}$)|(508(\\d){6}$)|(800(\\d){6}$)\"\n    # consider there are many countries, and every country has several phone number formats,\n    # which means it is very hard to consider validity based on the format of the phone number\n    phone_reg = \"(\\d)+$\"\n\n    # extract all the email contents from column and save to a dictionary\n    phone_records = data_framed['phone_number'].to_dict()\n    calculate_validity_of_single_column(phone_records,phone_reg,\"phone_number\")\n\n    # use #non-null/#records to calculate the validity of the column\n    for i in range(1,len(fieldsname)):\n        # setting new name for non_null count\n        name = fieldsname[i]\n        # count non-null of a single column\n        non_null_result = data_framed[fieldsname[i]].count()\n\n        # calculate the non-null probability\n        null_possibility = non_null_result/len(phone_records)\n        print(name, \"not null possibility:\",null_possibility)\n\n# calculate the specific possibility of different fields' validity and null situation\ndef calculate_validity_of_single_column(records, regexpression,name):\n\n    print(\"starts!!\", name)\n    # find and calculate the validity of email column\n    em_invalid_count = 0\n    em_null_count = 0\n\n    # loop all the items in the input records\n    for (k,v) in records.items():\n\n        # initialize a list, and add the single v to the list\n        single_field_records = []\n\n        # identify phone field, extract the contents of phone list\n        if \"[\" in str(v) and \"]\" in str(v):\n\n            v = v.strip(\"[\").strip(\"]\").strip(\"\\'\")\n            if \",\" in v:\n                single_field_records = v.split(\"', '\")\n            # print(\"single_field_records:\",single_field_records)\n        else:\n            single_field_records.append(v)\n            # print(\"single_field_records:\",single_field_records)\n\n\n        # during dataframe, phone_1 transfer from numeric class to float class,\n        # has mixed types in the single column; so need to deal with it separately\n        # if isinstance(v, float):\n        #     if \".\" in str(v):\n        #         v = int(v)\n\n\n        # loop all the contents in the single_field_record, and identify if every\n        # single content is qualified\n        for single_field_item in single_field_records:\n            # use regex to find valid format of contents\n            reg_result = re.match(regexpression,str(single_field_item))\n            # count the number of invalid emails\n            if not reg_result:\n                em_invalid_count += 1\n                # print(\"may not emails\",v)\n                # count the number of null\n                if str(v) == \"nan\":\n                    em_null_count += 1\n                    # print(\"may not emails\",v)\n    print(\"may not emails count:\", em_invalid_count)\n    print(\"null contents:\", em_null_count)\n    # calculate the validity possibility of email column\n    validity_possibility = 1 - (em_invalid_count/len(records))\n    # calculate the null possibility of email column\n    null_possibility = em_null_count/len(records)\n    print(name, \"info validity:\", validity_possibility)\n    print(name, \"null possibility:\",null_possibility)\n\n\n\n\n\n\n# assess_columns_using_dataframe(input_file)\nassess_columns_using_dataframe_and_reg(input_file)\n","sub_path":"assess_data.py","file_name":"assess_data.py","file_ext":"py","file_size_in_byte":6110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"477046647","text":"\"\"\" Code for HW3 part 2 pianist hand detection\n\nAuthor: Shawn Lin (shawnlin@bu.edu)\n        Alex Wong (awong1@bu.edu)\n\"\"\"\n\nimport os, sys\nimport time\nimport re\n\nimport cv2\nimport numpy as np\n\ncv2.namedWindow(\"Orig_video\")\n\n# Constants\nSCALE_FACTOR = 4\nFPS = 20\nDEBUG = False\n\n# Dummy callback function\ndef nothing(x):\n    pass\n\n# HSV filter bar\nif DEBUG:\n    cv2.createTrackbar(\"low_h\", \"Orig_video\", 0, 255, nothing)   # 11\n    cv2.createTrackbar(\"high_h\", \"Orig_video\", 12, 255, nothing)    # 11\n    cv2.createTrackbar(\"low_s\", \"Orig_video\", 94, 255, nothing)  # 190\n    cv2.createTrackbar(\"high_s\", \"Orig_video\", 178, 255, nothing)    # 190\n    cv2.createTrackbar(\"low_v\", \"Orig_video\", 117, 255, nothing)  # 48\n    cv2.createTrackbar(\"high_v\", \"Orig_video\", 255, 255, nothing)    # 48\n\n# Parse frame ID from file name \ndef get_frame_id(fn):\n    return int(re.sub(r\"piano_(\\d+)\\.png\", \"\\\\1\", fn))\n\n# Get an unused new color\ndef get_next_new_color(usedColors):\n    newColor = (np.random.choice(range(256), size=3))\n    while np.any([np.all(uc == newColor) for uc in usedColors]): # if newColor matches any of the oldColors\n        newColor = (np.random.choice(range(256), size=3))\n    return newColor\n\n\ndef get_average_video_frame(video_dir):\n    \"\"\" Compute the average video frame.\n    \n        video_dir -- name of the video directory\n    \"\"\"\n    frames = []\n    for _, _, file_list in os.walk(video_dir):\n        file_list = sorted(file_list, key=lambda x: get_frame_id(x))\n\n        for fn in file_list:\n            frame = cv2.imread(\"%s/%s\" % (video_dir, fn), cv2.IMREAD_COLOR)\n            new_shape = (frame.shape[1]//SCALE_FACTOR, frame.shape[0]//SCALE_FACTOR)\n            frame = cv2.resize(frame, new_shape)\n            frames.append(frame)\n        print(\"shape\", np.array(frames).shape)\n        avg_frame = np.average(np.array(frames).astype(np.uint32), axis=0).astype(np.uint8)\n        print(np.max(avg_frame), np.min(avg_frame))\n        print(\"avg shape\", avg_frame.shape)\n        return avg_frame\n\ndef video_frame_iterator(video_dir, debug):\n    \"\"\" Parse and traverse all the files in the directory and return a generator\n        object of frames.\n    \n    Arguments:\n        video_dir -- directory name that contains all the frames\n        debug -- debug flag (freeze on first frame if set to True)\n    \"\"\"\n    for _, _, file_list in os.walk(video_dir):\n        file_list = sorted(file_list, key=lambda x: get_frame_id(x))\n\n        if debug:\n            while True:\n                frame = cv2.imread(\"%s/%s\" % (video_dir, file_list[0]), cv2.IMREAD_COLOR)\n                new_shape = (frame.shape[1]//SCALE_FACTOR, frame.shape[0]//SCALE_FACTOR)\n                frame = cv2.resize(frame, new_shape)\n                yield (0, frame)\n        else:\n            for fn in file_list:\n                time.sleep(1./FPS)\n                frame = cv2.imread(\"%s/%s\" % (video_dir, fn), cv2.IMREAD_COLOR)\n                new_shape = (frame.shape[1]//SCALE_FACTOR, frame.shape[0]//SCALE_FACTOR)\n                frame = cv2.resize(frame, new_shape)\n                yield (get_frame_id(fn), frame)\n\n# \ndef skin_masking(frame, debug):\n    \"\"\" Perform HSV thresholding for skin detection\n    \n    Arguments:\n        frame -- Input frame\n        debug -- debug flag. Show tracking bar for tuning HSV threshold when\n                 debug flag set to True.\n    \"\"\"\n    if debug:\n        low_h = cv2.getTrackbarPos(\"low_h\", \"Orig_video\")\n        high_h = cv2.getTrackbarPos(\"high_h\", \"Orig_video\")\n        low_s = cv2.getTrackbarPos(\"low_s\", \"Orig_video\")\n        high_s = cv2.getTrackbarPos(\"high_s\", \"Orig_video\")\n        low_v = cv2.getTrackbarPos(\"low_v\", \"Orig_video\")\n        high_v = cv2.getTrackbarPos(\"high_v\", \"Orig_video\")\n    else:\n        low_h, high_h, low_s, high_s, low_v, high_v = [0, 12, 94, 178, 117, 255]\n\n    lower_range = np.array([low_h, low_s, low_v], dtype= \"uint8\")\n    upper_range = np.array([high_h, high_s, high_v], dtype =\"uint8\")\n    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n    frame = cv2.inRange(frame, lower_range, upper_range);\n    \n    return frame\n\n# Filter unrelated binary object and return label map\ndef get_label_map(frame_th, num_labels, labels, stats, centroids):\n    label_map = np.zeros((frame_th.shape[0], frame_th.shape[1], 3), np.uint8)\n\n    \n    stats = [(i, stat) for i, stat in enumerate(stats)]\n\n    # Remove small objects\n    ignore_obj_idx = {i: True for i, stat in stats if stat[4] < 150.}\n    ignore_obj_idx[0] = True # Ignore background object\n\n    # Top-right object filter\n    top_right_ctrd = [0, 480]\n    top_right_idx = -1\n    for c, (i, stat) in zip(centroids, stats):\n        \n        if i in ignore_obj_idx:\n            continue\n\n        # print(\"obj: %i, area: %.3f, centroid: \" % (i, stat[4]), c)\n        if c[0] >= top_right_ctrd[0] and c[1] <= top_right_ctrd[1]:\n            top_right_ctrd = c\n            top_right_idx = i\n\n    # Ignore top-rightest object condition:\n    cur_obj_cnt = len(stats) - len(ignore_obj_idx)\n    if cur_obj_cnt >= 3:\n        ignore_obj_idx[top_right_idx] = True\n    elif 1 < cur_obj_cnt < 3 and max([s[4] for i, s in stats if i != 0]) > 400.:\n        ignore_obj_idx[top_right_idx] = True\n\n    # Only focus on coloring the top 2 largest object\n    filtered_stats = list(filter(lambda x: x[0] not in ignore_obj_idx, stats))\n    sorted_stats = sorted(filtered_stats, key=lambda x: x[1][4], reverse=True)[:2]\n\n    print(\"sorted_filtered_stats:\", sorted_stats)\n\n    color_map = [np.array([0, 0, 0])]\n    for _ in range(num_labels):\n        color = get_next_new_color(color_map)\n        color_map.append(color)\n\n    for i in range(labels.shape[0]):\n        for j in range(labels.shape[1]):\n            color_label = labels[i][j]\n            if color_label not in ignore_obj_idx:\n                color = color_map[color_label]\n                label_map[i][j] = color\n    \n    return label_map, sorted_stats\n\n\nif __name__ == \"__main__\":\n\n    avg_frame = get_average_video_frame(\"../CS585-PianoImages\")\n    if DEBUG:\n        cv2.imshow(\"AvgFrame\", avg_frame)\n\n    cv2.waitKey(0)\n    for frame_id, frame in video_frame_iterator(\"../CS585-PianoImages\", DEBUG):\n        \n        # Moving region to ROI_mask\n        frame_diff = cv2.absdiff(frame, avg_frame)\n        frame_diff = cv2.cvtColor(frame_diff, cv2.COLOR_BGR2GRAY)\n        _, roi_mask = cv2.threshold(frame_diff, 10, 255, cv2.THRESH_BINARY)\n        \n        if DEBUG:\n            cv2.imshow(\"mask\", roi_mask)\n\n        # Apply ROI mask on original frame\n        frame_roi = cv2.bitwise_and(frame, frame, mask=roi_mask)\n\n        # skin detection\n        frame_gs = skin_masking(frame_roi, DEBUG)\n\n        # Morphology\n        frame_blur = cv2.GaussianBlur(frame_gs, (3, 3), 0)\n        _, frame_th = cv2.threshold(frame_blur, 50, 255, cv2.THRESH_BINARY)\n        frame_blur = cv2.GaussianBlur(frame_th, (5, 5), 0)\n        _, frame_th = cv2.threshold(frame_blur, 80, 255, cv2.THRESH_BINARY)\n\n        kernel_3x3 = np.ones((5, 5), np.uint8)\n        kernel_7x7 = np.ones((3, 3), np.uint8)\n        frame_gs = cv2.dilate(frame_gs, kernel_3x3, iterations=1)\n        frame_gs = cv2.erode(frame_gs, kernel_3x3, iterations=1)\n        frame_gs = cv2.dilate(frame_gs, kernel_7x7, iterations=1)\n        frame_gs = cv2.erode(frame_gs, kernel_7x7, iterations=1)\n\n        frame_gs = cv2.GaussianBlur(frame_gs, (3, 3), 0)\n        _, frame_gs = cv2.threshold(frame_gs, 150, 255, cv2.THRESH_BINARY)\n\n        # Flood filling with object stats\n        num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(frame_th, 4, cv2.CV_32S)\n\n        # Object detection heuristics\n        label_map, stats = get_label_map(frame_th, num_labels, labels, stats, centroids)\n        \n        # Draw hand bounding box\n        for stat in stats:\n            x, y, w, h = stat[1][:4]\n            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)\n\n        cv2.imshow(\"Orig_video\", frame)\n\n        if cv2.waitKey(5) & 0xFF == ord('q'):\n            exit(0)\n","sub_path":"hw3/src/hw3_p2_piano.py","file_name":"hw3_p2_piano.py","file_ext":"py","file_size_in_byte":7984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"652285993","text":"'''\nDescripttion: \nversion: \nAuthor: Jim Huang\nDate: 2021-05-01 10:09:30\nLastEditors: CoderXZ\nLastEditTime: 2021-05-01 10:13:19\n'''\nfrom collections import namedtuple\nStudents = namedtuple(\"Student\",\"name age\")\nJim = Students(\"Jim\",20)\nprint(Jim.name)\nprint(Jim.age)","sub_path":"MagePython/Chapter2/2-namedtuple.py","file_name":"2-namedtuple.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"327293737","text":"from django.conf.urls import include, url\nfrom django.contrib import admin\n\nurlpatterns = [\n    # Examples:\n    # url(r'^$', 'zapt2.views.home', name='home'),\n    # url(r'^blog/', include('blog.urls')),\n\n    url(r'^admin/', include(admin.site.urls)),\n    url(r'^$', 'board.views.bottom', name='kong'),\n    url(r'^test$', 'board.views.test', name='test'),\n    url(r'^news', 'board.views.news', name='news'),\n    url(r'^leftfield', 'board.views.leftfield', name='leftfield'),\n    url(r'^loadpkg', 'board.views.loadpkg', name='loadpkg'),\n    url(r'^title', 'board.views.title', name='title'),\n    url(r'^index/', 'board.views.index', name='kong'),\n    url(r'^spr/', 'board.views.spr', name='spr'),\n    url(r'^sendemail$', 'dra.views.send_dra_email', name='email'),\n    url(r'^dra_report', 'dra.views.dra',{'tag':'ALL'}, name='dra_report'),\n    url(r'^dra_tps_report', 'dra.views.dra',{'tag':'TPS'}, name='dra_tps_report'),\n    url(r'^dra_cpu_report', 'dra.views.dra',{'tag':'CPU'}, name='dra_cpu_report'),\n]\n\n'''\nurl(r'^index/', 'board.views.index', name='kong'),\nurl(r'^$', 'board.views.bottom', name='kong'),\nurl(r'^test', 'board.views.test', name='test'),\nurl(r'^title', 'board.views.title', name='title'),\nurl(r'^board/(?P<v1>.*)/(?P<v2>.*)', 'board.views.board',{'test':'board'}, name='board'),\nurl(r'^welcome', 'board.views.welcome', name='welcome'),\nurl(r'^admin', include(admin.site.urls)),\nurl(r'^bottom', 'board.views.bottom', name='bottom'),\nurl(r'^leftfield', 'board.views.leftfield', name='leftfield'),\nurl(r'^rightfield', 'board.views.rightfield', name='rightfield'),\n\n#(r'^one1/$', redirect_to, {'url': '/another/'}),\n#(r'^one2/$', RedirectView.as_view(url='/another/')),\n'''\n","sub_path":"zapt2/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"566477902","text":"#!/usr/bin/python3\n# -*- coding:utf-8 -*-\n\"\"\"\n\n把phantomjs.exe移到python文件夹中的Scripts中，至此，就已经在Win的环境下配置好了环境。\n\nPhantomJS是一个基于Webkit的\"无界面\"(headless)浏览器，它会把网站加载到内存并执行页面上的JavaScript，因为不会展示图形界面，所以运行起来比完整的浏览器更高效。\n\n#如果没有在环境变量指定PhantomJS位置\n#driver = webdriver.PhantomJS(executable_path = \"./phantomjs\")\n\nhttps://www.cnblogs.com/miqi1992/p/8093958.html\n\n\"\"\"\n\nimport sys,os,json,datetime,time\nfrom lxml import etree\nimport requests\nfrom selenium import webdriver\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nimport warnings\nwarnings.simplefilter(\"ignore\", Warning)\n\nstart = time.time()\n\nheaders = {\n    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n    'Accept-Language': 'zh-CN,zh;q=0.8,en-US;q=0.5,en;q=0.3',\n    'User-Agent': 'chrome',\n    'referer':'http://www.google.com'\n}\n\n#使用copy()防止修改原代码定义dict\ndcap = DesiredCapabilities.PHANTOMJS.copy() \n\nfor key, val in headers.items():\n    dcap['phantomjs.page.customHeaders.{}'.format(key)] = val\n\ndcap[\"phantomjs.page.settings.loadImages\"] = False  # 禁止加载图片\n\nurl = \"https://www.banggood.com/BlitzWolf-BW-ES1-Graphene-In-ear-Sound-Wired-Control-Earphone-With-Microphone-p-1110873.html\"\n\nbrowser = webdriver.PhantomJS(desired_capabilities=dcap)\nbrowser.set_page_load_timeout(10)\nbrowser.set_script_timeout(10)\n\ntry:\n\n    browser.get(url)\n\nexcept Exception as e:\n    # print(e)\n    browser.execute_script('window.stop()')          # 当页面加载时间超过设定时间，通过执行Javascript来stop加载，然后继续执行后续动作\n\nprint(browser.title)\n\nn_sold = browser.find_element_by_xpath('//*[@class=\"list_sold\"]')\nprint(n_sold.text)\n\nn_wish = browser.find_element_by_xpath('//*[@class=\"wish_num\"]')\nprint(n_wish.text)\n\nend = time.time()\nprint (\"time: %.2fs\" % (end - start))\n\n","sub_path":"PhantomJS1/getjs2.py","file_name":"getjs2.py","file_ext":"py","file_size_in_byte":2025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"286415668","text":"#Matchlen\ndef matchlen(s, length):\n    return (s * (length//len(s)+1))[:length]\n\n#Intro\nchoice = input(\"Do you want to encrypt (e) or decrypt (d) a word?: \")\nprint(\"Input a word and a key: \")\npalabraoriginal = input(\"Word: \")\nclave = input(\"Key: \")\n\n#abc\nabc = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\", \"i\", \"j\", \"k\", \"l\", \"m\", \"n\", \"o\", \"p\", \"q\", \"r\", \"s\", \"t\", \"u\", \"v\", \"w\", \"x\", \"y\", \"z\"]\n\n#Contar\nlong = len(palabraoriginal)\n\n#Lista\nlista_palabra = list(palabraoriginal)\nlista_clave = list(clave)\nlista_resultado = []\n\n#ClaveLongitud\nlista_clavelongitud = matchlen(lista_clave, long)\n\n#Zippeado\nzipped = list(zip(lista_palabra, lista_clavelongitud))\n\nif choice == \"e\" or choice == \"E\":\n    for n in range(0, long):\n        indiceN=int((abc.index((zipped[n])[0]))+(abc.index((zipped[n])[1])))\n        if indiceN > 26:\n            indiceN = indiceN - 26\n            lista_resultado.append(abc[indiceN])\n        if indiceN > 52:\n            indiceN = indiceN - 52\n            lista_resultado.append(abc[indiceN])\n        else:\n            indiceN = indiceN - 26\n            lista_resultado.append(abc[indiceN])\n    print(\"The encrypted word is:\", \"\".join(lista_resultado))\n\nelse:\n    for n in range(0, long):\n        indiceN=int((abc.index((zipped[n])[0]))-(abc.index((zipped[n])[1])))\n        if indiceN > 26:\n            indiceN = indiceN - 26\n            lista_resultado.append(abc[indiceN])\n        if indiceN > 52:\n            indiceN = indiceN - 52\n            lista_resultado.append(abc[indiceN])\n        else:\n            lista_resultado.append(abc[indiceN])\n    print(\"The decrypted word is:\", \"\".join(lista_resultado))\n","sub_path":"Captures_de_Pantalla/Funcions.py","file_name":"Funcions.py","file_ext":"py","file_size_in_byte":1630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"545027786","text":"from django.urls import path, include\nfrom .views import (index,\n                    RestaurantListView,\n                    RestaurantCreateView,\n                    RestaurantDetailView,\n                    RestaurantUpdateView,\n                    RestaurantDeleteView,\n                    FoodListView,\n                    FoodCreateView,\n                    FoodDetailView,\n                    FoodUpdateView,\n                    FoodDeleteView,\n                    create_restaurant,\n                    IndexView)\napp_name = 'res_app'\n\nurlpatterns = [\n    path('', IndexView.as_view(), name='index'),\n    path('restaurant_list/', RestaurantListView.as_view(), name='restaurant_list'),\n    path('restaurant_create/', RestaurantCreateView.as_view(), name='restaurant_create'),\n    path('restaurant_detail/<int:pk>/', RestaurantDetailView.as_view(), name='restaurant_detail'),\n    path('restaurant_update/<int:pk>/', RestaurantUpdateView.as_view(), name='restaurant_update'),\n    path('restaurant_delete/<int:pk>/', RestaurantDeleteView.as_view(), name='restaurant_delete'),\n    path('api/', include('res_app.api.urls')),\n    path('food_list/', FoodListView.as_view(), name='food_list'),\n    path('food_create/', FoodCreateView.as_view(), name='food_create'),\n    path('food_detail/<int:pk>/', FoodDetailView.as_view(), name='food_detail'),\n    path('food_update/<int:pk>/', FoodUpdateView.as_view(), name='food_update'),\n    path('food_delete/<int:pk>/', FoodDeleteView.as_view(), name='food_delete'),\n    path('create_restaurant_with_func/', create_restaurant, name='create_restaurant_with_func')\n]","sub_path":"res_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"252321240","text":"#write the import statements for homework5 functions\n\n#With the functions created in homework5...\n#Prompt user for number of sales records to input.\n#Open a file for text writing.\n#Iterate and prompt user for item name and price.\n#Save item name and price to file in one line\n#Calculate sum of price and write to file\n#Calculate average price and write to file\n\n#Open a file for text reading.\n#Read the saved file and output table\n\nfrom homework5 import write_sales_data\nfrom homework5 import read_sales_data\n\ndef main():\n    infile = open('salesrecord.txt', 'w')\n    infile.write('name' + '\\t' + 'price' + '\\n')\n\n    num_of_items = int(input(\"Enter number of items: \"))\n\n    total = 0\n    for items in range(1, num_of_items+1):\n        item = input('Enter name of item: ')\n        price = float(input('Enter price of item: '))\n        infile.write(str(item)+ '\\t')\n        infile.write(str(\"{0:.2f}\".format(price))+ '\\n')\n        total += price\n\n    infile.write('total' + '\\t'+ str(\"{0:.2f}\".format(total))+ '\\n')\n\n    avg = total / num_of_items\n    infile.write('Avg' + '\\t'+ str(\"{0:.2f}\".format(avg)))\n    infile.close()\n        \n    infile = open('salesrecord.txt', 'r')\n    file_contents = infile.read()\n    infile.close()\n    print(file_contents)\n        \nmain()\n\n","sub_path":"src/homework/main/main_homework5.py","file_name":"main_homework5.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"141545411","text":"\ndef main(input_matrix):\n    with open(input_matrix, \"r\")as f:\n        rawin = f.read()\n    rawin = rawin.splitlines()\n\n    # strip away comments at the beginning of the matrix file\n    inp = []\n    for line in rawin:\n        if line[0] == '#':\n            continue\n        else:\n            inp = inp + [line]\n    # print \"inp: \", inp\n\n    # get the labels along the top of the matrix\n    toprow = \"\"\n    for char in inp[0]:\n        if char != ' ':\n            toprow = toprow + char\n\n    # get the labels along the left side\n    lefcol = \"\"\n    for line in inp:\n        if line[0] != ' ':\n            lefcol = lefcol + line[0]\n\n    # get a label for every data point in the matrix\n    # labels are in order from top->bottom and right->left\n    labels = []\n    for x in toprow:\n        for y in lefcol:\n            labels.append(x+y)\n\n    numbers = []\n    # get a list of every data point in same order as labels\n    for line in inp[1:]:\n        for i in range(2, len(line[1:]), 3):\n            numbers.append(int(line[i:i+2]))\n\n    return dict(zip(labels, numbers))\n","sub_path":"archive/fourth/sub_matrix.py","file_name":"sub_matrix.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"230131284","text":"# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n#       OS : GNU/Linux Ubuntu 16.04 \n# COMPILER : Python 3.5.2\n#   AUTHOR : Klim V. O.\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n\n'''\nПредназначен для построения и обучения сети LeNet, и классификации изображений рукописных цифр.\n'''\n\nimport sys\nimport numpy\nimport time\nfrom PIL import Image as pilImage\nfrom io import BytesIO\nimport tensorflow\nfrom keras.models import model_from_json\nfrom keras.preprocessing import image\nfrom keras.datasets import mnist\nfrom keras.models import Sequential, model_from_json\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras import utils\n        \n# Для повторяемости результатов (опорное значение для генератора случайных чисел)\nnumpy.random.seed(42)\n\nclass LeNet:\n    ''' Предназначен для построения и обучения сети LeNet, и классификации изображений рукописных цифр.\n    1. f_net_model - имя .json файла с описанием модели сети\n    2. f_net_weights - имя .h5 файла с весами обученной сети\n    3. f_test_classification_img - имя .jpg/.png/.bmp/.tiff файла с тестовым изображением цифры '''\n    def __init__(self, f_net_model, f_net_weights, f_test_classification_img):\n        self.f_net_model = f_net_model\n        self.f_net_weights = f_net_weights\n        self.f_test_classification_img = f_test_classification_img\n        self.lenet_model = None\n        self.__init_classification()        \n        self.is_training = False\n        \n\n    def train(self, f_source_data, for_out_in_file = False):\n        ''' Обучение сети на наборе рукописных цифр MNIST с параметрами по умолчанию. \n        1. f_source_data - имя .npz файла с обучающей выборкой\n        2. for_out_in_file - изменяет вывод при обучении модели для удобства записи в файл \n        3. возвращает строку с точностью классификации на тестовых данных из MNIST и затраченное на обучение время '''       \n        \n        self.is_training = True\n\n        # Размер изображений в обучающей выборке\n        img_width, img_height = 28, 28\n\n        # Загрузка обучающей выборки mnist (X_train и X_test - наборы изображений, y_train и y_test - \n        # цифры, которые на них изображены (т.е. правильные ответы, метки классов))\n        print('[i] Загрузка обучающей выборки из %s' % f_source_data)\n        f = numpy.load(f_source_data)\n        x_train, y_train = f['x_train'], f['y_train']\n        x_test, y_test = f['x_test'], f['y_test']\n        f.close()\n        \n        print('[i] Подготовка обучающей выборки...')\n        # Добавление всех изображений в один массив\n        # n x m x p x k, где n - номер изображения, m - столбец в изображении, p - строка в изображении, k - интенсивность цвета пикселя (0..255) \n        x_train = x_train.reshape(x_train.shape[0], img_width, img_height, 1)\n        x_test = x_test.reshape(x_test.shape[0], img_width, img_height, 1)\n        input_shape = (img_width, img_height, 1)\n\n        # Приведение типа к float32 и нормализация (перевод интенсивности цвета пикселя в диапазон 0..1)\n        x_train = x_train.astype('float32')\n        x_test = x_test.astype('float32')\n        x_train /= 255\n        x_test /= 255\n\n        # Преобразование меток изображений в категории (т.е. преобразование цифры в массив из 10 элементов (т.к. 10 классов), где все элементы \n        # равны 0 кроме того, порядковый номер которого равен исходной цифре, он равен 1). \n        # Например: была цифра 4, в результате преобразования получится массив - [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n        Y_train = utils.to_categorical(y_train, 10)\n        Y_test = utils.to_categorical(y_test, 10)\n\n        print('[i] Построение сети...')\n        # Создание модели однонаправленной (последовательной) сети\n        model = Sequential()\n\n        start_time = None\n        end_time = None\n        scores = None\n        \n        # Слой свертки, 75 карт признаков, размер ядра свертки: 5х5\n        # Представляет из себя разбиение каждого изображения на матрицы 5х5, интенсивность цвета каждого пикселя умножается\n        # на веса соответствующего данной части изображения нейрона, результаты суммируются и передаются дальше \n        # 75 карт признаков - т.е. 75 наборов слоёв, которые используют разные ядра свёртки (т.е. по сути, выделяют 75 признаков)\n        model.add(Conv2D(75, kernel_size=(5, 5), activation='relu', input_shape=input_shape))\n\n        # Слой подвыборки (субдискретизирующий слой), размер пула 2х2\n        # Нужен что бы распознавать цифры разного масштаба на изображении, на этом слое берутся результаты матрицы нейронов 2х2 \n        # предыдущего слоя и выбирается из них максимальное\n        # Dropout - слой регуляризации, нужен что бы предотвратить переобучение (на этом слое каждый раз, когда подаётся новый \n        # объект (т.е. происходит передача сигналов между предыдущим слоем и следующим), случайным образом отключаются нейроны \n        # с вероятностью 0.2, т.е. вероятность 20% того, что нейрон не будет участвовать в обучении на следующем слое, он будет «отключён»)\n        model.add(MaxPooling2D(pool_size=(2, 2)))\n        model.add(Dropout(0.2))\n\n        # Слой свертки, 100 карт признаков, размер ядра свертки 5х5\n        model.add(Conv2D(100, kernel_size=(5, 5), activation='relu'))\n\n        # Слой подвыборки, размер пула 2х2\n        model.add(MaxPooling2D(pool_size=(2, 2)))\n        model.add(Dropout(0.2))\n\n        # Полносвязный слой, 500 нейронов, необходим для классификации\n        # Flatten - преобразование из двумерного вида в одномерный\n        # В слое типа Dense происходит соединение всех нейронов предыдущего уровня со всеми нейронами следующего уровня\n        model.add(Flatten())\n        model.add(Dense(500, activation='relu'))\n        model.add(Dropout(0.5))\n\n        # Полносвязный выходной слой, 10 нейронов, которые соответствуют классам рукописных цифр от 0 до 9\n        # При успешном распознавании на выходе одного из этих нейронов будет значение, близкое к 1, а на остальных - близкие к 0\n        model.add(Dense(10, activation='softmax'))\n\n        # Компиляция модели и вывод данных о ней\n        model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n        print(model.summary())\n\n        # Обучение сети\n        print('\\n[i] Обучение сети...')\n        start_time = time.time()\n        if for_out_in_file:\n            model.fit(x_train, Y_train, batch_size=200, epochs=10, validation_split=0.2, verbose=2)\n        else:\n            model.fit(x_train, Y_train, batch_size=200, epochs=10, validation_split=0.2, verbose=1)\n\n        # Оценка качества обучения на тестовых данных, loss - значение функции ошибки, acc - точность\n        scores = model.evaluate(x_test, Y_test, verbose=0)\n        end_time = time.time()\n        print('[i] Точность работы на тестовой выборке: %.2f%%' % (scores[1]*100))\n        print('[i] Время обучения: %.2f минут.' % ((end_time - start_time)/60.0))\n\n        # Генерация описания модели в формате json и запись её в файл\n        print('[i] Сохранение модели сети и весов в %s и %s' % (self.f_net_model, self.f_net_weights))\n        model_json = model.to_json()\n        json_file = open(self.f_net_model, 'w')\n        json_file.write(model_json)\n        json_file.close()\n\n        # Сохранение весов в файл (для работы необходим пакет h5py (sudo pip3 install h5py) и libhdf5-dev (sudo apt-get install libhdf5-dev))\n        model.save_weights(self.f_net_weights)\n\n        self.is_training = False\n        self.__init_classification()\n        return (round(scores[1]*100, 2), round((end_time - start_time)/60, 2)) # точность в %, время в минутах\n\n\n    def __init_classification(self):\n        ''' Загрузка модели сети и её весов, компиляция полученной сети и проверка её работоспособности. '''\n        # Загрузка модели из файла\n        json_file = open(self.f_net_model, 'r')\n        loaded_model_json = json_file.read()\n        json_file.close()\n        self.lenet_model = model_from_json(loaded_model_json)\n\n        # Загрузка весов в модель\n        self.lenet_model.load_weights(self.f_net_weights)\n\n        # Компиляция модели\n        self.lenet_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n        # Загрузка тестового изображения\n        test_image_value = 0\n        image_for_classification = pilImage.open(self.f_test_classification_img)\n        image_for_classification = image_for_classification.convert('L')\n        image_for_classification.thumbnail((28, 28), pilImage.ANTIALIAS)\n\n        # Преобразование изображения в массив numpy\n        x = image.img_to_array(image_for_classification)\n        \n        # Инвертирование цвета и нормализация\n        x = 255 - x\n        x = x / 255\n        x = numpy.expand_dims(x, axis=0)\n\n        # Классификация\n        categories = self.lenet_model.predict(x)\n        classified_digit = int(numpy.argmax(categories))\n        if classified_digit == test_image_value:\n            print('[i] Тестовое изображение классифицированно правильно.')\n        else:\n            print('[E] Тестовое изображение классифицировано неверно. Рекомендуется обучить сеть заново.')        \n        \n\n    def classify(self, image_for_classification):\n        ''' Классификация изображения с помощью заранее обученной сети.\n        1. image_for_classification - .jpg/.png/.bmp/.tiff файл с изображением цифры в виде бинарной строки\n        2. возвращает строку с распознанной цифрой или: error_1 - если выполняется обучение сети, error_2 - если что-то не так с изоражением '''\n        \n        if self.is_training == True:\n            return 'error_1'\n        \n        try:\n            # Преобразование бинарной строки в PIL Image\n            stream = BytesIO(image_for_classification)\n            image_for_classification = pilImage.open(stream)\n\n            # Если изображение прямоугольное, то преобразование его в квадратное\n            width, height = image_for_classification.size\n            if width > height:\n                background = pilImage.new('RGB', (width, width), (255, 255, 255))\n                background.paste(image_for_classification, (0, (width - height) // 2))\n                image_for_classification = background\n            elif width < height:\n                background = pilImage.new('RGB', (height, height), (255, 255, 255))\n                background.paste(image_for_classification, ((height - width) // 2, 0))\n                image_for_classification = background\n\n            # Преобразование изображение в чёрно-белое и уменьшение размера до 28х28\n            image_for_classification = image_for_classification.convert('L')\n            image_for_classification.thumbnail((28, 28), pilImage.ANTIALIAS)\n        except Exception:\n            return 'error_2'\n\n        # Преобразование изображения в массив numpy\n        x = image.img_to_array(image_for_classification)\n        \n        # Инвертирование цвета и нормализация\n        x = 255 - x\n        x = x / 255\n        x = numpy.expand_dims(x, axis=0)\n\n        # Распознавание\n        categories = self.lenet_model.predict(x)\n        classified_digit = int(numpy.argmax(categories))\n        return str(classified_digit)\n\n\n\n\ndef main():\n    f_source_data = 'data/mnist.npz'\n    f_net_model = 'data/lenet_model.json'\n    f_net_weights = 'data/lenet_weights.h5'\n    f_test_classification_img = 'images/test_classification.jpg'\n    lenet = LeNet(f_net_model, f_net_weights, f_test_classification_img)\n    lenet.train(f_source_data)\n\n\nif __name__ == '__main__':\n    main()","sub_path":"lenet.py","file_name":"lenet.py","file_ext":"py","file_size_in_byte":15033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"19910461","text":"def cipher(x):\n    ans = \"\"\n    for c in x:\n        if c.islower():\n            c = chr(219-ord(c))\n        ans += c\n    return ans\n\ns = \"This is a pen\"\ns = cipher(s)\nprint(s)\nprint(cipher(s))\n\n","sub_path":"hotate/chapter01/knock08.py","file_name":"knock08.py","file_ext":"py","file_size_in_byte":194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"533376407","text":"def check_window(needs, window):\n    for k in needs.keys():\n        if k not in window or window.get(k, -1) < needs[k]:\n            return False\n    return True\n\n\ndef func():\n    origin_str = input()\n    target = input()\n\n    res = \"\"\n    if not target or len(origin_str) < len(target):\n        return \"\"\n\n    left, right = 0, 0\n    needs = {}  # target dict\n    window = {}  # now window\n    \n    for c in target:\n        needs[c] = 1 if c not in needs else needs[c] + 1\n        \n    while right < len(origin_str):\n        window[origin_str[right]] = 1 if origin_str[right] not in window else window[origin_str[right]] + 1\n        while check_window(needs, window):\n            # print(needs, window)\n            if len(res) > len(origin_str[left: right + 1]) or not res:\n                res = origin_str[left: right + 1]\n            window[origin_str[left]] -= 1\n            left += 1\n\n        right += 1\n\n    print(res)\n\n\nfunc()\n","sub_path":"Code/CodeRecords/2640/58547/286933.py","file_name":"286933.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"531842096","text":"from __future__ import print_function\nimport matplotlib.cm as mpl_color_map\nimport copy\nimport pdb\nfrom transforms.radar_transforms import microdoppler_transform as transform\nimport matplotlib.pylab as plt\nfrom torch.nn import ReLU\nimport numpy as np\nfrom PIL import Image\nimport functools\nimport csv\nimport os\nimport collections\nfrom collections import Counter\nfrom torch.autograd import Variable\nimport torch\nfrom utils import load_checkpoint\nfrom datasets import RadarDataset\nfrom models.models import CNN3DNet_Modified\nimport sys\nimport random\nsys.path.insert(0, '../')\n\n\nclass VanillaBackprop():\n    \"\"\"\n        Produces gradients generated with vanilla back propagation from the image\n    \"\"\"\n\n    def __init__(self, model):\n        self.model = model\n        self.gradients = None\n        # Put model in evaluation mode\n        self.model.eval()\n        # Hook the first layer to get the gradient\n        self.hook_layers()\n\n    def hook_layers(self):\n        def hook_function(module, grad_in, grad_out):\n            self.gradients = grad_in[0]\n\n        # Register hook to the first layer\n        first_layer = list(self.model.features._modules.items())[0][1]\n        first_layer.register_backward_hook(hook_function)\n\n    def generate_gradients(self, input_image, target_class):\n        # Forward\n        model_output = self.model(input_image)\n        # Zero grads\n        self.model.zero_grad()\n        # Target for backprop\n        one_hot_output = torch.FloatTensor(1, model_output.size()[-1]).zero_()\n        one_hot_output[0][target_class] = 1\n        # Backward pass\n        model_output.backward(gradient=one_hot_output)\n        # Convert Pytorch variable to numpy array\n        # [0] to get rid of the first channel (1,3,224,224)\n        gradients_as_arr = self.gradients.data.numpy()[0]\n        return gradients_as_arr\n\n\nif __name__ == '__main__':\n    dataset = 'gestures'\n    base_location = '../data'\n    csv_location = '../data'\n    sample_length = 50\n    filename_dataset = '../data/gestures-random-train.csv'\n    features = 'range_doppler_log'\n\n    values = dict()\n    values['microdoppler'] = {'mean': -18548.79815690202, 'std': 1202.8846406929292,\n                              'min': -20102.396484375, 'max': 0.0}\n    values['range_doppler_log'] = {'mean': -115.96167423431216, 'std': 47.856299075087996,\n                                   'min': -200.0, 'max': -24.600444793701172,\n                                   'median': np.load('/home/gp/Documents/radar_stuff/range_doppler_median.npy')}\n\n    with open(os.path.join(csv_location, '%s-labels.csv' % dataset), 'r') as f:\n        reader = csv.reader(f, delimiter=';')\n        labels = [v for v, in reader]\n    print(labels)\n    sample_sizes = {\n        'microdoppler': {0: (sample_length, 256), 1: (sample_length, 253)},\n        'range_doppler_log': {0: (sample_length, 80, 128), 1: (sample_length, 80, 126)},\n    }\n\n    # Dataset for forward pass\n    transform = functools.partial(transform,\n                                  sample_length=sample_length,\n                                  features=features,\n                                  scaling='standard',\n                                  preprocessing=True,\n                                  values=values[features])\n    dataset2 = RadarDataset(filename_dataset, labels,\n                            transform=transform,\n                            feature=features,\n                            select='center',\n                            sample_length=sample_length,\n                            base_location=base_location)\n    # Model\n    net = CNN3DNet_Modified(num_classes=len(\n        labels), sample_size=sample_sizes[features][1])\n    # Load model\n    load_checkpoint(\n        net, None, '/home/gp/Documents/radar_stuff/models/gestures_random_nopool_cpu.pt')\n    net.cpu()\n    net.eval()\n\n    VIS_TARGET_FOLDER = '../results'\n    if not os.path.exists('../results'):\n        os.makedirs('../results')\n\n    # frame for padding\n    pad_sample, pad_im_label = dataset2[254]\n    padding = pad_sample[0][49]\n\n    master_logit_diff_list = [0] * 50\n    master_magnitude_list = [0] * 50\n    master_stddev_logit_list = []\n    std_dev = [0] * 50\n    vbp_magnitude_frame_replace = []\n    avg_logit_diff_list = []\n    upper_confidence_list = [0] * 50\n    lower_confidence_list = [0] * 50\n\n    # Vanilla Backprop\n    VBP = VanillaBackprop(net)\n    sample_amount = len(dataset2)\n    for i in range(0, sample_amount):\n        sample_id = i\n        vbp_sample, label1 = dataset2[sample_id]\n        # making copies for modification\n        vbp_sample_copy = copy.deepcopy(vbp_sample)\n        # Forward pass\n        out = net(vbp_sample)\n        # current prediction\n        org_pred_logit = out[0][label1].item()\n        # Get gradients\n        var_sample_1 = Variable(vbp_sample_copy, requires_grad=True)\n        vanilla_grads = VBP.generate_gradients(var_sample_1, label1)\n        split_grads_np1 = vanilla_grads[0]\n        split_grads_np1 = np.clip(split_grads_np1, a_min=0, a_max=100000000)\n        vbp_magnitude_list = []\n        for j in range(0, 50):\n            vbp_magnitude_list.append(np.linalg.norm(split_grads_np1[j]))\n        logit_difference_list = []\n        sorted_logit_list = []\n        for single_frame_replace_id in range(0, 50):\n            vbp_sample_copy = copy.deepcopy(vbp_sample)\n            vbp_sample_copy[0][single_frame_replace_id] = padding\n            # Forwad pas with changed frame\n            out = net(vbp_sample_copy)\n            logit_difference = org_pred_logit - out[0][label1].item()\n            logit_difference_list.append(logit_difference)\n        # Sort logit changes according to grad magnitude, the largest magnitude is the last\n        # so the first element is the smallest magnitude ( ascending order )\n        sorted_logit_list = [x for _, x in sorted(zip(vbp_magnitude_list, logit_difference_list))]\n        master_stddev_logit_list.extend(sorted_logit_list)\n    master_stddev_logit_as_np = np.split(np.array(master_stddev_logit_list), sample_amount)\n    std_dev = np.std(np.vstack(master_stddev_logit_as_np), axis=0)\n    # avg_magnitude_list to get the change_in_logit-vs-Frame plot (frames in descending order of magnitude)\n    for loc in range(50):\n        avg_logit_diff_list = [x / sample_amount for x in master_logit_diff_list]\n        master_magnitude_list[loc] = master_magnitude_list[loc] + \\\n            vbp_magnitude_list[loc]\n    # calcuating the 95 percent confidennce interval\n    for loc in range(50):\n        upper_confidence_list[loc] = avg_logit_diff_list[loc] + \\\n            std_dev[loc] * (1.96 / np.sqrt(sample_amount))\n        lower_confidence_list[loc] = avg_logit_diff_list[loc] - \\\n            std_dev[loc] * (1.96 / np.sqrt(sample_amount))\n\nprint('vanilla backprop completed')\n","sub_path":"vanilla_backprop.py","file_name":"vanilla_backprop.py","file_ext":"py","file_size_in_byte":6787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"618304107","text":"import numpy as np\nfrom numpy import array\nimport json\nfrom keras.utils import to_categorical\nfrom sklearn.preprocessing import LabelEncoder\n\nONE_HOT_ENCODING = {\n    'Core': (1,0,0),\n    'Delta': (0,1,0),\n    'Neg': (0,0,1),\n    'Sing': (1,0),\n    'Not_sing':(0,1)\n}\n\nclass NumpyEncoder(json.JSONEncoder):\n    def default(self, obj):\n        if isinstance(obj, np.ndarray):\n            return obj.tolist()\n        return json.JSONEncoder.default(self, obj)\n\ndef nparray_to_json(key,img):\n    return json.dumps({key: img}, cls=NumpyEncoder)\n\ndef json_to_nparray(key,gson):\n    json_load = json.loads(gson)\n    return np.asarray(json_load[key])\n\ndef decode_prediction(y):\n    dtype = [('score', float), ('pos', int)]\n    c = []  \n    values = []\n    for j in range(0,3):\n        values.append((y[j], j))\n    \n    c = np.array(values, dtype=dtype)\n    c = np.sort(c, order='score')\n    \n    y[c[2][1]] = 1\n    y[c[1][1]] = 0\n    y[c[0][1]] = 0\n    return y\n    \ndef oneHotEncoding(data):\n\n    lb = []\n\n    for k in data.keys():\n        lb.append(k)\n\n    encoding = array(lb)\n\n    label_encoder = LabelEncoder()\n\n    encoding = label_encoder.fit_transform(encoding)\n\n    encoding = to_categorical(encoding)\n\n    return generate_labels(data, encoding, lb)\n\ndef label_encoding(data):\n\n    lb = []\n\n    for k in data.keys():\n        lb.append(k)\n\n    encoding = array(lb)\n\n    label_encoder = LabelEncoder()\n\n    encoding = label_encoder.fit_transform(encoding)\n\n    return generate_labels(data, encoding, lb)\n\ndef label_to_categorial_encoding(y):\n\n    return to_categorical(y)\n\ndef generate_labels(data, encoding, lb):\n\n    dataTemp = []\n    labels = np.array([])\n    names = []\n    gt_label = {}\n    i = 0\n\n    for (k, v) in data.items():\n\n        if labels.shape[0] == 0:\n#             labels = np.tile(encoding[i], (len(v), 1))\n            labels = np.tile(ONE_HOT_ENCODING[k], (len(v), 1))\n        else:\n#             label = np.tile(encoding[i], (len(v), 1))\n            label = np.tile(ONE_HOT_ENCODING[k], (len(v), 1))\n            labels = np.concatenate((labels, label), axis=0)\n\n#         gt_label[str(encoding[i])] = lb[i]\n        gt_label[str(ONE_HOT_ENCODING[k])] = lb[i]\n\n        for j in range(0, len(v)):\n            dataTemp.append(v[j].img)\n            names.append(v[j].name)\n\n        i = i + 1\n\n    return np.array(dataTemp), labels, gt_label, np.array(names)\n\n\ndef binaryEncoding(data):\n\n\n    dataTemp = []\n    encoding = [0,1]\n    labels = np.array([])\n    names = []\n    gt_label = {}\n    i = 0\n\n    for (k, v) in data.items():\n\n        if labels.shape[0] == 0:\n            labels = np.tile(encoding[i], (len(v), 1))\n        else:\n            label = np.tile(encoding[i], (len(v), 1))\n            labels = np.concatenate((labels, label), axis=0)\n\n        gt_label[str(encoding[i])] = encoding[i]\n\n        for j in range(0, len(v)):\n            dataTemp.append(v[j].img)\n            names.append(v[j].name)\n\n        i = i + 1\n\n    return np.array(dataTemp), labels, gt_label, np.array(names)\n\n\ndef checkEncoding(gt, labels):\n\n    for (k,v) in gt.items():\n        c = 0\n        p = 0\n        for i in range(0, len(labels)):\n            if k == str(labels[i]):\n                c += 1\n                if p == 0:\n                    p = i\n        print('%d : %s - %d (%s)' % (p, v, c, k))","sub_path":"Utils/Encoder.py","file_name":"Encoder.py","file_ext":"py","file_size_in_byte":3300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"306188596","text":"from rest_framework import serializers\nfrom .models import Course\nfrom files.models import CourseFile\nfrom instructors.models import Instructor\nfrom lectures.serializers import SectionPlayerSerializer\nfrom taggit_serializer.serializers import (TagListSerializerField,\n                                           TaggitSerializer)\nfrom targets.serializers import GoalSerializer, ExperienceSerializer, RequirementSerializer                                        \nfrom categories.serializers import CategorySerializer\nimport six\n\nclass CreateVideoSerializer(serializers.ModelSerializer):\n    \n    class Meta:\n        model = Course\n\n        fields = [\n            'video_url',  \n        ]\n\n\nclass CreateCourseSerializer(serializers.ModelSerializer):\n    id = serializers.ReadOnlyField()\n    class Meta:\n        model = Course\n        fields = [\n            'id',\n            'title',\n            'category',\n            'description',\n            \n        ]\n\nclass LevelOptionsSerializer(serializers.Serializer):\n    levels = serializers.MultipleChoiceField(Course.CourseLevelOptions.choices)\n\nclass InstructorSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Instructor\n        \n        fields = [\n          \n          'first_name',\n          'last_name',\n          'email',\n          'courses',\n          'headline',\n          'avatar',\n          'description'\n        ]\n        depth = 1\n    \n\nclass CourseDetailSerializer(TaggitSerializer, serializers.ModelSerializer):\n    tags = TagListSerializerField()\n    instructor = InstructorSerializer()\n    sections = SectionPlayerSerializer(many=True)\n    requirements = RequirementSerializer(many=True)\n    goals = GoalSerializer(many=True)\n    experiences = ExperienceSerializer(many=True)\n    category = CategorySerializer()\n    id = serializers.ReadOnlyField()\n    class Meta:\n        model = Course\n        fields = [\n            'id',\n            'title',\n            'category',\n            'description',\n            'headline',\n            'level',\n            'cover_image',\n            'instructor',\n            'video_url',\n            'subcategory',\n            'sections',\n            'requirements',\n            'goals',\n            'slug',\n            'experiences',\n            'tags',\n            'price',\n            'state'\n        ]\n\n\n\n      \nclass CourseUpdateSerializer(TaggitSerializer, serializers.ModelSerializer):\n    tags = TagListSerializerField()\n    id = serializers.ReadOnlyField()\n    class Meta:\n        model = Course\n        fields = [\n            'id',\n            'title',\n            'category',\n            'description',\n            'headline',\n            'level',\n            'cover_image',\n            'video_url',\n            'subcategory',\n            'tags',\n            'price',\n            'state'\n        ]\n\nclass NewTagListSerializerField(TagListSerializerField):\n    def to_internal_value(self, value):\n        if isinstance(value, six.string_types):\n            value = value.split(',')\n\n        if not isinstance(value, list):\n            self.fail('not_a_list', input_type=type(value).__name__)\n\n        for s in value:\n            if not isinstance(s, six.string_types):\n                self.fail('not_a_str')\n\n            self.child.run_validation(s)\n        return value\n\nclass ReviewCourseSerializer(TaggitSerializer, serializers.ModelSerializer):\n\n    tags = TagListSerializerField()\n\n    class Meta:\n        model = Course\n        exclude = [\n            'title'\n        ]\n\nclass CourseSubmitReviewSerializer(serializers.ModelSerializer):\n    id = serializers.ReadOnlyField()\n    class Meta:\n        model = Course\n        fields = [\n            'id',\n            'state',\n           \n        ]\n       \n  \n\nclass SearchTagsSerializer(TaggitSerializer, serializers.ModelSerializer):\n    tags = TagListSerializerField()\n\n    class Meta:\n        model = Course\n        fields = [\n            'title',\n            'tags'\n        ]\n\n\nclass CourseFileSerializer(serializers.ModelSerializer):\n    id = serializers.ReadOnlyField()\n    class Meta:\n        model = CourseFile\n        fields = [\n            'id',\n            'course',\n            'name',\n            'key',\n            'filetype',\n            'size'\n        ]\n\n","sub_path":"src/courses/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":4247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"347135980","text":"\"\"\" This is a document similar to that of the shared gui that we used\nfor sprint one and two. It will be where I construct all my frames for the final\n\"\"\"\n\nimport tkinter\nfrom tkinter import ttk\nimport time\n\n# Space for the frames\n\n\"\"\"Frame for the sleep uses radio buttons to use a new type of button\"\"\"\ndef frame_for_sleep(frame, mqtt_sender):\n    frame_sleep = ttk.Frame(frame, padding=10, borderwidth=10, relief=\"ridge\")\n    frame_sleep_label = ttk.Label(frame_sleep, text=\"Sleep Frame\")\n    frame_sleep.grid()\n\n    ask_to_sleep_button = ttk.Button(frame_sleep, text=\"Do you want to sleep baby?\")\n    how_long_label = ttk.Label(frame_sleep, text=\"How long do you want the baby to sleep for?\")\n    radio_1 = ttk.Radiobutton(frame_sleep, text=0, value=0)\n    radio_2 = ttk.Radiobutton(frame_sleep, text=3, value=3)\n    radio_3 = ttk.Radiobutton(frame_sleep, text=6, value=6)\n    radio_4 = ttk.Radiobutton(frame_sleep, text=9, value=9)\n    radio_5 = ttk.Radiobutton(frame_sleep, text=12, value=12)\n\n    ask_to_sleep_button.grid(row=1, column=0)\n    frame_sleep_label.grid(row=0, column=1)\n    how_long_label.grid(row=1, column=2)\n    radio_1.grid(row=2, column=3)\n    radio_2.grid(row=2, column=4)\n    radio_3.grid(row=2, column=5)\n    radio_4.grid(row=2, column=6)\n    radio_5.grid(row=2, column=7)\n\n\n    radio_observer = tkinter.StringVar()\n    for radio in [radio_1, radio_2, radio_3, radio_4, radio_5]:\n        radio['variable'] = radio_observer\n\n    ask_to_sleep_button[\"command\"] = lambda: handle_sleep(radio_observer, mqtt_sender)\n\n    return frame\n\n\n\"\"\"\"Frame for crawl using typical buttons\"\"\"\n\n\ndef frame_for_crawl(frame, mqtt_sender):\n    frame_crawl = ttk.Frame(frame, padding=10, borderwidth=10, relief=\"ridge\")\n    frame_crawl_label = ttk.Label(frame_crawl, text=\"Crawl Frame\")\n    frame_crawl.grid()\n\n    crawl_button = ttk.Button(frame_crawl, text=\"Crawl\")\n    speed_label = ttk.Label(frame_crawl, text=\"Speed\")\n    speed_entry = ttk.Entry(frame_crawl, width=8)\n    color_label = ttk.Label(frame_crawl, text=\"Color\")\n    color_entry = ttk.Entry(frame_crawl, width=8)\n\n    frame_crawl_label.grid(row=0, column=1)\n    crawl_button.grid(row=1, column=0)\n    speed_label.grid(row=3, column=0)\n    speed_entry.grid(row=4, column=0)\n    color_label.grid(row=3, column=2)\n    color_entry.grid(row=4, column=2)\n\n    crawl_button[\"command\"] = lambda: handle_crawl(color_entry, speed_entry, mqtt_sender)\n\n    return frame\n\n\n\"\"\"Frame for cry using typical buttons\"\"\"\ndef frame_for_cry(frame, mqtt_sender):\n    frame_cry = ttk.Frame(frame, padding=10, borderwidth=10, relief=\"ridge\")\n    frame_cry_label = ttk.Label(frame_cry, text=\"Cry Frame\")\n    frame_cry.grid()\n\n    cry_button = ttk.Button(frame_cry, text=\"Cry\")\n    how_long_label = ttk.Label(frame_cry, text=\"Cry for \")\n    how_long_entry = ttk.Entry(frame_cry, width=8)\n\n    frame_cry_label.grid(row=0, column=1)\n    cry_button.grid(row=1, column=0)\n    how_long_label.grid(row=1, column=2)\n    how_long_entry.grid(row=2, column=2)\n\n    cry_button[\"command\"] = lambda: handle_cry(how_long_entry, mqtt_sender)\n\n    return frame\n\n\n\"\"\"Frame for food and uses the typical buttons\"\"\"\ndef frame_for_food(frame, mqtt_sender):\n    frame_food = ttk.Frame(frame, padding=10, borderwidth=10, relief=\"ridge\")\n    frame_food_label = ttk.Label(frame_food, text=\"Food Frame\")\n    frame_food.grid()\n\n    go_to_food_button = ttk.Button(frame_food, text=\"Go to food\")\n    send_image_back_button = ttk.Button(frame_food, text=\"Send Image to Computer\")\n    speed_label = ttk.Label(frame_food, text=\"Speed\")\n    speed_entry = ttk.Entry(frame_food, width=8)\n    area_label = ttk.Label(frame_food, text=\"Area\")\n    area_entry = ttk.Entry(frame_food, width=8)\n\n    frame_food_label.grid(row=0, column=1)\n    go_to_food_button.grid(row=1, column=0)\n    send_image_back_button.grid(row=1, column=2)\n    speed_label.grid(row=2, column=0)\n    speed_entry.grid(row=3, column=0)\n    area_label.grid(row=2, column=2)\n    area_entry.grid(row=3, column=2)\n\n    go_to_food_button[\"command\"] = lambda: handle_food(mqtt_sender, speed_entry, area_entry)\n    send_image_back_button[\"command\"] = lambda: handle_send_back(mqtt_sender)\n\n    return frame\n\n\n\"\"\"Frame for changing the baby using the typical buttons\"\"\"\ndef frame_for_change(frame, mqtt_sender):\n    frame_change = ttk.Frame(frame, padding=10, borderwidth=10, relief=\"ridge\")\n    frame_change_label = ttk.Label(frame_change, text=\"Signal To Be Changed Frame\")\n    frame_change.grid()\n\n    change_button = ttk.Button(frame_change, text=\"Change Me!\")\n    speed_label = ttk.Label(frame_change, text=\"Speed\")\n    speed_entry = ttk.Entry(frame_change, width=8)\n    distance_label = ttk.Label(frame_change, text=\"Distance\")\n    distance_entry = ttk.Entry(frame_change, width=8)\n\n    frame_change_label.grid(row=0, column=1)\n    change_button.grid(row=1, column=1)\n    speed_label.grid(row=1, column=0)\n    speed_entry.grid(row=2, column=0)\n    distance_label.grid(row=1, column=2)\n    distance_entry.grid(row=2, column=2)\n\n    change_button[\"command\"] = lambda: handle_change(speed_entry, distance_entry, mqtt_sender)\n\n    return frame\n\n\n\"\"\"Frame for screaming and uses the typical buttons\"\"\"\n\n\ndef frame_for_scream(frame, mqtt_sender):\n    frame_scream = ttk.Frame(frame, padding=10, borderwidth=10, relief=\"ridge\")\n    frame_scream_label = ttk.Label(frame_scream, text=\"Scream Frame\")\n    frame_scream.grid()\n\n    scream_button = ttk.Button(frame_scream, text=\"Scream\")\n    duration_label = ttk.Label(frame_scream, text=\"Duration\")\n    duration_entry = ttk.Entry(frame_scream, width=8)\n\n    frame_scream_label.grid(row=0, column=1)\n    scream_button.grid(row=1, column=0)\n    duration_label.grid(row=1, column=2)\n    duration_entry.grid(row=2, column=2)\n\n    scream_button[\"command\"] = lambda: handle_scream(duration_entry, mqtt_sender)\n\n    return frame\n\n# Handlers\n\n\n\"\"\"Sets up the function to run the sleep\"\"\"\ndef handle_sleep(how_long_button, mqtt_sender):\n    print(how_long_button.get())\n    value = int(how_long_button.get())\n    print('I am sleeping')\n    mqtt_sender.send_message(\"sleep\", [value])\n\n\n\"\"\"Sets up function for crawl\"\"\"\ndef handle_crawl(color, speed, mqtt_sender):\n    print('Crawl')\n    print(color.get(), speed.get())\n    color = color.get()\n    speed = int(speed.get())\n    mqtt_sender.send_message(\"go_straight_until_color_is_not\", [color, speed])\n\n\n\"\"\"Sets up functions for cry \"\"\"\ndef handle_cry(how_lonng_entry, mqtt_sender):\n    value = int(how_lonng_entry.get())\n    print('Cry')\n    mqtt_sender.send_message(\"cry\", [value])\n\n\n\"\"\"Sets up functions for food\"\"\"\ndef handle_food(mqtt_sender, speed, area):\n    speed = int(speed.get())\n    area = int(area.get())\n    mqtt_sender.send_message(\"spin_clockwise_until_sees_object\", [speed, area])\n\n\n\"\"\"Sets up function to send info back to laptop\"\"\"\ndef handle_send_back(mqtt_sender):\n    mqtt_sender.send_message(\"display_camera_data\")\n\n\n\"\"\"Sets up function for change\"\"\"\ndef handle_change(distance, speed, mqtt_sender):\n    distance = int(distance.get())\n    speed = int(speed.get())\n    mqtt_sender.send_message(\"go_forward_with_ir\", [distance, speed])\n\n\n\"\"\"Sets up functions for scream\"\"\"\ndef handle_scream(duration, mqtt_sender):\n    duration = int(duration.get())\n    mqtt_sender.send_message(\"scream\", [duration])\n\n","sub_path":"src/m3_shared_gui.py","file_name":"m3_shared_gui.py","file_ext":"py","file_size_in_byte":7275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"220957892","text":"import logging\nfrom Config import config\n\n# config['api_log_fn'] = 'api.log'\n# config['log_level'] = 'DEBUG'\n\n\ndef setup_custom_logger(name):\n    logger = logging.getLogger(name)\n\n    stream_handler = logging.StreamHandler()\n    # formatter = logging.Formatter(\n    #     '[%(asctime)s] [%(name)s:%(module)s:%(lineno)d] [%(levelname)s] %(message)s')\n    formatter = logging.Formatter(\n        '[%(asctime)s] %(levelname)s [%(filename)s->%(funcName)s:%(lineno)s] %(message)s')\n    stream_handler.setFormatter(formatter)\n    logger.addHandler(stream_handler)\n\n    file_handler = logging.FileHandler(config.api_log_fn)\n    file_handler.setFormatter(formatter)\n    logger.addHandler(file_handler)\n    logger.setLevel(config.log_level)\n\n    return logger\n","sub_path":"api/services/custom_logger.py","file_name":"custom_logger.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"245903951","text":"# coding=utf-8\n# Copyright 2019 Google LLC\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\"\"\"Discriminator Agent code.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow.compat.v2 as tf\nfrom valan.framework import base_agent\nfrom valan.framework import common\nfrom valan.framework import utils\nfrom valan.r2r import constants\nfrom valan.r2r import image_encoder\nfrom valan.r2r import instruction_encoder\n\n_INFINITY = 1e9\n\n\nclass DiscriminatorAgent(base_agent.BaseAgent):\n  \"\"\"R2R Agent.\"\"\"\n\n  def __init__(self, config):\n    \"\"\"Initialize R2R Agent.\"\"\"\n    super(DiscriminatorAgent, self).__init__(name='discriminator_r2r')\n\n    self._instruction_encoder = instruction_encoder.InstructionEncoder(\n        num_hidden_layers=2,\n        output_dim=256,\n        pretrained_embed_path=config.pretrained_embed_path,\n        oov_bucket_size=config.oov_bucket_size,\n        vocab_size=config.vocab_size,\n        word_embed_dim=config.word_embed_dim,\n    )\n    self._image_encoder = image_encoder.ImageEncoder(\n        256, 512, num_hidden_layers=2)\n    self.affine_a = tf.Variable(1.0, dtype=tf.float32, trainable=True)\n    self.affine_b = tf.Variable(0.0, dtype=tf.float32, trainable=True)\n\n  def _get_initial_state(self, observation, batch_size):\n    text_token_ids = observation[constants.INS_TOKEN_IDS]\n    text_enc_outputs, final_state = self._instruction_encoder(text_token_ids)\n    return (final_state, text_enc_outputs)\n\n  def _torso(self, observation):\n    # For R2R, torso does not do anything but pass all the variables.\n    outputs = {}\n    outputs[constants.PANO_ENC] = observation[constants.PANO_ENC]\n    outputs[constants.INS_TOKEN_IDS] = observation[constants.INS_TOKEN_IDS]\n    outputs[constants.IS_START] = observation[constants.IS_START]\n    outputs[constants.DISC_MASK] = observation[constants.DISC_MASK]\n    return outputs\n\n  def _neck(self, torso_outputs, state):\n    current_lstm_state, text_enc_outputs = state\n    image_features = tf.cast(torso_outputs[constants.PANO_ENC], tf.float32)\n    lstm_output, next_lstm_state = self._image_encoder(image_features,\n                                                       current_lstm_state)\n    # <tf.float32>[batch_size, 1, hidden_dim]\n    lstm_output = tf.expand_dims(lstm_output, axis=1)\n    # The next_lstm_state are ListWrappers. In order to make it consistent with\n    # get_initial_state, we convert them to tuple.\n    result_state = []\n    for one_state in next_lstm_state:\n      result_state.append((one_state[0], one_state[1]))\n    torso_outputs['visual_feature'] = lstm_output\n    torso_outputs['text_feature'] = text_enc_outputs\n    return (torso_outputs, (result_state, text_enc_outputs))\n\n  def _head(self, neck_outputs):\n\n    # <tf.float32>[time * batch_size, 1, hidden_dim]\n    visual_feature = neck_outputs['visual_feature']\n    # <tf.float32>[time * batch_size, num_tokens, hidden_dim]\n    text_feature = neck_outputs['text_feature']\n\n    # <tf.float32>[time, batch_size, 1, hidden_dim]\n    visual_feature = tf.reshape(\n        visual_feature,\n        [self._current_num_timesteps, self._current_batch_size] +\n        visual_feature.shape[1:].as_list())\n\n    # <tf.float32>[batch_size, time, hidden_dim]\n    visual_feature = tf.squeeze(visual_feature, axis=2)\n    visual_feature = tf.transpose(visual_feature, [1, 0, 2])\n\n    first_true = utils.get_first_true_column(\n        tf.reshape(neck_outputs[constants.DISC_MASK],\n                   [self._current_num_timesteps, self._current_batch_size]))\n\n    # <tf.float32>[batch_size, num_tokens, hidden_dim]\n    text_feature = tf.cond(\n        tf.keras.backend.any(first_true),\n        lambda: tf.boolean_mask(text_feature, tf.reshape(first_true, [-1])),\n        lambda: tf.reshape(text_feature, [\n            self._current_num_timesteps, self._current_batch_size\n        ] + text_feature.shape[1:].as_list())[0, :, :, :])\n    # visual_feature = tf.nn.l2_normalize(visual_feature, axis=2)\n    # text_feature = tf.nn.l2_normalize(text_feature, axis=2)\n\n    # <tf.float32>[batch_size, time, num_tokens]\n    alpha_i_j = tf.matmul(visual_feature,\n                          tf.transpose(text_feature, perm=[0, 2, 1]))\n    # <tf.float32>[batch_size, time, num_tokens]\n    ealpha_i_j = tf.exp(alpha_i_j)\n    sum_i_j = tf.tile(\n        tf.expand_dims(tf.reduce_sum(ealpha_i_j, 2), 2),\n        [1, 1, tf.shape(ealpha_i_j)[2]])\n    mask = tf.cast(\n        tf.transpose(\n            tf.reshape(neck_outputs[constants.DISC_MASK],\n                       [self._current_num_timesteps, self._current_batch_size]),\n            perm=[1, 0]), tf.float32)\n    # <tf.float32>[batch, time, num_tokens]\n    c_i_j = tf.divide(ealpha_i_j, sum_i_j)\n    # <tf.float32>[batch, time]\n    score = tf.reduce_sum(c_i_j * alpha_i_j, 2)\n\n    escore = tf.exp(-1 * score) * mask\n    sum_escore = tf.tile(\n        tf.expand_dims(tf.reduce_sum(escore, 1), 1), [1, tf.shape(escore)[1]])\n    score_weight = tf.divide(escore, sum_escore)\n    similarities = tf.reduce_sum(mask * score * score_weight, 1)\n    similarities = tf.expand_dims(similarities, axis=0)\n    # [time_step, batch_size]\n    similarities = tf.tile(similarities, [self._current_num_timesteps, 1])\n\n    # Apply an affine transform.\n    similarities = similarities * self.affine_a + self.affine_b\n\n    output_a = tf.reshape(tf.convert_to_tensor(self.affine_a), [1, 1])\n    output_b = tf.reshape(tf.convert_to_tensor(self.affine_b), [1, 1])\n\n    output_a = tf.tile(output_a,\n                       [self._current_num_timesteps, self._current_batch_size])\n    output_b = tf.tile(output_b,\n                       [self._current_num_timesteps, self._current_batch_size])\n\n    return common.AgentOutput(\n        policy_logits=similarities, baseline=(output_a, output_b))\n","sub_path":"r2r/discriminator_agent.py","file_name":"discriminator_agent.py","file_ext":"py","file_size_in_byte":6293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"398986295","text":"'''\nGiven an integer n, return the number of trailing zeroes in n!.\n\nNote: Your solution should be in logarithmic time complexity.\n'''\n\n\ndef trailing_zeros(num):\n    count, k = 0, 5\n    while num:\n        k = num // 5\n        count += k\n        num = k\n    return count\n\n\nprint(trailing_zeros(5))\n","sub_path":"leetcode/python/172_factorial_trailing_zeros.py","file_name":"172_factorial_trailing_zeros.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"261140500","text":"#9/10/18\n#Description: We were asked to make a program that comunicated with you. I made one so that depending on your age and different answers you would get\n#different questions and answers\n\nname = input(\"Hello, what's your name?\")\nprint(\"Hi, \"+name+\"!\")\nage = int(input(\"If you don't mind me asking, how old are you?\"))\nif age < 20:\n\tschool = input(\"What school do you go to?\")\n\tgood = input(\"Is \"+school+\" a good school?\")\n\tif good == \"yes\" or good == \"Yes\":\n\t\tprint(\"I'm glad that you go to a good school\")\n\telse: \n\t\tprint(\"I'm sorry that you don't go to a good school\")\n\n\nelse:\n\twork = input(name + \" do you work?\")\n\tif work == \"yes\" or work == \"Yes\":\n\t\twork_company = input(\"What company do you work for?\")\n\t\tprint(work_company + \" sounds like a good company to work for!\")\n\telse:\n\t\twork_planned = input(\"Do you plan to get a job?\")\n\t\tif work_planned == \"yes\" or work_planned == \"Yes\":\n\t\t\tprint(\"That's good to hear! I hope that at \" + str(age)+ \" you are happy\")\n\t\telse:\n\t\t\tprint(\"That's too bad \"+ name+ \". You should find somewhere to work because at \"+str(age)+\", it is important to find a job!\")\n\n\n","sub_path":"Submitted homework/Last_attempt.py","file_name":"Last_attempt.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"644995430","text":"# Допущение: первое число в ряду Фибоначчи = 0, порядковый номер первого числа = 1\n\ndef fibonacci_list(n_x, n):\n    # n_x - порядковый номер числа в ряду (начинается с 1)\n    # n - количество чисел в ряду\n    fibonacci_1 = 1\n    fibonacci_2 = 1\n    l_fibonacci = []\n    if n == 1:\n        l_fibonacci = [0]\n    if n > 1:\n        l_fibonacci = [0, fibonacci_1]\n    if n > 2:\n        l_fibonacci.append(fibonacci_2)\n    i = 0\n    while i < n - 3:\n        fibonacci_sum = fibonacci_1 + fibonacci_2\n        fibonacci_1, fibonacci_2 = fibonacci_2, fibonacci_sum\n        i +=1\n        l_fibonacci.append(fibonacci_sum)\n    if n_x < n + 1 and n_x > 0:\n        print(l_fibonacci[n_x-1])\n        print(l_fibonacci)\n    elif n < 1:\n        print(f'Числовой ряд не сформирован')\n    else:\n        print(f'Не обнаружена числа в ряду с порядковым № {n_x}')\n\ndef fibonacci(n_x):\n    fibonacci_1 = 1\n    fibonacci_2 = 1\n    if n_x == 1:\n        print(0)\n    if n_x == 2:\n        print(fibonacci_1)\n    if n_x == 3:\n        print(fibonacci_2)\n    i = 0\n    while i < n_x - 3:\n        fibonacci_sum = fibonacci_1 + fibonacci_2\n        fibonacci_1, fibonacci_2 = fibonacci_2, fibonacci_sum\n        i += 1\n        if n_x == i + 3:\n            print(fibonacci_sum)\n\n# fibonacci_list(4,4)\n\nfibonacci(8)\n","sub_path":"hw_alg_3/fibonacci_specified.py","file_name":"fibonacci_specified.py","file_ext":"py","file_size_in_byte":1458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"30211071","text":"\"\"\"\nAuthor: Jing (https://github.com/gnijuohz)\n\nLetter Combinations of a Phone Number: https://oj.leetcode.com/problems/letter-combinations-of-a-phone-number  \n\nGiven a digit string, return all possible letter combinations that the number could represent.\n\n\n\nA mapping of digit to letters (just like on the telephone buttons) is given below.\n\n\n\nInput:Digit string \"23\"\nOutput: [\"ad\", \"ae\", \"af\", \"bd\", \"be\", \"bf\", \"cd\", \"ce\", \"cf\"].\n\n\n\nNote:\nAlthough the above answer is in lexicographical order, your answer could be in any order you want.  \nTags\nBacktracking, String  \n\"\"\"\n\nclass Solution:\n    # @return a list of strings, [s1, s2]\n    def letterCombinations(self, digits):\n        dic = {'2':'abc', '3':'def', '4':'ghi', '5':'jkl', '6':'mno', '7':'pqrs', '8':'tuv', '9':'wxyz'}\n        res = []\n        if not digits:\n            return [\"\"]\n        res = [l for l in dic[digits[0]]]\n        if len(digits) == 1:\n            return res\n        for i in range(1, len(digits)):\n            newRes = [r+l for r in res for l in dic[digits[i]]]\n            res = newRes\n        return res","sub_path":"solutions/Letter-Combinations-of-a-Phone-Number.py","file_name":"Letter-Combinations-of-a-Phone-Number.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"68540836","text":"\n# %%\ndef draw(df):\n    from matplotlib import pyplot as plt\n    import pandas as pd\n    import seaborn as sns\n    raw_time = pd.to_datetime(df.pop('trade_date'), format='%Y%m%d')\n    raw_time.head()\n    plt.plot(raw_time, df['close'])\n    plt.xlabel('time')\n    plt.ylabel('share price')\n    plt.title('trend')\n    plt.show()\n    daily_return = df['close'][0::2].pct_change().dropna()\n    plt.plot(raw_time[0::2][:10], daily_return[:10])\n    plt.xlabel('Time')\n    plt.ylabel('Rise and Fall')\n    plt.show()\n    sns.kdeplot(daily_return)\n\n","sub_path":"firstStock.py","file_name":"firstStock.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"295784093","text":"# Problem\n#  Want to keep the last few items during processing\n\n# Solution\n#  Keeping limited history is prefect fo rcollactions.deque\n\nfrom collections import deque\n\n#  Performs a text match on sequence of lines and yields the matching\n#   line along with the previous N lines\n#  This is an example of a generator function that seems to return two\n#   objects, but actually returns a generator (an iterator) that can be\n#   iterated over.\ndef search(lines, pattern, history=5):\n    previous_lines = deque(maxlen=history)\n    for line in lines:\n        if pattern in line:\n            yield line, previous_lines\n        previous_lines.append(line)\n\nif __name__ == '__main__':\n    with open('somefile.txt') as f:\n        for line, prevlines in search(f, 'python', 5):\n            for pline in prevlines:\n                print(pline, end='')\n            print(line, end='')\n            print('-'*20)\n\n# Discussion\n#  It is commen to use a generator funciton when writing code to\n#   search for items. This helps with decoupling the searching\n#   process from the code that utilizes the results.\n#  deque(maxlen=N) creates N-sized queue. When new items are\n#   added and the queue is full, then the oldest item is removed\n\nq = deque(maxlen=3)\nq.append(1)\nq.append(2)\nq.append(3)\nprint(q)  # deque([1, 2, 3], maxlen=3)\nq.append(4)\nprint(q)  # deque([2, 3, 4], maxlen=3)\nq.append(5)\nprint(q)  # deque([3, 4, 5], maxlen=3)\n\n#  When not giving it a maximum size, an unbounded queue is\n#   created, letting you append on either end.\n\nq = deque()\nq.append(1)\nq.append(2)\nq.append(3)\nprint(q)  # deque([1, 2, 3])\nq.appendleft(4)\nprint(q)  # deque([4, 1, 2, 3])\nprint(q.pop())  # 3\nprint(q)  # deque([4, 1, 2])\nprint(q.popleft())  # 4\n\n#  Adding or popping from the ends of a queue is O(1) complexity\n","sub_path":"01_data_structures_and_algorithms/03_keeping_last_n_items.py","file_name":"03_keeping_last_n_items.py","file_ext":"py","file_size_in_byte":1790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"580306501","text":"#!/usr/bin/env python\n\n# Command line script to read a sparse matrix and a clusters.txt file\n# Then output an ordering for the clusters\n\nimport sys\nimport networkx\nimport argparse\n\ndef main():\n    # Parse args\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--sparse-matrix', '-s', required=True)\n    parser.add_argument('--clusters', '-c', required=True)\n    args = parser.parse_args()\n\n    # Read clusters file, store a list of lists\n    clusters = []\n    with open(args.clusters, 'r') as clust:\n        for line in clust:\n            clusters.append(line.strip().split())\n\n    # Read sparse matrix file, store a graph\n    all_nodes = networkx.Graph()\n    with open(args.sparse_matrix, 'r') as matrix:\n        for line in matrix:\n            fields = line.strip().split()\n            all_nodes.add_edge(fields[0], fields[1], weight=int(fields[2]))\n\n    for i, cluster in enumerate(clusters):\n        # Create a graph\n        g = networkx.Graph()\n        for j, node in enumerate(cluster):\n            for other_node in cluster[j+1:]:\n                if other_node in all_nodes[node]:\n                    g.add_edge(node, other_node, weight=all_nodes[node][other_node])\n\n        # Find strongest path\n        # Create yet another graph, add all nodes from g\n        strongest_path = networkx.Graph()\n        for node in g.nodes():\n            strongest_path.add_node(node)\n        # Sort edges in this cluster according to weight\n        all_edges = []\n        for edge in g.edges():\n            node1 = edge[0]\n            node2 = edge[1]\n            weight = all_nodes[node1][node2]['weight']\n            all_edges.append( (node1, node2, weight) )\n        all_edges = sorted(all_edges, key=lambda x: x[2], reverse=True)\n        # Iterate through edges from strongest to weakest\n        for edge in all_edges:\n            node1 = edge[0]\n            node2 = edge[1]\n            weight = all_nodes[node1][node2]['weight']\n            node1_edge_count = len(strongest_path[node1])\n            node2_edge_count = len(strongest_path[node2])\n            # Connect nodes in strongest_path if neither has > 1 edge already\n            if node1_edge_count <= 1 and node2_edge_count <= 1:\n                strongest_path.add_edge(node1, node2, weight=weight)\n\n        # Print path\n        print(strongest_path.edges())\n        # Save graph\n        filename = \"{0}.cluster_{1}.gexf\".format(args.clusters, i)\n        networkx.write_gexf(strongest_path, filename)\n\n        # Clean up (?)\n        del g\n        del strongest_path\n\n\n\n\n##########################\n\nif __name__ == '__main__':\n    main()\n","sub_path":"pipeline/orderer.py","file_name":"orderer.py","file_ext":"py","file_size_in_byte":2603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"129299747","text":"def do_power_two(number):\n    number **= 2 \n    return number\n\ndef mapper (func, iterable):\n    retlist = []\n    for elem in iterable:\n        retlist.append (func(elem))\n    return retlist\n\nlist_my = [1,2,3,4,5]\nprint (mapper (do_power_two, list_my))\n","sub_path":"znachkov_oleksandr/03/task3.2.py","file_name":"task3.2.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"593709537","text":"\nclass GarageDoor:\n    \"\"\"Solves https://redd.it/4cb7eh\"\"\"\n    # Possible states\n    states = ['OPEN', 'CLOSING', 'CLOSED', 'OPENING', 'STOPPED_WHILE_OPENING',\n              'STOPPED_WHILE_CLOSING']\n\n    # Map of values to add to change state\n    button_map = [1, 4, 1, 1, -3, -2]\n    complete_map = [0, 1, 0, -3, 0, 0]\n\n    def __init__(self):\n        # Initialize to closed\n        self.current_state = 2\n\n    def button_click(self):\n        self.current_state += self.button_map[self.current_state]\n        self.emit_state()\n\n    def change_complete(self):\n        self.current_state += self.complete_map[self.current_state]\n        self.emit_state()\n\n    def emit_state(self):\n        print('Door: ', self.states[self.current_state])\n\nif __name__ == \"__main__\":\n    input_array = ['button_clicked', 'cycle_complete', 'button_clicked',\n                   'button_clicked', 'button_clicked', 'button_clicked',\n                   'button_clicked', 'cycle_complete']\n    door = GarageDoor()\n\n    door.emit_state()\n    # Get input\n    for ip in input_array:\n        if ip == 'button_clicked':\n            print('> Button clicked.')\n            door.button_click()\n        elif ip == 'cycle_complete':\n            print('> Cycle complete.')\n            door.change_complete()\n        else:\n            raise ValueError","sub_path":"DailyProgrammer/Garage.py","file_name":"Garage.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"118838275","text":"###Glossary project with hands on #2\r\n\r\nglossary = {\r\n    \"dictionary\": \"Python data type that stores key values:value pairs\",\r\n    \"tuples\":\"A list of items that do not change.\",\r\n    \"list\": \"A list of items that can change.\",\r\n    \"for\":\"used to create a counting loop\",\r\n    \"while\":\"used to create a conditional loop\"\r\n}\r\n\r\n\r\nprint(\"Dictionary\")\r\nprint(\"\\t\",glossary[\"dictionary\"])\r\nprint(\"Tuples\")\r\nprint(\"\\t\",glossary[\"tuples\"])\r\nprint(\"list\")\r\nprint(\"\\t\",glossary[\"list\"])\r\nprint(\"For Loops\")\r\nprint(\"\\t\",glossary[\"for\"])\r\nprint(\"While loops\")\r\nprint(\"\\t\",glossary[\"while\"])\r\n\r\n#TRY IT YOURSELF NUMBER 6-4\r\nfor x,y in glossary.items():\r\n    print(x, \":\" ,y)\r\n\r\nglossary = {\r\n    \"dictionary\": \"Python data type that stores key values:value pairs\",\r\n    \"tuples\":\"A list of items that do not change.\",\r\n    \"list\": \"A list of items that can change.\",\r\n    \"for\":\"used to create a counting loop\",\r\n    \"while\":\"used to create a conditional loop\",\r\n    \"int\": \"variable that should only be a number\",\r\n    \"bool\":\"A true or false variable\"\r\n}\r\n\r\nfor x,y in glossary.items():\r\n    print(x, \":\" ,y)\r\n\r\n","sub_path":"Lesson6/glossary.py","file_name":"glossary.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"108475453","text":"\"\"\"\nTest smd0 and eventbuilder for handling step dgrams.\nSee https://docs.google.com/spreadsheets/d/1VlVCwEVGahab3omAFJLaF8DJWFcz-faI9Q9aHa7QTUw/edit?usp=sharing for test setup.\n\n\"\"\"\nimport os, time, glob, sys\nfrom psana.smdreader import SmdReader\nfrom psana.dgram import Dgram\nfrom setup_input_files import setup_input_files\nfrom psana import DataSource\nimport numpy as np\nfrom mpi4py import MPI\ncomm = MPI.COMM_WORLD\nrank = comm.Get_rank()\nsize = comm.Get_size()\n\ntest_xtc_dir = os.environ.get('TEST_XTC_DIR', '.')\nxtc_dir = os.path.join(test_xtc_dir, '.tmp_smd0')\n\ndef run_smd0(n_events):\n    filenames = glob.glob(os.path.join(xtc_dir, '.tmp', 'smalldata', '*.xtc2'))\n    fds = [os.open(filename, os.O_RDONLY) for filename in filenames]\n\n    # Move file ptrs to datagram part\n    configs = [Dgram(file_descriptor=fd) for fd in fds]\n    \n    limit = len(filenames)\n    if len(sys.argv) > 1:\n        limit = int(sys.argv[1])\n    \n    st = time.time()\n    smdr = SmdReader(fds[:limit])\n    got_events = -1\n    processed_events = 0\n    smdr.get(n_events)\n    got_events = smdr.got_events\n    result = {'each_read':[], 'total_n_events':0}\n    cn_i = 0\n    while got_events != 0:\n        step_chunk_nbytes = 0\n        smd_chunk_nbytes = 0\n        for i in range(limit):\n            smd_view = smdr.view(i)\n            if smd_view:\n                smd_chunk_nbytes += smd_view.nbytes\n            step_view = smdr.view(i, update=True)\n            if step_view:\n                step_chunk_nbytes += step_view.nbytes\n        result['each_read'].append([got_events, smd_chunk_nbytes, step_chunk_nbytes])\n        processed_events += got_events\n       \n        # Read more events\n        smdr.get(n_events)\n        got_events = smdr.got_events\n        cn_i += 1\n\n    en = time.time()\n    result['total_n_events'] = processed_events\n\n    for fd in fds:\n        os.close(fd)\n\n    return result\n\ndef my_filter(evt):\n    return True\n\ndef run_serial_read(n_events, batch_size=1, filter_fn=0):\n    exp_xtc_dir = os.path.join(xtc_dir, '.tmp')\n    os.environ['PS_SMD_N_EVENTS'] = str(n_events)\n    ds = DataSource(exp='xpptut13', run=1, dir=exp_xtc_dir, batch_size=batch_size, filter=filter_fn)\n    cn_steps = 0\n    cn_events = 0\n    result = {'evt_per_step':[0,0,0], 'n_steps': 0, 'n_events':0}\n    for run in ds.runs():\n        edet = run.Detector('HX2:DVD:GCC:01:PMON')\n        sdet = run.Detector('motor2')\n        for i, step in enumerate(run.steps()):\n            cn_evt_per_step = 0\n            for j, evt in enumerate(step.events()):\n                cn_evt_per_step += 1\n                cn_events += 1\n            cn_steps +=1\n            result['evt_per_step'][i] = cn_evt_per_step\n        \n    result['n_steps'] = cn_steps\n    result['n_events'] = cn_events\n    return result\n    \ndef check_results(results, expected_result):\n    for result in results:\n        assert result == expected_result\n\ndef test_runsinglefile_steps():\n    ds = DataSource(files=os.path.join(xtc_dir,'.tmp','data-r0001-s00.xtc2'))\n    cn_steps = 0\n    cn_events = 0\n    result = {'evt_per_step':[0,0,0], 'n_steps': 0, 'n_events':0}\n    for run in ds.runs():\n        for i, step in enumerate(run.steps()):\n            cn_evt_per_step = 0\n            for j, evt in enumerate(step.events()):\n                cn_evt_per_step += 1\n                cn_events += 1\n            cn_steps +=1\n            result['evt_per_step'][i] = cn_evt_per_step\n        \n    result['n_steps'] = cn_steps\n    result['n_events'] = cn_events\n    return result\n\n\nif __name__ == \"__main__\":\n    import pathlib\n    p = pathlib.Path(xtc_dir)\n    if not p.exists():\n        if rank == 0:\n            p.mkdir()\n            setup_input_files(p, n_files=2, slow_update_freq=4, n_motor_steps=3, n_events_per_step=10, gen_run2=False)\n    \n    comm.Barrier()\n    \n    \"\"\"\n    # Expected result: \n    # each_read n_events, smd_chunk_nbytes, step_chunk_nbytes\n    # total_n_events\n    # Test 1: No. of chunk-read events covers the entire smds\n    expected_result = {'each_read': [[30, 7896, 3108]], 'total_n_events': 30}\n    result = run_smd0(51)\n    assert result == expected_result\n\n    # Test 2: No. of chunk-read events covers beyond the next BeginStep\n    expected_result = {'each_read': [[20, 5208, 1848], [10, 2688, 1260]], 'total_n_events': 30}\n    result = run_smd0(20)\n    assert result == expected_result\n\n    # Test 3: No. of chunk-read events covers the next BeginStep\n    expected_result = {'each_read': [[19, 5040, 1848], [11, 2856, 1260]], 'total_n_events': 30}\n    result = run_smd0(19)\n    assert result == expected_result\n    \"\"\"\n    # Test run.steps() \n    test_cases = [\\\n            (51, 1, 0), \\\n            (51, 1, my_filter), \\\n            (51, 5, 0), \\\n            (51, 5, my_filter), \\\n            (20, 1, 0), \\\n            (19, 1, 0), \\\n            (1, 1, my_filter), \\\n            (1, 1, 0), \\\n            (3, 4, my_filter), \n            (3, 4, 0), \\\n             ]\n    \n    for test_case in test_cases:\n        result = run_serial_read(test_case[0], batch_size=test_case[1], filter_fn=test_case[2])\n        result = comm.gather(result, root=0)\n        if rank == 0:\n            sum_events_per_step = np.zeros(3, dtype=np.int)\n            sum_events = 0\n            n_steps = 0\n            for i in range(size):\n                if result[i]['evt_per_step']:\n                    sum_events_per_step += np.asarray(result[i]['evt_per_step'], dtype=np.int)\n                sum_events += result[i]['n_events']\n                n_steps = np.max([n_steps, result[i]['n_steps']])\n            \n            assert all(sum_events_per_step == [10,10,10]) \n            assert sum_events == 30\n            assert n_steps == 3\n    \n    # Test run.steps() for RunSingleFile\n    if size == 1:\n        result = test_runsinglefile_steps()\n        assert result == {'evt_per_step': [10, 10, 10], 'n_steps': 3, 'n_events': 30}        \n\n","sub_path":"psana/psana/tests/run_steps.py","file_name":"run_steps.py","file_ext":"py","file_size_in_byte":5870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"629635996","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n\n\nx = np.array([ 1, 2, 3, 4, 5])\ny = np.array([1, 4, 9, 16, 25])\ndegree = 2\n\nprint(\"X Matrix\")\nprint(x)\nprint(\"Y Matrix\")\nprint(y)\n\n\ndef plot_regression_line(x, y, coe, a, b, n):\n    # plotting the actual points as scatter plot\n    plt.scatter(x, y, color = \"m\",\n               marker = \"o\", s = 30)\n\n    # predicted response vector\n    xv = np.linspace(a,b,n)\n    y_pred = np.zeros(len(xv))\n    for i in range(0, len(coe)):\n        if i == 0:\n            y_pred = coe[i] + y_pred\n        else:\n            y_pred = coe[i] * (xv ** i) + y_pred\n\n    # plotting the regression line\n    plt.plot(xv, y_pred, color = \"g\", )\n\n    # putting labels\n    plt.xlabel('x')\n    plt.ylabel('y')\n\n    # function to show plot\n    plt.show()\n\n\ndef pfit(x, y, e):\n    e +=1\n    # Build A matrix\n    A = np.zeros((e,e))\n    total, power = 0, 1\n    for i in range(0, len(A[0])):\n        for j in range(0, len(A[0])):\n            if total == 0:\n                A[i,j] = len(x)\n            else:\n                powered = x ** power\n                value = np.sum(powered)\n                A[i,j] = value\n                power += 1\n\n            total += 1\n        power = i + 1\n\n    # Build b matrix\n    b = np.zeros((e))\n    for i in range(0, len(b)):\n        if i == 0:\n            b[i] = np.sum(y)\n        else:\n            poweredx = x ** i\n            b[i] = np.sum(poweredx * y)\n\n    print(\"A matrix\")\n    print(A)\n    print(\"b matrix\")\n    print(b)\n\n    return np.linalg.solve(A,b)\n\n\nmy_ans = pfit(x,y, degree)\np_ans = np.flip(np.polyfit(x,y, degree))\n\nprint(\"My polyfit coefficients\")\nprint(my_ans)\n\nprint(\"Python polyfit coefficients\")\nprint(p_ans)\n\n# plotting regression line\nplot_regression_line(x, y, my_ans, x[0],x[len(x)-1],1000)\n","sub_path":"interpolation/poly_fit.py","file_name":"poly_fit.py","file_ext":"py","file_size_in_byte":1774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"507769281","text":"\"\"\"PYTHONRC - Really Neat way to make the python shell fun and usable.\n\nCopy this file to $HOME/.pythonrc.py\nIn windows, copy it to $HOMEDRIVE:$HOMEPATH/.pythonrc.py\n\nThis is called when interactive python interpreter is instructed to:\n    import user\nYou can force this to import everytime by setting an environment variable:\n    export PYTHONSTARTUP=\"$HOME/.pythonrc.py\"\nIt makes command history persistent, allows TAB expansion, adds time\nstamp.\n\"\"\"\n\n\nclass PythonRC:\n    \"\"\"Defines Python RC class.\"\"\"\n\n    histfile = None\n    debug = None\n\n    @classmethod\n    def init(cls):\n        \"\"\"Initialize a nice environment to run in.\"\"\"\n        import os\n        cls.debug = os.getenv('PYTHONRC_DEBUG')\n\n        if os.getenv('NOPYTHONRC'):\n            # Not loading the python environment\n            cls.debug_print(\"Skipping loading the pythonrc environment stuff\")\n            return\n\n        try:\n            import readline\n        except ImportError:\n            readline = None\n        cls.readline = readline\n\n        if readline:\n            # Set the history file\n            cls.histfile = os.path.join(os.getenv('HOME', '.'), '.python_history')\n            cls.debug_print(\"Using history file '%s'\" % cls.histfile)\n\n            # Read the history file\n            if os.path.exists(cls.histfile):\n                readline.read_history_file(cls.histfile)\n                cls.debug_print(\"Using existing history information\")\n            else:\n                cls.debug_print(\"Creating new history file\")\n\n            # Set the number of history items to 3000\n            readline.set_history_length(3000)\n            cls.debug_print(\"History size set to 3000\")\n\n            # Put a marker for the starting time.\n            readline.add_history(\"# starting %s\" % cls.strtime())\n            cls.debug_print(\"Marked start of history file\")\n\n            # Allow Tab Completion\n            import rlcompleter  # This sets up python specific completion pylint: disable=unused-import\n            readline.parse_and_bind(\"tab: complete\")\n            cls.debug_print(\"Started completion\")\n        else:\n            cls.debug_print(\"No readline available\")\n\n        # Set the prompts\n        import sys\n        sys.ps1 = \"python%s> \" % \".\".join(map(str, sys.version_info[:2]))\n        sys.ps2 = \" \" * (len(sys.ps1) - 2) + \"> \"\n        cls.debug_print(\"Set prompts\")\n\n        # The the exit function\n        import atexit\n        atexit.register(cls.exit)\n        cls.debug_print(\"Registered exit function\")\n\n    @classmethod\n    def debug_print(cls, *msgs):\n        \"\"\"Debugging Print.\"\"\"\n        if cls.debug:\n            for msg in msgs:\n                print(\"PYTHONRC: %s\" % msg)\n\n    @classmethod\n    def strtime(cls):\n        \"\"\"Return the time in a nicely formatted string.\"\"\"\n        import time\n        str_format = \"%A %I:%M %p, %B %d, %Y %Z\"\n        return time.strftime(str_format)\n\n    @classmethod\n    def exit(cls):\n        \"\"\"Run when everything exits.\"\"\"\n        cls.debug_print(\"Exiting...\")\n        if cls.readline:\n            cls.readline.add_history(\"# leaving %s\" % cls.strtime())\n            cls.readline.write_history_file(cls.histfile)\n        print(\"Exiting\")\n\n\nif __name__ == \"__main__\":\n    PythonRC.init()\n","sub_path":"development/python/pythonrc.py","file_name":"pythonrc.py","file_ext":"py","file_size_in_byte":3237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"559345060","text":"import torch, gym, argparse\nfrom datetime import datetime, timedelta, timezone\nfrom nn_model import LinearModel\nfrom func import get_args, arg2kwargs\n\nclass CartEnv(gym.RewardWrapper):\n    def step(self, action):\n        obs, reward, done, info = self.env.step(action)\n        return obs, self.reward(reward, done), done, info\n\n    def reward(self, reward, done):\n        if done:\n            reward = -1.\n        return reward\n\ndef make_env(ENV):\n    def env():\n        env = gym.make(ENV).unwrapped\n        return CartEnv(env)\n    return env\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    get_args(parser)\n    parser.add_argument(\"--path\", default=\"cart/\")\n    parser.add_argument(\"--n_mid\", type=int, default=10)\n    args = parser.parse_args()\n    kwargs = arg2kwargs(args)\n    alg_name = args.algo.lower()\n    if alg_name == \"ppo\":\n        from algo import PPO as Algo\n    elif alg_name == \"dqn\":\n        from algo import DQN as Algo\n    ENV = \"CartPole-v0\"\n    kwargs[\"n_mid\"] = args.n_mid\n    if args.no_cuda:\n        device = torch.device(\"cpu\")\n    else:\n        device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n    path = args.path\n    JST = timezone(timedelta(hours=+9), \"JST\")\n    name = alg_name + \"-lr\" + str(kwargs[\"lr\"])\n    print(datetime.now(JST), name)\n    \n    algo = Algo(ENV=ENV, model=LinearModel, make_env=make_env, **kwargs, device=device)\n    algo(path, name)\n    \n    print(datetime.now(JST))","sub_path":"cartpole.py","file_name":"cartpole.py","file_ext":"py","file_size_in_byte":1477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"573954549","text":"import os\nimport colorsys\n\nimport numpy as np\nfrom keras import backend as K\nfrom keras.models import load_model\nfrom keras.layers import Input\n\nfrom yolo4.model import yolo_eval, yolo4_body\nfrom yolo4.utils import letterbox_image\n\nfrom PIL import Image, ImageFont, ImageDraw\nfrom timeit import default_timer as timer\nimport matplotlib.pyplot as plt\nimport argparse\nimport cv2\n\nclass Yolo4(object):\n    def get_class(self):\n        classes_path = os.path.expanduser(self.classes_path)\n        with open(classes_path) as f:\n            class_names = f.readlines()\n        class_names = [c.strip() for c in class_names]\n        return class_names\n\n    def get_anchors(self):\n        anchors_path = os.path.expanduser(self.anchors_path)\n        with open(anchors_path) as f:\n            anchors = f.readline()\n        anchors = [float(x) for x in anchors.split(',')]\n        return np.array(anchors).reshape(-1, 2)\n\n    def load_yolo(self):\n        model_path = os.path.expanduser(self.model_path)\n        assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'\n\n        self.class_names = self.get_class()\n        self.anchors = self.get_anchors()\n        print('self.anchors', self.anchors)\n\n        num_anchors = len(self.anchors)\n        num_classes = len(self.class_names)\n\n        # Generate colors for drawing bounding boxes.\n        hsv_tuples = [(x / len(self.class_names), 1., 1.)\n                      for x in range(len(self.class_names))]\n        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))\n        self.colors = list(\n            map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),\n                self.colors))\n\n        self.sess = K.get_session()\n\n        # Load model, or construct model and load weights.\n        self.yolo4_model = yolo4_body(Input(shape=(self.input_size[1], self.input_size[0], 3)), num_anchors//3, num_classes, self.alpha, True)\n        self.yolo4_model.load_weights(model_path)\n\n        print('{} model, anchors, and classes loaded.'.format(model_path))\n\n        if self.gpu_num>=2:\n            self.yolo4_model = multi_gpu_model(self.yolo4_model, gpus=self.gpu_num)\n\n        self.input_image_shape = K.placeholder(shape=(2, ))\n        self.boxes, self.scores, self.classes = yolo_eval(self.yolo4_model.output, self.anchors,\n                len(self.class_names), self.input_image_shape,\n                score_threshold=self.score)\n\n    def __init__(self, score, iou, input_size, anchors_path, classes_path, model_path, alpha=1, gpu_num=1):\n        self.score = score\n        self.iou = iou\n        self.input_size = input_size\n        self.anchors_path = anchors_path\n        self.classes_path = classes_path\n        self.model_path = model_path\n        self.gpu_num = gpu_num\n        self.alpha = alpha\n        self.load_yolo()\n\n    def close_session(self):\n        self.sess.close()\n\n    def detect_image(self, image, model_image_size=(608, 608)):\n        start = timer()\n\n        boxed_image = letterbox_image(image, tuple(reversed(model_image_size)))\n        image_data = np.array(boxed_image, dtype='float32')\n\n        print(image_data.shape)\n        image_data /= 255.\n        image_data = np.expand_dims(image_data, 0)  # Add batch dimension.\n\n        out_boxes, out_scores, out_classes = self.sess.run(\n            [self.boxes, self.scores, self.classes],\n            feed_dict={\n                self.yolo4_model.input: image_data,\n                self.input_image_shape: [image.size[1], image.size[0]],\n                K.learning_phase(): 0\n            })\n\n        print('Found {} boxes for {}'.format(len(out_boxes), 'img'))\n\n        font = ImageFont.truetype(font='FreeSerif.ttf',\n                    size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))\n        thickness = (image.size[0] + image.size[1]) // 300\n\n        for i, c in reversed(list(enumerate(out_classes))):\n            predicted_class = self.class_names[c]\n            box = out_boxes[i]\n            score = out_scores[i]\n\n            label = '{} {:.2f}'.format(predicted_class, score)\n            draw = ImageDraw.Draw(image)\n            label_size = draw.textsize(label, font)\n\n            top, left, bottom, right = box\n            top = max(0, np.floor(top + 0.5).astype('int32'))\n            left = max(0, np.floor(left + 0.5).astype('int32'))\n            bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))\n            right = min(image.size[0], np.floor(right + 0.5).astype('int32'))\n            print(label, (left, top), (right-left, bottom-top))\n\n            if top - label_size[1] >= 0:\n                text_origin = np.array([left, top - label_size[1]])\n            else:\n                text_origin = np.array([left, top + 1])\n\n            # My kingdom for a good redistributable image drawing library.\n            for i in range(thickness):\n                draw.rectangle(\n                    [left + i, top + i, right - i, bottom - i],\n                    outline=self.colors[c])\n            draw.rectangle(\n                [tuple(text_origin), tuple(text_origin + label_size)],\n                fill=self.colors[c])\n            draw.text(text_origin, label, fill=(0, 0, 0), font=font)\n            del draw\n\n        end = timer()\n        print(end - start)\n        return image\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--alpha\", type = float, default = 1)\n    parser.add_argument(\"--model_path\", type = str, default = 'yolov4.h5')\n    parser.add_argument(\"--anchors_path\", type = str, default = 'model_data/yolo4_anchors.txt')\n    parser.add_argument(\"--classes_path\", type = str, default = 'model_data/coco_classes.txt')\n    parser.add_argument(\"--input_size\", nargs='*', type=int, default=[512, 288]) # skip images in each cats for coco\n    parser.add_argument(\"--thd\", type=float, default=0.5)\n    parser.add_argument(\"-i\", type = str, default = '')\n    parser.add_argument(\"-o\", type = str, default = '')\n    args = parser.parse_args()\n    print(args)\n\n    iou = 0.5\n\n    input_size = (args.input_size[0], args.input_size[1])\n\n    yolo4_model = Yolo4(args.thd, iou, input_size, args.anchors_path, args.classes_path, args.model_path, alpha=args.alpha)\n\n\n    if len(args.i) == 0:\n        cap = cv2.VideoCapture(0)\n    else:\n        cap = cv2.VideoCapture(args.i)\n\n    if (cap.isOpened() == False):\n        print(\"Unable to read camera feed\")\n        exit(0)\n\n    if len(args.o) != 0:\n        videoWriter = cv2.VideoWriter(args.o, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 30, input_size)\n        print('[video]', videoWriter)\n\n    while True:\n        ret, img = cap.read()\n        if not ret:\n            break\n\n        img = cv2.resize(img, input_size, interpolation=cv2.INTER_AREA)\n        img = img[...,::-1]\n        result = yolo4_model.detect_image(Image.fromarray(img), model_image_size=tuple(reversed(input_size)))\n        # plt.imshow(result)\n        # plt.show()\n        result = np.asarray(result)[...,::-1]\n        cv2.imshow('r', result)\n        if (cv2.waitKey(1) & 0xFF == ord('q')):\n            if len(args.o) != 0:\n                videoWriter.release()\n            exit()\n\n        if len(args.o) != 0:\n            videoWriter.write(result)\n\n    yolo4_model.close_session()\n","sub_path":"src/keras-yolo4-converter-master/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":7259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"141449010","text":"from django.shortcuts import render, get_object_or_404\nfrom django.db.models import Prefetch\nfrom django.conf import settings\nfrom django.core.mail import send_mail, BadHeaderError\nfrom .form import Contact\nfrom django.views.generic import (\n\n\tTemplateView,\n\tDetailView,\n\t)\nfrom .models import (\n\tHome, \n\tAbout, \n\tProjectType, \n\tProject, \n\tSubproject, \n\tContactUS, \n\tSocialMediaInfo,\n\t) \n\n\n\n\nclass IndexView(TemplateView):\n    template_name = \"index.html\"\n\n    def get_context_data(self, **kwargs):\n\n    \tcontext = super().get_context_data(**kwargs)\n\n    \tnew_context = {\n    \t'homes' : Home.objects.all(),\n    \t'abouts': About.objects.all(),\n    \t'projecttypes' : ProjectType.objects.prefetch_related('type').all(),\n    \t'infos' : ContactUS.objects.all(),\n    \t'medias': SocialMediaInfo.objects.all(),\n    \t}\n    \tcontext.update(new_context)\n    \treturn context\n\nclass ProjectView(DetailView):\n\ttemplate_name = \"project.html\"\n\n\tdef dispatch(self, *args, **kwargs):\n\t\treturn super(ProjectView, self).dispatch(*args, **kwargs)\n\n\tdef get_queryset(self):\n\t\tresult = self.kwargs.get(\"id\")\n\t\treturn result.objects.filter.all()\n\tdef get_context_data(self, **kwargs):\n\t\tcontext = super(ProjectView, self).get_context_data(**kwargs)\n\t\tname = self.kwargs.get(\"id\")\n\t\tnew_context = {\n\t\t\t'projectitems' : Subproject.objects.prefetch_related('project').filter(project = name),\n\t\t\t'projects' : Project.objects.prefetch_related('type').filter(project = name),\n\t\t\t'homes' : Home.objects.all(),\n\t\t\t'infos' : ContactUS.objects.all()\n\n\t\t}\n\t\tcontext.update(new_context)\n\n\t\treturn context\n\tdef get_object(self):\n\t\tid = self.kwargs.get(\"id\")\n\t\treturn get_object_or_404(Subproject, id=id)\n\nclass GeneralProjectView(DetailView):\n\ttemplate_name = \"generalproject.html\"\n\n\tdef dispatch(self, *args, **kwargs):\n\t\treturn super(GeneralProjectView, self).dispatch(*args, **kwargs)\n\n\tdef get_queryset(self):\n\t\tresult = self.kwargs.get(\"id\")\n\t\treturn result.objects.filter.all()\n\n\tdef get_context_data(self, **kwargs):\n\t\tcontext = super(GeneralProjectView, self).get_context_data(**kwargs)\n\t\tname = self.kwargs.get(\"id\")\n\t\tnew_context = {\n\t\t\t'projects' : Project.objects.prefetch_related('project_types').filter(project = name).all(),\n\t\t\t'homes' : Home.objects.all(),\n\t\t\t'infos' : ContactUS.objects.all()\n\n\t\t}\n\t\tprint (new_context['projects'])\n\t\tcontext.update(new_context)\n\n\t\treturn context\n\tdef get_object(self):\n\t\tid = self.kwargs.get(\"id\")\n\t\treturn get_object_or_404(Project, id=id)\n\n\t\ndef contact_view(request):\n\n\tif request.method == 'POST':\n\t\tform = Contact()\n\telif(request.method == 'GET'):\n\t\tprint (\"hi\")\n\t\tform = Contact(request.POST)\n\t\tif form.is_valid():\n\t\t\tprint (form)\n\t\t\tname = form.cleaned_data['name']\n\t\t\tsubject = form.cleaned_data['subject']\n\t\t\temail = form.cleaned_data['email']\n\t\t\tmessage = form.cleaned_data['message']\n\t\t\tprint (name , subject, email, message)\n\t\t\ttry:\n\t\t\t\tsend_mail(subject, message, email, ['emre.kyd11@gmail.com', 'kaydu16@itu.edu.tr', None])\n\t\t\texcept BadHeaderError:\n\t\t\t\treturn HttpResponse('Invalid header found.')\n\t\t\treturn redirect('success/')\n\treturn render(request, \"contact.html\", {'form': form})\n\ndef success_view(request):\n\tmessage = \"Mail başarıyla gönderildi..\"\n\tcontext = {'message': message}\n\treturn render(request, \"contact.html\", context)","sub_path":"web/page/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"629990740","text":"import glob\nimport logging\nimport os\nimport platform\nimport time\nfrom collections import OrderedDict\n\nimport cv2\nimport mlflow as ml\nimport numpy as np\nimport torch\nimport torch.autograd as autograd\nimport torchvision\nfrom torch.nn import DataParallel\nfrom torch.optim import Adam, RMSprop, SGD, lr_scheduler\nfrom torch.utils.tensorboard import SummaryWriter\nfrom torchsummary import summary as modelsummary\nfrom tqdm import tqdm\n\nfrom core import CenterNet\nfrom core import HeatmapFocalLoss, NormedL1Loss\nfrom core import Prediction\nfrom core import Voc_2007_AP\nfrom core import plot_bbox, PrePostNet\nfrom core import traindataloader, validdataloader\n\nlogfilepath = \"\"\nif os.path.isfile(logfilepath):\n    os.remove(logfilepath)\nlogging.basicConfig(filename=logfilepath, level=logging.INFO)\n\n\n# 초기화 참고하기\n# https://pytorch.org/docs/stable/nn.init.html?highlight=nn%20init#torch.nn.init.kaiming_normal_\n\ndef run(mean=[0.485, 0.456, 0.406],\n        std=[0.229, 0.224, 0.225],\n        epoch=100,\n        input_size=[512, 512],\n        input_frame_number=2,\n        batch_size=16,\n        batch_log=100,\n        subdivision=4,\n        train_dataset_path=\"Dataset/train\",\n        valid_dataset_path=\"Dataset/valid\",\n        data_augmentation=True,\n        num_workers=4,\n        optimizer=\"ADAM\",\n        lambda_off=1,\n        lambda_size=0.1,\n        save_period=5,\n        load_period=10,\n        learning_rate=0.001, decay_lr=0.999, decay_step=10,\n        GPU_COUNT=0,\n        base=18,\n        pretrained_base=True,\n        valid_size=8,\n        eval_period=5,\n        tensorboard=True,\n        valid_graph_path=\"valid_Graph\",\n        valid_html_auto_open=True,\n        using_mlflow=True,\n        topk=100,\n        iou_thresh=0.5,\n        nms=False,\n        except_class_thresh=0.01,\n        nms_thresh=0.5,\n        plot_class_thresh=0.5):\n    if GPU_COUNT == 0:\n        device = torch.device(\"cpu\")\n    elif GPU_COUNT == 1:\n        device = torch.device(\"cuda\")\n    else:\n        device = [torch.device(f\"cuda:{i}\") for i in range(0, GPU_COUNT)]\n\n    if isinstance(device, (list, tuple)):\n        context = device[0]\n    else:\n        context = device\n\n    # 운영체제 확인\n    if platform.system() == \"Linux\":\n        logging.info(f\"{platform.system()} OS\")\n    elif platform.system() == \"Windows\":\n        logging.info(f\"{platform.system()} OS\")\n    else:\n        logging.info(f\"{platform.system()} OS\")\n\n    # free memory는 정확하지 않은 것 같고, torch.cuda.max_memory_allocated() 가 정확히 어떻게 동작하는지?\n    if isinstance(device, (list, tuple)):\n        for i, d in enumerate(device):\n            total_memory = torch.cuda.get_device_properties(d).total_memory\n            free_memory = total_memory - torch.cuda.max_memory_allocated(d)\n            free_memory = round(free_memory / (1024 ** 3), 2)\n            total_memory = round(total_memory / (1024 ** 3), 2)\n            logging.info(f'{torch.cuda.get_device_name(d)}')\n            logging.info(f'Running on {d} / free memory : {free_memory}GB / total memory {total_memory}GB')\n    else:\n        if GPU_COUNT == 1:\n            total_memory = torch.cuda.get_device_properties(device).total_memory\n            free_memory = total_memory - torch.cuda.max_memory_allocated(device)\n            free_memory = round(free_memory / (1024 ** 3), 2)\n            total_memory = round(total_memory / (1024 ** 3), 2)\n            logging.info(f'{torch.cuda.get_device_name(device)}')\n            logging.info(f'Running on {device} / free memory : {free_memory}GB / total memory {total_memory}GB')\n        else:\n            logging.info(f'Running on {device}')\n\n    if GPU_COUNT > 0 and batch_size < GPU_COUNT:\n        logging.info(\"batch size must be greater than gpu number\")\n        exit(0)\n\n    if data_augmentation:\n        logging.info(\"Using Data Augmentation\")\n\n    logging.info(\"training Center Detector\")\n    input_shape = (1, 3 * input_frame_number) + tuple(input_size)\n\n    scale_factor = 4  # 고정\n    logging.info(f\"scale factor {scale_factor}\")\n\n    train_dataloader, train_dataset = traindataloader(augmentation=data_augmentation,\n                                                      path=train_dataset_path,\n                                                      input_size=input_size,\n                                                      input_frame_number=input_frame_number,\n                                                      batch_size=batch_size,\n                                                      pin_memory=True,\n                                                      num_workers=num_workers,\n                                                      shuffle=True, mean=mean, std=std, scale_factor=scale_factor,\n                                                      make_target=True)\n\n    train_update_number_per_epoch = len(train_dataloader)\n    if train_update_number_per_epoch < 1:\n        logging.warning(\"train batch size가 데이터 수보다 큼\")\n        exit(0)\n\n    valid_list = glob.glob(os.path.join(valid_dataset_path, \"*\"))\n    if valid_list:\n        valid_dataloader, valid_dataset = validdataloader(path=valid_dataset_path,\n                                                          input_size=input_size,\n                                                          input_frame_number=input_frame_number,\n                                                          batch_size=valid_size,\n                                                          num_workers=num_workers,\n                                                          pin_memory=True,\n                                                          shuffle=True, mean=mean, std=std, scale_factor=scale_factor,\n                                                          make_target=True)\n        valid_update_number_per_epoch = len(valid_dataloader)\n        if valid_update_number_per_epoch < 1:\n            logging.warning(\"valid batch size가 데이터 수보다 큼\")\n            exit(0)\n\n    num_classes = train_dataset.num_class  # 클래스 수\n    name_classes = train_dataset.classes\n\n    optimizer = optimizer.upper()\n    if pretrained_base:\n        model = str(input_size[0]) + \"_\" + str(input_size[1]) + \"_\" + optimizer + \"_P\" + \"CENTER_RES\" + str(base)\n    else:\n        model = str(input_size[0]) + \"_\" + str(input_size[1]) + \"_\" + optimizer + \"_CENTER_RES\" + str(base)\n\n    # https://discuss.pytorch.org/t/how-to-save-the-optimizer-setting-in-a-log-in-pytorch/17187\n    weight_path = os.path.join(\"weights\", f\"{model}\")\n    param_path = os.path.join(weight_path, f'{model}-{load_period:04d}.pt')\n\n    start_epoch = 0\n    net = CenterNet(base=base,\n                    input_frame_number=input_frame_number,\n                    heads=OrderedDict([\n                        ('heatmap', {'num_output': num_classes, 'bias': -2.19}),\n                        ('offset', {'num_output': 2}),\n                        ('wh', {'num_output': 2})\n                    ]),\n                    head_conv_channel=64,\n                    pretrained=pretrained_base)\n\n    # https://github.com/sksq96/pytorch-summary\n    modelsummary(net.to(context), input_shape[1:])\n\n    if tensorboard:\n        summary = SummaryWriter(log_dir=os.path.join(\"torchboard\", model), max_queue=10, flush_secs=10)\n        summary.add_graph(net.to(context), input_to_model=torch.ones(input_shape, device=context), verbose=False)\n\n    if os.path.exists(param_path):\n        start_epoch = load_period\n        checkpoint = torch.load(param_path)\n        if 'model_state_dict' in checkpoint:\n            try:\n                net.load_state_dict(checkpoint['model_state_dict'])\n            except Exception as E:\n                logging.info(E)\n            else:\n                logging.info(f\"loading model_state_dict\")\n\n    if start_epoch + 1 >= epoch + 1:\n        logging.info(\"this model has already been optimized\")\n        exit(0)\n\n    net.to(context)\n\n    if optimizer.upper() == \"ADAM\":\n        trainer = Adam(net.parameters(), lr=learning_rate, betas=(0.9, 0.999), weight_decay=0.000001)\n    elif optimizer.upper() == \"RMSPROP\":\n        trainer = RMSprop(net.parameters(), lr=learning_rate, alpha=0.99, weight_decay=0.000001, momentum=0)\n    elif optimizer.upper() == \"SGD\":\n        trainer = SGD(net.parameters(), lr=learning_rate, momentum=0.9, weight_decay=0.000001)\n    else:\n        logging.error(\"optimizer not selected\")\n        exit(0)\n\n    if os.path.exists(param_path):\n        # optimizer weight 불러오기\n        checkpoint = torch.load(param_path)\n        if 'optimizer_state_dict' in checkpoint:\n            try:\n                trainer.load_state_dict(checkpoint['optimizer_state_dict'])\n            except Exception as E:\n                logging.info(E)\n            else:\n                logging.info(f\"loading optimizer_state_dict\")\n\n    if isinstance(device, (list, tuple)):\n        net = DataParallel(net, device_ids=device, output_device=context, dim=0)\n\n    # optimizer\n    # https://pytorch.org/docs/master/optim.html?highlight=lr%20sche#torch.optim.lr_scheduler.CosineAnnealingLR\n    unit = 1 if (len(train_dataset) // batch_size) < 1 else len(train_dataset) // batch_size\n    step = unit * decay_step\n    lr_sch = lr_scheduler.StepLR(trainer, step, gamma=decay_lr, last_epoch=-1)\n\n    heatmapfocalloss = HeatmapFocalLoss(from_sigmoid=True, alpha=2, beta=4)\n    normedl1loss = NormedL1Loss()\n    prediction = Prediction(batch_size=valid_size, unique_ids=name_classes, topk=topk, scale=scale_factor, nms=nms,\n                            except_class_thresh=except_class_thresh, nms_thresh=nms_thresh)\n    precision_recall = Voc_2007_AP(iou_thresh=iou_thresh, class_names=name_classes)\n\n    # torch split이 numpy, mxnet split과 달라서 아래와 같은 작업을 하는 것\n    if batch_size % subdivision == 0:\n        chunk = int(batch_size) // int(subdivision)\n    else:\n        logging.info(f\"batch_size / subdivision 이 나누어 떨어지지 않습니다.\")\n        logging.info(f\"subdivision 을 다시 설정하고 학습 진행하세요.\")\n        exit(0)\n\n\n    start_time = time.time()\n    for i in tqdm(range(start_epoch + 1, epoch + 1, 1), initial=start_epoch + 1, total=epoch):\n\n        heatmap_loss_sum = 0\n        offset_loss_sum = 0\n        wh_loss_sum = 0\n        time_stamp = time.time()\n\n        # multiscale을 하게되면 여기서 train_dataloader을 다시 만드는 것이 좋겠군..\n        for batch_count, (image, _, heatmap_target, offset_target, wh_target, mask_target, _) in enumerate(\n                train_dataloader,\n                start=1):\n\n            trainer.zero_grad()\n\n            image = image.to(context)\n\n            '''\n            이렇게 하는 이유?\n            209 line에서 net = net.to(context)로 함\n            gpu>=1 인 경우 net = DataParallel(net, device_ids=device, output_device=context, dim=0) 에서 \n            output_device - gradient가 계산되는 곳을 context로 했기 때문에 아래의 target들도 context로 지정해줘야 함\n            '''\n            heatmap_target = heatmap_target.to(context)\n            offset_target = offset_target.to(context)\n            wh_target = wh_target.to(context)\n            mask_target = mask_target.to(context)\n\n            image_split = torch.split(image, chunk, dim=0)\n            heatmap_target_split = torch.split(heatmap_target, chunk, dim=0)\n            offset_target_split = torch.split(offset_target, chunk, dim=0)\n            wh_target_split = torch.split(wh_target, chunk, dim=0)\n            mask_target_split = torch.split(mask_target, chunk, dim=0)\n\n            heatmap_losses = []\n            offset_losses = []\n            wh_losses = []\n            total_loss = []\n\n            for image_part, heatmap_target_part, offset_target_part, wh_target_part, mask_target_part in zip(\n                    image_split,\n                    heatmap_target_split,\n                    offset_target_split,\n                    wh_target_split,\n                    mask_target_split):\n                heatmap_pred, offset_pred, wh_pred = net(image_part)\n                '''\n                pytorch는 trainer.step()에서 batch_size 인자가 없다.\n                Loss 구현시 고려해야 한다.(mean 모드) \n                '''\n                heatmap_loss = torch.div(heatmapfocalloss(heatmap_pred, heatmap_target_part), subdivision)\n                offset_loss = torch.div(normedl1loss(offset_pred, offset_target_part, mask_target_part) * lambda_off,\n                                        subdivision)\n                wh_loss = torch.div(normedl1loss(wh_pred, wh_target_part, mask_target_part) * lambda_size, subdivision)\n\n                heatmap_losses.append(heatmap_loss.item())\n                offset_losses.append(offset_loss.item())\n                wh_losses.append(wh_loss.item())\n\n                total_loss.append(heatmap_loss + offset_loss + wh_loss)\n\n            # batch size만큼 나눠줘야 하지 않나?\n            autograd.backward(total_loss)\n\n            trainer.step()\n            lr_sch.step()\n\n            heatmap_loss_sum += sum(heatmap_losses)\n            offset_loss_sum += sum(offset_losses)\n            wh_loss_sum += sum(wh_losses)\n\n            if batch_count % batch_log == 0:\n                logging.info(f'[Epoch {i}][Batch {batch_count}/{train_update_number_per_epoch}],'\n                             f'[Speed {image.shape[0] / (time.time() - time_stamp):.3f} samples/sec],'\n                             f'[Lr = {lr_sch.get_last_lr()}]'\n                             f'[heatmap loss = {sum(heatmap_losses):.3f}]'\n                             f'[offset loss = {sum(offset_losses):.3f}]'\n                             f'[wh loss = {sum(wh_losses):.3f}]')\n            time_stamp = time.time()\n\n        train_heatmap_loss_mean = np.divide(heatmap_loss_sum, train_update_number_per_epoch)\n        train_offset_loss_mean = np.divide(offset_loss_sum, train_update_number_per_epoch)\n        train_wh_loss_mean = np.divide(wh_loss_sum, train_update_number_per_epoch)\n        train_total_loss_mean = train_heatmap_loss_mean + train_offset_loss_mean + train_wh_loss_mean\n\n        logging.info(\n            f\"train heatmap loss : {train_heatmap_loss_mean} / train offset loss : {train_offset_loss_mean} / train wh loss : {train_wh_loss_mean} / train total loss : {train_total_loss_mean}\")\n\n        if i % save_period == 0:\n\n            if not os.path.exists(weight_path):\n                os.makedirs(weight_path)\n\n            module = net.module if isinstance(device, (list, tuple)) else net\n            auxnet = Prediction(unique_ids=name_classes, topk=topk, scale=scale_factor, nms=nms, except_class_thresh=except_class_thresh,\n                                nms_thresh=nms_thresh)\n            prepostnet = PrePostNet(net=module, auxnet=auxnet, input_frame_number=input_frame_number)  # 새로운 객체가 생성\n\n            try:\n                torch.save({\n                    'model_state_dict': net.state_dict(),\n                    'optimizer_state_dict': trainer.state_dict()}, os.path.join(weight_path, f'{model}-{i:04d}.pt'))\n\n                # torch.jit.trace() 보다는 control-flow 연산 적용이 가능한 torch.jit.script() 을 사용하자\n                # torch.jit.script\n                script = torch.jit.script(module)\n                script.save(os.path.join(weight_path, f'{model}-{i:04d}.jit'))\n\n                script = torch.jit.script(prepostnet)\n                script.save(os.path.join(weight_path, f'{model}-prepost-{i:04d}.jit'))\n\n                # # torch.jit.trace - 안 써짐\n                # 오류 : Expected object of device type cuda but got device type cpu for argument #2 'other' in call to _th_fmod\n                # trace = torch.jit.trace(prepostnet, torch.rand(input_shape[0], input_shape[1], input_shape[2], input_shape[3], device=context))\n                # trace.save(os.path.join(weight_path, f'{model}-{i:04d}.jit'))\n\n            except Exception as E:\n                logging.error(f\"pt, jit export 예외 발생 : {E}\")\n            else:\n                logging.info(\"pt, jit export 성공\")\n\n        if i % eval_period == 0 and valid_list:\n\n            heatmap_loss_sum = 0\n            offset_loss_sum = 0\n            wh_loss_sum = 0\n\n            # loss 구하기\n            for image, label, heatmap_target, offset_target, wh_target, mask_target, _ in valid_dataloader:\n                image = image.to(context)\n                label = label.to(context)\n                gt_box = label[:, :, :4]\n                gt_id = label[:, :, 4:5]\n\n                heatmap_target = heatmap_target.to(context)\n                offset_target = offset_target.to(context)\n                wh_target = wh_target.to(context)\n                mask_target = mask_target.to(context)\n\n                heatmap_pred, offset_pred, wh_pred = net(image)\n                id, score, bbox = prediction(heatmap_pred, offset_pred, wh_pred)\n                precision_recall.update(pred_bboxes=bbox,\n                                        pred_labels=id,\n                                        pred_scores=score,\n                                        gt_boxes=gt_box * scale_factor,\n                                        gt_labels=gt_id)\n\n                heatmap_loss = heatmapfocalloss(heatmap_pred, heatmap_target)\n                offset_loss = normedl1loss(offset_pred, offset_target, mask_target) * lambda_off\n                wh_loss = normedl1loss(wh_pred, wh_target, mask_target) * lambda_size\n\n                heatmap_loss_sum += heatmap_loss.item()\n                offset_loss_sum += offset_loss.item()\n                wh_loss_sum += wh_loss.item()\n\n            valid_heatmap_loss_mean = np.divide(heatmap_loss_sum, valid_update_number_per_epoch)\n            valid_offset_loss_mean = np.divide(offset_loss_sum, valid_update_number_per_epoch)\n            valid_wh_loss_mean = np.divide(wh_loss_sum, valid_update_number_per_epoch)\n            valid_total_loss_mean = valid_heatmap_loss_mean + valid_offset_loss_mean + valid_wh_loss_mean\n\n            logging.info(\n                f\"valid heatmap loss : {valid_heatmap_loss_mean} / valid offset loss : {valid_offset_loss_mean} / valid wh loss : {valid_wh_loss_mean} / valid total loss : {valid_total_loss_mean}\")\n\n            AP_appender = []\n            round_position = 2\n            class_name, precision, recall, true_positive, false_positive, threshold = precision_recall.get_PR_list()\n            for j, c, p, r in zip(range(len(recall)), class_name, precision, recall):\n                name, AP = precision_recall.get_AP(c, p, r)\n                logging.info(f\"class {j}'s {name} AP : {round(AP * 100, round_position)}%\")\n                AP_appender.append(AP)\n            mAP_result = np.mean(AP_appender)\n\n            logging.info(f\"mAP : {round(mAP_result * 100, round_position)}%\")\n            precision_recall.get_PR_curve(name=class_name,\n                                          precision=precision,\n                                          recall=recall,\n                                          threshold=threshold,\n                                          AP=AP_appender, mAP=mAP_result, folder_name=valid_graph_path, epoch=i,\n                                          auto_open=valid_html_auto_open)\n            precision_recall.reset()\n\n            if tensorboard:\n\n                batch_image = []\n                ground_truth_colors = {}\n                for k in range(num_classes):\n                    ground_truth_colors[k] = (0, 1, 0) # RGB\n\n                dataloader_iter = iter(valid_dataloader)\n                image, label, _, _, _, _, _ = next(dataloader_iter)\n\n                image = image.to(context)\n                label = label.to(context)\n                gt_boxes = label[:, :, :4]\n                gt_ids = label[:, :, 4:5]\n\n                heatmap_pred, offset_pred, wh_pred = net(image)\n                ids, scores, bboxes = prediction(heatmap_pred, offset_pred, wh_pred)\n\n                for img, gt_id, gt_box, heatmap, id, score, bbox in zip(image, gt_ids, gt_boxes, heatmap_pred, ids,\n                                                                        scores, bboxes):\n\n                    split_img = torch.split(img, 3, dim=0) # numpy split과 다르네...\n                    hconcat_image_list = []\n\n                    for j, ig in enumerate(split_img):\n\n                        ig = ig.permute((1, 2, 0)) * torch.tensor(std, device=ig.device) + torch.tensor(mean, device=ig.device)\n                        ig = (ig * 255).clamp(0, 255)\n                        ig = ig.to(torch.uint8)\n                        ig = ig.detach().cpu().numpy().copy()\n\n                        if j == len(split_img) - 1:  # 마지막 이미지\n                            # heatmap 그리기\n                            heatmap = heatmap.detach().cpu().numpy().copy()\n                            heatmap = np.multiply(heatmap, 255.0)  # 0 ~ 255 범위로 바꾸기\n                            heatmap = np.amax(heatmap, axis=0, keepdims=True)  # channel 축으로 가장 큰것 뽑기\n                            heatmap = np.transpose(heatmap, axes=(1, 2, 0))  # (height, width, channel=1)\n                            heatmap = np.repeat(heatmap, 3, axis=-1)\n                            heatmap = heatmap.astype(\"uint8\")  # float32 -> uint8\n                            heatmap = cv2.resize(heatmap, dsize=(input_size[1], input_size[0]))  # 사이즈 원복\n                            heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)\n                            heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)\n\n                            # ground truth box 그리기\n                            ground_truth = plot_bbox(ig, gt_box * scale_factor, scores=None, labels=gt_id, thresh=None,\n                                                     reverse_rgb=False,\n                                                     class_names=valid_dataset.classes,\n                                                     absolute_coordinates=True,\n                                                     colors=ground_truth_colors)\n                            # # prediction box 그리기\n                            prediction_box = plot_bbox(ground_truth, bbox, scores=score, labels=id,\n                                                       thresh=plot_class_thresh,\n                                                       reverse_rgb=False,\n                                                       class_names=valid_dataset.classes,\n                                                       absolute_coordinates=True,\n                                                       heatmap=heatmap)\n                            hconcat_image_list.append(prediction_box)\n                        else:\n                            hconcat_image_list.append(ig)\n\n                    hconcat_images = np.concatenate(hconcat_image_list, axis=1)\n\n                    # Tensorboard에 그리기 위해 (height, width, channel) -> (channel, height, width) 를한다.\n                    hconcat_images = np.transpose(hconcat_images, axes=(2, 0, 1))\n                    batch_image.append(hconcat_images)  # (batch, channel, height, width)\n\n                img_grid = torchvision.utils.make_grid(torch.as_tensor(batch_image), nrow=1)\n                summary.add_image(tag=\"valid_result\", img_tensor=img_grid, global_step=i)\n\n                summary.add_scalar(tag=\"heatmap_loss/train_heatmap_loss_mean\",\n                                   scalar_value=train_heatmap_loss_mean,\n                                   global_step=i)\n                summary.add_scalar(tag=\"heatmap_loss/valid_heatmap_loss_mean\",\n                                   scalar_value=valid_heatmap_loss_mean,\n                                   global_step=i)\n\n                summary.add_scalar(tag=\"offset_loss/train_offset_loss_mean\",\n                                   scalar_value=train_offset_loss_mean,\n                                   global_step=i)\n                summary.add_scalar(tag=\"offset_loss/valid_offset_loss_mean\",\n                                   scalar_value=valid_offset_loss_mean,\n                                   global_step=i)\n\n                summary.add_scalar(tag=\"wh_loss/train_wh_loss_mean\",\n                                   scalar_value=train_wh_loss_mean,\n                                   global_step=i)\n                summary.add_scalar(tag=\"wh_loss/valid_wh_loss_mean\",\n                                   scalar_value=valid_wh_loss_mean,\n                                   global_step=i)\n\n                summary.add_scalar(tag=\"total_loss/train_total_loss\",\n                                   scalar_value = train_total_loss_mean,\n                                   global_step=i)\n                summary.add_scalar(tag=\"total_loss/valid_total_loss\",\n                                   scalar_value = valid_total_loss_mean,\n                                   global_step=i)\n\n                for name, param in net.named_parameters():\n                    summary.add_histogram(tag=name, values=param, global_step=i)\n\n    end_time = time.time()\n    learning_time = end_time - start_time\n    logging.info(f\"learning time : 약, {learning_time / 3600:0.2f}H\")\n    logging.info(\"optimization completed\")\n\n    if using_mlflow:\n        ml.log_metric(\"learning time\", round(learning_time / 3600, 2))\n\n\nif __name__ == \"__main__\":\n    run(mean=[0.485, 0.456, 0.406],\n        std=[0.229, 0.224, 0.225],\n        epoch=100,\n        input_size=[512, 512],\n        input_frame_number=2,\n        batch_size=16,\n        batch_log=100,\n        subdivision=4,\n        train_dataset_path=\"Dataset/train\",\n        valid_dataset_path=\"Dataset/valid\",\n        data_augmentation=True,\n        num_workers=4,\n        optimizer=\"ADAM\",\n        lambda_off=1,\n        lambda_size=0.1,\n        save_period=5,\n        load_period=10,\n        learning_rate=0.001, decay_lr=0.999, decay_step=10,\n        GPU_COUNT=0,\n        base=18,\n        pretrained_base=True,\n        valid_size=8,\n        eval_period=5,\n        tensorboard=True,\n        valid_graph_path=\"valid_Graph\",\n        valid_html_auto_open=True,\n        using_mlflow=True,\n        topk=100,\n        iou_thresh=0.5,\n        nms=False,\n        except_class_thresh=0.01,\n        nms_thresh=0.5,\n        plot_class_thresh=0.5)\n","sub_path":"CenterNet/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":26298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"192289986","text":"import threading\nimport time\nimport inspect\nimport ctypes\n\nclass WrapperThread(threading.Thread):\n  def __init__(self, target, args):\n    super().__init__()\n    self.target = target\n    self.args = args\n    self.run = self.target\n    \nclass Thread:\n  def __init__(self, target, args = ()):\n    self.target = target\n    self.args = args\n    self.thread = None\n\n  def start(self):\n    self.thread = WrapperThread(self.target, self.args)\n    self.thread.start()\n    \n  def stop(self):\n    print('Thread was killed')\n    if not self.thread.isAlive():\n      return\n    tid = self.thread.ident\n    exctype = SystemExit\n\n    \"\"\"Raises an exception in the threads with id tid\"\"\"\n    if not inspect.isclass(exctype):\n      raise TypeError(\"Only types can be raised (not instances)\")\n    res = ctypes.pythonapi.PyThreadState_SetAsyncExc(\n      ctypes.c_long(tid),\n      ctypes.py_object(exctype)\n    )\n    if res == 0:\n      raise ValueError(\"invalid thread id\")\n    elif res != 1:\n      # \"\"\"if it returns a number greater than one, you're in trouble,\n      # and you should call it again with exc=NULL to revert the effect\"\"\"\n      ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, None)\n      raise SystemError(\"PyThreadState_SetAsyncExc failed\")","sub_path":"python/src/ThreadHelper.py","file_name":"ThreadHelper.py","file_ext":"py","file_size_in_byte":1237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"62095331","text":"import numpy as np\nfrom pingouin.tests._tests_pingouin import _TestPingouin\nfrom pingouin.power import (ttest_power, anova_power)\n\n\nclass TestPower(_TestPingouin):\n    \"\"\"Test power.py.\"\"\"\n\n    def test_ttest_power(self):\n        \"\"\"Test function ttest_power.\"\"\"\n        nx, ny = 20, 20\n        d = 0.5\n        power = ttest_power(d, nx, ny, paired=True, tail='one-sided')\n        # Compare values with GPower 3.1.9\n        assert np.allclose(power, 0.695)\n        power = ttest_power(d, nx, ny, paired=False, tail='two-sided')\n        assert np.allclose(power, 0.338)\n        ttest_power(d, nx)\n\n    def test_anova_power(self):\n        \"\"\"Test function anova_power.\"\"\"\n        ntot, ngroups = 60, 3\n        eta = .20\n        power = anova_power(eta, ntot, ngroups)\n        assert np.allclose(power, 0.932)\n","sub_path":"pingouin/tests/test_power.py","file_name":"test_power.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"198105220","text":"# Copyright (c) 2012 Rackspace Hosting\n# All Rights Reserved.\n#\n#    Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n#    not use this file except in compliance with the License. You may obtain\n#    a copy of the License at\n#\n#         http://www.apache.org/licenses/LICENSE-2.0\n#\n#    Unless required by applicable law or agreed to in writing, software\n#    distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n#    WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n#    License for the specific language governing permissions and limitations\n#    under the License.\n\n\"\"\"\nCluster Service Manager\n\"\"\"\n\n# import datetime\n# import time\n\nfrom oslo.config import cfg\nfrom oslo import messaging as oslo_messaging\n\nfrom whip import manager\nfrom whip.openstack.common.gettextutils import _\nfrom whip.openstack.common import importutils\nfrom whip.openstack.common import log as logging\n\nimport starcluster as sc\nimport starcluster.config as sc_cfg\n\n# from starcluster.commands.base import CmdBase as StarCluster\n\n# from nova.cells import messaging\n# from nova.cells import state as cells_state\n# from nova.cells import utils as cells_utils\n# from nova import context\n# from nova import exception\n# from nova import manager\n# from nova.objects import instance as instance_obj\n# from nova.openstack.common.gettextutils import _\n# from nova.openstack.common import importutils\n# from nova.openstack.common import log as logging\n# from nova.openstack.common import periodic_task\n# from nova.openstack.common import timeutils\n\nfrom whip.cluster import rpcapi as capi\n\ncluster_opts = [\n    cfg.StrOpt('driver', default='whip.cluster.driver.StarClusterDriver',\n                help='Cluster management driver (DEFAULT: StarCluster'),\n]\n\n\nCONF = cfg.CONF\nCONF.register_opts(cluster_opts, group='cluster')\n# CONF.import_opt('name', 'nova.cells.opts', group='cells')\n\nLOG = logging.getLogger(__name__)\n\n\nclass ClusterManager(manager.Manager):\n    \"\"\"This is a cluster manager which performs basic tasks.\n\n    Example: Start, Stop\n    \"\"\"\n\n    # target = oslo_messaging.Target(version='1.27')\n\n    def __init__(self, *args, **kwargs):\n        self.cfg = sc_cfg.get_config()\n        self.ec2 = self.cfg.get_easy_ec2()\n        self.cm = self.cfg.get_cluster_manager()\n        self.nm = sc.node.NodeManager(self.cfg, ec2=self.ec2)\n        super(ClusterManager, self).__init__(service_name='cluster',\n                                             *args, **kwargs)\n\n    def list(self, ctxt):\n        \"\"\"List existing clusters.\"\"\"\n        # capi.list(ctxt)\n        # self.ec2.list_starcluster_public_images()\n        print('STARCLUSTER LIST')\n        return ''\n\n    def start(self, ctxt, tag, template):\n        \"\"\"Star the cluster.\"\"\"\n        # scluster = self.cm.get_cluster_group_or_none(tag)\n        # if scluster:\n        #     scluster = self.cm.get_cluster(tag, group=scluster,\n        #                                    load_receipt=False,\n        #                                    require_keys=False)\n        # template = self.cm.get_default_cluster_template()\n        # scluster = self.cm.get_cluster_template(template, tag)\n        # scluster.start(create=True, create_only=False,\n        #                validate=True, validate_only=False,\n        #                validate_running=True)\n        print('STARCLUSTER START')\n\n\n    def stop(self, ctxt, tag, template):\n        \"\"\"HELP.\"\"\"\n        print('STARCLUSTER STOP')\n        return\n","sub_path":"whip/cluster/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":3495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"474879814","text":"\n\n#calss header\nclass _JINX():\n\tdef __init__(self,): \n\t\tself.name = \"JINX\"\n\t\tself.definitions = [u'to cause a person or group to experience bad luck: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'verbs'\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/verbs/_jinx.py","file_name":"_jinx.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"402424147","text":"#Program for the Full Simplification of a Polynomial\nimport math\n\n\n\ndef reducePoly(cList, eList):\n\t#Combination of like terms\n\tfor i in range(len(eList)):\n\t\t# print(\"I\"+str(i))\n\t\tj=i+1\n\t\twhile j<=len(eList)-1:\n\t\t\t# print(j)\n\t\t\t# print(eList[i],eList[j])\n\t\t\tif eList[i]==eList[j]:\n\t\t\t\tcList[i] = cList[i]+cList[j]\n\t\t\t\tdel(cList[j])\n\t\t\t\tdel(eList[j])\n\t\t\t\treducePoly(cList,eList)\n\t\t\tj+=1\n\n\treturn cList, eList\n\n\n\ndef factorPoly(cList, eList):\n\tfactorList = list() #Use to see if factorization is possible\n\tfor i in range(1,1000000):\n\t\tfactorList.append(i)\n\n\tfactorables = list() #Factor\n\tfor i in range(len(factorList)):\n\t\tif all(x%factorList[i]==0 for x in cList):\n\t\t\tfactorables.append(factorList[i])\n\tmaxFactor = max(factorables)\n\tif maxFactor>1:\n\t\tfor i in range(len(cList)):\n\t\t\tcList[i]=cList[i]/maxFactor\n\n\treturn cList, eList, maxFactor\n\n\n\ndef printSimplifiedPoly(cList,eList,maxFactor):\n\tprint(\"Simplified Polynomial: \")\n\tsSimplePoly = str(maxFactor)+'('\n\tfor i in range(len(cList)):\n\t\tsSimplePoly=sSimplePoly+str(cList[i])+'x^'+str(eList[i])+'+'\n\tsSimplePoly=sSimplePoly[:-1]\n\tsSimplePoly=sSimplePoly+')'\n\tprint(sSimplePoly)\n\n\n\ndef main():\n\t#Capture polynomial\n\tprint(\"Input the polynomial in the csv form 'ax^(j) , bx^(k) , cx^(l) , ...'.\\nIf x does not exist (just a constant), use x^0 in it's place.\\n\")\n\tsPoly = [x.replace(\" \", \"\") for x in input(\"Polynomial = \").split(\",\")]\n\tprint(sPoly)\n\n\t#Capture constants and exponents into a list\n\tcList = list()\n\teList = list()\n\tfor i in range(len(sPoly)):\n\t\tc = \"\"\n\t\te = \"\"\n\t\tj = 0\n\n\t\t# constants before x, exponents after ^\n\t\twhile str(sPoly[i][j])!='x':\n\t\t\tc=c+(sPoly[i][j])\n\t\t\tj+=1\n\t\tj+=2 #pass x and ^ characters\n\t\twhile j<len(sPoly[i]):\n\t\t\te = e+(sPoly[i][j])\n\t\t\tj+=1\n\n\t\tcList.append(int(c))\n\t\teList.append(int(e))\n\n\tcList, eList = reducePoly(cList, eList)\n\tcList, eList, maxFactor = factorPoly(cList, eList)\n\n\tprintSimplifiedPoly(cList,eList,maxFactor)\n\nmain()","sub_path":"Slagle Math/Symbolic Polynomial Simplification.py","file_name":"Symbolic Polynomial Simplification.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"135564605","text":"import os, datetime, time\nimport library.db_utils as db_utils\nimport app.sequence.service as sequence_service\nimport app.role.service as role_service\nimport app.projectmember.service as projectmember_service\nfrom bson.objectid import ObjectId\n\ndomain = 'project'\n\n\ndef find(request, space_id):\n    # member_projects = find_member_projects(space_id, request.user_id)\n    # admin_projects = find_admin_projects(space_id, request.user_id)\n    all_projects = find_all_projects(space_id)\n    projectid_list = []\n    # for item in member_projects:\n    #     if item['projectId'] not in projectid_list:\n    #         projectid_list.append(ObjectId(item['projectId']))\n    # for item in admin_projects:\n    #     if item['domainId'] not in projectid_list:\n    #         projectid_list.append(ObjectId(item['domainId']))\n\n    # projects = db_utils.find(space_id, domain, {'_id': {'$in': projectid_list}})\n    projects = db_utils.find(space_id, domain, {})\n\n    return 200, {'data': projects}\n\n\ndef update(request, space_id, data):\n    new_record = False\n    if '_id' not in data:\n        new_record = True\n    updated_record = db_utils.upsert(space_id, domain, data, request.user_id)\n    if new_record:\n        sequence_service.create_sequence(space_id, 'taskOrder', updated_record['_id'], 1)\n        sequence_service.create_sequence(space_id, 'stageOrder', updated_record['_id'], 1)\n        sequence_service.create_sequence(space_id, 'taskId', updated_record['_id'], 1)\n        sequence_service.create_sequence(space_id, 'epicColor', updated_record['_id'], 1)\n        role_service.add(space_id,\n                         {'type': 'ProjectAdministrator', 'userId': request.user_id, 'domainId': updated_record['_id']},\n                         request.user_id)\n    return 200, {'data': updated_record}\n\n\ndef delete(request, space_id, id):\n    project_member_list = projectmember_service.find_by_projectid_userid(request, space_id, id, request.user_id)\n    for item in project_member_list:\n        print(item)\n        if item['type'] == 'ADMINISTRATOR':\n            result = db_utils.delete(space_id, domain, {'_id': id}, request.user_id)\n            return 200, {'deleted_count': result.deleted_count}\n        else:\n            return 406, {'data': 'User is not a Member or administrator'}\n\n\ndef find_by_id(request, space_id, id):\n    project_member_list = projectmember_service.find_by_projectid_userid(request, space_id, id, request.user_id)\n    for item in project_member_list:\n        print(item)\n        if item['type'] == 'MEMBER' or item['type'] == 'ADMINISTRATOR':\n            data = db_utils.find(space_id, domain, {'_id': id})\n            return 200, {'data': data}\n        else:\n            return 406, {'data': 'User is not a Member or administrator'}\n\n        # TBD deprecated should be removed\n\n\ndef find_all_projects(space_id):\n    return db_utils.find(space_id, domain, {})\n\n\ndef get_by_reference(space_id, project_reference):\n    data = db_utils.find(space_id, domain, {\"reference\": project_reference})\n    if len(data) == 1:\n        return data[0]\n    else:\n        return None\n","sub_path":"app/project/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":3085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"646003833","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\nimport cv2\nimport glob\nimport os\nimport imgaug as ia\nfrom PIL import Image\nfrom imgaug import augmenters as iaa\nfrom imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOnImage\nfrom tqdm import tqdm\n\nfrom object_detection.utils import label_map_util\nfrom object_detection.utils import visualization_utils as viz_utils\n\nfrom src.preproc import read_label_from_xml\n\ndef plot_train_images_w_labels(src_dir, label_map_path, num_img=6, img_per_row=3, lw=16):\n\n    src_dir = str(src_dir)\n    label_map_path = str(label_map_path)\n    category_index = label_map_util.create_category_index_from_labelmap(label_map_path)\n    label_map_dict = label_map_util.get_label_map_dict(label_map_path)\n\n    images = np.random.choice(glob.glob(os.path.join(src_dir,'*.jpg'))\n                            + glob.glob(os.path.join(src_dir,'*.JPG')),\n                            num_img, replace=False)\n    labels = [os.path.splitext(img)[0]+'.xml' for img in images]\n    images = sorted(images)\n    labels = sorted(labels)\n\n    num_row = (num_img+img_per_row-1)//img_per_row\n    plt.figure(figsize=(13,7*num_row*(2/img_per_row)))\n\n    for n in range(num_img):\n\n        img = cv2.cvtColor(cv2.imread(images[n]), cv2.COLOR_RGB2BGR)\n        lbl, _ = read_label_from_xml(labels[n])\n        class_names, boxes = lbl\n        class_ids = [label_map_dict[class_name] for class_name in class_names]\n        boxes = np.array(boxes)\n        boxes1 = boxes.copy()\n        if len(boxes) != 0:\n            # object detection API expects bboxes as [ymin,xmin,ymax,xmax]\n            boxes1[:,0] = boxes[:,1]\n            boxes1[:,1] = boxes[:,0]\n            boxes1[:,2] = boxes[:,3]\n            boxes1[:,3] = boxes[:,2]\n\n        dummy_scores = np.ones(len(class_names))\n        img_with_labels = img.copy()\n\n        viz_utils.visualize_boxes_and_labels_on_image_array(\n            img_with_labels,\n            boxes = boxes1,\n            classes = class_ids,\n            scores = dummy_scores,\n            category_index = category_index,\n            skip_scores=True,\n            line_thickness=lw,\n            )\n\n        plt.subplot(num_row, img_per_row, 1+n)\n        plt.axis('off')\n        plt.imshow(img_with_labels)\n        plt.title(os.path.split(images[n])[1])\n\n    plt.tight_layout()\n    plt.subplots_adjust(wspace=0.1, hspace=0.1)\n    plt.show()\n\ndef plot_train_images_w_labels_from_paths(images, label_map_path, num_img=6, img_per_row=3, lw=16):\n\n    label_map_path = str(label_map_path)\n    category_index = label_map_util.create_category_index_from_labelmap(label_map_path)\n    label_map_dict = label_map_util.get_label_map_dict(label_map_path)\n\n    images = [str(img) for img in images]\n    labels = [os.path.splitext(img)[0]+'.xml' for img in images]\n    images =  sorted(images)\n    labels = sorted(labels)\n\n    num_row = (num_img+img_per_row-1)//img_per_row\n    plt.figure(figsize=(13,7*num_row*(2/img_per_row)))\n\n    for n in range(num_img):\n\n        print(images[n])\n        img = cv2.cvtColor(cv2.imread(images[n]), cv2.COLOR_RGB2BGR)\n        lbl, _ = read_label_from_xml(labels[n])\n        class_names, boxes = lbl\n        class_ids = [label_map_dict[class_name] for class_name in class_names]\n        boxes = np.array(boxes)\n        boxes1 = boxes.copy()\n        if len(boxes) != 0:\n            # object detection API expects bboxes as [ymin,xmin,ymax,xmax]\n            boxes1[:,0] = boxes[:,1]\n            boxes1[:,1] = boxes[:,0]\n            boxes1[:,2] = boxes[:,3]\n            boxes1[:,3] = boxes[:,2]\n\n        dummy_scores = np.ones(len(class_names))\n        img_with_labels = img.copy()\n\n        viz_utils.visualize_boxes_and_labels_on_image_array(\n            img_with_labels,\n            boxes = boxes1,\n            classes = class_ids,\n            scores = dummy_scores,\n            category_index = category_index,\n            skip_scores=True,\n            line_thickness=lw,\n            )\n\n        plt.subplot(num_row, img_per_row, 1+n)\n        plt.axis('off')\n        plt.imshow(img_with_labels)\n        plt.title(os.path.split(images[n])[1])\n\n    plt.tight_layout()\n    plt.subplots_adjust(wspace=0.1, hspace=0.1)\n    plt.show()\n\ndef draw_predictions_on_images(image_names, detections_from_dir, label_map_path, min_score_thresh=0.5, linewidth = 16):\n    category_index = label_map_util.create_category_index_from_labelmap(label_map_path)\n    res_images = []\n    for image_name in image_names:\n\n        image_np, detections = detections_from_dir[image_name]\n        image_np_with_detections = image_np.copy()\n\n        res_images += [viz_utils.visualize_boxes_and_labels_on_image_array(\n            image_np_with_detections,\n            detections['detection_boxes'],\n            detections['detection_classes'],\n            detections['detection_scores'],\n            category_index,\n            use_normalized_coordinates=True,\n            min_score_thresh=min_score_thresh,\n            agnostic_mode=False,\n            line_thickness=linewidth)]\n\n    return res_images\n\n\ndef plot_predictions_from_paths(image_names, detections_from_dir, label_map_path,\n                                min_score_thresh=0.5,\n                                img_per_row=2, linewidth=16):\n\n    num_img = len(image_names)\n    num_row = (num_img+img_per_row-1)//img_per_row\n    res_images = draw_predictions_on_images(image_names, detections_from_dir,\n                                            label_map_path,\n                                            min_score_thresh = min_score_thresh,\n                                            linewidth = linewidth)\n    plt.figure(figsize=(13,8.*num_row*(2/img_per_row)))\n\n    for n in range(num_img):\n        plt.subplot(num_row, img_per_row, 1+n)\n        plt.axis('off')\n        plt.imshow(res_images[n])\n        plt.title(image_names[n])\n\n    plt.tight_layout()\n    plt.subplots_adjust(wspace=0.1, hspace=0.1)\n    plt.show()\n\ndef save_predictions(target_dir, image_names, detections_from_dir, label_map_path,\n                     min_score_thresh=0.5, linewidth=16):\n    res_images = draw_predictions_on_images(image_names, detections_from_dir,\n                                            label_map_path,\n                                            min_score_thresh=min_score_thresh,\n                                            linewidth=linewidth)\n    for i in range(len(res_images)):\n        im = Image.fromarray(res_images[i])\n        im.save(os.path.join(target_dir, image_names[i]), \"jpeg\")\n\n\ndef print_metrics(metrics, image_dir, label_map_path , min_score_thresh=0.5, iou_thresh=0.5):\n    label_map_dict = label_map_util.get_label_map_dict(label_map_path)\n    label_map_dict_rev = {value: key for key, value in label_map_dict.items()}\n    tot_TP = 0\n    tot_FP = 0\n    tot_FN = 0\n    tot_AP = 0\n    for key in label_map_dict.keys():\n        tot_TP += metrics[key+'_TP_num']\n        tot_FN += metrics[key+'_FN_num']\n        tot_FP += metrics[key+'_FP0_num']\n        for key2 in label_map_dict_rev.keys():\n            if not(key == label_map_dict_rev[key2]):\n                tot_FP += metrics[key+'_FP'+str(key2)+'_num']\n        tot_AP += metrics['AP_'+key]\n    tot_prec = tot_TP/(tot_TP+tot_FP)\n    tot_rec  = tot_TP/(tot_TP+tot_FN)\n    print('Metrics when evaluated on images in \\033[1n{0}\\033[0m at minimal score {1} and IoU threshold {2}:\\n'\n          .format(image_dir, min_score_thresh, iou_thresh))\n    for key in label_map_dict.keys():\n        print('\\033[1m'+key+' AP\\033[0m: ', round(metrics['AP_'+key],4))\n        print('\\033[1m'+key+' Prediction Types\\033[0m: \\t{0} True Positives,'.format(metrics[key+'_TP_num']))\n        print('\\t\\t\\t\\t{} False Positives (nothing there),'.format(metrics[key+'_FP0_num']))\n        for key2 in label_map_dict_rev.keys():\n            if not(key == label_map_dict_rev[key2]):\n                print(('\\t\\t\\t\\t{} False Positives (mistaken '+label_map_dict_rev[key2]+'),')\n                      .format(metrics[key+'_FP'+str(key2)+'_num']))\n        print('\\t\\t\\t\\t{} False Negatives,'.format(metrics[key+'_FN_num']))\n    print('\\033[1mTotal Precision\\033[0m: {}'.format(round(tot_prec,4)))\n    print('\\033[1mTotal Recall\\033[0m: {}'.format(round(tot_rec,4)))\n    print('\\033[1mF-Score\\033[0m: {}'.format(round(2*tot_prec*tot_rec/(tot_prec+tot_rec),4)))\n    print('\\033[1mmAP\\033[0m: {}'.format(round(tot_AP/len(label_map_dict.keys()),4)))\n\n\ndef plot_detection_confusion_matrix(metrics, label_map_path, title=None, rotate_label=45, save = None):\n\n    label_map_dict = label_map_util.get_label_map_dict(label_map_path)\n    label_map_dict_rev = {value: key for key, value in label_map_dict.items()}\n    category_num = len(label_map_dict.keys())\n\n    plt.figure(figsize=(10,7))\n    cm = np.zeros((category_num+1,category_num+1))\n    for i in range(category_num):\n        for j in range(category_num):\n            if i == j:\n                cm[i,j] = metrics[label_map_dict_rev[i+1]+'_TP_num']\n            else:\n                cm[i,j] = metrics[label_map_dict_rev[j+1]+'_FP'+str(i+1)+'_num']\n    for i in range(category_num):\n        cm[i,category_num] = metrics[label_map_dict_rev[i+1]+'_FN_num']\n        cm[category_num, i] = metrics[label_map_dict_rev[i+1] + '_FP0_num']\n    cm[category_num,category_num] = float('nan')\n\n    label_list = [key for key in label_map_dict.keys()]+['None']\n\n    df_cm = pd.DataFrame(cm, index = label_list, columns = label_list)\n    df_cm.rename_axis(\"ground truth\", axis='rows', inplace=True)\n    df_cm.rename_axis(\"predicted\", axis='columns', inplace=True)\n\n\n    fig, ax = plt.subplots(figsize=(4.5,4.5))\n    ax.xaxis.set_ticks_position('top')\n    ax.xaxis.set_label_position('top')\n\n    sns.set(font_scale=1.2) # for label size\n    g = sns.heatmap(df_cm, annot=True, annot_kws={\"size\": 14}, cmap='Blues', cbar=False)\n    g.set_yticklabels(g.get_yticklabels(), rotation = rotate_label, va=\"center\")\n    g.set_xticklabels(g.get_xticklabels(), rotation = 90 - rotate_label)\n    if title: plt.title(title, fontsize=14)\n    if save is not None:\n        plt.savefig(save, bbox_inches='tight')\n    plt.show()\n","sub_path":"src/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":10097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"327143952","text":"import os\nimport pprint\nfrom textwrap import dedent\nfrom . import common\n\n\ndef test_config_loading(tmpdir):\n    f0 = tmpdir.mkdir('subdir').join('file0.yaml')\n    dir_to_include = tmpdir.join('subdir')\n    f0.write(dedent('''\n    references:\n      species_to_keep:\n        tag_from_directory:\n          fasta:\n            url: \"https://from_directory\"\n\n        # Will get overwritten by a specific file\n        tag_from_file:\n          fasta:\n            url: \"https://from_directory\"\n\n        # Will get overwritten by specific file, and then that will get\n        # overwritten by the config\n        tag_from_config:\n          fasta:\n            url: \"https://from_directory\"\n    '''))\n    f1 = tmpdir.join('subdir', 'file1.yaml')\n    f1.write(dedent('''\n    references:\n      species2:\n        tag_only_in_directory:\n          fasta:\n            url: \"\"\n            indexes:\n              - bowtie2\n    '''))\n\n    f2 = tmpdir.join('file1.yaml')\n    f2.write(dedent('''\n    references:\n      species_to_keep:\n        tag_from_file:\n          fasta:\n            url: \"https://from_file\"\n        tag_from_config:\n          fasta:\n            url: \"https://from_file\"\n\n    '''))\n\n    f3 = tmpdir.join('file3.yaml')\n    f3.write(dedent('''\n    references_dir: \"/data\"\n    references:\n      species_to_keep:\n        tag_from_config:\n          fasta:\n            url: \"https://from_config\"\n\n    include_references:\n      - {dir_to_include}\n      - {f2}\n    '''.format(dir_to_include=dir_to_include, f2=f2)))\n\n    config = common.load_config(str(f3))\n\n    assert config == {\n        'references_dir': '/data',\n        'include_references': [\n            '{0}/subdir'.format(str(tmpdir)),\n            '{0}/file1.yaml'.format(str(tmpdir)),\n        ],\n        'references': {\n            'species_to_keep': {\n                'tag_from_config': {\n                    'fasta': {'url': 'https://from_config'}},\n                'tag_from_directory': {\n                    'fasta': {'url': 'https://from_directory'}},\n                'tag_from_file': {\n                    'fasta': {'url': 'https://from_file'}}\n            },\n            'species2': {\n                'tag_only_in_directory': {\n                    'fasta': {'indexes': ['bowtie2'], 'url': ''}}},\n        },\n    }\n\n","sub_path":"lib/test_suite.py","file_name":"test_suite.py","file_ext":"py","file_size_in_byte":2269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"118205525","text":"\"\"\"Creating an instance of app and create_app function.\"\"\"\n# Third party import\nfrom flask import Flask, Response\nfrom flask_jwt_extended import JWTManager\n\n# Standard Library imports\nimport os\nimport json\nfrom datetime import timedelta\n\n# Local Import\nfrom instance.config import APP_CONFIG\nfrom .database import create_tables, initialize_db, drop_tables\n\ndef create_app(config_name):\n    \"\"\"Create App function.\"\"\"\n    app = Flask(__name__, instance_relative_config=True)\n    app.url_map.strict_slashes = False\n    initialize_db()\n    create_tables()\n\n    \n    #JWT Configurations.\n    app.config[\"JWT_SECRET_KEY\"] = 'mementomori'\n    app.config[\"JWT_ACCESS_TOKEN_EXPIRES\"] = timedelta(hours=6)\n    app.config[\"JWT_BLACKLIST_ENABLED\"] = True\n    app.config[\"JWT_BLACKLIST_TOKEN_CHECKS\"] = ['access']\n    manager = JWTManager(app=app)\n\n    # API Error Handlers.\n    @app.errorhandler(400)\n    def bad_request_handler(message):\n        payload = dict(\n            status=400,\n            error=\"Bad request\",\n            message=\"Check you inputs\"\n        )\n        resp = Response(json.dumps(payload), status=400, mimetype=\"application/json\")\n        return resp\n\n    @app.errorhandler(404)\n    def not_found(message):\n        payload = dict(\n            status=404,\n            error=\"Entered URL doesn't exist.\",\n            message=\"Check if the URL entered is correct.\"\n        )\n        resp = Response(json.dumps(payload), status=404, mimetype=\"application/json\")\n        return resp\n\n    \n    # JWT Error Handlers.\n    @manager.token_in_blacklist_loader\n    def check_if_token_is_blacklisted(decoded_token):\n        pass\n\n    @manager.unauthorized_loader\n    def empty_token(msg):\n        \"\"\"Function dealing with \"\"\"\n        payload = dict(\n            status=401,\n            error=\"Missing token\",\n            message=\"Please enter a token\"\n        )\n        resp = Response(json.dumps(payload), status=401, mimetype=\"application/json\")\n        return resp\n \n    @manager.expired_token_loader\n    def handling_expired_token():\n        payload = dict(\n            status=401,\n            error=\"Token has expired.\",\n            message=\"Please log in.\"\n        )\n        resp= Response(json.dumps(payload), status=401, mimetype=\"application/json\")\n        return resp\n\n    \n    # Registering Blueprint\n    from .api.v2 import version2 as v2\n    from .api.v2 import api\n    app.register_blueprint(v2)\n    manager._set_error_handler_callbacks(api)\n\n    # Adding Configurations.\n    app.config.from_object(APP_CONFIG[config_name])\n    app.config.from_pyfile('config.py')\n\n    return app\n","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"11950373","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Feb 13 09:26:11 2020\n\n@author: NG78D39\n\"\"\"\n\nimport numpy as np\nimport matplotlib as plt\nimport time\n\ndef NACA4(c,t,m,p,NeL):\n    #NACA4: Outputs NACA4 Coordinates\n    #yc: y due to camber\n    #yt: y due to thickness\n    #c: chord\n    #t: maximum thickness\n    #m: maximum camber\n    #p: location of maximmum camber\n    import numpy as np\n    x=np.linspace(0,1,NeL)**2\n    #x=0.5*(1-np.cos(x))\n    yt,yc=x*0.0,x*0.0\n    for i in range(len(x)):\n        yt[i]=5*t*(0.2969*np.sqrt(x[i])-0.126*x[i]-0.3516*x[i]**2+0.2843*x[i]**3\\\n                    -0.1015*x[i]**4)*c\n        if x[i]<p:\n            yc[i]=m/p**2*(2*p*x[i]-x[i]**2)*c\n        else:\n            yc[i]=m/(1-p)**2*((1-2*p)+2*p*x[i]-x[i]**2)*c\n    x=x*c\n    #create points surrounding shape\n    x2=np.zeros((2*len(x)-1,2))\n    x2[0:len(x),0]=np.array(list(reversed(x)))\n    y2=yc+yt\n    #y2[-1]=0.0\n    x2[0:len(x),1]=np.array(list(reversed(y2)))\n    x2[len(x)::,0]=x[1:len(x)]\n    y2=yc-yt\n    #y2[-1]=0.0\n    x2[len(x)::,1]=y2[1:len(x)]\n    return x2\n\n\n\ndef Rotate(Xs,Ys,theta):\n    Xs2=Xs*np.cos(theta)-Ys*np.sin(theta)\n    Ys2=Xs*np.sin(theta)+Ys*np.cos(theta)\n    return Xs2,Ys2\n\ndef InducedVelocity(Gamma,Xwing,Xt,Xn,iel,Elements,Xp):\n    #Gamma: vorticity\n    #Xp: point coordinates\n    Pt1=Xwing[int(Elements[iel,0]),:]\n    Pt2=Xwing[int(Elements[iel,1]),:]\n    \n    #print('Pt1=',Pt1)\n    #print('Pt2=',Pt2)\n    #print('Xp=',Xp)\n    Xr=Xp-Pt1 #calculate relative position vector\n    #convert to panel coordinates\n    Xr_pc=np.array([np.dot(Xr,Xt[iel,:]),np.dot(Xr,Xn[iel,:])])\n    Pt1_pc=np.array([0.0,0.0])\n    Pt2_pc=np.array([np.dot(Pt2-Pt1,Xt[iel,:]),0.0])\n    \n    \n    #print('Xp_pc=',Xr_pc)\n    #print('X2_pc=',Pt2_pc)\n            \n    R1=np.linalg.norm(Xr_pc-Pt1_pc)\n    R2=np.linalg.norm(Xr_pc-Pt2_pc)#np.sqrt((X[0]-X2[0])**2+X[1]**2)\n    \n    \n    TH1=np.arctan2(Xr_pc[1],Xr_pc[0])\n    TH2=np.arctan2(Xr_pc[1],Xr_pc[0]-Pt2_pc[0])\n    \n    \n    UL=0.15916*(TH2-TH1)\n    WL=0.15916*np.log(R2/R1)\n    \n    #print(UL,WL)\n    Vind=UL*Xt[iel,:]+WL*Xn[iel,:]\n\n    return Vind\n\n\ndef CreateAirfElements(Xwing):\n    #create grid\n    Elements=np.zeros((Xwing.shape[0]-1,2))\n    Xc=Elements*0\n    Xn=Elements*0\n    Xt=Elements*0\n    DL=np.zeros(Elements.shape[0])\n    for i in range(len(Elements)):\n        Elements[i,:]=np.array([i+1,i])\n        \n        #calculate normal and tangential vectors\n        Xr=Xwing[i+1,:]-Xwing[i,:]\n        DL[i]=np.linalg.norm(Xr)\n        Xr=Xr/DL[i]\n        Xt[i,:]=-Xr\n        Xn[i,:]=np.cross(np.array([Xr[0],Xr[1],0.0]),np.array([0,0,1]))[0:2]\n        #calculate centroids\n        Xc[i,:]=0.5*(Xwing[i,:]+Xwing[i+1,:])-0.0*DL[i]*Xn[i,:] #centroids slightly inside panel\n    return Elements,Xc,Xn,Xt,DL\n\ndef CreateInfluenceMatrix(Xwing,Elements,Xc,Xn,Xt):\n    N_el=Elements.shape[0]\n    A=np.zeros((N_el,N_el)) #initialize influence matrix\n    for i in range(N_el):\n        for j in range(N_el):\n            if i==j:\n                A[i,j]=-0.5\n            else:\n                Vind=InducedVelocity(1.0,Xwing,Xt,Xn,j,Elements,Xc[i,:])\n                A[i,j]=np.dot(Vind,Xt[i,:])\n    #add equation for Kutta condition\n    m=int(3*N_el/4)\n    A[m,:]=A[m,:]*0\n    A[m,0],A[m,-1]=1.0,1.0\n    return A\n\ndef CreateRHSMatrix(Vinf,Xt,Xc,Vc):\n    N_el=Xt.shape[0]\n    B=np.zeros(N_el)\n    for i in range(N_el):\n        Vinfi=Vinf-Vc[i,:]\n        B[i]=-np.dot(Vinfi,Xt[i,:])\n    #equation for Kutta condition\n    m=int(3*N_el/4)\n    B[m]=0.0\n    return B\n\ndef GetFlowField(Elements,Xc,Xt,Xn,Vinf,Gamma,DL,A,Vc):\n    N_el=Xc.shape[0]\n    V_el=Elements*0\n    Cp=np.zeros(N_el)\n    CF=np.zeros((N_el,2))\n    normVinf=np.linalg.norm(Vinf)\n    for i in range(N_el):\n        Vinfi=Vinf-Vc[i,:]\n        A2=A*1\n        A2[i,i]=0.5\n        V_el[i,:]=np.dot(A2[i,:],Gamma[:])*Xt[i,:]+Xt[i,:]*np.dot(Vinfi,Xt[i,:])\n        Cp[i]=1-(np.linalg.norm(V_el[i,:])/normVinf)**2\n    for i in range(N_el):\n        if i==int(3*N_el/4):\n            Cp[i]=np.interp(Xc[i,0],[Xc[i-1,0],Xc[i+1,0]],[Cp[i-1],Cp[i+1]])\n        CF[i,:]=-Cp[i]*DL[i]*Xn[i,:]\n    CFt=np.sum(CF,axis=0)\n    CD=np.dot(CFt,Vinf/normVinf)\n    CL=np.linalg.norm(CFt-CD*Vinf/normVinf)*np.sign(CFt[1])\n    return V_el,Cp,CL,CD,CF,CFt\n\ndef InitializeAeroModel(Xwing):\n    #create elements, centroids, normal, tangent and length vectors\n    Elements,Xc,Xn,Xt,DL=CreateAirfElements(Xwing)\n    #create influence matrix\n    A=CreateInfluenceMatrix(Xwing,Elements,Xc,Xn,Xt)\n    return Elements,Xc,Xn,Xt,DL,A\n    \ndef GetAeroSolution(Elements,Xc,Xn,Xt,DL,A,Vinf,Vc):\n    B=CreateRHSMatrix(Vinf,Xt,Xc,Vc) #create Right-Hand side matrix\n    Gam=np.linalg.solve(A,B) #solve linear system\n    V_el,Cp,CL,CD,CF,CFt=GetFlowField(Elements,Xc,Xt,Xn,Vinf,Gam,DL,A,Vc) #get flowfield\n    return V_el,Cp,CL,CD,CF,CFt\n","sub_path":"Codes/NN _and_FSI_Modules/AeroCharacteristics.py","file_name":"AeroCharacteristics.py","file_ext":"py","file_size_in_byte":4780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"524325126","text":"import scrapy\n\n#定义一个爬虫类继承一个爬虫类型\nclass demo1(scrapy.Spider):\n\n    # 定义蜘蛛名\n    name = \"small_crawl1\"\n\n    #通过这个方法进入下面的初始链接\n    def start_requests(self):\n        url = 'http://lab.scrapyd.cn/'\n        tag = getattr(self, 'tag', None)  # 获取tag值\n        if tag is not None:  # 判断是否存在tag，若存在，重新构造url\n            url = url + 'tag/' + tag  # 构造url\n        yield scrapy.Request(url=url, callback=self.parse)\n\n    #start_requests爬取到页面后，在这个方法里进行提取内容\n    def parse(self, response):\n\n        page = response.css('div.quote')  # 提取首页q所有名言，保存至变量mingyan\n        for p in page:  # 循环获取每一条名言里面的：名言内容、作者、标签\n\n            text = p.css('.text::text').extract()[0]  # 提取名言\n            autor = p.css('.author::text').extract()[0]  # 提取作者\n            tags = p.css('.tags .tag::text').extract()  # 提取标签\n            tags = ','.join(tags)  # 数组转换为字符串\n\n            with open(\"名言.txt\", \"a+\",encoding='utf-8') as f:\n                f.write('作者：' +autor)\n                f.write('\\t')\n                f.write('名言：' + text)\n                f.write('\\t')\n                f.write('标签：' + tags)\n                f.write('\\n')\n\n        #使用：response.css('li.next a::attr(href)').extract()[0]查看有木有存在下一页链接\n        next_page = response.css('li.next a::attr(href)').extract()[-1]\n        if next_page is not None:\n            next_page = response.urljoin(next_page)\n            yield scrapy.Request(next_page, callback=self.parse)","sub_path":"python开发/爬虫资料/scrapy_demo1/scrapy_demo1/spiders/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":1701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"438336541","text":"import discord, random, asyncio\nfrom discord.ext import commands as client\nfrom Cogs.config import conf\n#Imports\n\n\nclass Delete(client.Cog):#Class thing no touchy!!!111\n\n    def __init__(self, bot):\n         self.b = bot #Please no touchy thx\n         self.arg = \"\"\n\n    @client.command()\n    async def delete(self,ctx, *, arg1=None): # we make arg1 so we can have the command as this \"n_ask my dad is in jail lmao\" and it will obviously respond, if your missing the \"answer arg\" which comes after the command then the command will obviously not run\n        if arg1 is None:\n            await ctx.send(\"What do you need me to delete?\")\n        elif arg1 == \"Sayori\" or arg1 == \"Natsuki\" or arg1 == \"Yuri\" or arg1 == \"sayori\" or arg1 == \"natsuki\" or arg1 == \"yuri\" or arg1 == f\"<@{conf.sayori_id}\" or arg1 == f\"<@{conf.natsuki_id}\" or arg1 == f\"<@{conf.yuri_id}\":\n            async with ctx.message.channel.typing():\n                await asyncio.sleep(conf.type_speed)\n            await ctx.send(\"Ahahahaha! You know, I tried that once. Didn't really work out well in the long run.\")\n\n        elif arg1 == \"me\" or arg1 == \"Me\" or arg1 == \"mE\" or arg1 == \"ME\":\n            async with ctx.message.channel.typing():\n                await asyncio.sleep(conf.type_speed)\n            await ctx.send(\"Why would i want to do that?\")\n\n        elif arg1 == \"<@270057011251642368>\":\n            async with ctx.message.channel.typing():\n                await asyncio.sleep(conf.type_speed)\n            await ctx.send(\"Why would you want to delete the man who gave you the ability to use me? Shame on you!\")\n\n        elif arg1 == f\"<@{conf.monika_id}>\" or arg1 == \"Monika\":\n            async with ctx.message.channel.typing():\n                await asyncio.sleep(conf.type_speed)\n            await ctx.send(\"Ahahahaha! No.\")\n        else:\n            async with ctx.message.channel.typing():\n                await asyncio.sleep(conf.type_speed) \n            await ctx.send(f'os.remove(\"./characters/{arg1}.chr\")')\n            self.arg = arg1\n\n    @client.Cog.listener()\n    async def on_message(self,message):\n        if \"not this shit again, monika.\" in message.content.lower() and message.author.id == conf.mc_id:\n            async with message.channel.typing():\n                await asyncio.sleep(conf.type_speed)\n            await message.channel.send(\"Ugh, fine.\")\n            await asyncio.sleep(1)\n            async with message.channel.typing():\n                await asyncio.sleep(conf.type_speed)\n            await message.channel.send(f\"os.restore(\\\"./characters/{self.arg}.chr)\\\"\")\n            self.arg = \"\"\n\ndef setup(bot):#No no child keep your hands off or this will break and not load\n    bot.add_cog(Delete(bot))\n","sub_path":"monika/Cogs/delete.py","file_name":"delete.py","file_ext":"py","file_size_in_byte":2718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"344180414","text":"import csv\nfrom django.core.management.base import BaseCommand\nfrom products.models import Category\n\n\nclass Command(BaseCommand):\n    help = 'Imports categories information to database from a CSV file'\n\n    def add_arguments(self, parser):\n        parser.add_argument('file_path', type=str, help=\"Path to CSV file\")\n\n    def get_category_data(self, file_path):\n        with open(file_path) as categories_csv:\n            categories_data = csv.reader(categories_csv)\n            next(categories_data)  # skip header\n\n            for category in categories_data:\n                yield str(category).strip(\"'[]\")\n\n    def handle(self, *args, **kwargs):\n        file_path = kwargs['file_path']\n\n        for category in self.get_category_data(file_path):\n            category_data = Category(name=category)\n            category_data.save()\n            self.stdout.write(\n                self.style.SUCCESS(f\"Category {category} added successfully!\")\n            )\n","sub_path":"products/management/commands/import_categories.py","file_name":"import_categories.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"581696935","text":"import numpy as np\nimport theano\nimport theano.tensor as T\nfrom lib import sigmoid, softmax, dropout, floatX, random_weights, zeros\n\n\nclass NNLayer:\n\n    def get_params(self):\n        return self.params\n\n    def save_model(self):\n        return\n\n    def load_model(self):\n        return\n\n    def updates(self):\n        return []\n\n    def reset_state(self):\n        return\n\nclass InputLayer(NNLayer):\n    \"\"\"\n    \"\"\"\n    def __init__(self, X, name=\"\"):\n        self.name = name\n        self.X = X\n        self.params=[]\n\n    def output(self, train=False):\n        return self.X\n\nclass FullyConnectedLayer(NNLayer):\n    \"\"\"\n    \"\"\"\n    def __init__(self, num_input, num_output, input_layer, name=\"\"):\n        self.num_input = num_input\n        self.num_output = num_output\n        self.X = input_layer.output()\n        self.W_yh = random_weights((num_input, num_output),name=\"W_yh\")\n        self.b_y = zeros(num_output, name=\"b_y\")\n        self.params = [self.W_yh, self.b_y]\n\n    def output(self):\n        return T.dot(self.X, self.W_yh) + self.b_y\n\n    def reset_state(self):\n        self.W_yh = random_weights((self.num_input, self.num_output),name=\"W_yh\")\n        self.b_y = zeros(self.num_output, name=\"b_y\")\n        self.params = [self.W_yh, self.b_y]\n\n\nclass LSTMLayer(NNLayer):\n\n    def __init__(self, num_input, num_cells, input_layer=None, name=\"\"):\n        \"\"\"\n        LSTM Layer\n        Takes as input sequence of inputs, returns sequence of outputs\n\n        Currently takes only one input layer\n        \"\"\"\n        self.name = name\n        self.num_input = num_input\n        self.num_cells = num_cells\n\n        #Setting the X as the input layer\n        self.X = input_layer.output()\n\n        self.h0 = theano.shared(floatX(np.zeros((1, num_cells))))\n        self.s0 = theano.shared(floatX(np.zeros((1, num_cells))))\n\n\n        #Initializing the weights\n        self.W_gx = random_weights((num_input, num_cells), name=self.name+\"W_gx\")\n        self.W_ix = random_weights((num_input, num_cells), name=self.name+\"W_ix\")\n        self.W_fx = random_weights((num_input, num_cells), name=self.name+\"W_fx\")\n        self.W_ox = random_weights((num_input, num_cells), name=self.name+\"W_ox\")\n\n        self.W_gh = random_weights((num_cells, num_cells), name=self.name+\"W_gh\")\n        self.W_ih = random_weights((num_cells, num_cells), name=self.name+\"W_ih\")\n        self.W_fh = random_weights((num_cells, num_cells), name=self.name+\"W_fh\")\n        self.W_oh = random_weights((num_cells, num_cells), name=self.name+\"W_oh\")\n\n        self.b_g = zeros(num_cells, name=self.name+\"b_g\")\n        self.b_i = zeros(num_cells, name=self.name+\"b_i\")\n        self.b_f = zeros(num_cells, name=self.name+\"b_f\")\n        self.b_o = zeros(num_cells, name=self.name+\"b_o\")\n\n        self.params = [self.W_gx, self.W_ix, self.W_ox, self.W_fx,\n                        self.W_gh, self.W_ih, self.W_oh, self.W_fh,\n                        self.b_g, self.b_i, self.b_f, self.b_o,]\n\n        self.output()\n\n    #Function to calculate the next step\n    def forward_step(self, x, h_tm1, s_tm1):\n\n        g = T.tanh(T.dot(x, self.W_gx) + T.dot(h_tm1, self.W_gh) + self.b_g)\n        i = T.nnet.sigmoid(T.dot(x, self.W_ix) + T.dot(h_tm1, self.W_ih) + self.b_i)\n        f = T.nnet.sigmoid(T.dot(x, self.W_fx) + T.dot(h_tm1, self.W_fh) + self.b_f)\n        o = T.nnet.sigmoid(T.dot(x, self.W_ox) + T.dot(h_tm1, self.W_oh) + self.b_o)\n\n        s = i*g + s_tm1 * f\n        h = T.tanh(s) * o\n\n        return h, s\n\n    def output(self, train=True):\n\n        outputs_info = [self.h0, self.s0]\n\n        ([outputs, states], updates) = theano.scan(\n                fn=self.forward_step,\n                sequences=self.X,\n                outputs_info = outputs_info)\n\n        return outputs\n\n    def reset_state(self):\n        self.h0 = theano.shared(floatX(np.zeros(self.num_cells)))\n        self.s0 = theano.shared(floatX(np.zeros(self.num_cells)))\n","sub_path":"LSTM/layers.py","file_name":"layers.py","file_ext":"py","file_size_in_byte":3905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"595310309","text":"\"\"\"utility function\"\"\"\n\ndef getiddversion(iddtxt):\n    \"\"\"return the version number of the iddfile\"\"\"\n    verstr = \"IDD_Version\"\n    lines = iddtxt.splitlines()\n    firstline = lines[0]\n    if firstline.find(verstr) == -1:\n        return \"version not known\"\n    else:\n        return firstline.split(verstr)[-1].strip()","sub_path":"diagrams/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"411295439","text":"#   Copyright (c) 2008-2013 Szczepan Faber, Serhiy Oplakanets, Herr Kaste\n#\n#   Permission is hereby granted, free of charge, to any person obtaining a copy\n#   of this software and associated documentation files (the \"Software\"), to deal\n#   in the Software without restriction, including without limitation the rights\n#   to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n#   copies of the Software, and to permit persons to whom the Software is\n#   furnished to do so, subject to the following conditions:\n#\n#   The above copyright notice and this permission notice shall be included in\n#   all copies or substantial portions of the Software.\n#\n#   THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n#   IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n#   FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n#   AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n#   LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n#   OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n#   THE SOFTWARE.\n\nimport inspect\nfrom mockito import invocation\nfrom mockito.mock_registry import mock_registry\nfrom mockito.mocking import TestDouble\nfrom mockito.mocking import _Dummy\nfrom mockito.mocking import mock\n\nfrom trove.patch.mockito.invocation import ksc_RememberedInvocation,ksc_StubbedInvocation,ksc_VerifiableInvocation\n\n\nclass ksc_mock(mock):\n  def __init__(self, mocked_obj=None, strict=True):\n    self.invocations = []\n    self.stubbed_invocations = []\n    self.original_methods = []\n    self.stubbing = None\n    self.verification = None\n    if mocked_obj is None:\n        mocked_obj = _Dummy()\n        strict = False\n    self.mocked_obj = mocked_obj\n    self.strict = strict\n    self.stubbing_real_object = False\n    \n    self.is_inherit_cls = False\n    \n    mock_registry.register(self)\n  \n  def __getattr__(self, method_name):\n    if self.stubbing is not None:\n      return ksc_StubbedInvocation(self, method_name)\n    \n    if self.verification is not None:\n      return ksc_VerifiableInvocation(self, method_name)\n      \n    return ksc_RememberedInvocation(self, method_name)\n  \n  def remember(self, invocation):\n    self.invocations.insert(0, invocation)\n  \n  def finish_stubbing(self, stubbed_invocation):\n    self.stubbed_invocations.insert(0, stubbed_invocation)\n    self.stubbing = None\n    \n  def expect_stubbing(self):\n    self.stubbing = True\n    \n  def pull_verification(self):\n    v = self.verification\n    self.verification = None\n    return v\n\n  def has_method(self, method_name):\n    return hasattr(self.mocked_obj, method_name)\n    \n  def get_method(self, method_name):\n#     _m = self.mocked_obj.__dict__.get(method_name)\n    _m = getattr(self.mocked_obj,method_name)\n    return _m\n\n  def set_method(self, method_name, new_method):\n    setattr(self.mocked_obj, method_name, new_method)\n\n  MODULE = 1\n  STATIC = 2\n  CLASS = 3\n  INSTANCE = 4\n  def _check_type(self,method_name,original_method):\n      if inspect.ismodule(self.mocked_obj):\n        #print 'module_func',method_name\n        return self.MODULE\n        \n      if inspect.isclass(self.mocked_obj):\n        if not hasattr(original_method,\"__self__\"):\n            #print 'static_func',method_name\n            return self.STATIC  \n        else:\n            if original_method.__self__ != None:\n                #print 'class_func',method_name\n                return self.CLASS\n            else:\n                #print 'instance_func',method_name\n                return self.INSTANCE\n      return None\n    \n  def replace_method(self, method_name, original_method):\n    \n    def new_mocked_method(*args, **kwargs): \n      # we throw away the first argument, if it's either self or cls\n#       if inspect.isclass(self.mocked_obj) and not isinstance(original_method, staticmethod): \n#           args = args[1:]\n      _type = self._check_type(method_name,original_method)\n      if _type:\n        if _type in [self.CLASS,self.INSTANCE]:\n            args = args[1:]\n      else:\n        raise Exception(\"method_name %s of type is unknown\" % method_name)\n      call = self.__getattr__(method_name) # that is: invocation.RememberedInvocation(self, method_name)\n      return call(*args, **kwargs)\n      \n#     static_str = \"function\"\n#     class_str = \"bound method type\"\n#     if isinstance(original_method, staticmethod) or (self.is_inherit_cls and str(original_method).find(static_str)>=0):\n#       new_mocked_method = staticmethod(new_mocked_method)  \n#     elif isinstance(original_method, classmethod) or (self.is_inherit_cls and str(original_method).find(class_str)>=0): \n#       new_mocked_method = classmethod(new_mocked_method)  \n    _type = self._check_type(method_name,original_method)\n    if _type:\n        if _type == self.STATIC:\n            new_mocked_method = staticmethod(new_mocked_method)\n        elif _type == self.CLASS:\n            new_mocked_method = classmethod(new_mocked_method)\n    else:\n        raise Exception(\"method_name %s of type is unknown\" % method_name)\n    \n    self.set_method(method_name, new_mocked_method)\n    #print '------------',method_name,self.get_method(method_name)\n    \n  def stub(self, method_name):\n    original_method = self.get_method(method_name)\n    original = (method_name, original_method)\n    \n    self.original_methods.append(original)\n    \n#     if not original_method:\n#         self.is_inherit_cls = True\n#         original_method = getattr(self.mocked_obj,method_name)\n\n    # If we're trying to stub real object(not a generated mock), then we should patch object to use our mock method.\n    # TODO: Polymorphism was invented long time ago. Refactor this.\n    if self.stubbing_real_object:\n      self.replace_method(method_name, original_method)\n\n  def unstub(self):  \n    while self.original_methods:  \n      method_name, original_method = self.original_methods.pop()  \n      if method_name == 'get_pyhhostname':\n          pass\n      _type = self._check_type(method_name,original_method)\n      if _type:\n        if _type == self.STATIC:\n            new_mocked_method = staticmethod(original_method)\n        elif _type == self.CLASS:\n            new_mocked_method = classmethod(original_method)\n        else:\n             new_mocked_method = original_method\n        self.set_method(method_name, new_mocked_method)\n      else:\n        raise Exception(\"method_name %s of type is unknown\" % method_name)\n","sub_path":"trove/patch/mockito/mocking.py","file_name":"mocking.py","file_ext":"py","file_size_in_byte":6489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"352519947","text":"# -*- coding: utf-8 -*-\nfrom odoo import models, fields, api, _\nfrom datetime import datetime, date\nfrom pytz import timezone\nfrom dateutil.parser import parse\nfrom odoo.exceptions import Warning, RedirectWarning\nfrom io import BytesIO\nfrom xlsxwriter.utility import xl_rowcol_to_cell, xl_cell_to_rowcol, xl_range\nimport xlsxwriter\nimport re\nimport time\nimport codecs\n\nclass PreShipmentCertificateReport(models.AbstractModel):\n\t_name = 'report.preship_cert_report'\n\t_inherit = 'report.report_xlsx.abstract'\n\n\n\tdef _get_class(self, rule, w, s, typ):\n\t\trules = rule[1:-1].split('),(')\n\t\tfor r in rules:\n\t\t\tr = (re.sub(',=,',',==,',r)).split(',')\n\t\t\tt = '%s %s (%s*%s/100)' % (s, r[1], w, r[2])\n\n\t\t\tprint (typ, t)\n\t\t\tif eval(t):\n\t\t\t\treturn r[0]\n\n\n\tdef _to_hold(self, h, c):\n\t\tif h == True and c == 'C':\n\t\t\treturn 'HOLD'\n\t\telse:\n\t\t\treturn ''\n\n\n\tdef generate_xlsx_report(self, workbook, data, obj):\n\t\tfor o in obj:\n\t\t\tdate_issue = parse(o.date_issue)\n\t\t\tdate_issue = datetime.strftime(date_issue, '%Y%m%d')\n\t\t\tsheet = workbook.add_worksheet('Pre-Ship Cert_%s_%s' % (o.container,date_issue))\n\n\t\t\t# PRINT SETTINGS\n\t\t\tsheet.hide_gridlines(2)\n\t\t\tsheet.print_area('A1:K68')\n\t\t\tsheet.set_portrait()\n\t\t\tsheet.center_horizontally()\n\t\t\tsheet.fit_to_pages(1,1)\n\n\t\t\t# SET COLUMN WIDTHS\n\t\t\tcols = 'ABCDEFGHIJK'\n\t\t\twidhts = [14.67, 13.83, 8.67, 10.67, 8.83, 7.5, 7.83, 11.67, 9.67, 7.33, 10]\n\t\t\tfmt = workbook.add_format({'font_name':'Arial'})\n\t\t\tfor i,w, in enumerate(widhts):\n\t\t\t\tsheet.set_column(cols[i]+':'+cols[i], w, fmt)\n\t\t\t\n\t\t\t# SET ORIGINS\n\t\t\text_r = 15\n\t\t\tint_r = 21\n\t\t\tpack_r = 28\n\t\t\tfoot_r = 35\n\t\t\timg_attach_r = 35\n\n\t\t\t# SET ROW HEIGHTS\n\t\t\tfor i in range(1,52):\n\t\t\t\tif i in [ext_r, int_r, pack_r]:\n\t\t\t\t\tsheet.set_row(i, 22)\n\t\t\t\telse:\n\t\t\t\t\tsheet.set_row(i, 12)\n\n\t\t\t# SET CELL FORMATS\n\t\t\tphead = workbook.add_format({'font_size': 10, 'font_name':'Arial'})\n\t\t\tphead_bold = workbook.add_format({'font_size': 10, 'font_name':'Arial', 'bold':True})\n\t\t\tphead_blue = workbook.add_format({'font_size': 10, 'italic':True, 'font_color':'#1510C4', 'font_name':'Arial'})\n\t\t\tphead_line = workbook.add_format({'font_size': 10, 'top':3, 'font_name':'Arial'})\n\n\t\t\thead_name = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'align':'left'})\n\t\t\thead_center = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'align':'center'})\n\t\t\thead_center_percent = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'align':'center', 'num_format':'0.0%'})\n\t\t\thead_dash = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'align':'left', 'border':3})\n\t\t\thead_tot = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'align':'center', 'top':1, 'left':1, 'right':1, 'bold':True})\n\t\t\thead_tot_val = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'align':'center', 'left':1, 'right':1, 'num_format':'0.0%'})\n\t\t\thead_tot_class = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'align':'center', 'bottom':1, 'left':1, 'right':1, 'bold':True})\n\n\t\t\thead_mrg_top = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'align':'center', 'top':1, 'left':1, 'right':1, 'bold':True})\n\t\t\thead_mrg_bot = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'align':'center', 'bottom':1, 'left':1, 'right':1, 'bold':True})\n\n\t\t\tdhead = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'align':'center', 'valign':'vcenter', 'border':1, 'text_wrap':1})\n\t\t\tdhead_bold = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'bold':True, 'border':1, 'valign':'vcenter'})\n\t\t\trhead = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'border':1})\n\t\t\tddata = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'border':1, 'align':'center', 'valign':'vcenter'})\n\t\t\tddata_percent = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'border':1, 'align':'center', 'valign':'vcenter', 'num_format':'0.0%'})\n\t\t\tddata_avg = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'border':1, 'align':'center', 'valign':'vcenter', 'font_color':'#0070C0', 'bold':True})\n\n\t\t\tmrg_left = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'border':1, 'align':'left', 'valign':'vcenter'})\n\t\t\tmrg_center = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'border':1, 'align':'center', 'valign':'vcenter', 'text_wrap':1})\n\t\t\tmrg_center_bold = workbook.add_format({'font_size': 8, 'font_name':'Arial', 'border':1, 'align':'center', 'valign':'vcenter', 'text_wrap':1, 'bold':True})\n\n\t\t\tsheet.write('C1','PHILPACK', phead_bold)\n\t\t\tsheet.write('C2','Fresh Fruit Quality Assurance', phead)\n\t\t\tsheet.write('C2','Fresh Fruit Quality Assurance', phead)\n\t\t\tsheet.write('C4','(This form is uncontrolled when printed)', phead_blue)\n\t\t\tsheet.write('I1','F-QUA-008,02', phead)\n\t\t\tsheet.write('I2','Effectivity date:', phead)\n\n\t\t\t\n\n\t\t\ttry:\n\t\t\t\timg = self.env['ir.attachment'].search([('datas_fname','like','philpack_logo.png')]).datas\n\t\t\t\timg = codecs.decode(img, 'base64')\n\t\t\t\timage_data = BytesIO(img)\n\t\t\t\tsheet.insert_image('B1', '', {'image_data':image_data, 'x_offset':-25.0, 'x_scale':0.865, 'y_scale':1})\n\t\t\texcept:\n\t\t\t\tpass\n\n\t\t\tfor c in cols:\n\t\t\t\tsheet.write(c+'5', '', phead_line)\n\n\t\t\t# # REPORT HEADERS\n\t\t\tsheet.write('A6','DATE OF ISSUE:',head_name)\n\t\t\tsheet.write('A7','TIME OF ISSUE:',head_name)\n\t\t\tsheet.write('A8','ISSUED BY:',head_name)\n\t\t\tsheet.write('A9','CONTAINER NO:',head_name)\n\t\t\tsheet.write('A10','CUSTOMER:',head_name)\n\t\t\tsheet.write('D6','PRODUCT SPECIFICATIONS:',head_name)\n\t\t\tsheet.write('D7','SC:',head_name)\n\t\t\tsheet.write('D8','SC:',head_name)\n\t\t\tsheet.write('D8','PS/ COUNT:',head_name)\n\t\t\tsheet.write('D9','PACK DATE:',head_name)\n\t\t\tsheet.write('D10','MARKET:',head_name)\n\t\t\tsheet.write('H7','SERIES NO:',head_name)\n\t\t\tsheet.write('H8','DATE LOADED:',head_name)\n\t\t\tsheet.write('H9','QA INSPECTOR:',head_name)\n\t\t\tsheet.write('H10','NO. OF BOXES:',head_name)\n\t\t\tsheet.write('A13','OVERALL SCORE', head_name)\n\t\t\tsheet.write('A14','OVERALL CLASS', head_name)\n\t\t\tsheet.write('B12','TOTAL', head_tot)\n\n\t\t\tsheet.write('C12','EXTERNAL', head_center)\n\t\t\tsheet.write('D12','INTERNAL', head_center)\n\t\t\tsheet.write('E12','PACKAGING', head_center)\n\n\t\t\t# DETAILS COLUMN HEADER\n\t\t\tsheet.write(ext_r,0,'EXTERNAL QUALITY', dhead_bold)\n\t\t\tsheet.write(int_r,0,'INTERNAL QUALITY', dhead_bold)\n\t\t\tsheet.write(pack_r,0,'PACKAGING QUALITY', dhead_bold)\n\n\t\t\t# # # DETAILS ROW HEADER\n\t\t\tdt_head = ['Frts w/ defect', '% w/ defect', 'External score:', 'Class:']\n\t\t\tfor i,d in enumerate(dt_head):\n\t\t\t\tsheet.write(ext_r+i+1,0,d, rhead)\n\n\t\t\tdt_head = ['Frts w/ defect', '% w/ defect', 'Average', 'External score:', 'Class:']\n\t\t\tfor i,d in enumerate(dt_head):\n\t\t\t\tsheet.write(int_r+i+1,0,d, rhead)\n\n\t\t\tdt_head = ['Frts w/ defect', '% w/ defect', 'External score:', 'Class:']\n\t\t\tfor i,d in enumerate(dt_head):\n\t\t\t\tsheet.write(pack_r+i+1,0,d, rhead)\n\n\t\t\t# SET DETAIL FORMATS\n\t\t\tsheet.write('B9','', head_dash)\n\t\t\tsheet.write('B10','', head_dash)\n\t\t\tsheet.write('B13','', head_tot_val)\n\t\t\tsheet.write('B14','', head_tot_class)\n\t\t\tsheet.merge_range('G12:H12','FIELD SOURCE:', head_mrg_top)\n\n\t\t\t# FOOTER\n\t\t\tr = foot_r\n\t\t\t# sheet.merge_range('A%s:B%s'%(r,r),'SUPPLY TEMP STARTING:', mrg_left)\n\t\t\t# sheet.merge_range('A%s:B%s'%(r+1,r+1),'SUPPLY TEMP UPON DEPARTURE:', mrg_left)\n\t\t\t# sheet.merge_range('A%s:B%s'%(r+2,r+2),'VAN TEMP BEFORE STUFFING:', mrg_left)\n\t\t\t# sheet.merge_range('A%s:B%s'%(r+3,r+3),'VAN TEMP SETTING:', mrg_left)\n\n\t\t\t# sheet.merge_range('A%s:A%s'%(r+4,r+5),'PACK DATE', mrg_center)\n\t\t\t# sheet.merge_range('B%s:B%s'%(r+4,r+5),'BEFORE PC', mrg_center)\n\t\t\t# sheet.merge_range('C%s:C%s'%(r+4,r+5),'AFTER PC', mrg_center)\n\t\t\t# sheet.merge_range('D%s:D%s'%(r+4,r+5),'COLD STORAGE', mrg_center)\n\t\t\t# sheet.merge_range('E%s:G%s'%(r+4,r+5),'PULP TEMP', mrg_center)\n\t\t\t# sheet.merge_range('H%s:H%s'%(r+4,r+5),'# OF PALLETS', mrg_center)\n\t\t\t# sheet.write('E%s'%(r+5),'1ST', mrg_center)\n\t\t\t# sheet.write('F%s'%(r+5),'MID', mrg_center)\n\t\t\t# sheet.write('G%s'%(r+5),'LAST', mrg_center)\n\n\t\t\t# INSERT DATA HERE\n\t\t\tquery = \"\"\"\nWITH totals as (\n\t\t\t\t-- get total samples per operation type\n\t\t\t\t-- problem may exist if no_sample\n\t\t\t\tselect b.type, sum(b.total_sample) as total_sample\n\t\t\t\tfrom (\n\t\t\t\t\t\t\tselect a.lot, a.type, max(a.no_sample) as total_sample\n\t\t\t\t\t\t\tfrom (\n\t\t\t\t\t\t\t\t\t\tSELECT\n\t\t\t\t\t\t\t\t\t\t\tCASE\n\t\t\t\t\t\t\t\t\t\t\t\tWHEN trim(dil.type) ilike '%%external%%' THEN 'external'\n\t\t\t\t\t\t\t\t\t\t\t\tWHEN trim(dil.type) ilike '%%internal%%' THEN 'internal'\n\t\t\t\t\t\t\t\t\t\t\t\tWHEN trim(dil.type) ilike '%%pack%%' THEN 'packaging'\n\t\t\t\t\t\t\t\t\t\t\tEND as type\n\t\t\t\t\t\t\t\t\t\t\t,dil.lot, dil.factor, dil.factor_num, dil.no_sample\n\t\t\t\t\t\t\t\t\t\tFROM dmpi_crm_inspection_lot dil\n\t\t\t\t\t\t\t\t\t\tWHERE dil.dr_id = %s\n\t\t\t\t\t\t\t) as a\n\t\t\t\t\t\t\tgroup by a.lot, a.type\n\t\t\t\t\t\t\torder by a.lot, a.type\n\t\t\t\t) as b\n\t\t\t\tgroup by b.type\n)\n\n,inspection_lots_mean as (\n\t\t\t-- get inspections lots mean data\n\t\t\tSELECT dil.*, f.is_mean, f.parent_id\n\t\t\tFROM (\n\t\t\t\t\t\t\tSELECT\n\t\t\t\t\t\t\t\tCASE\n\t\t\t\t\t\t\t\t\tWHEN trim(dil.type) ilike '%%external%%' THEN 'external'\n\t\t\t\t\t\t\t\t\tWHEN trim(dil.type) ilike '%%internal%%' THEN 'internal'\n\t\t\t\t\t\t\t\t\tWHEN trim(dil.type) ilike '%%pack%%' THEN 'packaging'\n\t\t\t\t\t\t\t\tEND as type,\n\t\t\t\t\t\t\t\tdil.factor_num, dil.factor, sum(dil.no_sample) as no_sample, sum(dil.no_defect) as no_defect, avg(dil.value) as average\n\t\t\t\t\t\t\tFROM dmpi_crm_inspection_lot dil\n\t\t\t\t\t\t\tWHERE dil.dr_id = %s\n\t\t\t\t\t\t\tGROUP BY dil.type, dil.factor, dil.factor_num\n\t\t\t) as dil\n\t\t\tLEFT JOIN dmpi_crm_factor f on (f.code = dil.factor_num and f.type = dil.type)\n\t\t\tWHERE f.is_mean is true and f.parent_id is not null\n\t\t\tORDER BY dil.type, dil.factor_num\n)\n\n\n,inspection_lots as (\n\t\t\t-- get inspections lots data\n\t\t\tSELECT dil.*\n\t\t\tFROM (\n\t\t\t\t\t\t\tSELECT\n\t\t\t\t\t\t\t\tCASE\n\t\t\t\t\t\t\t\t\tWHEN trim(dil.type) ilike '%%external%%' THEN 'external'\n\t\t\t\t\t\t\t\t\tWHEN trim(dil.type) ilike '%%internal%%' THEN 'internal'\n\t\t\t\t\t\t\t\t\tWHEN trim(dil.type) ilike '%%pack%%' THEN 'packaging'\n\t\t\t\t\t\t\t\tEND as type,\n\t\t\t\t\t\t\t\tdil.factor_num, dil.factor, sum(dil.no_sample) as no_sample, sum(dil.no_defect) as no_defect\n\t\t\t\t\t\t\tFROM dmpi_crm_inspection_lot dil\n\t\t\t\t\t\t\tWHERE dil.dr_id = %s\n\t\t\t\t\t\t\tGROUP BY dil.type, dil.factor, dil.factor_num\n\t\t\t) as dil\n\t\t\tLEFT JOIN dmpi_crm_factor f on (f.code = dil.factor_num and f.type = dil.type)\n\t\t\tWHERE f.is_mean is false and f.parent_id is null\n\t\t\tORDER BY dil.type, dil.factor_num\n)\n\nselect f.id as factor_id\n\t,tl.sequence\n\t,f.type\n\t,f.name as factor\n\t,f.code as factor_code\n\t,il.no_sample\n\t,il.no_defect\n-- \t,il.average\n\t,case\n\t\t\t\twhen ilm.average is null then 0\n\t\t\t\telse ilm.average\n\tend average\n\t,tot.total_sample\n\t,tl.rule\n\t,tl.weight\n\t,tl.is_hold\n\t,count(f.*) over (partition by f.type) as count\nfrom dmpi_crm_factor f\nleft join dmpi_crm_template_line tl on tl.factor_id = f.id\nleft join inspection_lots il on (il.type = f.type and il.factor_num = f.code)\n-- ) il on (il.type = f.type and il.factor = f.name )\nleft join totals tot on tot.type = f.type\nleft join inspection_lots_mean ilm on ilm.parent_id = f.id\nwhere tl.tmpl_id = %s\norder by type, factor_code\n\t\t\t\"\"\" % (o.dr_id.id, o.dr_id.id, o.dr_id.id, o.tmpl_id.id)\n\t\t\tprint(query)\n\t\t\tself._cr.execute(query)\n\t\t\tresult = self._cr.dictfetchall()\n\n\t\t\t# initialize counters\n\t\t\tcols = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n\t\t\tr = ext_r\n\t\t\tcol = 1\n\t\t\tgrp_weight = 0\n\t\t\tgrp_score = 0\n\t\t\tgrp_clss = ''\n\t\t\ttotal_score = 0\n\t\t\ttotal_clss = ''\n\n\t\t\tfor rec in result:\n\t\t\t\t# compute score and class\n\t\t\t\tname = rec['factor']\n\t\t\t\tcol_end = rec['count']\n\t\t\t\tno_defect = int(rec['no_defect'] or 0)\n\t\t\t\ttotal_sample = int(rec['total_sample'] or 1)\n\t\t\t\tpercent_defect = no_defect / total_sample\n\t\t\t\tavg = float(rec['average'] or 0)\n\t\t\t\tweight = float(rec['weight'] or 0) / 100.\n\t\t\t\tscore = weight * (1. - percent_defect)\n\t\t\t\trule = rec['rule']\n\t\t\t\tclss = self._get_class(rule, weight, score, 'factor') if rule else ''\n\t\t\t\tgrp_clss = self._to_hold(rec['is_hold'] or False, clss)\n\n\t\t\t\tif rec['type'] == 'external':\n\n\t\t\t\t\t# write data\n\t\t\t\t\tdata = [name, no_defect, percent_defect, score, clss]\n\t\t\t\t\tfmt = [dhead, ddata, ddata_percent, ddata_percent, ddata_percent]\n\t\t\t\t\ti = 0\n\t\t\t\t\tfor d,f in zip(data, fmt):\n\t\t\t\t\t\tsheet.write(r+i, col, d, f)\n\t\t\t\t\t\ti += 1\n\n\t\t\t\t\t# next column\n\t\t\t\t\tgrp_weight += weight\n\t\t\t\t\tgrp_score += score\n\t\t\t\t\tcol += 1\n\n\t\t\t\t\tif col == (col_end + 1):\n\t\t\t\t\t\tif grp_clss != 'HOLD':\n\t\t\t\t\t\t\tgrp_rule = o.tmpl_id.ext_rule\n\t\t\t\t\t\t\tgrp_clss = self._get_class(grp_rule, grp_weight, grp_score, 'grp')\n\t\t\t\t\t\telif (o.tmpl_id.ext_hold):\n\t\t\t\t\t\t\ttotal_clss = 'HOLD'\n\n\t\t\t\t\t\t# write totals\n\t\t\t\t\t\tdata = ['TOTAL', no_defect, percent_defect, score, grp_clss]\n\t\t\t\t\t\tfmt = [dhead, ddata, ddata_percent, ddata_percent, ddata]\n\t\t\t\t\t\ti = 0\n\t\t\t\t\t\tfor d,f in zip(data, fmt):\n\t\t\t\t\t\t\tif not (i==0 or i==len(data)-1):\n\t\t\t\t\t\t\t\td = '=SUM(%s%s:%s%s)' % (cols[1], r+i+1, cols[col-1], r+i+1)\n\t\t\t\t\t\t\tsheet.write(r+i, col, d, f)\n\t\t\t\t\t\t\ti += 1\n\n\t\t\t\t\t\tsheet.write('C13', '=%s%s' % (cols[col], r+4), head_center_percent)\n\t\t\t\t\t\tsheet.write('C14', '=%s%s' % (cols[col], r+5), head_center)\n\n\t\t\t\t\t\tcol += 1\n\t\t\t\t\t\tsheet.write(r, col, 'FRUITS EVALUATED', dhead)\n\t\t\t\t\t\tsheet.write(r+1, col, total_sample, ddata)\n\t\t\t\t\t\tsheet.write(r+2, col, '', ddata)\n\t\t\t\t\t\tsheet.write(r+3, col, '', ddata)\n\t\t\t\t\t\tsheet.write(r+4, col, '', ddata)\n\n\t\t\t\t\t\t# reset/update counters\n\t\t\t\t\t\ttotal_score += grp_score\n\t\t\t\t\t\tr = int_r\n\t\t\t\t\t\tcol = 1\n\t\t\t\t\t\tgrp_weight = 0\n\t\t\t\t\t\tgrp_score = 0\n\t\t\t\t\t\tgrp_clss = ''\n\n\t\t\t\tif rec['type'] == 'internal':\n\n\t\t\t\t\t# write data\n\t\t\t\t\tdata = [name, no_defect, percent_defect, avg, score, clss]\n\t\t\t\t\tfmt = [dhead, ddata, ddata_percent, ddata_avg if avg != 0 else ddata, ddata_percent, ddata_percent]\n\t\t\t\t\ti = 0\n\t\t\t\t\tfor d,f in zip(data, fmt):\n\t\t\t\t\t\tsheet.write(r+i, col, d, f)\n\t\t\t\t\t\ti += 1\n\n\t\t\t\t\t# next column\n\t\t\t\t\tgrp_weight += weight\n\t\t\t\t\tgrp_score += score\n\t\t\t\t\tcol += 1\n\n\t\t\t\t\tif col == (col_end + 1):\n\t\t\t\t\t\tif grp_clss != 'HOLD':\n\t\t\t\t\t\t\tgrp_rule = o.tmpl_id.ext_rule\n\t\t\t\t\t\t\tgrp_clss = self._get_class(grp_rule, grp_weight, grp_score, 'grp')\n\t\t\t\t\t\telif (o.tmpl_id.int_hold):\n\t\t\t\t\t\t\ttotal_clss = 'HOLD'\n\n\t\t\t\t\t\t# write totals\n\t\t\t\t\t\tdata = ['TOTAL', no_defect, percent_defect, '', score, grp_clss]\n\t\t\t\t\t\tfmt = [dhead, ddata, ddata_percent, ddata, ddata_percent, ddata]\n\t\t\t\t\t\ti = 0\n\t\t\t\t\t\tfor d,f in zip(data, fmt):\n\t\t\t\t\t\t\tif not (i==0 or i==len(data)-1 or i==3):\n\t\t\t\t\t\t\t\td = '=SUM(%s%s:%s%s)' % (cols[1], r+i+1, cols[col-1], r+i+1)\n\t\t\t\t\t\t\tsheet.write(r+i, col, d, f)\n\t\t\t\t\t\t\ti += 1\n\n\t\t\t\t\t\tsheet.write('D13', '=%s%s' % (cols[col], r+5), head_center_percent)\n\t\t\t\t\t\tsheet.write('D14', '=%s%s' % (cols[col], r+6), head_center)\n\n\t\t\t\t\t\tcol += 1\n\t\t\t\t\t\tsheet.write(r, col, 'FRUITS EVALUATED', dhead)\n\t\t\t\t\t\tsheet.write(r+1, col, total_sample, ddata)\n\t\t\t\t\t\tsheet.write(r+2, col, '', ddata)\n\t\t\t\t\t\tsheet.write(r+3, col, '', ddata)\n\t\t\t\t\t\tsheet.write(r+4, col, '', ddata)\n\t\t\t\t\t\tsheet.write(r+5, col, '', ddata)\n\n\t\t\t\t\t\t# reset/update counters\n\t\t\t\t\t\ttotal_score += grp_score\n\t\t\t\t\t\tr = pack_r\n\t\t\t\t\t\tcol = 1\n\t\t\t\t\t\tgrp_weight = 0\n\t\t\t\t\t\tgrp_score = 0\n\t\t\t\t\t\tgrp_clss = ''\n\n\n\t\t\t\tif rec['type'] == 'packaging':\n\n\t\t\t\t\t# write data\n\t\t\t\t\tdata = [name, no_defect, percent_defect, score, clss]\n\t\t\t\t\tfmt = [dhead, ddata, ddata_percent, ddata_percent, ddata_percent]\n\t\t\t\t\ti = 0\n\t\t\t\t\tfor d,f in zip(data, fmt):\n\t\t\t\t\t\tsheet.write(r+i, col, d, f)\n\t\t\t\t\t\ti += 1\n\n\t\t\t\t\t# next column\n\t\t\t\t\tgrp_weight += weight\n\t\t\t\t\tgrp_score += score\n\t\t\t\t\tcol += 1\n\n\t\t\t\t\tif col == (col_end + 1):\n\t\t\t\t\t\tif grp_clss != 'HOLD':\n\t\t\t\t\t\t\tgrp_rule = o.tmpl_id.ext_rule\n\t\t\t\t\t\t\tgrp_clss = self._get_class(grp_rule, grp_weight, grp_score, 'grp')\n\t\t\t\t\t\telif (o.tmpl_id.pack_hold):\n\t\t\t\t\t\t\ttotal_clss = 'HOLD'\n\n\t\t\t\t\t\t# write totals\n\t\t\t\t\t\tdata = ['TOTAL', no_defect, percent_defect, score, grp_clss]\n\t\t\t\t\t\tfmt = [dhead, ddata, ddata_percent, ddata_percent, ddata]\n\t\t\t\t\t\ti = 0\n\t\t\t\t\t\tfor d,f in zip(data, fmt):\n\t\t\t\t\t\t\tif not (i==0 or i==len(data)-1):\n\t\t\t\t\t\t\t\td = '=SUM(%s%s:%s%s)' % (cols[1], r+i+1, cols[col-1], r+i+1)\n\t\t\t\t\t\t\tsheet.write(r+i, col, d, f)\n\t\t\t\t\t\t\ti += 1\n\n\t\t\t\t\t\tsheet.write('E13', '=%s%s' % (cols[col], r+4), head_center_percent)\n\t\t\t\t\t\tsheet.write('E14', '=%s%s' % (cols[col], r+5), head_center)\n\n\t\t\t\t\t\tcol += 1\n\t\t\t\t\t\tsheet.write(r, col, 'BOXES EVALUATED', dhead)\n\t\t\t\t\t\tsheet.write(r+1, col, total_sample, ddata)\n\t\t\t\t\t\tsheet.write(r+2, col, '', ddata)\n\t\t\t\t\t\tsheet.write(r+3, col, '', ddata)\n\t\t\t\t\t\tsheet.write(r+4, col, '', ddata)\n\n\t\t\t\t\t\ttotal_score += grp_score\n\n\t\t\t# # WRITE TOTALS\n\t\t\ttotal_weight = o.tmpl_id.total_weight / 100.\n\t\t\toverall_rule = o.tmpl_id.overall_rule\n\t\t\tprint (total_clss, total_weight, total_score)\n\n\t\t\tif total_clss != 'HOLD':\n\t\t\t\ttotal_clss = self._get_class(overall_rule, total_weight, total_score, 'total')\n\t\t\tprint (total_clss, total_weight, total_score)\n\n\t\t\t# update record\n\t\t\t# o.total_score = total_score\n\t\t\t# o.total_class = total_clss\n\t\t\to.total_score = round(total_score,3)\n\t\t\to.total_class = total_clss\n\n\t\t\t# total_clss = '=IF(OR(C14=\"HOLD\",D14=\"HOLD\"),\"HOLD\",(IF(B13>=94%%*%s%%,\"AA\",IF(B13>88%%*%s%%,\"A\",\"C\"))))' % (total_weight,total_weight)\n\t\t\tsheet.write('B13', '=SUM(C13:E13)', head_tot_val)\n\t\t\tsheet.write('B14', total_clss, head_tot_class)\n\n\t\t\t# # OTHERS\n\t\t\td = datetime.strptime(o.date_issue, '%Y-%m-%d %H:%M:%S').replace(tzinfo=timezone('UTC'))\n\t\t\td_local = d.astimezone(timezone(o.create_uid.tz or 'Asia/Manila'))\n\t\t\tdate_issue = datetime.strftime(d_local, '%d/%m/%Y')\n\t\t\ttime_issue = datetime.strftime(d_local, '%H:%M %p')\n\n\t\t\tsheet.write('B6', date_issue or '', head_name)\n\t\t\tsheet.write('B7', time_issue or '', head_name)\n\t\t\tsheet.write('B8', o.issuer.name or '', head_name)\n\t\t\tsheet.write('B9', o.container or '', head_dash)\n\t\t\tsheet.write('B10', o.customer or '', head_dash)\n\n\t\t\t# sheet.write('E7', o.product_sc or '', head_name)\n\t\t\t# sheet.write('E8', o.product_ps or '', head_name)\n\t\t\tsheet.write('E7', o.shell_color or '', head_name)\n\t\t\tsheet.write('E8', o.pack_size or '', head_name)\n\t\t\tsheet.write('E9', o.date_pack or '', head_name)\n\t\t\tsheet.write('E10', o.market or '', head_name)\n\n\t\t\tsheet.write('I7', o.series_no or '', head_name)\n\t\t\tsheet.write('I8', o.date_load or '', head_name)\n\t\t\tsheet.write('I9', o.inspector.name or '', head_name)\n\t\t\tsheet.write('I10', o.no_box or '', head_name)\n\n\t\t\tsheet.merge_range('G13:H13',o.field_source or '', head_mrg_bot)\n\n\t\t\t# r = foot_r\n\t\t\t# sheet.merge_range('C%s:H%s'%(r,r),o.temp_start or '', mrg_center_bold)\n\t\t\t# sheet.merge_range('C%s:H%s'%(r+1,r+1),o.temp_end or '', mrg_center_bold)\n\t\t\t# sheet.merge_range('C%s:H%s'%(r+2,r+2),o.van_temp_start or '', mrg_center_bold)\n\t\t\t# sheet.merge_range('C%s:H%s'%(r+3,r+3),o.van_temp_end or '', mrg_center_bold)\n\n\t\t\t# sheet.merge_range('A%s:A%s'%(r+6,r+7),o.date_pack or '', mrg_center)\n\t\t\t# sheet.merge_range('B%s:B%s'%(r+6,r+7),o.pre_pc or '', mrg_center)\n\t\t\t# sheet.merge_range('C%s:C%s'%(r+6,r+7),o.post_pc or '', mrg_center)\n\t\t\t# sheet.merge_range('H%s:H%s'%(r+6,r+7),o.no_pallet or '', mrg_center)\n\n\t\t\t# sheet.merge_range('D%s:D%s'%(r+6,r+11),o.cold_store or '', mrg_center)\n\t\t\t# sheet.merge_range('E%s:E%s'%(r+6,r+11),o.pulp_temp_first or '', mrg_center)\n\t\t\t# sheet.merge_range('F%s:F%s'%(r+6,r+11),o.pulp_temp_mid or '', mrg_center)\n\t\t\t# sheet.merge_range('G%s:G%s'%(r+6,r+11),o.pulp_temp_last or '', mrg_center)\n\n\t\t\t# for c in 'ABCH':\n\t\t\t# \tsheet.merge_range('%s%s:%s%s'%(c,r+8,c,r+9),'', mrg_center)\n\t\t\t# \tsheet.merge_range('%s%s:%s%s'%(c,r+10,c,r+11),'', mrg_center)\n\n\t\t\t# ADD ATTACHMENT\n\t\t\t\n\t\t\tr = img_attach_r\n\t\t\ttry:\n\t\t\t\timg = o.img\n\t\t\t\timg = codecs.decode(img, 'base64')\n\t\t\t\timage_data = BytesIO(img)\n\t\t\t\t# sheet.insert_image('A%s' % (r+13), '', {'image_data':image_data})\n\t\t\t\tsheet.insert_image('A%s' % (r), '', {'image_data':image_data})\n\t\t\texcept:\n\t\t\t\tpass","sub_path":"dmpi_crm/reports/preship_certificate.py","file_name":"preship_certificate.py","file_ext":"py","file_size_in_byte":19411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"50524475","text":"import warnings\nimport time\nimport sys\n\nfrom .base import Attack\nfrom .base import call_decorator\nfrom ...distances import MSE, Linf\nfrom ...criteria import Misclassification\nimport numpy as np\nimport math\nfrom warnings import warn\n\n\nclass HopSkipJumpAttack(Attack):\n    \"\"\"A powerful adversarial attack that requires neither gradients\n    nor probabilities.\n\n    Notes\n    -----\n    Features:\n    * ability to switch between two types of distances: MSE and Linf.\n    * ability to continue previous attacks by passing an instance of the\n      Adversarial class\n    * ability to pass an explicit starting point; especially to initialize\n      a targeted attack\n    * ability to pass an alternative attack used for initialization\n    * ability to specify the batch size\n\n    References\n    ----------\n    ..\n    HopSkipJumpAttack was originally proposed by Chen, Jordan and\n    Wainwright.\n    It is a decision-based attack that requires access to output\n    labels of a model alone.\n    Paper link: https://arxiv.org/abs/1904.02144\n    The implementation in Foolbox is based on Boundary Attack.\n\n    \"\"\"\n\n    @call_decorator\n    def __call__(\n        self,\n        input_or_adv,\n        label=None,\n        unpack=True,\n        iterations=64,\n        initial_num_evals=100,\n        max_num_evals=10000,\n        stepsize_search=\"geometric_progression\",\n        gamma=1.0,\n        starting_point=None,\n        batch_size=256,\n        internal_dtype=np.float64,\n        log_every_n_steps=1,\n        verbose=False,\n    ):\n        \"\"\"Applies HopSkipJumpAttack.\n\n        Parameters\n        ----------\n        input_or_adv : `numpy.ndarray` or :class:`Adversarial`\n            The original, correctly classified input. If it is a\n            numpy array, label must be passed as well. If it is\n            an :class:`Adversarial` instance, label must not be passed.\n        label : int\n            The reference label of the original input. Must be passed\n            if input is a numpy array, must not be passed if input is\n            an :class:`Adversarial` instance.\n        unpack : bool\n            If true, returns the adversarial input, otherwise returns\n            the Adversarial object.\n        iterations : int\n            Number of iterations to run.\n        initial_num_evals: int\n            Initial number of evaluations for gradient estimation.\n            Larger initial_num_evals increases time efficiency, but\n            may decrease query efficiency.\n        max_num_evals: int\n            Maximum number of evaluations for gradient estimation.\n        stepsize_search: str\n            How to search for stepsize; choices are 'geometric_progression',\n            'grid_search'. 'geometric progression' initializes the stepsize\n            by ||x_t - x||_p / sqrt(iteration), and keep decreasing by half\n            until reaching the target side of the boundary. 'grid_search'\n            chooses the optimal epsilon over a grid, in the scale of\n            ||x_t - x||_p.\n        gamma: float\n            The binary search threshold theta is gamma / d^1.5 for\n                   l2 attack and gamma / d^2 for linf attack.\n\n        starting_point : `numpy.ndarray`\n            Adversarial input to use as a starting point, required\n            for targeted attacks.\n        batch_size : int\n            Batch size for model prediction.\n        internal_dtype : np.float32 or np.float64\n            Higher precision might be slower but is numerically more stable.\n        log_every_n_steps : int\n            Determines verbositity of the logging.\n        verbose : bool\n            Controls verbosity of the attack.\n\n        \"\"\"\n\n        self.initial_num_evals = initial_num_evals\n        self.max_num_evals = max_num_evals\n        self.stepsize_search = stepsize_search\n        self.gamma = gamma\n        self.batch_size = batch_size\n        self.verbose = verbose\n        self._starting_point = starting_point\n        self.internal_dtype = internal_dtype\n        self.log_every_n_steps = log_every_n_steps\n        self.verbose = verbose\n\n        # Set constraint based on the distance.\n        if self._default_distance == MSE:\n            self.constraint = \"l2\"\n        elif self._default_distance == Linf:\n            self.constraint = \"linf\"\n\n        # Set binary search threshold.\n        self.shape = input_or_adv.unperturbed.shape\n        self.d = np.prod(self.shape)\n        if self.constraint == \"l2\":\n            self.theta = self.gamma / (np.sqrt(self.d) * self.d)\n        else:\n            self.theta = self.gamma / (self.d * self.d)\n        print(\"HopSkipJumpAttack optimized for {} distance\".format(self.constraint))\n\n        if not verbose:\n            print(\"run with verbose=True to see details\")\n\n        return self.attack(input_or_adv, iterations=iterations)\n\n    def attack(self, a, iterations):\n        \"\"\"\n        iterations : int\n            Maximum number of iterations to run.\n        \"\"\"\n        self.t_initial = time.time()\n\n        # ===========================================================\n        # Increase floating point precision\n        # ===========================================================\n\n        self.external_dtype = a.unperturbed.dtype\n\n        assert self.internal_dtype in [np.float32, np.float64]\n        assert self.external_dtype in [np.float32, np.float64]\n\n        assert not (\n            self.external_dtype == np.float64 and self.internal_dtype == np.float32\n        )\n\n        a.set_distance_dtype(self.internal_dtype)\n\n        # ===========================================================\n        # Construct batch decision function with binary output.\n        # ===========================================================\n        # decision_function = lambda x: a.forward(\n        #     x.astype(self.external_dtype), strict=False)[1]\n        def decision_function(x):\n            outs = []\n            num_batchs = int(math.ceil(len(x) * 1.0 / self.batch_size))\n            for j in range(num_batchs):\n                current_batch = x[self.batch_size * j : self.batch_size * (j + 1)]\n                current_batch = current_batch.astype(self.external_dtype)\n                out = a.forward(current_batch, strict=False)[1]\n                outs.append(out)\n            outs = np.concatenate(outs, axis=0)\n            return outs\n\n        # ===========================================================\n        # intialize time measurements\n        # ===========================================================\n        self.time_gradient_estimation = 0\n\n        self.time_search = 0\n\n        self.time_initialization = 0\n\n        # ===========================================================\n        # Initialize variables, constants, hyperparameters, etc.\n        # ===========================================================\n\n        # make sure repeated warnings are shown\n        warnings.simplefilter(\"always\", UserWarning)\n\n        # get bounds\n        bounds = a.bounds()\n        self.clip_min, self.clip_max = bounds\n\n        # ===========================================================\n        # Find starting point\n        # ===========================================================\n\n        self.initialize_starting_point(a)\n\n        if a.perturbed is None:\n            warnings.warn(\n                \"Initialization failed.\"\n                \" it might be necessary to pass an explicit starting\"\n                \" point.\"\n            )\n            return\n\n        self.time_initialization += time.time() - self.t_initial\n\n        assert a.perturbed.dtype == self.external_dtype\n        # get original and starting point in the right format\n        original = a.unperturbed.astype(self.internal_dtype)\n        perturbed = a.perturbed.astype(self.internal_dtype)\n\n        # ===========================================================\n        # Iteratively refine adversarial\n        # ===========================================================\n        t0 = time.time()\n\n        # Project the initialization to the boundary.\n        perturbed, dist_post_update = self.binary_search_batch(\n            original, np.expand_dims(perturbed, 0), decision_function\n        )\n\n        dist = self.compute_distance(perturbed, original)\n\n        distance = a.distance.value\n        self.time_search += time.time() - t0\n\n        # log starting point\n        self.log_step(0, distance)\n\n        for step in range(1, iterations + 1):\n\n            t0 = time.time()\n\n            # ===========================================================\n            # Gradient direction estimation.\n            # ===========================================================\n            # Choose delta.\n            delta = self.select_delta(dist_post_update, step)\n\n            # Choose number of evaluations.\n            num_evals = int(\n                min([self.initial_num_evals * np.sqrt(step), self.max_num_evals])\n            )\n\n            # approximate gradient.\n            gradf = self.approximate_gradient(\n                decision_function, perturbed, num_evals, delta\n            )\n\n            if self.constraint == \"linf\":\n                update = np.sign(gradf)\n            else:\n                update = gradf\n            t1 = time.time()\n            self.time_gradient_estimation += t1 - t0\n\n            # ===========================================================\n            # Update, and binary search back to the boundary.\n            # ===========================================================\n            if self.stepsize_search == \"geometric_progression\":\n                # find step size.\n                epsilon = self.geometric_progression_for_stepsize(\n                    perturbed, update, dist, decision_function, step\n                )\n\n                # Update the sample.\n                perturbed = np.clip(\n                    perturbed + epsilon * update, self.clip_min, self.clip_max\n                )\n\n                # Binary search to return to the boundary.\n                perturbed, dist_post_update = self.binary_search_batch(\n                    original, perturbed[None], decision_function\n                )\n\n            elif self.stepsize_search == \"grid_search\":\n                # Grid search for stepsize.\n                epsilons = np.logspace(-4, 0, num=20, endpoint=True) * dist\n                epsilons_shape = [20] + len(self.shape) * [1]\n                perturbeds = perturbed + epsilons.reshape(epsilons_shape) * update\n                perturbeds = np.clip(perturbeds, self.clip_min, self.clip_max)\n                idx_perturbed = decision_function(perturbeds)\n\n                if np.sum(idx_perturbed) > 0:\n                    # Select the perturbation that yields the minimum\n                    # distance after binary search.\n                    perturbed, dist_post_update = self.binary_search_batch(\n                        original, perturbeds[idx_perturbed], decision_function\n                    )\n            t2 = time.time()\n\n            self.time_search += t2 - t1\n\n            # compute new distance.\n            dist = self.compute_distance(perturbed, original)\n\n            # ===========================================================\n            # Log the step\n            # ===========================================================\n            # Using foolbox definition of distance for logging.\n            if self.constraint == \"l2\":\n                distance = dist ** 2 / self.d / (self.clip_max - self.clip_min) ** 2\n            elif self.constraint == \"linf\":\n                distance = dist / (self.clip_max - self.clip_min)\n            message = \" (took {:.5f} seconds)\".format(t2 - t0)\n            self.log_step(step, distance, message)\n            sys.stdout.flush()\n\n        # ===========================================================\n        # Log overall runtime\n        # ===========================================================\n\n        self.log_time()\n\n    # ===============================================================\n    #\n    # Other methods\n    #\n    # ===============================================================\n\n    def initialize_starting_point(self, a):\n        starting_point = self._starting_point\n\n        if a.perturbed is not None:\n            print(\n                \"Attack is applied to a previously found adversarial.\"\n                \" Continuing search for better adversarials.\"\n            )\n            if starting_point is not None:  # pragma: no cover\n                warnings.warn(\n                    \"Ignoring starting_point parameter because the attack\"\n                    \" is applied to a previously found adversarial.\"\n                )\n            return\n\n        if starting_point is not None:\n            a.forward_one(starting_point)\n            assert (\n                a.perturbed is not None\n            ), \"Invalid starting point provided. Please provide a starting point that is adversarial.\"\n            return\n\n        \"\"\"\n        Apply BlendedUniformNoiseAttack if without\n        initialization.\n        Efficient Implementation of BlendedUniformNoiseAttack in Foolbox.\n        \"\"\"\n        success = 0\n        num_evals = 0\n\n        while True:\n            random_noise = np.random.uniform(\n                self.clip_min, self.clip_max, size=self.shape\n            )\n            _, success = a.forward_one(random_noise.astype(self.external_dtype))\n            num_evals += 1\n            if success:\n                break\n            if num_evals > 1e4:\n                return\n\n        # Binary search to minimize l2 distance to the original input.\n        low = 0.0\n        high = 1.0\n        while high - low > 0.001:\n            mid = (high + low) / 2.0\n            blended = (1 - mid) * a.unperturbed + mid * random_noise\n            _, success = a.forward_one(blended.astype(self.external_dtype))\n            if success:\n                high = mid\n            else:\n                low = mid\n\n    def compute_distance(self, x1, x2):\n        if self.constraint == \"l2\":\n            return np.linalg.norm(x1 - x2)\n        elif self.constraint == \"linf\":\n            return np.max(abs(x1 - x2))\n\n    def project(self, unperturbed, perturbed_inputs, alphas):\n        \"\"\" Projection onto given l2 / linf balls in a batch. \"\"\"\n        alphas_shape = [len(alphas)] + [1] * len(self.shape)\n        alphas = alphas.reshape(alphas_shape)\n        if self.constraint == \"l2\":\n            projected = (1 - alphas) * unperturbed + alphas * perturbed_inputs\n        elif self.constraint == \"linf\":\n            projected = np.clip(\n                perturbed_inputs, unperturbed - alphas, unperturbed + alphas\n            )\n        return projected\n\n    def binary_search_batch(self, unperturbed, perturbed_inputs, decision_function):\n        \"\"\" Binary search to approach the boundary. \"\"\"\n\n        # Compute distance between each of perturbed and unperturbed input.\n        dists_post_update = np.array(\n            [\n                self.compute_distance(unperturbed, perturbed_x)\n                for perturbed_x in perturbed_inputs\n            ]\n        )\n\n        # Choose upper thresholds in binary searchs based on constraint.\n        if self.constraint == \"linf\":\n            highs = dists_post_update\n            # Stopping criteria.\n            thresholds = dists_post_update * self.theta\n        else:\n            highs = np.ones(len(perturbed_inputs))\n            thresholds = self.theta\n\n        lows = np.zeros(len(perturbed_inputs))\n\n        # Call recursive function.\n        while np.max((highs - lows) / thresholds) > 1:\n            # projection to mids.\n            mids = (highs + lows) / 2.0\n            mid_inputs = self.project(unperturbed, perturbed_inputs, mids)\n\n            # Update highs and lows based on model decisions.\n            decisions = decision_function(mid_inputs)\n            lows = np.where(decisions == 0, mids, lows)\n            highs = np.where(decisions == 1, mids, highs)\n\n        out_inputs = self.project(unperturbed, perturbed_inputs, highs)\n\n        # Compute distance of the output to select the best choice.\n        # (only used when stepsize_search is grid_search.)\n        dists = np.array(\n            [self.compute_distance(unperturbed, out) for out in out_inputs]\n        )\n        idx = np.argmin(dists)\n\n        dist = dists_post_update[idx]\n        out = out_inputs[idx]\n        return out, dist\n\n    def select_delta(self, dist_post_update, current_iteration):\n        \"\"\"\n        Choose the delta at the scale of distance\n        between x and perturbed sample.\n        \"\"\"\n        if current_iteration == 1:\n            delta = 0.1 * (self.clip_max - self.clip_min)\n        else:\n            if self.constraint == \"l2\":\n                delta = np.sqrt(self.d) * self.theta * dist_post_update\n            elif self.constraint == \"linf\":\n                delta = self.d * self.theta * dist_post_update\n\n        return delta\n\n    def approximate_gradient(self, decision_function, sample, num_evals, delta):\n        \"\"\" Gradient direction estimation \"\"\"\n        # Generate random vectors.\n        noise_shape = [num_evals] + list(self.shape)\n        if self.constraint == \"l2\":\n            rv = np.random.randn(*noise_shape)\n        elif self.constraint == \"linf\":\n            rv = np.random.uniform(low=-1, high=1, size=noise_shape)\n\n        axis = tuple(range(1, 1 + len(self.shape)))\n        rv = rv / np.sqrt(np.sum(rv ** 2, axis=axis, keepdims=True))\n        perturbed = sample + delta * rv\n        perturbed = np.clip(perturbed, self.clip_min, self.clip_max)\n        rv = (perturbed - sample) / delta\n\n        # query the model.\n        decisions = decision_function(perturbed)\n        decision_shape = [len(decisions)] + [1] * len(self.shape)\n        fval = 2 * decisions.astype(self.internal_dtype).reshape(decision_shape) - 1.0\n\n        # Baseline subtraction (when fval differs)\n        vals = fval if abs(np.mean(fval)) == 1.0 else fval - np.mean(fval)\n        gradf = np.mean(vals * rv, axis=0)\n\n        # Get the gradient direction.\n        gradf = gradf / np.linalg.norm(gradf)\n\n        return gradf\n\n    def geometric_progression_for_stepsize(\n        self, x, update, dist, decision_function, current_iteration\n    ):\n        \"\"\" Geometric progression to search for stepsize.\n          Keep decreasing stepsize by half until reaching\n          the desired side of the boundary.\n        \"\"\"\n        epsilon = dist / np.sqrt(current_iteration)\n        while True:\n            updated = np.clip(x + epsilon * update, self.clip_min, self.clip_max)\n            success = decision_function(updated[None])[0]\n            if success:\n                break\n            else:\n                epsilon = epsilon / 2.0  # pragma: no cover\n\n        return epsilon\n\n    def log_step(self, step, distance, message=\"\", always=False):\n        if not always and step % self.log_every_n_steps != 0:\n            return\n        print(\"Step {}: {:.5e} {}\".format(step, distance, message))\n\n    def log_time(self):\n        t_total = time.time() - self.t_initial\n        rel_initialization = self.time_initialization / t_total\n        rel_gradient_estimation = self.time_gradient_estimation / t_total\n        rel_search = self.time_search / t_total\n\n        self.printv(\"Time since beginning: {:.5f}\".format(t_total))\n        self.printv(\n            \"   {:2.1f}% for initialization ({:.5f})\".format(\n                rel_initialization * 100, self.time_initialization\n            )\n        )\n        self.printv(\n            \"   {:2.1f}% for gradient estimation ({:.5f})\".format(\n                rel_gradient_estimation * 100, self.time_gradient_estimation\n            )\n        )\n        self.printv(\n            \"   {:2.1f}% for search ({:.5f})\".format(rel_search * 100, self.time_search)\n        )\n\n    def printv(self, *args, **kwargs):\n        if self.verbose:\n            print(*args, **kwargs)\n\n\ndef BoundaryAttackPlusPlus(\n    model=None, criterion=Misclassification(), distance=MSE, threshold=None\n):\n    warn(\"BoundaryAttackPlusPlus is deprecated; use HopSkipJumpAttack.\")\n    return HopSkipJumpAttack(model, criterion, distance, threshold)\n","sub_path":"cnns/foolbox/foolbox_2_3_0/v1/attacks/hop_skip_jump_attack.py","file_name":"hop_skip_jump_attack.py","file_ext":"py","file_size_in_byte":20102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"218694891","text":"# Very inefficient code for generating low-entropy anagram-free strings\nimport random\nimport collections\nimport math\nimport sys\n\ndef is_anagram(s):\n    return len(s) % 2 == 0 and sorted(s[:len(s)//2]) == sorted(s[len(s)//2:])\n\ndef entropy(hist, n):\n    h = 0\n    for c in hist:\n        pc = hist[c]/n\n        h += pc*math.log(1/pc, 2)\n    return h\n\ndef high_entropy_suffix(s, t):\n    \"\"\"Return the length of the shortest suffix of s whose entropy exceeds t or return -1 if no such suffix exists\"\"\"\n    hist = collections.defaultdict(int)\n    for i in range(len(s)-1, -1, -1):\n        hist[s[i]] += 1\n        if entropy(hist, len(s)-i) >= t: return len(s)-i\n    return -1\n\ndef equal_histograms(hist0, hist1):\n    for c in hist0:\n        if hist0[c] != hist1[c]: return False\n    for c in hist1:\n        if hist0[c] != hist1[c]: return False\n    return True\n\ndef anagram_suffix(s, amax):\n    \"\"\"Return half the length of the shortest suffix of s that is an anagram or -1 if no such suffix exists\"\"\"\n    if len(s) < 2: return -1\n    hist0 = collections.defaultdict(int)\n    hist1 = collections.defaultdict(int)\n    hist0[s[len(s)-2]] = 1\n    hist1[s[len(s)-1]] = 1\n    # print(hist0.items(), hist1.items())\n    if amax < 1 and equal_histograms(hist0, hist1): return 1\n    for t in range(2, len(s)//2+1):\n        hist0[s[len(s)-2*t]] += 1\n        hist0[s[len(s)-2*t+1]] += 1\n        hist0[s[len(s)-t]] -= 1\n        hist1[s[len(s)-t]] += 1\n        # print(hist0.items(), hist1.items())\n        if amax < t and equal_histograms(hist0, hist1): return t\n    return -1\n\ndef empirical_entropy(s):\n    d = collections.defaultdict(int)\n    for c in s:\n        d[c] += 1\n    h = 0\n    for c in d:\n        pc = d[c]/len(s)\n        h += pc*math.log(1/pc, 2)\n    return h\n\nif __name__ == \"__main__\":\n    alphabet = \"abcedefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n    c = 50\n    if len(sys.argv) >= 2:\n        c = int(sys.argv[1])\n        if c > len(alphabet):\n            print(\"Error: max alphabet size is {}\".format(len(alphabet)))\n            sys.exit(-1)\n    alphabet = alphabet[:c]\n\n    amax = c//2\n    if len(sys.argv) >= 3:\n        amax = int(sys.argv[2])\n\n    sanity_check = False\n\n    hmax = math.log(c, 2) - 1\n    s = \"\"\n    erejections = 0\n    arejections = 0\n    iterations = 0\n    while 1 < 2:\n        s += random.choice(alphabet)\n        iterations += 1\n        if high_entropy_suffix(s, hmax) >= 0:\n            s = s[:len(s)-1]\n            erejections += 1\n        else:\n            t = anagram_suffix(s, amax)\n            if t > 0:\n                s = s[:len(s)-t]\n                arejections += t\n            elif len(s) % 1000 == 0:\n                filename = \"long-string-{}-{}.txt\".format(c, amax)\n                with open(filename, 'w') as fp:\n                    fp.write(s)\n        # # output long strings\n        # if iterations % 10000 == 0:\n        #     filename = \"\"\n        #     with(\"\")\n\n        # sanity check\n        if sanity_check and len(s) < 500 and iterations % 100 == 0:\n            for i in range(len(s)-1):\n                for j in range(i+1, len(s)):\n                    if empirical_entropy(s[i:j]) > hmax:\n                        print(\"Fatal error, string has high entropy substring\")\n                        sys.exit(-3)\n                    if 2*amax < j-i and is_anagram(s[i:j]):\n                        print(\"Fatal error, string contains anagram: {}\".format(s[i:j]))\n                        sys.exit(-2)\n\n        # This produces gnuplot output\n        print(\"{:10} {:10} {:10} {:10} {}\".format(iterations, len(s), erejections, arejections, empirical_entropy(s)))\n","sub_path":"entropy-anagram.py","file_name":"entropy-anagram.py","file_ext":"py","file_size_in_byte":3605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"653332955","text":"import torch\nfrom torchtext.datasets import text_classification\nimport pandas as pd\nimport numpy as np\nimport pickle\nimport os\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nimport time\nfrom torch.utils.data.dataset import random_split\n\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n\nwith open('vocab.pkl', 'rb') as f1:\n    vocab = pickle.load(f1)\n\n\nimport logging\nimport torch\nimport io\nfrom torchtext.utils import download_from_url, extract_archive, unicode_csv_reader\nfrom torchtext.data.utils import ngrams_iterator\nfrom torchtext.data.utils import get_tokenizer\nfrom torchtext.vocab import build_vocab_from_iterator\nfrom torchtext.vocab import Vocab\nfrom tqdm import tqdm\nfrom  torchtext.datasets.text_classification import TextClassificationDataset\n\n\nNGRAMS=2\n\ndef generate_batch(batch):\n    indexes=torch.tensor([int(entry[0]) for entry in batch])\n    label = torch.tensor([int(entry[1]) for entry in batch])\n    text = [entry[2] for entry in batch]\n    offsets = [0] + [len(entry) for entry in text]\n\n    offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)\n    text = torch.cat(text)\n    text=text.type(torch.LongTensor)\n    return indexes, text, offsets, label\n\n\n\n\n\n\nclass TextSentiment(nn.Module):\n    def __init__(self, vocab_size, embed_dim, num_class):\n        super().__init__()\n        self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)\n        self.fc = nn.Linear(embed_dim, num_class)\n        self.init_weights()\n\n\n    def init_weights(self):\n        initrange = 0.5\n        self.embedding.weight.data.uniform_(-initrange, initrange)\n        self.fc.weight.data.uniform_(-initrange, initrange)\n        self.fc.bias.data.zero_()\n\n    def forward(self, text, offsets):\n        embedded = self.embedding(text, offsets)\n        return self.fc(embedded)\n    \n    \n    def create_iterator_from_file_for_testing(self, vocab, ngrams, data, include_unk=False):\n        #each element contains text and label\n        article_index=1\n        label_index=2\n        datas = []\n        labels = []\n        tokenizer = get_tokenizer(\"basic_english\")\n\n        with tqdm(unit_scale=0, unit='lines') as t:\n            for index, row in data.iterrows():\n                tokens = ' '.join([str(row['article'])])\n                tokens=tokenizer(tokens)\n                if 'label' in row: #for compatibility, will not be used, and is not necessary\n                    cls=int(row['label'])\n                else:\n                    cls=1\n                index=row.name\n\n                if include_unk:\n                    tokens = torch.tensor([vocab[token] for token in tokens])\n                else:\n                    token_ids = list(filter(lambda x: x is not Vocab.UNK, [vocab[token] for token in tokens]))\n                    tokens = torch.tensor(token_ids)\n                if len(tokens) == 0:\n                    logging.info('Row contains no tokens.')\n                datas.append((index, cls, tokens))\n                labels.append(cls)\n                t.update(1)\n        return datas, set(labels)\n    \n    \n    \n    def setup_datasets_testing_from_df(self, vocab, df, include_unk=False):\n        test_data, test_labels=self.create_iterator_from_file_for_testing(vocab, NGRAMS, df)\n        return TextClassificationDataset(vocab, test_data, test_labels), vocab\n    \n    \n    \n    def predict(self, df, vocab):\n        test_dataset, vocab=self.setup_datasets_testing_from_df(vocab, df)\n        all_indexes=[]\n        predictions=[]\n        data = DataLoader(test_dataset, batch_size=df.shape[0], shuffle=False, collate_fn=generate_batch)\n        \n        for indexes, text, offsets, cls in data:\n            text, offsets, cls = text.to(device), offsets.to(device), cls.to(device)\n            with torch.no_grad():\n                #print(indexes, cls)\n                #print(text, offsets)\n                output = self.forward(text, offsets)\n                \n                for i in range(output.shape[0]):\n                    predictions.append(np.argmax(output[i].numpy()))\n                    all_indexes.append(indexes[i])\n                \n        return predictions\n\n\nN_EPOCHS = 20\nBATCH_SIZE = 16\nVOCAB_SIZE = len(vocab)\nEMBED_DIM = 32\nNUN_CLASS = 2\n\n\n\nwith open('model.pkl', 'rb') as f1:\n    model = pickle.load(f1)\n\n\n#returns a list of the predicted labels\npredictions=model.predict(pd.read_csv(\"./data/test_2.csv\", delimiter=\",\"), vocab)\n\n\n#and next we get the true labels for comparison\nlabels=pd.read_csv(\"./data/test_2.csv\", delimiter=\",\")['label']\nlabels\n\n# 1-Accuracy\nerror=0\nfor i in range(len(predictions)):\n    if predictions[i]!=labels[i]:\n        error+=1\nprint(error/len(predictions))\n\n","sub_path":"analysis_seb/first_model/final.py","file_name":"final.py","file_ext":"py","file_size_in_byte":4705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"653669724","text":"#CSCI 1133 Homework 4\n#August Eggers\n#Problem 4A\n\ndef two_sums(aList, target):\n    \"\"\"Finds the two integers in aList that sum to certain target value\"\"\"\n    for i in aList:\n        if type(i) != int:                                                      #Going through entire list searching for non integers. If one is not an integer, then the function returns\n            print(\"The list may only contain integers... exiting\")\n            return\n    for i in aList:\n        for ii in aList:\n            if i + ii == target:                                                #Going through entire list searching for a pair summing to target\n                return(i, ii)                                                   #Returns first two terms in aList that add to target\n","sub_path":"Homework4/egger235_4A.py","file_name":"egger235_4A.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"160127072","text":"#!/usr/bin/env python3\n\n# ========================\n# QuickPIC Deck Generator\n# ========================\nimport numpy as _np\nimport jinja2 as _jj\nfrom . import classes as _cl\n\n\ndef deckgen(\n        plasma, bunches, box, magic, qpic, filename=None, fid=None, verbose=True\n        ):\n    \"\"\"\n    Generates a QuickPIC rpinput deck.\n    \"\"\"\n    beampos = _cl.BeamPositioning()\n    phas_samp = _cl.PhaseSpaceSampling()\n    # =====================================\n    # Generate jinja template object\n    # =====================================\n    loader            = _jj.PackageLoader('pyqpic', 'resources/templates')\n    env               = _jj.Environment(loader=loader, trim_blocks=True)\n    template          = env.get_template('rpinput')\n    \n    # =====================================\n    # Create output file for writing\n    # =====================================\n    if (fid is None and filename is None):\n        filename = 'rpinput'\n    if (fid is not None and filename is not None):\n        raise ValueError('Must use either fid or filename')\n    if filename is not None:\n        fid = open(filename, 'w+')\n    \n    # =====================================\n    # Use jinja template object to create\n    # a stream with substitutions in it\n    # =====================================\n    stream = template.stream(\n        qpic              = qpic,\n        magic             = magic,\n        bunches           = bunches,\n        plasma            = plasma,\n        box               = box,\n        beampos           = beampos,\n        phas_samp         = phas_samp,\n        verbose           = verbose,\n        nbeam             = _np.size(bunches)\n        )\n    # =====================================\n    # Write stream contents to file\n    # =====================================\n    stream.disable_buffering()\n    stream.dump(fid)\n\n    # myfid = open('mytemp', 'w+')\n    # stream.dump(myfid)\n    # myfid.close()\n    \n    if filename is not None:\n        fid.close()\n    else:\n        return fid\n","sub_path":"pyqpic/deckgen.py","file_name":"deckgen.py","file_ext":"py","file_size_in_byte":2004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"218160011","text":"# This is meant to be a doubly, cirularly linked list to simulate the \n# turns being taken.\n\n\n# Creating the node class for a player's turn which will hold a player \n# object as data\nclass PlayerTurn:\n    def __init__(self, data, next = None, prev = None):\n        # Instantiates a node with a default next of None\n        self.data = data # player data object\n        self.next = next # will point to next clockwise player\n        self.prev = prev # will point to next counter-clockwise player\n        \n        \nclass TurnList:\n    def __init__(self):\n        self.head = None #creating head and tail for the list\n        self.tail = None # May not really need tail once it is connected\n        self.__direction = 1 # This will indicate the direction the token is moving\n        self.__token = None # This will track current player node thus indicating turn\n        self.__dealer = None # Marks if this player is currently the dealer\n        self.__change = False # Tracks changes made and Unity's state of reflecting change\n\n    # Add a player node to our linked list\n    def append(self, data):\n        # Instantiate a new node\n        newPlayer = PlayerTurn(data)\n        \n        # Is there something in our linked list yet\n        if self.head is None:\n            newPlayer.prev = None\n            self.head = newPlayer\n            self.tail = self.head\n        # There are node(s) in our linked list\n        else:\n            self.tail.next = newPlayer\n            newPlayer.prev = self.tail\n            self.tail = self.tail.next\n    \n    # Method to make the list circular once fully populated and place the token at the head\n    def complete(self):\n        self.head.prev = self.tail\n        self.tail.next = self.head\n        self.__dealer = self.head\n        self.__token = self.__dealer.next\n\n    def nextTurn(self):\n        if (self.__direction == 1):\n            self.__token = self.__token.next\n        else:\n            self.__token = self.__token.prev\n\n    def reverse(self):\n        if (self.__direction == 1):\n            self.__direction = 0\n        elif(self.__direction == 0):\n            self.__direction = 1\n\n    def skip(self):\n        self.nextTurn()\n        self.nextTurn()\n\n    def toggleChange(self):\n        if (self.__change == False):\n            self.__change = True\n        elif (self.__change == True):\n            self.__change = False\n\n    def printCurrent(self):\n        print(self.__token.data)\n\n    # This is meant to return the current player object\n    def get_player(self):\n        return self.__token.data\n\ndef main():\n    names = [\"player1\", \"player2\", \"player3\", \"player4\"]\n    turns = TurnList()\n    for name in names:\n        turns.append(name)\n    turns.complete()\n    print(turns.get_player())\n    turns.nextTurn()\n    print(turns.get_player())\n    turns.skip()\n    print(turns.get_player())\n    turns.reverse()\n    turns.nextTurn()\n    print(turns.get_player())\n\nmain()\n\n\n\n\n","sub_path":"Projects/UNO/turns.py","file_name":"turns.py","file_ext":"py","file_size_in_byte":2927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"71055216","text":"import asyncio\nimport click\nimport datetime\nimport time\n\nimport aioredis\n\nimport shared.redis\nimport shared.settings\n\nfrom . import *\n\n\nasync def doit(downloads, notify_channel, sleep, settings, loop):\n    async with shared.redis.redis_context(settings, loop) as redis:\n        for i, download in enumerate(downloads):\n            await shared.redis.store_download(redis, download, notify_channel)\n            print(i+1, download)\n            if sleep is not None:\n                await asyncio.sleep(sleep)\n\n\ndef pastyear_dates():\n    stop_date = datetime.datetime.now()\n    start_date = stop_date - datetime.timedelta(365)\n    return random_uniform_dates(start_date, stop_date)\n\n\n@click.command()\n@click.option(\n    '--pastyear',\n    is_flag=True,\n    help='Generate downloads for the past year.')\n@click.option(\n    '--notify',\n    is_flag=True,\n    help='Publish redis notifications.')\n@click.option(\n    '--count',\n    type=int,\n    help='Generate [count] downloads.')\n@click.option(\n    '--sleep',\n    type=float,\n    help='Sleep between each download for [sleep] seconds.')\ndef main(pastyear, notify, count, sleep):\n    settings = shared.settings.Settings()\n\n    coordinates = random_coordinates(settings.WORLD_GEOJSON_FILE)\n    app_ids = random_choices(['IOS_ALERT', 'IOS_MATE'])\n    dates = pastyear_dates() if pastyear else always_now()\n    notify_channel = settings.REDIS_NOTIFY_CHANNEL if notify else None\n\n    downloads = iter_count(random_downloads(coordinates, dates, app_ids), count)\n    loop = asyncio.get_event_loop()\n    loop.run_until_complete(\n        doit(downloads, notify_channel, sleep, settings, loop)\n    )\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"backend/generate_downloads/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"427353554","text":"# topics = [\"数组\"]\n\nfrom typing import List\nfrom math import ceil\n\n\nclass Solution:\n    def shipWithinDays(self, weights: List[int], D: int) -> int:\n        \"\"\"\n        Binary Search\n        time O(nlog(Σw)), space O(1), n 为 weights 的长度, Σw 是 weights 中所有元素的和\n        \"\"\"\n        w = max(max(weights), ceil(sum(weights) / D))\n\n        # 判断运载能力为 w 时，能否在 D 天内运输完成\n        def helper(w: int) -> bool:\n            total = day = 0\n            for weight in weights:\n                if total + weight > w:\n                    total = weight\n                    day += 1\n                else:\n                    total += weight\n            return day + bool(total) <= D\n\n        while True:\n            if helper(w):\n                return w\n            w += 1\n","sub_path":"algorithms/[1011]在 D 天内送达包裹的能力/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"83149636","text":"#!/usr/bin/python\n\nimport os\nimport sys\nimport re\nmaxDictionaryLength=3000000\n\nwordcounts = dict()\n\nfor file in os.listdir('../texts/textids'):\n    for line in open('../texts/textids/' + file,'r'):\n        filename = line.split('\\t')[1]\n        filename = re.sub('\\n','',filename)\n        reading = open('../texts/unigrams/'+filename+'.txt')\n        for wordEntry in reading:\n            wordEntry = wordEntry.split(' ')\n            wordEntry[1] = int(re.sub('\\n','',wordEntry[1]))\n            try:\n                wordcounts[wordEntry[0]]+=wordEntry[1]\n            except KeyError:\n                wordcounts[wordEntry[0]] = wordEntry[1]\n#Now we need to delete the words that appear below a cutoff that we find dynamically:\ncountcounts = dict()\nfor key in wordcounts:\n    try:\n        countcounts[wordcounts[key]] += 1\n    except KeyError:\n        countcounts[wordcounts[key]] = 1\n\nticker = 0\n#cutoff is the \nminimumCountsForInclusion = 1 \nfor key in sorted(countcounts.keys(),reverse=True):\n    ticker += countcounts[key]\n#    print str(key) + ' has ' + str(countcounts[key]) + ' types of things ticker is at ' + str(ticker)\n    if ticker > maxDictionaryLength:\n        minimumCountsForInclusion = key\n        break\n\nfor key in wordcounts.keys():\n    if wordcounts[key] < minimumCountsForInclusion:\n        del wordcounts[key]\n\nOUTFILE = open(\"../texts/wordlist/wordlist.txt\",'w')\n\nwordid = 0\nfor word in sorted(wordcounts,key=wordcounts.get,reverse=True):\n    wordid = wordid + 1\n    OUTFILE.write(str(wordid) + '\\t' + word + '\\t' + str(wordcounts[word]) + '\\n')\n","sub_path":"WordsTableCreate.py","file_name":"WordsTableCreate.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"586033242","text":"#!/usr/bin/env python3\n\nimport numpy as np\n\ndef make_grid():\n    nx = 51\n    x = np.linspace(0, 1, nx)\n    gx, gy = np.meshgrid(x, x)\n    u = np.zeros_like(gx)\n    u[0,:] = 0\n    u[-1,:] = 1 * np.sin(np.linspace(0,np.pi,nx))\n    u[:,0] = 0\n    u[:,-1] = 0\n    return nx, x, u\n\nnx, x, uoriginal = make_grid()\n\n# [begin example]\nimport solve_cpp_xarray\n# [end example]\n\nimport time\n\nclass Timer:\n    def __enter__(self):\n        self._t0 = time.time()\n    def __exit__(self, *args, **kw):\n        self._t1 = time.time()\n        print(\"Wall time: {:g} s\".format(self._t1 - self._t0))\n\n# [begin pycon]\nwith Timer():\n    u, step, norm = solve_cpp_xarray.solve_cpp(uoriginal)\n# [end pycon]\n\nfrom matplotlib import pyplot as plt\n\ndef show_result(u, step, norm, size=7):\n    print(\"step\", step, \"norm\", norm)\n    fig, ax = plt.subplots(figsize=(size,size))\n    cs = ax.contour(x, x, u.T)\n    ax.clabel(cs, inline=1, fontsize=10)\n\n    ax.set_xticks(np.linspace(0,1,6))\n    ax.set_yticks(np.linspace(0,1,6))\n    ax.set_xlabel('$x$')\n    ax.set_ylabel('$y$')\n\n    ax.grid(True, which='minor')\n\nshow_result(u, step, norm)\n\nplt.rc('figure', figsize=(8, 8))\n\nimport os\nimagedir = os.path.join(os.path.dirname(__file__), '..', 'image')\nimagebase = os.path.splitext(os.path.basename(__file__))[0] + '.png'\nimagepath = os.path.join(imagedir, imagebase)\nprint('write to {}'.format(imagepath))\nplt.savefig(imagepath, dpi=150)\n\n# vim: set ff=unix fenc=utf8 et sw=4 ts=4 sts=4 tw=79:\n","sub_path":"nsd/11arraydesign/code/03_solve_cpp_xarray.py","file_name":"03_solve_cpp_xarray.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"94731946","text":"import timeit\nimport functools\nimport numpy as np\n\n\ndef timefunc(number=10000):\n    def _timefunc(func):\n        @functools.wraps(func)\n        def time_func_wrapper(*args, **kwargs):\n            t0 = timeit.default_timer()\n            for _ in range(number):\n                value = func(*args, **kwargs)\n            t1 = timeit.default_timer()\n            print(\"func: {}(args={}, kwargs={}) time: {}\".format(func.__name__, str(args), str(kwargs), t1-t0))\n            return value\n        return time_func_wrapper\n    return _timefunc\n\nvolumes= [6, 7, 8]\n@timefunc(number=100000)\ndef modify_np(x, i, j):\n    y = x.copy()\n    free = volumes[j] - x[j]\n    spill = min(free, x[i])\n    y[i] -= spill\n    y[j] += spill\n    #y = x\n    h = hash(y.tostring())\n    return h\n\nx = np.array([1,2,3])\nmodify_np(x, 1, 2)\n\n\n\n@timefunc(number=100000)\ndef modify_np(x, i, j):\n    y = x.copy()\n    free = volumes[j] - x[j]\n    spill = min(free, x[i])\n    y[i] -= spill\n    y[j] += spill\n    hash(tuple(y))\n\nx = [1,2,3]\nmodify_np(x, 1, 2)","sub_path":"2019/problems/MINSPILL/timefunc.py","file_name":"timefunc.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"482336372","text":"#!/usr/bin/python3.5\n# -*-coding:Utf-8 -*\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\nwine_url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\"\ndf_wine = pd.read_csv(wine_url,header=None)\ncolumns = [\"Class label\",\"Alcohol\",\"Malic acid\",\"Ash\",\"Alcalinity of ash\",\"Magnesium\",\"Total phenols\",\"Flavanoids\",\"Nonflavanoid phenols\",\"Proanthocyanins\",\"Color intensity\",\"Hue\",\"OD280/OD315 of diluted wines\",\"Proline\"]\ndf_wine.columns = columns \n\nprint(\"Class labels: \", np.unique(df_wine[\"Class label\"]),'\\n')\nprint(df_wine.head())\n\n# First column is class label\nclasses =  df_wine.iloc[:, 0].values\n# the rest is the data\nvalues =  df_wine.iloc[:, 1:].values\n\n# 30 percent of the dataset will the test set\ntrain_values, test_values, train_classes, test_classes = train_test_split(values,classes,test_size=0.3,random_state=0)\n\n","sub_path":"DataPreprocessing/PartitioningData/PartitioningData.py","file_name":"PartitioningData.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"635649999","text":"def write_polar_deck():\n\n    from mpl_toolkits.mplot3d import Axes3D\n    import os                            #dir(os) lists all the functions avaliable within os\n    import struct\n    import numpy as np\n    import math as m\n    import matplotlib as mpl\n    from matplotlib import pyplot as plt\n    from mpl_toolkits.mplot3d import Axes3D\n    import pyh2d as h2d\n\n    if(h2d.g.testing==1):\n        win+=1;h2d.g.win+=1;fig3d= plt.figure(num=h2d.g.win,figsize=(10,10),facecolor='white');plt.clf()\n        h2d.g.ax3d = fig3d.add_subplot(111, projection='3d')\n        plt.autoscale(enable=True, axis='both', tight=True)\n        plt.xlabel('X $(\\mu m)$')\n        plt.ylabel('Y $(\\mu m)$')\n        plt.title('Reconstructed rays')\n        plt.axis('equal')\n\n\n    h2d.g.region_num      = np.asarray(h2d.g.region_num     ).transpose()\n    h2d.g.region_density  = np.asarray(h2d.g.region_density ).transpose()\n    h2d.g.region_tele     = np.asarray(h2d.g.region_tele    ).transpose()/(11605.*1000.)#K => keV\n    h2d.g.region_tion     = np.asarray(h2d.g.region_tion    ).transpose()/(11605.*1000.)\n    h2d.g.region_trad     = np.asarray(h2d.g.region_trad    ).transpose()/(11605.*1000.)\n    h2d.g.region_bfld     = np.asarray(h2d.g.region_bfld    ).transpose()\n    h2d.g.region_ratios_z = np.asarray(h2d.g.region_ratios_z)\n    h2d.g.region_ratios_r = np.asarray(h2d.g.region_ratios_r)\n    h2d.g.target_coords_r = np.asarray(h2d.g.target_coords_r)\n    h2d.g.target_coords_z = np.asarray(h2d.g.target_coords_z)\n    h2d.g.region_cell_n_r = np.asarray(h2d.g.region_cell_n_r)\n    h2d.g.region_cell_n_z = np.asarray(h2d.g.region_cell_n_z)\n\n    print('Writing new h2d input deck...',end='')\n    f              = open(h2d.g.original_h2d_file,'r')\n    lines_original = []\n    lines_new      = []\n\n    for line in f:\n        lines_original = np.append(lines_original,line.strip())\n    f.close()\n\n    i_line = []\n    i_line = np.append(i_line,np.where(lines_original=='c start zoning'     )[0][0]) #0\n    i_line = np.append(i_line,np.where(lines_original=='c end zoning'       )[0][0]) #1\n    i_line = np.append(i_line,np.where(lines_original=='c start rays'       )[0][0]) #2\n    i_line = np.append(i_line,np.where(lines_original=='c end rays'         )[0][0]) #3\n    i_line = np.append(i_line,np.where(lines_original=='c start laser power')[0][0]) #4\n    i_line = np.append(i_line,np.where(lines_original=='c end laser power'  )[0][0]) #5\n\n    lines_new = np.copy(lines_original[0:int(i_line[0])+1])\n    reg=0\n    print(h2d.g.region_cell_n_z)\n    for reg_r in range(h2d.g.num_regions_r):\n        for reg_z in range(h2d.g.num_regions_z):\n            reg+=1\n            lines_new = np.append(lines_new,'c      k1  l1  k2  l2    r0         z0          r1         a1       r2      a2: Region %d: radial region %d, z region %d'%(reg,reg_r+1,reg_z+1))\n\n            try:\n                type(h2d.g.tgt_cen_r)\n                #mesh region                   c     k1    l1    k2    l2    r0    z0    r1    a1    r2    a2\n                lines_new=np.append(lines_new,'mesh  %3.0d %3.0d %3.0d %3.0d %2.10e %2.10e %2.10e %2.10e %2.10e %2.10e $ mesh'%(\n                    h2d.g.region_cell_n_z[reg_z  ],h2d.g.region_cell_n_r[reg_r  ],\n                    h2d.g.region_cell_n_z[reg_z+1],h2d.g.region_cell_n_r[reg_r+1],\n                    h2d.g.tgt_cen_r[reg_r]        ,h2d.g.tgt_cen_z[reg_r],\n                    h2d.g.target_coords_r[reg_r  ],h2d.g.target_angles[reg_z  ],\n                    h2d.g.target_coords_r[reg_r+1],h2d.g.target_angles[reg_z+1]))\n            except:\n                #mesh region                   c     k1    l1    k2    l2    r0    z0    r1    a1    r2    a2\n                lines_new=np.append(lines_new,'mesh  %3.0d %3.0d %3.0d %3.0d %2.10e %2.10e %2.10e %2.10e %2.10e %2.10e $ mesh'%(\n                    h2d.g.region_cell_n_z[reg_z  ],h2d.g.region_cell_n_r[reg_r  ],\n                    h2d.g.region_cell_n_z[reg_z+1],h2d.g.region_cell_n_r[reg_r+1],\n                    h2d.g.tgt_cen[0]              ,h2d.g.tgt_cen[1],\n                    h2d.g.target_coords_r[reg_r  ],h2d.g.target_angles[reg_z  ],\n                    h2d.g.target_coords_r[reg_r+1],h2d.g.target_angles[reg_z+1]))\n\n            if h2d.g.region_ratios_z[reg_z] > 0:\n                #line with Z ratio at mesh minimum in R(l) direction\n                lines_new=np.append(lines_new,'ratio %3.0d %3.0d %3.0d %3.0d %2.3f $line with Z ratio at mesh minimum in R(l) direction'%(\n                    h2d.g.region_cell_n_z[reg_z  ],h2d.g.region_cell_n_r[reg_r],\n                    h2d.g.region_cell_n_z[reg_z+1],h2d.g.region_cell_n_r[reg_r],\n                    h2d.g.region_ratios_z[reg_z]))\n                \n                #line with Z ratio at mesh maximum in R(l) direction\n                lines_new=np.append(lines_new,'ratio %3.0d %3.0d %3.0d %3.0d %2.3f $line with Z ratio at mesh maximum in R(l) direction'%(\n                    h2d.g.region_cell_n_z[reg_z  ],h2d.g.region_cell_n_r[reg_r+1],\n                    h2d.g.region_cell_n_z[reg_z+1],h2d.g.region_cell_n_r[reg_r+1],\n                    h2d.g.region_ratios_z[reg_z  ]))\n\n            #line with R ratio at mesh minimum in Z(k) direction\n            lines_new=np.append(lines_new,'ratio %3.0d %3.0d %3.0d %3.0d %2.3f $line with R ratio at mesh minimum in Z(k) direction'%(\n                h2d.g.region_cell_n_z[reg_z  ],h2d.g.region_cell_n_r[reg_r  ],\n                h2d.g.region_cell_n_z[reg_z  ],h2d.g.region_cell_n_r[reg_r+1],\n                h2d.g.region_ratios_r[reg_r]))\n\n            #line with R ratio at mesh maximum in Z(k) direction\n            lines_new=np.append(lines_new,'ratio %3.0d %3.0d %3.0d %3.0d %2.3f $line with R ratio at mesh maximum in Z(k) direction' %(\n                h2d.g.region_cell_n_z[reg_z+1],h2d.g.region_cell_n_r[reg_r  ],\n                h2d.g.region_cell_n_z[reg_z+1],h2d.g.region_cell_n_r[reg_r+1],\n                h2d.g.region_ratios_r[reg_r]))\n\n            #Sweep ratios from lines across the rest of the mesh\n            lines_new=np.append(lines_new,'fill  %3.0d %3.0d %3.0d %3.0d $Region %d: Fill in mesh ratios from lines'%(\n                h2d.g.region_cell_n_z[reg_z  ],h2d.g.region_cell_n_r[reg_r  ],\n                h2d.g.region_cell_n_z[reg_z+1],h2d.g.region_cell_n_r[reg_r+1],\n                reg))\n\n            #Apply regional densities, temperatures, bfld\n            lines_new=np.append(lines_new,'c      k1  l1  k2  l2 reg dens te   ti   tr bfld')\n            lines_new=np.append(lines_new,'region %3.0d %3.0d %3.0d %3.0d %3.0d %2.5e %2.5e %2.5e %2.5e %2.5e'%(\n                h2d.g.region_cell_n_z[reg_z  ]+1,h2d.g.region_cell_n_r[reg_r  ]+1,\n                h2d.g.region_cell_n_z[reg_z+1]  ,h2d.g.region_cell_n_r[reg_r+1]  ,\n                reg,\n                h2d.g.region_density[reg_r],\n                h2d.g.region_tele   [reg_r],\n                h2d.g.region_tion   [reg_r],\n                h2d.g.region_trad   [reg_r],\n                h2d.g.region_bfld   [reg_r]))\n            lines_new=np.append(lines_new,'c')\n            #end of for loop\n\n    if h2d.g.implosion:\n        lines_new=np.append(lines_new,'source norad %d %d %d %d  $prevent radiation escaping along open k line'%(h2d.g.region_cell_n_z[-1],region_cell_n_r_cum[0]+1,h2d.g.region_cell_n_z[-1],region_cell_n_r_cum[-1]))\n        lines_new=np.append(lines_new,'tv 0.000e+00 0.000e+00')\n        lines_new=np.append(lines_new,'tv 1.000e+00 0.000e+00')\n    else:\n        print('WARNING variable h2d.g.implosion set to FALSE: radiation will escape along open k line')\n    if(np.max(h2d.g.region_bfld)>0):\n        #lines_new=np.append(lines_new,'magnetic zonly $Bfield along z axis only. Units unclear: Gauss?')\n        lines_new=np.append(lines_new,'magnetic       $Azimuthal Bfield (magnitude set per-region)')\n        lines_new=np.append(lines_new,'parm lmag t         $logical magnetic field switch')\n\n    #Add the rays\n    lines_new = np.append(lines_new,lines_original[int(i_line[1]):int(i_line[2]+1)])\n    e3        = np.linspace(1000,100000,100)\n    num_rays  = 0\n    #3d rays\n    if(h2d.g.ray_trace_type=='3d'):\n        i_ray     = np.where(h2d.g.ray[5,]==1)[0]\n        ray_strs  = []\n        print('Creating ray strings...',end='')\n        for r in i_ray:\n            if(h2d.g.define_rays_at_tgt==1):\n                ray_rad_xy = np.sqrt(h2d.g.ray_tgt[0,r]**2.+h2d.g.ray_tgt[1,r]**2.)\n                ray_str    = 'RAY %2.2e %2.2e %2.3f %2.4f %2.0f %2.3f %2.3f'%(\n                    ray_rad_xy,h2d.g.ray_tgt[2,r],h2d.g.h2d_angles[0,r],h2d.g.ray[3,r],h2d.g.ray[4,r],\n                    h2d.g.h2d_angles[1,r],h2d.g.h2d_angles[2,r])\n            else:\n                ray_rad_xy = np.sqrt(h2d.g.ray[0,r]**2.+h2d.g.ray[1,r]**2.)\n                ray_str    = 'RAY %2.9e %2.9e %2.9f %2.9f %2.0f %2.9f %2.9f'%(\n                    ray_rad_xy,h2d.g.ray[2,r],h2d.g.h2d_angles[0,r],h2d.g.ray[3,r],h2d.g.ray[4,r],\n                    h2d.g.h2d_angles[1,r],h2d.g.h2d_angles[2,r])                    \n\n            if(h2d.g.testing==1):\n                #h2d_angles[0,r] xz plane\n                #h2d_angles[1,r] xy plane\n                #h2d_angles[2,r] yz plane\n                xy_ang = h2d.g.h2d_angles[1,r]\n                ray_x1 = ray_rad_xy*np.cos((np.pi/180.)*(xy_ang))\n                ray_y1 = ray_rad_xy*np.sin((np.pi/180.)*(xy_ang))\n                signx  = -1. if h2d.g.ray[0,r]<0 else 1.\n                xz_ang = signx*(180-h2d.g.h2d_angles[0,r])\n                signy  = -1. if h2d.g.ray[1,r]<0 else 1.\n                yz_ang = h2d.g.h2d_angles[2,r]\n                ray_z1 = h2d.g.ray[2,r]\n                dz     = 0-h2d.g.ray_z1\n                dx     = dz*np.tan((np.pi/180.)*xz_ang)\n                dy     = dz*np.tan((np.pi/180.)*yz_ang)\n                ray_x2 = ray_x1+dx\n                ray_y2 = ray_y1+dy\n                ray_z2 = ray_z1+dz\n                print('ray_rad_xy=%2.2e, xy ang=%2.0f, ray_x1=%2.0f, ray_y1=%2.0f, ray_z1=%2.0f, xz ang = %2.0f, dx=%2.0f, yz_ang = %2.0f, dy=%2.0f, dz=%2.0f, ray_x2=%2.0f, ray_y2=%2.0f, ray_z2=%2.0f'%(ray_rad_xy,xy_ang,ray_x1*1e4,ray_y1*1e4,ray_z1*1e4,xz_ang,dx*1e4,yz_ang,dy*1e4,dz*1e4,ray_x2*1e4,ray_y2*1e4,ray_z2*1e4))\n                h2d.g.ax3d.plot([ray_x1*1e4,ray_x2*1e4],[ray_y1*1e4,ray_y2*1e4],[ray_z1*1e4,ray_z2*1e4],zdir='z',c=(ray[3,r],0,0))\n                h2d.g.ax3d.scatter(ray_x1*1e4,ray_y1*1e4,ray_z1*1e4,'or')\n                h2d.g.ax3d.scatter(ray_x2*1e4,ray_y2*1e4,ray_z2*1e4,'ob')\n\n            num_rays += 1\n            ray_strs.append(ray_str)\n            if r in e3: print(str(r)+', ',end='')\n    #2d rays\n    elif(h2d.g.ray_trace_type=='2d'):\n        print('Creating 2d rays...',end='')\n\n        ray_strs  = []\n        for r in range(h2d.g.ray[:,0].size):\n            if h2d.g.ray[r,3]>0:\n                ray_str   = 'RAY %2.5e %2.5e %2.6f %2.5e %d'%(h2d.g.ray[r,0],h2d.g.ray[r,1],\n                                                                  h2d.g.ray[r,2],h2d.g.ray[r,3],\n                                                                  h2d.g.ray[r,-1])\n\n                ray_strs.append(ray_str)\n                num_rays += 1\n                if r in e3: print(str(r)+', ',end='')\n        print(' kept %d of %d 2d rays'%(num_rays,h2d.g.ray[:,0].size))\n\n        #end of creating 2d rays\n    #this uses the 3d ray bundle but doesn't define the final 2 angles h2d requires for 3d, this should have the \n    #effect of collapsing the rays back to 2d\n    elif(h2d.g.ray_trace_type=='3dto2d'):\n        i_ray     = np.where(h2d.g.ray[5,]==1)[0]\n        ray_strs  = []\n        print('Creating %d ray strings using 3dto2d method...'%(len(i_ray)),end='')\n        for r in i_ray:\n            if(h2d.g.define_rays_at_tgt==1):\n                ray_rad_xy = np.sqrt(h2d.g.ray_tgt[0,r]**2.+h2d.g.ray_tgt[1,r]**2.)\n                ray_str    = 'RAY %2.5e %2.5e %2.6f %2.6f %2.0f'%(\n                    ray_rad_xy,h2d.g.ray_tgt[2,r],h2d.g.h2d_angles[0,r],h2d.g.ray[3,r],h2d.g.ray[4,r])\n            else:\n                ray_rad_xy = np.sqrt(h2d.g.ray[0,r]**2.+h2d.g.ray[1,r]**2.)\n                ray_str    = 'RAY %2.5e %2.5e %2.6f %2.6f %2.0f'%(\n                    ray_rad_xy,h2d.g.ray[2,r],h2d.g.h2d_angles[0,r],h2d.g.ray[3,r],h2d.g.ray[4,r])                    \n                ray_rad_xy = np.sqrt(h2d.g.ray[0,r]**2.+h2d.g.ray[1,r]**2.)\n            num_rays += 1\n            ray_strs.append(ray_str)\n            if r in e3: print(str(r)+', ',end='')\n\n    else:\n        print('Unrecognised laser ray trace type \\'%s\\': no ; rays created!!!'%(h2d.g.ray_trace_type))\n\n    lines_new = np.append(lines_new,ray_strs)\n    #add the total laser power in\n    lines_new = np.append(lines_new,lines_original[int(i_line[3]):int(i_line[4]+1)])\n    n_times = h2d.g.laser_pwr.shape[0]\n    for t in range(n_times):\n        pwr_str   = 'tv %5.4e %5.4e'%(h2d.g.laser_pwr[t,0],h2d.g.laser_pwr[t,1]*1e7)\n        lines_new = np.append(lines_new,pwr_str)\n\n    lines_new = np.append(lines_new,lines_original[int(i_line[5]):])\n\n    print('Writing rays to file %s'%h2d.g.final_h2d_file)\n    f = open(h2d.g.final_h2d_file,'w')\n    \n    for line in lines_new:\n        f.writelines(line+'\\n')\n    f.close()\n\n    if h2d.g.ifcopy2server==1:\n        a=h2d.cp2server(ip_address = h2d.g.ip_address, username = h2d.g.username)\n","sub_path":"build/lib/pyh2d/setup_polar.py","file_name":"setup_polar.py","file_ext":"py","file_size_in_byte":13210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"508952504","text":"import os\n\nfrom flask import Flask\nfrom flask import render_template\nfrom flask_uploads import UploadSet, IMAGES, AUDIO, configure_uploads, DOCUMENTS, ARCHIVES\nfrom flask_sqlalchemy import SQLAlchemy\nimport pymysql\n\nfrom apps.utils import create_folder\n\napp = Flask(__name__)\napp.debug = True\n\napp.config[\"SECRET_KEY\"] = 'who am i ? do you know?'\napp.config['MAX_CONTENT_LENGTH'] = 100 * 1024 * 1024\n\n# app.config[\"SQLALCHEMY_DATABASE_URI\"] = \"sqlite:///flasker.db\"\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = \"mysql+pymysql://root:root@localhost/flasker\"\napp.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = True\n\ndb = SQLAlchemy(app)\n\nAPPS_DIR = os.path.dirname(__file__)\nSTATIC_DIR = os.path.join(APPS_DIR, 'static')\napp.config[\"UPLOADS_RELATIVE\"] = 'uploads'\napp.config[\"UPLOADS_FOLDER\"] = os.path.join(STATIC_DIR, app.config[\"UPLOADS_RELATIVE\"])\n# 第一步：配置 上传文件保存地址\napp.config['UPLOADED_PHOTOS_DEST'] = app.config[\"UPLOADS_FOLDER\"]\napp.config['UPLOADED_IMGS_DEST'] = app.config[\"UPLOADS_FOLDER\"]\napp.config['UPLOADED_FILES_DEST'] = app.config[\"UPLOADS_FOLDER\"]\napp.config['UPLOADED_MUSICCOVERS_DEST'] = app.config[\"UPLOADS_FOLDER\"]\napp.config['UPLOADED_MUSICAUDIOS_DEST'] = app.config[\"UPLOADS_FOLDER\"]\napp.config['UPLOADED_MUSICLRCS_DEST'] = app.config[\"UPLOADS_FOLDER\"]\napp.config['UPLOADED_MOVIECOVERS_DEST'] = app.config[\"UPLOADS_FOLDER\"]\napp.config['UPLOADED_MOVIEVIDEOS_DEST'] = app.config[\"UPLOADS_FOLDER\"]\n# 创建 UploadSet 类的实例\nphotosSet = UploadSet(name='photos', extensions=IMAGES)\nimgsSet = UploadSet(name='imgs', extensions=IMAGES)\nfilesSet = UploadSet(name='files', extensions=AUDIO + ARCHIVES + DOCUMENTS)\nmusicCoversSet = UploadSet(name='musiccovers', extensions=IMAGES)\nmusicAudiosSet = UploadSet(name='musicaudios', extensions=AUDIO)\nmusicLrcsSet = UploadSet(name='musiclrcs', extensions=('lrc',))\nmovieCoversSet = UploadSet(name='moviecovers', extensions=IMAGES)\nmovieVideosSet = UploadSet(name='movievideos', extensions=('mp4',))\n# 配置FlaskUpLoad 和 app\nconfigure_uploads(app, photosSet)\nconfigure_uploads(app, imgsSet)\nconfigure_uploads(app, filesSet)\nconfigure_uploads(app, musicCoversSet)\nconfigure_uploads(app, musicAudiosSet)\nconfigure_uploads(app, musicLrcsSet)\nconfigure_uploads(app, movieCoversSet)\nconfigure_uploads(app, movieVideosSet)\n\ncreate_folder(app.config[\"UPLOADS_FOLDER\"])\n\nfrom apps.admin import admin_bp\nfrom apps.home import home_bp\n\napp.register_blueprint(admin_bp, url_prefix='/admin')\napp.register_blueprint(home_bp)\n\n\n@app.errorhandler(404)\ndef page_not_found(error):\n    return render_template('error/page_not_found.html'), 404\n","sub_path":"flask015/apps/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"179335697","text":"import prody as pr\nimport numpy as np\n\n__all__ = ['renumber_chids_resnums']\n\n\ndef renumber_chids_resnums(obj, new_chid):\n    \"\"\"Assumes the resindices of a selection are monotonically increasing by 1\"\"\"\n    try:\n        sele = obj.ifg_frag\n    except:\n        sele = obj.sele\n\n    sele_copy = sele.copy()\n    sele_copy.setResnums(sele.getResindices() - np.min(obj.resindex) + 10)\n    sele_copy.setChids([new_chid]*len(sele))\n\n    return sele_copy","sub_path":"combs_pub/apps/renumber.py","file_name":"renumber.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"633025865","text":"import os\nimport collections\n\ndef work(srcPath, realPath):\n    with open(srcPath, \"r\", encoding=\"utf-8\") as fRead:\n        while True:\n            # laphae11985@163.com----198587\n            lineInfo = fRead.readline()\n\n            if len(lineInfo) < 5:\n                break\n\n            # 邮箱的字符串\n            # laphae11985@163.com\n            mailStr = lineInfo.split(\"----\")[0]\n            # 邮箱类型的目录\n            # E:/Code/Python/经典Python教程入门到精通/PythonTutorialProj/pers/cyj/day10/res/163\n            mailTypeStr = mailStr.split(\"@\")[1].split(\".\")[0]\n            dirStr = os.path.join(realPath, mailTypeStr)\n            if not os.path.exists(dirStr):\n                # 存在，创建\n                os.mkdir(dirStr)\n            # 创建文件\n            filePath = os.path.join(dirStr, mailTypeStr + \".txt\")\n            with open(filePath, \"a\") as fWrite:\n                fWrite.write(mailStr+\"\\n\")\n\n\ndef getAllDirQueue(srcPath, realPath):\n    \"\"\"队列模拟递归遍历目录\"\"\"\n\n    queue = collections.deque()\n    queue.append(srcPath)\n\n    while len(queue) != 0:\n        # 出队数据\n        dirPath = queue.popleft()\n        # 找出所有的文件\n        fileList = os.listdir(dirPath)\n\n        for fileName in fileList:\n            # 绝对路径\n            fileAbsPath = os.path.join(dirPath, fileName)\n            # 判断是否是目录，是目录就进队，不是就打印\n            if os.path.isdir(fileAbsPath):\n                queue.append(fileAbsPath)\n            else:\n                # 处理普通文件\n                work(fileAbsPath, realPath)\n\n\nif __name__ == \"__main__\":\n    srcPath = os.path.abspath(\".\") + \"/file/newdir\"\n    realPath = os.path.abspath(\".\") + \"/file/res\"\n    getAllDirQueue(srcPath, realPath)","sub_path":"pers/cyj/day10/04-周末练习/周末练习.py","file_name":"周末练习.py","file_ext":"py","file_size_in_byte":1790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"295224543","text":"def solution(A):\n    \"\"\"\n    Problem: http://codility.com/demo/take-sample-test/perm_missing_elem\n    :param A:\n    :return:\n    \"\"\"\n    left = A[0]\n    right = sum(A) - left\n\n    least_diff = abs(right - left)\n    for i in range(1, len(A) - 1):\n        left += A[i]\n        right -= A[i]\n        diff = abs(right - left)\n        if diff < least_diff:\n            least_diff = diff\n\n    return least_diff\n\n\nif __name__ == '__main__':\n    assert solution([3, 1, 2, 4, 3]) == 1\n","sub_path":"codility/time_complexity/tape_equilibrium.py","file_name":"tape_equilibrium.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"435044366","text":"from django.urls import path\nfrom homework_api.views import (GroupList,\n                                GroupDetail,\n                                GroupChildGroups,\n                                ElementList,\n                                GroupChildElements,\n                                ElementDetail)\n\nurlpatterns = [\n    path('groups/', GroupList.as_view()),\n    path('groups/<int:id>/', GroupDetail.as_view()),\n    path('groups/<int:parent_group_id>/childgroups/', GroupChildGroups.as_view()),\n    path('groups/<int:parent_group_id>/childelements/', GroupChildElements.as_view()),\n    path('elements/', ElementList.as_view()),\n    path('elements/<int:id>/', ElementDetail.as_view()),\n]\n","sub_path":"homework_api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"141701346","text":"import os\nimport sys\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.layers import Layer, Dense\nfrom tensorflow.keras.optimizers import Adam\nimport tensorflow_probability as tfp\n\nclass SenderEncoder(tf.keras.Model):\n    \"\"\"\n    Encoder class for building speaker's Encoder\n    The encoder receives a concept as binary vector\n    and encodes it into a dense (embedding_dim) representation u\n    \"\"\"\n    def __init__(self, units=44, categories_dim=595):\n        # TODO: categories_dim anpassen!\n        super(SenderEncoder, self).__init__()\n        self.categories_dim = categories_dim\n        self.input_layer = tf.keras.layers.InputLayer(input_shape=(self.categories_dim))\n        self.act = tf.keras.layers.Dense(units, activation='sigmoid')\n\n    def call(self, input_concept):\n        \"\"\"\n        input_concept: binary list representing a concept\n        \"\"\"\n        x = self.input_layer(input_concept)\n        output = self.act(x)\n\n        return output\n\nclass Sender(tf.keras.Model):\n    \"\"\"\n    class that calls sender encoder\n    \"\"\"\n    def __init__(self, num_options, batch_size):\n        super(Sender, self).__init__()\n        self.encoder = SenderEncoder()\n        self.num_options = num_options\n        self.batch_size = batch_size\n\n    def call(self, sender_input):\n        encoded_input = []\n        for i in range(self.num_options):\n            encoded_input.append(self.encoder(sender_input[i]))\n        encoded_input = tf.stack(encoded_input)\n        return encoded_input\n\nclass SenderOnlyTarget(tf.keras.Model):\n\n    def __init__(self, batch_size):\n        super(SenderOnlyTarget, self).__init__()\n        self.encoder = SenderEncoder()\n        self.batch_size = batch_size\n\n    def call(self, sender_input):\n        encoded_input = self.encoder(sender_input)\n        encoded_input = tf.stack(encoded_input)\n        return encoded_input\n\nclass Sender_LSTM(tf.keras.Model):\n    \"\"\"\n    LSTM network of the sender that creates\n    the message of length max_m\n    \"\"\"\n\n    def __init__(self, agent, embed_dim, num_cells, hidden_size, max_len, vocab_size=99, training=True, batch_size=32, see_all_input=True):\n        super(Sender_LSTM, self).__init__()\n        self.agent = agent\n        self.vocab_size = vocab_size\n        self.embed_dim = embed_dim\n        self.training = training\n        # set max_len-1 to always replace eos with 0\n        self.max_len = max_len-1\n        self.training = training\n        #self.states = None\n        self.batch_size = batch_size\n        self.see_all_input = see_all_input\n\n        self.output_layer = tf.keras.layers.Dense(units=vocab_size, activation='softmax')\n        #self.lstm = tf.keras.layers.LSTM(units=hidden_size, activation=None, return_sequences=True, return_state=True)\n        self.lstm = tf.keras.layers.LSTM(units=num_cells, activation=None, return_sequences=False, return_state=True)\n\n    def call(self, input):\n\n        # preprocess input\n        input = self.agent(input)\n        if self.see_all_input:\n            input = tf.squeeze(input)\n        input = tf.transpose(input, [1, 0, 2])\n\n        message = []\n        entropy = []\n        logits = []\n        state_h = None\n\n        #print(\"input-shape:\")\n        #print(input.shape)\n\n\n        for i in range(self.max_len):\n            states = None\n            output = None\n            if i == 0:\n                output, state_h, state_c = self.lstm(input)\n                states = [state_h, state_c]\n            else:\n                output, state_h, state_c = self.lstm(input, initial_state=states)\n                states = [state_h, state_c]\n            step_probs = self.output_layer(output)\n            dist = tfp.distributions.Categorical(probs=step_probs)\n\n            if self.training:\n                single_char = dist.sample()\n            else:\n                single_char = tf.math.reduce_max(step_probs, 1)\n            logits.append(dist.log_prob(single_char))\n\n            message.append(single_char)\n\n            entropy.append(dist.entropy())\n\n        #print(\"entropy: \", entropy)\n        entropy = tf.transpose(entropy, perm=[1,0])\n        zeros_ent = tf.zeros_like(entropy)\n        entropy = tf.concat([entropy, zeros_ent], 1)\n        entropy = tf.stack(entropy)\n\n        logits = tf.transpose(logits, perm=[1,0])\n        zeros_log = tf.zeros_like(logits)\n        logits = tf.concat([logits, zeros_log], 1)\n        logits = tf.stack(logits)\n\n        # zip / reshape tensors to have batch_size messages with length max_len\n        message = tf.transpose(message, perm=[1, 0])\n        # adding zeros to end of message\n        zeros = tf.zeros_like(message)\n        message = tf.concat([message, zeros], 1)\n\n        return message, logits, entropy, state_h\n        # problem: logits has shape seq_length x output_size\n        # do we sample only from first output logits[0]\n        # logits[0] is output corresponding to target\n\n        # turn lstm output into distribution\n\n        # compute entropy of distribution\n\n        # sample from distribution for every _ in range(self.max_len)\n        # append sample to message\n\n        # embed message to max_len, turn eos to 0\n\n        # maybe loop multiple times (max_len) trough network\n        # save last hidden state and give as input\n\n        # return message, last_state[0], logits, entropy\n","sub_path":"FinalProject/Archive/sender.py","file_name":"sender.py","file_ext":"py","file_size_in_byte":5302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"281654639","text":"import xmltodict\n\ndef processXML(filename): # function parses xml files to dictionaries\n\twith open(filename,\"r\") as infileXML: # open the file under the name infileXML\n\t\tfileContent = infileXML.read() # read all XML file contents\n\t\txmlDict = xmltodict.parse(fileContent) # parse the contents to a dictionary using the xmltodict module\n\t\treturn xmlDict # return the xml file stored in a dictionary\n\ndictArtiekelen = processXML(\"./producten.xml\") # store the dictionary in a variable\n#  store the dictionary items under [\"artiekelen\"][\"artikel\"] in a list\nartiekelen = dictArtiekelen[\"artiekelen\"][\"artikel\"]\n\nfor artikel in artiekelen: # for every item in the artiekelen list\n    if artikel[\"naam\"] is not None: # if the value of the item with tag [\"name\"] is not None\n        print(artikel[\"naam\"]) # print the value of the item with tag [\"none\"]","sub_path":"Les10/prE10_1.py","file_name":"prE10_1.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"552416881","text":"# Delete Object Properties\r\n\r\nclass Users:\r\n    def __init__(self, name, age):\r\n        self.name = name\r\n        self.age = age\r\n\r\nuserA = Users(\"George\", 34)\r\n\r\n# deleting object property\r\ndel userA.age\r\n\r\nprint(f\"my name is {userA.name}, and I am {userA.age} years\")\r\n","sub_path":"pyouts/145pyout.py","file_name":"145pyout.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"90411582","text":"import re\nimport requests\nfrom conf import settings\nfrom .decorators import memoize\n\nhandle_pattern = re.compile(r\"/openaccess-disseminate/(?P<handle>[0-9.]+/[0-9]+)\")\n\ndef fetch_metadata(request):\n    data = _make_request(get_handle(request.get(\"request\")))\n    if not data.get('success'):\n        return False\n    request['dlcs'] = filter(None, data.get(\"departments\", []))\n    request['handle'] = data.get(\"uri\")\n    request['title'] = data.get(\"title\")\n    authors = data.get(\"ids\")\n    if authors:\n        request['authors'] = authors[0]\n    return request\n\ndef get_handle(req_string):\n    matches = handle_pattern.search(req_string)\n    return matches.groupdict().get('handle')\n\n@memoize\ndef _make_request(handle):\n    r = requests.get(settings.DSPACE_IDENTITY_SERVICE,\n                     params={'handle': handle})\n    data = r.raise_for_status() or r.json()\n    return data\n","sub_path":"pipeline/dspace.py","file_name":"dspace.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"62567210","text":"from django.forms import ModelForm, TextInput, Select, BaseInlineFormSet\nfrom .models import Course, Book, Chapter, Objective, Source, Question, Answer\n\nclass ChapterForm(ModelForm):\n    chapter = forms.ModelChoiceField(\n            queryset = Chapter.objects.all(),\n            widget   = Select(attrs={'class': 'chapter'}),\n            )\n\n    class Meta:\n        model = Question\n\n\nclass ObjectiveForm(ModelForm):\n    objective = forms.ModelChoiceField(\n        queryset=Objective.objects.all(),\n        widget=Select(attrs={'class': 'objective'}),\n    )\n\n    class Meta:\n        model = Question\n        #fields = ('name', '')\n        #widgets = {'name': TextInput(attrs={'class': 'name'})}\n\n\nclass BookForm(ModelForm):\n    book = forms.ModelChoiceField(\n            queryset = Objective.objects.all(),\n            widget = Select(attrs={'class': 'book'}),\n            )\n\n    class Meta:\n        model = Question\n\n\nclass SourceForm(ModelForm):\n    source = forms.ModelChoiceField(\n            queryset = Objective.objects.all(),\n            widget = Select(attrs={'class': 'source'}),\n            )\n\n    class Meta:\n        model = Question\n","sub_path":"questions/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"490167237","text":"\"\"\"\n        CDNSim\n\n    file: hl_sim.py\n\n        NEC Europe Ltd. PROPRIETARY INFORMATION\n\n    This software is supplied under the terms of a license agreement\n    or nondisclosure agreement with NEC Europe Ltd. and may not be\n    copied or disclosed except in accordance with the terms of that\n    agreement. The software and its source code contain valuable trade\n    secrets and confidential information which have to be maintained in\n    confidence.\n    Any unauthorized publication, transfer to third parties or duplication\n    of the object or source code - either totally or in part - is\n    prohibited.\n\n        Copyright (c) 2016 NEC Europe Ltd. All Rights Reserved.\n\n    Author: Anton Ivanov <anton.ivanov@neclab.eu>\n\n    NEC Europe Ltd. DISCLAIMS ALL WARRANTIES, EITHER EXPRESS OR IMPLIED,\n    INCLUDING BUT NOT LIMITED TO IMPLIED WARRANTIES OF MERCHANTABILITY\n    AND FITNESS FOR A PARTICULAR PURPOSE AND THE WARRANTY AGAINST LATENT\n    DEFECTS, WITH RESPECT TO THE PROGRAM AND THE ACCOMPANYING\n    DOCUMENTATION.\n\n    No Liability For Consequential Damages IN NO EVENT SHALL NEC Europe\n    Ltd., NEC Corporation OR ANY OF ITS SUBSIDIARIES BE LIABLE FOR ANY\n    DAMAGES WHATSOEVER (INCLUDING, WITHOUT LIMITATION, DAMAGES FOR LOSS\n    OF BUSINESS PROFITS, BUSINESS INTERRUPTION, LOSS OF INFORMATION, OR\n    OTHER PECUNIARY LOSS AND INDIRECT, CONSEQUENTIAL, INCIDENTAL,\n    ECONOMIC OR PUNITIVE DAMAGES) ARISING OUT OF THE USE OF OR INABILITY\n    TO USE THIS PROGRAM, EVEN IF NEC Europe Ltd. HAS BEEN ADVISED OF THE\n    POSSIBILITY OF SUCH DAMAGES.\n\n        THIS HEADER MAY NOT BE EXTRACTED OR MODIFIED IN ANY WAY.\n\"\"\"\n\nfrom __future__ import print_function\nimport matplotlib.pyplot as plt\nfrom decorations import printWithClock\nimport treap\n\n\nclass highLevelSimulation:\n    def __init__(self, args, resDirName):\n        self.eventQueue = treap.treap()\n        self.lastEventTime = 0\n        self.urRef = None\n        self.topArgs = args\n        self.simulatorReady = False if args.backnoise else True\n        self.simulationDone = False\n        self.simulationStatistics = []\n        self.urStatistics_nActCons = []\n        self.urStatistics_nReqPSec = []\n        self.simResDirName = resDirName\n        return\n\n    def step(self):\n        minKey = self.eventQueue.find_min()\n        e = self.eventQueue[minKey]\n        self.eventQueue.remove(minKey)\n        self.lastEventTime = e.time\n        e.objRef.process(e)\n        return\n\n    def eventPush(self, ev):\n        self.eventQueue[ev] = ev\n        return\n\n    def eventUpdateTime(self, e, newTime):\n        self.eventQueue.remove(e)\n        e.time = newTime\n        self.eventQueue[e] = e\n        return\n\n    def deleteEvent(self, e):\n        self.eventQueue.remove(e)\n        return\n\n    def plotSimStats(self):\n        import netStreamingPrimitives\n        printWithClock(\"Plotting simulation results..\")\n        sTypes, ids, chnls, startTs, buffTs, buffEvs, playTs, avgTRs, consRs,\\\n            toCaches, srcIPs, dstIPs = zip(*self.simulationStatistics)\n        setRates = set()\n        for i in consRs:\n            setRates.add(i)\n        avgTRperCR = dict.fromkeys(setRates, [])\n        for k in avgTRperCR.keys():\n            avgTRperCR[k] = [float(i[7])/float(i[8])\n                             for i in self.simulationStatistics if i[8] == k]\n        buffPlayRatio = [float(i[4])/float(i[4] + i[6])\n                         for i in self.simulationStatistics]\n\n        plt.clf()\n        plt.suptitle('Histogram: Distribution of channel popularity')\n        plt.ylabel('Fraction of users')\n        plt.xlabel('Channel #')\n        plt.hist(\n            chnls,\n            netStreamingPrimitives.NUMBER_CHANNELS,\n            histtype='stepfilled',\n            normed=True\n        )\n        plt.savefig(self.simResDirName + '/fig_channelPopularity.pdf')\n\n        plt.clf()\n        plt.suptitle('Histogram: Start times')\n        plt.ylabel('Number of viewers')\n        plt.xlabel('Start time (s)')\n        plt.hist(\n            startTs,\n            max(startTs),\n            histtype='stepfilled',\n            cumulative=True,\n            normed=True\n        )\n        plt.savefig(self.simResDirName + '/fig_startTimes.pdf')\n\n        plt.clf()\n        plt.suptitle('Histogram: Buffering times to playbacktime ratio')\n        plt.ylabel('Fraction of viewers')\n        plt.xlabel('Buffering time')\n        plt.hist(\n            buffPlayRatio,\n            100,\n            histtype='stepfilled',\n            cumulative=True,\n            normed=True\n        )\n        plt.savefig(self.simResDirName + '/fig_buffTimes.pdf')\n\n        plt.clf()\n        plt.suptitle('Histogram: Buffering events')\n        plt.ylabel('Number of viewers')\n        plt.xlabel('Buffering events')\n        maxBufEvntVal = max(buffEvs)\n        plt.hist(\n            buffEvs,\n            maxBufEvntVal if maxBufEvntVal > 0 else 10,\n            histtype='stepfilled',\n            cumulative=True,\n            normed=True\n        )\n        plt.savefig(self.simResDirName + '/fig_buffEvents.pdf')\n\n        plt.clf()\n        plt.suptitle('Histogram: Distribution of play times')\n        plt.ylabel('Fraction of viewers')\n        plt.xlabel('Play time (s)')\n        plt.hist(\n            playTs,\n            100,\n            histtype='stepfilled',\n            cumulative=False,\n            normed=False\n        )\n        plt.savefig(self.simResDirName + '/fig_playTimes.pdf')\n\n        for k in avgTRperCR.keys():\n            plt.clf()\n            plt.suptitle(\n                'Histogram: Average download rate, playback = ' +\n                str(k) + ' bps.'\n            )\n            plt.ylabel('Number of viewers')\n            plt.xlabel('Download / consume rate')\n            plt.hist(\n                avgTRperCR[k],\n                histtype='stepfilled',\n                cumulative=False,\n                normed=False\n            )\n            plt.savefig(self.simResDirName + '/fig_avgTRates_' +\n                        str(k) + '.pdf')\n\n        plt.clf()\n        plt.suptitle('Server side statistics')\n        ax1 = plt.gca()\n        ax1.set_xlabel('Time (s)')\n        ax1.set_ylabel('# active connections', color='b')\n        x, y = zip(*self.urStatistics_nActCons)\n        ax1.plot(x, y)\n        for tl in ax1.get_yticklabels():\n            tl.set_color('b')\n        ax2 = ax1.twinx()\n        ax2.set_ylabel('# requests per minute', color='r')\n        x, y = zip(*self.urStatistics_nReqPSec)\n        ax2.plot(x, y, color='r')\n        for tl in ax2.get_yticklabels():\n            tl.set_color('r')\n        plt.savefig(self.simResDirName + '/fig_serverStats.pdf')\n\n        return\n\n    def saveSimStatsToFile(self):\n        import csv\n        resFileName = 'results' +\\\n                      '_nh' + str(len(self.urRef.listOfHosts)) +\\\n                      '_ac' + str(self.urRef.activeStreamsMax) +\\\n                      '_ba' + str(self.urRef.activeNoiseStreamsMax) +\\\n                      '_to' + str(self.urRef.totalStreams) +\\\n                      '_cp' + str(self.topArgs.percentCache) +\\\n                      '_cb' + str(self.topArgs.cachesec) +\\\n                      '_ci' + str(self.topArgs.cacheinit) +\\\n                      '_ct' + str(self.topArgs.cachethreshold) +\\\n                      '_on' + str(self.topArgs.ondemandCache) +\\\n                      '_st' + str(self.topArgs.streaming)\n        fOutName = self.simResDirName + '/' + resFileName + '.csv'\n        printWithClock(\"Saving simulation results to: \" + fOutName)\n        with open(fOutName, 'wb') as csvfile:\n            writer = csv.writer(csvfile)\n            for entry in self.simulationStatistics:\n                writer.writerow(entry)\n        return\n","sub_path":"hl_sim.py","file_name":"hl_sim.py","file_ext":"py","file_size_in_byte":7673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"598302020","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Feb  3 10:27:56 2019\r\n\r\n@author: victo\r\n\"\"\"\r\n\r\nimport numpy as np\r\n\r\nimport tensorflow as tf\r\n\r\nfrom tensorflow.examples.tutorials.mnist import input_data\r\n\r\n##############################加载样本数据集\r\nmnist = input_data.read_data_sets(\"data/\", one_hot = True)\r\n\r\ntrainimg = mnist.train.images\r\ntrainlabel = mnist.train.labels\r\ntestimg = mnist.test.images\r\ntestlabel = mnist.test.labels\r\n\r\n##############################参数初始化\r\nn_hidden_1 = 256\r\nn_hidden_2 = 128\r\nn_input = 784\r\nn_classes = 10\r\n\r\nx = tf.placeholder(\"float\",[None,n_input])\r\ny = tf.placeholder(\"float\",[None,n_classes])\r\n\r\n#########################################网络结构\r\nstddev=0.1\r\nweights = {\r\n        'W1':tf.Variable(tf.random_normal([n_input,n_hidden_1],stddev=stddev)),\r\n        'W2':tf.Variable(tf.random_normal([n_hidden_1,n_hidden_2],stddev=stddev)),\r\n        'out':tf.Variable(tf.random_normal([n_hidden_2,n_classes],stddev=stddev))\r\n}\r\n\r\nbiais = {\r\n        'b1':tf.Variable(tf.random_normal([n_hidden_1])),\r\n        'b2':tf.Variable(tf.random_normal([n_hidden_2])),\r\n        'out':tf.Variable(tf.random_normal([n_classes]))\r\n        }\r\n#########################################前向传播\r\ndef multilayer_perception(_X,_weights,_biais):\r\n    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(_X,_weights['W1']),_biais['b1']))\r\n    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1,_weights['W2']),_biais['b2']))\r\n    return (tf.matmul(layer_2,_weights['out']+biais['out']))\r\n\r\n###########################################反向传播\r\npred = multilayer_perception(x,weights,biais)\r\n\r\ncost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))  ###交叉熵函数\r\n\r\noptm = tf.train.GradientDescentOptimizer(learning_rate = 0.05).minimize(cost)##梯度下降求最优参数\r\n\r\ncorr = tf.equal(tf.argmax(pred,1),tf.arg_max(y,1))\r\n\r\naccr = tf.reduce_mean(tf.cast(corr,\"float\"))\r\n\r\n##############################################\r\ninit = tf.global_variables_initializer()\r\nprint(\"Function Ready\")\r\n\r\ntraining_epochs = 60\r\nbatch_size = 100\r\ndisplay_step = 1\r\n\r\nsess = tf.Session()\r\nsess.run(init)\r\n\r\nfor epoch in range(training_epochs):\r\n    avg_cost = 0\r\n    total_batch = int(mnist.train.num_examples/batch_size)\r\n    \r\n    for i in range(total_batch):\r\n        batch_xs,batch_ys = mnist.train.next_batch(batch_size)\r\n        feeds = {x:batch_xs,y:batch_ys}\r\n        sess.run(optm,feed_dict=feeds)\r\n        avg_cost = avg_cost+sess.run(cost,feed_dict=feeds)\r\n    avg_cost = avg_cost / total_batch\r\n    feeds_train = {x:batch_xs, y:batch_ys}\r\n    feeds_test = {x: mnist.test.images, y:mnist.test.labels}\r\n    train_acc = sess.run(accr,feed_dict=feeds_train)\r\n    test_acc = sess.run(accr,feed_dict=feeds_test)\r\n    print(\" Epoch: %03d/%03d cost: %.9f train_acc: %.3f test_acc: %.3f\" \r\n          %(epoch,training_epochs,avg_cost,train_acc,test_acc))\r\nprint(\"DONE\")\r\n        \r\n","sub_path":"neural_network.py","file_name":"neural_network.py","file_ext":"py","file_size_in_byte":2933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"632895197","text":"from datetime import datetime\nimport os\nimport pytz\nimport requests\nimport math\n\n\napi_key='432a128cb02deb7fc9bbd109b0a5c177'\napi_url=('http://api.openweathermap.org/data/2.5/weather?q={}&mode=json&units=metric&appid={}')\n\napi_url2=('http://api.openweathermap.org/data/2.5/forecast?q={}&mode=json&units=metric&appid={}')\n\ndef query_api(city):\n    try:\n        #print(api_url.format(city,api_key))\n        data=requests.get(api_url.format(city,api_key)).json()\n       \n    except Exception as exc:\n        print(exc)\n        data=None\n    return data\n\n\ndef query_api2(city):\n     try:\n        #print(api_url.format(city,api_key))\n        \n        data2=requests.get(api_url2.format(city,api_key)).json()\n        #print(data2)\n     except Exception as exc:\n        print(exc)\n        data2=None\n     return data2","sub_path":"Small_Weather_App_1.3/weather.py","file_name":"weather.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"405738628","text":"import asyncio\nimport logging\nimport json\nfrom collections import deque\nimport src.persistent.sessions\n\nLOG = logging.getLogger(__name__)\n\nauth = src.persistent.sessions.Authentication()\nloop = asyncio.get_event_loop()\nclients = []\n\n\ndef run(host: str=\"127.0.0.1\", port: int=12346):\n    \"\"\"Main method for running the trader server process.\"\"\"\n    coro = loop.create_server(TraderServerProtocol, host, port)\n    server = loop.run_until_complete(coro)\n\n    try:\n        loop.run_forever()\n    except KeyboardInterrupt:\n        LOG.info(\"Private server received KeyboardInterrupt, shutting down.\")\n    finally:\n        server.close()\n        loop.until_complete(server.wait_closed())\n        loop.close()\n\n\nclass TraderServerProtocol(asyncio.Protocol):\n    \"\"\"Server Protocol for listening to private Trader channels\"\"\"\n\n    # If TIMEOUT_SEC is set to 0.0 then there is no timeout period\n    TIMEOUT_SEC = 0.0\n\n    def timeout(self):\n        \"\"\"Called if client doesn't respond in TIMEOUT_SEC seconds.\"\"\"\n        LOG.info(\"Connection with {} timed out. Closing.\".format(\n            self.peername\n        ))\n        self.transport.write(\"Timeout limit reached. Closing connection.\\n\"\n                             .encode(\"utf-8\"))\n        self.transport.close()\n\n    def connection_made(self, transport):\n        \"\"\"\"Connection initiated.\"\"\"\n        # TODO: Implement data comm sequence number\n        self.peername = transport.get_extra_info(\"peername\")\n        self.transport = transport\n        self.authenticated = False\n        self.in_queue = deque()\n\n        # start timeout timer\n        if self.TIMEOUT_SEC > 0.0:\n            self.timeout_timer = asyncio.get_event_loop().call_later(\n                self.TIMEOUT_SEC, self.timeout\n            )\n\n        LOG.info(\"Connection from {}\".format(self.peername))\n\n        asyncio.ensure_future(self._in_consume())\n\n    async def _in_consume(self):\n        \"\"\"Loop processing data input queue\"\"\"\n        while True:\n            self.in_wait = asyncio.Future()\n            await self.in_wait\n            while len(self.in_queue):\n                data = self.in_queue.popleft()\n                if self.transport:\n                    try:\n                        result = self._process_data(data)\n                        if isinstance(result, asyncio.Future) or asyncio.iscoroutinefunction(result):\n                            result = await result\n                    except Exception as ex:\n                        LOG.debug(ex)\n\n    async def _process_data(self, data):\n        \"\"\"Protocol expects JSON message\"\"\"\n        try:\n            json_data = json.loads(data.decode())\n        except json.JSONDecodeError:\n            LOG.info(\"Received nonJSON data from {}\".format(self.peername))\n\n            self.transport.write(\"Bad data format. Closing connection.\\n\".encode(\"utf-8\"))\n            self.transport.close()\n\n        # TODO: Secure authentication, password is insecure! implement optional SSL via asyncio\n        if not self.authenticated and json_data[\"type\"] == \"authenticate\":\n\n            if auth.authenticate_local(json_data[\"email\"], json_data[\"password\"]):\n                self.authenticated = True\n            else:\n                LOG.info(\"Client {} failed to authenticate.\".format(self.peername))\n                self.authenticated = False\n                return\n\n            LOG.info(\"Trader {} (peername: {}) successfully authenticated.\".format(json_data[\"email\"], self.peername))\n            self.transport.write(\"Successfully authenticated. Welcome {}\\n\"\n                                 .format(json_data[\"email\"])\n                                 .encode('utf-8'))\n        elif json_data[\"type\"] == \"authenticate\":\n            self.transport.write(\"Trader {} already authenticated.\\n\"\n                                 .format(json_data[\"email\"])\n                                 .encode(\"utf-8\"))\n\n            # if self.authenticated == True:\n            #    if json_data[\"type\"] == \"\"\n\n            # TODO: Custom(?) protocol\n\n    def data_received(self, data):\n        self.in_queue.append(data)\n        if not self.in_wait.done():\n            self.in_wait.set_result(None)\n        # Reset timeout timer\n        if self.TIMEOUT_SEC > 0.0:\n            self.timeout_timer.cancel()\n            self.timeout_timer = asyncio.get_event_loop().call_later(\n                self.TIMEOUT_SEC, self.timeout\n            )\n\n    def eof_received(self):\n        \"\"\"EOF received from the client\"\"\"\n        LOG.info(\"Closed connection with client {}\".format(self.peername))\n        self.transport.close()\n\n    def connection_lost(self, exc):\n        \"\"\"EOF or transport error indicating that the\n        client has disconnected.\"\"\"\n        self.transport = None\n        if exc:\n            LOG.info(\"Connection lost with client {}. Error: {}\".format(\n                self.peername, exc\n            ))\n\nif __name__ == '__main__':\n    pass","sub_path":"src/servers/traderserver.py","file_name":"traderserver.py","file_ext":"py","file_size_in_byte":4898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"602147651","text":"from typing import List\n\n\nclass Solution:\n    \"\"\"\n    26. 删除排序数组中的重复项\n    给定一个排序数组，你需要在 原地 删除重复出现的元素，使得每个元素只出现一次，返回移除后数组的新长度。\n    不要使用额外的数组空间，你必须在 原地 修改输入数组 并在使用 O(1) 额外空间的条件下完成。\n    https://leetcode-cn.com/problems/remove-duplicates-from-sorted-array\n    \"\"\"\n    # 双指针\n    def removeDuplicates(self, nums: List[int]) -> int:\n        if len(nums) <= 1:\n            return len(nums)\n\n        i = 0\n        j = 1\n        while j < len(nums):\n            # 当快指针等于慢指针的时候，快指针向后移动一位\n            # 当快指针不等于慢指针的时候，将快指针和慢指针的后一位交换，两者同时向后移动\n            if nums[i] != nums[j]:\n                i += 1\n                nums[i] = nums[j]\n            j += 1\n        return i+1\n\n\nso = Solution()\nprint(so.removeDuplicates([1, 1, 2, 3 , 3, 4, 5]))\n\n","sub_path":"arr.remove-duplicates-from-sorted-array.py","file_name":"arr.remove-duplicates-from-sorted-array.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"49866936","text":"import time\nimport sys\nimport ibmiotf.application\nimport ibmiotf.device\nimport random\n#Provide your IBM Watson Device Credentials\norganization = \"xyz1kg\"\ndeviceType = \"Arduino\"\ndeviceId = \"ard123\"\nauthMethod = \"token\"\nauthToken = \"12345678\"\n\n\ndef myCommandCallback(cmd):\n        print(\"Command received: %s\" % cmd.data)#Commands\n        \n\ntry:\n\tdeviceOptions = {\"org\": organization, \"type\": deviceType, \"id\": deviceId, \"auth-method\": authMethod, \"auth-token\": authToken}\n\tdeviceCli = ibmiotf.device.Client(deviceOptions)\n\t#..............................................\n\t\nexcept Exception as e:\n\tprint(\"Caught exception connecting device: %s\" % str(e))\n\tsys.exit()\n\n# Connect and send a datapoint \"hello\" with value \"world\" into the cloud as an event of type \"greeting\" 10 times\ndeviceCli.connect()\n\nwhile True:\n        \n        hum=random.randint(60, 77)\n        #print(hum)\n        temp =random.randint(96, 98)\n        #Send Temperature & Humidity to IBM Watson\n        data = { 'Temperature' : temp, 'Humidity': hum }\n        #print (data)\n        def myOnPublishCallback():\n            print (\"Published Temperature = %s C\" % temp, \"Humidity = %s %%\" % hum, \"to IBM Watson\")\n\n        success = deviceCli.publishEvent(\"Weather\", \"json\", data, qos=0, on_publish=myOnPublishCallback)\n        if not success:\n            print(\"Not connected to IoTF\")\n        time.sleep(2)\n        \n        deviceCli.commandCallback = myCommandCallback\n\n# Disconnect the device and application from the cloud\ndeviceCli.disconnect()\n","sub_path":"ibmp.py","file_name":"ibmp.py","file_ext":"py","file_size_in_byte":1516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"183864477","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 23 15:26:01 2019\n\n@author: busra\n\"\"\"\n\nfrom bs4 import BeautifulSoup\nimport requests\n\nchunk_size = 256\n\ndef change_char(name):\n    liste = str.maketrans(\"ÇĞİÖŞÜçğıöşü\\n \",\"CGIOSUcgiosu _\")\n    name = name.translate(liste)\n    return name.lower()\n\ndef source_code(url,name):\n    req = requests.get(url)\n    \n    if(req.status_code == 200):\n        req = req.text\n    else:\n        print(\"Not Found\")\n    \n    soup = BeautifulSoup(req,'lxml')\n    data = soup.find(\"meta\",property=\"og:video\")['content']\n   \n    name = change_char(name)\n    \n    r = requests.get(data, stream=True)\n    \n    with open(name+\".mp4\",\"wb\") as f:\n        for chunk in r.iter_content(chunk_size=chunk_size):\n            f.write(chunk)","sub_path":"SourceCode.py","file_name":"SourceCode.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"466500985","text":"import math\n\nf = open('visible_evaluation_triplets.txt', 'r')\nfileVisible = open('visible.txt', 'a')\nfileHidden = open('hidden.txt', 'a')\nprev = \"\"\ndat = []\ncount = 1\nsplitFactor = 0.5\nfor line in f:\n\n    tokens = line.split()\n    curr = tokens[0]\n    if curr == prev:\n        dat.append(line)\n\n    else:\n\n        prev = curr\n        length = len(dat)\n        if count != 1:\n            split = math.ceil(splitFactor * length)\n            split = int(split)\n            firstPart = dat[0:split]\n            secondPart = dat[split:length]\n            dat = []\n            dat.append(line)\n            for obj in firstPart:\n                fileVisible.write(obj)\n            for obj in secondPart:\n                fileHidden.write(obj)\n        else:\n            dat.append(line)\n    count += 1\n\nlength = len(dat)\nsplit = math.ceil(splitFactor * length)\nsplit = int(split)\nfirstPart = dat[0:split]\nsecondPart = dat[split:length]\nfor obj in firstPart:\n    fileVisible.write(obj)\nfor obj in secondPart:\n    fileHidden.write(obj)\n\n\n\n\n","sub_path":"Split_Test_Data.py","file_name":"Split_Test_Data.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"77589298","text":"'''\nMMU Simulator in python\nAssignment 3, Computer Architecture\nAuthors: Akilesh K, Arjun Menon V\nSept 2021\nClass Definition for the proc module\n'''\nimport numpy as np\n\nclass proc:\n    def __init__(self, pid):\n        self.pid = pid\n        self.hits = 0\n        self.misses = 0\n        self.requests = 0\n        self.pageEvictions = 0\n        self.tableEvictions = 0\n        self.pTableCopy = [[None]*1024]*1024   # Helper array to model copy of page Table in disk\n\n    def pagewalk(self, kernelMem, userMem, offsets):\n        self.requests += 1\n        dirOffset = offsets[0]\n        tabOffset = offsets[1]\n        pDir = kernelMem.mem[self.pid]\n        pTableMiss = False\n        pMiss = False\n        wb = 0\n        invalidateCopy = 0\n        # Check if page table present or not\n        # Bring it to memory and continue if not\n        if pDir[dirOffset] == None:\n            pTableMiss = True\n            if kernelMem.freeFrames.empty():\n                self.tableEvictions += 1\n                evictedKernelFrame = kernelMem.evictFrame()\n                evictedTable = kernelMem.mem[evictedKernelFrame]\n                wb, evictedPID, evictedOffset = kernelMem.invalidateEntry(\"kernel\", evictedKernelFrame)\n            mappedKernelFrame = kernelMem.freeFrames.get()\n            pDir[dirOffset] = mappedKernelFrame\n            # in case of pTable Miss, write the Page Table Entry from \"disk\"\n            kernelMem.mem[pDir[dirOffset]] = self.pTableCopy[dirOffset]\n\n        pTableAddr = pDir[dirOffset]\n        pTable = kernelMem.mem[pTableAddr]\n        kernelMem.updateLRU(pTableAddr)\n\n        # Check if page present or not\n        # Bring it to memory and continue if not\n        if pTable[tabOffset] == None:\n            pMiss = True\n            if userMem.freeFrames.empty():\n                self.pageEvictions += 1\n                evictedUserFrame = userMem.evictFrame()\n                invalidateCopy = kernelMem.invalidateEntry(\"user\", evictedUserFrame)\n            mappedUserFrame = userMem.freeFrames.get()\n            pTable[tabOffset] = mappedUserFrame\n\n        pageAddr = pTable[tabOffset]\n        page = userMem.mem[pageAddr]\n        userMem.updateLRU(pageAddr)\n\n        if pTableMiss:\n            self.misses += 1\n        else:\n            if pMiss:\n                self.misses += 1\n            else:\n                self.hits += 1\n\n        if wb:\n            rvec1 = [wb, evictedPID, evictedOffset, evictedTable]\n        else:\n            rvec1 = [wb, None, None, None]\n        if invalidateCopy:\n            rvec2 = [invalidateCopy, evictedUserFrame]\n        else:\n            rvec2 = [invalidateCopy, None]\n\n        return page, rvec1, rvec2\n","sub_path":"src/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":2665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"18485047","text":"\"\"\"\nAuxiliary funtions that replicate Plagborg-Moller and Wolf (2020)\nAuthor: Joao B. Duarte\nLast update: 2021-04-14\nThis is an alpha version of the code. Any bugs or mistakes are my own. If you find them please report them to joao.duarte@novasbe.pt\n\"\"\"\nimport math\nimport numpy as np\nfrom scipy.stats import chi2\nfrom scipy.stats import invgauss\nfrom scipy.stats import norm\nfrom scipy import linalg\nfrom scipy import optimize as opt\nfrom statsmodels.tsa.tsatools import lagmat\nfrom scipy.optimize import NonlinearConstraint\nimport scipy.integrate as integrate\n\n\ndef stoye_bound(c, rho, Delta_over_sigma):\n    # Integral used in Stoye(2009) confidence interval routine\n    # See Appendix B of Stoye(2009)\n    integr = integrate.quad(lambda z: norm.cdf((rho * z + c(2) + Delta_over_sigma) / np.sqrt(1 - rho ** 2)) * norm.pdf(z),\n                          - 10,\n                          c[0])\n    return integr\n\n\ndef stoye_CI(lower_estim, upper_estim, varcov, signif_level):\n    # Stoye (2009) confidence interval for partially identified parameter\n    # Inputs to routine\n    Delta = upper_estim - lower_estim  # Point estimate of length of identif. set\n    sigma_lower = np.sqrt(varcov[0, 0])  # Std. dev. of lower bound estimate\n    sigma_upper = np.sqrt(varcov[1, 1])  # Std. dev. of upper bound estimate\n    rho = varcov[0, 1] / (sigma_lower * sigma_upper)  # Correlation of lower and upper bound estimates\n\n    # Numerically minimize CI length subject to coverage constraints\n    con = lambda c: [0, 1 - signif_level - np.r_[stoye_bound(c, rho, Delta / sigma_upper),\n                                                 stoye_bound(np.fliplr(c), rho, Delta / sigma_lower)]]\n    nlc = NonlinearConstraint(con, -np.inf, 1.9)\n    c_opt = opt.minimize(lambda c: np.c_[sigma_lower, sigma_upper] @ c.T,\n                         x0=np.array([lower_estim - invgauss.cdf(1 - signif_level / 2) * sigma_lower,\n                                      upper_estim + invgauss.cdf(1 - signif_level / 2) * sigma_upper]),\n                         constraints=nlc)\n\n    # Confidence interval\n    CI = np.c_[lower_estim - sigma_lower * c_opt(1), upper_estim + sigma_upper * c_opt(2)]\n    return CI\n\n\ndef CI_fun(bounds_boot, bounds_OLS, settings):\n    # Bootstrap confidence intervals\n\n    # ----------------------------------------------------------------\n    # Get Inputs\n    # ----------------------------------------------------------------\n\n    fields = list(bounds_OLS.__dict__.keys())\n    signif_level = settings.signif_level\n\n    # optimopts = settings.optimopts\n\n    # ----------------------------------------------------------------\n    # Quantiles of Bootstrap Draws\n    # ----------------------------------------------------------------\n    class bounds_boot_mean():\n        for j in range(len(fields)):\n            locals()[fields[j]] = np.squeeze(np.mean(getattr(bounds_boot, fields[j]), 2))  # Average\n\n    class bounds_boot_plow():\n        for j in range(len(fields)):\n            if type(getattr(bounds_OLS, fields[j])) == np.float64:\n                locals()[fields[j]] = np.squeeze(np.quantile(getattr(bounds_boot, fields[j]) - (getattr(bounds_OLS,\n                                                                                                        fields[j])),\n                                                             signif_level / 2, axis=2))  # Lower quantile\n            elif type(getattr(bounds_OLS, fields[j])) == np.ndarray:\n                locals()[fields[j]] = np.squeeze(np.quantile(getattr(bounds_boot, fields[j]) - (getattr(bounds_OLS,\n                                                                                                        fields[j]))[:,\n                                                                                               :,\n                                                                                               np.newaxis],\n                                                             signif_level / 2, axis=2))  # Lower quantile\n            else:\n                locals()[fields[j]] = []\n\n    class bounds_boot_phigh():\n        for j in range(len(fields)):\n            if type(getattr(bounds_OLS, fields[j])) == np.float64:\n                locals()[fields[j]] = np.squeeze(np.quantile(getattr(bounds_boot, fields[j]) - (getattr(bounds_OLS,\n                                                                                                        fields[j])),\n                                                             1 - signif_level / 2, axis=2))  # Upper quantile\n            elif type(getattr(bounds_OLS, fields[j])) == np.ndarray:\n                locals()[fields[j]] = np.squeeze(np.quantile(getattr(bounds_boot, fields[j]) - (getattr(bounds_OLS,\n                                                                                                        fields[j]))[:,\n                                                                                               :,\n                                                                                               np.newaxis],\n                                                             1 - signif_level / 2, axis=2))  # Upper quantile\n            else:\n                locals()[fields[j]] = []\n\n    bounds_boot_mean = bounds_boot_mean()\n    bounds_boot_plow = bounds_boot_plow()\n    bounds_boot_phigh = bounds_boot_phigh()\n\n    # ----------------------------------------------------------------\n    # CI for IS\n    # ----------------------------------------------------------------\n\n    class bounds_CI_IS():\n        class OLS_biascorr():\n            for j in range(len(fields)):\n                locals()[fields[j]] = 2 * getattr(bounds_OLS, fields[j]) - getattr(bounds_boot_mean, fields[j])  #\n                # Bias correction\n\n        class lower():\n            for j in range(len(fields)):\n                if getattr(bounds_OLS, fields[j]) == []:\n                    locals()[fields[j]] = []\n                else:\n                    locals()[fields[j]] = getattr(bounds_OLS, fields[j]) - getattr(bounds_boot_phigh,\n                                                                                   fields[j])  # Hall's\n                # bootstrap percentile interval, lower bound\n\n        class upper():\n            for j in range(len(fields)):\n                if getattr(bounds_OLS, fields[j]) == []:\n                    locals()[fields[j]] = []\n                else:\n                    locals()[fields[j]] = getattr(bounds_OLS, fields[j]) - getattr(bounds_boot_plow,\n                                                                                   fields[j])  # Hall's\n\n    bounds_CI_IS = bounds_CI_IS()\n    bounds_CI_IS.OLS_biascorr = bounds_CI_IS.OLS_biascorr()\n    bounds_CI_IS.lower = bounds_CI_IS.lower()\n    bounds_CI_IS.upper = bounds_CI_IS.upper()\n\n    # ----------------------------------------------------------------\n    # CI for Parameter (Stoye, 2009)\n    # ----------------------------------------------------------------\n\n    bounds_CI_para = {'lower': {}, 'upper': {}}\n\n    if not settings.CI_para:\n        return bounds_CI_IS, bounds_CI_para\n\n    for j in range(len(fields)):\n        lb_pos = fields[j].index('_LB')\n        if lb_pos == None:\n            continue\n        else:\n            the_param = fields[j][:lb_pos]  # Name of parameter\n\n        field_LB = fields[j]  # Lower bound field\n        field_UB = the_param + '_UB'  # Upper bound field\n\n        if type(getattr(bounds_OLS, field_LB)) == np.float64:\n            bounds_CI_para['lower'][the_param] = np.empty(1)\n            bounds_CI_para['upper'][the_param] = np.empty(1)\n            size_l = np.size(getattr(bounds_OLS, field_LB))\n            size_m = np.size(getattr(bounds_OLS, field_LB))\n        else:\n            bounds_CI_para['lower'][the_param] = np.empty(getattr(bounds_OLS, field_LB).shape)\n            bounds_CI_para['upper'][the_param] = np.empty(getattr(bounds_OLS, field_LB).shape)\n            size_l = np.size(getattr(bounds_OLS, field_LB), 0)\n            size_m = np.size(getattr(bounds_OLS, field_LB), 1)\n        for l in range(size_l):\n            for m in range(size_m):\n                # Bootstrap var-cov matrix of estimated lower and upper bounds\n                varcov = np.cov(\n                    np.column_stack((np.squeeze(getattr(bounds_boot, field_LB)[l, m, :]), np.squeeze(getattr(\n                        bounds_boot, field_UB)[l, m, :]))))\n                # Enforce parameter in [0,1] (except alpha)\n                if the_param != 'alpha':\n                    if type(getattr(bounds_CI_IS.OLS_biascorr, field_LB)) == np.float64:\n                        getattr(bounds_CI_IS.OLS_biascorr, field_LB)[l, m] = max(0, getattr(bounds_CI_IS.OLS_biascorr,\n                                                                                            field_LB))\n                        getattr(bounds_CI_IS.OLS_biascorr, field_UB)[l, m] = max(0, getattr(bounds_CI_IS.OLS_biascorr,\n                                                                                            field_UB))\n                    else:\n                        getattr(bounds_CI_IS.OLS_biascorr, field_LB)[l, m] = max(0, getattr(bounds_CI_IS.OLS_biascorr,\n                                                                                            field_LB)[l, m])\n                        getattr(bounds_CI_IS.OLS_biascorr, field_UB)[l, m] = max(0, getattr(bounds_CI_IS.OLS_biascorr,\n                                                                                            field_UB)[l, m])\n\n                if not (('FVD_LB' == field_LB) or ('R2_recov_LB' == field_LB)):\n                    # Compute Stoye (2009) confidence interval\n                    if type(getattr(bounds_CI_IS.OLS_biascorr, field_LB)) == np.float64:\n                        CI = stoye_CI(getattr(bounds_CI_IS.OLS_biascorr, field_LB),\n                                      getattr(bounds_CI_IS.OLS_biascorr, field_UB),\n                                      varcov, signif_level)\n                        bounds_CI_para['lower'][the_param] = CI[0]\n                        bounds_CI_para['upper'][the_param] = CI[1]\n                    else:\n                        CI = stoye_CI(getattr(bounds_CI_IS.OLS_biascorr, field_LB)[l, m],\n                                      getattr(bounds_CI_IS.OLS_biascorr, field_UB)[l, m],\n                                      varcov,\n                                      signif_level)\n                        bounds_CI_para['lower'][the_param][l, m] = CI[0]\n                        bounds_CI_para['upper'][the_param][l, m] = CI[1]\n                else:\n                    # FVD and R2_recov: one-sided lower confidence interval,\n                    # since upper bound is always 1\n                    if type(getattr(bounds_CI_IS.OLS_biascorr, field_LB)) == np.float64:\n                        bounds_CI_para['lower'][the_param] = getattr(bounds_CI_IS.OLS_biascorr,\n                                                                     field_LB) + invgauss.cdf(signif_level) * np.sqrt(\n                            varcov[0, 0])\n                        bounds_CI_para['lower'][the_param] = 1\n                    else:\n                        bounds_CI_para['lower'][the_param][l, m] = getattr(bounds_CI_IS.OLS_biascorr,\n                                                                           field_LB) + invgauss.cdf(\n                            signif_level) * np.sqrt(\n                            varcov[0, 0])\n                        bounds_CI_para['lower'][the_param][l, m] = 1\n    return bounds_CI_IS, bounds_CI_para\n\n\ndef FVD_fun(var, hor, yzt_aux, alpha):\n    # Forecast variance decomposition bound\n\n    Sigma_yzt = yzt_aux.Sigma_yzt\n    maxVar_yt_ythor = yzt_aux.maxVar_yt_ythor[:, :, hor - 1]\n    FVD = sum(((1 / alpha) * Sigma_yzt[:hor, var]) ** 2) / (\n            sum(((1 / alpha) * Sigma_yzt[:hor, var]) ** 2) + maxVar_yt_ythor[var, var])\n    return FVD\n\n\ndef FVD_IS(yzt_aux, dataobj, settings, alpha):\n    # Identified set for forecast variance decomposition\n\n    # ----------------------------------------------------------------\n    # Preparations\n    # ----------------------------------------------------------------\n\n    n_y = dataobj.n_y\n    FVD_hor = settings.FVD_hor\n    FVD_LB = np.empty((np.size(FVD_hor, 0), n_y))\n    VMA_hor = settings.VMA_hor\n    alpha_UB = alpha.alpha_UB\n    Sigma_yzt = yzt_aux.Sigma_yzt\n\n    Cov_y_shock1_LB = [None] * VMA_hor\n    Cov_yt_LB = [None] * VMA_hor\n    for i in range(VMA_hor):\n        Cov_y_shock1_LB[i] = 0\n        for j in range((VMA_hor - (i + 1) + 1)):\n            Cov_y_shock1_LB[i] = Cov_y_shock1_LB[i] + ((1 / alpha_UB) * Sigma_yzt[j, :][:, np.newaxis]) @ \\\n                                 ((1 / alpha_UB) * Sigma_yzt[j + i, :][np.newaxis, :])\n        Cov_yt_LB[i] = yzt_aux.Cov_y[i] - Cov_y_shock1_LB[i]\n\n    Sigma_yt_big_LB = np.empty((n_y * VMA_hor, n_y * VMA_hor))\n    for i in range(VMA_hor):\n        for j in range(VMA_hor):\n            if i > j:\n                Sigma_yt_big_LB[i * n_y: (i + 1) * n_y, j * n_y: (j + 1) * n_y] = Cov_yt_LB[abs(i - j)]\n            else:\n                Sigma_yt_big_LB[i * n_y: (i + 1) * n_y, j * n_y: (j + 1) * n_y] = Cov_yt_LB[abs(i - j)].T\n\n    yzt_aux.maxVar_yt_ythor = np.empty((n_y, n_y, FVD_hor[-1]))\n\n    for hor in range(FVD_hor[-1]):\n        Sigma_yt_ythor = Sigma_yt_big_LB\n        Cov_yt = Cov_yt_LB\n        if hor >= 1:\n            for j in range(1, VMA_hor - hor):\n                Sigma_yt_ythor[:n_y, j * n_y: (j + 1) * n_y] = Cov_yt[abs((j + hor))].T\n            Sigma_yt_ythor[:n_y, (VMA_hor - hor + 2) * n_y:] = 0\n            for i in range(1, VMA_hor - hor):\n                Sigma_yt_ythor[i * n_y: (i + 1) * n_y, :n_y] = Cov_yt[abs(i + hor)]\n            Sigma_yt_ythor[(VMA_hor - hor + 2) * n_y:, :n_y] = 0\n        yzt_aux.maxVar_yt_ythor[:, :, hor] = Sigma_yt_ythor[:n_y, :n_y] - Sigma_yt_ythor[:n_y, n_y:] \\\n                                             @ np.linalg.solve(Sigma_yt_ythor[n_y:, n_y:], Sigma_yt_ythor[n_y:, :n_y])\n\n    # ----------------------------------------------------------------\n    # Lower Bound\n    # ----------------------------------------------------------------\n\n    for hor_indx in range(np.size(FVD_hor, 0)):\n        hor = FVD_hor[hor_indx]\n        for j in range(n_y):\n            FVD_LB[hor_indx, j] = FVD_fun(j, hor, yzt_aux, alpha.alpha_UB)\n    return FVD_LB\n\n\ndef FVR_fun(var, hor, yzt_aux, alpha):\n    # Forecast variance ratio, given alpha\n    Sigma_yzt = yzt_aux.Sigma_yzt\n    Var_y_yhor = yzt_aux.Var_y_yhor[:, :, hor - 1]\n    FVR = np.sum(((1 / alpha) * Sigma_yzt[:hor, var]) ** 2) / Var_y_yhor[var, var]\n    return FVR\n\n\ndef FVR_IS(yzt_aux, dataobj, settings, alpha):\n    # Identified set for forecast variance ratio\n\n    # ----------------------------------------------------------------\n    # Preparations\n    # ----------------------------------------------------------------\n\n    # imports\n\n    n_y = dataobj.n_y\n    FVR_hor = settings.FVR_hor\n    FVR_true = np.empty((np.size(FVR_hor, 0), n_y))\n    FVR_UB = np.empty((np.size(FVR_hor, 0), n_y))\n    FVR_LB = np.empty((np.size(FVR_hor, 0), n_y))\n\n    VMA_hor = settings.VMA_hor\n    Sigma_y_yhor = yzt_aux.Sigma_y_big\n    Cov_y = yzt_aux.Cov_y\n\n    # get forecasting variance\n\n    yzt_aux.Var_y_yhor = np.empty((n_y, n_y, FVR_hor[-1]))\n\n    if settings.use_KF == 0:\n        for hor in range(FVR_hor[-1]):\n            if hor >= 1:\n                for j in range(1, VMA_hor - hor):\n                    Sigma_y_yhor[:n_y, j * n_y: (j + 1) * n_y] = Cov_y[abs(j + hor)].T\n                Sigma_y_yhor[:n_y, (VMA_hor - hor + 2) * n_y:] = 0\n                for i in range(1, VMA_hor - hor):\n                    Sigma_y_yhor[i * n_y: (i + 1) * n_y, :n_y] = Cov_y[abs(i + hor)]\n                Sigma_y_yhor[(VMA_hor - hor + 2) * n_y:, :n_y] = 0\n\n            yzt_aux.Var_y_yhor[:, :, hor] = Sigma_y_yhor[:n_y, :n_y] - \\\n                                            Sigma_y_yhor[:n_y, n_y:] @ np.linalg.solve(Sigma_y_yhor[n_y:, n_y:],\n                                                                                       Sigma_y_yhor[n_y:, :n_y])\n    else:\n        A_KF = yzt_aux.A_KF\n        B_KF = yzt_aux.B_KF\n        C_KF = yzt_aux.C_KF\n        cvar_states_1 = yzt_aux.cvar_states_1\n\n        for hor in range(FVR_hor[-1]):\n            if hor == 0:\n                cvar_states = cvar_states_1\n            else:\n                cvar_states = A_KF @ cvar_states @ A_KF.T + B_KF @ B_KF.T\n\n            yzt_aux.Var_y_yhor[:, :, hor] = C_KF @ cvar_states @ C_KF.T\n\n    # ----------------------------------------------------------------\n    # Truth\n    # ----------------------------------------------------------------\n\n    if hasattr(alpha, 'alpha_true') == 0:\n        for hor_indx in range(np.size(FVR_hor, 0)):\n            hor = FVR_hor[hor_indx]\n            for j in range(n_y):\n                FVR_true[hor_indx, j] = FVR_fun(j, hor, yzt_aux, alpha.alpha_true)\n    else:\n        FVR_true = []\n\n    # ----------------------------------------------------------------\n    # Upper Bound\n    # ----------------------------------------------------------------\n    for hor_indx in range(np.size(FVR_hor, 0)):\n        hor = FVR_hor[hor_indx]\n        for j in range(n_y):\n            FVR_UB[hor_indx, j] = FVR_fun(j, hor, yzt_aux, alpha.alpha_LB)\n\n    # ----------------------------------------------------------------\n    # Lower Bound\n    # ----------------------------------------------------------------\n    for hor_indx in range(np.size(FVR_hor, 0)):\n        hor = FVR_hor[hor_indx]\n        for j in range(n_y):\n            FVR_LB[hor_indx, j] = FVR_fun(j, hor, yzt_aux, alpha.alpha_UB)\n\n    return FVR_LB, FVR_UB, FVR_true\n\n\ndef R2_IS(yzt_aux, dataobj, settings, R2_hor, alpha):\n    # Identified set for degree of invertibility out to time t+l\n    # ----------------------------------------------------------------\n    # Get Inputs\n    # ----------------------------------------------------------------\n    Var_zt = yzt_aux.Var_zt\n    Sigma_yzt = yzt_aux.Sigma_yzt\n    Sigma_y_big = yzt_aux.Sigma_y_big\n    VMA_hor = settings.VMA_hor\n    n_y = dataobj.n_y\n    alpha_true = alpha.alpha_true\n    alpha_LB = alpha.alpha_LB\n    alpha_UB = alpha.alpha_UB\n\n    # ----------------------------------------------------------------\n    # Auxiliary Computations\n    # ----------------------------------------------------------------\n\n    Cov_zty_lagged = np.zeros((1, n_y * VMA_hor))\n    for i in range(R2_hor):\n        Cov_zty_lagged[0, i * n_y: (i + 1) * n_y] = Sigma_yzt[R2_hor - i -1, :]\n\n    Var_zt_yhor = Var_zt - Cov_zty_lagged @ np.linalg.solve(Sigma_y_big, Cov_zty_lagged.T)\n\n    Rtilde2 = 1 - Var_zt_yhor[0, 0] / Var_zt\n\n    # ----------------------------------------------------------------\n    # Truth\n    # ----------------------------------------------------------------\n    if (not alpha.alpha_true) == 0:\n        R2_true = Var_zt / alpha_true ** 2 * Rtilde2\n    else:\n        R2_true = []\n\n    # ----------------------------------------------------------------\n    # Upper Bound\n    # ----------------------------------------------------------------\n    R2_UB = Var_zt / alpha_LB ** 2 * Rtilde2\n\n    # ----------------------------------------------------------------\n    # Lower Bound\n    # ----------------------------------------------------------------\n    R2_LB = Var_zt / alpha_UB ** 2 * Rtilde2\n    return R2_LB, R2_UB, R2_true\n\n\ndef alpha_IS(yzt_aux, dataobj, settings):\n    # Identified set for scale parameter alpha\n\n    # ----------------------------------------------------------------\n    # Get Inputs\n    # ----------------------------------------------------------------\n    global alpha_LB\n    Var_zt = yzt_aux.Var_zt\n    s_y = yzt_aux.s_y\n    s_yzt = yzt_aux.s_yzt\n    n_y = dataobj.n_y\n    ngrid = settings.alpha_ngrid\n    bnd_recov = settings.bnd_recov\n    hor_pred = round(settings.VMA_hor / 2) - 1  # largest you can go with a two-sided prediction\n\n    # ----------------------------------------------------------------\n    # Truth\n    # ----------------------------------------------------------------\n    if hasattr(dataobj, 'alpha'):\n        alpha_true = dataobj.alpha\n    else:\n        alpha_true = []\n\n    # ----------------------------------------------------------------\n    # Upper Bound\n    # ----------------------------------------------------------------\n    alpha_UB = np.sqrt(Var_zt)\n\n    # ----------------------------------------------------------------\n    # Lower Bound\n    # ----------------------------------------------------------------\n    class alpha_plot():\n        pass\n\n    alpha_plot = alpha_plot()\n\n    if bnd_recov == 0:  # Sharp lower bound\n\n        alpha_LB_fun = lambda omega: np.sqrt((s_yzt(omega).conj().T @ np.linalg.solve(s_y(omega), s_yzt(omega))).real)[\n            0, 0]\n\n        omega_grid = np.linspace(0, 2 * math.pi, ngrid).T\n        alpha_LB_vals = np.empty((ngrid, 1))\n        for i in range(ngrid):\n            alpha_LB_vals[i] = alpha_LB_fun(omega_grid[i])\n        alpha_LB = np.max(alpha_LB_vals)\n        alpha_plot.alpha_LB_vals = alpha_LB_vals\n        alpha_plot.omega_grid = omega_grid\n\n    elif bnd_recov == 1:  # Weaker lower bound (sharp under recoverability)\n        Var_y = np.empty((((2 * hor_pred + 1) * n_y, (2 * hor_pred + 1) * n_y)))\n        for i in range(2 * hor_pred + 1):\n            for j in range(2 * hor_pred + 1):\n                if i > j:\n                    Var_y[i * n_y: (i + 1) * n_y, j * n_y: (j + 1) * n_y] = yzt_aux.Cov_y[abs(i - j)]\n                else:\n                    Var_y[i * n_y: (i + 1) * n_y, j * n_y: (j + 1) * n_y] = yzt_aux.Cov_y[abs(i - j)].T\n\n        Cov_zy = np.zeros((1, (2 * hor_pred + 1) * n_y))\n\n        for i in range(hor_pred + 1):\n            Cov_zy[0, i * n_y: (i + 1) * n_y] = yzt_aux.Sigma_yzt[hor_pred + 1 - (i + 1), :]\n\n        alpha_LB = np.sqrt(Cov_zy @ np.linalg.solve(Var_y, Cov_zy.T))[0, 0]\n\n        alpha_plot.alpha_LB_vals = []\n        alpha_plot.omega_grid = []\n    return alpha_LB, alpha_UB, alpha_true, alpha_plot\n\n\ndef get_IS(yzt_aux, dataobj, settings):\n    class bounds():\n        pass\n\n    bounds = bounds()\n    # ----------------------------------------------------------------\n    # Alpha\n    # ----------------------------------------------------------------\n    [bounds.alpha_LB, bounds.alpha_UB, bounds.alpha_true, bounds.alpha_plot] = alpha_IS(yzt_aux, dataobj, settings)\n\n    class alpha():\n        pass\n\n    alpha = alpha()\n    alpha.alpha_LB = bounds.alpha_LB\n    alpha.alpha_UB = bounds.alpha_UB\n    if hasattr(dataobj, 'alpha'):\n        alpha.alpha_true = dataobj.alpha\n    else:\n        alpha.alpha_true = []\n    # ----------------------------------------------------------------\n    # R2\n    # ----------------------------------------------------------------\n    # invertibility\n    if settings.CI_for_R2_inv == 1:\n        [bounds.R2_inv_LB, bounds.R2_inv_UB, bounds.R2_inv_true] = R2_IS(yzt_aux, dataobj, settings, 1, alpha)\n    else:\n        bounds.R2_inv_LB = []\n        bounds.R2_inv_UB = []\n\n    # recoverability\n    if settings.CI_for_R2_recov == 1:\n        [bounds.R2_recov_LB, bounds.R2_recov_UB, bounds.R2_recov_true] = R2_IS(yzt_aux, dataobj, settings,\n                                                                               round(settings.VMA_hor / 2) - 1,\n                                                                               alpha)  # use exactly same bound as for two-sided alpha recoverability computation\n    else:\n        bounds.R2_recov_LB = []\n        bounds.R2_recov_UB = []\n\n    # ----------------------------------------------------------------\n    # FVR\n    # ----------------------------------------------------------------\n    if settings.CI_for_FVR == 1:\n        [bounds.FVR_LB, bounds.FVR_UB, bounds.FVR_true] = FVR_IS(yzt_aux, dataobj, settings, alpha)\n    else:\n        bounds.FVR_LB = []\n        bounds.FVR_UB = []\n\n    # ----------------------------------------------------------------\n    # FVD\n    # ----------------------------------------------------------------\n    if settings.CI_for_FVD == 1:\n        bounds.FVD_LB = FVD_IS(yzt_aux, dataobj, settings, alpha)\n        bounds.FVD_UB = np.ones((np.size(bounds.FVD_LB, 0), dataobj.n_y))\n    else:\n        bounds.FVD_LB = []\n        bounds.FVD_UB = []\n    return bounds\n\n\ndef cross_sd_fun(omega, Sigma_yztilde):\n    # preparations\n    hor = np.size(Sigma_yztilde, 0)\n\n    # derive lambda_yz_star(omega)\n    cross_sd = 0\n    for l in range(hor):\n        cross_sd = cross_sd + Sigma_yztilde[l, :] * np.exp(complex(-1) * omega * l)\n    cross_sd = cross_sd[np.newaxis, :]\n    cross_sd = cross_sd.reshape((np.size(cross_sd, 1), np.size(cross_sd, 0)), order='F')\n    return cross_sd\n\n\ndef sd_fun(omega, Theta_Wold):\n    # Spectral density of moving average\n\n    # preparations\n    hor = np.size(Theta_Wold, 0)\n\n    # derive Theta(omega)\n    Theta_aux = 0\n    for l in range(hor):\n        Theta_aux = Theta_aux + np.squeeze(Theta_Wold[l, :, :]) * np.exp(complex(-1) * omega * l)\n\n    # derive final result\n    sd = Theta_aux @ Theta_aux.conj().T\n    return sd\n\n\ndef kalman_filter(A, B, C, y):\n    # Kalman filter for conditional variance calculations\n\n    # number of observations and variables\n    T = np.size(y, 0)\n    n_s = np.size(A, 0)\n\n    # initial conditions\n    st = np.zeros((n_s, 1))\n    Pt = np.zeros((n_s, n_s))\n\n    # filtering\n    for t in range(T):\n        yt = y[t, :].T\n\n        # prediction equations\n        st_1 = A @ st\n        Pt_1 = A @ Pt @ A.T + B @ B.T\n\n        # innovations\n        yt_1 = C @ st_1\n        vt = yt - yt_1\n\n        # updating equations\n        Kt = Pt_1 @ C.T @ np.linalg.inv(C @ Pt_1 @ C.T)\n        st = st_1 + Kt @ vt\n        Pt = Pt_1 - Kt @ C @ Pt_1\n\n    cond_var = Pt\n    cond_var_1 = Pt_1\n\n    return cond_var, cond_var_1\n\n\ndef get2ndmoments_VAR(VAR, dataobj, settings):\n    # Compute autocovariances implied by reduced-form VAR\n\n    # ----------------------------------------------------------------\n    # Preparations\n    # ----------------------------------------------------------------\n\n    VAR_coeff = VAR.VAR_coeff\n    Sigma_u = VAR.Sigma_u\n    n_x = dataobj.n_x\n    n_y = dataobj.n_y\n    p = VAR.laglength\n    VMA_hor = settings.VMA_hor\n\n    # ----------------------------------------------------------------\n    # Wold Representation\n    # ----------------------------------------------------------------\n\n    Theta_Wold = np.empty((VMA_hor, n_x, n_x))\n    VAR_coeff = np.row_stack((VAR_coeff[:p * n_x, :], np.zeros((1 + VMA_hor * n_x - p * n_x, n_x))))\n\n    for i in range(VMA_hor):\n        if i == 0:\n            Theta_Wold[i, :, :] = linalg.sqrtm(Sigma_u)\n        else:\n            Theta_Wold[i, :, :] = np.zeros((n_x, n_x))\n            for j in range(i):\n                Theta_Wold[i, :, :] = np.squeeze(Theta_Wold[i, :, :]) + VAR_coeff[j * n_x: (j + 1) * n_x,\n                                                                        :].T @ np.squeeze(\n                    Theta_Wold[i - j - 1, :, :])  # just compute Cov(x_t, eps_{t-h})\n\n    Theta_Wold_y = Theta_Wold[:, :n_y, :]\n    Theta_Wold_z = Theta_Wold[:, n_y, :]\n    Theta_Wold_z = Theta_Wold_z[:, np.newaxis, :]\n    Theta_Wold_zt = np.squeeze(Theta_Wold[0, n_y, :])\n\n    # ----------------------------------------------------------------\n    # Variances and Covariances\n    # ----------------------------------------------------------------\n\n    Cov_x = [None] * VMA_hor\n    for i in range(VMA_hor):\n        Cov_x[i] = np.zeros((n_x, n_x))\n        for j in range(VMA_hor - i):\n            Cov_x[i] = Cov_x[i] + np.squeeze(Theta_Wold[j, :, :]) @ np.squeeze(Theta_Wold[j + i, :, :]).T\n\n    Cov_y = [None] * VMA_hor\n    for i in range(VMA_hor):\n        Cov_y[i] = Cov_x[i][:n_y, :n_y]\n\n    Var_zt = Theta_Wold_zt.T @ Theta_Wold_zt\n    Sigma_yzt = np.empty((VMA_hor, n_y))\n    for i in range(VMA_hor):\n        Sigma_yzt[i, :] = np.squeeze(Theta_Wold[i, :-1, :]) @ np.squeeze(Theta_Wold[0, -1, :])\n\n    Sigma_y_big = np.empty((n_y * VMA_hor, n_y * VMA_hor))\n    for i in range(VMA_hor):\n        for j in range(VMA_hor):\n            if i > j:\n                Sigma_y_big[i * n_y: (i + 1) * n_y, j * n_y: (j + 1) * n_y] = Cov_y[abs(i - j)]\n            else:\n                Sigma_y_big[i * n_y: (i + 1) * n_y, j * n_y: (j + 1) * n_y] = Cov_y[abs(i - j)].T\n\n    if settings.use_KF == 1:\n        A_KF = np.zeros((n_x * p, n_x * p))\n        for i in range(1, p + 1):\n            A_KF[:n_x, (i - 1) * n_x: i * n_x] = VAR_coeff[(i - 1) * n_x: i * n_x, :].T\n\n        A_KF[n_x: n_x * p, :n_x * (p - 1)] = np.eye(n_x * (p - 1))\n\n        B_KF = np.zeros((n_x * p, n_x))\n        B_KF[:n_x, :] = linalg.sqrtm(Sigma_u)\n\n        C_KF = np.zeros((n_y, n_x * p))\n        C_KF[:, :n_y] = np.eye(n_y)\n\n        T_KF = 100\n        y_KF = np.zeros((T_KF, n_y))\n        _, cvar_states_1 = kalman_filter(A_KF, B_KF, C_KF, y_KF)\n    else:\n        A_KF = []\n        B_KF = []\n        C_KF = []\n        cvar_states_1 = []\n\n    # ----------------------------------------------------------------\n    # Spectral Densities\n    # ----------------------------------------------------------------\n\n    s_y = lambda omega: sd_fun(omega, Theta_Wold_y)\n    s_yzt = lambda omega: cross_sd_fun(omega, Sigma_yzt)\n\n    # ----------------------------------------------------------------\n    # Collect Results\n    # ----------------------------------------------------------------\n    class yzt_aux():\n        pass\n\n    yzt_aux = yzt_aux()\n    yzt_aux.Theta_Wold = Theta_Wold\n    yzt_aux.Theta_Wold_y = Theta_Wold_y\n    yzt_aux.Theta_Wold_z = Theta_Wold_z\n    yzt_aux.Theta_Wold_zt = Theta_Wold_zt\n    yzt_aux.Cov_x = Cov_x\n    yzt_aux.Cov_y = Cov_y\n    yzt_aux.Var_zt = Var_zt\n    yzt_aux.Sigma_yzt = Sigma_yzt\n    yzt_aux.Sigma_u = Sigma_u\n    yzt_aux.s_y = s_y\n    yzt_aux.s_yzt = s_yzt\n    yzt_aux.Sigma_y_big = Sigma_y_big\n    yzt_aux.cvar_states_1 = cvar_states_1\n    yzt_aux.A_KF = A_KF\n    yzt_aux.B_KF = B_KF\n    yzt_aux.C_KF = C_KF\n    return yzt_aux\n\n\ndef bootstrapVAR(VAR, data, settings):\n    # Homoskedastic recursive residual VAR bootstrap\n\n    # ----------------------------------------------------------------\n    # Estimate VAR\n    # ----------------------------------------------------------------\n\n    # preliminaries\n    n_x = data.n_x\n    data = data.data.x\n    T = settings.T\n    p = VAR.laglength\n    n_boot = settings.n_boot\n\n    # sample size\n    T_VAR = T - p\n\n    # estimate VAR\n    X = lagmat(data, p, original=\"ex\")\n    X = X[p:, :]\n    Y = data[p:, :]\n    VAR_coeff = np.linalg.lstsq(np.c_[X, np.ones((len(X), 1))], Y, rcond=None)[0]\n    VAR_res = Y - np.c_[X, np.ones((len(X), 1))] @ VAR_coeff\n\n    # ----------------------------------------------------------------\n    # Bootstrap\n    # ----------------------------------------------------------------\n\n    VAR_coeff_boot = np.empty((p * n_x + 1, n_x, n_boot))\n    Sigma_u_boot = np.empty((n_x, n_x, n_boot))\n    data_start = data[:p, :]\n\n    for i in range(n_boot):\n        if T >= 1000:\n            if i % 10 == 0:\n                print('I am at iteration ', i)\n        u_boot = VAR_res[np.floor(np.size(VAR_res, 0) * np.random.rand(T_VAR, 1)).astype(int), :]  # reshuffle residuals\n\n        # create new artificial data\n        data_boot = np.empty((T, n_x))\n        data_boot[:p, :] = data_start\n        Xlag = X[0, :]\n\n        for t in range(T_VAR):\n            data_boot[p + t, :] = np.append(Xlag, 1.0) @ VAR_coeff + u_boot[t, :]\n            Xlag = np.concatenate((data_boot[p + t, :], Xlag[:-n_x]), axis=0)\n\n        # estimate VAR on artificial data\n        X_boot = lagmat(data_boot, p, original='ex')\n        X_boot = X_boot[p:, :]\n        Y_boot = data_boot[p:, :]\n        VAR_coeff_boot[:, :, i] = np.linalg.lstsq(np.c_[X_boot, np.ones((len(X_boot), 1))], Y_boot, rcond=None)[0]\n        VAR_res_boot = Y_boot - np.c_[X_boot, np.ones((len(X_boot), 1))] @ VAR_coeff_boot[:, :, i]\n        Sigma_u_boot[:, :, i] = (VAR_res_boot.T @ VAR_res_boot) / (T_VAR - n_x * p - 1)\n\n    # collect results\n    class VAR_boot():\n        pass\n\n    VAR_boot = VAR_boot()\n\n    VAR_boot.VAR_coeff = VAR_coeff_boot\n    VAR_boot.Sigma_u = Sigma_u_boot\n    return VAR_boot\n\n\ndef test_invertibility(VAR):\n    # Granger causality pre-test for invertibility\n\n    # Dimensions\n    [k, n_x] = VAR.VAR_coeff.shape\n    p = int((k - 1) / n_x)\n\n    # Var-cov matrix of vec(VAR_coeff)\n    varcov = np.kron(VAR.Sigma_u, np.linalg.inv(VAR.X.T @ VAR.X))\n\n    # Selection matrix picking out lags of z in each y equation\n    the_eye = np.eye(n_x)\n    sel = np.r_[np.tile(the_eye[:, n_x - 1], p), 0] == 1\n\n    # Test in each y equation separately\n    class wald_stat():\n        pass\n    wald_stat = wald_stat()\n    class df():\n        pass\n    df = df()\n    class pval():\n        pass\n    pval = pval()\n\n    wald_stat.eqns = np.empty((1, n_x - 1))\n    df.eqns = p * np.ones((1, n_x - 1))\n    pval.eqns = np.empty((1, n_x - 1))\n    for i in range(n_x - 1):\n        the_coef = VAR.VAR_coeff[:, i]\n        the_varcov = varcov[i * k:(i + 1) * k, i * k: (i + 1) * k]\n        wald_stat.eqns[0, i] = the_coef[sel] @ np.linalg.lstsq(the_varcov[np.ix_(sel, sel)], the_coef[sel],\n                                                                rcond=None)[0]\n        pval.eqns[0, i] = 1 - chi2.cdf(wald_stat.eqns[0, i], df.eqns[0, i])\n\n    # Overall test for all y equations jointly\n    sel2 = np.row_stack((np.tile(sel.reshape(k, 1), (n_x - 1, 1)), np.zeros((k, 1)))) == 1\n    sel2 = sel2.flatten()\n    coef_vec = VAR.VAR_coeff.flatten(order='F')\n    wald_stat.all = coef_vec[sel2].T @ ((np.linalg.lstsq(varcov[np.ix_(sel2, sel2)], coef_vec[sel2][:, np.newaxis], rcond=None))[0])\n    df.all = p * (n_x - 1)\n    pval.all = 1 - chi2.cdf(wald_stat.all, df.all)\n    return wald_stat, df, pval\n\n\ndef selectlag_IC(data, maxlag, penalty):\n    # Select VAR lag length using information criterion\n\n    # preliminaries\n    n_x = np.size(data, 1)\n\n    # compute information criterion for each possible lag length\n    IC = np.empty((maxlag + 1, 1))\n    for i in range(1, maxlag + 1):\n        # set lag length\n        p = i\n\n        # estimate VAR\n        data_est = data[maxlag - p:, :]\n        T = np.size(data_est, 0)\n        T_VAR = T - p\n        X = lagmat(data_est, p, original='ex')\n        X = X[p:, :]\n        Y = data_est[p:, :]\n        X = np.column_stack((X, np.ones((len(X), 1))))\n        VAR_coeff = (np.linalg.inv(X.T @ X)) @ (X.T @ Y)\n        VAR_res = Y - X @ VAR_coeff\n        #     Sigma_u    = (VAR_res'*VAR_res)/(T_VAR-n_x*p-1)\n        Sigma_u = (VAR_res.T @ VAR_res) / T_VAR\n        # compute information criterion\n        T = np.size(data, 0) - maxlag\n        #     IC(i)  = log(det(Sigma_u)) + i * n_x^2 * 2/T\n        IC[i] = np.log(np.linalg.det(Sigma_u)) + i * n_x ** 2 * penalty(T)\n    lag = np.where(IC == min(IC))\n    return int(lag[0][0])\n\n\ndef estimateVAR(data, settings):\n    # Least-squares VAR estimation\n    # preliminaries\n    n_x = np.size(data, 1)\n    T = np.size(data, 0)\n\n    # select lag length\n    if settings.select_VAR_simlaglength == 1:\n        p = selectlag_IC(data, settings.max_simlaglength, settings.penalty)\n    else:\n        p = settings.VAR_simlaglength\n\n    T_VAR = T - p\n\n    # estimate VAR\n    X = lagmat(data, p, original='ex')\n    X = X[p:, :]\n    X = np.column_stack((X, np.ones((len(X), 1))))\n    Y = data[p:, :]\n    VAR_coeff = (np.linalg.inv(X.T @ X)) @ (X.T @ Y)\n    VAR_res = Y - X @ VAR_coeff\n    # Sigma_u    = (VAR_res'*VAR_res)/(T_VAR-n_x*p-1)\n    Sigma_u = (VAR_res.T @ VAR_res) / T_VAR\n\n    # collect results\n    class VAR_sim():\n        pass\n\n    VAR_sim = VAR_sim()\n    VAR_sim.T_VAR = T_VAR\n    VAR_sim.VAR_coeff = VAR_coeff\n    VAR_sim.Sigma_u = Sigma_u\n    VAR_sim.VAR_res = VAR_res\n    VAR_sim.X = X\n    VAR_sim.laglength = p\n    return VAR_sim","sub_path":"python/functions/aux_functions.py","file_name":"aux_functions.py","file_ext":"py","file_size_in_byte":35690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"603856330","text":"#!/usr/bin/python3\n\nfrom unittest import TestCase, main\n\ndef do_redirect_print():\n    import sys\n\n    print(\"default encoding:\", sys.getdefaultencoding())\n    with open('mylog.log', 'w') as f:\n        print(\"writing to log1\", file=f)\n        print(\"writing to log2\", file=f)\n        print(\"done\")\n\n    with open('mylog.log', 'r', newline='') as f:\n        s = f.read()\n\ndef do_print_sep_end():\n    print('ACME', 50, 91.5, sep=',')\n    print('ACME', 50, 91.5, sep=',', end='!!\\n')\n\n    for i in range(10):\n        print(i, end=' ')\n    print('\\n', end='')\n\n    row = ('ACME', 50, 91.5)\n    print(row)\n    print(*row)\n    print(*row, sep=',')\n\n    s = \"abcdefg\"\n    print(s)\n    print(*s)\n    print(list(s))\n\n    row = {\"leo\": 10, \"john\": 11, \"jack\": 12}\n    print(row)\n    print(*row)\n    print(*row.keys())\n    print(*row.values())\n    try:\n        print(**row) #error\n    except:\n        pass\n\ndef do_binary():\n    t = \"hello world\"\n    b = b\"hello world\"\n    r = r\"hello world\"\n    for i, j, k in zip(t, b, r):\n        print(i, j, k)\n\n    with open(\"my.bin\", \"wb\") as f:\n        f.write(t.encode(\"utf-8\"))\n    with open(\"my.bin\", \"rb\") as f:\n        s0 = f.read(8)\n        s = s0.decode(\"utf-8\")\n        print(s0)\n        print(s)\n\n    import array\n    k = [1, 2, 3, 4, 5]\n    nums = array.array('i', k) # 'i' means signed integer\n    print(nums)\n    with open(\"my0.bin\", \"wb\") as f:\n        f.write(nums)\n    with open(\"my0.bin\", \"rb\") as f:\n        n0 = f.read()\n        n = array.array('i', n0)\n        print(n)\n    n1 = array.array('i', [0] * 10)\n    with open(\"my0.bin\", \"rb\") as f:\n        f.readinto(n1)\n        print(n1)\n\ndef do_stringIO_bytesIO():\n    import io\n\n    s = io.StringIO()\n    s.write(\"test1\")\n    print(\"test2\", file=s)\n    print(s.getvalue())\n\n    s = io.BytesIO()\n    s.write(b'test byte 0')\n    print(s.getvalue())\n\ndef do_gz_bz2():\n    f = open('c01_struct_algo.py', 'rb')\n    text = f.read()\n    f.close()\n\n    import bz2, gzip\n    with bz2.open('my.bz2', 'wb', compresslevel=9) as f0:\n        with gzip.open('my.gz', 'wb', compresslevel=9) as f1:\n            f0.write(text)\n            f1.write(text)\n\n    with open('mysample.txt', 'wb') as f:\n        with bz2.open(f, 'wb') as b: # Open from f\n            b.write(text)\n\ndef do_fixed_block():\n    from functools import partial\n\n    MYSIZE = 100\n    with open('sample.txt', 'rb') as f:\n        records = iter(partial(f.read, MYSIZE), b'')\n        for r in records:\n            print(r)\n\ndef do_readinto():\n    import os.path\n\n    size = os.path.getsize('sample.txt')\n    print(size)\n    s = bytearray(size)\n    with open('sample.txt', 'rb') as f: # must open as 'b'\n        f.readinto(s)\n    print(s)\n\n    s0 = memoryview(s)\n    s1 = s0[-5:]\n    print(s1)\n\n    s1[:] = b'KKKKK'\n    print(s)\n\ndef do_mmap():\n    import os\n    import mmap\n\n    size = 100\n    s = b'ABCKKK'\n    slen = len(s)\n    fn = 'mmap.bin'\n    with open(fn, 'wb') as f:\n        f.seek(size - len(s))\n        f.write(s)\n    print(os.path.getsize(fn))\n\n    fd = os.open(fn, os.O_RDWR) # need to open by os.open to get fd\n    with mmap.mmap(fd, size, access=mmap.ACCESS_WRITE) as arr:\n        print(len(arr))\n        print(arr[-slen:])\n\n        ns = b'MYJAC'\n        ns_len = len(ns)\n        arr[-ns_len:] = ns\n        print(arr[-ns_len:])\n\ndef do_os_path():\n    import os\n\n    path = '~/study/code_practice/python/python_cookbook/'\n    print(\"expanduser:\", os.path.expanduser(path))\n    print(\"dirname:\", os.path.dirname(path))\n    print(\"basename:\", os.path.basename(path))\n\ndef do_os_listdir():\n    import os\n\n    mydir = '~/study/code_practice/python'\n    mydir = os.path.expanduser(mydir)\n    names = os.listdir(mydir)\n    files = [os.path.join(mydir, name) for name in names\n                if os.path.isfile(os.path.join(mydir, name))]\n    dirs = [os.path.join(mydir, name) for name in names\n                if os.path.isdir(os.path.join(mydir, name))]\n    print(files)\n    print(dirs)\n\n    import glob\n    pyfiles = glob.glob(os.path.join(mydir, '*.py'))\n    print(\"py files:\".rjust(30, '*'), pyfiles)\n\n    import fnmatch\n    pycfiles = [os.path.join(mydir, name) for name in os.listdir(mydir)\n            if fnmatch.fnmatch(name, \"*.pyc\")]\n    print(\"pyc files:\".rjust(30, '*'), pycfiles)\n\n    for fn in pycfiles:\n        print(os.stat(fn))\n        mtime = os.path.getmtime(fn)\n        print(mtime)\n\n        import time\n        lt = time.localtime(mtime)\n        print(lt)\n        asct = time.asctime(lt)\n        print(asct)\n\ndef do_unicode_file():\n    import os\n\n    fn = '你好.txt'\n\n    with open(fn, 'w') as f:\n        f.write('好的')\n\n    names = os.listdir(b'.') # use b'.' to convert unicode fn into byte string\n    for name in names:\n        print(name)\n    print(names)\n\n    with open(b'\\xe4\\xbd\\xa0\\xe5\\xa5\\xbd.txt', 'r') as f:\n        print(f.read())\n\ndef do_file_detach_3layers():\n    import io\n\n    fn = 'sample.txt'\n    f = open(fn, 'w')\n    print('f:', f)\n    print('f.buffer:', f.buffer)\n    print('f.buffer.raw:', f.buffer.raw)\n\n    f0 = f.detach()\n    print('f0:', f0)\n\n    f1 = io.TextIOWrapper(f0, encoding='latin-1')\n    print('f1:', f1)\n\ndef do_read_binary_into_textfile():\n    fn = 'sample0.txt'\n\n    with open(fn, 'at') as f:\n        f.buffer.write(b'FFABC\\n') # write to file's binary buffer directly\n\ndef do_tempfile():\n    import tempfile\n\n    with tempfile.TemporaryFile('w+t') as f:\n        print(f)\n        print(f.name)\n        f.write('testing')\n        f.seek(0)\n        print(f.readline())\n\n    print(\"temp folder:\", tempfile.gettempdir())\n    with tempfile.NamedTemporaryFile('w+t') as f:\n        print(f)\n        print(f.name)\n\n    with tempfile.TemporaryDirectory() as dirname:\n        print('dir is:', dirname)\n\ndef do_serialize_obj():\n    import os\n    import pickle\n\n    fn = 'serialize_obj.pickle'\n    if not os.path.exists(fn):\n        with open(fn, 'xb') as f:\n            pass\n\n    with open(fn, 'wb') as f:\n        print(f)\n        pickle.dump([1, 2, 3, 4, 5], f)\n        pickle.dump({\"leo\": 100, \"john\": 10, \"mike\": 60}, f)\n        pickle.dump(\"this is good\", f)\n\n    with open(fn, 'rb') as f:\n        print(pickle.load(f))\n        print(pickle.load(f))\n        print(pickle.load(f))\n\n    s0 = pickle.dumps([1, 2, 3, 4, 5])\n    s1 = pickle.dumps({\"leo\": 100, \"john\": 10, \"mike\": 60})\n    print(s0)\n    print(s1)\n\n    obj0 = pickle.loads(s0)\n    obj1 = pickle.loads(s1)\n    print(obj0)\n    print(obj1)\n\n    print(' use json: '.center(25, '#'))\n    import json\n    fn = 'serialize_obj.json'\n    if not os.path.exists(fn):\n        with open(fn, 'xt') as f:\n            pass\n\n    ''' jason can only dump/load one object, so need to combine all the\n    record into one obj'''\n    with open(fn, 'w') as f:\n        print(f)\n        save = []\n        save.append([1, 2, 3, 4, 5])\n        save.append({\"leo\": 100, \"john\": 10, \"mike\": 60})\n        save.append(\"this is good\")\n        json.dump(save, f)\n\n    with open(fn, 'r') as f:\n        print(f)\n        save = json.load(f)\n        for obj in save:\n            print(obj)\n\n    s0 = json.dumps([1, 2, 3, 4, 5])\n    s1 = json.dumps({\"leo\": 100, \"john\": 10, \"mike\": 60})\n    print(s0)\n    print(s1)\n\n    obj0 = json.loads(s0)\n    obj1 = json.loads(s1)\n    print(obj0)\n    print(obj1)\n\nclass C05TestCase(TestCase):\n    def test_test(self):\n        #s = input(\"input something:\")\n        do_serialize_obj()\n        print(\"Test Done\".center(80, '-'))\n\nif __name__ == '__main__':\n    main()\n","sub_path":"python/python_cookbook/c05_file_and_io.py","file_name":"c05_file_and_io.py","file_ext":"py","file_size_in_byte":7432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"72784370","text":"#!/usr/bin/python3\n'''Lists all State objects from the database hbtn_0e_6_usa'''\nfrom sys import argv\nfrom relationship_state import Base, State\nfrom relationship_city import City\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\n\nif __name__ == '__main__':\n    connection = 'mysql+mysqldb://{}:{}@localhost/{}'\n    engine = create_engine(connection.format(*argv[1:]),\n                           pool_pre_ping=True)\n\n    Base.metadata.create_all(engine)\n    session_m = sessionmaker(bind=engine)\n    session = session_m()\n\n    response = session.query(City, State).filter(City.state_id == State.id)\\\n                      .order_by(City.id).all()\n    for city, state in response:\n        print('{}: {} -> {}'.format(city.id, city.name, state.name))\n\n    session.close()\n","sub_path":"0x0F-python-object_relational_mapping/102-relationship_cities_states_list.py","file_name":"102-relationship_cities_states_list.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"30779338","text":"import discord\nfrom discord.ext import commands\n\nbot = commands.Bot(command_prefix=commands.when_mentioned_or('!'))\n\ngame = discord.Game(\"Under construction\")\n\n\n@bot.event\nasync def on_ready():\n    print('Bot is ready.')\n    await bot.change_presence(status=discord.Status.do_not_disturb, activity=game)\n\n\n@bot.command(name='test')\n@commands.is_owner()\nasync def test(ctx):\n    await ctx.message.add_reaction('\\U0001F4AA')\n    await ctx.author.send('This is not a test.')\n\nwith open('token.txt') as f:\n    token = f.read()\n\nbot.run(token)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"537042701","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# 8.\tОпределить, является ли год, который ввел пользователем,\n# високосным или не високосным.\n# Год является високосным в двух случаях: либо он кратен 4,\n# но при этом не кратен 100, либо кратен 400.\n\n\ndef is_leap_year(year):\n    if (year % 400 == 0\n            or (year % 100 != 0\n                and year % 4 == 0)):\n        return True\n    return False\n\n\nif __name__ == '__main__':\n    year = int(input(\"Введите год: \"))\n    print(\"Это високосный год:\", 'Да' if is_leap_year(year) else 'Нет')\n","sub_path":"Lesson_1/8.py","file_name":"8.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"229267268","text":"from dataflows import Flow, load, printer, set_type, update_resource\nfrom datapackage_pipelines_migdar.flows.dump_to_es import DumpToElasticSearch\nfrom datapackage_pipelines_migdar.flows.constants import PUBLICATIONS_ES_REVISION\nimport os\n\n\nos.environ.setdefault('DPP_ELASTICSEARCH', 'localhost:19200')\n\n\ndef update_schema(package):\n    for resource in package.pkg.descriptor['resources']:\n        resource['schema']['primaryKey'] = ['migdar_id']\n    yield package.pkg\n    yield from package\n\n\ndef flow(*args):\n    is_dpp = len(args) > 3\n    return Flow(\n        load('data/publications_for_es/datapackage.json'),\n        lambda row: dict(row, json=''),\n        update_schema,\n        set_type('json', resources=None, type='object', **{'es:index': False}),\n        DumpToElasticSearch({'migdar': [{'resource-name': 'unique_records',\n                                         'doc-type': 'publications',\n                                         'revision': PUBLICATIONS_ES_REVISION}]})(),\n        DumpToElasticSearch({'migdar': [{'resource-name': 'search_app_records',\n                                         'doc-type': 'publications',\n                                         'revision': PUBLICATIONS_ES_REVISION}]})(),\n        printer(tablefmt='plain' if is_dpp else 'html', num_rows=1, fields=['doc_id']),\n        update_resource(None, **{'dpp:streaming': True})\n    )\n","sub_path":"datapackage_pipelines_migdar/flows/dump_publications_to_es.py","file_name":"dump_publications_to_es.py","file_ext":"py","file_size_in_byte":1372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"645918114","text":"# Python bytecode 2.7 (decompiled from Python 2.7)\n# Embedded file name: scripts/client/cgf_components/portal_manager.py\nimport CGF\nfrom cgf_script.managers_registrator import onAddedQuery, onRemovedQuery\nfrom cgf_components import IsSelected, IsHighlighted\nfrom EventPortal import EventPortal\nfrom EventVehicle import EventVehicle\nfrom gui.shared import events, EVENT_BUS_SCOPE, g_eventBus\nfrom gui.wt_event.wt_event_helpers import g_execute_after_all_event_vehicles_loaded\nfrom skeletons.gui.game_control import IEventBattlesController\nfrom skeletons.gui.shared.utils import IHangarSpace\nfrom skeletons.prebattle_vehicle import IPrebattleVehicle\nfrom helpers import dependency\n\nclass PortalSelectionManager(CGF.ComponentManager):\n    gameEvent = dependency.descriptor(IEventBattlesController)\n    prebattleVehicle = dependency.descriptor(IPrebattleVehicle)\n    hangarSpace = dependency.descriptor(IHangarSpace)\n    portalQuery = CGF.QueryConfig(EventPortal, CGF.GameObject)\n    vehicleQuery = CGF.QueryConfig(EventVehicle, CGF.GameObject)\n    selectedQuery = CGF.QueryConfig(CGF.GameObject, EventVehicle, IsSelected)\n    highlightedQuery = CGF.QueryConfig(CGF.GameObject, IsHighlighted)\n\n    def activate(self):\n        if self.hangarSpace.spaceInited:\n            self.__onSpaceCreated()\n        else:\n            self.hangarSpace.onSpaceCreate += self.__onSpaceCreated\n        g_eventBus.handleEvent(events.LobbySimpleEvent(events.HangarSimpleEvent.PORTAL_MANAGER_ACTIVATED), scope=EVENT_BUS_SCOPE.LOBBY)\n\n    def deactivate(self):\n        self.hangarSpace.onSpaceCreate -= self.__onSpaceCreated\n\n    @onAddedQuery(IsSelected, EventVehicle)\n    def onVehicleSelected(self, _, vehicle):\n        self.prebattleVehicle.switchCamera(vehicle)\n\n    @onAddedQuery(IsSelected, EventPortal)\n    def onPortalSelected(self, _, __):\n        self.__removeHighlight()\n\n    @onRemovedQuery(IsSelected, EventPortal)\n    def onPortalDeselected(self, _, __):\n        self.__removeHighlight()\n        self.__removeSelection()\n\n    @onAddedQuery(IsHighlighted, EventVehicle)\n    def onVehicleHighlighted(self, _, entity):\n        if not entity.isDestroyed:\n            entity.setHighlight(True, fallback=True)\n\n    @onRemovedQuery(IsHighlighted, EventVehicle)\n    def onVehicleFaded(self, _, entity):\n        if not entity.isDestroyed:\n            entity.setHighlight(False, fallback=True)\n\n    @onAddedQuery(IsHighlighted, EventPortal)\n    def onPortalHighlighted(self, _, entity):\n        if not self.gameEvent.isEventPrbActive():\n            for _, gameObject in self.vehicleQuery:\n                if gameObject.findComponentByType(IsHighlighted) is None:\n                    gameObject.createComponent(IsHighlighted)\n\n            if not entity.isDestroyed:\n                entity.setHighlight(True, fallback=True)\n        return\n\n    @onRemovedQuery(IsHighlighted, EventPortal)\n    def onPortalFaded(self, _, entity):\n        for _, gameObject in self.vehicleQuery:\n            gameObject.removeComponentByType(IsHighlighted)\n\n        if not entity.isDestroyed:\n            entity.setHighlight(False, fallback=True)\n\n    @g_execute_after_all_event_vehicles_loaded\n    def __onSpaceCreated(self):\n        if self.gameEvent.isEventPrbActive():\n            self.prebattleVehicle.selectAny()\n\n    def __removeHighlight(self):\n        for go, _ in self.highlightedQuery:\n            go.removeComponentByType(IsHighlighted)\n\n    def __removeSelection(self):\n        for go, _, __ in self.selectedQuery:\n            go.removeComponentByType(IsSelected)\n","sub_path":"source/res/scripts/client/cgf_components/portal_manager.py","file_name":"portal_manager.py","file_ext":"py","file_size_in_byte":3534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"156493955","text":"from database.models import User, Location, Step\n\n\nclass QueryManagerMixin:\n    model = None\n\n    @staticmethod\n    def add(session, instance):\n        session.add(instance)\n\n    @staticmethod\n    def add_all(session, instances):\n        session.add_all(instances)\n\n    @classmethod\n    def get(cls, session, **kwargs):\n        return session.query(cls.model).filter_by(**kwargs).first()\n\n    @classmethod\n    def create(cls, session, **kwargs):\n        instance = cls.model(**kwargs)\n        session.add(instance)\n        return instance\n\n    @classmethod\n    def get_or_create(cls, session, **kwargs):\n        instance = cls.get(session, **kwargs)\n        if instance is None:\n            instance = cls.create(session, **kwargs)\n        return instance\n\n\nclass UserQueryManager(QueryManagerMixin):\n    model = User\n\n\nclass LocationQueryManager(QueryManagerMixin):\n    model = Location\n\n\nclass StepQueryManager(QueryManagerMixin):\n    model = Step\n","sub_path":"database/query.py","file_name":"query.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"108755185","text":"#! /usr/bin/env python\n\"\"\"\nA simple Burrows-Wheeler transform function in python.\nAlgorithm presented in:\nBurrows M, Wheeler DJ: A Block Sorting Lossless Data Compression Algorithm.\n    Technical Report 124. Palo Alto, CA: Digital Equipment Corporation; 1994.\nUSAGE: bwt.py [-h] [-i INDEX] STRING\n\"\"\"\n\nimport argparse\n\ndef bw_transform(s):\n    n = len(s)\n    m = sorted([s[i:n]+s[0:i] for i in range(n)])\n    I = m.index(s)\n    L = ''.join([q[-1] for q in m])\n    return (I, L)\n\nfrom operator import itemgetter\n\ndef bw_restore(I, L):\n    n = len(L)\n    X = sorted([(i, x) for i, x in enumerate(L)], key=itemgetter(1))\n\n    T = [None for i in range(n)]\n    for i, y in enumerate(X):\n        j, _ = y\n        T[j] = i\n\n    Tx = [I]\n    for i in range(1, n):\n        Tx.append(T[Tx[i-1]])\n\n    S = [L[i] for i in Tx]\n    S.reverse()\n    return ''.join(S)\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(\n        description='Transforms strings using Burrows-Wheeler transform')\n    parser.add_argument('-i', '--index', metavar=\"INDEX\", help='Performs ' +\n        'inverse BWT. A transform index must be specified as an INDEX value.')\n    parser.add_argument('STRING', type=str, help='Transform this string.')\n    args = parser.parse_args()\n\n    if args.index:\n        print(bw_restore(args.index, args.STRING))\n    else:\n        print(\"%d %s\" % bw_transform(args.STRING))\n","sub_path":"ass2/onlineBwtencode.py","file_name":"onlineBwtencode.py","file_ext":"py","file_size_in_byte":1386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"470045252","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar  8 13:33:43 2018\n\n@author: pourasgh\n\"\"\"\n\n\nimport nibabel\nfrom matplotlib import pyplot as plt\nimport os\nimport numpy as np\nfrom config import *\nfrom STN import *\nimport tensorflow as tf\nimport glob\n\nfrom scipy import ndimage\n\nconfig = Config(is_train= True)\n\ndispm_dir = config.dispmDir\n\n\n\n\n\nKorM=1\npaitent_num=10\ngt_frame =13\n\n\nimages = []\nfiles = glob.glob (\"/home/ameneh/UofA/Datasets/mnist_png/training/0/*.png\")\nfor myFile in files:\n    image = ndimage.imread (myFile)\n    image = image / 255.\n    image = image.astype('float32')\n    images.append (image)\ncount = len(images)\n        \nmov_data = np.empty([count, 28, 28], dtype=np.float32)\nfor i in range (count):\n    mov_data[i,:,:]= images[i]\nfor slice_ in range(count):#range():\n\n    \n\n    \n    \n    \n    \n    \n    \n    # Iterate through slices\n    # for slice_ in range(4,5):  \n    \n    \n    \n    st=r'/home/ameneh/UofA/git/DIRegistration_Manifold/tmp/tmp_0000/lr_1E-04,lr_1E-04,bn=false,do=true,usefc,1,_,5,_,1000.npy'\n    for fdmname2 in [st]:\n               #str3 + str(paitent_num) + str(slice_)+str4 + str(slice_)+',_,2000.npy',\n               #str3 + str(paitent_num) + str(slice_)+str4 + str(slice_)+',_,3000.npy']:\n               #,str3 + str(paitent_num) + str(slice_)+str4 + str(slice_)+',_,4000.npy'\n               #,str3 + str(paitent_num) + str(slice_)+str4 + str(slice_)+',_,5000.npy']:# range(data_im.shape[2]): \n        print(fdmname2)\n        if not os.path.exists(\"/home/ameneh/results_dl/\"+str(1000)):\n            os.mkdir(\"/home/ameneh/results_dl/\"+str(1000))\n        \n        \n        \n        file = open(\"/home/ameneh/results_dl/\"+str(1000)+\"/test1file.txt\",\"a+\") \n       \n        # check if there is a displacement matrix\n        if os.path.isfile(fdmname2):    \n            \n           \n            DM_ = np.load(open(fdmname2, 'rb'))\n           \n            \n            \n            \n            \n            sess = tf.Session()\n            im_warp = mov_data[slice_,:,:]\n            #        image = image/255\n#             plt.figure(100)\n#             plt.imshow(im_warp, 'gray')\n#             plt.show()\n            \n\n            \n            image1 =ndimage.imread('/home/ameneh/UofA/Datasets/mnist_png/training/fixed.png')\n            im_target = image1.astype('float32')     \n            im_target = np.squeeze(im_target)\n            \n            pixelMap = im_warp\n            pixelMap2 = im_target\n            print(im_warp.shape)\n            im_mov= tf.placeholder(tf.float32, [1,im_warp.shape[0],im_warp.shape[1]])\n            dispM = tf.placeholder(tf.float32, [1,im_warp.shape[0],im_warp.shape[1],2])\n            #####################################################################\n            \n#             for i in range(im_warp.shape[0]):\n#                 for j in range(im_warp.shape[1]):\n#                     #print(float(im_warp[i,j]))\n#                     if float(im_warp[i,j]) <3:\n#                         pixelMap[i,j] =255\n#                     else:\n#                         pixelMap[i,j] = 0\n#                         #         print(\"frame 0 \")\n#                         #         plt.imshow(pixelMap, cmap = 'gray')\n#                         #         plt.show()\n#                         \n#             for i in range(im_target.shape[0]):\n#                 for j in range(im_target.shape[1]):\n#                     #print(float(im_warp[i,j]))\n#                     if float(im_target[i,j]) <3:\n#                         pixelMap2[i,j] =255\n#                     else:\n#                         pixelMap[i,j] = 0\n#                         \n#                         #         print(\"frame 12 \")\n#                         #         plt.imshow(pixelMap2, cmap = 'gray')\n#                         #         plt.show()\n#                         #         \n#             im_warp = pixelMap/255\n            im_warp = np.expand_dims(im_warp, 0)\n#             im_target = pixelMap2/255\n            #im_target2 = np.expand_dims(im_target, 0)\n            \n            im = im_warp\n            im_warp = np.squeeze(im_warp)\n#             plt.imshow(im_warp, 'gray')\n#             plt.show()\n            if KorM == 0:\n                pass\n            else:\n                for i in range (1):\n        \n                    print(i)\n                    disp=DM_[i,:,:,:]\n                    print(slice_, \"disp: \", disp.shape)\n                    disp = np.expand_dims(disp,0)\n                    im_warp2 =STN(im_mov, dispM)\n                    im=sess.run(im_warp2, {im_mov:im, dispM : disp})\n                    im2 = np.squeeze(im)\n                \n               \n               # plt.figure()\n    \n                #plt.imshow(im_target, 'gray', interpolation='none', alpha=0.9)\n                if fdmname2.endswith('.npy'):\n                    fdmname2 = fdmname2[:-4]\n                    fdmname2 = fdmname2[105:]\n#                 str2 = \"/home/ameneh/results_dl/\"+str(1000)+'/'+str(slice_)+fdmname2 + '.png'\n#                 print(str2)\n#                 \n#                 plt.savefig(str2, bbox_inches='tight')\n#                 \n#                 plt.figure(2)\n#                 plt.imshow(im_target, 'gray')\n#                 \n#                 plt.savefig(\"/home/ameneh/results_dl/\"+str(1000)+'/'+str(slice_)+fdmname2 + '_gt.png', bbox_inches='tight')\n#                 \n                plt.figure(3)\n                from skimage import util  \n                #im2 = util.invert(im2)\n                \n                plt.imshow(im2, 'gray')\n                \n                plt.savefig(\"/home/ameneh/results_dl/\"+str(1000)+'/'+str(slice_)+fdmname2 + '_re.png', bbox_inches='tight')\n                #plt.show()\n        \n#        for i in range (12):\n#        \n#            print(i)\n#            dispm=DM[i,:,:,:]\n#            dispm = np.expand_dims(dispm,0)\n#            im_warp3 =STN(im_mov, dispm)\n#            im3=sess.run(im_warp3, {im_mov:im3, dispM : dispm})\n#            im4 = np.squeeze(im3)\n#              \n        #plt.imshow(im2, cmap = 'gray' )  \n                im = np.squeeze(im) \n                file.write(str( np.sum(im[im_target==1])*2.0 / (np.sum(im)+ np.sum(im_target)))+\"\\r\\n\")\n                print( np.sum(im[im_target>=1])*2.0 / (np.sum(im)+ np.sum(im_target)))\n        else:\n            continue\n   \n    file.close()\n        \n#        print( np.sum(im3[im_target==0])*2.0 / (np.sum(im3)+ np.sum(im_target) ))    \n","sub_path":"dice_digit.py","file_name":"dice_digit.py","file_ext":"py","file_size_in_byte":6407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"335765200","text":"import pandas as pd\n\ndef pandasToInsertSQL(\n    dataframe,\n    schemaname='myschema',\n    tablename='mytable'):\n    \"\"\"Convert a pandas dataframe to an SQL INSERT statement.\n    \n    Keyword arguments:\n      dataframe (pandas dataframe):\n        The data to INSERT.\n      schemaname (string):\n        The database schema to write to.\n      tablename (string):\n        The database table to write to.\n    \n    Example 1:\n      >>> mydf = pd.DataFrame(\n      ... ['a','b'],\n      ... columns=['col1'])\n      >>> pandasToInsertSQL(mydf)\n      \"INSERT INTO myschema.mytable (col1) VALUES ('a'),('b')\"\n    \n    Example 2:\n      >>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})\n      >>> pandasToInsertSQL(df2)\n      'INSERT INTO myschema.mytable (a,b) VALUES (1,3),(2,4)'\n    \n    Returns:\n      String. An SQL INSERT statement.\n    \"\"\"\n    \n    ## First, store dataframe values as comma-separated strings\n    ## in a list\n    allcsrows = []\n    for i in range(dataframe.shape[0]):\n        ## Store each cell value for 1 row into a list\n        currow = []\n        for curcell in dataframe.loc[i,:]:\n            ## Add quotation marks to any strings\n            if str(type(curcell))==\"<class 'str'>\":\n                curcell = \"'\" + curcell + \"'\"\n            else:\n                curcell = str(curcell)\n            currow.append(curcell)\n        ## Construct 1 Comma-separated row\n        csrow = \",\".join(currow)\n        csrow = \"(\" + csrow + \")\"\n        allcsrows.append(csrow)\n    \n    ## Construct the SQL INSERT statement\n    dfcolnames = dataframe.columns.values.astype('str')\n    sql = \"INSERT INTO %s.%s (%s) VALUES %s\" %(\n        schemaname,\n        tablename,\n        \",\".join(dfcolnames),\n        \",\".join(allcsrows))\n    \n    ## pandas nan's SQL equivalent is NULL so we have to replace\n    ## those\n    NULLalias = 'nan'\n    sql = sql.replace(',' + NULLalias + ',', ',NULL,')\n    sql = sql.replace('(' + NULLalias + ',', '(NULL,')\n    sql = sql.replace(',' + NULLalias + ')', ',NULL)')\n    \n    return(sql)","sub_path":"toolbox.py","file_name":"toolbox.py","file_ext":"py","file_size_in_byte":2018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"161507803","text":"import os\nimport tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\n\nINPUT_NODE = 784\nOUTPUT_NODE = 10\nLAYER1_NODE = 700\nREGULARIZER = 0.0001\nBATCH_SIZE = 100\nLEARNING_RATE_BASE = 0.8\nLEARNING_RATE_DECAY = 0.99\nMOVING_AVERAGE_DECAY = 0.99\nTRAIN_STEPS = 10000\n\nMODEL_SAVE_PATH = './model'\nMODEL_NAME = 'mnist_model'\n\ndef get_weight(shape , regularizer):\n    w = tf.Variable(tf.truncated_normal(shape , stddev = 0.1))\n    if regularizer !=None:\n        tf.add_to_collection('losses' , tf.contrib.layers.l2_regularizer(regularizer)(w))\n    return w\n\ndef get_bias(shape):\n    b = tf.Variable(tf.constant(0.1, shape=shape))\n    return b\n\ndef forward(input_tensor , average_class , regularizer ):\n    # 定义神经网络参数\n    w1 = get_weight([INPUT_NODE, LAYER1_NODE], regularizer)\n    b1 = get_bias([LAYER1_NODE])\n    w2 = get_weight([LAYER1_NODE , OUTPUT_NODE] , regularizer)\n    b2 = get_bias([OUTPUT_NODE])\n    # 判断是否使用滑动平均\n    if average_class == None :\n        layer1 = tf.nn.relu(tf.matmul(input_tensor , w1) + b1)\n        out_tensor = tf.matmul(layer1 , w2) + b2\n    else :\n        layer1 =tf.nn.relu(tf.matmul(input_tensor , average_class.average(w1)) + average_class.average(b1))\n        out_tensor = tf.nn.relu(tf.matmul(layer1 , average_class.average(w2)) + average_class.average(b2))\n    return out_tensor\n\n\ndef train(mnist):\n    # 占位函数定义输入输出\n    x = tf.placeholder(tf.float32 , [None , INPUT_NODE] , name = 'x-input')\n    y_ = tf.placeholder(tf.float32 , [None , OUTPUT_NODE] , name = 'y-output')\n    # 定义前向传播过程\n    y = forward(x , None , REGULARIZER)\n    # 定义反向传播过程\n    ## 定义全局训练轮数张量（不可训练）\n    global_step = tf.Variable(0 , trainable = False)\n    ## 定义损失函数\n    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels = tf.argmax(y_ , 1) , logits = y)\n    cross_entropy_mean = tf.reduce_mean(cross_entropy)\n    loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))\n    ## 设置指数衰减学习率\n    learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE , global_step , mnist.train.num_examples/BATCH_SIZE , LEARNING_RATE_DECAY , staircase = True)\n    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss , global_step = global_step)\n    ## 定义滑动平均\n    variable_average = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY , global_step)\n    variable_average_op = variable_average.apply(tf.trainable_variables())\n    train_op = tf.group(train_step , variable_average_op)\n    # 定义一个saver\n    saver = tf.train.Saver()\n    # 开始训练\n    with tf.Session() as sess:\n        # 初始化计算图张量\n        tf.global_variables_initializer().run()\n        # 判断是否已有训练模型\n        ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)\n        if ckpt and ckpt.model_checkpoint_path:\n            saver.restore(sess , ckpt.model_checkpoint_path)\n            # global_step= tf.Variable(eval(ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]) ,\n            #                          trainable = False )\n        for i in range(TRAIN_STEPS):\n            xs , ys = mnist.train.next_batch(BATCH_SIZE)\n\n            _ , loss_value , step = sess.run([train_op , loss , global_step] , feed_dict = {x:xs , y_:ys})\n            if i%1000 ==0:\n                print(\"after %d training step(s) , loss on training batch is %g\"%(step , loss_value))\n                # 保存模型\n                saver.save(sess , os.path.join(MODEL_SAVE_PATH , MODEL_NAME) , global_step = global_step)\n\ndef main():\n    mnist = input_data.read_data_sets(\"./data_sets\" , one_hot = True)\n    train(mnist)\n\nif __name__ == '__main__':\n    main()\n\n\n\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"232291285","text":"\"\"\"Eular Problem 21 -\n    Evaluate the sum of all the amicable numbers under 10000.\n\"\"\"\n\nimport primes\nimport timer\n\nDESC = 'Amicable numbers:'\nSOLUTION = 31626\n\n\ndef add_amicables(limit):\n    \"\"\" Adds all the amicable numbers up to limit and returns the sum. \"\"\"\n    amicables = set()\n    div_sums = primes.divisor_sum_dict(limit)\n\n    for k, v in div_sums.items():\n        # Skip divisor sums that are equal to or over the limit - they won't be in the keys.\n        # Also - k and v cannot be equal. k != v.\n        if v >= limit or k == v:\n            continue\n\n        # Since we have the divisor sum of k as v, we can conversely lookup the opposite\n        # div_sums[v] and check if it equals k.\n        if div_sums.get(v, -1) == k:\n            amicables.add(k)\n\n    return sum(amicables)\n\n\n@timer.timeit\ndef solve():\n    LIMIT = 10000\n    # Add all of the amicable numbers together\n    return add_amicables(LIMIT)\n","sub_path":"problem21.py","file_name":"problem21.py","file_ext":"py","file_size_in_byte":921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"446675779","text":"#!env/bin/python\n\nimport os\nimport numpy as np\nimport scipy\nfrom scipy.misc import imsave,imresize\nimg_dir = 'original_sketch/'\nnew_img_dir = 'adjusted_sketch/'\nimg_file_list = []\n\nimg_file = open('original_list.txt','r')\n\nfor line in img_file:\n    line = line.strip()\n    img_file_list.append(line)\n\nfor img in img_file_list:\n    try:\n        img_raw = scipy.misc.imread(os.path.join(img_dir,img))\n        img_raw = np.array(img_raw)\n        img_raw = img_raw[121:581,201:571]#new img size is [460,370]\n        img_raw = imresize(img_raw,[256,256])\n        imsave(os.path.join(new_img_dir,img),img_raw)\n    except:\n        print('%s does not exist' % img)\n","sub_path":"tensorlayer/hd_img_adjust.py","file_name":"hd_img_adjust.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"118488276","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\nimport tweepy\n\nconsumer_key=\"コンシューマキー\"\nconsumer_secret=\"コンシューマシークレット\"\naccess_token=\"アクセストークン\"\naccess_secret=\"アクセスシークレット\"\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_secret)\napi = tweepy.API(auth)\nprint('Done!')","sub_path":"TwitterAPI/tp.py","file_name":"tp.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"513736141","text":"import concurrent.futures\nimport sys\n\npool = concurrent.futures.ThreadPoolExecutor()\n\ndef utf8_check(path, encod='UTF-8'):\n  fo = open(path, \"rb\").read()\n  try:\n    fo.decode(\"utf-8\")\n    print(f'{path} is in UTF-8')\n  except UnicodeDecodeError:\n    print(f'{path} is NOT in utf-8')\n\n\npool.map(utf8_check, sys.argv[1:])\n","sub_path":"homework/homework12/peer_review_hw12/vj.py","file_name":"vj.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"514398418","text":"from __future__ import print_function\n\nimport sys\nfrom bisect import insort\nfrom itertools import izip\nfrom collections import defaultdict\n\n\nclass ValidationError(ValueError):\n    pass\n\n\ndef append_to_log(filename, timestamps, employees, guests, encryptor):\n    with open(filename, 'wb') as opened_file:\n        opened_file.write(encryptor.encrypt([timestamps, employees, guests]))\n\n\ndef change_status(human, history, humans_in, rooms):\n    room_id = history[1][-1]\n    if room_id >= 0:\n        insort(humans_in, human)\n        insort(rooms[room_id], human)\n    elif room_id == -1:\n        insort(humans_in, human)\n\n\ndef print_status(employees, guests):\n    rooms = defaultdict(list)\n    employees_in = []\n    guests_in = []\n    for employee, history in employees.iteritems():\n        change_status(employee, history, employees_in, rooms)\n    print(','.join(employees_in))\n\n    for guest, history in guests.iteritems():\n        change_status(guest, history, guests_in, rooms)\n    print(','.join(guests_in))\n\n    for room in sorted(rooms.iterkeys()):\n        print(str(room) + ': ' + ','.join(rooms[room]))\n\n\ndef print_room_id(arguments, employees, guests):\n    if arguments['employee']:\n        room_history = employees[arguments['employee'][0]][1]\n    else:\n        room_history = guests[arguments['guest'][0]][1]\n    print(','.join([str(room) for room in room_history if room >= 0]))\n\n\ndef print_total_time(arguments, timestamp, employees, guests):\n    if arguments['employee']:\n        history = employees[arguments['employee'][0]]\n    else:\n        history = guests[arguments['guest'][0]]\n    if not history:\n        sys.exit(0)\n\n    total_time = 0\n    in_gallery = False\n    for time, position in izip(*history):\n        if position == -1 and not in_gallery:\n            in_gallery = True\n            arrival_time = time\n        if position == -2:\n            in_gallery = False\n            total_time += time - arrival_time\n    if in_gallery:\n        total_time += timestamp - arrival_time\n\n    print(total_time)\n\n\ndef print_rooms(arguments, employees, guests):\n    history = []\n    humans = {}\n    for employee in arguments['employee']:\n        if employee not in employees:\n            return\n        human = 'E' + employee\n        humans[human] = -2\n        for time, position in izip(*employees[employee]):\n            insort(history, [time, position, human])\n    for guest in arguments['guest']:\n        if guest not in guests:\n            return\n        human = 'G' + guest\n        humans[human] = -2\n        for time, position in izip(*guests[guest]):\n            insort(history, [time, position, human])\n    if not history:\n        sys.exit(0)\n\n    rooms = set()\n    for time, position, human in history:\n        humans[human] = position\n        if len(set(humans.itervalues())) == 1 and position >= 0:\n            rooms.add(str(position))\n\n    print(','.join(sorted(rooms)))\n","sub_path":"129/code/build/log_libraries/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"114793538","text":"import cv2\nimport mediapipe as mp\n\n\ncapture = cv2.VideoCapture(0)\n\nmpHands = mp.solutions.hands\nhands = mpHands.Hands()\nmpDraw = mp.solutions.drawing_utils\n\npTime = 0\ncTime = 0\nface_cascade = cv2.CascadeClassifier('haar_face.xml')\nwhile True:\n    istrue, shus = capture.read()\n    frame = cv2.resize(shus, (1100, 800), interpolation=cv2.INTER_AREA)\n    colorgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n    result = hands.process(colorgb)\n\n\n\n\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n    faces = face_cascade.detectMultiScale(gray, 1.1, 4)\n    for (x, y, w, h) in faces:\n        cv2.putText(frame,\"SHUSANKET BASYAL\", (x,y-80), cv2.FONT_HERSHEY_DUPLEX, 1.0,\n                    (170,255,0),2)\n        cv2.putText(frame, \"AGE:20\", (x, y - 30), cv2.FONT_HERSHEY_DUPLEX, 1.0,\n                    (170,255,0), 2)\n        cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 255), 2)\n\n    # print(result.multi_hand_landmarks)\n\n    if result.multi_hand_landmarks:\n        for handif in result.multi_hand_landmarks:\n            for id, lm in enumerate(handif.landmark):\n                print(id, lm)\n                h, w, c = frame.shape\n                cx, cy = int(lm.x * w), int(lm.y * h)\n\n\n\n            mpDraw.draw_landmarks(frame, handif, mpHands.HAND_CONNECTIONS)\n\n\n\n    # cTime = time.time()\n    # fps = 1/(cTime-pTime)\n    # pTime = cTime\n\n\n\n\n\n\n\n    cv2.imshow(\"frame\", frame)\n\n    if cv2.waitKey(1) == ord('s'):\n        break\n\ncapture.release()\ncv2.destroyAllWindows()\n\n\n\n\n\n\n\n\n","sub_path":"shusanket.py","file_name":"shusanket.py","file_ext":"py","file_size_in_byte":1490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"568122796","text":"import networkx as nx\nimport tnetwork as tn\nimport leidenalg as la\nimport sortedcontainers\nimport time\n\nfrom tnetwork.utils.community_utils import single_list_community2nodesets\ntry:\n    import igraph as ig\nexcept ModuleNotFoundError:\n    ig = None\n\n\ndef __from_graph_tool_to_nx(graph, node_map=None, directed=None):\n\n    if directed is None:\n        directed = graph.is_directed()\n\n    if directed:\n        tp = nx.DiGraph()\n    else:\n        tp = nx.Graph()\n\n    tp.add_nodes_from([int(v) for v in graph.vertices()])\n    tp.add_edges_from([(int(e.source()), int(e.target()))\n                       for e in graph.edges()])\n    if node_map:\n        nx.relabel_nodes(tp, node_map, copy=False)\n\n    return tp\n\n\ndef __from_nx_to_igraph(g, directed=None):\n    \"\"\"\n    :param g:\n    :param directed:\n    :return:\n    \"\"\"\n\n    if ig is None:\n        raise ModuleNotFoundError(\n            \"Optional dependency not satisfied: install igraph to use the selected feature.\")\n\n    if directed is None:\n        directed = g.is_directed()\n\n    gi = ig.Graph(directed=directed)\n\n    ##Two problems to handle:\n    # 1)in igraph, names have to be str.\n    # 2)since we can ask to compute metrics with found communities and the the original graph, we need to keep\n    #the original nodes types in communities. Therefore we need to handle some transparent conversion for non-str nodes.\n    if type(list(g.nodes)[0]) is str: #if nodes are string, no problem\n        gi.add_vertices([n for n in g.nodes()])\n        gi.add_edges([(u, v) for (u, v) in g.edges()])\n\n    else:\n        if set(range(len(g.nodes)))==set(g.nodes()):#if original names are well formed contiguous ints, keep this for efficiency.\n            # Put these int as str with identitiers in the name attribute\n            gi.add_vertices(len(g.nodes))\n            gi.add_edges([(u, v) for (u, v) in g.edges()])\n            gi.vs[\"name\"]=[\"\\\\\"+str(n) for n in g.nodes()]\n        else: #if names are not well formed ints, convert to string and use the identifier to remember converting back to int\n            #convert = {str(x):x for x in g.nodes()}\n            gi.add_vertices([\"\\\\\"+str(n) for n in g.nodes()])\n            gi.add_edges([(\"\\\\\"+str(u), \"\\\\\"+str(v)) for (u, v) in g.edges()])\n            #for k,v in convert.items():\n            #    gi.vs[\"name\"][k]=v\n\n\n    edgelist = nx.to_pandas_edgelist(g)\n    for attr in edgelist.columns[2:]:\n        gi.es[attr] = edgelist[attr]\n\n    return gi\n\n\n\n\ndef transversal_network_leidenalg(dyn_graph:tn.DynGraphSN, interslice_weight=1,elapsed_time=False):\n    \"\"\"\n    Multiplex community detection reimplemented in leidenalg\n\n    Algorithm described in [1]\n    (see method `mucha_original` for more information)\n    This function use the implementation in the leidenalg library instead of the original matlab implementation.\n    It requires the installation of the leidenalg library (including igraph).\n    It is usually slower than the original implementation (but does not require matlab)\n\n    [1]Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J. P. (2010).\n    Community structure in time-dependent, multiscale, and multiplex networks.\n    science, 328(5980), 876-878.\n\n    :param dyn_graph: dynamic network\n    :param interslice_weight:\n    :param elapsed_time:\n    :return:\n    \"\"\"\n    print(\"preprocessing transversal network leidenalg \")\n\n\n    graphs = dyn_graph.snapshots()\n    igraph_graphs = sortedcontainers.SortedDict()\n    for t,g in graphs.items():\n        igraph_graphs[t]=__from_nx_to_igraph(g)\n\n    start_time = time.time()\n    print(\"calling external code\")\n\n    coms,scores = la.find_partition_temporal(list(igraph_graphs.values()),\n                               la.ModularityVertexPartition,\n                               interslice_weight=interslice_weight,\n                               vertex_id_attr=\"name\")\n    duration = time.time() - start_time\n    print(\"postprocessing \")\n\n    to_return = tn.DynCommunitiesSN()\n    ts  =list(igraph_graphs.keys())\n    for i in range(len(coms)):\n        t= ts[i]\n        partition = single_list_community2nodesets(coms[i],igraph_graphs[t].vs[\"name\"])\n        to_return.set_communities(t,partition)\n\n    print(\"sucessfully finished transversal network leidenalg  \")\n\n    if elapsed_time:\n        return (to_return,{\"total\":duration})\n    return to_return","sub_path":"build/lib/tnetwork/DCD/externals/MuchaLeiden.py","file_name":"MuchaLeiden.py","file_ext":"py","file_size_in_byte":4331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"418545868","text":"# Easy    Codewriting     300\n#\n# Given a positive integer number and a certain length, we need to modify the given number to have a specified length. We are allowed to do that either by cutting out leading digits (if the number needs to be shortened) or by adding 0s in front of the original number.\n#\n# Example\n#\n# For number = 1234 and width = 2, the output should be\n# integerToStringOfFixedWidth(number, width) = \"34\";\n# For number = 1234 and width = 4, the output should be\n# integerToStringOfFixedWidth(number, width) = \"1234\";\n# For number = 1234 and width = 5, the output should be\n# integerToStringOfFixedWidth(number, width) = \"01234\".\n# Input/Output\n#\n# [execution time limit] 4 seconds (py3)\n#\n# [input] integer number\n#\n# A non-negative integer.\n#\n# Guaranteed constraints:\n# 0 ≤ number ≤ 109.\n#\n# [input] integer width\n#\n# A positive integer representing the desired length.\n#\n# Guaranteed constraints:\n# 1 ≤ width ≤ 50.\n#\n# [output] string\n#\n# The modified version of number as described above.\n\ndef integerToStringOfFixedWidth(number, width):\n    if len(str(number)) > width:\n        number = list(str(number))\n        for i in range(len(number) - width):\n            del number[0]\n        return ''.join(number)\n\n    if len(str(number)) <= width:\n        for i in range(width - len(str(number))):\n            number = '0' + str(number)\n        return str(number)\n","sub_path":"codesignal/The Core/Well of Integration/Integer to String Of Fixed Width.py","file_name":"Integer to String Of Fixed Width.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"365561969","text":"from migen.fhdl.std import *\nfrom migen.bank.description import *\nfrom migen.bank.eventmanager import *\nfrom migen.genlib.fifo import SyncFIFOBuffered\n\nfrom liteusb.ftdi.std import *\n\nclass FtdiUART(Module, AutoCSR):\n\tdef __init__(self, tag, fifo_depth=64):\n\t\tself.tag = tag\n\n\t\tself._rxtx = CSR(8)\n\n\t\tself.submodules.ev = EventManager()\n\t\tself.ev.tx = EventSourcePulse()\n\t\tself.ev.rx = EventSourceLevel()\n\t\tself.ev.finalize()\n\n\t\tself.source = source = Source(user_layout)\n\t\tself.sink = sink = Sink(user_layout)\n\n\t\t###\n\n\t\t# TX\n\t\ttx_start = self._rxtx.re\n\t\ttx_done  = self.ev.tx.trigger\n\n\t\tself.sync += \\\n\t\t\tIf(tx_start,\n\t\t\t\tsource.stb.eq(1),\n\t\t\t\tsource.d.eq(self._rxtx.r),\n\t\t\t).Elif(tx_done,\n\t\t\t\tsource.stb.eq(0)\n\t\t\t)\n\n\t\tself.comb += [\n\t\t\tsource.sop.eq(1),\n\t\t\tsource.eop.eq(1),\n\t\t\tsource.length.eq(1),\n\t\t\tsource.dst.eq(self.tag),\n\t\t\ttx_done.eq(source.stb & source.ack),\n\t\t]\n\n\t\t# RX\n\t\trx_available = self.ev.rx.trigger\n\n\t\trx_fifo = SyncFIFOBuffered(8, fifo_depth)\n\t\tself.submodules += rx_fifo\n\t\tself.comb += [\n\t\t\trx_fifo.we.eq(sink.stb),\n\t\t\tsink.ack.eq(sink.stb & rx_fifo.writable),\n\t\t\trx_fifo.din.eq(sink.d),\n\n\t\t\trx_available.eq(rx_fifo.readable),\n\t\t\trx_fifo.re.eq(self.ev.rx.clear),\n\t\t\tself._rxtx.w.eq(rx_fifo.dout)\n\t\t]\n","sub_path":"misoclib/com/liteusb/frontend/uart.py","file_name":"uart.py","file_ext":"py","file_size_in_byte":1220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"189303957","text":"import os\n\n\n\n\n\n# откриваєш Телегу шукаєшь @BotFather, создаешь нового бота копируєш токен в кавички нижче\n\nTOKEN = \"1903114881:AAHPnaUsbGWxb-vu3r4cSrrZpT42ON5vBqw\" #цей удалить\n\n# пользовательське сооглашение пиши сам\nRULES = \"Пользовательське соглашение и т.д... https://telegra.ph/Polzovatelskoe-soglashenie-12-12\"\n\n# имя твого бота типо @BotMy \nBOT_NAME=\"@KOSMOLOT_WIN_Bot\" \n\n# силка на челика из тех поддержки мож свою добавить типо @myname_123\nSUPPORT_LINK = \"@ONI_CHan\"\n\n\n\n\n\n# регайся на wayforpay.com создай магазин(сам розберешся) введеш данні і т.д.\n# зайдеш в меню (Мои магазини/Настройки магазина) вибираєш свій магазин шукаєш пункт Реквизити магазина\n\n# копіруєш значення MERCHANT LOGIN в MERCH_ID:\n\nMERCH_API = \"cc510ade846c7c4739e4bcc0c7edfd736d7ef34f\"\nMERCH_ID = \"freelance_user_6114532096e3a\"\n\n#советую разообраться с настройкой магазина и активировать так как изначально он в тестовом режиме тоесть если оплатить \n# товар то через 15 минут деньги вернуться но товар через бота уже будет показан!!!\n\n\n\n# 1 регестрируйся на https://github.com/ \n# 2 создай репозиторий\n# 3 перекинь туда всі файли з архіву\n# 4 зарегестрируйся на heroku.com\n# 5 создай нове приложения на цьом сайті\n# 6 назви його і це названія встав сюди\n\nHEROKU_APP_NAME = \"telegrambotcs\"\n\n# 7 Deployment method вибери GitHub и встав названія репозиторія\n# 8 ждеш поки за білдиться проект\n\n# ГОТОВО!\n\n\n# ============================== Не трогать ===========================================\n\n# webhook settings\nWEBHOOK_HOST = f'https://{HEROKU_APP_NAME}.herokuapp.com'\nWEBHOOK_PATH = f'/webhook/{TOKEN}'\nWEBHOOK_URL = f'{WEBHOOK_HOST}{WEBHOOK_PATH}'\n\n# webserver settings\nWEBAPP_HOST = '0.0.0.0'\nWEBAPP_PORT = int(os.getenv('PORT'))\n\n\nMERCH_BOT_URL = \"https://t.me/{}\".format(BOT_NAME[1:])\n","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"325678921","text":"from bs4 import BeautifulSoup\nfrom requests import session\nimport config\nimport sqlite3, re\n\n'''\n This module uses BeautifulSoup to get all event names from the <strong> tag.\n The links are provided from event_links.txt (which I won't need later)\n The database is updated with new event names, links, and event ids\n'''\n\ndbn = \"saltybet_schema\"\n\n# Open database connection\ndb = sqlite3.connect(config.saltystats)\ndbc = db.cursor()\n\n# Sign into Salty Bet\nwith session() as c:\n    c.post('https://www.saltybet.com/authenticate?signin=1', data=config.payload)\n\n    # Read event links file\n    with open(config.events,'r') as f:\n        for link in f:\n            response = c.get(link)\n\n            try:\n\n                # open page text in BeautifulSoup\n                page = response.text\n                soup = BeautifulSoup(page, \"lxml\")\n\n                # Get event name from top of page\n                event_tag = soup.find('strong')\n                event = event_tag.text\n\n                # Get event id from URL\n                event_split = re.search('(\\d+)$', link)\n                event_d = event_split.group(1)\n\n                query = 'INSERT OR IGNORE INTO events (`event_name`,`event_link`,`event_id`)\\\n                VALUES (\"{0}\", \"{1}\", {2};'.format(event, link, event_d)\n\n                dbc.execute(query)\n                db.commit()\n\n                print(\"Name: {0} ID: {2} - Link: {1}\".format(event, link, event_d))\n\n            except Exception as e:     # most generic exception you can catch\n                log = open(config.eventslog, 'w')\n                log.write(\"{0}\".format(str(e)))\n\n            finally:\n                pass\n\ndb.close()","sub_path":"Resources/Tests/fetches/events.py","file_name":"events.py","file_ext":"py","file_size_in_byte":1669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"16796883","text":"from django.db import models\nfrom django.contrib.auth.models import User\nfrom django.utils.timezone import localtime\n\nimport datetime\nimport math\n\nclass SleepManager(models.Manager):\n    def totalSleep(self):\n        sleeps =  Sleep.objects.all()\n        return sum((sleep.end_time - sleep.start_time for sleep in sleeps),datetime.timedelta(0))\n\n    def sleepTimes(self,res=1):\n        sleeps = Sleep.objects.all()\n        atTime = [0] * (24 * 60 / res) \n        for sleep in sleeps:\n            startDate = localtime(sleep.start_time).date()\n            endDate = localtime(sleep.end_time).date()\n            dr = [startDate + datetime.timedelta(i) for i in range((endDate-startDate).days + 1)]\n            for d in dr:\n                if d == startDate:\n                    startTime = localtime(sleep.start_time).time()\n                else:\n                    startTime = datetime.time(0)\n                if d == endDate:\n                    endTime = localtime(sleep.end_time).time()\n                else:\n                    endTime = datetime.time(23,59)\n                for i in range((startTime.hour * 60 + startTime.minute) / res, (endTime.hour * 60 + endTime.minute + 1) / res):\n                    atTime[i]+=1\n        return atTime\n\n\nclass Sleep(models.Model):\n    objects = SleepManager()\n\n    user = models.ForeignKey(User)\n    start_time = models.DateTimeField()\n    end_time = models.DateTimeField()\n    comments = models.TextField(blank=True)\n    date = models.DateField()\n\n    def __unicode__(self):\n        return \"Sleep from %s to %s\" % (self.start_time,self.end_time)\n\n    def length(self):\n        return self.end_time - self.start_time\n\n    def overlaps(self, otherSleep):\n        return (min(self.end_time, otherSleep.end_time) - max(self.start_time, otherSleep.start_time) < datetime.timedelta(0))\n\n    def validate_unique(self, exclude=None):\n        #Yes this is derpy and inefficient and really should actually use the exclude field\n        from django.core.exceptions import ValidationError, NON_FIELD_ERRORS\n        sleepq = Sleep.objects.filter(user=self.user)\n        for i in sleepq:\n            if not overlaps(self, i): raise ValidationError(u'Error: Overlapping Sleep Detected! You can\\' be asleep twice at the same time!')\n\nclass Allnighter(models.Model):\n    user = models.ForeignKey(User)\n    date = models.DateField()\n    comments = models.TextField(blank=True)\n\nclass SleeperProfile(models.Model):\n    user = models.OneToOneField(User)\n    # all other fields should have a default\n    PRIVACY_HIDDEN = -100\n    PRIVACY_REDACTED = -50\n    PRIVACY_NORMAL = 0\n    PRIVACY_STATS = 50\n    PRIVACY_PUBLIC = 100\n    PRIVACY_CHOICES = (\n            (PRIVACY_HIDDEN, 'Hidden'),\n            (PRIVACY_REDACTED, 'Redacted'),\n            (PRIVACY_NORMAL, 'Normal'),\n            (PRIVACY_STATS, 'Stats Public'),\n            (PRIVACY_PUBLIC, 'Sleep Public'),\n            )\n    privacy = models.SmallIntegerField(choices=PRIVACY_CHOICES,default=PRIVACY_NORMAL,verbose_name='Privacy to anonymous users')\n    privacyLoggedIn = models.SmallIntegerField(choices=PRIVACY_CHOICES,default=PRIVACY_NORMAL,verbose_name='Privacy to logged-in users')\n    privacyFriends = models.SmallIntegerField(choices=PRIVACY_CHOICES,default=PRIVACY_NORMAL,verbose_name='Privacy to friends')\n    friends = models.ManyToManyField(User,related_name='friends+',blank=True)\n    follows = models.ManyToManyField(User,related_name='follows+',blank=True)\n    use12HourTime = models.BooleanField(default=False)\n\n    emailreminders = models.BooleanField(default=False)\n\n    def __unicode__(self):\n        return \"SleeperProfile for user %s\" % self.user\n\nclass SleeperManager(models.Manager):\n    def sorted_sleepers(self,sortBy='zScore',user=None):\n        sleepers = Sleeper.objects.all().prefetch_related('sleep_set','sleeperprofile')\n        scored=[]\n        extra=[]\n        for sleeper in sleepers:\n            if len(sleeper.sleepPerDay())>2 and sleeper.sleepPerDay(packDates=True)[-1]['date'] >= datetime.date.today()-datetime.timedelta(5):\n                p = sleeper.getOrCreateProfile()\n                if user is None:\n                    priv = p.PRIVACY_HIDDEN\n                elif user is 'all':\n                    priv = p.PRIVACY_PUBLIC\n                elif user.is_anonymous():\n                    priv = p.privacy\n                elif user.pk==sleeper.pk:\n                    priv = p.PRIVACY_PUBLIC\n                else:\n                    priv = p.privacyLoggedIn\n\n                if priv<=p.PRIVACY_REDACTED:\n                    sleeper.displayName=\"[redacted]\"\n                else:\n                    sleeper.displayName=sleeper.username\n                if priv>p.PRIVACY_HIDDEN:\n                    d=sleeper.decayStats()\n                    d['user']=sleeper\n                    if user.is_authenticated() and user.pk==sleeper.pk:\n                        d['opcode']='me' #I'm using opcodes to mark specific users as self or friend.\n                    else:\n                        d['opcode'] = None\n                    scored.append(d)\n            else:\n                if 'is_authenticated' in dir(user) and user.is_authenticated() and user.pk == sleeper.pk:\n                    d = sleeper.decayStats()\n                    d['rank']='n/a'\n                    sleeper.displayName=sleeper.username\n                    d['user']=sleeper\n                    d['opcode']='me'\n                    extra.append(d)\n        scored.sort(key=lambda x: -x[sortBy])\n        for i in xrange(len(scored)):\n            scored[i]['rank']=i+1\n        return scored+extra\n\nclass Sleeper(User):\n    class Meta:\n        proxy = True\n\n    objects = SleeperManager()\n\n    def getOrCreateProfile(self):\n        return SleeperProfile.objects.get_or_create(user=self)[0]\n\n    def timeSlept(self,start=datetime.date.min,end=datetime.datetime.max):\n        sleeps = self.sleep_set.filter(date__gte=start,date__lte=end)\n        return sum([s.end_time-s.start_time for s in sleeps],datetime.timedelta(0))\n\n    def sleepPerDay(self,start=datetime.date.min,end=datetime.date.max,packDates=False,hours=False):\n        if start==datetime.date.min and end==datetime.date.max:\n            sleeps = self.sleep_set.values('date','start_time','end_time')\n        else:\n            sleeps = self.sleep_set.filter(date__gte=start,date__lte=end).values('date','start_time','end_time')\n        if sleeps:\n            dates=map(lambda x: x['date'], sleeps)\n            first = min(dates)\n            last = max(dates)\n            n = (last-first).days + 1\n            dateRange = [first + datetime.timedelta(i) for i in range(0,n)]\n            byDays = [sum([(s['end_time']-s['start_time']).total_seconds() for s in filter(lambda x: x['date']==d,sleeps)]) for d in dateRange]\n            if hours:\n                byDays = map(lambda x: x/3600,byDays)\n            if packDates:\n                return [{'date' : first + datetime.timedelta(i), 'slept' : byDays[i]} for i in range(0,n)]\n            else:\n                return byDays\n        else:\n            return []\n\n    def movingStats(self,start=datetime.date.min,end=datetime.date.max):\n        sleep = self.sleepPerDay(start,end)\n        d = {}\n        try:\n            avg = sum(sleep)/len(sleep)\n            d['avg']=avg\n            if len(sleep)>2:\n                stDev = math.sqrt(sum(map(lambda x: (x-avg)**2, sleep))/(len(sleep)-1.5)) #subtracting 1.5 is correct according to wikipedia\n                d['stDev']=stDev\n                d['zScore']=avg-stDev\n        except:\n            pass\n        try:\n            avgSqrt = (sum(map(lambda x: math.sqrt(x),sleep))/len(sleep))**2\n            d['avgSqrt']=avgSqrt\n        except:\n            pass\n        for k in ['avg','stDev','zScore','avgSqrt']:\n            if k not in d:\n                d[k]=datetime.timedelta(0)\n            else:\n                d[k]=datetime.timedelta(0,d[k])\n        return d\n\n    def decaying(self,data,hl,stDev=False):\n        s = 0\n        w = 0\n        for i in range(len(data)):\n            s+=2**(-i/float(hl))*data[-i-1]\n            w+=2**(-i/float(hl))\n        if stDev:\n            w = w*(len(data)-1.5)/len(data)\n        return s/w\n\n    def decayStats(self,end=datetime.date.max,hl=4):\n        sleep = self.sleepPerDay(datetime.date.min,end)\n        d = {}\n        try:\n            avg = self.decaying(sleep,hl)\n            d['avg']=avg\n            stDev = math.sqrt(self.decaying(map(lambda x: (x-avg)**2,sleep),hl,True))\n            d['stDev']=stDev\n            d['zScore']=avg-stDev\n        except:\n            pass\n        try:\n            avgSqrt = self.decaying(map(lambda x: math.sqrt(x),sleep),hl)**2\n            d['avgSqrt']=avgSqrt\n        except:\n            pass\n        for k in ['avg','stDev','zScore','avgSqrt']:\n            if k not in d:\n                d[k]=datetime.timedelta(0)\n            else:\n                d[k]=datetime.timedelta(0,d[k])\n        return d\n\n","sub_path":"sleep/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":8916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"415564777","text":"from django.conf.urls.defaults import *\n\n# Uncomment the next two lines to enable the admin:\nfrom django.contrib import admin\n\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n    # Uncomment the admin/doc line below to enable admin documentation:\n#    (r'^dds/admin/doc/', include('django.contrib.admindocs.urls')),\n\n    #(r'^dds/', 'scrape.dds.views.subscribe'),\n\n    # Uncomment the next line to enable the admin:\n    (r'^admin/', include(admin.site.urls)),\n\n    (r'', include('scrape.dds.urls')),\n    (r'^app/', include('scrape.dds.urls')),\n\n\n)\n","sub_path":"urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"348050381","text":"__author__ = 'verasraul'\nimport requests\nimport json\n\n# https://api.github.com/usrs/:gitUser\ndef git_user_api(User):\n    site = requests.get(f\"https://api.github.com/users/{User}\")\n    siteResponse = site.json()\n    return json.dumps(siteResponse, indent=4, sort_keys=True)\n\n\ndef gemini():\n    site = requests.get(\"https://api.gemini.com/v1/pubticker/btcusd\")\n    siteResponse = site.json()\n    return json.dumps(siteResponse, indent=4, sort_keys=True)\n\n\nprint(git_user_api(\"verasraul\"))\n# print(gemini())","sub_path":"PracticeProjects/MiniCalc/MiniCalcClient/ClienTester.py","file_name":"ClienTester.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"78083876","text":"from sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import cross_val_score, train_test_split\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport PartB\n\n\nneighbors = [1, 3, 5, 10, 15]\nlegends = [f'{neighbor} neighbors' for neighbor in neighbors]\nkNNClassifiers = []\ndistanceMethods = {\"manhattan\": 1, \"euclidean\": 2}\n\nfig, ax = plt.subplots()\nax.set_xlabel(\"Number of neighbors\")\nax.set_ylabel(\"Mean accuracy\")\n\nfor method in distanceMethods.values():\n    for numberOfNeighBors in neighbors:\n        kNNClassifiers.append(KNeighborsClassifier(\n            n_neighbors=numberOfNeighBors, p=method))\n\n\ndata = np.loadtxt(open('Lab4PokerData.txt', 'rb'),\n                  delimiter='\\n ', dtype='str')\n\nactions_list = PartB.removeCommas(data)\nPartB.removeEmptyString(actions_list)\nactions_list = PartB.ConvertToNumerical(actions_list)\n\n# Split data.\ntrain_set, test_set = train_test_split(actions_list, test_size=0.2)\n\n# Extract final actions of p2 (target values).\np2_final_actions_train = PartB.extractPlayer2FinalActionsTest(train_set)\np2_final_actions_test = PartB.extractPlayer2FinalActionsTest(test_set)\n\n\nfor kNN in kNNClassifiers:\n    cross_validation_score = cross_val_score(\n        kNN, test_set, p2_final_actions_test, cv=3)\n    mean_acc = np.mean(cross_validation_score)\n    ax.scatter(kNN.n_neighbors, mean_acc,\n               label=f'{kNN.n_neighbors} neighbors with {list(distanceMethods.keys())[list(distanceMethods.values()).index(list(kNN.get_params().values())[6])]} distance')\nplt.ylabel(\"Average accuracy\")\nplt.xlabel(\"Number of neighbors\")\nplt.legend()\nplt.show()\n","sub_path":"Lab4/Lab4/Task 2/PartC.py","file_name":"PartC.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"104669880","text":"from module.protocol.network.messages.NetworkMessage import NetworkMessage\n\n\nclass HouseGuildRightsMessage(NetworkMessage):\n    def __init__(self, buffer_reader, len_type, length, count=None):\n        NetworkMessage.__init__(self, buffer_reader, len_type, length, count)\n        self.id = 8382\n        self.houseId = {\"type\": \"uint\", \"value\": \"\"}\n        self.instanceId = {\"type\": \"uint\", \"value\": \"\"}\n        self.secondHand = {\"type\": \"Boolean\", \"value\": \"\"}\n        self.guildInfo = {\"type\": \"GuildInformations\", \"value\": \"\"}\n        self.rights = {\"type\": \"uint\", \"value\": \"\"}\n","sub_path":"module/protocol/network/messages/HouseGuildRightsMessage.py","file_name":"HouseGuildRightsMessage.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"630497598","text":"import subprocess\nimport numpy as np\nfrom scipy import signal\n\nclass Converter(object):\n    def __init__(self, defaultBits = 16, defaultFs=16000, defaultChannel=1):\n        self.defaultBits = defaultBits\n        self.defaultFs = defaultFs\n        self.defaultChannel = defaultChannel\n\n    def convertFile(self, filePath, bits=0, fs=0, channels=0):\n        \"\"\"\n        Adapted from https://github.com/mozilla/DeepSpeech/blob/master/native_client/python/client.py\n        \"\"\"\n        if bits == 0 or fs == 0 or channels == 0:\n            # Use defaults\n            bits = self.defaultBits\n            fs = self.defaultFs\n            channels = self.defaultChannel\n        soxCmd = 'sox {} --type raw --bits {} --channels {} --rate {} - '.format(\n                filePath, bits, channels, fs)\n\n        try:\n            p = subprocess.Popen(soxCmd.split(),\n                                stderr=subprocess.PIPE, stdout=subprocess.PIPE)\n            output, err = p.communicate()\n\n            if p.returncode:\n                raise RuntimeError('SoX returned non-zero status: {}'.format(err))\n\n        except OSError as e:\n            raise OSError('SoX not found, use 16kHz files or install it: ', e)\n\n        audio = np.fromstring(output, dtype=np.int16)\n        return fs, audio\n\n    def convert(self, audio, currentFs, bits=0, fs=0, channels=0):\n        if bits == 0 or fs == 0 or channels == 0:\n            # Use defaults\n            bits = self.defaultBits\n            fs = self.defaultFs\n            channels = self.defaultChannel\n\n        resampleRatio = float(currentFs) / fs\n\n        resampledAudio = signal.resample(audio, int(audio.shape[0]/resampleRatio))\n        resampledAudio = resampledAudio.astype(np.int16)\n\n        return fs, resampledAudio\n\nif __name__ == '__main__':\n    import scipy.io.wavfile as wav\n    conv = Converter()#defaultFs=3000)\n    fs, audio = wav.read('/home/aslab/csse4011/speechAnalysis/speechAnalysis/teddyResampledTo3000.wav')#'/home/aslab/csse4011/speechAnalysis/speechAnalysis/en-0533.wav')\n    newfs, resamp = conv.convert(audio, fs)\n    print(fs, '-->', newfs)\n    wav.write('teddy3000UpsampledTo16000.wav', newfs, resamp)","sub_path":"speechFeatures/Converter.py","file_name":"Converter.py","file_ext":"py","file_size_in_byte":2161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"458035665","text":"from django.shortcuts import render\nfrom django.http import HttpResponse, JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom rest_framework.parsers import JSONParser\nfrom rest_framework import status\nfrom .models import User, Users, MeetingsTable, CommentsTable\nfrom rest_framework import status\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom schedule.serializers import UserDemoSerializer, UsersSerializer, MeetingsTableSerializer\nfrom datetime import datetime\nfrom django.utils import timezone\nimport pytz\n# Create your views here.\n\n\ndef home(request):\n    user = request.user\n    if request.method=='GET' and user.is_authenticated:\n        email = user.email\n        User2 = Users.objects.get(email=email)\n        serializer = UsersSerializer(User2)\n        return JsonResponse(serializer.data, safe=False)\n    else:\n        return HttpResponse(\"please login first\")\n\ndef register(request):\n    if request.method=='GET':\n        return HttpResponse(\"Please register first\")\n\nclass UserCreate(APIView):\n    def post(self, request, format='json'):\n        serializer = UserDemoSerialzier(data=request.data)\n        serializer_data = UsersSerializer(data=request.data)\n        if serializer.is_valid():\n            user = serializer.save()\n            user2 = serializer_data.save()\n            if user and user2:\n                return Response(serializer.data, status=status.HTTP_201_CREATED)\n        return Response(serializer.error, status=status.HTTP_400_BAD_REQUEST)\n\n@csrf_exempt\ndef schedule_list(request):\n    \n    user = request.user\n    if user.is_authenticated:\n        if request.method == 'GET':\n            meetings_invited_in = []\n            user = Users.objects.get(email=user.email)\n            if user.admin==1:\n                meetings = MeetingsTable.objects.filter(meeting_date_time__gte=datetime.now())\n            else:\n                all_meetings = MeetingsTable.objects.all()\n                for meet in all_meetings:\n                    if email == meet.scheduler_id.email:\n                        meetings_invited_in.append(meet.id)\n                    else:\n                        invited_people = meet.people_invited.all()\n                        for person in invited_people:\n                            if email == person.email:\n                                meetings_invited_in.append(meet.id)\n                meetings = MeetingsTable.objects.filter(meeting_date_time__gte=timezone.now()).filter(id__in=meetings_invited_in)\n            serializer = MeetingsTableSerializer(meetings, many=True)\n            return JsonResponse(serializer.data, safe=False)\n\n        elif request.method == 'POST':\n            data = JSONParser().parse(request)\n            serializer = MeetingsTableSerializer(data=data)\n            if serializer.is_valid():\n                serializer.save()\n                return Jsonresponse(serializer.data, status=201)\n            return JsonResponse(serializer.errors, status=400)\n\n@csrf_exempt\ndef meeting_detail(request, pk):\n    user = request.user\n    if user.is_authenticated():\n        try:\n            meeting = MeetingsTable.object.get(id=pk)\n            user = Users.objects.get(email=user.email)\n        except MeetingsTable.DoesNotExist and Users.DoesNotExist:\n            return HttpResponse(status=404)\n\n        if request.method == 'GET':\n            serializer = MeetingsTableSerializer(meeting)\n            return Jsonresponse(serializer.data)\n\n        elif request.method == 'DELETE':\n            if user.admin==1 or user.email==meeting.scheduler_id:\n                meeting.delete()\n                return HttpResponse(status=204)\n            else:\n                return HttpResponse(\"You are not authorized to delete this meeting\")\n\n        elif request.method == 'PUT':\n            if user.admin==1 or user.email==meeting.scheduler_id:\n                data = JSONParser().parse(request)\n                serializer = MeetingsTableSerializer(meetings, data=data)\n                if serializer.is_valid():\n                    return Jsonresponse(serializer.data)\n                return JsonResponse(serializer.errors, status=400)\n            else:\n                return HttpResponse(\"You are not authorized to change this meetings details\")\n","sub_path":"IMGSched_project/schedule/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"372978570","text":"\"\"\"\n1. Написать любую детерменированную функцию (Детерменированная функция = функция, которая возвращает одно и тоже вне\nзависимости от парамеметров)\n\"\"\"\nimport json\n\n\"\"\"\n2. Написать функцию, которая вернет True если число четное и False если не четное\n\"\"\"\n\n\ndef is_even(number):\n    return not bool(number % 2)\n\n\ndef is_odd(number):\n    return bool(number % 2)\n\n\n\"\"\"\n3. Напишите функция is_prime, которая принимает 1 аргумент (число) и возвращает True, если число простое, иначе False\nПростое число - это число, которое делится без остатка только на себя и на 1\n\"\"\"\n\n\ndef is_prime(value):\n    if value == 1:\n        return False\n    d = 2\n    while d * d <= value and value % d != 0:\n        d += 1\n    return d * d > value\n\n\n# print(is_prime(1))\n# print(is_prime(2))\n# print(is_prime(3))\n# print(is_prime(4))\n# print(is_prime(5))\n# print(is_prime(6))\n# print(is_prime(7))\n# print(is_prime(8))\n# print(is_prime(9))\n\n\"\"\"\n4. Напишите функцию, которая принимает 1 аргумент (строка) и выполняет следующие действия на каждую из букв строки:\ni - инкремент (+1)\nd - дикремент (-1)\ns - возведение в квадрат\no - добавить число в результативный список\nостальные буквы игнорируются\nИсходное число = 0\nРезультативный список = []\nВернуть результативный список\nparse(\"iiisdoso\")  ==>  [8, 64] <- это как пример\n\"\"\"\n\n\ndef parse(operations):\n    initial = 0\n    res = []\n    for op in operations:\n        if op == 'i':\n            initial += 1\n        elif op == 'o':\n            res.append(initial)\n        elif op == 'd':\n            initial -= 1\n        elif op == 's':\n            initial **= 2\n    return res\n\n\n# assert parse(\"iiisdoso\") == [8, 64]\n\n\"\"\"\n5. Написать программу, которая откроет файл (questions.json) и после ответов на вопросы, запишет их назад \nв этот же файл.\n\"\"\"\n\n\ndef answer_questions(file_path):\n    with open(file_path) as file:\n        data = json.load(file)\n\n    for idx, q in enumerate(data.get('questions', [])):\n        data['questions'][idx]['answer'] = input(q['q'])\n\n    with open(file_path, 'w') as file:\n        json.dump(data, file, indent=4)\n\n\nanswer_questions('questions.json')\n","sub_path":"lk7/home_review.py","file_name":"home_review.py","file_ext":"py","file_size_in_byte":2757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"381139932","text":"# -*- coding: utf-8 -*-\n# vim: set fenc=utf-8 ai ts=4 sw=4 sts=4 et:\n\n\"\"\" Inner Task list. \"\"\"\n\n# ::AUTOFILL copyleft::\n# ::AUTOFILL license::\n\n# ::IDEA http://www.tummy.com/Community/Articles/django-pagination/::\n\nfrom django import template\n\nfrom tasks.views import TaskListView\n\nregister = template.Library()\n\n\n@register.inclusion_tag('tasks/inline_list.html', takes_context=True)\ndef inline_task_list(context, qs, sort_get='tasksort'):\n    \"\"\"\n    Displays a sortable list of tasks for the given task queryset.\n    \"\"\"\n    view = TaskListView()\n    view.sort_get = sort_get\n    view.request = context['request']\n\n    return {\n        'fields': view.build_context_fields(),\n        'task_list': view.extend_queryset(qs),\n    }\n\n\n","sub_path":"tasks/templatetags/inline_task_list.py","file_name":"inline_task_list.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"455187917","text":"#!/usr/bin/python3\nfrom tkinter import *\nwindow = Tk()\nwindow.title(\"Welcom to python gui\")\nwindow.geometry('350x200')\nlbl = Label(window, text=\"Hello\", font=(\"Arial Bold\", 12))\nlbl.grid(column=0, row=0)\ntxt = Entry(window, width=10)\ntxt.grid(column=1, row=0)\ndef clicked():\n    res = \"Welcom to \" + txt.get()\n    lbl.configure(text=res)\nbtn = Button(window, text=\"Click Me\", command=clicked)\nbtn.grid(column=0, row=1)\nwindow.mainloop()\n","sub_path":"python/mygui.py","file_name":"mygui.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"361985003","text":"\"\"\"Tests for Runge-Kutta initialization.\"\"\"\nimport numpy as np\nimport pytest\n\nfrom probnum import diffeq, randvars\nfrom tests.test_diffeq.test_odefilter.test_initialization_routines import (\n    _interface_initialize_test,\n)\n\n\nclass TestRungeKuttaInitialization(\n    _interface_initialize_test.InterfaceInitializationRoutineTest\n):\n    @pytest.fixture(autouse=True)\n    def _setup(self):\n        self.rk_init = (\n            diffeq.odefilter.initialization_routines.RungeKuttaInitialization(\n                dt=1e-1, method=\"RK45\"\n            )\n        )\n\n    def test_call(\n        self, lotka_volterra, lotka_volterra_inits, lotka_volterra_testcase_order\n    ):\n\n        prior_process = self._construct_prior_process(\n            order=lotka_volterra_testcase_order,\n            spatialdim=lotka_volterra.dimension,\n            t0=lotka_volterra.t0,\n        )\n\n        received_rv = self.rk_init(ivp=lotka_volterra, prior_process=prior_process)\n\n        # Extract the relevant values\n        expected = lotka_volterra_inits\n\n        # The higher derivatives will have absolute difference ~8%\n        # if things work out correctly\n        assert isinstance(received_rv, randvars.Normal)\n        np.testing.assert_allclose(received_rv.mean, expected, rtol=0.25)\n        assert np.linalg.norm(received_rv.std) > 0\n\n    def test_is_exact(self):\n        assert self.rk_init.is_exact is False\n\n    def test_requires_jax(self):\n        assert self.rk_init.requires_jax is False\n","sub_path":"tests/test_diffeq/test_odefilter/test_initialization_routines/test_runge_kutta.py","file_name":"test_runge_kutta.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"483106447","text":"\"\"\"\nLeast Recently Used Cache.\n\"\"\"\n\nclass Node(object):\n    def __init__(self, val, pre=None, next=None):\n        self.val = val\n        self.pre = pre\n        self.next = next\n\nclass DoublyLinkedList(object):\n    def __init__(self):\n        self.head = None\n        self.tail = None\n        self.size = 0\n        \n    def insert(self, val, index=0):\n        if self.head is None:\n            self.head = self.tail = Node(val)\n        elif index == 0:\n            node = Node(val, next=self.head)\n            self.head.pre = node\n            self.head = node\n        else:\n            node = self.head\n            while index > 0 and node.next is not None:\n                index -= 1\n                node = node.next\n            if index == 0:\n                newnode = Node(val, node.pre, node)\n                node.pre.next = newnode\n                node.pre = newnode\n            else:\n                newnode = Node(val, pre=node)\n                node.next = newnode\n                self.tail = newnode\n        self.size += 1\n    \n    def popright(self):\n        if self.tail is None:\n            pass\n        else:\n            if self.tail.pre is not None:\n                self.tail.pre.next = None\n            self.tail = self.tail.pre\n            self.size -= 1\n    \n    def printl(self):\n        node = self.head\n        while node is not None:\n            print(node.val,)\n            node = node.next\n        print\n\nclass LRUCache(object):\n\n    def __init__(self, capacity):\n        \"\"\"\n        :type capacity: int\n        \"\"\"\n        self.size = capacity\n        self.deque = DoublyLinkedList()\n        self.hashmap = dict()\n\n    def remove(self, node):\n        if node is None:\n            return\n        if node.pre is None:\n            self.deque.head = node.next\n        else:\n            node.pre.next = node.next\n        if node.next is None:\n            self.deque.tail = node.pre\n        else:\n            node.next.pre = node.pre\n        del self.hashmap[node.val[0]]\n        del node\n        self.deque.size -= 1\n        \n    def get(self, key):\n        \"\"\"\n        :rtype: int\n        \"\"\"\n        if key not in self.hashmap:\n            return -1\n        \n        node = self.hashmap[key]\n        val = node.val[1]\n        \n        self.remove(node)\n        self.deque.insert((key, val))\n        self.hashmap[key] = self.deque.head\n        \n        return val\n\n    def set(self, key, value):\n        \"\"\"\n        :type key: int\n        :type value: int\n        :rtype: nothing\n        \"\"\"\n        if key not in self.hashmap:\n            if self.deque.size == self.size:\n                del self.hashmap[self.deque.tail.val[0]]\n                self.deque.popright()\n        else:\n            node = self.hashmap[key]\n            self.remove(node)\n        \n        self.deque.insert((key, value))\n        self.hashmap[key] = self.deque.head\n","sub_path":"Python/Exercises/lru.py","file_name":"lru.py","file_ext":"py","file_size_in_byte":2860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"636394922","text":"# Import libraries\nimport pandas as pd\nimport numpy as np\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt #%matplotlib inline\n\nfrom plotly.offline import iplot, init_notebook_mode\nfrom geopy.geocoders import Nominatim\nimport plotly.plotly as py\n\nFIFA18 = pd.read_csv('CompleteDataset.csv', low_memory=False)\nFIFA18.columns\n\ninteresting_columns = [\n    'Name',\n    'Age',\n    'Nationality',\n    'Overall',\n    'Potential',\n    'Club',\n    'Value',\n    'Wage',\n    'Preferred Positions'\n]\nFIFA18 = pd.DataFrame(FIFA18, columns=interesting_columns)\n\ndef str2number(amount):\n    if amount[-1] == 'M':\n        return float(amount[1:-1]) * 1000000\n    elif amount[-1] == 'K':\n        return float(amount[1:-1]) * 1000\n    else:\n        return float(amount[1:])\n\n\nFIFA18['ValueNum'] = FIFA18['Value'].apply(lambda x: str2number(x))\nFIFA18['WageNum'] = FIFA18['Wage'].apply(lambda x: str2number(x))\nFIFA18['Position'] = FIFA18['Preferred Positions'].str.split().str[0]\n\n# Grouping the data by countries\nvalcon = FIFA18.groupby(\"Nationality\").size().reset_index(name=\"Count\")\n\n#best squad analysis\nFIFA18 = FIFA18[['Name', 'Age', 'Nationality', 'Overall', 'Potential', 'Club', 'Position', 'Value', 'Wage']]\n#print (FIFA18.head(10))\n\n\ndef get_best_squad(formation):\n    FIFA18_copy = FIFA18.copy()\n    store = []\n\n    # iterate through all positions in the input formation and get players with highest overall respective to the position\n    for i in formation:\n        store.append([\n            i,\n            FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'] == i]['Overall'].idxmax()]]['Name'].to_string(\n                index=False),\n            FIFA18_copy[FIFA18_copy['Position'] == i]['Overall'].max(),\n            FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'] == i]['Overall'].idxmax()]]['Age'].to_string(\n                index=False),\n            FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'] == i]['Overall'].idxmax()]]['Club'].to_string(\n                index=False),\n            FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'] == i]['Overall'].idxmax()]]['Value'].to_string(\n                index=False),\n            FIFA18_copy.loc[[FIFA18_copy[FIFA18_copy['Position'] == i]['Overall'].idxmax()]]['Wage'].to_string(\n                index=False)\n        ])\n\n        FIFA18_copy.drop(FIFA18_copy[FIFA18_copy['Position'] == i]['Overall'].idxmax(),\n                         inplace=True)\n\n    # return store with only necessary columns\n    return pd.DataFrame(np.array(store).reshape(11, 7),\n                        columns=['Position', 'Player', 'Overall', 'Age', 'Club', 'Value', 'Wage']).to_string(\n        index=False)\n# 4-3-3\nsquad_433 = ['GK', 'RB', 'CB', 'CB', 'LB', 'CDM', 'CM', 'CAM', 'RW', 'ST', 'LW']\nprint ('4-3-3')\nprint (get_best_squad(squad_433))\n\n# 4-4-2\nsquad_442 = ['GK', 'RB', 'CB', 'CB', 'LB', 'RM', 'CM', 'CM', 'LM', 'ST', 'ST']\nprint ('4-4-2')\nprint (get_best_squad(squad_442))\n\n# 4-2-3-1\nsquad_4231 = ['GK', 'RB', 'CB', 'CB', 'LB', 'CDM', 'CDM', 'CAM', 'CAM', 'CAM', 'ST']\nprint ('4-2-3-1')\nprint (get_best_squad(squad_4231))","sub_path":"best_squad_fifa.py","file_name":"best_squad_fifa.py","file_ext":"py","file_size_in_byte":3076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"633795242","text":"# Equipe-02\r\n# Scrum master: Matheus Serrão Uchôa\r\n# Devs:\r\n# Paulo Henrique Munhoz\r\n# Ana Beatriz Pimentel\r\n# Kariny Oliveira\r\n\r\n#  print the word in ascendent\r\n\r\n\r\ndef fn_descendent(str_word):\r\n    str_word = str_word.replace(\" \", \"\")  # replace the blank spaces\r\n    str_word = sorted(str_word, reverse=True)  # sort the letters\r\n    str_word = ''.join(str_word)  # join the letters in str_word\r\n    print(str_word)  # print\r\n\r\n\r\n# main function\r\nstr_s = input(\"Enter the word: \")\r\nif len(str_s) <= 20:\r\n    fn_descendent(str_s)\r\nelse:\r\n    print(\"Word length more than expected\")\r\n","sub_path":"exercicio-strings-and-numbers/Extras/Strings/fn_descendent_Equipe2.py","file_name":"fn_descendent_Equipe2.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"304786808","text":"'''\r\nn개로 이루어진 정수 집합에서 원하는 수의 위치를 찾는 문제\r\n(단, 입력되는 집합은 오름차순으로 정렬되어 있으며, 같은 수는 없음)\r\n\r\n입력:\r\n정수 n 입력 후, n개의 정수를 입력한 후 찾고자 하는 수를 입력하면\r\n그 수의 위치를 찾아주는 프로그램\r\n'''\r\n\r\nn = int(input(\"정수 n 입력 \"))\r\nli = []\r\nfor i in range(0, n):\r\n    li.append(input(\"원소 입력, 줄로 구분 \"))\r\nprint(li)\r\n\r\nitem = input(\"찾고자 하는 원소 \")\r\n\r\nPosition = 0\r\nfound = False\r\ncounter = 0\r\n\r\nwhile Position < len(li) and not found:\r\n    counter = counter + 1\r\n    if li[Position] == item:\r\n        found = True\r\n    Position = Position + 1\r\n\r\nprint(counter)\r\n","sub_path":"linearstructure.py","file_name":"linearstructure.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"29850300","text":"from selenium  import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions  as  EC\n# from bst.bst_datatest.test_case.models.get_driver_moni_ip import get_driver_moni_ip\nfrom time import sleep\nfrom BSTAuto_Python.bst_new.util.my_to_excel import *\nfrom BSTAuto_Python.bst_new.util.my_read_excel import *\nfrom datetime import datetime\nfrom selenium.webdriver.common.by import By\nfrom selenium.common.exceptions import StaleElementReferenceException\n\ndef  open(url):\n    # 代理IP\n    # driver = get_driver_moni_ip()\n    # driver.get(\"http://httpbin.org/ip\")\n    # print(driver.page_source)\n    driver = webdriver.Chrome()\n    driver.get(url)\n    return driver\n\n\n# 得到导入企业的链接\ndef get_zhiding_qy_yj(driver,outfilename,sheet1data,zhiding_qy_yj_data):\n    # 选择企业业绩\n    driver.find_element_by_css_selector('.nav-box>div:nth-child(1)>a').click()\n    sleep(3)\n\n    for row in sheet1data:\n        qyname = row[1].strip()\n        driver.find_element_by_css_selector('input[placeholder=\"请输入企业名称关键字\"]').clear()\n        driver.find_element_by_css_selector('input[placeholder=\"请输入企业名称关键字\"]').send_keys(qyname)\n        driver.find_element_by_xpath('//span[text()=\"查询\"]').click()\n        sleep(3)\n\n        qy_name = driver.find_element_by_css_selector\\\n            ('.table>div:nth-child(2)>div:nth-child(1)>ul>li:nth-child(2)>a>em').text.strip()\n        if qy_name[-3:] == \"已认证\":\n            qy_name = qy_name[0:-4]\n        # https://hhb.cbi360.net/sg_549433/\n        href = driver.find_element_by_css_selector\\\n            ('.table>div:nth-child(2)>div:nth-child(1)>ul>li:nth-child(2)>a').get_attribute('href')\n        # print(href)\n        zbsl = driver.find_element_by_css_selector\\\n            ('.table>div:nth-child(2)>div:nth-child(1)>dl>dd>a>strong').text.strip()\n        tmp = [qy_name,href,zbsl]\n        zhiding_qy_yj_data.append(tmp)\n        # print(zhiding_qy_yj_data)\n\n    tablenamehouzui = datetime.now().strftime('%Y%m%d_%H%M%S')\n    # 到数据到excle中\n    columnRows = [\"qy_name\", \"href\", \"zbsl\"]\n    wirteDataToExcel(outfilename+'href_' + tablenamehouzui + \".xlsx\", \"jst_qyyj_zhejiang\", columnRows, zhiding_qy_yj_data)\n    print(\"qyhref  to   excel  sucess\")\n\n# 得到相应企业的全部企业资质信息\ndef get_qyzz_detail(driver,outfilename,zhiding_qy_yj_data,qyzz_data):\n    for qy_name, href,zbsl in zhiding_qy_yj_data:\n        driver.get(href)\n        print(href)\n        sleep(3)\n\n        ry_count = int((driver.find_element_by_xpath\n                        (\"//div[@class='module'][6]//div[@class='text-count']/span\").text.strip())[1:-1])\n        if ry_count == 0: break\n\n        # 点击企业人员\n        # driver.find_element_by_xpath('//*[@id=\"newrisk\"]/li[4]/h3/a').click()\n        # sleep(3)\n\n\n        # 计算该企业中有几页人员数据\n        if ry_count > 15:\n            if ry_count % 10 == 0:\n                total_ye = ry_count // 10\n            else:\n                total_ye = ry_count // 10 + 1\n        else:\n            total_ye = 1\n        print(\"总页数：\" + str(total_ye))\n        if total_ye > 15: total_ye = 15\n\n        cur_ye = driver.find_element_by_xpath(\"//div[@class='module'][6]//ul/li[@class='number active']\").text.strip()\n        print(cur_ye)\n        for gotoyema in range(1, total_ye + 1):\n            print(\"第\" + str(gotoyema) + \"页\")\n            if cur_ye != gotoyema:\n                locator2 = (By.XPATH, \"//div[@class='module'][6]//slot/div/input\")\n                WebDriverWait(driver, 10).until(EC.visibility_of_element_located(locator2)).clear()\n                WebDriverWait(driver, 10).until(EC.visibility_of_element_located(locator2)).send_keys(str(gotoyema))\n\n                driver.find_element_by_xpath('//div[@class=\"module\"][6]//slot/button').click()\n                sleep(3)\n\n            qyzzname = ' '\n            content_list = driver.find_elements_by_xpath('//div[@class=\"module\"][6]//tbody/tr')[:]\n            # print(','.join(str(s) for s in content_list if s not in [None]))\n            for content in content_list:\n                if content.find_element_by_xpath('./td[2]//span'):\n                    qyzzname = content.find_element_by_xpath('./td[2]//span').text.strip()\n\n                qy_name = qy_name\n\n                tmp = [qy_name, qyzzname]\n                qyzz_data.append(tmp)\n                print(qyzz_data)\n    tablenamehouzui = datetime.now().strftime('%Y%m%d_%H%M%S')\n    # 到数据到excle中\n    columnRows = [ \"qy_name\", \"qyzzname\"]\n    wirteDataToExcel(outfilename+\"ryxx_\" + tablenamehouzui + \".xlsx\", \"Sheet1\", columnRows, qyzz_data)\n    print(\"qyzz  to   excel  sucess\")\n\n\nif  __name__ == '__main__':\n    root_dir = os.path.dirname(os.path.abspath('.')) + '/data'\n    infilename = root_dir + r\"\\qy_list\\云南省企业列表_20200820.xlsx\"\n    outfilename = root_dir + r\"\\get_jst_qyyj\\建设通企业业绩_数据准备_贺家斌_\"\n    url = \"https://passport.cbi360.net/login?url=https%3A%2F%2Fwww.cbi360.net%2Fhyjd%2F20200528%2F203181.html\"\n\n    # 读要查询的项目经理和相应企业进来\n    all_sheet_data = read_excel(infilename)\n    sheet1data = all_sheet_data[0][1][1:]\n    print(sheet1data)\n\n    driver=open(url)\n    zhiding_qy_yj_data = []\n    qyyj_data = []\n    qyzz_data = []\n    s = input(\"input  your  unm: \")\n    if int(s) == 1:\n        # 得到导入企业的链接\n        get_zhiding_qy_yj(driver,outfilename,sheet1data,zhiding_qy_yj_data)\n\n        # datalist=[['广州', '广州敏城建设工程有限公司', 'https://hhb.cbi360.net/sg_47307/', '726']]\n\n\n        # 得到导入企业的全部企业业绩\n        # max_ye指定最多查询建设通多少页的业绩\n        get_qyzz_detail(driver,outfilename,zhiding_qy_yj_data,qyzz_data)\n\n        # print(zhiding_qy_yj_data)\n        # 得到导入企业的全部人员信息\n        # get_ryxx_detail(driver,outfilename, zhiding_qy_yj_data, ryxx_data)","sub_path":"BSTAuto_Python/bst_new/jst/get_jst_qyzz.py","file_name":"get_jst_qyzz.py","file_ext":"py","file_size_in_byte":5992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"287908862","text":"from django.conf.urls import url\nfrom . import views\nfrom .feeds import LatestPostsFeed\n\nurlpatterns = [\n    url(r'^$', views.post_list, name='post_list'),\n    url(r'^(?P<post_id>[0-9]+)/share/$', views.post_share, name='post_share'),\n    url(r'^(?P<year>[0-9]{4})/(?P<month>[0-9]{2})/(?P<day>[0-9]{2})/(?P<post>[-\\w]+)/$',\n        views.post_detail,\n        name='post_detail'),\n    url(r'^tag/(?P<tag_slug>[-\\w]+)/$', views.post_list, name='post_list_by_tag'),\n    url(r'^feed/$', LatestPostsFeed(), name='post_feed'),\n]\n","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"103316647","text":"import xlrd\r\n\r\ndata1 = xlrd.open_workbook('2020年10月.xlsx')\r\ntable1 = data1.sheets()[0]\r\ndata2 = xlrd.open_workbook('2020年11月.xlsx')\r\ntable2 = data2.sheets()[0]\r\ndata3 = xlrd.open_workbook('2020年12月.xlsx')\r\ntable3 = data3.sheets()[0]\r\n\r\nnrows = table1.nrows  # 获取行数\r\nncols = table1.ncols  # 获取列数\r\n\r\n\r\n# 提取表格中单元格中的打卡数据，并存储在列表a中\r\ndef getcontent1():\r\n    a = []\r\n    b = []\r\n    c = []\r\n\r\n    for i in range(4, 16):\r\n        b = []\r\n        for j in range(6, 37):\r\n            # print('-'*10)\r\n            c = table1.cell(i, j).value\r\n            b.append(c)\r\n            # print('-' * 10)\r\n            # print(b)\r\n\r\n        a.append(b)\r\n    return a\r\n\r\n\r\ndef getcontent2():\r\n    a = []\r\n    b = []\r\n    c = []\r\n\r\n    for i in range(4, 16):\r\n        b = []\r\n        for j in range(6, 36):\r\n            # print('-'*10)\r\n            c = table2.cell(i, j).value\r\n            b.append(c)\r\n            # print('-' * 10)\r\n            # print(b)\r\n\r\n        a.append(b)\r\n    return a\r\n\r\n\r\ndef getcontent3():\r\n    a = []\r\n    b = []\r\n    c = []\r\n\r\n    for i in range(4, 16):\r\n        b = []\r\n        for j in range(6, 37):\r\n            # print('-'*10)\r\n            c = table3.cell(i, j).value\r\n            b.append(c)\r\n            # print('-' * 10)\r\n            # print(b)\r\n\r\n        a.append(b)\r\n    return a\r\n\r\n\r\n# w = getcontent()\r\n#\r\n# print(w[0][1])\r\n\r\n\r\n#   处理每个单元格中的数据，去掉换行，拆分时和分，并存储在列表a中\r\ndef dealdata(string):\r\n    c = string.replace('  \\n', \":\")\r\n    c = c.split(':')\r\n\r\n    flag = 0\r\n    a = []\r\n    b = []\r\n    i = 0\r\n    while i < len(c):\r\n        b.append(c[i])\r\n        if i % 2 != 0:\r\n            a.append(b)\r\n            b = []\r\n        i = i + 1\r\n\r\n    return a\r\n\r\n\r\n# print(dealdata(w[0][1]))\r\n\r\n\r\n#  整理数据，提取出每个时段中最早到达时间和最晚离开时间\r\ndef simplify(aray):\r\n    j = 0\r\n    a = []\r\n    l = len(aray) - 1\r\n\r\n    # 判断是否存在18:20这个特殊的时间点，如果存在的话，看他和哪个时间段闭合\r\n    flag = 0\r\n    flag1 = 0\r\n    flag2 = 0\r\n    for i in range(len(aray)):\r\n        if int(aray[i][0]) * 60 + int(aray[i][1]) == 1100:\r\n            flag = 1\r\n        if 780 <= int(aray[i][0]) * 60 + int(aray[i][1]) <= 945:\r\n            flag1 = 1\r\n        if 1200 <= int(aray[i][0]) * 60 + int(aray[i][1]) <= 1350:\r\n            flag2 = 1\r\n\r\n    while j < l:\r\n        # print('-'*10)\r\n        # print(aray)\r\n        # print(l)\r\n        # print(j)\r\n        if 390 <= int(aray[j][0]) * 60 + int(aray[j][1]) <= 645 and 390 <= int(aray[j + 1][0]) * 60 + int(\r\n                aray[j + 1][1]) <= 645:\r\n            aray.pop(j + 1)\r\n            j = 0\r\n            l = l - 1\r\n            continue\r\n        if 660 <= int(aray[j][0]) * 60 + int(aray[j][1]) <= 750 and 660 <= int(aray[j + 1][0]) * 60 + int(\r\n                aray[j + 1][1]) <= 750:\r\n            aray.pop(j)\r\n            j = 0\r\n            l = l - 1\r\n            continue\r\n        if 780 <= int(aray[j][0]) * 60 + int(aray[j][1]) <= 945 and 780 <= int(aray[j + 1][0]) * 60 + int(\r\n                aray[j + 1][1]) <= 945:\r\n            aray.pop(j + 1)\r\n            j = 0\r\n            l = l - 1\r\n            continue\r\n        if 1020 <= int(aray[j][0]) * 60 + int(aray[j][1]) <= 1100 and 1020 <= int(aray[j + 1][0]) * 60 + int(\r\n                aray[j + 1][1]) <= 1100:\r\n            aray.pop(j)\r\n            j = 0\r\n            l = l - 1\r\n            continue\r\n        if 1100 <= int(aray[j][0]) * 60 + int(aray[j][1]) <= 1170 and 1100 <= int(aray[j + 1][0]) * 60 + int(\r\n                aray[j + 1][1]) <= 1170:\r\n            aray.pop(j + 1)\r\n            j = 0\r\n            l = l - 1\r\n            continue\r\n        if 1200 <= int(aray[j][0]) * 60 + int(aray[j][1]) <= 1350 and 1200 <= int(aray[j + 1][0]) * 60 + int(\r\n                aray[j + 1][1]) <= 1350:\r\n            aray.pop(j)\r\n            j = 0\r\n            l = l - 1\r\n            continue\r\n\r\n        j = j + 1\r\n\r\n    return aray, flag, flag1, flag2\r\n\r\n\r\n# 计算每个人每天的工作时间\r\ndef calday(b):\r\n    # a = getcontent()\r\n    # b = dealdata(a[0][0])\r\n    aray, flag, flag1, flag2 = simplify(b)\r\n    time1 = 0\r\n    time2 = 0\r\n    time3 = 0\r\n    time4 = 0\r\n    time5 = 0\r\n    time6 = 0\r\n    for j in range(len(aray)):\r\n        if 390 <= int(aray[j][0]) * 60 + int(aray[j][1]) <= 645:\r\n            time1 = int(aray[j][0]) * 60 + int(aray[j][1])\r\n        if 660 <= int(aray[j][0]) * 60 + int(aray[j][1]) <= 750:\r\n            time2 = int(aray[j][0]) * 60 + int(aray[j][1])\r\n        if 780 <= int(aray[j][0]) * 60 + int(aray[j][1]) <= 945:\r\n            time3 = int(aray[j][0]) * 60 + int(aray[j][1])\r\n        if flag == 1 and flag1 == 1:\r\n            time4 = 1100\r\n        if flag == 0 and 1020 <= int(aray[j][0]) * 60 + int(aray[j][1]) < 1100:\r\n            time4 = int(aray[j][0]) * 60 + int(aray[j][1])\r\n        if flag == 1 and flag2 == 1:\r\n            time5 = 1100\r\n        if flag == 0 and 1100 < int(aray[j][0]) * 60 + int(aray[j][1]) < 1170:\r\n            time5 = int(aray[j][0]) * 60 + int(aray[j][1])\r\n        if 1200 <= int(aray[j][0]) * 60 + int(aray[j][1]) <= 1350:\r\n            time6 = int(aray[j][0]) * 60 + int(aray[j][1])\r\n    if time1 > 0 and time2 > 0:\r\n        sum1 = time2 - time1\r\n    else:\r\n        sum1 = 0\r\n    if time3 > 0 and time4 > 0:\r\n        sum2 = time4 - time3\r\n    else:\r\n        sum2 = 0\r\n    if time5 > 0 and time6 > 0:\r\n        sum3 = time6 - time5\r\n    else:\r\n        sum3 = 0\r\n\r\n    sum = sum1 + sum2 + sum3\r\n    # print(\"time1:%d\" % time1)\r\n    # print(\"time2:%d\" % time2)\r\n    # print(\"time3:%d\" % time3)\r\n    # print(\"time4:%d\" % time4)\r\n    # print(\"time5:%d\" % time5)\r\n    # print(\"time6:%d\" % time6)\r\n    # print(sum)\r\n    return sum\r\n\r\n\r\n# 打印每个人每天的工作时间\r\ndef calmon(fun):\r\n    list = []\r\n    # a = getcontent1()\r\n    a = fun\r\n    for i in range(len(a)):\r\n        # print(\"-\" * 20)\r\n        sum = 0\r\n        for j in range(len(a[i])):\r\n            b = dealdata(a[i][j])\r\n            c = calday(b)\r\n            sum = sum + c\r\n        list.append(sum)\r\n    # print(list)\r\n    return list\r\n\r\n\r\ndef min2hour(list):\r\n    newlist = []\r\n    for i in range(len(list)):\r\n        a = ''\r\n        c = list[i]%60\r\n        b = (list[i]-c)//60\r\n        a = str(b)+\"小时\"+str(c)+\"分钟\"\r\n        newlist.append(a)\r\n    return newlist\r\n\r\n\r\nlist1 = calmon(getcontent1())\r\nslist1 = min2hour(list1)\r\nlist2 = calmon(getcontent2())\r\nslist2 = min2hour(list2)\r\nlist3 = calmon(getcontent3())\r\nslist3 = min2hour(list3)\r\n# print(list1)\r\n# print(list2)\r\n# print(list3)\r\n","sub_path":"dataprocessing.py","file_name":"dataprocessing.py","file_ext":"py","file_size_in_byte":6623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"182641355","text":"# read_rrfile.py\n# Douglas York\n# 7/21/2015\n#\n# Parse data from the input repair rate file and store into dataframes\ndef read_rrfile(rrfile):\n\timport numpy as np\n\t# rrfile = \"C:/Users/Douglas/Documents/work/AWSS Hydraulic Model/Simulation_Data/Simulation Data/RR/GIRAFFE_RR_Boore2008_PGV_Sim1_Case121.txt\"\n\trrInfo = {}\n\tpipeInfo = {}\n\tPipe_IDs = []\n\tLengths_km = []\n\tRRs = []\n\tMaterials = []\n\twith open(rrfile) as RRfile:\n\t\tnext(RRfile)\n\t\tfor line in RRfile:\n\t\t\tstuff = line.split()\n\t\t\tPipe_IDs.append(str(stuff[0]))\n\t\t\tLengths_km.append(np.float(stuff[1]))\n\t\t\tRRs.append(np.float(stuff[2]))\n\t\t\tMaterials.append(str(stuff[3]))\n\t\t\tpipeInfo = {'Length_km':np.float(stuff[1]),'RR':np.float(stuff[2]),'Material':str(stuff[3])}\n\t\t\trrInfo[str(stuff[0])] = pipeInfo\n\t\t\t\n\t\tPipe_IDs = np.asarray(Pipe_IDs)\n\t\tLengths_km = np.array(Lengths_km)\n\t\tRRs = np.array(RRs)\n\t\tMaterials = np.asarray(Materials)\n\t\tnp.save('C:/Users/Douglas/Documents/work/AWSS Hydraulic Model/Simulation Data'+'EPANETinfo',rrInfo)\n\treturn rrInfo, Pipe_IDs, Lengths_km, RRs, Materials","sub_path":"read_rrfile.py","file_name":"read_rrfile.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"265774738","text":"# -*- coding: utf-8 -*-\n\nfrom .models import Function\nfrom .fields import function_fields\nfrom flask_restful import marshal\n\nfrom flask import Response\nfrom flask import render_template\n\nfrom app import app\n\n\n@app.route('/')\ndef index():\n    fs = Function.query.all()\n    #funcs = sorted([marshal(f, function_fields) for f in fs],\n    #        key=lambda x:x['invoked'], reverse=True)[:100]\n    funcs = [marshal(f, function_fields) for f in fs] # sort by dataTables.\n    return Response(\n            render_template('index.html',\n                title='Unicorn Service',\n                funcs=funcs),\n            mimetype='text/html')\n\n\n@app.route('/help')\ndef help():\n    return Response(\n            render_template('help.html',\n                title='Unicorn Service - Help',\n                ),\n            mimetype='text/html')\n","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"35525398","text":"import numpy as np\n\nN = 4\nM = 5\n\nmatrix = np.random.randint(low=-9, high=10, size=(N, M))\nprint(\"Матрица:\\r\\n{}\".format(matrix))\n\nsum_cols = np.sum(np.absolute(matrix), axis=0)\nprint(\"Суммы столбцов:\\r\\n {}\".format(sum_cols))\n\nmax_sum = np.max(sum_cols)\nmax_sum_col = np.argmax(sum_cols)\ncol = matrix[:, max_sum_col]\nprint(\"Наибольшая сумма: {} в столбце №{}\".format(max_sum, max_sum_col+1))\nprint(\"Элементы столбца: {}\".format(col))\n\nmax_el = np.max(col)\nprint(\"Наибольший элемент: {}\".format(max_el))\n","sub_path":"Курсовая работа/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"16300669","text":"from django.http import HttpResponse\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom bot_extra.functions import *\n\n\n@csrf_exempt\ndef index(request):\n    if request.method == \"GET\":\n        return HttpResponse('It works!!!!')\n    else:\n        chat_id = get_chat_id(request)\n        text = get_text(request)\n\n        send_message(chat_id, text)\n\n        return HttpResponse('Post works!')\n\n","sub_path":"shop/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"543528607","text":"#!/usr/bin/python\nimport argparse\n\nfrom wikivision.app import app\n\n\ndef get_parser():\n    parser = argparse.ArgumentParser(\n        prog='python -m wikivision',\n        description=\"Visualize Wikipedia article revision histories.\",\n    )\n    parser.add_argument('article_slug', nargs='?')\n    return parser\n\n\nif __name__ == '__main__':\n    parser = get_parser()\n    args = parser.parse_args()\n    app.run(debug=True)\n","sub_path":"wikivision/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"352312825","text":"\"\"\"\npython实战练习2\n课后作业2\n用类和面向对象的思想，“描述”生活中任意接触到的东西（比如动物、小说里面的人物，不做限制，随意发挥），数量为5个\n\"\"\"\n\n\n# 定义一个可爱的小姐姐\nclass People:\n    # 定义变量\n    lovely = \"我很可爱\"\n    hand = \"我有一双手\"\n    feet = \"我有一双大长腿\"\n    eye = \"我是单眼皮\"\n\n    # 定义一个吃的方法\n    def eat(self):\n        print(\"我是一个小吃货\")\n\n    # 定义一个跑的方法\n    def run(self):\n        print(\"我跑的很快\")\n\n\n# 类的实例化\npp = People()\n# 调用类中的方法及属性\npp.run()\npp.eat()\nprint(pp.lovely)\nprint(pp.eye)\n","sub_path":"task_two/py_oop_two/people.py","file_name":"people.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"442488658","text":"#_*_ coding:gb2312_*_\n#encoding=utf-8\n# #如果是汉字注释，需要在代码开头加上_*_ coding:gb2312_*_\nimport requests\nfrom urllib.parse import  urlencode\nfrom requests.exceptions import  ConnectionError\nfrom pyquery import PyQuery as pq\nimport io\nimport sys\nimport re\nimport pymongo\n\nsys.stdout = io.TextIOWrapper(sys.stdout.buffer,encoding='gb18030')\nclient = pymongo.MongoClient('localhost')\ndb = client['weixin']\n\n\n\nbase_url = 'https://weixin.sogou.com/weixin?'\nheaders = {\n    'Cookie': 'SUV=006B71D7DF5BD8345B7660A775D2B520; SMYUV=1539747960285644; IPLOC=CN1304; SUID=F909C6B7541C940A000000005BD26754; ABTEST=6|1540515669|v1; SNUID=9464AADA6C69157B2C2A33A36DF7D0A3; weixinIndexVisited=1; sct=1; JSESSIONID=aaa4ZSXSubIC1ae6-lIzw; ppinf=5|1540517252|1541726852|dHJ1c3Q6MToxfGNsaWVudGlkOjQ6MjAxN3x1bmlxbmFtZToyNDpKdXN0JTIwYSUyMCUyMFglRUYlQkMlODF8Y3J0OjEwOjE1NDA1MTcyNTJ8cmVmbmljazoyNDpKdXN0JTIwYSUyMCUyMFglRUYlQkMlODF8dXNlcmlkOjQ0Om85dDJsdUlKLXFDbDdKUE5VOGlRREFJeTdoNWNAd2VpeGluLnNvaHUuY29tfA; pprdig=Kh_XW3wdeuljDBo5A3hA1gPS_EqVAdaCKCoxG0tQ5TCk_h6ksm9_QaBeePcg8uYtwRjriU6mvebrkXdfbM1R6LhJNyGQ7Di8lw13uj8JuoOAEbEIAQrqq2umx9CbAKdSWbkK5vbgyY4HYZynd27iVNtWS5wS9qVpAKdsiyJTvlY; sgid=04-35568889-AVvSbYQY4ayenEEia81OSsRQ; ppmdig=15405172530000000eaa3477479224333f117411eb04b128',\n    'Host': 'weixin.sogou.com',\n    'Referer': 'https://weixin.sogou.com/weixin?query=%E9%A3%8E%E6%99%AF&_sug_type_=&sut=1833&lkt=1%2C1540515730996%2C1540515730996&s_from=input&_sug_=y&type=2&sst0=1540515731099&page=100&ie=utf8&w=01019900&dr=1',\n    'Upgrade-Insecure-Requests': '1',\n    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36'\n}\nkeyword = '风景'\nproxy_pool_url = 'http://127.0.0.1:5555/random'\nproxy = None\nmax_count = 5 # 最大请求次数的判断\n\n\ndef get_proxy():\n    try:\n        response = requests.get(proxy_pool_url)\n        if response.status_code == 200:\n            return response.text\n        return  None\n    except ConnectionError:\n        return None\n\ndef get_html(url,count=1):\n    print('Crawling:',url)\n    print('Tring Count:',count)\n    global proxy\n    if count >max_count:\n        print('Tried Too Many Counts')\n        return None\n    try:\n        if proxy:\n            proxies={\n                'http':'http://'+proxy\n            }\n            # 不让它自动处理跳转\n            response = requests.get(url,allow_redirects=False,headers = headers,proxies=proxies)\n        else :\n            response = requests.get(url,allow_redirects=False,headers = headers)\n\n\n        if response.status_code == 200:\n            return  response.text\n        if response.status_code == 302:\n            print('302')\n            #当前IP已经被封，需要代理\n            proxy = get_proxy()\n            if proxy:\n                print('Using:',proxy)\n                return get_html(url)\n            else :\n                print('Get Proxy Failed')\n                return None\n    except ConnectionError as e:\n        print('Error Occurred!',e.args)\n        proxy = get_proxy()\n        count +=1\n        return get_html(url,count)\n\ndef get_indx(keyword,page):\n    data = {\n        'query' : keyword,\n        'type' : 2,\n        'page' : page\n    }\n    queries = urlencode(data)\n    url = base_url + queries\n    html = get_html(url)\n    return html\n\ndef parse_index(html):\n    doc = pq(html)\n    items = doc('.news-box .news-list li .txt-box h3 a').items()\n    for item in items:\n        yield  item.attr('href')\n\ndef get_detail(url):\n    try:\n        response = requests.get(url)\n        if response.status_code == 200:\n            return  response.text\n        return None\n    except ConnectionError:\n        return None\n\ndef parse_detail(html):\n    try:\n        doc = pq(html)\n\n        title = doc('#activity-name').text()\n        content = doc('#js_content').text().replace('\\n','')\n        date = doc('#publish_time').text()#获取不到时间信息\n        nickname = doc('#js_name').text()\n        wechat = doc('#js_profile_qrcode > div > p:nth-child(3) > span').text()\n\n        return {\n            'title':title,\n            'content':content,\n            'date': date,\n            'nickname' : nickname,\n            'wechat':wechat\n        }\n    except :\n        return None\n\ndef save_to_mongo(data):\n    # 去重\n    if db['articles'].update({'title':data['title']},{'$set':data},True):\n        print('save to Mongo',data['title'])\n    else :\n        print('save to Mongo failed',data['title'])\n\n\ndef main():\n    for i in range(1,101):\n        html =get_indx(keyword,i)\n        if html:\n            article_urls = parse_index(html)\n            for article_url in article_urls:\n                article_html = get_detail(article_url)\n                if article_html:\n                    article_data = parse_detail(article_html)\n                    print(article_data)\n                    #if data: save_to_mondo(data)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"使用代理爬取搜狗浏览器上的微信文章/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":4975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"404788905","text":"from parser_andrea import parse,ordinaLista,ordinaListaDistanza\nfrom OutputClass import OutputClass\n\nparam, rides_list = parse(\"./input/e.in\")\n\nROW = param[0]\nCOLUMN = param[1]\nFLEET = param[2]\nRIDES = param[3]\nBONUS = param[4]\nDEADLINE = param[5]\n\nprint(DEADLINE)\nprint(\"BONUS = \" + str(BONUS))\n\ndef getStartPosix(arr):\n    return(arr[0])\ndef getEndPosix(arr):\n    return(arr[1])\ndef prendiTempi(arr):\n    return arr[2]\ndef prendiNCorsa(arr):\n    return arr[3]\ndef prendiDistaza(arr):\n    return arr[4]\ndef prendiValidita(arr):\n    return arr[5]\ndef prendiWaste(arr):\n    return arr[6]\ndef prendiDurataToT(arr):\n    return arr[7]\ndef prendihasBonus(arr):\n    return arr[8]\n\n\nout = OutputClass(\"./e3.txt\", FLEET)\n\n\n'''\ndef ordinaListaWasted(carPosix, simTime):\n    for index in rides_list:\n        dist = calcolaDistanza(carPosix, getStartPosix(index))\n        setCalculatedTime(index, dist)\n        # setCalculatedTime(index,dist+simTime-getSettedTimes(index)[0])\n        setSimulationTime(index, carPosix, simTime)\n        print(index)\n        print(\"tempo DEAD : \" + str(DEADLINE))\n        print(\"tempo disd : \" + str(dist))\n        print(\"tempo ORA : \" + str(simTime))\n        print(\"tempo travel : \" + str(getFixedTravelTime(index)))\n        print(\"tempo sim : \" + str(DEADLINE - (dist + simTime + getFixedTravelTime(index))))\n        wastedTime = calcolaWastedTime(index, carPosix, simTime)\n        # print(\"waste : \" + str(wastedTime))\n        if DEADLINE - (dist + simTime + getFixedTravelTime(index))>0 :\n            print(\"presa\")\n        else :\n            print(\"non presa\")\n        if wastedTime < 0:\n            setFinishOnTime(index, -1)\n        else:\n            setFinishOnTime(index, DEADLINE - (dist + simTime + wastedTime + getFixedTravelTime(index)))\n    return sorted(rides_list, key=itemgetter(5))\n\n'''\n\n\n\nlistaAuto = list()\ndictAuto = {}\nfound = True\nfor car in range(FLEET):\n    dictAuto.update({car : (0,0,0)})\ncounter = 0\n\nwhile counter<=BONUS:\n    print(\"contatore : \" + str(counter))\n\n    for car in range(FLEET):\n        inserted = 0\n        while inserted < 2:\n            #print(\"auto : \" + str(car))\n            parametriAuto = dictAuto.get(car)\n            carPosix = (parametriAuto[0],parametriAuto[1])\n            globalTime = parametriAuto[2]\n            rides_list = ordinaLista(rides_list,carPosix,globalTime)\n            failed = True\n            for arr in rides_list:\n                if prendiValidita(arr)==1 and prendiWaste(arr)<=counter and prendiWaste(arr)!=0:\n                    '''print(arr)\n                    print(\"globaltime + distance : \" + str(globalTime+prendiDistaza(arr)))\n                    print(\"start time : \"+ str(prendiTempi(arr)[0]))\n                    print(\"waste : \" + str(prendiWaste(arr)))'''\n                    globalTime = globalTime + prendiDurataToT(arr)\n                    carPosix = getEndPosix(arr)\n                    out.addCar(car, prendiNCorsa(arr))\n                    rides_list.remove(arr)\n                    rides_list = ordinaLista(rides_list, carPosix, globalTime)\n                    parametriAuto = (carPosix[0],carPosix[1],globalTime)\n                    dictAuto.update({car: parametriAuto})\n                    failed = False\n            if failed == True and len(rides_list) >0 :\n                inserted = inserted +1\n                rides_list = ordinaLista(rides_list, carPosix, globalTime)\n                for arr2 in rides_list:\n                    if prendiValidita(arr2) == 1:\n                        globalTime = globalTime + prendiDurataToT(arr2)\n                        carPosix = getEndPosix(arr2)\n                        out.addCar(car, prendiNCorsa(arr2))\n                        rides_list.remove(arr2)\n                        parametriAuto = (carPosix[0], carPosix[1], globalTime)\n                        dictAuto.update({car: parametriAuto})\n                        break\n                    else :\n                        pass\n\n    counter += 1\n\n\nfound = True\nwhile found:\n    found = False\n    for car in range(FLEET):\n        parametriAuto = dictAuto.get(car)\n        carPosix = (parametriAuto[0], parametriAuto[1])\n        globalTime = parametriAuto[2]\n        rides_list = ordinaLista(rides_list,carPosix,globalTime)\n        for arr in rides_list:\n            if prendiValidita(arr)==1:\n                #print(arr)\n                globalTime = globalTime + prendiDurataToT(arr)\n                carPosix = getEndPosix(arr)\n                out.addCar(car, prendiNCorsa(arr))\n                rides_list.remove(arr)\n                rides_list = ordinaLista(rides_list, carPosix, globalTime)\n                parametriAuto = (carPosix[0], carPosix[1], globalTime)\n                found = True\n                break\n\n        dictAuto.update({car : parametriAuto})\n\n\n\nout.creaFileOutput()\nprint(dictAuto)\n'''NON MODIFICAREEEEEEEEE'''\n'''AGGIUNGI DEADTIME DI OGNI MACCHINA PER VEDERE SE SONO ARRIVATE FINO ALLA FINE DAVVERO'''","sub_path":"questoFunzionaMoltoBene.py","file_name":"questoFunzionaMoltoBene.py","file_ext":"py","file_size_in_byte":4937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"451395177","text":"__author__ = \"Anne Evered\"\n\n# %% REQUIRED LIBRARIES\nimport os\nimport numpy as np\nimport os\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom celluloid import Camera\nfrom matplotlib.lines import Line2D\nfrom src.visualization.save_view_fig import save_view_fig, save_animation\nfrom src.visualization.simulation_figures_shared_functions import (\n    data_loading_and_preparation,\n    get_features_dictionary,\n)\n\n# These functions are for creating an animation or static figure of simulation\n# output in Matplotlib that roughly follows Tidepool design color scheme, etc. for\n# different simulation elements (bg, temp_basal, sbr, etc.).\n\n# This animation depends on imagemagick (for saving image). You can download imagemagick here:\n# http://www.imagemagick.org/script/download.php\n\n\n# Create figure\ndef create_simulation_figure_matplotlib(\n    file_location,\n    file_names,\n    traces,\n    subplots,\n    time_range=(0, 8),\n    main_title=\"\",\n    subplot_titles=[],\n    animate_figure=True,\n    figure_name=\"simulation_figure\",\n    save_fig_path=os.path.join(\n        \"..\", \"..\", \"reports\", \"figures\", \"simulation_figure_examples\"\n    ),\n):\n    \"\"\"\n    Create matplotlib animation or static image of simulation output that generally\n    aligns with Tidepool's color scheme/styling for simulation elements (blood glucose,\n    scheduled basal rates, temp basals, etc.).\n\n    Parameters\n    ----------\n    file_location: str\n        folder location where files to visualize data from are located\n    file_names: list of strings\n        list of filenames within that folder that want to pull data from for the animation/figure\n    traces: list of dictionaries\n        a list of dictionaries where each dictionary corresponds to one of the passed in filenames or dataframes,\n        the keys of the dictionaries are subplots (0-indexed), and the values are a list of values for\n        that subplot from that filename (ex. traces = [{0: [\"bg\", \"bg_sensor\"], 1: [\"iob\"], 2: [\"sbr\"]}])\n    subplots: int\n        number of subplots (1-indexed)\n    time_range: tuple\n        the range of hours of the simulation want to show (i.e. (0, 8) for\n        8 hours starting at t=0\n    main_title: str\n        main title for the figure\n    subplot_titles: list of strings\n        list of subplot titles\n    animate_figure: bool\n        boolean for whether to make the figure an animation or a static figure\n    figure_name: str\n        the name to use for the figure\n    save_fig_path: str\n        file path to save figures to\n\n    Returns\n    -------\n\n    \"\"\"\n    # Load data files\n    data_frames = []\n    for file in file_names:\n        df = data_loading_and_preparation(\n            os.path.abspath(os.path.join(file_location, file))\n        )\n        data_frames.append(df)\n\n    # Set fonts\n    font = {\"size\": 8}\n    plt.rc(\"font\", **font)\n\n    # Set up figure and axes\n    fig, axes = plt.subplots(subplots, constrained_layout=True)\n\n    # Initialize Camera for animation\n    camera = Camera(fig)\n\n    # Add figure layout elements\n    fig.set_size_inches(10, 3 * subplots)\n    fig.suptitle(\n        main_title, fontsize=10\n    )  # fontsize=11, x=.05, horizontalalignment=\"left\")\n\n    # Add axes\n    axes = add_axes(\n        traces=traces,\n        num_subplots=subplots,\n        axes=axes,\n        data_frames=data_frames,\n        time_range=time_range,\n        subplot_titles=subplot_titles,\n    )\n\n    # Create custom legend and add to axes\n    axes = add_default_legend(axes, subplots, traces)\n\n    # Add in different animation traces\n\n    # Determine time values to use for stepping through animation\n    if animate_figure:\n        time_values = data_frames[0][\"hours_post_simulation\"]\n\n    # Just use maximum time value in case of no animation\n    else:\n        time_values = [max(data_frames[0][\"hours_post_simulation\"])]\n\n    # Iterate over each time value and create a snapshot at that time value\n    for t in time_values:\n        for index, df in enumerate(data_frames):\n            # Create subset of the data\n            subset_df = df[df[\"hours_post_simulation\"] < t]\n            for subplot in traces[index]:\n                for field in traces[index][subplot]:\n                    axes = add_plot(\n                        subset_df, subplots, axes, field=field, subplot=subplot\n                    )\n        camera.snap()\n\n    # Animate figure\n    animation = camera.animate()\n\n    # Set layout\n    # fig.tight_layout(rect=[0, 0.03, 1, 0.95]) #[0, 0.03, 1, 0.85])\n\n    # Save figure\n\n    if animate_figure:\n        figure_name = figure_name + \"-animation\"\n    else:\n        figure_name = figure_name + \"-static\"\n\n    save_animation(\n        animation,\n        figure_name=figure_name,\n        analysis_name=\"simulation_matplotlib_example\",\n        save_fig=True,\n        save_fig_path=save_fig_path,\n        fps=5,\n        dpi=100,\n    )\n\n    return\n\n\ndef add_default_legend(axes, subplots, traces):\n    \"\"\"\n    Add legend to the axes of the plot. This is needed to be done using matplotlib shapes\n    rather than the build in matplotlib legend because otherwise the animation will add\n    a legend at each time step rather than just once.\n\n    Parameters\n    ----------\n    axes: axes object\n        the axes of the matplotlib figure\n    subplots: int\n        number of subplots in the figure\n    traces: list of dictionaries\n        a list of dictionaries where each dictionary corresponds to one of the passed in filenames or dataframes,\n        the keys of the dictionaries are subplots (0-indexed), and the values are a list of values for\n        that subplot from that filename (ex. traces = [{0: [\"bg\", \"bg_sensor\"], 1: [\"iob\"], 2: [\"sbr\"]}])\n\n\n    Returns\n    -------\n\n    \"\"\"\n\n    # Add the corresponding shape and label for each field in the plot to the legend\n    for subplot in range(subplots):\n        legend_items = []\n        for trace_dict in traces:\n            if subplot in trace_dict.keys():\n                for field in trace_dict[subplot]:\n                    features = get_features_dictionary(field)\n                    legend_items.append(\n                        Line2D(\n                            [0],\n                            [0],\n                            color=features[\"color\"],\n                            label=features[\"legend_label\"],\n                            marker=features[\"marker\"],\n                            markersize=3,\n                            linestyle=features[\"linestyle\"],\n                        )\n                    )\n        # Syntax is slightly different if there is only 1 subplot\n        if subplots < 2:\n            add_to = axes\n        else:\n            add_to = axes[subplot]\n        add_to.legend(handles=legend_items, loc=\"upper right\")\n\n    # Return the updated axes\n\n    return axes\n\n\ndef add_axes(\n    traces, num_subplots, axes, data_frames, subplot_titles, time_range=(0, 8)\n):\n    \"\"\"\n\n    Parameters\n    ----------\n    traces: list of dictionaries\n        a list of dictionaries where each dictionary corresponds to one of the passed in filenames or dataframes,\n        the keys of the dictionaries are subplots (0-indexed), and the values are a list of values for\n        that subplot from that filename (ex. traces = [{0: [\"bg\", \"bg_sensor\"], 1: [\"iob\"], 2: [\"sbr\"]}])\n    num_subplots: int\n        number of subplots (1-indexed)\n    axes: axes object\n        the axes of the matplotlib figure\n    data_frames: list of dataframes\n    subplot_titles: list of strings\n        list of subplot titles\n    time_range: tuple\n        the range of hours of the simulation want to show (i.e. (0, 8) for\n        8 hours starting at t=0v\n\n    Returns\n    -------\n    axes: axes object\n        the axes of the matplotlib figure updated with the new axes elements\n\n    \"\"\"\n    # Get all of the df[field] for the particular subplot\n    for subplot in range(num_subplots):\n        fields_in_subplot = []\n        min_value = 100\n        max_value = 0\n        for index, traces_per_file in enumerate(traces):\n            if subplot in traces_per_file:\n                fields = traces_per_file[subplot]\n            else:\n                fields = []\n            fields_in_subplot = fields_in_subplot + fields\n\n            # Find the min and max of all of the fields in the subplot\n            for field in fields:\n                if max(data_frames[index][field]) > max_value:\n                    max_value = max(data_frames[index][field])\n                if min(data_frames[index][field]) < min_value:\n                    min_value = min(data_frames[index][field])\n\n        # Get the particular axis going to be updating based on subplot number\n        # Different syntax if only one subplot\n        if num_subplots < 2:\n            add_to = axes\n        else:\n            add_to = axes[subplot]\n\n        # Set subplot titles\n        if subplot < len(subplot_titles):\n            add_to.set_title(subplot_titles[subplot])\n\n        # See if subplot contains any of the common glucose bg traces, otherwise do insulin axis\n        # Call the function for the appropriate axis\n        if \"bg\" in fields_in_subplot or \"bg_sensor\" in fields_in_subplot:\n            axes = glucose_axis(\n                axes, add_to, time_range, y_range=(min_value, max_value)\n            )\n        else:\n            axes = basal_axis(axes, add_to, time_range, y_range=(0, max_value))\n            # set the y-min for the basal axis always to zero\n\n    return axes\n\n\ndef glucose_axis(axes, add_to, time_range=(0, 8), y_range=(70, 250)):\n    \"\"\"\n    Add elements for a y axis of a plot that's showing glucose data.\n\n    Parameters\n    ----------\n    axes: axes object\n        the axes of the matplotlib figure\n    add_to: subplot of figure\n        the particular subplot of the figure to use\n    time_range: tuple\n        the range of hours of the simulation want to show (i.e. (0, 8) for\n        8 hours starting at t=0\n    y_range: tuple\n        range for the y-axis (i.e. (y min, y max) )\n\n    Returns\n    -------\n    axes: axes object\n        the axes of the matplotlib figure updated with the new axis\n\n    \"\"\"\n    # Add elements for a y axis of a plot that's showing glucose data\n    add_to.set_ylabel(\"Glucose (mg/dL)\")\n    add_to.set_xlabel(\"Hours\")\n    add_to.set_xlim(time_range[0], time_range[1])\n    add_to.set_ylim(y_range[0] - 20, y_range[1] + 50)\n    add_to.set_xticks(np.arange(time_range[0], time_range[1] + 1, 2.0))\n    add_to.grid(True)\n    return axes\n\n\ndef basal_axis(axes, add_to, time_range=(0, 8), y_range=(0, 1)):\n    \"\"\"\n    Add elements for a y axis of a plot that's showing insulin data.\n\n     Parameters\n    ----------\n    axes: axes object\n        the axes of the matplotlib figure\n    add_to: subplot of figure\n        the particular subplot of the figure to use\n    time_range: tuple\n        the range of hours of the simulation want to show (i.e. (0, 8) for\n        8 hours starting at t=0\n    y_range: tuple\n        range for the y-axis (i.e. (y min, y max) )\n\n    Returns\n    -------\n    axes: axes object\n        the axes of the matplotlib figure updated with the new axis\n\n    \"\"\"\n    # Add elements for a y axis of a plot that's showing insulin data\n    add_to.set_ylabel(\"Insulin (U or U/hr)\")\n    add_to.set_xlabel(\"Hours\")\n    add_to.set_xlim(time_range[0], time_range[1])\n    add_to.set_ylim(y_range[0], y_range[1] + 0.5)\n    add_to.set_xticks(np.arange(time_range[0], time_range[1] + 1, 2.0))\n    add_to.grid(True)\n    return axes\n\n\ndef add_plot(df, num_subplots, axes, field=\"bg\", subplot=2):\n    \"\"\"\n\n    Parameters\n    ----------\n    df: dataframe\n        the dataframe adding data from for the plot\n    num_subplots: int\n        number of subplots (1-indexed)\n    axes: axes object\n        the axes of the matplotlib figure\n    field: basestring\n        the field of the dataframe for the plot\n    subplot: int\n        the particular subplot number adding this particular trace to\n\n    Returns\n    -------\n    axes: axes object\n        the axes of the matplotlib figure updated with the new plot\n\n    \"\"\"\n\n    if num_subplots < 2:\n        add_to = axes\n    else:\n        add_to = axes[subplot]\n\n    # Get dictionary of style features for that particular field\n    features_dictionary = get_features_dictionary(field)\n\n    # Filter out the zeros for certain fields\n    # (i.e. do not want to show a true_bolus of zero)\n    if field in [\n        \"true_bolus\",\n        \"reported_bolus\",\n        \"true_carb_value\",\n        \"reported_carb_value\",\n        \"true_carb_duration\",\n        \"reported_carb_duration\",\n        \"undelivered_basal_insulin\",\n    ]:\n        # filter out the zero values\n        df = df[df[field] != 0]\n\n    # For delivered basal insulin, need additional syntax for stem plot\n    if field == \"delivered_basal_insulin\":\n        (markerLines, stemLines, baseLines) = add_to.stem(\n            df[\"hours_post_simulation\"],\n            df[\"delivered_basal_insulin\"],\n            linefmt=\"#f9706b\",\n            use_line_collection=True,\n        )\n        plt.setp(\n            markerLines,\n            color=features_dictionary[\"color\"],\n            markersize=features_dictionary[\"markersize\"],\n        )\n        plt.setp(\n            stemLines,\n            color=features_dictionary[\"color\"],\n            linewidth=features_dictionary[\"linewidth\"],\n        )\n        plt.setp(\n            baseLines,\n            color=features_dictionary[\"color\"],\n            linewidth=features_dictionary[\"linewidth\"],\n        )\n\n    # Add the data trace to the particular subplot with the particular design features\n    else:\n        add_to.plot(\n            df[\"hours_post_simulation\"],\n            df[field],\n            color=features_dictionary[\"color\"],\n            alpha=features_dictionary[\"alpha\"],\n            linestyle=features_dictionary[\"linestyle\"],\n            linewidth=features_dictionary[\"linewidth\"],\n            marker=features_dictionary[\"marker\"],\n            markersize=features_dictionary[\"markersize\"],\n            drawstyle=features_dictionary[\"drawstyle\"],\n        )\n\n    # Add fill in for temp_basal_sbr_if_nan\n    if field == \"temp_basal_sbr_if_nan\":\n        add_to.fill_between(\n            df[\"hours_post_simulation\"],\n            df[field],\n            color=features_dictionary[\"color\"],\n            step=features_dictionary[\"step\"],\n            alpha=0.4,\n        )\n\n    # Return updated axes object\n\n    return axes\n\n\ndef generate_figure_from_user_input():\n    \"\"\"\n    Generate the matplotlib simulation figure from user input.\n\n    \"\"\"\n\n    # Get user input for features in plot\n    file_location = os.path.join(\"..\", \"..\", \"data\", \"processed\")\n\n    # Get number of subplots from user\n    num_subplots = int(input(\"How many subplots in the figure?\"))\n\n    # Get filenames to show in plot from user (ex. risk_scenarios_PyLoopkit v0.1.csv)\n    file_names = list(\n        input(\n            \"What file(s) do you want to visualize in the plot (as comma-separated list)?\"\n        ).split(\",\")\n    )\n\n    traces = []\n\n    # Get fields per filename to show in plot from user\n    for file in file_names:\n        subplot_dictionary = dict()\n        for subplot in range(num_subplots):\n            traces_for_subplot = list(\n                input(\n                    \"What fields do you \"\n                    \"want to show from \" + str(file) + \"\"\n                    \", in subplot \" + str(subplot + 1) + \"\"\n                    \" (as comma\"\n                    \" separated list)?\"\n                ).split(\",\")\n            )\n            subplot_dictionary[subplot] = traces_for_subplot\n        traces.append(subplot_dictionary)\n\n    # Get number of hours to show from user\n    hours = int(input(\"How many hours do you want to show?\"))  # 8\n\n    # Get title of plot from user\n    title_text = input(\"Title of plot:\")\n\n    create_simulation_figure_matplotlib(\n        file_location=file_location,\n        file_names=file_names,\n        traces=traces,\n        subplots=num_subplots,  # type of axis and reference dataframe\n        time_range=[0, hours],\n        main_title=title_text,\n        subplot_titles=[\"\"],\n        animate_figure=False,\n    )\n\n    return\n","sub_path":"src/visualization/simulation_figure_matplotlib.py","file_name":"simulation_figure_matplotlib.py","file_ext":"py","file_size_in_byte":16079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"390945423","text":"# 写一个程序,输入任意行文字,当输入空行时结束输入\n# 打印带有行号的输入结果\ndef get_list():\n    while True:\n        s =input(\"请输入任意文字:\")\n        if s == \"\":   # if not s:\n            break\n        yield s\ndef main():\n    L=list(get_list())\n    # 用 enumerate 将L形成能提供的行号的可迭代对象\n    for  t in enumerate(L,1):\n        print(\"第 %d 行: %s\" % t)\nmain()","sub_path":"_1.Python/Pbase/_4.Package/_4.Iterator/_13.exe_enu.py","file_name":"_13.exe_enu.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"337955938","text":"import os\nimport sys\nimport fnmatch\nimport zipfile\n\ncurrent_date = None\ncurrent_max = 0\ncurrent_min = sys.float_info.max\ncurrent_sum = 0\ncurrent_count = 0\ncurrent_spread = 0\nlast_line = None\nlast_avg = 0\n\nfor root, dir, files in os.walk(sys.argv[1]):\n\t#Traverse the dataset directory\n\tfor items in fnmatch.filter(files, \"*USD-*.zip\"):\n\t\tzfile = zipfile.ZipFile(root + \"/\" + items)\n\t\tfor finfo in zfile.infolist():\n\t\t\t#Read lines from zipfile\n\t\t\tfile = zfile.open(finfo)\n\t\t\tfor line in file:\n\t\t\t\t#Parse lines and calculate the prices \n\t\t\t\tcurrency, timestamp, bid, ask = line.split(\",\")\n\t\t\t\tdate = timestamp.split(\":\")[0]\n\t\t\t\tmid = (float(bid) + float(ask)) / 2\n\t\t\t\tspread = (float(ask) - float(bid)) / float(bid)\n\t\t\t\tif (current_date == date):\n\t\t\t\t\tif (mid > current_max): current_max = mid\n\t\t\t\t\tif (mid < current_min): current_min = mid\n\t\t\t\t\tcurrent_sum += mid\n\t\t\t\t\tcurrent_spread += spread\n\t\t\t\t\tcurrent_count += 1\n\t\t\t\telse:\n\t\t\t\t\t#If the timestamp is different from previous one, output the previous one\n\t\t\t\t\tif (current_date):\n\t\t\t\t\t\tif (last_line):\n\t\t\t\t\t\t\tif (current_sum / current_count > last_avg):\n\t\t\t\t\t\t\t\tlabel = 1\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tlabel = 0\n\t\t\t\t\t\t\tsys.stdout.write(last_line + \",\" + str(label) + \"\\n\")\n\t\t\t\t\t\tlast_avg = current_sum / current_count\n\t\t\t\t\t\tlast_line = current_date + \",\" + currency + \",\" + str(current_max) + \",\" + str(current_min) + \",\" + str(last_avg) + \",\" + str(current_spread / current_count)\n\t\t\t\t\tcurrent_date = date\n\t\t\t\t\tcurrent_max = mid\n\t\t\t\t\tcurrent_min = mid\n\t\t\t\t\tcurrent_sum = mid\n\t\t\t\t\tcurrent_count = 1\n\t\t\t\t\tcurrent_spread = spread\n\t\t\tcurrent_date = None\n\t\tif (current_date == date):\n\t\t\tif (last_line):\n\t\t\t\tif (current_sum / current_count > last_avg):\n\t\t\t\t\tlabel = 1\n\t\t\t\telse:\n\t\t\t\t\tlabel = 0\n\t\t\t\tsys.stdout.write(last_line + \",\" + str(label) + \"\\n\")","sub_path":"Assignment1/prepdata.py","file_name":"prepdata.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"225808485","text":"import pyautogui as gui\n\n# # 특정 좌표로 이동 후 클릭하기\n# gui.moveTo(44, 17)\n# gui.click()\n#\n# # 특정 좌표 바로 클릭하기\n# gui.click(44, 17)\n#\n# # 이미지 위치 알아내기\n# i = gui.locateOnScreen('txt.png')\n# print(i)\n# # 이미지의 가운데 좌표 알아내기\n# q = gui.center(i)\n# gui.click(q)\n#\n# # 이미지의 가운데 좌표 한번에 알아내기\n# i = gui.locateCenterOnScreen('txt.png')\n# gui.click(i)\n\n# 현재 마우스 위치 알아오기\nprint(gui.position())\n\n# 계산기 번호 별로 이미지 만들기\n#gui.screenshot('7.png', region=(1065, 477, 30, 30))\nnum7 = gui.locateCenterOnScreen('7.png')\nnum1 = gui.locateCenterOnScreen('1.png')\n\ngui.click(num7)\ngui.click(num1)\ngui.click(num1)","sub_path":"Automations/pyautogui/이미지_위치.py","file_name":"이미지_위치.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"586167815","text":"import os\nimport h5py\nimport numpy as np\nfrom keras import optimizers\nfrom keras.models import Sequential\nfrom keras.layers import Convolution2D, MaxPooling2D, Activation, Dropout, Flatten, Dense, ZeroPadding2D\nfrom vgg16 import VGG16\nfrom dataGenerators import getGenerators\n\nnumberOfEpochs = 15\nnumberOfTrainingSamples = 1204\nnumberOfValidationSamples = 200\nnumberOfTestingSamples = 428\nnumberOfChannels = 3\nimageWidth = imageHeight = 150\n\ndataPath = './car_data/'\ndataPaths = {\n    'training': dataPath + 'training',\n    'validation': dataPath + 'validation',\n    'testing': dataPath + 'testing'\n}\n\n# Data generators\ngenerator = getGenerators(imageHeight, imageWidth, dataPaths)\n\n# VGG16 model topology\nmodel = Sequential()\nmodel.add(ZeroPadding2D((1, 1), input_shape=(imageWidth, imageHeight, numberOfChannels)))\n\nmodel.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1'))\nmodel.add(ZeroPadding2D((1, 1)))\nmodel.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))\nmodel.add(MaxPooling2D((2, 2), strides=(2, 2)))\n\nmodel.add(ZeroPadding2D((1, 1)))\nmodel.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1'))\nmodel.add(ZeroPadding2D((1, 1)))\nmodel.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))\nmodel.add(MaxPooling2D((2, 2), strides=(2, 2)))\n\nmodel.add(ZeroPadding2D((1, 1)))\nmodel.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1'))\nmodel.add(ZeroPadding2D((1, 1)))\nmodel.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))\nmodel.add(ZeroPadding2D((1, 1)))\nmodel.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3'))\nmodel.add(MaxPooling2D((2, 2), strides=(2, 2)))\n\nmodel.add(ZeroPadding2D((1, 1)))\nmodel.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_1'))\nmodel.add(ZeroPadding2D((1, 1)))\nmodel.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_2'))\nmodel.add(ZeroPadding2D((1, 1)))\nmodel.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_3'))\nmodel.add(MaxPooling2D((2, 2), strides=(2, 2)))\n\nmodel.add(ZeroPadding2D((1, 1)))\nmodel.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1'))\nmodel.add(ZeroPadding2D((1, 1)))\nmodel.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_2'))\nmodel.add(ZeroPadding2D((1, 1)))\nmodel.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3'))\nmodel.add(MaxPooling2D((2, 2), strides=(2, 2)))\n\nvgg16model = VGG16(weights='imagenet', include_top=False)\nmodel.set_weights(vgg16model.get_weights())\n\nprint('VGG16 model loaded')\n\n# Define final fully connected layers\nfcModel = Sequential()\nfcModel.add(Flatten(input_shape=model.output_shape[1:]))\nfcModel.add(Dense(256, activation='relu'))\nfcModel.add(Dropout(0.5))\nfcModel.add(Dense(1, activation='sigmoid'))\n\n# Add on top of VGG16 model\nmodel.add(fcModel)\n\n# Freeze all layers up to last convolutional block\nfor layer in model.layers[:25]:\n    layer.trainable = False\n\n# Compile the model\nmodel.compile(loss='binary_crossentropy', optimizer=optimizers.SGD(lr=1e-4, momentum=0.9), metrics=['accuracy'])\n\n# Stop training when a monitored quantity has stopped improving\n# early_stopping = EarlyStopping(monitor='val_loss', mode='min')\n\n# fine-tune the model\nmodel.fit_generator(\n    generator['training'],\n    samples_per_epoch=numberOfTrainingSamples,\n    nb_epoch=numberOfEpochs,\n    validation_data=generator['validation'],\n    nb_val_samples=numberOfValidationSamples\n    #       callbacks=[early_stopping]\n)\n\nmodel.save_weights('pretrained_model_weights.hdf5')\n\n# Test the accuracy of the model\ntest = model.evaluate_generator(generator['testing'], val_samples=numberOfTestingSamples)\nprint(\"Loss: %.2f\" % test[0])\nprint(\"Accuracy: %.2f%%\" % (test[1] * 100))\n","sub_path":"pretrainedClassifier.py","file_name":"pretrainedClassifier.py","file_ext":"py","file_size_in_byte":3696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"321003386","text":"from rsyscall.tests.trio_test_case import TrioTestCase\nfrom rsyscall import local_thread\nfrom rsyscall.handle.pointer import UseAfterFreeError\n\nclass TestPointer(TrioTestCase):\n    async def asyncSetUp(self) -> None:\n        self.thr = local_thread\n\n    async def test_use_after_free_ptr(self) -> None:\n        buf = await self.thr.malloc(bytes, 16)\n        buf.free()\n        with self.assertRaises(UseAfterFreeError):\n            buf.near\n        str(buf)\n\n    async def test_use_after_free_allocation(self) -> None:\n        buf = await self.thr.malloc(bytes, 16)\n        buf.allocation.free()\n        with self.assertRaises(UseAfterFreeError):\n            buf.near\n        buf = await self.thr.ptr(b'foo')\n        buf.allocation.free()\n        with self.assertRaises(UseAfterFreeError):\n            buf.near\n        str(buf)\n","sub_path":"python/rsyscall/tests/test_pointer.py","file_name":"test_pointer.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"358749840","text":"from os import environ\nimport sys\nimport unittest\n\nfrom flask import Flask, render_template_string, session\nimport itsdangerous\n\nimport pytest\n\nfrom coaster.app import (\n    KeyRotationWrapper,\n    RotatingKeySecureCookieSessionInterface,\n    _additional_config,\n    init_app,\n    load_config_from_file,\n)\nfrom coaster.logger import LocalVarFormatter\nfrom coaster.logger import init_app as logger_init_app\n\n\nclass TestCoasterApp(unittest.TestCase):\n    def setUp(self):\n        self.app = Flask(__name__)\n        self.another_app = Flask(__name__)\n\n    def test_load_config_from_file(self):\n        load_config_from_file(self.app, \"settings.py\")\n        assert self.app.config['SETTINGS_KEY'] == \"settings\"\n\n    def test_additional_settings_from_file(self):\n        env = 'COASTER_ENV'\n        environ[env] = \"gibberish\"\n        assert _additional_config.get(environ[env]) is None\n        for k, v in _additional_config.items():\n            environ[env] = k\n            assert _additional_config.get(environ[env]) == v\n\n    def test_init_app(self):\n        environ['FLASK_ENV'] = \"testing\"\n        init_app(self.app)\n        assert self.app.config['SETTINGS_KEY'] == \"settings\"\n        assert self.app.config['TEST_KEY'] == \"test\"\n\n    def test_logging_handler(self):\n        load_config_from_file(self.another_app, \"testing.py\")\n        logger_init_app(self.another_app)\n        for handler in self.another_app.logger.handlers:\n            try:\n                raise Exception\n            except Exception:  # noqa: B902 # skipcq: PYL-W0703\n                formatter = handler.formatter\n                if isinstance(formatter, LocalVarFormatter):\n                    formatter.formatException(sys.exc_info())\n\n    def test_load_config_from_file_ioerror(self):\n        app = Flask(__name__)\n        assert not load_config_from_file(app, \"notfound.py\")\n\n    def test_current_auth(self):\n        environ['FLASK_ENV'] = \"testing\"\n        init_app(self.app)\n        with self.app.test_request_context():\n            assert (\n                render_template_string(\n                    '{% if current_auth.is_authenticated %}Yes{% else %}No{% endif %}'\n                )\n                == 'No'\n            )\n\n\ndef test_key_rotation_wrapper():\n    payload = {'test': 'value'}\n\n    secret_keys1 = ['key1', 'key2', 'key3']\n    secret_keys2 = list(reversed(secret_keys1))\n\n    # These serializers share the same secret keys in different orders of priority\n    serializer1a = KeyRotationWrapper(itsdangerous.URLSafeSerializer, secret_keys1)\n    serializer2a = KeyRotationWrapper(itsdangerous.URLSafeSerializer, secret_keys2)\n\n    # These are truncated to drop the last secret key (which is the first of the other)\n    serializer1b = KeyRotationWrapper(itsdangerous.URLSafeSerializer, secret_keys1[:-1])\n    serializer2b = KeyRotationWrapper(itsdangerous.URLSafeSerializer, secret_keys2[:-1])\n\n    out1 = serializer1a.dumps(payload)\n    out1b = serializer1b.dumps(payload)\n    out2 = serializer2a.dumps(payload)\n    out2b = serializer2b.dumps(payload)\n\n    assert out1 == out1b\n    assert out2 == out2b\n    assert out1 != out2\n    # We'll ignore the b outputs from here onward since they are the same\n\n    # The serializers can load their own output\n    assert serializer1a.loads(out1) == payload\n    assert serializer1b.loads(out1) == payload\n    assert serializer2a.loads(out2) == payload\n    assert serializer2b.loads(out2) == payload\n\n    # The serializers that share a full set of secret keys can read each others' outputs\n    assert serializer1a.loads(out2) == payload\n    assert serializer2a.loads(out1) == payload\n\n    # However, the serializers with missing secret keys will raise BadSignature\n    with pytest.raises(itsdangerous.exc.BadSignature):\n        serializer1b.loads(out2)\n    with pytest.raises(itsdangerous.exc.BadSignature):\n        serializer2b.loads(out1)\n\n    # The KeyRotationWrapper has a safety catch for when a string secret is provided\n    with pytest.raises(ValueError):\n        KeyRotationWrapper(itsdangerous.URLSafeSerializer, 'secret_key')\n\n\ndef test_app_key_rotation():\n    app = Flask(__name__)\n    app.session_interface = RotatingKeySecureCookieSessionInterface()\n\n    @app.route('/set')\n    def route_set():\n        session['test'] = 'value'\n        return 'set'\n\n    @app.route('/get')\n    def route_get():\n        return str(session.get('test') == 'value')\n\n    app.config['SECRET_KEYS'] = ['key1', 'key2']\n    app.config['SECRET_KEY'] = app.config['SECRET_KEYS'][0]\n\n    with app.test_client() as c:\n        rv = c.get('/set')\n        assert 'Set-Cookie' in rv.headers\n        rv = c.get('/get')\n        assert 'Set-Cookie' not in rv.headers\n        assert rv.data == b'True'\n\n        # Rotate secret keys and confirm cookie still works\n        app.config['SECRET_KEYS'] = ['key_new', 'key1']\n        app.config['SECRET_KEY'] = app.config['SECRET_KEYS'][0]\n\n        rv = c.get('/get')\n        assert 'Set-Cookie' not in rv.headers  # Won't be set until amended\n        assert rv.data == b'True'\n\n        # Now change secret keys to something entirely new and confirm cookie is invalid\n        app.config['SECRET_KEYS'] = ['new']\n        app.config['SECRET_KEY'] = app.config['SECRET_KEYS'][0]\n\n        rv = c.get('/get')\n        assert rv.data == b'False'\n\n    # We'll get a RuntimeError without a secret keys list\n    del app.config['SECRET_KEYS']\n    # SECRET_KEY is present but no longer consulted\n    assert app.config['SECRET_KEY']\n    with app.test_client() as c:\n        rv = c.get('/set')\n        assert rv.status_code == 500  # RuntimeError was raised\n","sub_path":"tests/test_app.py","file_name":"test_app.py","file_ext":"py","file_size_in_byte":5593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"255277053","text":"import functools\nfrom threading import Lock\nfrom .utils import supports_terminal_sequences, TERMINAL_SEQUENCES, write_string\n\n\nclass MultilinePrinterBase:\n    def __init__(self, stream=None, lines=1):\n        self.stream = stream\n        self.maximum = lines - 1\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, *args):\n        self.end()\n\n    def print_at_line(self, text, pos):\n        pass\n\n    def end(self):\n        pass\n\n    def _add_line_number(self, text, line):\n        if self.maximum:\n            return f'{line + 1}: {text}'\n        return text\n\n    def write(self, *text):\n        write_string(''.join(text), self.stream)\n\n\nclass QuietMultilinePrinter(MultilinePrinterBase):\n    pass\n\n\nclass MultilineLogger(MultilinePrinterBase):\n    def write(self, *text):\n        self.stream.debug(''.join(text))\n\n    def print_at_line(self, text, pos):\n        # stream is the logger object, not an actual stream\n        self.write(self._add_line_number(text, pos))\n\n\nclass BreaklineStatusPrinter(MultilinePrinterBase):\n    def print_at_line(self, text, pos):\n        self.write(self._add_line_number(text, pos), '\\n')\n\n\nclass MultilinePrinter(MultilinePrinterBase):\n    def __init__(self, stream=None, lines=1, preserve_output=True):\n        super().__init__(stream, lines)\n        self.preserve_output = preserve_output\n        self._lastline = self._lastlength = 0\n        self._movelock = Lock()\n        self._HAVE_FULLCAP = supports_terminal_sequences(self.stream)\n\n    def lock(func):\n        @functools.wraps(func)\n        def wrapper(self, *args, **kwargs):\n            with self._movelock:\n                return func(self, *args, **kwargs)\n        return wrapper\n\n    def _move_cursor(self, dest):\n        current = min(self._lastline, self.maximum)\n        yield '\\r'\n        distance = dest - current\n        if distance < 0:\n            yield TERMINAL_SEQUENCES['UP'] * -distance\n        elif distance > 0:\n            yield TERMINAL_SEQUENCES['DOWN'] * distance\n        self._lastline = dest\n\n    @lock\n    def print_at_line(self, text, pos):\n        if self._HAVE_FULLCAP:\n            self.write(*self._move_cursor(pos), TERMINAL_SEQUENCES['ERASE_LINE'], text)\n\n        text = self._add_line_number(text, pos)\n        textlen = len(text)\n        if self._lastline == pos:\n            # move cursor at the start of progress when writing to same line\n            prefix = '\\r'\n            if self._lastlength > textlen:\n                text += ' ' * (self._lastlength - textlen)\n            self._lastlength = textlen\n        else:\n            # otherwise, break the line\n            prefix = '\\n'\n            self._lastlength = textlen\n        self.write(prefix, text)\n        self._lastline = pos\n\n    @lock\n    def end(self):\n        # move cursor to the end of the last line, and write line break\n        # so that other to_screen calls can precede\n        text = self._move_cursor(self.maximum) if self._HAVE_FULLCAP else []\n        if self.preserve_output:\n            self.write(*text, '\\n')\n            return\n\n        if self._HAVE_FULLCAP:\n            self.write(\n                *text, TERMINAL_SEQUENCES['ERASE_LINE'],\n                f'{TERMINAL_SEQUENCES[\"UP\"]}{TERMINAL_SEQUENCES[\"ERASE_LINE\"]}' * self.maximum)\n        else:\n            self.write(*text, ' ' * self._lastlength)\n","sub_path":"minicurses.py","file_name":"minicurses.py","file_ext":"py","file_size_in_byte":3330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"244691928","text":"import general\n# from sqlalchemy import create_engine\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column, Integer, Float, String\nfrom sqlalchemy import MetaData\nfrom sqlalchemy import select\n\n\n# Type conversion functions (python types, sql types, sqlalchemy types)\ndef get_py_type(tbl_dict):\n    \"\"\"Given a table dictionary (formatted as key:[list]), returns a dictionary of tbl_key: py_type where py_type can\n    be 'integer', 'float', or 'string' (at the moment)\"\"\"\n\n    tbl_keys = list(tbl_dict.keys())\n    py_types = [general.get_type(tbl_dict[key]) for key in list(tbl_dict.keys())]\n    py_types_dict = dict(zip(tbl_keys, py_types))\n    return py_types_dict\n\n\ndef get_sql_type(tbl_dict):\n    \"\"\"Given a table dictionary (formatted as key:[list]), returns a dictionary of tbl_key: sql_type\n    where sql_type is a sqlalchemy sql_type class\"\"\"\n\n    py_types = get_py_type(tbl_dict)  # py_types is a dict\n    sql_types = py_type_to_sql_type(py_types)\n    return sql_types\n\n\ndef py_type_to_sql_type(py_types):\n    \"\"\"Converts a dictionary of python types to a dictionary of sql types where sql type is a sqlalchemy sql_type\n    class\"\"\"\n\n    sql_types = dict()\n    for key in py_types:\n        if py_types[key] == \"integer\":\n            sql_types[key] = Integer\n        elif py_types[key] == \"float\":\n            sql_types[key] = Float\n        elif py_types[key] == \"string\":\n            sql_types[key] = String\n        else:\n            raise Exception(\"Error: py_type {} is not an integer, float, or string\".format(py_types[key]))\n    return sql_types\n\n\ndef create_col_definitions(tbl_name, id_type_dict):\n    \"\"\"Returns a dictionary that begins with __table__name and an integer id followed by columns as specified\n    in the id_type_dict\n    tbl_name: name of the desired table\n    id_type_dict: dictionary of column id's and associated sql_alchemy sql types\"\"\"\n\n    col_specs = {'__tablename__': '{}'.format(tbl_name),\n                 'id': Column(Integer, primary_key=True)}\n    for key in id_type_dict:\n        col_specs[key] = Column(id_type_dict[key])\n\n    return col_specs\n\n\n# Database table functions (i.e. create, drop, access)\ndef create_table(engine, name, cols, overwrite=False):\n    \"\"\"Creates a table(name) in the engine with the specified cols\n    engine: sql_alchemy create_engine(url) output\n    name: name for the created table\n    cols: dictionary of column names and sql types with a table name specified\"\"\"\n\n    base = declarative_base()\n    base.metadata.reflect(engine)\n    if name in base.metadata.tables and not overwrite:\n        print(\"Table exists and overwrite is False. Returning without making changes\")\n        return\n    elif name in base.metadata.tables and overwrite:\n        print(\"Table exists and overwrite is True. Overwriting table\")\n        drop_table(engine, name)\n    table = type(name, (base, ), cols)\n    table.__table__.create(bind=engine)\n\n\ndef drop_table(engine, drop_tbl):\n    \"\"\"Function takes an engine, finds table in the engine metadata, and drops the drop_tbl from the engine\"\"\"\n\n    meta = MetaData(bind=engine)\n    meta.reflect(bind=engine)\n    drop_tbl_class = meta.tables[drop_tbl]\n    drop_tbl_class.drop()\n\n\ndef get_table(engine, tbl):\n    meta = MetaData(bind=engine)\n    meta.reflect(bind=engine)\n    return meta.tables[tbl]\n\n\n# Table modification functions\ndef insert_row(engine, table, row):\n\n    conn = engine.connect()\n    conn.execute(table.insert(), row)\n    # conn.execute(table.insert(), [\n    #   {'l_name': 'Hi', 'f_name': 'bob'},\n    #   {'l_name': 'yo', 'f_name': 'alice'}])\n\n\ndef insert_rows(engine, table, rows):\n    \"\"\"To Improve: concatenate rows so that only one call to the DB is made when inserting\"\"\"\n\n    for row in rows:\n        insert_row(engine, table, row)\n\n\ndef dict_to_rows(tbl_dict):\n    \"\"\"Converts an input dictionary into rows compatible with sqlalchemy's insert function\n    Currently presumes each dictionary object is a list of equivalent length\"\"\"\n\n    rows = []\n    keys = list(tbl_dict.keys())\n    length = len(tbl_dict[keys[0]])\n    for i in range(length):\n        row_dict = dict()\n        for key in keys:\n            row_dict[key] = tbl_dict[key][i]\n        rows.append(row_dict)\n    return rows\n\n\ndef select_rows(conn, table):\n    select_st = select([table]).where(\n        table.c.l_name == 'efg_pct')\n    res = conn.execute(select_st)\n    for _row in res:\n        print(_row)\n","sub_path":"database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":4422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"248807089","text":"def dig_pow(n, p):\n    sum = sum2 = 0\n    t = m = n\n    while n:\n        n = n // 10\n        sum += 1\n    sum = sum + p - 1\n    while sum >= p:\n        k = t % 10\n        t = t // 10\n        sum2 += k ** sum\n        sum = sum - 1\n    return sum2//m if sum2 % m == 0 else -1","sub_path":"CodeWars/Python/6kyu/Playing with digits.py","file_name":"Playing with digits.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"612797798","text":"import os\nimport argparse\nimport torch\nimport torch.nn.parallel\nimport torch.optim\nimport torch.utils.data.distributed\nimport torch.distributed as dist\nimport torchvision.transforms as transforms\nfrom torch.optim import lr_scheduler\nfrom src.helper_functions.helper_functions import mAP, CocoDetection, CutoutPIL, ModelEma, add_weight_decay, strip_prefix_if_present\nfrom src.models import create_model\nfrom src.loss_functions.losses import AsymmetricLoss\nfrom randaugment import RandAugment\nfrom torch.cuda.amp import GradScaler, autocast\n\nparser = argparse.ArgumentParser(description='PyTorch MS_COCO Training')\nparser.add_argument('data', metavar='DIR', help='path to dataset', default='/home/MSCOCO_2014/')\nparser.add_argument('--lr', default=1e-4, type=float)\nparser.add_argument('--model-name', default='', type=str)\nparser.add_argument('--model-path', default='', type=str)\nparser.add_argument('--num-classes', default=80)\nparser.add_argument('-j', '--workers', default=8, type=int, metavar='N',\n                    help='number of data loading workers (default: 16)')\nparser.add_argument('--image-size', default=224, type=int,\n                    metavar='N', help='input image size (default: 448)')\nparser.add_argument('--thre', default=0.8, type=float,\n                    metavar='N', help='threshold value')\nparser.add_argument('-b', '--batch-size', default=128, type=int,\n                    metavar='N', help='mini-batch size (default: 16)')\nparser.add_argument('--print-freq', '-p', default=64, type=int,\n                    metavar='N', help='print frequency (default: 64)')\nparser.add_argument('--drop', default=0.0, type=float,\n                    metavar='N', help='attention drop rate')\nparser.add_argument('--drop_path', default=0.2, type=float,\n                    metavar='N', help='vit drop path rate')\nparser.add_argument('--dtgfl', action='store_true',\n            help='using disable_torch_grad_focal_loss in ASL loss')\nparser.add_argument('--output', metavar='DIR',\n                    default=os.getenv('PT_OUTPUT_DIR', '/tmp'),\n                    help='path to output folder')\n# distribution training\nparser.add_argument(\"--local_rank\", type=int, default=-1,\n                    help='local rank for DistributedDataParallel')\n\n\ndef main():\n    args = parser.parse_args()\n    args.do_bottleneck_head = False\n\n    # setup dist training\n    args.distributed = False\n    if 'WORLD_SIZE' in os.environ:\n        args.distributed = int(os.environ['WORLD_SIZE']) > 1\n    args.device = 'cuda'\n    args.world_size = 1\n    args.rank = 0  # global rank\n\n    if args.distributed:\n        args.device = 'cuda:%d' % args.local_rank\n        torch.cuda.set_device(args.local_rank)\n        torch.distributed.init_process_group(backend='nccl', init_method='env://')\n        args.world_size = torch.distributed.get_world_size()\n        args.rank = torch.distributed.get_rank()\n        print('Training in distributed mode with multiple processes, 1 GPU per process. Process %d, total %d.' % (args.rank, args.world_size))\n    else:\n        print('Training with a single process on 1 GPUs.')\n    assert args.rank >= 0\n\n    # Setup model\n    print('creating model...')\n    model = create_model(args).cuda()\n    if args.model_path:  # make sure to load pretrained ImageNet model\n        state = torch.load(args.model_path, map_location='cpu')\n        if 'net' in state:\n            state = state['net']\n        if 'model' in state:\n            state = state['model']\n        state = strip_prefix_if_present(\n            state, prefix=\"module.\"\n        )\n        if args.model_name.startswith('resne'):\n            filtered_dict = {k: v for k, v in state.items() if\n                             (k in model.state_dict() and 'fc' not in k)}\n        elif args.model_name.startswith('deit_'):\n            filtered_dict = {k: v for k, v in state.items() if\n                             (k in model.state_dict() and 'head' not in k)}\n        else:\n            filtered_dict = {k: v for k, v in state.items() if\n                             (k in model.state_dict() and 'head.fc' not in k)}\n        model.load_state_dict(filtered_dict, strict=False)\n    print('done\\n')\n\n    if args.distributed:\n        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], broadcast_buffers=False)\n\n    # COCO Data loading\n    instances_path_val = os.path.join(args.data, 'annotations/instances_val2014.json')\n    instances_path_train = os.path.join(args.data, 'annotations/instances_train2014.json')\n    # data_path_val = args.data\n    # data_path_train = args.data\n    data_path_val   = f'{args.data}/val2014'    # args.data\n    data_path_train = f'{args.data}/train2014'  # args.data\n    val_dataset = CocoDetection(data_path_val,\n                                instances_path_val,\n                                transforms.Compose([\n                                    transforms.Resize((args.image_size, args.image_size)),\n                                    transforms.ToTensor(),\n                                    # normalize, # no need, toTensor does normalization\n                                ]))\n    train_dataset = CocoDetection(data_path_train,\n                                  instances_path_train,\n                                  transforms.Compose([\n                                      transforms.Resize((args.image_size, args.image_size)),\n                                      CutoutPIL(cutout_factor=0.5),\n                                      RandAugment(),\n                                      transforms.ToTensor(),\n                                      # normalize,\n                                  ]))\n    print(\"len(val_dataset)): \", len(val_dataset))\n    print(\"len(train_dataset)): \", len(train_dataset))\n    if args.distributed:\n        assert args.batch_size // dist.get_world_size() == args.batch_size / dist.get_world_size(), 'Batch size is not divisible by num of gpus.'\n        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset, shuffle=True)\n        val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False)\n        train_loader = torch.utils.data.DataLoader(\n            train_dataset, batch_size=args.batch_size // dist.get_world_size(),\n            num_workers=args.workers, pin_memory=True, sampler=train_sampler,\n            drop_last=True)\n\n        val_loader = torch.utils.data.DataLoader(\n            val_dataset, batch_size=args.batch_size // dist.get_world_size(),\n            num_workers=args.workers, pin_memory=False, sampler=val_sampler)\n    else:\n        # Pytorch Data loader\n        train_loader = torch.utils.data.DataLoader(\n            train_dataset, batch_size=args.batch_size, shuffle=True,\n            num_workers=args.workers, pin_memory=True)\n\n        val_loader = torch.utils.data.DataLoader(\n            val_dataset, batch_size=args.batch_size, shuffle=False,\n            num_workers=args.workers, pin_memory=False)\n\n    # Actuall Training\n    train_multi_label_coco(model, train_loader, val_loader, args.lr, args)\n\n\ndef train_multi_label_coco(model, train_loader, val_loader, lr, args):\n    ema = ModelEma(model, 0.9997)  # 0.9997^641=0.82\n\n    # set optimizer\n    Epochs = 80\n    Stop_epoch = 40\n    weight_decay = 1e-4\n    criterion = AsymmetricLoss(gamma_neg=4, gamma_pos=0, clip=0.05, disable_torch_grad_focal_loss=args.dtgfl)\n    parameters = add_weight_decay(model, weight_decay)\n    optimizer = torch.optim.Adam(params=parameters, lr=lr, weight_decay=0)  # true wd, filter_bias_and_bn\n    steps_per_epoch = len(train_loader)\n    scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr, steps_per_epoch=steps_per_epoch, epochs=Epochs,\n                                        pct_start=0.2)\n\n    highest_mAP = 0\n    trainInfoList = []\n    scaler = GradScaler()\n    for epoch in range(Epochs):\n        if args.distributed:\n            train_loader.sampler.set_epoch(epoch)\n        if epoch > Stop_epoch:\n            break\n        for i, (inputData, target) in enumerate(train_loader):\n            inputData = inputData.cuda()\n            target = target.cuda()  # (batch,3,num_classes)\n            target = target.max(dim=1)[0]\n            with autocast():  # mixed precision\n                output = model(inputData).float()  # sigmoid will be done in loss !\n            loss = criterion(output, target)\n            model.zero_grad()\n\n            scaler.scale(loss).backward()\n            # loss.backward()\n\n            scaler.step(optimizer)\n            scaler.update()\n            # optimizer.step()\n\n            scheduler.step()\n\n            ema.update(model)\n            # store information\n            if i % 100 == 0:\n                trainInfoList.append([epoch, i, loss.item()])\n                print('Epoch [{}/{}], Step [{}/{}], LR {:.1e}, Loss: {:.1f}'\n                      .format(epoch, Epochs, str(i).zfill(3), str(steps_per_epoch).zfill(3),\n                              scheduler.get_last_lr()[0], loss.item()))\n\n        model.eval()\n        mAP_score_regular, mAP_score_ema = validate_multi(val_loader, model, ema)\n        model.train()\n\n        if (not args.distributed) or (args.distributed and dist.get_rank() == 0):\n            save_checkpoint({\n                'state_dict': model.state_dict(),\n                'epoch': epoch,\n                'mAP': mAP_score_regular\n            },\n                savedir=args.output,\n                savedname='model-{}-{}.ckpt'.format(epoch + 1, i + 1),\n                is_best=mAP_score_regular > mAP_score_ema and mAP_score_regular > highest_mAP)\n\n            save_checkpoint({\n                'state_dict': ema.module.state_dict(),\n                'epoch': epoch,\n                'mAP': mAP_score_ema\n            },\n                savedir=args.output,\n                savedname='model-{}-{}-{}.ckpt'.format('ema', epoch + 1, i + 1),\n                is_best=mAP_score_ema > mAP_score_regular and mAP_score_ema > highest_mAP)\n\n        mAP_score = max(mAP_score_regular, mAP_score_ema)\n        highest_mAP = max(highest_mAP, mAP_score)\n        print('current_mAP = {:.4f}, highest_mAP = {:.4f}\\n'.format(\n            mAP_score, highest_mAP)\n        )\n\n\ndef save_checkpoint(state_dict, savedir, savedname, is_best):\n    torch.save(state_dict, os.path.join(savedir, savedname))\n    if is_best:\n        torch.save(state_dict, os.path.join(savedir, 'model-highest.ckpt'))\n\n\ndef validate_multi(val_loader, model, ema_model):\n    print(\"starting validation\")\n    Sig = torch.nn.Sigmoid()\n    preds_regular = []\n    preds_ema = []\n    targets = []\n    for i, (input, target) in enumerate(val_loader):\n        target = target\n        target = target.max(dim=1)[0]\n        # compute output\n        with torch.no_grad():\n            with autocast():\n                output_regular = Sig(model(input.cuda())).cpu()\n                output_ema = Sig(ema_model.module(input.cuda())).cpu()\n\n        # for mAP calculation\n        preds_regular.append(output_regular.cpu().detach())\n        preds_ema.append(output_ema.cpu().detach())\n        targets.append(target.cpu().detach())\n\n    mAP_score_regular = mAP(torch.cat(targets).numpy(), torch.cat(preds_regular).numpy())\n    mAP_score_ema = mAP(torch.cat(targets).numpy(), torch.cat(preds_ema).numpy())\n    print(\"mAP score regular {:.2f}, mAP score EMA {:.2f}\".format(mAP_score_regular, mAP_score_ema))\n    return mAP_score_regular, mAP_score_ema\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":11439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"252723255","text":"import socket\nimport threading\n\nbind_ip = \"0.0.0.0\"\nbind_port = 9999\n\nserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\nserver.bind((bind_ip,bind_port))\n\n# Set the maximum number of connections\nserver.listen(5)\n\nprint(f\"[*] Listen on {bind_ip}:{bind_port}\")\n\ndef handle_client(client_socket:socket):\n    req = client_socket.recv(1024)\n    print(f\"[*] Recived:{bytes.decode(req)}\")\n    client_socket.send(bytes(\"ACK!\", encoding=\"utf8\"))\n    client_socket.close()\n\nwhile True:\n    client, addr = server.accept()\n\n    print(f\"[*] Accepted connection from {addr[0]}:{addr[1]}\")\n\n    client_handle = threading.Thread(target=handle_client, args=(client,))\n    client_handle.start()","sub_path":"netbase/tcp_server.py","file_name":"tcp_server.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"87498602","text":"\"\"\"\ndecorator to make functions that only ever return the first non-false value they create\n\"\"\"\nfrom datetime import datetime, timedelta\nimport time\n\n\nclass CachedInstance(object):\n    \"a decorator\"\n\n    def __init__(self, timeout=0, file=''):\n        \"timeout: int seconds        file='string path to store result'\"\n        self.result = {}\n        self.timeouts = {}\n        self.time = datetime.now()\n        self.timeout = timeout\n        self.file = file\n\n    def __call__(self, fn):\n        \"wrap our function\"\n\n        def wrapper(*a, **k):\n            uid = a[0].uid\n            # regenerate content on finding a recache parameter\n            if k.get('recache', 0):\n                self.result[uid] = ''\n                del k['recache']\n            if self.timedout(uid):\n                self.result[uid] = ''\n            # regenerate if no content available\n            if not self.result.get(uid, ''):\n                self.result[uid] = fn(*a, **k)\n                self.timeouts[uid] = datetime.now()\n                if self.file:\n                    open(self.file % uid, 'w').write(self.result[uid])\n            return self.result[uid]\n\n        return wrapper\n\n    def timedout(self, uid):\n        \"returns True if we have timed out\"\n        # if there's no timeout we will not regenerate\n        if not self.timeout:\n            return False\n        if not self.timeouts.get(uid):\n            return False\n        return datetime.now() >= self.timeouts[uid] + timedelta(\n            seconds=self.timeout)\n\n\ndef cached_instance(fn):\n    return CachedInstance()(fn)\n\n\nif __name__ == '__main__':\n\n    class X(object):\n        def __init__(self, uid):\n            self.uid = uid\n\n        @cached_instance\n        def fn(self):\n            return datetime.now()\n\n        @CachedInstance(timeout=1)\n        def fn2(self):\n            return datetime.now()\n\n        @CachedInstance(file='/tmp/test_%d.html')\n        def fn3(self):\n            return repr(datetime.now())\n\n    xs = [X(i) for i in range(3)]\n    t1s = [x.fn() for x in xs]\n    print(t1s)\n    #assert list(set(t1s))==xs, 'each instance should have an unique value'\n    t2s = [x.fn() for x in xs]\n    assert t1s == t2s, 'should return cached values'\n    t3s = [x.fn() for x in xs]\n    assert t1s == t3s, 'should return cached values'\n    t4s = [x.fn(recache=True) for x in xs]\n    assert t1s != t4s, 'should return new values'\n\n    t5s = [x.fn2() for x in xs]\n    t6s = [x.fn2() for x in xs]\n    assert t5s == t6s, \"values should be cached\"\n    time.sleep(2)\n    t7s = [x.fn2() for x in xs]\n    assert t5s != t7s, \"values should refreshed\"\n\n    t8s = [x.fn3() for x in xs]\n    t9s = [open('/tmp/test_%d.html' % x.uid).read() for x in xs]\n    assert t8s == t9s, \"should have been copied to file\"\n","sub_path":"evoke/serve/cached_instance.py","file_name":"cached_instance.py","file_ext":"py","file_size_in_byte":2764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"155160212","text":"from PyDSS.common import DATE_FORMAT\nfrom datetime import datetime\nimport os\n\nsettings_dict = {\n        \"Exports\": {\n            'Export Mode': {'type': str, 'Options': [\"byClass\", \"byElement\"]},\n            'Export Style': {'type': str, 'Options': [\"Single file\", \"Separate files\"]},\n            # Feather is not supported because its underlying libraries do not support complex numbers\n            'Export Format': {'type': str, 'Options': [\"csv\", \"h5\"]},\n            'Export Compression': {'type': bool, 'Options': [True, False]},\n            'Export Iteration Order': {'type': str, 'Options': [\"ElementValuesPerProperty\",\n                                                                \"ValuesByPropertyAcrossElements\"]},\n            'Export Elements': {'type': bool, 'Options': [True, False]},\n            'Export Event Log': {'type': bool, 'Options': [True, False]},\n            'Export Data Tables': {'type': bool, 'Options': [True, False]},\n            'Export Data In Memory': {'type': bool, 'Options': [True, False]},\n            'HDF Max Chunk Bytes': {'type': int, 'Options': range(16 * 1024, 1024 * 1024 + 1)},\n            'Export PV Profiles': {'type': bool, 'Options': [True, False]},\n            'Log Results': {'type': bool, 'Options': [True, False]},\n        },\n        \"Frequency\": {\n            'Enable frequency sweep': {'type': bool, 'Options': [True, False]},\n            'Fundamental frequency': {'type': int, 'Options': [50, 60]},\n            'Start frequency': {'type': float},\n            'End frequency': {'type': float},\n            'frequency increment': {'type': float},\n            'Neglect shunt admittance': {'type': bool, 'Options': [True, False]},\n            'Percentage load in series': {'type': float, 'Options': range(0, 100)},\n        },\n        \"Helics\": {\n            'Co-simulation Mode': {'type': bool, 'Options': [True, False]},\n            'Iterative Mode': {'type': bool, 'Options': [True, False]},\n            'Max co-iterations': {'type': int, 'Options': range(1, 1000)},\n            'Error tolerance': {'type': float},\n            'Federate name': {'type': str},\n            'Broker': {'type': str},\n            'Broker port': {'type': int},\n            'Time delta': {'type': float},\n            'Core type': {'type': str},\n            'Uninterruptible': {'type': bool, 'Options': [True, False]},\n            'Helics logging level': {'type': int, 'Options': range(0, 10)},\n        },\n        \"Logging\": {\n            'Logging Level': {'type': str, 'Options': [\"DEBUG\", \"INFO\", \"WARNING\" , \"ERROR\"]},\n            'Log to external file': {'type': bool, 'Options': [True, False]},\n            'Display on screen': {'type': bool, 'Options': [True, False]},\n            'Clear old log file': {'type': bool, 'Options': [True, False]},\n            'Pre-configured logging': {'type': bool, 'Options': [True, False]},\n        },\n        \"MonteCarlo\": {\n            'Number of Monte Carlo scenarios': {'type': int},\n        },\n        \"Plots\": {\n            'Create dynamic plots': {'type': bool, 'Options': [True, False]},\n            'Open plots in browser': {'type': bool, 'Options': [True, False]},\n        },\n        \"Project\": {\n            'Project Path': {'type': str},\n            'Start time': {'type': str},\n            'Simulation duration (min)': {'type': float},\n            'Step resolution (sec)': {'type': float},\n            'Loadshape start time': {'type': str},\n            'Max Control Iterations' : {'type': int},\n            'Error tolerance': {'type': float},\n            'Simulation Type': {'type': str, 'Options': [\"QSTS\", \"Dynamic\", \"Snapshot\", \"Monte Carlo\"]},\n            'Active Project': {'type': str},\n            'Scenarios': {'type': list},\n            'Active Scenario': {'type': str},\n            'DSS File': {'type': str},\n            'DSS File Absolute Path': {'type': bool, 'Options': [True, False]},\n            'Return Results': {'type': bool, 'Options': [True, False]},\n            'Control mode': {'type': str, 'Options': [\"Static\", \"Time\"]},\n            'Disable PyDSS controllers': {'type': bool, 'Options': [True, False]},\n            'Use Controller Registry': {'type': bool, 'Options': [True, False]},\n        },\n        \"Profiles\": {\n            \"Use profile manager\":  {'type': bool, 'Options': [True, False]},\n            \"source_type\": {'type': str},\n            \"source\": {'type': str},\n            \"Profile mapping\": {'type': str},\n            \"is_relative_path\":  {'type': bool, 'Options': [True, False]},\n            \"settings\": {'type': dict},\n        },\n        \"Reports\": {\n            'Format': {'type': str, 'Options': [\"csv\", \"h5\"]},\n            'Types': {'type': list}\n        },\n    }\n\n\ndef validate_settings(dss_args):\n    for category, params in dss_args.items():\n        valid_settings = settings_dict[category]\n        for key, ctype in params.items():\n            assert (key in valid_settings), \"category='{}' field='{}' is not a valid PyDSS argument\".format(category,\n                                                                                                            key)\n            if valid_settings[key]['type'] == float and isinstance(ctype, int):\n                ctype = float(ctype)\n            assert (isinstance(ctype,\n                               valid_settings[key]['type'])), \"'{}' can only be a '{}' data type. Was passed {}\".format(\n                key, valid_settings[key]['type'], type(ctype)\n            )\n            if 'Options' in valid_settings[key]:\n                if isinstance(valid_settings[key]['Options'], list):\n                    assert (ctype in (valid_settings[key]['Options'])), \\\n                        \"Invalid argument value '{}'. Possible values are: {}\".format(ctype,\n                                                                                      valid_settings[key]['Options'])\n                elif isinstance(valid_settings[key]['Options'], range):\n                    assert (min(valid_settings[key]['Options']) <= ctype <= max(valid_settings[key]['Options'])), \\\n                        \"Value '{}' out of bounds for '{}'. Allowable range is: {}-{}\".format(\n                            ctype,\n                            key,\n                            min(valid_settings[key]['Options']),\n                            max(valid_settings[key]['Options'])\n                        )\n\n    for category, params in settings_dict.items():\n        for key, ctype in params.items():\n            assert (key in dss_args[\n                category]), \"category='{}' field='{}' definition is missing in the TOML file\".format(category, key)\n\n    try:\n        Date = datetime.strptime(dss_args['Project'][\"Start time\" ], DATE_FORMAT)\n    except:\n        raise Exception(\"For category='Project', field='Start time' should be a datetime string with format {}\".format(\n            DATE_FORMAT\n        ))\n\n    try:\n        Date = datetime.strptime(dss_args['Project'][\"Loadshape start time\"], DATE_FORMAT)\n    except:\n        raise Exception(\"For category='Project', field='Loadshape start time' should be a datetime string with format {}\".format(\n            DATE_FORMAT\n        ))\n\n\n\n    assert (dss_args['Frequency']['End frequency'] >= dss_args['Frequency']['Start frequency']), \\\n        \"'End frequency' can not be smaller than 'Start frequency'\"\n    assert (os.path.exists(dss_args['Project']['Project Path'])), \\\n        \"Project path {} does not exist.\".format(dss_args['Project']['Project Path'])\n    assert (os.path.exists(os.path.join(dss_args['Project']['Project Path'], dss_args['Project'][\"Active Project\"]))), \\\n        \"Project '{}' does not exist.\".format(dss_args['Project'][\"Active Project\"])\n\n    assert (os.path.exists(os.path.join(dss_args['Project']['Project Path'],\n                                        dss_args['Project'][\"Active Project\"],\n                                        'Scenarios',\n                                        dss_args['Project']['Active Scenario']))), \\\n        \"Scenario '{}' does not exist.\".format(dss_args['Project']['Active Scenario'])\n    assert (os.path.exists(os.path.join(dss_args['Project']['Project Path'],\n                                        dss_args['Project'][\"Active Project\"],\n                                        'DSSfiles',\n                                        dss_args['Project']['DSS File']))), \\\n        \"Master DSS file '{}' does not exist.\".format(dss_args['Project']['DSS File'])\n    return","sub_path":"PyDSS/valiate_settings.py","file_name":"valiate_settings.py","file_ext":"py","file_size_in_byte":8434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"96086436","text":"import requests\r\nimport re\r\nimport csv\r\nimport os\r\nfrom urllib.parse import quote\r\n\r\n\r\n#### NOTE:- I AM NOT THE AUTHOR OF THIS CODE #####\r\n\r\n# Script Configuration\r\nlinkedin_email = \"odelapradeep12@gmail.com\" # place your linkedin login email\r\nlinkedin_password = \"PASSWORD\" # place your linkedin login password\r\ntarget_company_link = \"https://www.linkedin.com/company/johnson-&-johnson/\" # place company URL \r\n# End Script Configuration\r\n\r\nclass LinkedIn:\r\n\r\n    def __init__(self):\r\n        self.s = requests.Session()\r\n        self.headers = {\r\n            \"user-agent\": \"Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.132 Safari/537.36 OPR/67.0.3575.97\"\r\n            }\r\n\r\n    \r\n    def login(self,email,password):\r\n        # creates a session\r\n        try:\r\n            sc = self.s.get(\"https://www.linkedin.com/login\", headers=self.headers).text\r\n        except:\r\n            return False\r\n        csrfToken = sc.split('csrfToken\" value=\"')[1].split('\"')[0]\r\n        sid = sc.split('sIdString\" value=\"')[1].split('\"')[0]\r\n        pins = sc.split('pageInstance\" value=\"')[1].split('\"')[0]\r\n        lcsrf = sc.split('loginCsrfParam\" value=\"')[1].split('\"')[0]\r\n        data = {\r\n            'csrfToken': csrfToken,\r\n            'session_key': email,\r\n            'ac': '2',\r\n            'sIdString': sid,\r\n            'parentPageKey': 'd_checkpoint_lg_consumerLogin',\r\n            'pageInstance': pins,\r\n            'trk': 'public_profile_nav-header-signin',\r\n            'authUUID': '',\r\n            'session_redirect': 'https://www.linkedin.com/feed/',\r\n            'loginCsrfParam': lcsrf,\r\n            'fp_data': 'default',\r\n            '_d': 'd',\r\n            'showGoogleOneTapLogin': 'true',\r\n            'controlId': 'd_checkpoint_lg_consumerLogin-login_submit_button',\r\n            'session_password': password,\r\n            'loginFlow': 'REMEMBER_ME_OPTIN'\r\n            }\r\n        try:\r\n            after_login = self.s.post(\"https://www.linkedin.com/checkpoint/lg/login-submit\",headers=self.headers,data=data).text\r\n        except:\r\n            return False\r\n        is_logged_in = after_login.split('<title>')[1].split('</title>')[0]\r\n        if is_logged_in == \"LinkedIn\":\r\n            return True\r\n        else:\r\n            return False\r\n\r\n    def bulkScan(self, profiles):\r\n        # scans a list of profiles for email\r\n        all_emails = []\r\n        for profile in profiles:\r\n            profile = profile + \"/detail/contact-info/\"\r\n            sc = self.s.get(profile, headers=self.headers, allow_redirects=True).text\r\n            emails_found = re.findall(r'[a-zA-Z0-9\\.\\-\\_i]+@[\\w.]+',sc)\r\n            all_emails.extend(emails_found)\r\n        return all_emails\r\n\r\n    def singleScan(self, profile):\r\n        # scans a single account for email\r\n        profile = profile + \"/detail/contact-info/\"\r\n        sc = self.s.get(profile, headers=self.headers, allow_redirects=True).text\r\n        emails_found = re.findall(r'[a-zA-Z0-9\\.\\-\\_i]+@[\\w.]+',sc)\r\n        return emails_found\r\n\r\n    def saveRecord(self, data):\r\n        with open('empolys.csv', mode='a+', encoding='utf-8', newline='') as csvFile:\r\n            fieldnames = [\"Profile Link\", \"Full Name\", \"Headline\", \"Country\"]\r\n            writer = csv.DictWriter(csvFile, fieldnames=fieldnames)\r\n            if os.stat('leads.csv').st_size == 0:\r\n                writer.writeheader()\r\n            writer.writerow({\"Profile Link\":data[0], \"Full Name\":data[1], \"Headline\":data[2], \"Country\":data[3]})\r\n\r\n    def saveEmail(self, email):\r\n        with open('emails.csv', mode='a+', encoding='utf-8') as emFile:\r\n            if os.stat('emails.csv').st_size == 0:\r\n                emFile.write(\"Email\\n\")\r\n            emFile.write(email + \"\\n\")\r\n\r\n\r\n\r\n    def listProfiles(self, company_id, page_no, need_count=False):\r\n        resp = self.s.get('https://www.linkedin.com/search/results/people/?facetCurrentCompany=%5B%22{}%22%5D&origin=COMPANY_PAGE_CANNED_SEARCH&page={}'.format(company_id, page_no), headers=self.headers).text\r\n        token = self.s.cookies.get_dict().get('JSESSIONID').replace('\"','')\r\n        headers = {\r\n            'accept': 'application/vnd.linkedin.normalized+json+2.1',\r\n            'csrf-token': token,\r\n            'dnt': '1',\r\n            'referer': 'https://www.linkedin.com/search/results/people/?facetCurrentCompany=%5B%22{}%22%5D&origin=COMPANY_PAGE_CANNED_SEARCH&page={}'.format(company_id, page_no),\r\n            'user-agent': 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.121 Safari/537.36',\r\n            'x-li-lang': 'en_US',\r\n            'x-li-page-instance': 'urn:li:page:d_flagship3_search_srp_people;dhIfWyZ7T8m+SR5/ck6/vw==',\r\n            'x-li-track': '{\"clientVersion\":\"1.7.3019\",\"osName\":\"web\",\"timezoneOffset\":6,\"deviceFormFactor\":\"DESKTOP\",\"mpName\":\"voyager-web\",\"displayDensity\":1,\"displayWidth\":1366,\"displayHeight\":768}',\r\n            'x-restli-protocol-version': '2.0.0'\r\n        }\r\n        resp = self.s.get('https://www.linkedin.com/voyager/api/search/blended?count=10&filters=List(currentCompany-%3E{},resultType-%3EPEOPLE)&origin=COMPANY_PAGE_CANNED_SEARCH&q=all&queryContext=List(spellCorrectionEnabled-%3Etrue)&start={}'.format(company_id,(int(page_no)-1) * 10), headers=headers).json()\r\n        profiles = resp.get('data').get('elements')[0].get('elements')\r\n        all_profile_links = []\r\n        if need_count:\r\n            page_count = resp.get('data').get('paging').get('total')\r\n        for profile in profiles:\r\n            person_name = profile.get('title').get('text')\r\n            profile_link = profile.get('navigationUrl')\r\n            headline = profile.get('headline').get('text')\r\n            country = profile.get('subline').get('text')\r\n            print(\"Profile Link: {}\".format(profile_link))\r\n            print(\"Full Name: {}\".format(person_name))\r\n            print(\"Headline: {}\".format(headline))\r\n            print(\"Country: {}\".format(country))\r\n            data = []\r\n            data.append(profile_link)\r\n            data.append(person_name)\r\n            data.append(headline)\r\n            data.append(country)\r\n            self.saveRecord(data)\r\n        if need_count:\r\n            return all_profile_links, page_count\r\n        else:\r\n            return all_profile_links\r\n\r\n    def getCompanyID(self, company_link):\r\n        try:\r\n            company_username = company_link.split('.com/company/')[1].replace('/','')\r\n        except:\r\n            print(\"Use company link like https://www.linkedin.com/company/unilever/ only!\")\r\n            return None\r\n        resp = self.s.get(company_link, headers=self.headers).text\r\n        token = self.s.cookies.get_dict().get('JSESSIONID').replace('\"','')\r\n        headers = {\r\n            'csrf-token': token,\r\n            'referer': 'https://www.linkedin.com/',\r\n            'user-agent': 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.121 Safari/537.36'\r\n        }\r\n        api_link = 'https://www.linkedin.com/voyager/api/organization/companies?decorationId=com.linkedin.voyager.deco.organization.web.WebCompanyStockQuote-2&q=universalName&universalName={}'.format(quote(company_username))\r\n        resp = self.s.get(api_link, headers=headers).json()\r\n        company_id = resp.get('elements')[0].get('entityUrn').split(':')[-1]\r\n        return company_id\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    connection = LinkedIn()\r\n    login_state = connection.login(linkedin_email, linkedin_password)\r\n    if login_state:\r\n        print(\"Logged in to LinkedIn!\")\r\n        company_id = connection.getCompanyID(target_company_link)\r\n        if company_id is not None:\r\n            try:\r\n                print(\"Collecting all company member profiles upto 10000!\")\r\n                profile_list, page_count = connection.listProfiles(company_id, 1, True)\r\n                for page_no in range(2,page_count+1):\r\n                    profile_list.extend(connection.listProfiles(company_id, page_no))\r\n            except:\r\n                print(\"Profile list collected and saved! Extracting emails ...\")\r\n                all_emails = connection.bulkScan(profile_list)\r\n                for email in all_emails:\r\n                    print(email)\r\n                    connection.saveEmail(email)\r\n    else:\r\n        print(\"Unable to login to LinkedIn!\")\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"476716528","text":"import requests\nimport json\nimport math\nfrom geopy.distance import vincenty\nimport traceback\nimport logging\n\n# Setup logging\nlogger = logging.getLogger('mainlog')\nlogger.setLevel(logging.DEBUG)\nstream = logging.StreamHandler()\nstream.setLevel(logging.DEBUG)\nlogger.addHandler(stream)\n\nurl = 'https://api-v3.mbta.com'\n\nheaders = {'api_key':'k9rojBi20kesV8k_yW1WOA'}\n\nlat = 42.3438654\nlon = -71.0880786\nradius = 0.00000005\n\ncall = '/predictions?filter[latitude]='+str(lat)+'&filter[longitude]='+str(lon)+'&filter[radius]='+str(radius)+'&include=stop,route,trip'\n\nr = requests.get(url+call, headers=headers)\n\nprint('code: ' + str(r.status_code))\n#print(json.dumps(r.text, indent=4))\nj = json.loads(r.text)\n#print(json.dumps(j['included'][0], indent=4))\n#print(json.dumps(j['included'][1], indent=4))\n#print(json.dumps(j['included'][2], indent=4))\n#print(json.dumps(j['included'][3], indent=4))\n#print(json.dumps(j['included'][0], indent=4))\n#print(json.dumps(j['included'][31], indent=4))\n\n#print('vince: ' + str(vincenty((lat, lon), (42.347779, -71.085476))))\n\n\nprint(len(j['data']))\nprint(len(j['included']))\n\nfor i in range(0, 50):\n    print(str(i) + ' ' + str(j['included'][i].keys()))\n\nwith open('j.txt', 'w') as f:\n    pass\n    #f.write(j['included'])\n    \n    \n    \n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"302881471","text":"# Copyright 2021 Katteli Inc.\n# TestFlows.com Open-Source Software Testing Framework (http://testflows.com)\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# to the end flag\nimport contextvars\n\nfrom collections import namedtuple\n\nfrom concurrent.futures import CancelledError\nfrom concurrent.futures import TimeoutError\nfrom concurrent.futures import Future as ConcurrentFuture\n\nfrom .asyncio import asyncio, wrap_future, OptionalFuture\nfrom .asyncio import Future as AsyncFuture\nfrom .asyncio import is_running_in_event_loop\nfrom .asyncio import TimeoutError as AsyncTimeoutError\nfrom .asyncio import CancelledError as AsyncCancelledError\n\n\ndef Context(**kwargs):\n    \"\"\"Convenience function to create\n    a namedtuple to store parallel context variables.\n    \"\"\"\n    return namedtuple(\"ParallelContext\", \" \".join(kwargs.keys()))(*kwargs.values())\n\n\ncontext = Context(\n    current=contextvars.ContextVar(\"_testflows_current\", default=None),\n    previous=contextvars.ContextVar(\"_testflows_previous\", default=None),\n    top=contextvars.ContextVar(\"_testflows_top\", default=None),\n    is_valid=contextvars.ContextVar(\"_testflows_is_valid\", default=None),\n)\n\n# set current parallel context as valid\ncontext.is_valid.set(True)\n\nContextVar = contextvars.ContextVar\ncopy_context = contextvars.copy_context\n\n\ndef convert_result_to_concurrent_future(fn, args=None, kwargs=None):\n    \"\"\"Make concurrent future out of result of a function call.\"\"\"\n    if args is None:\n        args = ()\n    if kwargs is None:\n        kwargs = {}\n\n    future = ConcurrentFuture()\n    future.set_running_or_notify_cancel()\n    try:\n        future.set_result(fn(*args, **kwargs))\n    except BaseException as exc:\n        future.set_exception(exc)\n\n    return future\n\n\ndef _get_parallel_context():\n    \"\"\"Return parallel context based on contextvars\n    with all the user context variables cleared\n    to None.\n    \"\"\"\n    ctx = contextvars.copy_context()\n    # clear any user context variables to None\n    for var in ctx.keys():\n        if not var.name.startswith(\"_testflows_\"):\n            # in Python 3.8 ContextVar can't be cleared\n            # and therefore the best we can do is to set\n            # user context variable to None\n            var.set(None)\n    return ctx\n\n\ndef _check_parallel_context():\n    \"\"\"Check if parallel parallel context is valid.\"\"\"\n    if not context.is_valid.get():\n        raise RuntimeError(\"parallel context was not set\")\n\n\ndef top(value=None):\n    \"\"\"Highest level test.\"\"\"\n    if value is not None:\n        context.top.set(value)\n    return context.top.get()\n\n\ndef current(value=None, set_value=False):\n    \"\"\"Currently executing test.\"\"\"\n    if value is not None or set_value:\n        context.current.set(value)\n    return context.current.get()\n\n\ndef previous(value=None):\n    \"\"\"Last executed test.\"\"\"\n    if value is not None:\n        context.previous.set(value)\n    return context.previous.get()\n\n\ndef join(\n    *future,\n    futures=None,\n    test=None,\n    filter=None,\n    all=False,\n    no_async=False,\n    cancel_pending=False\n):\n    \"\"\"Wait for parallel test futures to complete.\n    Returns a list of completed tests.\n\n    Terminates current test if any of the parallel\n    tests raise an exception.\n\n    If no futures specified uses test.futures().\n    If test is not specified test is set to current().\n\n    :param *future: one or more futures (optional)\n    :param futures: list of futures (optional)\n    :param test: current test, default: current()\n    :param filter: filter function, default: None\n    :param all: wait and join all the tests, default: False\n    :param no_async: force non async join while running in event loop\n    :param cancel_pending: cancel any pending futures\n    \"\"\"\n    if no_async is False and is_running_in_event_loop():\n        return _async_join(\n            *future,\n            futures=futures,\n            test=test,\n            filter=filter,\n            all=all,\n            cancel_pending=cancel_pending\n        )\n\n    if test is None:\n        test = current()\n\n    futures = list(future) or futures or test.futures\n    tests = []\n    exception = None\n    filtered_count = 0\n\n    while True:\n        if not futures or filtered_count >= len(futures):\n            break\n\n        future = None\n        try:\n            future = futures.pop(0)\n\n            if filter and not filter(future):\n                futures.append(future)\n                filtered_count += 1\n                continue\n\n            filtered_count = 0\n\n            if cancel_pending:\n                if future.cancel():\n                    continue\n            try:\n                exc = future.exception(timeout=0.1)\n                if exc is not None:\n                    if exception is None:\n                        exception = exc\n                        cancel_pending = True\n                        if test:\n                            test.terminate()\n                    if not all:\n                        raise exception\n                else:\n                    tests.append(future.result(timeout=0.1))\n            except CancelledError:\n                continue\n            except TimeoutError:\n                futures.append(future)\n                continue\n        except BaseException:\n            if future is not None:\n                futures.append(future)\n            raise\n\n    if exception is not None:\n        raise exception\n    return tests\n\n\nasync def _async_join(\n    *future, futures=None, test=None, filter=None, all=False, cancel_pending=False\n):\n    \"\"\"Wait for async parallel test futures to complete.\n    Returns a list of completed tests.\n\n    Terminates current test if any of the parallel\n    tests raise an exception.\n\n    If no futures specified uses test.futures().\n    If test is not specified test is set to current().\n\n    :param *future: one or more futures (optional)\n    :param futures: list of futures (optional)\n    :param test: current test, default: current()\n    :param filter: filter function, default: None\n    :param all: wait and join all the tests, default: False\n    :param cancel_pending: cancel any pending futures\n    \"\"\"\n    if test is None:\n        test = current()\n\n    futures = list(future) or futures or test.futures\n    tests = []\n    exception = None\n    filtered_count = 0\n\n    while True:\n        if not futures or filtered_count >= len(futures):\n            break\n\n        future = None\n        try:\n            future = futures.pop(0)\n\n            if filter and not filter(future):\n                futures.append(future)\n                filtered_count += 1\n                continue\n\n            filtered_count = 0\n\n            try:\n                f = future\n                if isinstance(future, ConcurrentFuture):\n                    f = wrap_future(future, new_future=OptionalFuture())\n\n                if cancel_pending:\n                    if future.cancel():\n                        continue\n\n                tests.append(await asyncio.wait_for(asyncio.shield(f), timeout=0.1))\n            except AsyncCancelledError:\n                continue\n            except AsyncTimeoutError:\n                futures.append(future)\n                continue\n        except BaseException as exc:\n            if future is not None:\n                if future.done():\n                    if test:\n                        test.terminate()\n                    if not all:\n                        raise\n                    if exception is None:\n                        exception = exc\n                        cancel_pending = True\n                    continue\n                else:\n                    futures.append(future)\n            raise\n\n    if exception is not None:\n        raise exception\n    return tests\n","sub_path":"testflows/_core/parallel/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":8200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"23024080","text":"# @Author: J.Y.\n# @Date:   2019-05-03T11:51:59+09:00\n# @Project: NLP\n# @Last modified by:   J.Y.\n# @Last modified time: 2019-05-27T14:08:58+09:00\n# @License: JeeY\n# @Copyright: J.Y. JeeY\n\nimport os\nimport sys\nimport numpy as np\nsys.path.append(r'../module')\n\nimport parsing_module_2 as p2\n\nfname1 = 'd:/Program_Data/raw_test_dataset_07.test'\nfname2 = 'd:/Program_Data/raw_test_dataset_08.test'\n\ntrain_data = list()\nword_dict = p2.making_dictionary_of_words()\npos_dict = p2.making_pos_dictionary()\n\n# print(word_dict)\n\nwith open(fname1, 'r', encoding='utf-8') as f, open(fname2, 'a', encoding='utf-8') as fw:\n    switch = 1\n    sent = list()\n    while True:\n        if switch == 1:\n            new = list()\n            new.append('ROOT')\n            new.append(str(word_dict['ROOT']))\n            new.append(str(word_dict['ROOT']))\n            new.append(str(pos_dict['ROOT']))\n            new.append(str(pos_dict['ROOT']))\n            fw.write('    '.join(new) + '\\n')\n            switch = 0\n            continue\n        new = list()\n        line = f.readline()\n        if not line:break\n        line = line.split()\n        if line == []:\n            switch = 1\n            fw.write('\\n')\n            continue\n        # print(line)\n        new.append(line[0])\n        new.append(str(line[1]))\n        new.append(str(line[2]))\n        new.append(str(line[3]))\n        new.append(str(line[4]))\n        fw.write('    '.join(new) + '\\n')\n\n\n\n\n\n\n\n\n\n\n## endl\n","sub_path":"Keras_parsing/making_data_3/make_test_data_2.py","file_name":"make_test_data_2.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"193825804","text":"import service as s \nimport image_service as ims\nimport tornado.ioloop\nimport tornado.web\nimport json\nimport platform\nimport tempfile\ntempdir = '/tmp' if platform.system() == 'Darwin' else tempfile.gettempdir()\n\nclass BaseHandler(tornado.web.RequestHandler):\n    def set_default_headers(self):\n        self.set_header(\"Access-Control-Allow-Origin\", \"*\")\n        self.set_header(\"Access-Control-Allow-Headers\", \"x-requested-with\")\n        self.set_header('Access-Control-Allow-Methods', 'POST, GET, OPTIONS, DELETE')\n\n    def options(self):\n        self.set_status(204)\n        self.finish()\n\nclass TranslateHandler(BaseHandler):\n    def get(self, text, suite):\n        r = s._translate(text, suite)\n        self.write(r)\n\nclass ImageCaptionHandler(BaseHandler):\n    def post(self, t=None):\n        \n        p = tempdir + '/tmp.jpg'\n        data = self.request.body\n        with open(p, 'wb') as fp:\n            fp.write(data)\n\n        if t == 'c':\n            ret = ims.image_caption(p)\n            self.write(json.dumps(dict(c=ret[\"caption\"])))\n        elif t == 'd':\n            ret = ims.object_detection(p)\n            self.write(json.dumps(dict(d=ret)))\n        elif t == 'w':\n            ret = ims.word_detection(p)\n            self.write(json.dumps(dict(w=ret)))\n        else:\n            ret = ims.image_caption(p)\n            ret2 = ims.object_detection(p)\n            ret3 = ims.word_detection(p)\n            self.write(json.dumps(dict(c=ret[\"caption\"], d=ret2, w=ret3)))\n\ndef make_app():\n    # prefix = r'/hackathon'\n    prefix = r''\n    return tornado.web.Application([\n        (prefix + r\"/translate/(?P<text>[^/]+)/(?P<suite>.*)\", TranslateHandler),\n        (prefix + r\"/image-caption/(?P<t>.*)\", ImageCaptionHandler),\n        (prefix + r\"/(.*)\",tornado.web.StaticFileHandler, {\"path\": \"../frontend/dist\"},),\n    ])\n\nif __name__ == \"__main__\":\n    app = make_app()\n    app.listen(8888)\n    tornado.ioloop.IOLoop.current().start()\n","sub_path":"backend/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"260485447","text":"#my professor told me to write a comment on each\nfrom PIL import Image, ImageDraw\n#statement. I don't know why though.\nimport random\n\n#In this 'challenge', we shall use python to create the image. It is easier to do, and it also will allow me to dust up on python since I have not used it since Luigi's Poker. That was a while ago.\n\n#make the image random upon each successful run\nrandom.seed(None)\n\n#make the resolution of the image random cause why not!\nIMAGESIZE = random.randint(150, 300)\n\n#The Image File\nimage = Image.new('RGB',(IMAGESIZE,IMAGESIZE))\n#The draw object that will actually draw on the image\ndraw = ImageDraw.Draw(image)\n\n#the color variable is a 3-tuple object which is randomized upon each iteration\n#had to crack up Wikipedia for this one\n#In mathematics, a tuple is a finite ordered list (sequence) of elements. - Wikipedia\n#In Laymans terms, it is a ordered list.\n\nfor x in range(0, IMAGESIZE):\n\tfor y in range(0, IMAGESIZE):\n\t\tcolor = (random.randint(0,255),random.randint(0,255),random.randint(0,255))\n\t\tdraw.point((x,y),color)\n\n#finally, save the image\nimage.save(\"random.png\")","sub_path":"64 - Random Image Generator/ImageGenerator.py","file_name":"ImageGenerator.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"249496703","text":"\"\"'''\n述語と格パターンに続けて,項（述語に係っている文節そのもの）をタブ区切り形式で出力せよ．\n45の仕様に加えて，以下の仕様を満たすようにせよ:\n項は述語に係っている文節の単語列とする（末尾の助詞を取り除く必要はない）\n述語に係る文節が複数あるときは，助詞と同一の基準・順序でスペース区切り��並べる\n'''\n\n\nfrom knock41 import load_chunk\n\n\nif __name__ ==  '__main__':\n    filepath = './data/ai.ja/ai.ja.txt.parsed'\n    res = load_chunk(filepath)\n    with open('./ans46.txt','w', encoding='utf-8') as f:\n        for sentence in res:\n            for chunk in sentence.chunks:\n                for morph in chunk.morphs:\n                    if morph.pos == '動詞':\n                        cases = []\n                        modi_chunks = []\n                        for src in chunk.srcs:           # 動詞の係り元chunkから助詞を探す\n                            case = [morph.surface for morph in sentence.chunks[src].morphs if morph.pos == '助詞']\n                            if len(case) > 0:           # 助詞を含むchunkの場合は、助詞と項を取得\n                                cases = cases + case\n                                modi_chunks.append(''.join(morph.surface for morph in sentence.chunks[src].morphs if morph.pos != '記号'))\n                        if len(cases) > 0:\n                            cases = sorted(list(set(cases)))\n                            line = '{}\\t{}\\t{}'.format(morph.base, ' '.join(cases), ' '.join(modi_chunks))\n\n                            print(line, file=f)\n\n\n","sub_path":"wei/chapter05/knock46.py","file_name":"knock46.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"53246326","text":"import operator\nimport xlrd     # 엑셀을 사용하기 위한 모듈\n\ndef magic(file_path):\n    wb = xlrd.open_workbook(file_path)\n    per_sh = wb.sheet_by_name(\"PER\")\n\n    per_dict = {}       # 딕셔너리\n    for i in range(1, per_sh.nrows):\n        data = per_sh.row_values(i)     # i 번째의 행의 내용을 가져온다.\n        name = data[0]                  # 회사명 추출\n        per = data[1]                   # 회사의 per 추출\n        if per > 0:\n            per_dict[name] = per        # 딕셔너리에 저장\n\n    # key 값이 아닌 뒤의 데이터를 기준으로 정렬할 경우\n    sorted_per = sorted(per_dict.items(), key=operator.itemgetter(1))\n\n    #PER 랭킹 매기기\n    per_rank = {}                       # 랭킹을 관리하는 딕셔너리\n    for num, firm in enumerate(sorted_per):\n        per_rank[firm[0]] = num + 1     # 이름 : 랭킹 형태로 저장\n\n    roa_sh = wb.sheet_by_name(\"ROA\")\n\n    roa_dict = {}\n\n    for i in range(1, roa_sh.nrows):\n        data = roa_sh.row_values(i)  # i 번째의 행의 내용을 가져온다.\n        name = data[0]      # 회사명 추출\n        roa = data[1]       # 회사의 roa 추출\n        if roa != '':\n            roa_dict[name] = roa  # 딕셔너리에 저장\n\n    # key 값이 아닌 뒤의 데이터를 기준으로 내림 차순으로 정렬할 경우\n    sorted_roa = sorted(roa_dict.items(), key=operator.itemgetter(1), reverse=True)\n\n    roa_rank = {}\n\n    for num, firm in enumerate(sorted_roa):\n        roa_rank[firm[0]] = num + 1\n\n    # 종합 랭킹 매기기\n    total_rank = {}\n\n    for name in roa_rank.keys():\n        if name in per_rank.keys():\n            total_rank[name] = roa_rank[name] + per_rank[name]\n\n    sorted_total = sorted(total_rank.items(), key=operator.itemgetter(1))\n\n    file_path = \"D:\\\\Projects\\\\New_Python\\\\Quant_Strategy\\\\datas\\\\마법공식 데이터2.xlsx\"\n\n    magic_rank = {}\n    for num, firm in enumerate(sorted_total):\n        magic_rank[firm[0]] = num + 1\n\n    # 상위 30개 항목 출력하기\n    for num, key in enumerate(magic_rank.keys()):\n        if num == 30:\n            break\n        print(key,':',magic_rank[key])\n\nif __name__ == \"__main__\":\n    file_path = \"D:\\\\Projects\\\\New_Python\\\\Quant_Strategy\\\\datas\\\\마법공식 데이터.xlsx\"\n    magic(file_path)","sub_path":"Quant_Strategy/Magic/magic_module.py","file_name":"magic_module.py","file_ext":"py","file_size_in_byte":2316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"645722432","text":"class Solution:\n    def longestCommonPrefix(self, strs: List[str]) -> str:\n        pre=''        \n        if strs==None or len(strs)==0:\n            return pre\n        s=strs[0]\n        for i in range(len(s)):\n            for _ in strs:\n                if i>=len(_) or _[i]!=s[i]:\n                    return pre\n            pre+=s[i]\n        return pre\n","sub_path":"Longest Common Prefix.py","file_name":"Longest Common Prefix.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"45924281","text":"\"\"\"A setuptools based setup module.\nSee:\nhttps://packaging.python.org/en/latest/distributing.html\nhttps://github.com/pypa/sampleproject\n\"\"\"\n\nfrom setuptools import setup, find_packages\nfrom codecs import open\nfrom os import path\n\nhere = path.abspath(path.dirname(__file__))\n\nwith open(path.join(here, 'README.rst'), encoding='utf-8') as f:\n    long_description = f.read()\n\nsetup(\n    name='paetom-utils',\n    version='1.0.0',\n    description='Biblioteca úteis para desenvolvimento no padrão de projeto Paetom',  # Required\n    long_description=long_description,  # Optional\n    url='http://paetom.com/python/utils',  # Optional\n    author='Cleverson de Souza Carneiro',  # Optional\n    author_email='cleverson@paetom.com',  # Optional\n    classifiers=[  # Optional\n        'Development Status :: 2 - Alpha',\n        'Framework :: Paetom',\n        'License :: OSI Approved :: MIT License',\n        'Programming Language :: Python :: 3'\n    ],\n\n    keywords='design patern framework',  # Optional\n    packages=find_packages(exclude=['contrib', 'docs', 'tests']),  # Required\n    install_requires=['flask','sqlalchemy'],  # Optional\n    python_requires=\">=3,<4\",\n    extras_require={  # Optional\n        'dev': ['check-manifest'],\n        'test': ['coverage'],\n    },\n\n    # If there are data files included in your packages that need to be\n    # installed, specify them here.\n    #\n    # If using Python 2.6 or earlier, then these have to be included in\n    # MANIFEST.in as well.\n    # package_data={  # Optional\n    #    'sample': ['package_data.dat'],\n    #},\n\n    # Although 'package_data' is the preferred approach, in some case you may\n    # need to place data files outside of your packages. See:\n    # http://docs.python.org/3.4/distutils/setupscript.html#installing-additional-files\n    #\n    # In this case, 'data_file' will be installed into '<sys.prefix>/my_data'\n    # data_files=[('my_data', ['data/data_file'])],  # Optional\n\n    # To provide executable scripts, use entry points in preference to the\n    # \"scripts\" keyword. Entry points provide cross-platform support and allow\n    # `pip` to create the appropriate form of executable for the target\n    # platform.\n    #\n    # For example, the following would provide a command called `sample` which\n    # executes the function `main` from this package when invoked:\n    # entry_points={  # Optional\n    #    'console_scripts': [\n    #        'sample=sample:main',\n    #    ],\n    #},\n\n    # List additional URLs that are relevant to your project as a dict.\n    #\n    # This field corresponds to the \"Project-URL\" metadata fields:\n    # https://packaging.python.org/specifications/core-metadata/#project-url-multiple-use\n    #\n    # Examples listed include a pattern for specifying where the package tracks\n    # issues, where the source is hosted, where to say thanks to the package\n    # maintainers, and where to support the project financially. The key is\n    # what's used to render the link text on PyPI.\n    #project_urls={  # Optional\n    #    'Bug Reports': 'https://github.com/pypa/sampleproject/issues',\n    #    'Funding': 'https://donate.pypi.org',\n    #    'Say Thanks!': 'http://saythanks.io/to/example',\n    #    'Source': 'https://github.com/pypa/sampleproject/',\n    #},\n)","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":3258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"544749189","text":"from typing import Dict\nfrom pydantic import BaseModel\n\n\nclass DataInDB(BaseModel):\n    nombreGasto: str\n    valor: int\n\n\ndatabase_data = Dict[str, DataInDB]\n\n\ndef get_data(nombreGasto: str):\n    if nombreGasto in database_data.keys():\n        return database_data[nombreGasto]\n    else:\n        return None\n\n\ndatabase_transactions = []\ngenerator = {\"id\": 0}\n\n\ndef crear_data(creardata_in_db: DataInDB):\n    database_data[creardata_in_db.nombreGasto] = creardata_in_db\n    database_data[creardata_in_db.valor] = creardata_in_db\n    generator[\"id\"] = generator[\"id\"] + 1\n    database_transactions.append(creardata_in_db)\n\n    return database_transactions\n\n\ndef get_alldata():\n    return database_transactions\n\n\ndatabase_data = {\n    \"gasto1\": DataInDB(**{\"nombreGasto\": \"peluqueria\", \"valor\": 5000}),\n\n}\n","sub_path":"db/Data_db.py","file_name":"Data_db.py","file_ext":"py","file_size_in_byte":803,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"641869858","text":"def target_category_loss(x, category_index, nb_classes):\n    return tf.multiply(x, K.one_hot([category_index], nb_classes))\n\ndef target_category_loss_output_shape(input_shape):\n    return input_shape\n\ndef grad_cam(input_model, image, class_score, category_index, layer_name):\n    nb_classes = 2\n    target_layer = lambda x: target_category_loss(x, category_index, nb_classes)\n\n    x = input_model.layers[-1].output\n    x = Lambda(target_layer, output_shape=target_category_loss_output_shape)(x)\n    model = keras.models.Model(input_model.layers[0].input, x)\n\n    loss = K.sum(model.layers[-1].output)\n    # conv_output = [l for l in model.layers[0].layers if l.name is layer_name][0].output\n    # conv_output = [l for l in model.layers if l.name is layer_name][0].output\n    conv_output = model.get_layer(layer_name).output\n\n    K.set_learning_phase(0)\n    grads = normalize(K.gradients(loss, conv_output)[0])\n    gradient_function = K.function([model.layers[0].input], [conv_output, grads])\n    # gradient_function = K.function([model.layers[0].input], [conv_output])\n\n    output, grads_val = gradient_function([image])\n    output, grads_val = output[0, :], grads_val[0, :, :, :]\n    # output = gradient_function([image])\n    # output = output[0,:]\n\n    weights = np.mean(grads_val, axis=(0, 1))\n    cam = np.zeros(image.shape[1: 3], dtype=np.float32)\n\n    for i, w in enumerate(weights):\n        cam += w * cv2.resize(output[:, :, i], (224, 224), interpolation=cv2.INTER_CUBIC)\n        # plt.imshow(cam, cmap = 'gray')\n        # plt.show()\n    # print weights[0]\n\n    # cam = cv2.resize(cam, (224, 224), interpolation = cv2.INTER_CUBIC)\n    cam = np.maximum(cam, 0)\n    # print np.max(cam)\n    heatmap = cam * 1.0 / np.max(cam)\n    cam[0, 0] = np.max(cam) * 1.0 / class_score\n    cam = cam * 1.0 / np.max(cam)\n    cam = cv2.applyColorMap(np.uint8(255 * cam), cv2.COLORMAP_JET)\n    cam = cv2.cvtColor(cam, cv2.COLOR_BGR2RGB)\n    # Return to BGR [0..255] from the preprocessed image\n    # image = image[0, :]\n    # image -= np.min(image)\n    # image = np.minimum(image, 255)\n\n    # cam = cv2.applyColorMap(np.uint8(cam), cv2.COLORMAP_JET)\n    # cam = np.float32(cam) + np.float32(image)\n    # cam = 255 * cam / np.max(cam)\n    # cam = cam.astype(np.int8)\n    # return np.uint8(cam), heatmap\n    return cam, heatmap, output, grads_val","sub_path":"eigenface/gradcam.py","file_name":"gradcam.py","file_ext":"py","file_size_in_byte":2333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"349263831","text":"from qt_gui.mouse_event import MouseEvent\n\nimport start\nfrom objects import *\nfrom state import State\n\n\nclass ConnectNodesState(State):\n    def __init__(self, globals):\n        super(ConnectNodesState, self).__init__(globals)\n\n    def mouse_event(self, event, x, y):\n        self.enableHover(self.globals.graph.getNodeAt(x, y))\n        if event == MouseEvent.EVENT_LBUTTONDOWN:\n            self.globals.graph.addNode(Node((x, y)))\n\n    def keyboard_event(self, key):\n        if key == 10:\n            self.globals.state = start.StartState(self.globals)\n        if key == ord('z'):\n            self.globals.graph.popLastNode()\n\n    def overlay_event(self):\n        if self.snapy_node:\n            self.globals.overlay.writeLine(2, \"Node Id: %s, Node Type; \" % (self.snapy_node.id))\n\n        self.globals.overlay.writeLine(0, 'Press \"ENTER\" to exit Mode')\n","sub_path":"code/qt/state_machine/connect_line.py","file_name":"connect_line.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"586089534","text":"\n\n#calss header\nclass _UNENDING():\n\tdef __init__(self,): \n\t\tself.name = \"UNENDING\"\n\t\tself.definitions = [u'used to describe activities or events, especially unpleasant ones, when they seem to continue for ever: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'adjectives'\n\n\n\tdef run(self, obj1, obj2):\n\t\tself.jsondata[obj2] = {}\n\t\tself.jsondata[obj2]['properties'] = self.name.lower()\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/adjectives/_unending.py","file_name":"_unending.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"142554268","text":"import theano\nimport theano.tensor as T\nfrom theano.tensor.shared_randomstreams import RandomStreams\n\nimport numpy as np\nfrom numpy.random import shuffle\n\n\nimport os\nimport sys\nimport timeit\nfrom utils import *\n\nfrom cost_update import *\nfrom sdae_part import *\nfrom cf_part import *\n\ndef train_sdae(xc_values,\n\tR_value,\n\tlambda_n=1,\n\tlambda_u=1,\n\tlambda_v=1,\n\tlambda_w=0.005,\n\tn_k=40,\n\talpha=0,\n\tdesign=[1542,500,20]):\n\n\n\n\tdesign[0] = xc_values.shape[1]\n\tdesign[-1] = n_k\n\tstring_param='Loop, lambda_n= '+str(design)\n\tprint('start '+string_param)\n\tn_u, n_i = 50, 1000\n\txc_values[xc_values<0.01] = 0\n\ttrain_set_x = theano.shared(value=xc_values[:n_i,:], name='xc_train', borrow=True)\n\n\tx = T.fmatrix('x')\n\tvec = T.lvector()\n\trng = np.random.RandomState(123)\n\ttheano_rng = RandomStreams(rng.randint(2 ** 30))\n\tdesign[0] = xc_values.shape[1]\n\tdesign[-1] = n_k\n\tdropout_rate = 0.1\n\n\tsdae = SDAE(\n\t    numpy_rng=rng,\n\t    theano_rng=theano_rng,\n\t    hidden_layers_size=design,\n\t    activation=T.nnet.sigmoid,\n\t    dropout_rate=dropout_rate,\n\t    lambda_w=lambda_w,\n\t    index=vec,\n\t    input=x\n\t)\n\n\tinitial_learning_rate = 0.1\n\tlearning_rate = theano.shared(np.cast[theano.config.floatX](initial_learning_rate))\n\n\tinitial_momentum = 0.9\n\tmomentum = theano.shared(np.cast[theano.config.floatX](initial_momentum), name='momentum')\n\n\tbatch_size = 5\n\tn_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size\n\n\tcost_sdae, updates_sdae = cost_sdae_alone(da=sdae,learning_rate=learning_rate,momentum=momentum,\\\n\t\tlambda_w=lambda_w,lambda_n=lambda_n)\n\n\tindex = T.iscalar()\n\n\ttrain_da = theano.function(\n\t    [index],\n\t    cost_sdae,\n\t    updates=updates_sdae,\n\t    givens={\n\t        x: train_set_x[index * batch_size: (index + 1) * batch_size]\n\t    }\n\t)\n\n\tprint(\"Training\")\n\tn_epochs = 100\n\tc = []\n\n\tstart_time = timeit.default_timer()\n\tfor epoch in range(n_epochs):\n\t    score_batch = []\n\t    for batch_index in range(n_train_batches):\n\t        score_batch.append(train_da(batch_index))\n\t    c.append(np.mean(score_batch))\n\n\treturn c","sub_path":"train_sdae.py","file_name":"train_sdae.py","file_ext":"py","file_size_in_byte":2041,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"195563629","text":"import calendar\n\n\ndef unlucky_day(year):\n    count = 0\n    for i in range(12):\n        if calendar.weekday(year, i+1, 13) == 4:\n            count += 1\n    return count\n\n\n\"\"\"\nfrom datetime import date\n\ndef unlucky_days(year):\n    return sum(date(year, m, 13).weekday() == 4 for m in range(1, 13))\n\"\"\"\n\n\n\"\"\"\nfrom calendar import weekday\n\ndef unlucky_days(year):\n    return sum(1 for i in xrange(1, 13) if weekday(year, i, 13) == 4)\n\"\"\"\n\n\n\"\"\"\nfrom datetime import date\n\nunlucky_days = lambda year: sum(date(year, month, 13).weekday() == 4 for month in range(1, 13))\n\"\"\"\n","sub_path":"7kyu/unlucky_day.py","file_name":"unlucky_day.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"562720720","text":"from tkinter import *\nimport keyboard\nimport numpy as np\nfrom scipy.interpolate import interp1d\nimport matplotlib.pyplot as plt\n\ncanvas_width = 500\ncanvas_height = 500\n\npts = []\ndef paint(event):\n    if keyboard.is_pressed('q'):\n        master.destroy()\n        plot(pts)\n    python_green = \"#476042\"\n    x1, y1 = ( event.x - 1 ), ( event.y - 1 )\n    x2, y2 = ( event.x + 1 ), ( event.y + 1 )\n    w.create_oval( x1, y1, x2, y2, fill = python_green )\n    pts.append([event.x, event.y])\n\ndef plot(pts):\n    print(str(pts))\n    pad = 3\n    pts = np.pad(pts, [(pad,pad), (0,0)], mode='wrap')\n    x, y = pts.T\n    i = np.arange(0, len(pts))\n\n    interp_i = np.linspace(pad, i.max() - pad + 1, 5 * (i.size - 2*pad))\n\n    xi = interp1d(i, x, kind='cubic')(interp_i)\n    yi = interp1d(i, y, kind='cubic')(interp_i)\n\n    fig, ax = plt.subplots()\n    ax.plot(xi, yi)\n    ax.plot(x, y, 'ko')\n    plt.show()\n\n\nmaster = Tk()\nmaster.title( \"test - getting down the points\" )\nw = Canvas(master,\n           width=canvas_width,\n           height=canvas_height)\nw.pack(expand = YES, fill = BOTH)\nw.bind( \"<B1-Motion>\", paint)\n\nmainloop()\n","sub_path":"flow.py","file_name":"flow.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"485425257","text":"import urllib\nfrom datetime import datetime\nimport xml.etree.ElementTree as ET\nimport sys\n\n\ndef request_timestamp(time_stamp=\"last\"):\n    try:\n        url = \"https://beacon.nist.gov/rest/record/\" + time_stamp\n        uh = urllib.urlopen(url)\n        data = uh.read()\n        tree = ET.fromstring(data)\n        return tree.find(\"{http://beacon.nist.gov/record/0.1/}outputValue\").text\n    except:\n        return \"404\"\n\n\ndef calc(val_list):\n    result = {}\n    for val in val_list:\n        for letter in val:\n            if (letter not in result):\n                result[letter] = 1\n            else:\n                result[letter] += 1\n    return result\n\n\ndef datetime_to_unix_time(y=1970, m=1, d=1, h=0):\n    dt = datetime(y, m, d, h).strftime(\"%s\")\n    return dt\n\n\ndef period_timestamp_gen(stamp_from, stamp_to):\n    if (stamp_from < stamp_to and stamp_from != stamp_to and (stamp_to - stamp_from) >= 3600):\n        return range(stamp_from, stamp_to, 3600)\n\n\ndef main(argv):\n    dt_now = datetime.now()\n\n    if ('--from' in argv and '--to' in argv):\n        if (argv[1] == '--from' and argv[3] == '--to'):\n            date_from = argv[2]\n            date_to = argv[4]\n\n            dt_from = dt_now.replace(month=dt_now.month - int(date_from.split(\" \")[0].replace(\"m\", \"\")),\n                                     day=dt_now.day - int(date_from.split(\" \")[1].replace(\"d\", \"\")),\n                                     hour=dt_now.hour - int(date_from.split(\" \")[2].replace(\"h\", \"\")))\n\n            dt_to = dt_now.replace(month=dt_now.month - int(date_to.split(\" \")[0].replace(\"m\", \"\")),\n                                   day=dt_now.day - int(date_to.split(\" \")[1].replace(\"d\", \"\")),\n                                   hour=dt_now.hour - int(date_to.split(\" \")[2].replace(\"h\", \"\")))\n\n            dt_stamp_from = datetime_to_unix_time(dt_from.year, dt_from.month, dt_from.day, dt_from.hour)\n            dt_stamp_to = datetime_to_unix_time(dt_to.year, dt_to.month, dt_to.day, dt_to.hour)\n\n            out_val_list = []\n            if (dt_stamp_from < dt_stamp_to):\n                for stamp in period_timestamp_gen(int(dt_stamp_from), int(dt_stamp_to)):\n                    out_val_list.append(request_timestamp(str(stamp)))\n\n                for ul, ulc in calc(out_val_list).items():\n                    print (\"{}, {}\".format(ul, ulc))\n            else:\n                print (\"Incorrect time period\")\n\n    else:\n        out_val_list = [request_timestamp()]\n        for ul, ulc in calc(out_val_list).items():\n            print (\"{}, {}\".format(ul, ulc))\n\n\nif __name__ == \"__main__\":\n    main(sys.argv)\n","sub_path":"summarizebeacon.py","file_name":"summarizebeacon.py","file_ext":"py","file_size_in_byte":2595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"176623129","text":"import time\r\nimport os\r\nimport sys\r\nimport RPi.GPIO as GPIO\r\nfrom DCWheel import DCWheel\r\nfrom Ports import Ports\r\nfrom threading import Thread\r\n\r\nGPIO.setmode(GPIO.BOARD)\r\n\r\n#Class designed for DC Motor Control\r\n#Written by Mike Michaels 3/2/15\r\nthreads = []\r\ntry:\r\n    for i in range(0,5):\r\n        leftWheel = DCWheel(Ports.LEFT_WHEEL)\r\n        rightWheel = DCWheel(Ports.RIGHT_WHEEL)\r\n        threads.append(Thread(target = leftWheel.TurnOnTimedRatio, args = (.1,1,)))\r\n        leftId = len(threads) - 1\r\n        threads[leftId].name = \"left\"\r\n        rightThread = Thread(target = rightWheel.TurnOnTimedRatio, args = (.1,1,))\r\n        threads[leftId].start()\r\n        rightThread.start()\r\n        threads[leftId].join()\r\n        del threads[leftId]\r\n        rightThread.join()\r\n        print(threads)\r\n        print(\"done\")\r\n\r\nfinally:\r\n    GPIO.cleanup()\r\n\r\n\r\n","sub_path":"MikeBot/TestWheel.py","file_name":"TestWheel.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"46539111","text":"import os\nimport copy\nfrom pymongo import MongoClient\nfrom flask import Response\nfrom bson.json_util import dumps\nimport logging\nimport time\nimport pdsdpimockfhir.cache as cache\nimport requests\nimport sys\nfrom urllib.parse import urlsplit\nfrom tx.fhir.utils import bundle, unbundle\nfrom tx.functional.either import Left, Right\nfrom tx.logging.utils import getLogger\n\n\nlogger = getLogger(__name__, logging.INFO)\n\n\nfhir_server_url_base = os.environ.get(\"FHIR_SERVER_URL_BASE\")\n\n\ndef _get_patient(patient_id):\n    resc = cache.get_patient(patient_id)\n\n    if resc is not None:\n        return resc\n    elif fhir_server_url_base is not None and fhir_server_url_base != \"\":\n        resp = requests.get(f\"{fhir_server_url_base}/Patient/{patient_id}\")\n        if resp.status_code == 404:\n            return None\n        else:\n            resc = resp.json()\n            cache.update_patient(resc)\n            return resc\n    else:\n        return None\n        \n\ndef get_patient(patient_id):\n    resc = _get_patient(patient_id)\n\n    if resc is not None:\n        return resc\n    else:\n        return \"not found\", 404\n        \n\ndef _get_resource(resc_type, patient_id):\n    bundle = cache.get_resource(resc_type, patient_id)\n\n    if bundle is not None:\n        return bundle\n    elif fhir_server_url_base is not None and fhir_server_url_base != \"\":\n        resp = requests.get(f\"{fhir_server_url_base}/{resc_type}?patient={patient_id}\")\n        logger.debug(f\"{fhir_server_url_base}/{resc_type}?patient={patient_id} => {resp.status_code}\")\n        if resp.status_code == 404:\n            return None\n        else:\n            bundle = resp.json()\n            print(bundle)\n            cache.update_resource(resc_type, patient_id, bundle)\n            print(bundle)\n            sys.stdout.flush()\n            return bundle\n    else:\n        return {\"resourceType\":\"Bundle\", \"entry\": []}\n\n    \ndef post_resources(resc_types, patient_ids):\n    patients = []\n    for patient_id in patient_ids:\n        requests = []\n        for resc_type in resc_types:\n            if resc_type == \"Patient\":\n                requests.append({\n                    \"url\": f\"/Patient/{patient_id}\",\n                    \"method\": \"GET\"\n                })\n            else:\n                requests.append({\n                    \"url\": f\"/{resc_type}?patient={patient_id}\",\n                    \"method\": \"GET\"\n                })\n        batch = bundle(requests, \"batch\")\n        patient = _post_batch(batch).value\n        patients.append(patient)\n    return patients                \n\n\ndef get_resource(resource_name, patient_id):\n    bundle = _get_resource(resource_name, patient_id)\n    if bundle is None:\n        return \"not found\", 404\n    else:\n        return bundle\n\n                    \ndef post_patient(resource):\n    cache.update_patient(resource)\n    return \"success\", 200\n    \n\ndef post_resource(resource):\n    cache.post_resource(resource)\n    return \"success\", 200\n\n\ndef post_batch(batch):\n    return _post_batch(batch).rec(lambda x: (x, 500), lambda x: x)\n\n\ndef _post_batch(batch):\n    def handle_requests(requests):\n        rescs = []\n        for request in requests:\n            method = request[\"method\"]\n            url = request[\"url\"]\n            result = urlsplit(url)\n            pcs = result.path.split(\"/\")\n            qcs = map(lambda x: x.split(\"=\"), result.query.split(\"&\"))\n            if pcs[1] == \"Patient\":\n                rescs.append(_get_patient(pcs[2]))\n            else:\n                patient_id = None\n                for qc in qcs:\n                    if qc[0] == \"patient\":\n                        patient_id = qc[1]\n                rescs.append(_get_resource(pcs[1], patient_id))\n        return Right(bundle(rescs, \"batch-response\"))\n\n    return unbundle(batch).bind(handle_requests)\n\n\ndef post_bundle(bundle):\n    cache.post_bundle(bundle)\n    return \"success\", 200\n\n\ndef delete_resource():\n    cache.delete_resource()\n    return \"success\", 200\n\n\n","sub_path":"pdsdpimockfhir/dispatcher.py","file_name":"dispatcher.py","file_ext":"py","file_size_in_byte":3948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"533109098","text":"def get_max_score(\n    keys: int, simulator_united_str, players=2, symbols='123456789'\n):\n\n    return sum(\n        0 < simulator_united_str.count(symbol) <= (players * keys)\n        for symbol in symbols\n    )\n\n\nif __name__ == '__main__':\n    keys = int(input())\n    simulator_generated_input = f'{input()}{input()}{input()}{input()}'\n    print(get_max_score(keys, simulator_generated_input))\n","sub_path":"YandexPracticum/contest/sprint11/lovkost_ruk.py","file_name":"lovkost_ruk.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"574266778","text":"from _loadlib.rh019 import *\n\n## Preprocess data\n\n#```python\n#### We are loading and using the union aligned Pru datasets\ndf_id = pd.read_csv(gene_dictionary_input, index_col=0, sep='\\t')\ngpd = {x:y for x,y in zip(df_id['gene'].values,\n                                      df_id['product'].values)}\ndata = sat.pp.convert_id_to_gene(sat.pp.load_dataframes(htseq_input,\\\n                                remove_last_nrows=5).T, df_id,\n                                 start='transcript')\nadata = anndata.AnnData(data)\nadata.X = sp.sparse.csr_matrix(adata.X)\nadata.obs.index = ['_'.join(x.split('_')[:4]) for x in adata.obs_names]\n#```\n\n#```python\n#### Process adata and add meta annotation information\ndf_star = sat.pp.load_dfStar(star_input)\ndf_star.index = ['_'.join(x.split('_')[:4]) for x in df_star.index.values]\nadata.var['product'] = sat.get_product(adata.var_names.values, gpd)\nfor key in df_star.columns.values:\n    adata.obs[key] = df_star.loc[adata.obs_names, key]\nadata.obs = adata.obs.fillna(0)\n\n#```python\nsc.write(output_dir+'adata_raw.h5ad', adata)\n#```\n\n### Filter cells\n\n#```python\nadata_raw = sc.read_h5ad(output_dir+'adata_raw.h5ad')\ngpd = {x:y for x,y in zip(adata_raw.var.index.values, adata_raw.var['product'].values)};\nribosomalRNA = 'TGGT1_412150'\nercc = sat.get_gene('ERCC-', gpd)['gene'].values\nread_sum = adata_raw.X.sum(axis=1).A.flatten()\ngene_count = np.greater(adata_raw.X.A, 0).sum(axis=1)\nper_rrna = (adata_raw[:, ribosomalRNA].X / adata_raw.X.sum(axis=1).flatten()).\\\nA.flatten()*100\nper_ercc = (adata_raw[:, ercc].X.sum(axis=1) / adata_raw.X.sum(axis=1)).A.flatten()*100\n#```\n\n#```python\nplt.hist(per_rrna, bins=20);\nplt.title('% rRNA');\n#```\n\n#```python\nfig, ax = plt.subplots(figsize=(3, 3))\nplt.scatter(read_sum, \\\n            per_rrna, s=5, c='', alpha=0.5, edgecolor='steelblue');\nmax_rrna = 0.02\nplt.axhline(max_rrna, ls='--', c='k');\nplt.ylabel('% rRNA'); plt.xlabel('Read sum');\nfig.savefig(output_dir+'rs_vs_rrna.pdf', bbox_inches='tight')\n#```\n\n#```python\nfig, ax = plt.subplots(figsize=(3, 3))\nplt.scatter(read_sum, \\\n            gene_count, s=5, c='', alpha=0.5, edgecolor='steelblue');\nplt.xscale('log')\nmax_gc = 4500\nmin_gc = 600\nmin_read = 5*10**4\nplt.axhline(min_gc, ls='--', c='k');\nplt.axhline(max_gc, ls='--', c='k');\nplt.axvline(min_read, ls='--', c='k');\nplt.ylabel('Gene count'); plt.xlabel('Read sum');\nfig.savefig(output_dir+'rs_vs_gc.pdf', bbox_inches='tight')\n#```\n\n#```python\nfig, ax = plt.subplots(figsize=(3, 3))\nplt.scatter(read_sum, \\\n            per_ercc, s=5, c='', alpha=0.5, edgecolor='steelblue');\nplt.xscale('log')\nmin_read = 5*10**4\nmax_ercc = 70\nplt.axhline(max_ercc, ls='--', c='k');\nplt.axvline(min_read, ls='--', c='k');\nplt.ylabel('% ERCC'); plt.xlabel('Read sum');\nfig.savefig(output_dir+'rs_vs_ercc.pdf', bbox_inches='tight')\n#```\n\n#```python\nfig, ax = plt.subplots(figsize=(3, 3))\nplt.scatter(read_sum, \\\n            adata_raw.obs['percent_mapped'],\\\n            s=5, c='', alpha=0.5, edgecolor='steelblue');\nplt.xscale('log')\nmin_read = 5*10**4\nmin_per_mapped = 28\nplt.axhline(min_per_mapped, ls='--', c='k');\nplt.axvline(min_read, ls='--', c='k');\nplt.ylabel('% mapped'); plt.xlabel('Read sum'); plt.legend();\nfig.savefig(output_dir+'rs_vs_mapped.pdf', bbox_inches='tight')\n#```\n\n#### Threshold for cell quality\n\n#```python\nmax_gc = 4500\nmin_gc = 600\nmin_read = 5*10**4\nmax_ercc = 70\nmax_rrna = 0.02\nmin_per_mapped = 28\nsubset = np.repeat(True, adata_raw.shape[0])\nsubset[per_rrna >= max_rrna] = False\nsubset[per_ercc >= max_ercc] = False\nsubset[gene_count <= min_gc] = False\nsubset[gene_count >= max_gc] = False\nsubset[read_sum <= min_read] = False\nsubset[adata_raw.obs['percent_mapped'].values <= min_per_mapped] = False\n#```\n\n\n#```python\nadata_allg = adata_raw[subset].copy()\nadata_allg.obs['gene_count'] = gene_count[subset]\nadata_allg.obs['read_sum'] = read_sum[subset]\nadata_allg.X = sp.sparse.csr_matrix(sat.cpm_normalize(adata_allg.X.A))\nsc.write(output_dir+'adata_allg.h5ad', adata_allg)\n#```\n\n#```python\nsubset = np.array([True if x in adata_allg.obs_names else False for x in\n          adata_raw.obs_names])\nrs_filtered = adata_raw[subset].X.A.sum(axis=1)\ngc_filtered = np.greater(adata_raw[subset].X.A, 0).sum(axis=1)\nrs_analyzed = adata_raw[subset != True].X.A.sum(axis=1)\ngc_analyzed = np.greater(adata_raw[subset != True].X.A, 0).sum(axis=1)\nfig, ax = plt.subplots(figsize=(3, 3))\nax.scatter(rs_filtered, gc_filtered, c='xkcd:blue', label='Analyzed', s=5)\nax.scatter(rs_analyzed, gc_analyzed, c='xkcd:crimson', label='Filtered', s=5)\nax.set_xscale('log')\nax.set_xlim(10**2,)\nax.set_xlabel('Mapped read', fontsize=12)\nax.set_ylabel('Gene count', fontsize=12)\nax.set_title('Gene detection', fontsize=12)\nax.legend(frameon=False, loc=(0.05, 0.7))\nfig.savefig(output_dir+'gc_rs_filter.pdf', bbox_inches='tight',\n            transparent=True)\n#```\n\n### Filter genes\n\n\n# Now we remove ribosomal RNA, ERCC and transgenes genes from the dataset\n\n#```python\nadata_allg = sc.read_h5ad(output_dir+'adata_allg.h5ad')\ntransgenes = ['transgene:GFP', 'transgene:TdTomato']\nkeep_genes = np.setdiff1d(adata_allg.var.index.values,\n                          np.concatenate([ercc, [ribosomalRNA], transgenes]))\nadata = adata_allg[:, keep_genes].copy()\nadata.X = sp.sparse.csr_matrix(sat.cpm_normalize(adata.X.A))\nadata.obs['gene_count'] = np.greater(adata.X.A, 0).sum(axis=1)\nsc.write(output_dir+'adata_filtered.h5ad', adata)\n#```\n","sub_path":"Scripts/_preprocess/rh019.py","file_name":"rh019.py","file_ext":"py","file_size_in_byte":5408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"89523540","text":"#!/usr/bin/env pythonw\n# -*- coding: utf8 -*-\n\nimport sys\nimport os\nsys.path.append(os.path.abspath(\"..\"))\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib as mpl\nmpl.use('agg')\nimport matplotlib.pyplot as plt  # noqa\nfrom sklearn import preprocessing, linear_model, metrics, model_selection\nfrom imblearn.pipeline import make_pipeline\nimport pickle\nfrom time import sleep\nimport seaborn as sns\nsns.set(context='paper', style='whitegrid', color_codes=True, font_scale=1.8)\ncolorcycle = [(0.498, 0.788, 0.498),\n              (0.745, 0.682, 0.831),\n              (0.992, 0.753, 0.525),\n              (0.220, 0.424, 0.690),\n              (0.749, 0.357, 0.090),\n              (1.000, 1.000, 0.600),\n              (0.941, 0.008, 0.498),\n              (0.400, 0.400, 0.400)]\nsns.set_palette(colorcycle)\n\nfrom speclib import plotting, pushbulletNotifier\nfrom imblearn import over_sampling\n\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\nnp.set_printoptions(linewidth=145)\n\nimport logging\nlogging.basicConfig(filename='joachimExercise_expanded_feature_space_pca_reduction.log', level=logging.INFO,\n                    format=\"%(asctime)s :: %(filename)s:%(lineno)s :: %(funcName)s() ::    %(message)s\")\nlogger = logging.getLogger('joachimExercise')\n\nlogger.info(f\"Loading data\")\ndf0 = pd.read_table('RGender.dat', sep=' ').T\n\ndf1 = df0.drop('gender', axis=1)\ndf1.rename(columns=lambda s: s.replace('bfi_', '').replace('.answer', ''), inplace=True)\ngender = df0.gender\nnd1 = preprocessing.scale(df1.values)\n\nlogger.info(f\"Data loaded\")\n\njn = pushbulletNotifier.JobNotification(devices=\"phone\")\n\nprocesses = 25\ntry:\n    X_train, X_test, y_train, y_test = model_selection.train_test_split(nd1, gender.values,\n                                                                        test_size=0.2, stratify=gender.values)\n\n    logger.info(f\"Split data in to training set and validation set.\")\n    classifier = ['logisticregression', linear_model.LogisticRegression(max_iter=250)]\n    sampler_lst = [['smote', over_sampling.SMOTE()],\n                   ['adasyn', over_sampling.ADASYN()],\n                   ['random¬oversampler', over_sampling.RandomOverSampler()]]\n    pipeline_lst = [ [f'{sampler[0]}-{classifier[0]}', make_pipeline(sampler[1], classifier[1])]\n                      for sampler in sampler_lst ]  # noqa\n    param_grid = {\n        'logisticregression__C': 2.0**np.linspace(-8, 5, 15)\n        }  # noqa\n    for name, pipe in pipeline_lst:\n        jn.send(message=f\"Starding cross validation with resampling method {name}\")\n        logger.info(f\"Starting cross validation\")\n        est = model_selection.GridSearchCV(pipe, param_grid, scoring='roc_auc', cv=5, verbose=49, refit=True,\n                                           n_jobs=processes, pre_dispatch=processes, return_train_score=True)\n        est.fit(X_train, y_train)\n        _, yhat = est.predict_proba(X_test).T\n        try:\n            logger.info(f\"Cross validation done, best score was {est.best_score_}\")\n            logger.info(f\"Best params were {est.best_params_}\")\n            logger.info(f\"Best estimator were {est.best_estimator_}\")\n            logger.info(f\"Checking using the validation set.\")\n        except Exception as e:\n            print(f\"Logging exception: {e}\")\n        validation_auc_score = metrics.roc_auc_score(y_test, yhat)\n        logger.info(f\"AUC score for validation set of size {len(y_test)} is {validation_auc_score:.5f}\")\n        logger.info(f\"AUC score for validation set of size {len(y_test)} is {validation_auc_score:.5f}\")\n        fig, ax, aucscore = plotting.plotROC(y_test, yhat)\n        fig.savefig(f'figs/joachim_exercise_resampling_{name}_resampled.pdf')\n        est.y_test = y_test  # save for plotting ROC curve later\n        est.yhat = yhat  # save for plotting ROC curve later\n        est.validation_auc_score = validation_auc_score\n        est.X_test = X_test\n        est.y_test = y_test\n        with open(f\"joachim_exercise_resampling_{name}_resampled.pkl\", 'bw') as fid:\n            pickle.dump(est, fid)\nexcept Exception as err:\n    jn.send(err)\n    sleep(8)\n    raise err\n\njn.send(message=f\"Cross validation is done.\")\n","sub_path":"joachim_exercise/joachim_exercise_resampling.py","file_name":"joachim_exercise_resampling.py","file_ext":"py","file_size_in_byte":4177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"356660991","text":"#!/usr/bin/python2\n\ndef midifreq(p):\n\treturn pow(2, (p - 69) / 12.0) * 440\n\nrate = 48e3\nnotes = range(128)\nfreqs = map(midifreq, notes)\n\ncoefs = [rate / (4 * freq * (1 - freq / rate))\n\t\tfor freq in freqs]\n\nticks = [freq / (rate / 2)\n\t\tfor freq in freqs]\n\nopen(\"dpw_coefs.asm\", \"w\").write(\n\t\"DpwCoefs: ; rate / (4 * freq * (1 - freq / rate)), midi notes 0..127\\n\" +\n\t\"\\n\".join(\"\\tdc\\t%f/2048\" % c for c in coefs) +\n\t\"\\n\"\n)\n\nopen(\"saw_ticks.asm\", \"w\").write(\n\t\"SawTicks: ; freq / (rate / 2), midi notes 0..127\\n\" +\n\t\"\\n\".join(\"\\tdc\\t%f\" % c for c in ticks) +\n\t\"\\n\"\n)\n","sub_path":"units/oscs/dsp/gen_dsp_coefs.py","file_name":"gen_dsp_coefs.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"548639687","text":"class Solution:\n    '''\n    考察转换 n进制的方法\n    '''\n    def convertToBase7(self, num: int) -> str:\n        if num == 0:\n            return \"0\"\n        negative = 0\n        if num< 0:\n            negative = 1\n            num = abs(num)\n        result = \"\"\n        while num > 0:\n            digit = num%7\n            result = str(digit) + result\n            num //= 7\n        if negative:\n            result = '-' + result\n        return result","sub_path":"504.Base7.py","file_name":"504.Base7.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"458206759","text":"from collections import deque\n\nd = deque()\nn = int(input())\n\nfor i in range(n):\n    line = input().split()\n    if len(line) == 2:\n        if line[0] == 'append':\n            d.append(line[1])\n        elif line[0] == 'appendleft':\n            d.appendleft(line[1])\n    else:\n        if line[0] == 'pop':\n            d.pop()\n        elif line[0] == 'popleft':\n            d.popleft()\nprint(' '.join(list(d)))\n","sub_path":"Hackerrank/Python/Collections/collections.deque.py","file_name":"collections.deque.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"116626070","text":"#Import\nimport requests #HTML requests\nfrom flask import jsonify #Returning JSON\nimport json\n\n\ndef Change_Values(args):\n    nodeID=args[\"id\"]\n    intvValence=args[\"valence\"]\n    intvValue=args[\"value\"]\n    recompiledNodes = []\n    with open('models/data.json') as json_file:\n        dat = json.load(json_file)\n        for node in dat[\"nodes\"]:\n            if node[\"id\"] == nodeID:\n                nodeAct_init=node[\"activation\"]\n                if intvValence == \"+\":\n                    node[\"activation\"] += intvValue\n                elif intvValence == \"-\":\n                    node[\"activation\"] -= intvValue\n                print(\"Intervention: {} ({}-->{})\".format(node[\"id\"],nodeAct_init, node[\"activation\"]))\n            recompiledNodes.append(node)\n    changedDat = {\"nodes\":recompiledNodes, \"links\":dat[\"links\"]}\n    with open('models/data.json', 'w') as json_file:\n        json.dump(changedDat, json_file, indent=4, sort_keys=True)\n\ndef Propagation(param):\n    pass #convert javascript function to python","sub_path":"models/simulation.py","file_name":"simulation.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"156254684","text":"from __future__ import absolute_import, unicode_literals\n\nimport csv\nimport datetime\n\nfrom django.core.exceptions import PermissionDenied\nfrom django.http import HttpResponse\nfrom django.shortcuts import get_object_or_404, redirect, render\nfrom django.utils.encoding import smart_str\nfrom django.utils.translation import ugettext as _\n\nfrom wagtail.utils.pagination import paginate\nfrom wagtail.wagtailadmin import messages\nfrom wagtail.wagtailcore.models import Page\nfrom wagtail.wagtailforms.forms import SelectDateForm\nfrom wagtail.wagtailforms.models import FormSubmission, get_forms_for_user\n\n\ndef index(request):\n    form_pages = get_forms_for_user(request.user)\n\n    paginator, form_pages = paginate(request, form_pages)\n\n    return render(request, 'wagtailforms/index.html', {\n        'form_pages': form_pages,\n    })\n\n\ndef delete_submission(request, page_id, submission_id):\n    if not get_forms_for_user(request.user).filter(id=page_id).exists():\n        raise PermissionDenied\n\n    submission = get_object_or_404(FormSubmission, id=submission_id)\n    page = get_object_or_404(Page, id=page_id)\n\n    if request.method == 'POST':\n        submission.delete()\n\n        messages.success(request, _(\"Submission deleted.\"))\n        return redirect('wagtailforms:list_submissions', page_id)\n\n    return render(request, 'wagtailforms/confirm_delete.html', {\n        'page': page,\n        'submission': submission\n    })\n\n\ndef list_submissions(request, page_id):\n    form_page = get_object_or_404(Page, id=page_id).specific\n\n    if not get_forms_for_user(request.user).filter(id=page_id).exists():\n        raise PermissionDenied\n\n    data_fields = [\n        (field.clean_name, field.label)\n        for field in form_page.form_fields.all()\n    ]\n\n    submissions = FormSubmission.objects.filter(page=form_page)\n\n    select_date_form = SelectDateForm(request.GET)\n    if select_date_form.is_valid():\n        date_from = select_date_form.cleaned_data.get('date_from')\n        date_to = select_date_form.cleaned_data.get('date_to')\n        # careful: date_to should be increased by 1 day since the submit_time\n        # is a time so it will always be greater\n        if date_to:\n            date_to += datetime.timedelta(days=1)\n        if date_from and date_to:\n            submissions = submissions.filter(submit_time__range=[date_from, date_to])\n        elif date_from and not date_to:\n            submissions = submissions.filter(submit_time__gte=date_from)\n        elif not date_from and date_to:\n            submissions = submissions.filter(submit_time__lte=date_to)\n\n    if request.GET.get('action') == 'CSV':\n        # return a CSV instead\n        response = HttpResponse(content_type='text/csv; charset=utf-8')\n        response['Content-Disposition'] = 'attachment;filename=export.csv'\n\n        writer = csv.writer(response)\n\n        header_row = ['Submission date'] + [smart_str(label) for name, label in data_fields]\n\n        writer.writerow(header_row)\n        for s in submissions:\n            data_row = [s.submit_time]\n            form_data = s.get_data()\n            for name, label in data_fields:\n                data_row.append(smart_str(form_data.get(name)))\n            writer.writerow(data_row)\n        return response\n\n    paginator, submissions = paginate(request, submissions)\n\n    data_headings = [label for name, label in data_fields]\n    data_rows = []\n    for s in submissions:\n        form_data = s.get_data()\n        data_row = [s.submit_time] + [form_data.get(name) for name, label in data_fields]\n        data_rows.append({\n            \"model_id\": s.id,\n            \"fields\": data_row\n        })\n\n    return render(request, 'wagtailforms/index_submissions.html', {\n        'form_page': form_page,\n        'select_date_form': select_date_form,\n        'submissions': submissions,\n        'data_headings': data_headings,\n        'data_rows': data_rows\n    })\n","sub_path":"wagtail/wagtailforms/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"12221134","text":"# Create a function which checks on the conditions\r\ndef Ifleap(year):\r\n    # set the leap variable as False\r\n    leap = False\r\n    # Check if the year input meets the condition\r\n    if year % 400 == 0:\r\n        # set leap true if it meets the condition\r\n        leap = True\r\n    elif year % 100 == 0:\r\n        # else set leap False if it does not meet the condition\r\n        leap = False\r\n    elif year % 4 == 0:\r\n        leap = True\r\n    return leap\r\n\r\n# Prompt the user to enter a rando year\r\nyear = Ifleap(int(input(\"Enter any year:  \")))\r\n\r\n#print the answer by passing the input through the function\r\nprint ((year))\r\n\r\nprint(\"\\nWelcome \")","sub_path":"Day_1/Number1.py","file_name":"Number1.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"301371811","text":"N , W = map(int , input().split())\nvalue = []\nweight = []\ndp = [[0] * (N + 1) for _ in range(N)]\nfor _ in range(N):\n    v , w = map(int ,input().split())\n    value.append(v)\n    weight.append(w)\n\ndef knapsack(i , j):\n    dp = [[-1] * (W + 1) for _ in range(N + 1)]\n    def solve(i , j):\n        if dp[i][j] != -1:\n            return dp[i][j]\n        if i == N:\n            retval = 0\n        elif j < weight[i]:\n            retval = solve(i + 1 , j)\n        else:\n            retval = max(solve(i + 1 , j) , solve(i + 1 , j - weight[i]) + value[i])\n        dp[i][j] = retval\n        return retval\n    ans = solve(0,W)\n    return ans\n\nif __name__ == \"__main__\":\n    print(knapsack(0 , W))","sub_path":"study/ant/2_basic/2-3/2-3-1_knapsack/knapsack.py","file_name":"knapsack.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"124829845","text":"import unittest\nimport tinychain as tc\n\nfrom testutils import start_host\n\n\nCONSERVED = tc.Number(20)\n\n\nclass Balance(tc.Cluster):\n    __uri__ = tc.URI(\"/app/balance\")\n\n    def _configure(self):\n        self.weight = tc.chain.Sync(tc.UInt(10))\n\n\nclass Left(Balance):\n    __uri__ = tc.uri(Balance) + \"/left\"\n\n    @tc.post_method\n    def weigh(self, txn, weight: tc.Number):\n        right = tc.use(Right)\n\n        txn.total = CONSERVED\n        txn.update = tc.After(\n            self.weight.set(weight),\n            right.weigh({\"weight\": (txn.total - weight)}))\n\n        return tc.If(self.weight == weight, None, txn.update)\n\n\nclass Right(Balance):\n    __uri__ = tc.uri(Balance) + \"/right\"\n\n    @tc.post_method\n    def weigh(self, txn, weight: tc.Number):\n        left = tc.use(Left)\n\n        txn.total = CONSERVED\n        txn.update = tc.After(\n            self.weight.set(weight),\n            left.weigh({\"weight\": (txn.total - weight)}))\n\n        return tc.If(self.weight == weight, None, txn.update)\n\n\nclass InteractionTests(unittest.TestCase):\n    def testStartup(self):\n        expected = 10\n\n        host = start_host(\"test_multi_cluster_startup\", [Left, Right])\n\n        actual = host.get(\"/app/balance/left/weight\")\n        self.assertEqual(expected, actual)\n\n        actual = host.get(\"/app/balance/right/weight\")\n        self.assertEqual(expected, actual)\n\n    def testUpdate(self):\n        host = start_host(\"test_multi_cluster_update\", [Left, Right])\n        host.post(\"/app/balance/right/weigh\", {\"weight\": 5})\n        self.assertEqual(host.get(\"/app/balance/right/weight\"), 5)\n        self.assertEqual(host.get(\"/app/balance/left/weight\"), 15)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","sub_path":"tests/test_multi_cluster.py","file_name":"test_multi_cluster.py","file_ext":"py","file_size_in_byte":1706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"102233724","text":"def lrc2subRip(lrcLyrics, songLength):\n    def timeConvert(time):\n        mm, ss, xx = map(int, re.split(r\":|\\.\", time))\n        hh = mm / 60\n        mm %= 60\n        return \"%02d:%02d:%02d,%03d\" % (hh, mm, ss, xx*10)\n\n    ans = []\n    for k, line in enumerate(lrcLyrics):\n        t = timeConvert(line[1:9])\n        if ans: ans[-3] += \" --> \" + t\n        ans.extend([str(k+1), t, line[11:], \"\"])\n    if ans: ans[-3] += \" --> \" + songLength + \",000\"\n    return ans[:-1]\n\n","sub_path":"arcade/the_core/LRCToSubRip.py","file_name":"LRCToSubRip.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"285595782","text":"from collections import OrderedDict\nimport bokeh.models as bmd\nfrom bokeh.palettes import Plasma256\nimport holoviews as hv\nfrom holoviews.operation.datashader import datashade, dynspread\nimport datashader as ds\nimport panel as pn\nimport param\nimport functools\nimport os\n\nfrom figure import config\nfrom figure.query import get_data_cache\n\nhv.extension('bokeh')\nhv_renderer = hv.renderer('bokeh').instance(mode='server')\n\n# Note: We are now recreating the plot every time.\n# In principle it would be nice to just update the ColunmDataSource but I\n# I don't think there is a way around this in holoviews (?)\n\nplot_px = 600\ndynspread.threshold = 0.6  # maximum fraction of distance to neighboring point\ndynspread.max_px = 40  # resolution of dots (NOT: size)\noverlay_threshold = 2000  # start showing overlay when that many points left\n\nexplore_url = os.getenv(\n    'EXPLORE_URL', \"https://dev-www.materialscloud.org/explore/hcofs-co2\")\n\n\ndef filter_points(points, x_range, y_range):\n    \"\"\"Filter points by x/y range.\n\n    To be used with RangeXY stream.\n    \"\"\"\n    if x_range is None or y_range is None:\n        return points\n    return points[x_range, y_range]\n\n\ndef hover_points(points, threshold=overlay_threshold):\n    \"\"\"Filter points by threshold.\n\n    Returns empty list if number of input points exceeds threshold.\n    \"\"\"\n    if len(points) > threshold:\n        return points.iloc[:0]\n    return points\n\n\ndef prepare_data(inp_x, inp_y, inp_clr):\n    \"\"\"Prepare data source\"\"\"\n\n    # query for results\n    inp_list = [inp_x, inp_y, inp_clr]\n    results_wnone = get_data_cache(\n        inp_list)  #returns [inp_x_value, inp_y_value, inp_clr_value, cof-id]\n    # dump None lists that make bokeh crash! TODO: improve!\n    results = []\n    for l in results_wnone:\n        if None not in l:\n            results.append(l)\n\n    # prepare data for plotting\n    nresults = len(results)\n    if not results:\n        results = [[0, 0, 0, 'no data']]\n        msg = \"No matching COFs found.\"\n    else:\n        msg = \"{} COFs found.\".format(nresults)\n        if nresults > config.max_points:\n            results = results[:config.max_points]\n        msg += \"\\nPlotting {}...\".format(len(results))\n\n    uuid, group_label, x, y, clrs = list(zip(*results))\n    x = list(map(float, x))\n    y = list(map(float, y))\n    clrs = list(map(float, clrs))\n    uuid = list(map(str, uuid))\n\n    data = {'x': x, 'y': y, 'color': clrs, 'name': group_label, 'uuid': uuid}\n    return data, msg\n\n\ndef get_hover(inp_x, inp_y, inp_clr):\n    \"\"\"Returns hover tool\"\"\"\n    q_x = config.quantities[inp_x]\n    q_y = config.quantities[inp_y]\n    q_clr = config.quantities[inp_clr]\n    xhover = (q_x[\"label\"], \"@x {}\".format(q_x[\"unit\"]))\n    yhover = (q_y[\"label\"], \"@y {}\".format(q_y[\"unit\"]))\n    if 'unit' not in list(q_clr.keys()):\n        clr_val = \"@color\"\n    else:\n        clr_val = \"@color {}\".format(q_clr['unit'])\n    tooltips = [\n        (\"name\", \"@name\"),\n        xhover,\n        yhover,\n        (q_clr[\"label\"], clr_val),\n    ]\n    return bmd.HoverTool(tooltips=tooltips)\n\n\ndef get_plot(inp_x, inp_y, inp_clr):\n    \"\"\"Creates holoviews plot\"\"\"\n    data, msg = prepare_data(inp_x, inp_y, inp_clr)\n    source = bmd.ColumnDataSource(data=data)\n\n    # hovering\n    hover = get_hover(inp_x, inp_y, inp_clr)\n\n    # tap\n    tap = bmd.TapTool()\n    tap.callback = bmd.OpenURL(url=explore_url + \"/details/@uuid\")\n\n    # plot\n    points = hv.Points(\n        source.data,\n        kdims=['x', 'y'],\n        vdims=['color', 'name', 'uuid'],\n    )\n    filtered = points.apply(filter_points,\n                            streams=[hv.streams.RangeXY(source=points)])\n\n    p_shaded = datashade(\n        filtered,\n        width=plot_px,\n        height=plot_px,\n        cmap=Plasma256,\n        aggregator=ds.mean('color')  # we want color of mean value under pixel\n    )\n    p_hover = filtered.apply(hover_points)\n\n    update_fn = functools.partial(update_legends,\n                                  inp_x=inp_x,\n                                  inp_y=inp_y,\n                                  inp_clr=inp_clr)\n    hv_plot = (dynspread(p_shaded) * p_hover).opts(\n        hv.opts.Points(\n            tools=[\n                tap,\n                'pan',\n                'box_zoom',\n                'save',\n                'reset',\n                hover,\n            ],\n            active_tools=['wheel_zoom'],\n            #active_scroll='box_zoom',\n            #active_drag='box_zoom',\n            alpha=0.2,\n            hover_alpha=0.5,\n            size=10,\n            width=plot_px,\n            height=plot_px,\n            color='color',\n            cmap=Plasma256,\n            colorbar=True,\n            show_grid=True,\n        ),\n        hv.opts(toolbar='above', finalize_hooks=[update_fn]),\n    )\n    #     output_backend='webgl',\n\n    p_new = hv_renderer.get_plot(hv_plot).state\n\n    return p_new, msg\n\n\ndef update_legends(plot, _element, inp_x, inp_y, inp_clr):\n    p = plot.state\n\n    q_x = config.quantities[inp_x]\n    q_y = config.quantities[inp_y]\n\n    #title = \"{} vs {}\".format(q_x[\"label\"], q_y[\"label\"])\n    xlabel = \"{} [{}]\".format(q_x[\"label\"], q_x[\"unit\"])\n    ylabel = \"{} [{}]\".format(q_y[\"label\"], q_y[\"unit\"])\n\n    q_clr = config.quantities[inp_clr]\n    if 'unit' not in list(q_clr.keys()):\n        clr_label = q_clr[\"label\"]\n    else:\n        clr_label = \"{} [{}]\".format(q_clr[\"label\"], q_clr[\"unit\"])\n\n    p.xaxis.axis_label = xlabel\n    p.yaxis.axis_label = ylabel\n\n    p.title_location = 'right'\n    p.title.align = 'center'\n    p.title.text_font_size = '10pt'\n    p.title.text_font_style = 'italic'\n    p.title.text = clr_label\n\n\npn.extension()\nplot_dict = OrderedDict(\n    ((config.quantities[q]['label'], q) for q in config.quantities))\n\n\nclass StructurePropertyVisualizer(param.Parameterized):\n\n    preset = config.presets['default']\n    x = param.Selector(objects=plot_dict, default=preset['x'])\n    y = param.Selector(objects=plot_dict, default=preset['y'])\n    clr = param.Selector(objects=OrderedDict(plot_dict), default=preset['clr'])\n    msg = pn.pane.HTML(\"\")\n    _plot = None  # reference to current plot\n\n    @param.depends('x', 'y', 'clr')\n    def plot(self):\n        selected = [self.x, self.y, self.clr]\n        unique = set(selected)\n        if len(unique) < len(selected):\n            self.msg.object = \"<b style='color:red;'>Warning: {} contains duplicated selections.</b>\".format(\n                \", \".join([config.quantities[s]['label'] for s in selected]))\n            return self._plot\n\n        self._plot, self.msg.object = get_plot(self.x, self.y, self.clr)\n        return self._plot\n\n\nexplorer = StructurePropertyVisualizer()\n\ngspec = pn.GridSpec(sizing_mode='stretch_both', max_width=900)\ngspec[0, 0] = explorer.param\ngspec[:2, 1:4] = explorer.plot\ngspec[1, 0] = explorer.msg\n\ngspec.servable()\n","sub_path":"figure/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"82300225","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nimport tensorflow.contrib.slim as slim\nfrom tensorflow.python.ops.nn_ops import leaky_relu\n\ntry:\n    import tfplot\nexcept:\n    pass\n\nfrom ops import conv2d, fc\nfrom util import log\n\nfrom vqa_util import question2str, answer2str\n\n\nclass Model(object):\n\n    def __init__(self, config,\n                 debug_information=False,\n                 is_train=True):\n        self.debug = debug_information\n\n        self.config = config\n        self.batch_size = self.config.batch_size\n        self.img_size = self.config.data_info[0]\n        self.c_dim = self.config.data_info[2]\n        self.q_dim = self.config.data_info[3]\n        self.a_dim = self.config.data_info[4]\n        self.conv_info = self.config.conv_info\n\n        # create placeholders for the input\n        self.img = tf.placeholder(\n            name='img', dtype=tf.float32,\n            shape=[self.batch_size, self.img_size, self.img_size, self.c_dim],\n        )\n        self.q = tf.placeholder(\n            name='q', dtype=tf.float32, shape=[self.batch_size, self.q_dim],\n        )\n        self.a = tf.placeholder(\n            name='a', dtype=tf.float32, shape=[self.batch_size, self.a_dim],\n        )\n\n        self.is_training = tf.placeholder_with_default(bool(is_train), [], name='is_training')\n\n        self.build(is_train=is_train)\n\n    def get_feed_dict(self, batch_chunk, step=None, is_training=None):\n        fd = {\n            self.img: batch_chunk['img'],  # [B, h, w, c]\n            self.q: batch_chunk['q'],  # [B, n]\n            self.a: batch_chunk['a'],  # [B, m]\n        }\n        if is_training is not None:\n            fd[self.is_training] = is_training\n\n        return fd\n\n    def build(self, is_train=True):\n\n        n = self.a_dim\n        conv_info = self.conv_info\n\n        # build loss and accuracy {{{\n        def build_loss(logits, labels):\n            # Cross-entropy loss\n            loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))\n            l2_reqularization = tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))\n            loss += l2_reqularization\n            # Classification accuracy\n            correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))\n            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n            return loss, accuracy\n        # }}}\n\n        def concat_coor(o, i, d):\n            coor = tf.tile(tf.expand_dims(\n                [float(int(i / d)) / d, (i % d) / d], axis=0), [self.batch_size, 1])\n            o = tf.concat([o, tf.to_float(coor)], axis=1)\n            return o\n\n        def g_theta(o_i, o_j, q, scope='g_theta', reuse=True):\n            with tf.variable_scope(scope, reuse=reuse) as scope:\n                if not reuse: log.warn(scope.name)\n                g_1 = fc(tf.concat([o_i, o_j, q], axis=1), 256, name='g_1')\n                g_1 = tf.layers.dropout(g_1, rate=0.5, training=is_train)\n                g_2 = fc(g_1, 256, name='g_2')\n                g_2 = tf.layers.dropout(g_2, rate=0.5, training=is_train)\n                g_3 = fc(g_2, 256, name='g_3')\n                g_3 = tf.layers.dropout(g_3, rate=0.5, training=is_train)\n                g_4 = fc(g_3, 256, name='g_4')\n                return g_4\n\n        def attentional_relational_layer(inputs, q, slices):\n            input_shape = inputs.get_shape().as_list()\n\n            b, h, w, c = input_shape[0:4]\n            print(input_shape)\n            reuse = False\n            object_combos = []\n            attentional_condition_vector = tf.layers.flatten(inputs=inputs)\n            #attentional_condition_vector = tf.layers.dense(flattened_inputs, units=c, activation=leaky_relu)\n            # attentional_condition_vector = tf.layers.conv2d(inputs=inputs, filters=h*w, kernel_size=[3, 3],\n            #                                    strides=(1, 1),\n            #                                    padding='SAME', activation=leaky_relu)\n            #attentional_condition_vector = tf.layers.flatten(inputs=attentional_condition_vector)\n            for i in range(slices):\n                attentional_condition_vector_i = tf.concat([attentional_condition_vector,\n                                                            tf.expand_dims(self.batch_size * [float(i)], axis=1)],\n                                                           axis=1)\n                attentional_condition_vector_i = tf.layers.dense(attentional_condition_vector_i, 256,\n                                                                 activation=leaky_relu,\n                                              kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=0.0002),\n                                              reuse=reuse, name=\"attentional_features\")\n                attentional_condition_vector_i = tf.layers.dropout(attentional_condition_vector_i, rate=0.5,\n                                                                   training=is_train)\n                attentional_condition_vector_i = tf.concat([attentional_condition_vector_i,\n                                                            tf.expand_dims(self.batch_size * [float(i)], axis=1)],\n                                                           axis=1)\n                attention_a = tf.layers.dense(attentional_condition_vector_i, h * w, activation=tf.nn.softmax,\n                                              kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=0.0002),\n                                              name=\"attention_a_{}\".format(i))\n                attention_a = tf.reshape(attention_a, shape=[b, h, w, 1])\n\n                attention_b = tf.layers.dense(attentional_condition_vector_i, h * w, activation=tf.nn.softmax,\n                                              kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=0.0002),\n                                              name=\"attention_b_{}\".format(i))\n                attention_b = tf.reshape(attention_b, shape=[b, h, w, 1])\n\n                flattenA = inputs * attention_a\n                flattenA = tf.reduce_sum(flattenA, axis=[1, 2])\n                flattenB = inputs * attention_b\n                flattenB = tf.reduce_sum(flattenB, axis=[1, 2])\n                flattenA = tf.layers.flatten(flattenA)\n                flattenB = tf.layers.flatten(flattenB)\n                object_A = tf.concat([flattenA, tf.expand_dims(self.batch_size * [float(i)], axis=1)], axis=1)\n                object_B = tf.concat([flattenB, tf.expand_dims(self.batch_size * [float(i)], axis=1)], axis=1)\n                g_i = g_theta(object_A, object_B, q, reuse=reuse)\n\n                reuse = True\n                # print(dense_layer.get_shape())\n                object_combos.append(g_i)\n\n            all_g = tf.stack(object_combos, axis=0)\n            all_g = tf.reduce_mean(all_g, axis=0, name='all_g')\n            return all_g\n\n        # Classifier: takes images as input and outputs class label [B, m]\n        def CONV(img, q, scope='CONV'):\n            with tf.variable_scope(scope) as scope:\n                log.warn(scope.name)\n                conv_1 = conv2d(img, conv_info[0], is_train, s_h=3, s_w=3, name='conv_1')\n                conv_2 = conv2d(conv_1, conv_info[1], is_train, s_h=3, s_w=3, name='conv_2')\n                conv_3 = conv2d(conv_2, conv_info[2], is_train, name='conv_3')\n                conv_4 = conv2d(conv_3, conv_info[3], is_train, name='conv_4')\n\n                # eq.1 in the paper\n                # g_theta = (o_i, o_j, q)\n                # conv_4 [B, d, d, k]\n                all_g = attentional_relational_layer(inputs=conv_4, q=q, slices=32)\n                return all_g\n\n        def f_phi(g, scope='f_phi'):\n            with tf.variable_scope(scope) as scope:\n                log.warn(scope.name)\n                fc_1 = fc(g, 256, name='fc_1')\n                fc_2 = fc(fc_1, 256, name='fc_2')\n                fc_2 = slim.dropout(fc_2, keep_prob=0.5, is_training=is_train, scope='fc_3/')\n                fc_3 = fc(fc_2, n, activation_fn=None, name='fc_3')\n                return fc_3\n\n        g = CONV(self.img, self.q, scope='CONV')\n        logits = f_phi(g, scope='f_phi')\n        self.all_preds = tf.nn.softmax(logits)\n        self.loss, self.accuracy = build_loss(logits, self.a)\n\n        # Add summaries\n        def draw_iqa(img, q, target_a, pred_a):\n            fig, ax = tfplot.subplots(figsize=(6, 6))\n            ax.imshow(img)\n            ax.set_title(question2str(q))\n            ax.set_xlabel(answer2str(target_a)+answer2str(pred_a, 'Predicted'))\n            return fig\n\n        try:\n            tfplot.summary.plot_many('IQA/',\n                                     draw_iqa, [self.img, self.q, self.a, self.all_preds],\n                                     max_outputs=4,\n                                     collections=[\"plot_summaries\"])\n        except:\n            pass\n\n        tf.summary.scalar(\"loss/accuracy\", self.accuracy)\n        tf.summary.scalar(\"loss/cross_entropy\", self.loss)\n        log.warn('Successfully loaded the model.')\n","sub_path":"model_attentional_rn.py","file_name":"model_attentional_rn.py","file_ext":"py","file_size_in_byte":9141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"220796840","text":"#! /usr/bin/env python\r\n\r\nimport os\r\nimport json\r\nimport random\r\nimport sys\r\n\r\nname = 'settings'\r\ndata_file = name + '.json'\r\njson_dirs = 'directories'\r\njson_ext = 'file_extensions'\r\njson_excl = 'exclude_term'\r\njson_size = 'minimum_size_byte'\r\n\r\n\r\ndef get_files(folders, extensions=[], exclude=[], minimum=0):\r\n    files = []\r\n\r\n    # Exclude not implemented\r\n\r\n    for folder in folders:\r\n        for root, dirnames, filenames in os.walk(folder):\r\n            for filename in filenames:\r\n                absolute_path = os.path.join(root,filename)\r\n                if filename.lower().endswith(tuple(extensions)) and os.stat(absolute_path).st_size > minimum:\r\n                    files.append(absolute_path)\r\n\r\n    return files\r\n\r\n\r\ndef get_random(folders, extensions=[], exclude=[], minimum=0):\r\n    files = get_files(folders, extensions, exclude, minimum)\r\n\r\n    if files:\r\n        return random.choice(files)\r\n    else:\r\n        print('No files found.')\r\n        sys.exit(0)\r\n\r\n\r\ndef default_dictionary():\r\n    folders = []\r\n    extensions = ['.mkv', '.wmv', '.avi', '.mpg', '.mpeg', '.flv', '.mov']\r\n    exclude = []\r\n    minimum = 104857600\r\n    dictionary = {json_dirs: folders, json_ext: extensions, json_excl: exclude, json_size: minimum}\r\n    return dictionary\r\n\r\n\r\ndef load_data():\r\n    if os.path.isfile(data_file):\r\n        content = open(data_file).read()\r\n        return json.loads(content)\r\n    else:\r\n        return default_dictionary()\r\n\r\n\r\ndef write_data(dictionary):\r\n    with open(data_file, 'w+') as outfile:\r\n        json.dump(dictionary, outfile, indent=4)\r\n\r\n\r\ndef run_file(path):\r\n    import subprocess\r\n\r\n    # Windows\r\n    if os.name == 'nt':\r\n        os.startfile(path, 'open')\r\n    # Mac OS X\r\n    elif sys.platform.startswith('darwin'):\r\n        subprocess.call(('open', path))\r\n    # Linux\r\n    elif os.name == 'posix':\r\n        subprocess.call(('xdg-open', path))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    settings = load_data()\r\n\r\n    if not settings[json_dirs]:\r\n        print('You need to add a folder first.')\r\n    else:\r\n        file = get_random(settings[json_dirs], settings[json_ext], settings[json_excl])\r\n        run_file(file)","sub_path":"random_file.py","file_name":"random_file.py","file_ext":"py","file_size_in_byte":2165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"257554718","text":"import logging\nimport time\nimport re\n\nimport requests\nfrom pyshorteners import Shortener\n\n\nclass Scraper:\n    header = {\n        \"user-agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36 OPR/55.0.2994.61\"}\n\n    def __init__(self, dao, bot, user_id, sleep_time):\n        logging.debug(\"Erstelle neuen scraper\")\n        self.dao = dao\n        self.bot = bot\n        self.user_id = user_id\n        self.sleep_time = sleep_time\n        self.shortener = Shortener(\"Isgd\")\n\n    # Link processing\n    def shorten_link(self, link):\n        try:\n            proc_link = self.shortener.short(link)\n        except:\n            print(\"Shortener-Service nicht erreichbar. Verwende vollen Link.\")\n            proc_link = link\n        return proc_link\n\n    def scrape_hottest(self):\n        logging.debug(\"Fetching json for hottest deals\")\n        json_url = requests.get(\n            \"https://www.mydealz.de/widget/hottest?selectedRange=day&threadTypeTranslated=&merchant_name=&merchant_id=&eventId=&groupName=&context=listing\",\n            headers=self.header, timeout=20)\n        json_data = json_url.json()\n        logging.debug(\"Request completed\")\n        results = []\n\n        for thread in json_data[\"data\"][\"threads\"]:\n            res = {\n                \"id\": thread[\"id\"],\n                \"url\": thread[\"url\"],\n                \"origin\": \"hottest\",\n                \"title\": thread[\"title\"].strip()\n            }\n            results.append(res)\n        return results\n\n    def check_mydealz(self):\n        while True:\n            # wants\n            keywords = self.dao.get_keywords(self.user_id)\n            # has\n            found = self.dao.get_found(self.user_id)\n            # available\n            hottest = self.scrape_hottest()\n            available_not_found = list(\n                filter(lambda k: k[\"id\"] not in list(map(lambda e: e[\"mydealz_id\"], found)), hottest))\n\n            for keyword in keywords:\n                for thread in available_not_found:\n                    title = thread[\"title\"]\n                    logging.debug(\"looking for keyword %s in thread %s\", keyword[\"keyword\"], title)\n                    if re.search(keyword[\"keyword\"], title, re.IGNORECASE):\n                        self.bot.send_message(\n                            self.user_id,\n                            \"Schlüsselwort auf mydealz gefunden! Titel: \" + title + \" link: \" + self.shorten_link(\n                                thread[\"url\"]))\n                        self.dao.add_found(self.user_id, keyword[\"id\"], thread[\"id\"])\n\n            time.sleep(self.sleep_time)\n","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":2635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"221201171","text":"\"\"\"\n@package base\n\nWebDriver Factory class implementation\nIt creates a webdriver instance based on browser configurations\n\nExample:\n    wdf = WebDriverFactory(browser)\n    wdf.getWebDriverInstance()\n\"\"\"\nimport traceback\nfrom selenium import webdriver\nimport os\n\nclass WebDriverFactory():\n\n    def __init__(self, browser):\n        \"\"\"\n        Inits WebDriverFactory class\n\n        Returns:\n            None\n        \"\"\"\n        self.browser = browser\n\n    def getWebDriverInstance(self):\n        \"\"\"\n       Get WebDriver Instance based on the browser configuration\n\n        Returns:\n            'WebDriver Instance'\n        \"\"\"\n        if self.browser == \"iexplorer\":\n            # Set ie driver\n            driver = webdriver.Ie()\n        elif self.browser == \"firefox\":\n            driver = webdriver.Firefox()\n        elif self.browser == \"chrome\":\n            # Set chrome driver\n            driver = webdriver.Chrome()\n        else:\n            driver = webdriver.Firefox()\n        # Setting Driver Implicit Time out for An Element\n        driver.implicitly_wait(3)\n        # Maximize the window\n        driver.maximize_window()\n        return driver","sub_path":"04_Selenium/framework/core/webdriverfactory.py","file_name":"webdriverfactory.py","file_ext":"py","file_size_in_byte":1153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"462039091","text":"import requests\r\nimport time\r\nimport json\r\nimport random\r\nimport string\r\nfrom os import system\r\n\r\ndef Main():\r\n\r\n    s = requests.session()\r\n    game_id = input('Enter the game pin: ')\r\n    game_data = s.get('https://www.menti.com/core/objects/vote_ids/' + game_id);\r\n    if not game_data.status_code == 200:\r\n        system('cls')\r\n        return Main()\r\n\r\n    def inputBots():\r\n        try:\r\n            num_of_bots = int(input('Enter the amount of bots: '))\r\n            return num_of_bots\r\n        except ValueError:\r\n            return inputBots();\r\n\r\n\r\n    num_of_bots = inputBots()\r\n    \r\n\r\n\r\n    game_data_json = game_data.json()\r\n\r\n\r\n    id = game_data_json['id'];\r\n    key = game_data_json['questions'][0]['public_key']\r\n    type = game_data_json['questions'][0]['type']\r\n\r\n\r\n\r\n    def randomString(stringLength):\r\n        letters = string.ascii_lowercase\r\n        return ''.join(random.choice(letters) for i in range(stringLength))\r\n\r\n    def prio(wordDict):\r\n        which = input('Enter the id from 1 to ' + str(lenght) + ' or the sentance of the choice to bot: ')\r\n        bruh = None\r\n        try:\r\n            if which in wordDict:\r\n                bruh = {wordDict[which]: 100};\r\n                return bruh\r\n            elif int(which) > 1 and int(which) <= lenght:\r\n                bruh = {int(which) + 4: 100}\r\n                return bruh\r\n           \r\n        except ValueError:\r\n            print('Not found')\r\n        return prio(wordDict)\r\n\r\n    def ranking(wordDict):\r\n        which = input('Enter the id from 1 to ' + str(lenght) + ' or the sentance of the choice to bot: ')\r\n        bruh = None\r\n        try:\r\n            if which in wordDict:\r\n                bruh = [wordDict[which]]\r\n                return bruh\r\n            elif int(which) > 1 and int(which) <= lenght:\r\n                bruh = [int(which)]\r\n                return bruh\r\n        except ValueError:\r\n            print('Not found')\r\n        return ranking(wordDict)\r\n    vote = None\r\n    rand = False\r\n\r\n\r\n    if type == 'wordcloud':\r\n        vote = input('Enter word to be spammed, \"random\" if the word should be random: ')\r\n        maxInputs = int(game_data_json['questions'][0]['max_nb_words'])\r\n        if vote.lower() == 'random':\r\n            rand = True\r\n        else:\r\n            noSpace = vote.replace(\" \", \"_\")\r\n            vote = ''.join(noSpace + ' ' for i in range(maxInputs))\r\n    elif type == 'open':\r\n        vote = input('Enter word/sentence you want to be spammed, \"random\" if it should be randomized: ')\r\n        if vote.lower() == 'random':\r\n            rand = True\r\n    \r\n    elif type == 'ranking':\r\n        alternatives = game_data_json['questions'][0]['choices']\r\n        lenght = len(alternatives)\r\n        wordDict = {}\r\n        for x in range(lenght):\r\n            wordDict[alternatives[x]['label']] = alternatives[x]['id']\r\n\r\n        vote = ranking(wordDict)\r\n\r\n    elif type == 'prioritisation':\r\n        alternatives = game_data_json['questions'][0]['choices']\r\n        lenght = len(alternatives)\r\n        wordDict = {}\r\n        for x in range(lenght):\r\n            wordDict[alternatives[x]['label']] = alternatives[x]['id']\r\n\r\n\r\n        vote = prio(wordDict)\r\n    elif type == 'scales':\r\n        alternatives = game_data_json['questions'][0]['choices']\r\n        lenght = len(alternatives)\r\n        min = game_data_json['questions'][0]['range']['min']\r\n\r\n\r\n        vote = {}\r\n\r\n        for x in range(lenght):\r\n            vote[alternatives[x]['id']] = [min, min]\r\n\r\n\r\n\r\n    \r\n\r\n\r\n    for x in range(num_of_bots):\r\n        new_id = s.post(\"https://www.menti.com/core/identifier\").json()['identifier']\r\n\r\n\r\n\r\n        header = {\"x-identifier\": new_id}\r\n\r\n        if rand == True:\r\n            if type == 'wordcloud':\r\n                vote = randomString(8)\r\n            elif type == 'open':\r\n                vote = randomString(25)\r\n\r\n        r = s.post('https://www.menti.com/core/votes/' + key, json = {\"question_type\" : type, \"vote\" : vote}, headers = header)\r\n        print(r)\r\n    system('cls')\r\n    return Main()\r\n\r\n\r\n\r\nMain()\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"Mentimeter_Bot.py","file_name":"Mentimeter_Bot.py","file_ext":"py","file_size_in_byte":4068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"407973944","text":"from itertools import combinations\nn = int(input())\ndata = [list(map(int,input().split())) for _ in range(n)]\nmembers = [i for i in range(n)]\npossible_team = []\n\n#조합으로 가능한 팀 생성\nfor team in list(combinations(members, n//2)):\n    possible_team.append(team)\n\nmin_stat_gap = 10000\nfor i in range(len(possible_team) // 2):\n    team = possible_team[i]\n    stat_A = 0\n    for j in range(n//2):\n        member = team[j]\n        for k in team:\n            stat_A += data[member][k]\n\n    team = possible_team[-i-1]\n    stat_B = 0\n    for j in range(n//2):\n        member = team[j]\n        for k in team:\n            stat_B += data[member][k]\n\n    min_stat_gap = min(min_stat_gap, abs(stat_A - stat_B))\n\nprint(min_stat_gap)\n\n","sub_path":"baekjun/num_14889(스타트와 링크).py","file_name":"num_14889(스타트와 링크).py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"631547527","text":"#!/usr/bin/env python\n# coding=utf-8\nfrom __future__ import unicode_literals, absolute_import, print_function, division\nimport sopel.module\nimport sys\nimport os\nshareddir = os.path.dirname(os.path.dirname(__file__))\nsys.path.append(shareddir)\nfrom SpicebotShared import *\n\nspankweapons = ['paddle','belt']\n\n@sopel.module.commands('spank')\ndef mainfunction(bot, trigger):\n    enablestatus, triggerargsarray = spicebot_prerun(bot, trigger, trigger.group(1))\n    if not enablestatus:\n        execute_main(bot, trigger, triggerargsarray)\n    \ndef execute_main(bot, trigger, triggerargsarray):\n    target = get_trigger_arg(bot, triggerargsarray, 1)\n    reason = get_trigger_arg(bot, triggerargsarray, '2+')\n    message = \"Whoops, something went wrong.\"\n    # Nothing specified\n    if not target:\n        bot.say(\"Who/what would you like to spank?\")\n    \n    # Can't slap the bot\n    if target == bot.nick:\n        bot.say(\"I will not do that!!\")\n    \n    # Target is fine\n    else:\n        weapon = get_trigger_arg(bot, spankweapons, 'random')\n        if not reason:\n            message = trigger.nick + \" spanks \" + target + \" with a \" + weapon + \".\"\n        else:\n            if reason.startswith('for'):\n                message = trigger.nick + \" spanks \" + target + \" with a \" + weapon + \" \"+ reason + \".\"\n            else:\n                message = trigger.nick + \" spanks \" + target + \" with a \" + weapon + \" \"+ reason + \".\"\n        bot.say(message)\n","sub_path":"modules/Memes/Spank.py","file_name":"Spank.py","file_ext":"py","file_size_in_byte":1451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"64111831","text":"import torch\nfrom torch.distributions.normal import Normal\nfrom torch.distributions.multivariate_normal import MultivariateNormal\nfrom torch.nn.functional import one_hot\n\nfrom distributions import GaussianDistribution\n\ndef get_coeffs_from_generative(mc, query_node, evidence_node, dim=1):\n    \"\"\"\n    Computes the weight, bias, and covariance of the linear Gaussian posterior\n     P(query_node | evidence_node) using exact inference.\n\n    Parameters\n    ----------\n    mc : BayesNet\n        The generative model — CPDs must be linear Gaussian!\n\n    query_node : str\n        Name of the query node\n\n    evidence_node : str\n        Name of the evidence node\n\n    dim : int\n        Dimensionality of each node\n    \"\"\"\n    zero = 0 if dim == 1 else torch.zeros(dim)\n\n    true_bias = infer_from_generative(mc, query_node, evidence_node, zero).mean\n    true_cov = infer_from_generative(mc, query_node, evidence_node, zero).cov\n\n    if dim == 1:\n        true_weight = infer_from_generative(mc, query_node, evidence_node, 1).mean - true_bias\n    else:\n        cols = []\n        for i in range(dim):\n            x = one_hot(torch.tensor(i), dim)\n            col_i = infer_from_generative(mc, query_node, evidence_node, x).mean - true_bias\n            cols.append(col_i)\n        true_weight = torch.stack(cols).T\n\n    return true_weight, true_bias, true_cov\n\ndef infer_from_generative(mc, query_node, evidence_node, evidences):\n    \"\"\"\n    Returns a GaussianDistribution object representing the posterior \n     P(query_node|evidence_node = evidences) for fixed evidence values.\n    \"\"\"\n    mean, cov = compute_joint_linear(mc, query_node, evidence_node, evidences)\n    return GaussianDistribution(mean, cov)\n\ndef compute_joint_linear(mc, query_node, evidence_node, evidence=1):\n    \"\"\"\n    Returns the mean and covariance of P(query_node | evidence_node=evidence).\n    Assumes that `mc` is a BayesNet whose CPDs are *linear Gaussian*.\n    \"\"\"\n    dim = isinstance(evidence, torch.Tensor) and evidence.shape and evidence.shape[-1]\n    multi = dim > 1\n\n    evidence = {evidence_node: evidence}\n\n    zero = 0 if not multi else torch.zeros(dim)\n    identity = 1 if not multi else torch.eye(dim)\n\n    all_coeffs = []\n    means = []\n    covs = []\n    prev_mean = zero\n    prev_cov = identity\n    all_cov = []\n    for i, var in enumerate(mc.ordering):\n        node = mc.get_node(var)\n\n        # TODO: clean this up\n        if isinstance(node.cpd, GaussianDistribution):\n            coeffs = (zero, node.cpd.mean)\n            cov_node = node.cpd.cov\n        elif hasattr(node.cpd.cond_fn, \"weights\"):\n            coeffs = (node.cpd.cond_fn.weights[0].weight, node.cpd.cond_fn.weights[0].bias)\n            cov_node = node.cpd.cov\n        else:\n            coeffs = node.cpd.coeffs_list[0]\n            cov_node = node.cpd.cov\n\n        all_coeffs.append(coeffs) #Putting all the coefficients together\n        all_cov.append(cov_node)\n\n        # Mean computations\n        if multi:\n            curr_mean = prev_mean @ coeffs[0].T + coeffs[1]\n        else:\n            curr_mean = prev_mean * coeffs[0] + coeffs[1]\n        means.append(curr_mean)\n        prev_mean = curr_mean\n\n        # cov computations\n        if multi:\n            curr_cov = coeffs[0] @ prev_cov @ coeffs[0].T + cov_node\n        else:\n            curr_cov = coeffs[0]*prev_cov*coeffs[0] + cov_node\n        covs.append(curr_cov)\n        prev_cov = curr_cov\n\n    # Getting the things we need for cross covariances\n    query_idx = mc.ordering.index(query_node)\n    evidence_idx = mc.ordering.index(evidence_node)\n\n    # getting the front multiplier on the later variable in terms of the earlier one.\n    front_multiplier = identity\n    start = min(query_idx, evidence_idx)\n    end = max(query_idx, evidence_idx) + 1\n    for i in range(start + 1, end):\n        coeff = all_coeffs[i][0]\n        if multi:\n            front_multiplier = coeff @ front_multiplier\n        else:\n            front_multiplier *= coeff\n\n    # Marginalizing out everything else and computing the cross covariances\n    query_mean = means[query_idx]\n    query_cov = covs[query_idx]\n\n    evidence_mean = means[evidence_idx]\n    evidence_cov = covs[evidence_idx]\n\n    if multi:\n        cross_cov = query_cov @ front_multiplier.T\n    else:\n        cross_cov = front_multiplier * query_cov\n\n    # Conditioning: Conditioning on the evidence to get the query distribution\n    # Currently works both ways because it is a scalar\n    if query_idx < evidence_idx:\n        if isinstance(cross_cov, torch.Tensor):\n            cond_mean = query_mean + (evidence[evidence_node] - evidence_mean) @ (cross_cov @ torch.inverse(evidence_cov)).T\n            cond_cov = query_cov - cross_cov @ torch.inverse(evidence_cov) @ cross_cov.T\n        else:\n            cond_mean = query_mean + cross_cov*(1./evidence_cov)*(evidence[evidence_node] - evidence_mean)\n            cond_cov = query_cov - cross_cov * (1. / evidence_cov) * cross_cov\n    else:\n        # TODO: this isn't correct, not sure what the actual equations should be though?\n        cond_mean = query_mean\n        cond_cov = query_cov\n\n    return cond_mean, cond_cov\n\ndef infer_from_posterior(mc, query_node, evidence_node, evidences):\n    return mc.get_node(query_node).cpd.cond_fn([evidences])\n\ndef kl_div_gaussian(true_mean, true_cov, mean, cov):\n    if len(cov.shape) and cov.shape[-1] > 1:\n        return torch.distributions.kl.kl_divergence(MultivariateNormal(true_mean, true_cov), MultivariateNormal(mean, cov))\n    else:\n        return torch.distributions.kl.kl_divergence(Normal(true_mean, true_cov ** 0.5), Normal(mean, cov ** 0.5))\n\ndef expected_kl_gaussian(true_mc, inferred_mc, query_node, evidence_node, \n                        true_func=compute_joint_linear, posterior_func=infer_from_posterior, num_samples=5000):\n    \"\"\"\n    Given the true Gaussian posterior p(X|e) represented by `true_mc`, and the inferred posterior q(X|e) represented by `inferred_mc`,\n     returns the expected KL divergence between the two:\n\n     E_e~p(e)[KL(p(X|e) || q(X|e))]\n\n    The `true_func` and `posterior_func` are functions that take in a BayesNet, query node, evidence node, and evidence,\n     then return the true and learned posterior distributions respectively. These can be changed depending on the model;\n     for example, if your learned posterior is a generative model instead of a discriminative one, you should set posterior_func=compute_joint_linear.\n    \"\"\"\n    evidences = true_mc.sample_labeled(num_samples)[evidence_node]\n\n    true_mean, true_cov = true_func(true_mc, query_node, evidence_node, evidences)\n    posteriors = posterior_func(inferred_mc, query_node, evidence_node, evidences)\n\n    kl = kl_div_gaussian(true_mean, true_cov, posteriors.mean, posteriors.cov)\n    return torch.mean(kl)\n","sub_path":"inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":6769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"647081736","text":"#!/usr/bin/env python3.7\n# -*- coding: utf-8 -*\n\"\"\"\n\"\"\"\nimport json\nimport logging\nimport math\nimport os\nimport pathlib\nimport random\nimport re\nimport shutil\nimport string\nfrom typing import *\n\nfrom edinmt import CONFIG \n\nlogger = logging.getLogger('edinmt.text_processors.retagger')\nlogger.setLevel(CONFIG.LOG_LEVEL)\n#only translation output should go on stdout so we remove StreamHandlers\nhandlers = logger.handlers.copy()\nlogger.handlers = [h for h in handlers if not isinstance(h, logging.StreamHandler)]\n\n#We pass URLs, emails, etc. through MT unscathed by wrapping them in a\n#a special tag protector symbol (which is not BPEd), and then re-inserting\n#them back into the translation on the output side. Find them using regex:\n#\n# TAG regex source (Svetlana Tchistiakova): \n# https://gist.github.com/kaleidoescape/524f6f53a4562eaf6d8f1463f4d54670\nTAG_REGEX = r\"(?:\\s*<(?:[A-Za-z]+|/)[^<]*?>\\s*)\"\nTAG_TEMPL = ' [TAG{}] '\n#\n# URL regex source (Mathias Bynens / Gruber) @gruber (71 chars) version:\n# https://mathiasbynens.be/demo/url-regex\n# Note: I was tempted to use https://gist.github.com/gruber/8891611, but it's too inefficient\nURL_REGEX = r\"#\\b(?:(?:[\\w-]+://?|www[.])[^\\s()<>]+(?:\\([\\w\\d]+\\)|(?:[^[:punct:]\\s]|/)))#iS\"\nURL_TEMPL = ' [URL{}] '\n#\n# EMAIL regex source (Regular-Expressions.info) 2nd to last one: \n# http://www.regular-expressions.info/email.html\nEMAIL_REGEX = r\"(?:[^\\w][a-z0-9!#$%&'*+/=?^_‘{|}~-]+(?:\\.[a-z0-9!#$%&'*+/=?^_‘{|}~-]+)*@(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?[^\\w])\"\nEMAIL_TEMPL = ' [EML{}] '\n#\n# Twitter handle regex\nTWEET_REGEX = r'(?:@\\S+)'\nTWEET_TEMPL = ' [HANDLE{}] '\n#\n#NOTE: To OR the regexes together the order matters, and above regexes\n#should be surrounded by non-capture groups, and it is recommended to\n#surround the pattern with spaces or non-word chars for best results.\n#full_regex = f\"({TAG_REGEX})|({URL_REGEX})|({EMAIL_REGEX})|({TWEET_REGEX})\"\n#REGEX = re.compile(full_regex)\n#TEMPL = (TAG_TEMPL, URL_TEMPL, EMAIL_TEMPL, TWEET_TEMPL) #same order as ORed regexes\n\ndef extract_tags(text: str):\n    r\"\"\"\n    Extract urls/emails/tags/etc from the text and return a tuple\n    of the cleaned up text and a list of [(symbol, url/email/etc)].\n    \"\"\"\n    regexes = (TAG_REGEX, URL_REGEX, EMAIL_REGEX, TWEET_REGEX) \n    templs = (TAG_TEMPL, URL_TEMPL, EMAIL_TEMPL, TWEET_TEMPL) \n    for j, regex in enumerate(regexes):\n        matches = re.findall(regex, text)\n        tags = []\n        for i, match in enumerate(matches):\n            for j, m in enumerate(match):\n                if m:\n                    repl = templs[j].format(i)\n                    text = text.replace(m, repl, 1)\n                    tags.append([repl, m])\n    return text, tags \n\ndef reinsert_tags(text: str, tags: List[tuple]):\n    r\"\"\"\n    Reinsert urls/emails/tags/etc. into the text using the list of\n    [(symbol, url/email/etc)]. In case MT failed to add one of\n    the symbols into the output, the url/email will just be\n    appended to the end of the text.\n    \"\"\"\n    for tag, item in tags:\n        tag = tag.strip()\n        if tag in text:\n            text = text.replace(tag, item, 1)\n        else:\n            #MT didn't output it, but we don't want to lose it\n            text = f\"{text} {item}\"\n    return text\n\ndef extract_tags_file(input_fp: str, output_fp: str, tags_fp: str):\n    with open(input_fp, 'r', encoding='utf-8') as infile, \\\n         open(output_fp, 'w', encoding='utf-8') as outfile, \\\n         open(tags_fp, 'w', encoding='utf-8') as tags_fh:\n        for i, line in enumerate(infile):\n            line = line.strip()\n            line, tags = extract_tags(line)\n            outfile.write(line + '\\n')\n            tags_fh.write(json.dumps(tags) + '\\n')\n    return output_fp, tags_fp\n\ndef reinsert_tags_file(input_fp: str, tags_fp: str, output_fp: str):\n    with open(input_fp, 'r', encoding='utf-8') as infile, \\\n         open(tags_fp, 'r', encoding='utf-8') as tags_fh, \\\n         open(output_fp, 'w', encoding='utf-8') as outfile:\n        for line in infile:\n            line = line.strip()\n            tags = json.loads(tags_fh.readline().strip())\n            if tags:\n                line = reinsert_tags(line, tags)\n            outfile.write(line + '\\n')\n    return output_fp\n\n\n\nif __name__ == '__main__':\n    import fire\n    fire.Fire(reinsert_tags_file)","sub_path":"edinmt/retagger.py","file_name":"retagger.py","file_ext":"py","file_size_in_byte":4343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"442925719","text":"import os\nimport shutil\n\nMUTANTS_DIR = os.path.join('.', 'major')\nPACKAGE = \"org.apache.commons.cli\"\nPACKAGE_DIR = PACKAGE.replace('.', os.sep)\n\n\ndef main():\n\n    for mutant_dir in os.listdir(MUTANTS_DIR):\n        mutant_dir = os.path.join(MUTANTS_DIR, mutant_dir)\n        if os.path.isdir(mutant_dir):\n            with_package = os.path.join(mutant_dir, PACKAGE_DIR)\n            os.makedirs(with_package, exist_ok=True)\n            for file in os.listdir(mutant_dir):\n                file = os.path.join(mutant_dir, file)\n                if os.path.isfile(file):\n                    shutil.move(file, with_package)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"02/commons-cli3/mutants/pack.py","file_name":"pack.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"554548730","text":"#!/usr/bin/env python3\nimport re\n\n\nbox_rx = re.compile(r'(\\d+)x(\\d+)x(\\d+)')\n\ndef wrap(box):\n    n = [box[0] * box[1], box[0] * box[2], box[1] * box[2]]\n    t = min(n)\n    return sum(2 * x for x in n) + t\n\n\ndef solve(problem):\n    boxes = (tuple(int(x) for x in box_rx.findall(s)[0]) for s in problem.splitlines())\n    res = sum(wrap(b) for b in boxes)\n    return res\n\n\ndef test():\n    assert wrap((1, 2, 3)) == 2*2 + 2*3 + 2*6 + 2\n\n    assert solve('2x3x4') == 58\n    assert solve('1x1x10') == 43\n\n\ndef getinput():\n    import fileinput\n    with fileinput.input() as f:\n        return ''.join(f).strip()\n\n\nif __name__ == '__main__':\n    test()\n    print(solve(getinput()))\n","sub_path":"code/02-1-wrapping_paper/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"535027224","text":"\"\"\"Test a fast Direct ACK response after an ACK is received.\"\"\"\nimport asyncio\nfrom random import randint\nfrom unittest import TestCase\n\nfrom pyinsteon.constants import MessageFlagType\nfrom pyinsteon.handlers.to_device.on_level import OnLevelCommand\nfrom pyinsteon.handlers.to_device.status_request import StatusRequestCommand\nfrom pyinsteon.topics import ON, STATUS_REQUEST\nfrom pyinsteon.utils import build_topic\nfrom tests import set_log_levels\nfrom tests.utils import TopicItem, async_case, random_address, send_topics\n\n\nclass TestFastDirectAck(TestCase):\n    \"\"\"Test a fast Direct Ack response after an ACK is received.\"\"\"\n\n    @async_case\n    async def test_on_with_fast_direct_ack(self):\n        \"\"\"Test ON with fast direct ack response.\"\"\"\n        response_called = False\n\n        def handle_on_response(on_level):\n            \"\"\"Handle the ON command response.\"\"\"\n            nonlocal response_called\n            response_called = True\n\n        set_log_levels(logger_topics=True)\n\n        address = random_address()\n        target = random_address()\n        group = randint(0, 10)\n        cmd = OnLevelCommand(address, group)\n        cmd.subscribe(handle_on_response)\n        ack_topic = build_topic(ON, \"ack\", address, group, MessageFlagType.DIRECT)\n        direct_ack_topic = build_topic(\n            ON, None, address, None, MessageFlagType.DIRECT_ACK\n        )\n        ack_topic_item = TopicItem(\n            topic=ack_topic,\n            kwargs={\"cmd1\": 0x11, \"cmd2\": 0xFF, \"user_data\": None},\n            delay=0.1,\n        )\n        direct_ack_topic_item = TopicItem(\n            topic=direct_ack_topic,\n            kwargs={\n                \"cmd1\": 0x11,\n                \"cmd2\": 0xFF,\n                \"target\": target,\n                \"user_data\": None,\n                \"hops_left\": 0,\n            },\n            delay=0,\n        )\n        send_topics([ack_topic_item, direct_ack_topic_item])\n        await asyncio.sleep(0.2)\n        assert response_called\n\n    @async_case\n    async def test_status_with_fast_direct_ack(self):\n        \"\"\"Test STATUS REQUEST with fast direct ack response.\"\"\"\n        response_called = False\n\n        def handle_status_response(db_version, status):\n            \"\"\"Handle the ON command response.\"\"\"\n            nonlocal response_called\n            response_called = True\n\n        set_log_levels(logger_topics=True)\n\n        address = random_address()\n        target = random_address()\n        status_type = randint(0, 10)\n        cmd = StatusRequestCommand(address, status_type)\n        cmd.subscribe(handle_status_response)\n        ack_topic = build_topic(\n            STATUS_REQUEST, \"ack\", address, None, MessageFlagType.DIRECT\n        )\n        direct_ack_topic = build_topic(\n            ON, None, address, None, MessageFlagType.DIRECT_ACK\n        )\n        ack_topic_item = TopicItem(\n            topic=ack_topic,\n            kwargs={\"cmd1\": 0x19, \"cmd2\": status_type, \"user_data\": None},\n            delay=0.1,\n        )\n        direct_ack_topic_item = TopicItem(\n            topic=direct_ack_topic,\n            kwargs={\n                \"cmd1\": 0x11,\n                \"cmd2\": 0x02,\n                \"target\": target,\n                \"user_data\": None,\n                \"hops_left\": 0,\n            },\n            delay=0,\n        )\n        send_topics([ack_topic_item, direct_ack_topic_item])\n        await asyncio.sleep(0.2)\n        assert response_called\n","sub_path":"tests/test_handlers/test_fast_ack_direct_ack.py","file_name":"test_fast_ack_direct_ack.py","file_ext":"py","file_size_in_byte":3408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"384684581","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Aug 22 12:40:39 2018\r\n\r\n@author: Chintan Rajvir\r\n\"\"\"\r\n\r\nfrom PIL import Image\r\nfrom random import randint\r\nimport copy\r\n\r\ndef xTimesG(t0, xNew, yNew, xCurr, yCurr, p):\r\n    while t0!=0:\r\n        s = ((yNew-yCurr)*mmi((xNew-xCurr), p))%p\r\n        xNew = (pow(s, 2) - xNew - xCurr)%p\r\n        yNew = (s*(xCurr-xNew)-yCurr)%p\r\n        t0 -= 1\r\n    return [xNew%256, yNew%256]\r\n\r\ndef mmi(a0, p):\r\n    return pow(a0, p-2, p)\r\n\r\ndef multiply(A, B):\r\n    x = A[0][0]*B[0] + A[0][1]*B[1] + A[0][2]*B[2] + A[0][3]*B[3]\r\n    y = A[1][0]*B[0] + A[1][1]*B[1] + A[1][2]*B[2] + A[1][3]*B[3]\r\n    z = A[2][0]*B[0] + A[2][1]*B[1] + A[2][2]*B[2] + A[2][3]*B[3]\r\n    w = A[3][0]*B[0] + A[3][1]*B[1] + A[3][2]*B[2] + A[3][3]*B[3]\r\n    return [x%256, y%256, z%256, w%256]\r\n\r\ndef convertToMatrix(A, w):\r\n    tmp = []\r\n    for i in range(0, len(A), w):\r\n        tmp.append(A[i:i+w])\r\n    return tmp\r\n\r\ntestImg = 'res/p2.png'\r\nimg = Image.open(testImg)\r\nimg = img.convert(\"RGB\")\r\n\r\n#Let us define the ECC Parameters now. \r\n\r\na = 1\r\nb = 3\r\np = 31\r\nx0 = 1\r\ny0 = 6\r\nG = [x0, y0]\r\nxCurr = G[0]\r\nyCurr = G[1]\r\ns = ((3*pow(xCurr, 2) + a)*mmi(2*yCurr, p))%p\r\nxNew = (pow(s, 2)-2*xCurr)%p        #x2G\r\nyNew = (s*(xCurr-xNew)-yCurr)%p     #y2G\r\n\r\nalpha = 13\r\nbeta = 17\r\n\r\nalphaG = xTimesG(alpha-2, xNew, yNew, xCurr, yCurr, p)\r\nbetaG = xTimesG(beta-2, xNew, yNew, xCurr, yCurr, p)\r\n                        #(alpha*beta)%p\r\n\r\ntmpS = ((3*pow(betaG[0], 2) + a)*mmi(2*betaG[1], p))%p\r\nX2B = (pow(tmpS, 2)-2*betaG[0])%p\r\nY2B = (tmpS*(betaG[0]-X2B)-betaG[1])%p\r\ntwo_betaG = [X2B, Y2B]\r\nalphaBetaG = xTimesG(alpha-2, X2B, Y2B, betaG[0], betaG[1], p)\r\nprint(alphaBetaG)\r\n\r\nx = alphaBetaG[0]\r\ny = alphaBetaG[1]\r\n\r\nxG = xTimesG(x-2, xNew, yNew, xCurr, yCurr, p)\r\nyG = xTimesG(y-2, xNew, yNew, xCurr, yCurr, p)\r\n\r\nk = [xG, yG]\r\n\r\nK11 = copy.deepcopy(k)\r\nK12 = [[1-K11[0][0], 0-K11[0][1]], [0-K11[1][0], 1-K11[1][1]]]\r\nK21 = [[1+K11[0][0], K11[0][1]], [K11[1][0], 1+K11[1][1]]]\r\nK22 = [[-1*k[0][0], -1*k[0][1]], [-1*k[1][0], -1*k[1][1]]]\r\n\r\nKm = [[K11[0][0], K11[0][1], K12[0][0], K12[0][1]],\r\n      [K11[1][0], K11[1][1], K12[1][0], K12[1][1]],\r\n      [K21[0][0], K21[0][1], K22[0][0], K22[0][1]],\r\n      [K21[1][0], K21[1][1], K22[1][0], K22[1][1]]]\r\n\r\nprint(Km)\r\n\r\nw, h = img.size\r\nredList = []\r\ngreenList = []\r\nblueList = []\r\nfor i in range(w):\r\n    for j in range(h):\r\n        R, G, B = img.getpixel((i, j))\r\n        redList.append(R)\r\n        greenList.append(G)\r\n        blueList.append(B)\r\n     \r\nnewRedList = []\r\nnewGreenList = []\r\nnewBlueList = []\r\nfor i in range(0, w*h, 4):\r\n    arr = redList[i:i+4]\r\n    newRedList += multiply(Km, arr)\r\n    arr = greenList[i:i+4]\r\n    newGreenList += multiply(Km, arr)\r\n    arr = blueList[i:i+4]\r\n    newBlueList += multiply(Km, arr)\r\n\r\n#print(redMatrix)\r\nencryptedImg = Image.new(img.mode, img.size)\r\neI = encryptedImg.load()\r\nk = 0\r\nfor i in range(h):\r\n    for j in range(w):\r\n        eI[i, j] = (newRedList[k], newGreenList[k], newBlueList[k])\r\n        #encryptedImg.putpixel((j, i), tuple(newRedList[k], newGreenList[k], newBlueList[k]))\r\n        k += 1\r\n\r\nencryptedImg.save('res/encrypted.png')\r\n\r\n\"\"\"KmINV = [[K22[0][0]%256, K22[0][1]%256, (-1*K12[0][0])%256, (-1*K12[0][1])%256],\r\n      [K22[1][0]%256, K22[1][1]%256, (-1*K12[1][0])%256, (-1*K12[1][1])%256],\r\n      [(-1*K21[0][0])%256, (-1*K21[0][1])%256, K11[0][0]%256, K11[0][1]%256],\r\n      [(-1*K21[1][0])%256, (-1*K21[1][1])%256, K11[1][0]%256, K11[1][1]%256]]\"\"\"\r\n\r\nimg = Image.open('res/encrypted.png')\r\n\r\nw, h = img.size\r\nredList = []\r\ngreenList = []\r\nblueList = []\r\nfor i in range(w):\r\n    for j in range(h):\r\n        R, G, B = img.getpixel((i, j))\r\n        redList.append(R)\r\n        greenList.append(G)\r\n        blueList.append(B)\r\n        \r\nnewRedList = []\r\nnewGreenList = []\r\nnewBlueList = []\r\nfor i in range(0, w*h, 4):\r\n    arr = redList[i:i+4]\r\n    newRedList += multiply(Km, arr)\r\n    arr = greenList[i:i+4]\r\n    newGreenList += multiply(Km, arr)\r\n    arr = blueList[i:i+4]\r\n    newBlueList += multiply(Km, arr)\r\n\r\ndecryptedImg = Image.new(img.mode, img.size)\r\neI = decryptedImg.load()\r\nk = 0\r\nfor i in range(h):\r\n    for j in range(w):\r\n        eI[i, j] = (newRedList[k], newGreenList[k], newBlueList[k])\r\n        k += 1\r\n\r\ndecryptedImg.save('res/decrypted.png')","sub_path":"projDemo.py","file_name":"projDemo.py","file_ext":"py","file_size_in_byte":4286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"635582912","text":"# coding:utf-8\n\nimport smtplib\nfrom email.mime.text import MIMEText  #此模块可以用于发送正文\nfrom email.mime.multipart import MIMEMultipart #此模块用于发送带附件的邮件\n\n# ----1、跟发件相关的参数(163邮箱)------\nsmtpserver = \"smtp.163.com\" #发件服务器\nport = 25    #端口\nsender = \"wanglu172@163.com\" #发件箱\npsw = \"19920726sr\"\nreceiver = \"wanglu172@qq.com\" #收件箱\n\n# ----1、跟发件相关的参数(qq邮箱)------\n# smtpserver = \"smtp.qq.com\" #发件服务器\n# port =465    #端口\n# sender = \"qq@qq.com\" #发件箱\n# psw = \"oloupgvjhnbvbgge\"  #qq邮箱需要用授权码，163可以直接用密码\n# # receiver = \"qq@qq.com\" #收件箱\n\n\nreceiver = [\"172484647@qq.com\",\"wanglu172@163.com\"] #发给多个邮箱\n# ----2、编辑邮件的内容------\n#读文件\nfile_path=r\"D:\\2018_5m_08_14_26_08result.html\"  #读取本地的一个一个测试报告\nwith open(file_path,\"rb\") as fp:\n    mail_body=fp.read()\n\n#邮件发送、接收、主题调用\nmsg=MIMEMultipart()\nmsg[\"from\"]=sender\n# msg[\"to\"]=receiver  #发给单个邮箱\nmsg[\"to\"]=\";\".join(receiver)  #发给多个邮箱\nmsg[\"subject\"]=\"test\"\n\n#正文\nbody=MIMEText(mail_body,\"html\",\"utf-8\")\nmsg.attach(body)                               #读取附件中的正文作为邮件正文\n#附件\natt=MIMEText(mail_body,\"basse64\",\"utf-8\")\natt[\"Content-Type\"]=\"application/octet-stream\"\natt[\"Content-Disposition\"]='attachment; filename=\"test_report.html\"'  #将系统中的result.html重命名一下作为附件发送\nmsg.attach(att)\n\n# ----2、编辑邮件的内容------简化版\n# subject = \"subject\"\n# body = '<p>subjecttitle</p>'  #邮件的正文\n# msg = MIMEText(body,\"html\",\"utf-8\")\n# msg['from']=sender\n# msg['to']=\"111@qq.com\"\n# msg['subject']=subject\n\n# # ----3、发送邮件(163邮箱)------简化版\n# smtp = smtplib.SMTP() #连接服务器\n# smtp.connect(smtpserver)\n# smtp.login(sender,psw)    #登录\n# smtp.sendmail(sender,receiver,msg.as_string()) #发送\n# smtp.quit()    #关闭\n#\n# # ----3、发送邮件(qq邮箱)------简化版\n# smtp=smtplib.SMTP_SSL(smtpserver,port) #连接服务器\n# smtp.login(sender,psw)    #登录\n# smtp.sendmail(sender,receiver,msg.as_string()) #发送\n# smtp.quit()    #关闭\n\n# ----3、发送邮件(任意邮箱)------\ntry:\n    smtp = smtplib.SMTP()\n    smtp.connect(smtpserver)  #连接服务器\n    smtp.login(sender,psw)    #登录\nexcept:\n    smtp=smtplib.SMTP_SSL(smtpserver,port) #连接服务器\n    smtp.login(sender,psw)    #登录\nsmtp.sendmail(sender,receiver,msg.as_string()) #发送\nsmtp.quit()    #关闭","sub_path":"labledemo/email01.py","file_name":"email01.py","file_ext":"py","file_size_in_byte":2569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"586210878","text":"#!/usr/bin/python3\n\nfrom typing import List\nimport json\n\n\nclass Solution:\n    def longestStrChain(self, words: List[str]) -> int:\n        d = dict()\n        for word in words:\n            d[word] = 1\n        longest = 1\n        for word in sorted(words, key=len):\n            for i in range(len(word)):\n                prev = word[:i] + word[i+1:]\n                if prev in d:\n                    d[word] = max(d[word], d[prev] + 1)\n            longest = max(longest, d[word])\n        return longest\n\n\narr = json.loads(input())\n\nsol = Solution()\nprint(sol.longestStrChain(arr))\n","sub_path":"leetcode/1048_longest_string_chain/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"179094720","text":"#C:\\Users\\ahok\\PycharmProjects\\Python\\GH_Table_Export_to_Excel_xlsx.py\n# Script to connect to Oracle DB, runs a query and post results to excel 2007+ (xlsx) file.\n# April 3, 2017\n# Python - 3.5.4\n\nimport cx_Oracle\nimport xlsxwriter\nimport datetime\n\nselect_clause = \"select * from \"\ntable_name = \"calendar\"\nwhere_clause = \"\"#where col1 ='xvz'\"\norder_clause = \"\"#order by col1 asc\"\n\n# Prepare SQl statement\nsqlCommand = select_clause + \" \" + table_name + \" \" + where_clause + \" \" + order_clause\nprint(\"sqlCommand = \", sqlCommand)\n\n# Connection to Oracle DB\n\ndbcon = cx_Oracle.connect(\"username/password@tns_file_entry\")\n\ncursor = dbcon.cursor()\nprintHeader = True\n# Execute SQL command and extract results\nsqlResult = cursor.execute(sqlCommand)\n\n# Create excel workbook\nworkbook = xlsxwriter.Workbook(\"C:\\\\Users\\\\ahok\\\\Desktop\\\\Temp\\\\\"+table_name+\".xlsx\")\n# Add excel sheet to above workbook\nworksheet = workbook.add_worksheet(table_name)\n# Setting formats\nnum_format = workbook.add_format({'num_format': '###0.####'})\ndate_format = workbook.add_format({'num_format': 'm/d/yyyy h:mm:ss'})\n\nrow = 0\ncol = 0\n# Add column headers if requested\nstart_time = (datetime.datetime.now())\nprint(\"Export started at = \", start_time)\nprint(\"Exporting Header...\")\nif printHeader:\n    header = []\n    for header in cursor.description:\n        worksheet.write(row, col, header[0])\n        col = col+1\nprint(\"Header Exported\")\nprint(\"Exporting Rows...\")\n# Add data\nrow = 1\ncol = 0\nfor tupple_row in sqlResult:\n    col = 0\n    for list_item in tupple_row:\n        tab_desc = []\n        tab_desc = cursor.description[col]\n        # Get column data type\n        col_data_type = str(tab_desc[1])\n        #print(list_item)\n        # If cell value is blank then insert blank. Can add more\n        if list_item is None:\n            worksheet.write_blank(row, col, None)\n        # If cell value is data then format date time\n        elif col_data_type.find(\"DATETIME\") > 0:\n            worksheet.write_datetime(row, col, list_item, date_format)\n        # If cell value is Number then format date time\n        elif col_data_type.find(\"NUMBER\") > 0:\n            worksheet.write_number(row, col, list_item)\n        # String\n        else:\n            worksheet.write(row, col, list_item)\n        col = col + 1\n    row = row + 1\nworkbook.close()\nprint(\"Rows Exported. Total rows = \", row-1)\nend_time = (datetime.datetime.now())\nprint(\"Export completed at = \", end_time)\nprint(\"Export duration = \", end_time-start_time)\ncursor.close()\n\n\n","sub_path":"src_python/GH_Table_Export_to_Excel_xlsx.py","file_name":"GH_Table_Export_to_Excel_xlsx.py","file_ext":"py","file_size_in_byte":2504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"605264565","text":"import numpy as np\nimport scipy.io as sio\nimport time\n\n### INITIALIZATION ###\n\n# Compare skrelief and skrebate implementation of ReliefF algorithm.\nfrom skrelief.relieff import Relieff\nfrom skrebate import ReliefF\n# Set name of RBA implementation being evaluated.\nrba_name = \"ReliefF\"\n\nrba1 = Relieff()  # skrelief implementation of ReliefF algorithm.\nrba2 = ReliefF(n_neighbors=5, n_jobs=1)   # SKRebate implementation of ReliefF algorithm.\nrba3 = ReliefF(n_neighbors=5, n_jobs=-1)  # SKRebate implementation of ReliefF algorithm.\n\n# Fit skrelief implementation of ReliefF algorithm to compile modules.\ninit_data = np.random.rand(30, 100)\ninit_target = (init_data[:, 0] > 0.5).astype(int)\nrba1.fit(init_data, init_target)\n\nRESULTS_SAVE_PATH = 'results/'\n\n### /INITIALIZATION ###\n\n\n# Create test data size parameters.\nsamples = np.arange(100, 101)\nsample_dim = np.arange(100, 5100, 100)\nxx, yy = np.meshgrid(samples, sample_dim)\n\n# Get number of rows of data dimensionalities to compare.\nnum_pairs = xx.shape[0]\n\n# Allocate matrices for time, were nxt_row are stored.\ntime_mat_1 = np.empty((sample_dim.size, samples.size), dtype=np.float)\ntime_mat_2 = np.empty((sample_dim.size, samples.size), dtype=np.float)\ntime_mat_3 = np.empty((sample_dim.size, samples.size), dtype=np.float)\n\n# pairs indices.\nidx_pair = 0\n\n# Go over sizes of data matrices.\nfor pair in zip(xx, yy):\n    \n    # Allocate rows \n    nxt_row1 = np.empty(pair[0].size, dtype=np.float)\n    nxt_row2 = np.empty(pair[0].size, dtype=np.float)\n    nxt_row3 = np.empty(pair[0].size, dtype=np.float)\n    \n    # column index in next row of results.\n    col_idx = 0\n    for pair2 in zip(pair[0], pair[1]):\n\n        # Create dummy data and target values.\n        data = np.random.rand(pair2[0], pair2[1])\n        target = (data[:, 0] > 0.5).astype(int)\n        \n        ### Measure performance ###\n        start1 = time.perf_counter()\n        rba1.fit(data, target)\n        time1 = time.perf_counter() - start1\n\n        start2 = time.perf_counter()\n        rba2.fit(data, target)\n        time2 = time.perf_counter() - start2\n\n        start3 = time.perf_counter()\n        rba3.fit(data, target)\n        time3 = time.perf_counter() - start3\n        ###########################\n        \n        # Save results to row of results.\n        nxt_row1[col_idx] = time1\n        nxt_row2[col_idx] = time2\n        nxt_row3[col_idx] = time3\n        col_idx += 1\n\n    # Put rows in results matrices.\n    time_mat_1[idx_pair, :] = nxt_row1\n    time_mat_2[idx_pair, :] = nxt_row2\n    time_mat_3[idx_pair, :] = nxt_row3\n    \n    print(\"finished pair {0}/{1}\".format(idx_pair+1, num_pairs))\n    \n    # Increment index of data dimensionality pairs.\n    idx_pair += 1\n\n\n# Save matrices of results.\nsio.savemat(RESULTS_SAVE_PATH + 'time1_' + rba_name + '.mat', {'time1' : time_mat_1})\nsio.savemat(RESULTS_SAVE_PATH + 'time2_' + rba_name + '.mat', {'time2' : time_mat_2})\nsio.savemat(RESULTS_SAVE_PATH + 'time3_' + rba_name + '.mat', {'time3' : time_mat_3})\n\n\n","sub_path":"time-comparison/time_eval.py","file_name":"time_eval.py","file_ext":"py","file_size_in_byte":2995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"608864519","text":"from tkinter import *\nfrom openpyxl import *\n\nwb=load_workbook('data.xlsx')\nsheet=wb.active\n\ndef excel():\n    sheet.column_dimensions['A'].width= 30\n    sheet.column_dimensions['B'].width= 10\n    sheet.column_dimensions['C'].width= 10\n    sheet.column_dimensions['D'].width= 10\n    sheet.column_dimensions['E'].width= 20\n    sheet.column_dimensions['F'].width= 30\n    sheet.column_dimensions['G'].width= 50\n\n    sheet.cell(row=1, column=1).value = \"Name\"\n    sheet.cell(row=1, column=2).value = \"Course\"\n    sheet.cell(row=1, column=3).value = \"Sem\"\n    sheet.cell(row=1, column=4).value = \"Sex\"\n    sheet.cell(row=1, column=5).value = \"Contact no\"\n    sheet.cell(row=1, column=6).value = \"Email id\"\n    sheet.cell(row=1, column=7).value = \"Address\"\n\ndef focus1(event):\n    course_field.focus_set()\ndef focus2(event):\n    sem_field.focus_set()\ndef focus3(event):\n    sex_field.focus_set()\ndef focus4(event):\n    contact_no_field.focus_set()\ndef focus5(event):\n    email_id_field.focus_set()\ndef focus6(event):\n    address_field.focus_set()\n\ndef clear():\n    name_field.delete(0, END)\n    course_field.delete(0, END)\n    sem_field.delete(0, END)\n    sex_field.delete(0, END)\n    contact_no_field.delete(0, END)\n    email_id_field.delete(0, END)\n    address_field.delete(0, END)\n\ndef insert():\n    if (name_field.get() == \"\" and\n\t\tcourse_field.get() == \"\" and\n\t\tsem_field.get() == \"\" and\n\t\tsex_field.get() == \"\" and\n\t\tcontact_no_field.get() == \"\" and\n\t\temail_id_field.get() == \"\" and\n\t\taddress_field.get() == \"\"):\n\n\t\t      print(\"empty input\")\n\n    else:\n        current_row = sheet.max_row\n        current_column = sheet.max_column\n\n        sheet.cell(row=current_row + 1, column=1).value = name_field.get()\n        sheet.cell(row=current_row + 1, column=2).value = course_field.get()\n        sheet.cell(row=current_row + 1, column=3).value = sem_field.get()\n        sheet.cell(row=current_row + 1, column=4).value = sex_field.get()\n        sheet.cell(row=current_row + 1, column=5).value = contact_no_field.get()\n        sheet.cell(row=current_row + 1, column=6).value = email_id_field.get()\n        sheet.cell(row=current_row + 1, column=7).value = address_field.get()\n\n        wb.save('data.xlsx')\n        name_field.focus_set()\n        clear()\n\n\nroot=Tk()\nroot.configure(background='light green')\nroot.title(\"student details\")\nroot.geometry(\"500x300\")\nexcel()\n\nhead=Label(root, text=\"Form\", bg=\"light green\")\nhead.grid(row=0, column=1)\n\nname=Label(root, text=\"Name\", bg=\"light green\")\nname.grid(row=1, column=0)\n\ncourse=Label(root, text=\"Course\", bg=\"light green\")\ncourse.grid(row=2, column=0)\n\nsem=Label(root, text=\"Sem\", bg=\"light green\")\nsem.grid(row=3, column=0)\n\nsex=Label(root, text=\"Sex\", bg=\"light green\")\nsex.grid(row=4, column=0)\n\ncontact=Label(root, text=\"Contact No\", bg=\"light green\")\ncontact.grid(row=5, column=0)\n\nmail=Label(root, text=\"E-mail id\", bg=\"light green\")\nmail.grid(row=6, column=0)\n\nadd=Label(root, text=\"Address\", bg=\"light green\")\nadd.grid(row=7, column=0)\n\nname_field = Entry(root)\ncourse_field = Entry(root)\nsem_field = Entry(root)\nsex_field = Entry(root)\ncontact_no_field = Entry(root)\nemail_id_field = Entry(root)\naddress_field = Entry(root)\n\nname_field.bind(\"<Return>\", focus1)\ncourse_field.bind(\"<Return>\", focus2)\nsem_field.bind(\"<Return>\", focus3)\nsex_field.bind(\"<Return>\", focus4)\ncontact_no_field.bind(\"<Return>\", focus5)\nemail_id_field.bind(\"<Return>\", focus6)\n\nname_field.grid(row=1, column=1, ipadx=\"100\")\ncourse_field.grid(row=2, column=1, ipadx=\"100\")\nsem_field.grid(row=3, column=1, ipadx=\"100\")\nsex_field.grid(row=4, column=1, ipadx=\"100\")\ncontact_no_field.grid(row=5, column=1, ipadx=\"100\")\nemail_id_field.grid(row=6, column=1, ipadx=\"100\")\naddress_field.grid(row=7, column=1, ipadx=\"100\")\n\nexcel()\n\nsubmit=Button(root, text=\"Submit\", fg=\"Black\", bg=\"Red\", command=insert)\nsubmit.grid(row=8, column=1)\nroot.mainloop()\n","sub_path":"day7/stud_form.py","file_name":"stud_form.py","file_ext":"py","file_size_in_byte":3869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"304630248","text":"from __future__ import (absolute_import, division,\n                        unicode_literals, print_function)\nimport six\nimport numpy as np\nimport time\nfrom replay.pipeline.pipeline import DataMuggler, ColSpec, Unalignable\nfrom datetime import datetime\nfrom nose.tools import assert_true, assert_equal\nfrom numpy.testing import assert_array_equal\nfrom nose.tools import assert_raises, make_decorator\n\n\ndef use_fs_dec(func):\n    \"\"\"\n    A decorator to run a test with and with out fs\n\n    func : function\n        The signature must be f(use_fs)\n    \"\"\"\n    def wrapper():\n        for use_fs in [True, False]:\n            yield func, use_fs\n\n    return make_decorator(func)(wrapper)\n\n\ndef test_empty_DM():\n    dm = DataMuggler([])\n    assert_equal(len(dm.keys()), 0)\n\n\ndef test_maxframes():\n    col_list = [('a', 'ffill', 0),\n                ('b', 'ffill', 1),\n                ('c', 'ffill', 2)]\n\n    dm = DataMuggler(col_list, max_frames=5, use_pims_fs=False)\n    assert_equal(3, dm.ncols)\n\n    for j in range(12):\n        ts = datetime.now()\n        data_dict = {'a': j, 'b': np.ones(2) * j,\n                     'c': np.ones((2, 2)) * j}\n\n        dm.append_data(ts, data_dict)\n\n        a = dm.get_values('a', [])[1]['a']\n        b = dm.get_values('b', [])[1]['b']\n        c = dm.get_values('c', [])[1]['c']\n\n        assert_equal(len(a), j+1)\n        assert_equal(a[-1], j)\n        print(len(b))\n        assert_true(len(b) <= 5)\n        assert_equal(int(np.mean(b[-1])), j)\n        assert_true(len(c) <= 5)\n        assert_equal(int(np.mean(c[-1])), j)\n\n\n@use_fs_dec\ndef test_get_row(use_fs):\n    col_list = [('a', 'ffill', 0),\n                ('b', 'ffill', 1),\n                ('c', 'ffill', 2)]\n\n    dm = DataMuggler(col_list, use_pims_fs=use_fs)\n    assert_equal(3, dm.ncols)\n    ts_list = []\n    for j in range(12):\n        ts = datetime.now()\n        ts_list.append(ts)\n        data_dict = {'a': j, 'b': np.ones(2) * j,\n                     'c': np.ones((2, 2)) * j}\n\n        dm.append_data(ts, data_dict)\n\n    print(dm._dataframe)\n\n    for j, ts in enumerate(ts_list):\n        res = dm.get_row(ts, ['a', 'b', 'c'])\n        assert_equal(res['a'], j)\n        assert_array_equal(res['b'], np.ones(2) * j)\n        assert_array_equal(res['c'], np.ones((2, 2)) * j)\n\n\n@use_fs_dec\ndef test_get_col(use_fs):\n    col_list = [('a', 'ffill', 0),\n                ('b', 'ffill', 1),\n                ('c', 'ffill', 2)]\n\n    dm = DataMuggler(col_list, use_pims_fs=use_fs)\n    assert_equal(3, dm.ncols)\n\n    for j in range(12):\n        ts = datetime.now()\n        data_dict = {'b': np.ones(2) * j,\n                     'c': np.ones((2, 2)) * j}\n\n        if j % 2:\n            data_dict['a'] = j\n        dm.append_data(ts, data_dict)\n\n    ts_lst, col_vals = dm.get_column('a')\n    target_vals = [j * 2 + 1 for j in range(6)]\n    assert_array_equal(col_vals, target_vals)\n\n    ts_lst, col_vals = dm.get_column('b')\n    for j, cv in zip(range(12), col_vals):\n        assert_array_equal(np.ones(2) * j, cv)\n\n    assert_raises(ValueError, dm.get_column, 'aardvark')\n\n\n@use_fs_dec\ndef test_last_val(use_fs):\n    col_list = [('a', 'ffill', 0),\n                ('b', 'ffill', 1),\n                ('c', 'ffill', 2)]\n\n    dm = DataMuggler(col_list, use_pims_fs=use_fs)\n    ts_list = []\n    for j in range(12):\n        ts = datetime.now()\n        ts_list.append(ts)\n        data_dict = {'a': j, 'b': np.ones(2) * j,\n                     'c': np.ones((2, 2)) * j}\n\n        dm.append_data(ts, data_dict)\n\n        t, res = dm.get_last_value('a', ['b', 'c'])\n        assert_equal(ts, t)\n        for k in data_dict:\n            assert_array_equal(res[k], data_dict[k])\n\n\n@use_fs_dec\ndef test_get_times(use_fs):\n    col_list = [('a', 'ffill', 0),\n                ('b', 'ffill', 1),\n                ('c', 'ffill', 2)]\n\n    dm = DataMuggler(col_list, use_pims_fs=use_fs)\n    ts_list = []\n    for j in range(12):\n        ts = datetime.now()\n        ts_list.append(ts)\n        data_dict = {'a': j, 'b': np.ones(2) * j,\n                     'c': np.ones((2, 2)) * j}\n\n        dm.append_data(ts, data_dict)\n    res_ts = dm.get_times('a')\n    assert_equal(ts_list, list(res_ts))\n\n\ndef test_bad_col_append():\n    col_list = [('a', 'ffill', 0),\n                ('b', 'ffill', 1),\n                ('c', 'ffill', 2)]\n\n    dm = DataMuggler(col_list)\n\n    bad_dict = {'aardvark': 42}\n    assert_raises(ValueError, dm.append_data, datetime.now(), bad_dict)\n\n\ndef test_colspec():\n    assert_raises(ValueError, ColSpec, 'a', 'ffill', -1)\n    assert_raises(ValueError, ColSpec, 'a', 'aardvark', 1)\n\n\ndef test_props():\n    col_list = [(chr(j), vf, j - 97) for\n                j, vf in enumerate(ColSpec.valid_fill_methods, start=97)]\n\n    dm = DataMuggler(col_list)\n\n    fill_dict = {chr(j): vf for\n                j, vf in enumerate(ColSpec.valid_fill_methods, start=97)}\n\n    assert_equal(fill_dict, dm.col_fill_rules)\n\n    dim_dict = {chr(j): j-97 for j, vf\n                in enumerate(ColSpec.valid_fill_methods, start=97)}\n\n    assert_equal(dim_dict, dm.col_dims)\n\n\ndef test_align_against():\n    col_list = [('a', 'ffill', 0),\n                ('b', 'ffill', 0),\n                ('c', 'ffill', 0),\n                ('d', None, 0)]\n\n    dm = DataMuggler(col_list)\n    dm.append_data(datetime.now(), {'a': -1, 'd': -1})\n    for j in range(12):\n        ts = datetime.now()\n        data_dict = {'c': j}\n        if j % 2:\n            data_dict['a'] = j\n        if j % 3:\n            data_dict['b'] = j\n\n        dm.append_data(ts, data_dict)\n\n    ab_dict = dm.align_against('a', ['b', 'd'])\n    assert_equal({'a': True, 'b': False, 'd': False},\n                 ab_dict)\n\n    ba_dict = dm.align_against('b', ['a'])\n    assert_equal({'a': True, 'b': True}, ba_dict)\n\n    d_dict = dm.align_against('d')\n    assert_equal(d_dict,\n                 {'a': True, 'b': False, 'c': False, 'd': True})\n\n    assert_raises(ValueError, dm.align_against, 'aardvark')\n\n\ndef test_unique_keys():\n    col_list = [('a', 'ffill', 0),\n                ('a', 'ffill', 0),\n                ('c', 'ffill', 0),\n                ('d', None, 0)]\n\n    assert_raises(ValueError, DataMuggler, col_list)\n\n\ndef test__non_scalar_lookup_fail():\n    col_list = [('a', 'ffill', 0),\n                ('b', 'ffill', 0)]\n\n    dm = DataMuggler(col_list)\n\n    ts = datetime.now()\n    data_dict = {'a': 1, 'b': 1}\n    dm.append_data(ts, data_dict)\n\n    ts = datetime.now()\n    data_dict = {'b': 2}\n    dm.append_data(ts, data_dict)\n\n    assert_raises(Unalignable, dm._listify_output, dm._dataframe)\n\n\ndef test_add_column():\n    col_list = [('a', 'ffill', 0),\n                ('b', 'ffill', 0)]\n\n    new_col = ('c', None, 0)\n    dm = DataMuggler(col_list)\n    assert_equal(2, dm.ncols)\n    dm.add_column(new_col)\n    assert_equal(3, dm.ncols)\n\n    assert_equal(set(['a', 'b', 'c']), set(dm.keys()))\n\n    assert_raises(ValueError, dm.add_column, new_col)\n\n\ndef test_add_column_data():\n    col_list = [('a', 'ffill', 0),\n                ('b', 'ffill', 1)]\n\n    dm = DataMuggler(col_list)\n    assert_equal(2, dm.ncols)\n\n    for j in range(5):\n        ts = datetime.now()\n        data_dict = {'a': j, 'b': np.ones(2) * j}\n\n        dm.append_data(ts, data_dict)\n\n    new_col = ('c', None, 2)\n    dm.add_column(new_col)\n    assert_equal(3, dm.ncols)\n    for j in range(5, 10):\n        ts = datetime.now()\n        data_dict = {'a': j, 'b': np.ones(2) * j,\n                     'c': np.ones((2, 2)) * j}\n\n        dm.append_data(ts, data_dict)\n\n        a = dm.get_values('a', [])[1]['a']\n        b = dm.get_values('b', [])[1]['b']\n        c = dm.get_values('c', [])[1]['c']\n\n        assert_equal(len(a), j+1)\n        assert_equal(len(b), j+1)\n        assert_equal(len(c), j+1 - 5)\n        assert_equal(a[-1], j)\n        assert_equal(int(np.mean(b[-1])), j)\n        assert_equal(int(np.mean(c[-1])), j)\n\n\n@use_fs_dec\ndef test_bulk_add(use_pims_fs):\n    N = 5\n    a_data = list(np.rollaxis((np.ones((2, 2)).reshape(2, 2, 1) *\n                               np.arange(N).reshape(1, 1, -1)), 2, 0))\n\n    b_data = list(np.rollaxis((np.ones((2, 2)).reshape(2, 2, 1) *\n                               np.arange(N)[::-1].reshape(1, 1, -1)), 2, 0))\n\n    data_dict = {'a': a_data, 'b': b_data}\n    ts = []\n    for j in range(N):\n        ts.append(datetime.now())\n        time.sleep(.1)\n\n    col_list = [('a', 'ffill', 2),\n                ('b', 'ffill', 2)]\n    dm = DataMuggler(col_list, use_pims_fs=use_pims_fs)\n    cached_dd = dict(data_dict)\n    dm.append_data(ts, data_dict)\n    assert_equal(cached_dd, data_dict)\n\n    ts, a_res = dm.get_column('a')\n    for l, r in zip(a_data, a_res):\n        assert_array_equal(l, r)\n\n    ts, b_res = dm.get_column('b')\n    for l, r in zip(b_data, b_res):\n        assert_array_equal(l, r)\n","sub_path":"replay/tests/test_dm.py","file_name":"test_dm.py","file_ext":"py","file_size_in_byte":8712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"392228927","text":"from .dataset import Dataset\nimport torch\nimport numpy as np\n\n\nclass TensorDataset(Dataset):\n    \"\"\"\n    Accept:\n     * dict of tensors or numpy arrays\n     * list of tensors or numpy arrays\n     * tensor or numpy array\n    \"\"\"\n    def __init__(self, data):\n        super(TensorDataset, self).__init__()\n        if isinstance(data, dict):\n            assert len(data) > 0, \"Should have at least one element\"\n            # check that all fields have the same size\n            n_elem = len(list(data.values())[0])\n            for v in data.values():\n                assert len(v) == n_elem\n        elif isinstance(data, list):\n            assert len(data) > 0, \"Should have at least one element\"\n            n_elem = len(data[0])\n            for v in data:\n                assert len(v) == n_elem\n        self.data = data\n\n    def __len__(self):\n        if isinstance(self.data, dict):\n            return len(list(self.data.values())[0])\n        elif isinstance(self.data, list):\n            return len(self.data[0])\n        elif torch.is_tensor(self.data) or isinstance(self.data, np.ndarray):\n            return len(self.data)\n\n    def __getitem__(self, idx):\n        super(TensorDataset, self).__getitem__(idx)\n        if isinstance(self.data, dict):\n            return {k: v[idx] for k,v in self.data.items()}\n        elif isinstance(self.data, list):\n            return [v[idx] for v in self.data]\n        elif torch.is_tensor(self.data) or isinstance(self.data, np.ndarray):\n            return self.data[idx]\n","sub_path":"torchnet/dataset/tensordataset.py","file_name":"tensordataset.py","file_ext":"py","file_size_in_byte":1513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"553936317","text":"from net.config import ConfType\nfrom train.CVGAE.train_qm9 import train_qm9, QMDataset\n\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--seed', type=int, default=16880611)\nparser.add_argument('--pos', type=int, default=0)\narg = parser.parse_args()\nseed = arg.seed\npos = arg.pos\n\nif pos == 0:\n    conf_type = ConfType.NONE\n    name = 'QM8-Xconf'\nelse:\n    conf_type = ConfType.RDKIT\n    name = 'QM8-rdkit'\n\ntrain_qm9(\n    special_config={\n        'HV_DIM': 128,\n        'HE_DIM': 64,\n\n        'EPOCH': 300,\n        'BATCH': 50,\n        'SAMPLE': 5,\n        'LAMBDA': 1,\n        'LR': 5e-6,\n        'GAMMA': 0.995,\n        'DECAY': 1e-5,\n\n        'CONF_TYPE': conf_type,\n    },\n    dataset=QMDataset.QM8,\n    use_cuda=True,\n    max_num=-1,\n    data_name=f'CVGAE-{name}@{seed}',\n    seed=seed,\n    force_save=True,\n    tag=f'CVGAE-{name}@{seed}',\n    use_tqdm=False,\n)\n","sub_path":"CVGAE_qm8.py","file_name":"CVGAE_qm8.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"251083957","text":"#!/usr/bin/env python\n\"\"\"\n Created by Dai at 18-10-18.\n\"\"\"\nimport json\nfrom functools import wraps\n\n\ndef format_url(**kwargs):\n    fixed_url = ''\n    try:\n        fixed_url += '/' + kwargs.get('index')\n    except:\n        raise ('not index error')\n\n    try:\n        fixed_url += '/' + kwargs.get('doc_type')\n    except:\n        pass\n\n    try:\n        fixed_url += '/' + kwargs.get('id')\n    except:\n        pass\n\n    try:\n        body = kwargs.get('body')\n        data = json.dumps(body)\n        data = data.encode('utf-8')\n    except:\n        data = None\n    res = {\n        'fixed_url': fixed_url,\n        'data': data,\n        'params': kwargs.get('params', {}),\n        \"file_type\": kwargs.get(\"file_type\", 'json')\n    }\n    return res\n\n\ndef current_session(func):\n    \"\"\"\n    :param func: func\n    :return: instance\n    \"\"\"\n\n    @wraps(func)\n    async def instance(*args, **kwargs):\n        con = await args[0].pooling.get_connection()\n        kwargs['con'] = con['connect']\n        res = await func(*args, **kwargs)\n        await args[0].pooling.release_connection(con)\n        return res\n\n    return instance\n\n\nif __name__ == '__main__':\n    res = format_url(index='dai', doc_type='ji')\n    print(res)\n","sub_path":"src/elastic/client/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"116907600","text":"'''\r\n- 為「PCHome線上購物」建立一個離線版的商品搜尋程式。\r\n- 網址： https://shopping.pchome.com.tw/\r\n'''\r\n\r\nimport requests\r\nimport os\r\nimport json\r\nimport prettytable\r\n\r\ndef search_product(product_name, page_num):\r\n    rr = requests.get(\r\n        \"https://ecshweb.pchome.com.tw/search/v3.3/all/results\",\r\n        headers = {\r\n            \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36 OPR/67.0.3575.115\",\r\n            \"Accept-Language\": \"zh-TW,zh;q=0.9,en-US;q=0.8,en;q=0.7\",\r\n        },\r\n        params = {\r\n            \"q\" : prodcut_name,\r\n            \"page\" : str(page_num),\r\n            \"sort\" : \"sale/dc\"\r\n        }\r\n    )\r\n\r\n    data_json = json.loads(rr.text)\r\n    return data_json[\"prods\"]\r\n    # for d in data_json[\"prods\"]:\r\n    #     print(d[\"name\"], d[\"price\"])\r\n\r\ndef show_product(product_list):\r\n    os.system(\"cls\")\r\n    t = prettytable.PrettyTable([\"名稱\",\"價格\"],encoding=\"utf-8\")\r\n    t.align[\"名稱\"] = \"l\"\r\n    t.align[\"價格\"] = \"r\"\r\n    for d in product_list:\r\n        # print(d[\"name\"], d[\"price\"])\r\n        t.add_row([d[\"name\"], d[\"price\"]])\r\n    print(t)\r\n\r\nos.system(\"cls\")\r\nprodcut_name = input(\"產品關鍵字： \")\r\npage_num = 1\r\n\r\nwhile page_num != 0:\r\n    product_list = search_product(prodcut_name, page_num)\r\n    show_product(product_list)\r\n    print(f\"現在頁碼：{page_num}\")\r\n    try:\r\n        page_num = int(input(\"前往頁碼(輸入 0 結束): \"))\r\n    except:\r\n        pass\r\n\r\n\r\n\r\n","sub_path":"webcrawler/pchome-webcrawler.py","file_name":"pchome-webcrawler.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"578928871","text":"import time, signal, sys, atexit\nimport random\n\nimport pyupm_grove as grove\n\nimport pyupm_uln200xa as upmULN200XA\n\nimport pyupm_my9221 as upmMy9221\n\nimport pyupm_ttp223 as ttp223\n\nimport pyupm_buzzer as upmBuzzer\n\nimport pyupm_i2clcd as lcd\n\n# Initialize Jhd1313m1 at 0x3E (LCD_ADDRESS) and 0x62 (RGB_ADDRESS) \nmyLcd = lcd.Jhd1313m1(0, 0x3E, 0x62)\n\n# Create the buzzer object using GPIO pin 5\nbuzzer = upmBuzzer.Buzzer(5)\n\n# Create the TTP223 touch sensor object using GPIO pin 0\ntouch = ttp223.TTP223(4)\n\n# Create the button object using GPIO pin 0\nbutton = grove.GroveButton(6)\n\n# Instantiate a ULN2003XA stepper object\nmyUln200xa = upmULN200XA.ULN200XA(4096, 8, 9, 10, 11)\n\n# Instantiate a MY9221, we use D2 for the data, and D3 for the data clock\nmyLEDBar = upmMy9221.MY9221(8, 9)\n\n# Create the Grove LED object using GPIO pin 2\ngreenLed = grove.GroveLed(2)\nredLed = grove.GroveLed(7)\n\ncount = 0 \t# counts the no. of times swatter slaps, each user gets only 5 chances\nwincount = 0    #counts the no. of times user wins\n\nnumChances = 5; # total no. of chances user gets\n\nS = 675;\nB = 256;\n\nbar_index = 0;\n\ndef bluff():\n\tmyUln200xa.setSpeed(7) \n\tmyUln200xa.setDirection(upmULN200XA.ULN200XA.DIR_CW)\n\tmyUln200xa.stepperSteps(B)\n\n\tmyUln200xa.setDirection(upmULN200XA.ULN200XA.DIR_CCW)\n\tmyUln200xa.stepperSteps(B)\n\n\ttime.sleep(2);\n\ndef slap():\n\n\tglobal wincount;\n\tglobal count;\n\tglobal bar_index;\n\n\tmyUln200xa.setSpeed(7) \n\tmyUln200xa.setDirection(upmULN200XA.ULN200XA.DIR_CW)\n\tmyUln200xa.stepperSteps(S)\n\n\tif (touch.isPressed() == 0):\n\t\twincount += 1\n\t\tgreenLed.on()\n\t\ttime.sleep(2)\n\t\tgreenLed.off()\n\n\t\tmyLEDBar.setBarLevel(bar_index + 2)\n\t\tbar_index = bar_index + 2;\n\n\telse:\n\t\tredLed.on()\n\t\tbuzzer.playSound(upmBuzzer.RE, 1000000)\n\n\t\ttime.sleep(2)\n\t\t\n\t\tredLed.off()\n\t\tbuzzer.stopSound()\n\t\n\tmyUln200xa.setDirection(upmULN200XA.ULN200XA.DIR_CCW)\n\tmyUln200xa.stepperSteps(S)\n\t\n\tcount += 1\n\t\ndef reset():\n\n\tglobal wincount;\n\tglobal count;\n\tglobal bar_index;\n\n\tcount = 0\n\twincount = 0\n\tbar_index = 0\n\n\tgreenLed.off()\n\tredLed.off()\n\tmyLEDBar.setBarLevel(0)\n\tbuzzer.stopSound()\n\tmyLcd.clear()\n\ndef teardown():\n\tglobal touch, buzzer, greenLed, redLed, myLEDBar, button\n\tdel touch\n\tdel buzzer\n\tdel greenLed\n\tdel redLed\n\tdel myLEDBar\n\tdel button\n\ndef main():\n\twhile(1):\n\t\treset()\n\n\t\tmyLcd.write(\"Press Button\") \n\n\t\twhile(1):\n\t\t\tif(button.value() != 0):\n\t\t\t\tbreak\n\t\t\ttime.sleep(1)\n\t\t\n\t\tmyLcd.clear()\n\t\tmyLcd.write(\"Total chances: 5\")\n\n\t\ttime.sleep(3);\n\t\tmyLcd.clear();\n\n\t\tmyLcd.write(\"Place finger on\");\n\t\tmyLcd.setCursor(1, 0);\n\t\tmyLcd.write(\"touch sensor\");\n\n\t\twhile(count < numChances):\n\t\t\tif (touch.isPressed() == 1):\n\t\t\t\trand = random.randint(0,2) # generates random number from 0 and 2\n\t\t\t\tif(rand == 0):\n\t\t\t\t\tslap()\n\t\t\t\telse:\n\t\t\t\t\tbluff()\n\n\t\t\t\tmyLcd.clear()\n\t\t\t\tmyLcd.write(\"#Chances left:\" + str(numChances - count))\n\t\t\ttime.sleep(2)\n\t\t\n\t\tmyLcd.clear()\n\t\tmyLcd.write(\"Your score: \" + str(wincount) + \"/\" + str(numChances))\t\n\n\t\ttime.sleep(2)\n\t\tmyLcd.clear()\n\n\t\tif(wincount == numChances):\n\t\t\tmyLcd.write(\"Hurray!!\");\n\t\telse:\n\t\t\tmyLcd.write(\"Better luck\");\n\t\t\tmyLcd.setCursor(1,0);\n\t\t\tmyLcd.write(\"Next time\");\n\n\t\ttime.sleep(3);\n\t\tmyLcd.clear();\n\n\tteardown()\n\nmain()\n\n\n","sub_path":"robot-snap.py","file_name":"robot-snap.py","file_ext":"py","file_size_in_byte":3165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"534892234","text":"from cv2 import cv2  \nimport numpy as np\nfrom helper import stackImages\n\npath = 'resources/cards.jpg'\npath2 = 'resources/shapes.png'\n\ndef wrap_perspective(path):\n    img  = cv2.imread(path)\n\n    w, h = 250, 350\n    pts1 = np.float32([[111,219],[287,188],[154,482],[352,440]])\n    pts2 = np.float32([[0,0],[w,0],[0,h],[w,h]])\n    matrix = cv2.getPerspectiveTransform(pts1, pts2)\n    imgOutput = cv2.warpPerspective(img, matrix,(w,h))\n    cv2.imshow('Images', img)\n    cv2.imshow('Output',imgOutput)\n    cv2.waitKey(0)\n\n# wrap_perspective(path)\n\ndef join_image(path):\n    img  = cv2.imread(path)\n    imgh = np.hstack((img, img))\n    imgv = np.vstack((img, img))\n    cv2.imshow('Horizontal',imgh)\n    cv2.imshow('Vertical', imgv)\n    cv2.waitKey(0)\n\n# join_image()\n\ndef detect_color(path):\n    # add track bar\n    def empty(x):\n        pass\n\n    cv2.namedWindow('TrackBar')\n    cv2.resizeWindow('TrackBar', 640, 240)\n    cv2.createTrackbar('Hue min', 'TrackBar',0,179,empty)\n    cv2.createTrackbar('Hue max', 'TrackBar',179,179,empty)\n    cv2.createTrackbar('Saturation min', 'TrackBar',0,255,empty)\n    cv2.createTrackbar('Saturation max', 'TrackBar',255,255,empty)\n    cv2.createTrackbar('Val min', 'TrackBar',0,255,empty)\n    cv2.createTrackbar('Val max', 'TrackBar',255,255,empty)\n\n    while True:\n        img  = cv2.imread(path)\n        imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n\n        # get the values from track bar\n        h_min = cv2.getTrackbarPos('Hue min', 'TrackBar')\n        h_max = cv2.getTrackbarPos('Hue max', 'TrackBar')\n        s_min = cv2.getTrackbarPos('Saturation min', 'TrackBar')\n        s_max = cv2.getTrackbarPos('Saturation max', 'TrackBar')\n        v_min = cv2.getTrackbarPos('Val min', 'TrackBar')\n        v_max = cv2.getTrackbarPos('Val max', 'TrackBar')\n\n        lower = np.array([h_min, s_min, v_min])\n        upper = np.array([h_max, s_max, v_max]) \n        mask = cv2.inRange(imgHSV, lower, upper)\n\n        imgMasked = cv2.bitwise_and(img, img, mask=mask)\n\n        # cv2.imshow('Original image', img)\n        # cv2.imshow('HSV image', imgHSV)\n        # cv2.imshow('Mask image', mask)\n        # cv2.imshow('Masked image', imgMasked)\n\n        imgStack = stackImages(0.6,([img,imgHSV],[mask,imgMasked]))\n        cv2.imshow('Stacked Images',imgStack)\n\n        cv2.waitKey(1)\n\n# detect_color(path)\n\ndef getContours(img, imgContour):\n    contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n    for cnt in contours:\n        area = cv2.contourArea(cnt)\n        print(area)\n        cv2.drawContours(imgContour, cnt,-1,(255,0,0),3)\n        peri = cv2.arcLength(cnt, True)\n        approx = cv2.approxPolyDP(cnt, 0.02*peri, True)\n        print(len(approx))  # number of point in shape\n\n        x,y,w,h = cv2.boundingRect(approx)\n        if len(approx ) == 3: objType = 'Tri'\n        elif len(approx ) == 4:\n            aspratio = w/float(h)\n            if aspratio>0.95 and aspratio<1.05: objType = 'Square' \n            else: objType = 'Rect'\n        elif len(approx ) >4: \n            objType = 'Circle'  \n        else: objType = 'Others'\n        cv2.rectangle(imgContour,(x,y),(x+w,y+h),(0,255,0),2)\n        cv2.putText(imgContour, objType,(x+w//2-10,y+h//2-10),cv2.FONT_HERSHEY_COMPLEX, 0.5, (0,0,0),2)\n\ndef shape_contour(path):\n    img = cv2.imread(path)\n    imgContour = img.copy()\n   \n    imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # convert to gray scale\n    imgBlur = cv2.GaussianBlur(imgGray, (5,5), 1)   # blur image\n    imgCanny = cv2.Canny(img, 50, 50) # detecting edges\n    imgBlank = np.zeros_like(img)\n    getContours(imgCanny, imgContour)\n    # cv2.imshow('Original Imge', img)\n    # cv2.imshow('Gray Imge', imgGray)\n    # cv2.imshow('Blur Imge', imgBlur)\n\n    imgStack = stackImages(0.6,([img, imgGray, imgBlur],[imgCanny, imgContour, imgBlank]))\n    cv2.imshow('Stacked Images',imgStack)\n\n    cv2.waitKey(0)\n\n# shape_contour(path2)","sub_path":"Learning/Day2.py","file_name":"Day2.py","file_ext":"py","file_size_in_byte":3914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"286454924","text":"# -*- coding: utf-8 -*-\n#!/usr/bin/python2\n\"\"\"Toolkit for coordinate transformations.\n\n    CAUTION: All angles (input and output) are in radians (unless otherwise\n    stated)!\n\n    Classes\n    =======\n    This module defines three classes: Transform, Vector, Rotation.\n    - They work in 3D Euklidian space.\n    - Vector can handle different coordinate systems: cartesian, sherical and\n      cylindrical. Rotation operates always in cartesian coordinates. Transform\n      uses Vector and Rotation. Vector can be in any coordinate system but upon\n      application, its cartesian representation is used.\n    - Vector can also represent an array of n vectors (implemented in a\n      3xn-matrix).\n    - Most operations of Transform, Vector and Rotation on vector work as well\n      when the operand Vector is an array. These method exploit the optimized\n      numpy operations. In doing so, computations can be sped up substantially.\n\n    If you want to perform other coordinate transformations than these, have a\n    look at `transformations.py` by Christoph Gohlke:\n    https://www.lfd.uci.edu/~gohlke/code/transformations.py.html\n    It can elegantly perform many more coordinate transformation (such as\n    shear, projection, etc.) at the expense of being a bit less easily\n    accessible for non-mathematicians.\n\n\n    Todo / ideas\n    ============\n    Currently none. You got any? Let me know!\n\n\n    Reliability\n    ===========\n    All functions and methods have been tested moderately but not exhaustively.\n    So most probably, there are still undiscovered bugs.\n    I recommend that you perform plausibility checks on your results. (AA)\n\n\n    Coordinate Systems\n    ==================\n    *** CARTESIAN COORDINATES ***\n        x, y, z defined in the regular (right-handed) way\n\n\n    *** SPHERICAL COORDINATES ***\n        right-handed: r, phi, theta\n\n        symbol  range          interpretation                  ref. (\"0 at\")\n        ----------------------------------------------------------------------\n        r       [0..inf)       upwardness (dist. from origin)  origin\n        phi     [0..2pi)       eastwardness (longitude)        positive x-axis\n        theta   [-pi/2..pi/2]  northwardness (elevation, lat.) x-y-plane\n\n        uniqueness:\n        r == 0            -->  phi, theta == 0\n        theta == +- pi/2  -->  phi == 0\n\n\n    *** CYLINDRICAL COORDINATES ***\n        right-handed: rho, phi, z\n\n\n        symbol  range         interpretation                 reference (\"0 at\")\n        -----------------------------------------------------------------------\n        rho     [0...inf)     radius (distance from z-axis)  z-axis\n        phi     [0...2pi)     eastwardness (azimuth)         positive x-axis\n        z       (-inf...inf)  distance to x-y-plane          x-y-plane\n\n        uniqueness:\n        r == 0  -->  phi == 0\n\n\n    Author\n    ======\n    Andreas Anhaeuser (AA) <anhaeus@meteo.uni-koeln.de>\n    Institute for Geophysics and Meteorology\n    University of Cologne, Germany\n\n\n    History\n    =======\n    2017-11-22 (AA): Created.\n    2017-12-12 (AA): Extension for 3xn Vectors. Clean up. Documentation.\n    2018-01-10 (AA): Documentation and comments.\n    2018-05-18 (AA): Made Rotation and Transform instances callable.\n\"\"\"\n# Computer, please:\n\nfrom copy import deepcopy as copy\nimport numpy as np\n\nimport conversions as conv\n\n###################################################\n# CLASSES                                         #\n###################################################\nclass Vector(object):\n    \"\"\"A 3D vector that is aware of its type of coordinate system.\n\n        A Vector is a representation of a point (or shift, etc.) in 3D\n        Euklidian space.\n\n        It is aware of its type of coordinate system. This can be\n        - 'car' (cartesian)\n        - 'sph' (spherical)\n        - 'cyl' (cylindrical)\n        The representation of the Vector can be changed arbitrarily between\n        these coordinate systems.\n\n        Various operations are defined on a Vector:\n        - unary (abs, negate, unit vector, ...)\n        - arithmetics with another Vector (+, -, dot, cross)\n        - arithmetics with scalars (*, /)\n\n        Other fancy properties can be retrieved:\n        - zenith and azimuth angles\n\n        The class is 'defensive' w. r. t. input values: if they are beyond\n        bounds, they are converted automatically (e. g. r < 0, phi > 2pi),\n        using the interpretation that is most probably intended by the user.\n\n        CAUTION: All angles (input and output) are in radians!\n\n\n        Initialization\n        ==============\n        Instantiate the class with Vector(co, kind), where in the simple case,\n        `co` is a 3-element array. The interpretation of the 3 element of `co`\n        depends on the coordinate system, specified by `kind`, see Section\n        'Coordiante Systems' in the module docstring.\n\n\n        List of Vector\n        ==============\n        - To get one vector, instantiate the class with a 3-element array.\n        - To get a Vector object that actually represents a list of N\n          mathematical vectors, instantiate the class with a 3xN array.\n\n\n        History\n        =======\n        2018-12-12 (AA): Created.\n        2018-01-03 (AA): Azimuth and zenith angles.\n        2018-05-22 (AA): Reduced type identifier to three characters\n                         ('car[t]', 'sph[eric]'); kept backward compatible.\n    \"\"\"\n    _valid_kinds = (None, 'car', 'sph', 'cyl')\n\n    def __init__(self, co=np.zeros(3), kind='car'):\n        \"\"\"Initialize.\n\n            See class description for details.\n\n            CAUTION: All angles (input and output) are in radians!\n\n            Parameters\n            ----------\n            co : array-like, first dimensions must be 3\n                first dimension is interpreted as three coordinate axes.\n            kind : {'car', 'sph', 'cyl'}\n                How the coordinates are to be interpreted.\n        \"\"\"\n        if not isinstance(kind, str):\n            raise TypeError('kind must be a str.')\n        kind = kind[:3].lower()\n\n        if kind[:3] not in self._valid_kinds:\n            raise ValueError('kind must be in %s.' % str(self._valid_kinds))\n\n        # normalize\n        if kind == 'sph':\n            co = conv.normalize_sph(*co)\n        elif kind == 'cyl':\n            co = conv.normalize_cyl(*co)\n\n        co = np.array(co)\n        assert np.shape(co)[0] == 3\n\n        self.kind = kind\n        self.co = co\n\n    @classmethod\n    def from_azimuth_and_zenith_angle(cls, co=np.zeros(3)):\n        \"\"\"Initialize with meteorological angles.\n        \n            Parameters\n            ----------\n            co : array-like, first dimensions must be 3\n                first dimension is interpreted as three coordinate axes\n                (r, azimuth, zenith angle)\n                azimuth is measurued clockwise (!) in the x-y-plane, starting\n                at the positive y-axis (!).\n                zenith angle is the distance to the positive z-axis.\n        \"\"\"\n        r = co[0]\n        azi = co[1]\n        za = co[2]\n\n        phi = np.pi/2 - azi\n        theta = np.pi/2 - za\n        co_new = np.array((r, phi, theta))\n        return cls(co_new, kind='sph')\n\n    @classmethod\n    def from_azimuth_and_view_angle(cls, co=np.zeros(3)):\n        \"\"\"Initialize with meteorological angles.\n        \n            Parameters\n            ----------\n            co : array-like, first dimensions must be 3\n                first dimension is interpreted as three coordinate axes\n                (r, azimuth, view angle)\n                azimuth is measurued clockwise (!) in the x-y-plane, starting\n                at the positive y-axis (!).\n                view angle is the distance to the negative z-axis.\n        \"\"\"\n        r = co[0]\n        azi = co[1]\n        va = co[2]\n\n        phi = np.pi/2 - azi\n        theta = va - np.pi/2\n        co_new = np.array((r, phi, theta))\n        return cls(co_new, kind='sph')\n\n    def __repr__(self):\n        \"\"\"Return string representation.\"\"\"\n        # kind\n        kind = self.kind\n        if kind == 'car':\n            k = 'cartesian'\n            n = '(x, y, z)'\n        elif kind == 'sph':\n            k = 'spherical'\n            n = '(r, phi, theta)'\n        elif kind == 'cyl':\n            k = 'cylindrical'\n            n = '(rho, phi, z)'\n\n        # coordinates\n        c = str(self.get_coords())\n\n        # infix\n        if len(np.shape(self.co)) > 1:\n            i = '\\n'\n        else:\n            i = ' '\n\n        # compose string\n        s = 'Vector in %s coordinates %s:%s%s' % (k, n, i, c)\n        return s\n\n    def __abs__(self):\n        \"\"\"Return absolute value as a number.\"\"\"\n        co = self.co\n        if self.kind == 'car':\n            a = np.sqrt(np.sum(co**2, 0))\n        elif self.kind == 'sph':\n            a = co[0]\n        elif self.kind == 'cyl':\n            rho = co[0]\n            z = co[2]\n            a = np.sqrt(rho**2 + z**2)\n        return a\n\n    def __neg__(self):\n        \"\"\"Return negation as a Vector.\"\"\"\n        kind = self.kind\n        if kind == 'car':\n            co = - self.co\n        elif kind == 'sph':\n            r = self.co[0]\n            phi = self.co[1]\n            theta = self.co[2]\n            co = conv.normalize_sph(-r, phi, theta)\n        elif kind == 'cyl':\n            rho = self.co[0]\n            phi = self.co[1]\n            z = self.co[2]\n            co = conv.normalize_cyl(-rho, phi, -z)\n        return Vector(co, kind)\n\n    def __add__(self, other):\n        \"\"\"Add another Vector and return result as Vector.\"\"\"\n        co1 = self.get_car()\n        co2 = other.get_car()\n        s1 = np.shape(co1)\n        s2 = np.shape(co2)\n\n        # regular case (both vectors have same shape):\n        if s1 == s2:\n            co = co1 + co2\n\n        # first vector (3x1), the other (3xn):\n        elif len(s1) == 1:\n            co = (co1 + co2.T).T\n\n        # first vector (3xn), the other (3x1):\n        elif len(s2) == 1:\n            co = (co1.T + co2).T\n\n        # this should not happen:\n        else:\n            raise IndexError()\n\n        return Vector(co, 'car')\n\n    def __iadd__(self, other):\n        return self.__add__(other)\n\n    def __sub__(self, other):\n        \"\"\"Substract another Vector and return result as Vector.\"\"\"\n        return self + (-other)\n\n    def __isub__(self, other):\n        return self.__sub__(other)\n\n    def __mul__(self, a):\n        \"\"\"Multiply by a scalar and return result as Vector.\"\"\"\n        kind = self.kind\n        co = copy(self.co)\n        assert np.shape(a) == np.shape(co[0])\n        if kind == 'car':\n            co *= a\n        elif kind == 'sph':\n            co[0] *= a\n        elif kind == 'cyl':\n            co[0] *= a\n            co[2] *= a\n        return Vector(co, kind)\n\n    def __rmul__(self, a):\n        return self.__mul__(a)\n\n    def __imul__(self, a):\n        return self.__mul__(a)\n\n    def __div__(self, a):\n        \"\"\"Devide by a non-zero scalar and return result as Vector.\"\"\"\n        if np.shape(a) == () and a == 0:\n            raise ZeroDivisionError()\n        return self.__mul__(1. / a)\n\n    def __idiv__(self, a):\n        return self.__div__(a)\n\n    def __truediv__(self, a):\n        return self.__div__(a)\n\n    def __itruediv__(self, a):\n        return self.__div__(a)\n\n    def __len__(self):\n        return self.length()\n\n    def __bool__(self):\n        return abs(self) != 0\n\n    ###################################################\n    # UNARY OPERATORS                                 #\n    ###################################################\n    def unit_vector(self):\n        \"\"\"Return a unit Vector pointing in the same direction.\"\"\"\n        return self / abs(self)\n\n    ###################################################\n    # BINARY OPERATORS                                #\n    ###################################################\n    def inner(self, other):\n        \"\"\"Return inner product with another Vector. (alias to 'dot')\"\"\"\n        return self.dot(other)\n\n    def dot(self, other):\n        \"\"\"Return inner product with another Vector.\n\n            If both `self` and `other` are Vector lists, shapes must match.\n        \"\"\"\n        assert isinstance(other, Vector)\n        co1 = self.get_car()\n        co2 = other.get_car()\n        S1 = np.shape(co1)\n        S2 = np.shape(co2)\n        if S1 == S2:\n            P = co1.dot(co2)\n        elif len(S1) == 1:\n            P = co1.dot(co2)\n        elif len(S2) == 1:\n            P = co2.dot(co1)\n        else:\n            raise IndexError('Vector do not have matching shapes.')\n        return P\n\n    def cross(self, other):\n        \"\"\"Return outer product. Only works for 3x1 Vectors.\"\"\"\n        assert isinstance(other, Vector)\n        assert len(np.shape(self.co)) == 1\n        assert len(np.shape(other.co)) == 1\n        co1 = self.get_car()\n        co2 = other.get_car()\n        return Vector(np.cross(co1, co2))\n\n    ###################################################\n    # GETTERS: BASIC                                  #\n    ###################################################\n    def get_coords(self):\n        \"\"\"Get coordinates, unrespective of kind.\n\n            This is merely intended as a helper to other class methods. It is\n            discouraged to access it from outside as a user function.\n            Use get_car(), get_sph() and get_cyl() instead unless you have\n            good reason not to do so.\n\n            Caution\n            -------\n            This returns the Vector coordinates in its native kind! Make sure\n            you know which system you are working in for correct\n            interpretation.\n        \"\"\"\n        return self.co\n\n    def get_kind(self):\n        \"\"\"Often useful in combination with get_coords().\"\"\"\n        return self.kind\n\n    def get_car(self):\n        \"\"\"Get coordinates in cartesian representation.\"\"\"\n        kind = self.kind\n        co = self.get_coords()\n        if kind == 'sph':\n            co = np.array(conv.sph2car(*co))\n        if kind == 'cyl':\n            co = np.array(conv.cyl2car(*co))\n        return co\n\n    # alias\n    def get_cart(self):\n        return self.get_car()\n\n    def get_sph(self):\n        \"\"\"Get coordinates in spherical representation.\"\"\"\n        kind = self.kind\n        co = self.get_coords()\n        if kind == 'car':\n            co = np.array(conv.car2sph(*co))\n        if kind == 'cyl':\n            co = np.array(conv.cyl2sph(*co))\n        return co\n\n    # alias\n    def get_spheric(self):\n        return self.get_sph()\n\n    def get_cyl(self):\n        \"\"\"Get coordinates in cylindrical representation.\"\"\"\n        kind = self.kind\n        co = self.get_coords()\n        if kind == 'car':\n            co = np.array(conv.car2cyl(*co))\n        if kind == 'sph':\n            co = np.array(conv.sph2cyl(*co))\n        return co\n\n    ###################################################\n    # GETTERS: ANGLES                                 #\n    ###################################################\n    def get_azimuth(self):\n        \"\"\"Return azimuth angle in rad.\n\n            The azimuth angle is measured in the x-y-plane, starting at the\n            y-axis towards the positive x-axis, i. e. the angle is measured in\n            mathematically negative sense.\n        \"\"\"\n        co = self.get_car()\n        return np.arctan2(co[0], co[1])\n\n    def get_zenith_angle(self):\n        \"\"\"Return zenith angle (distance from z-axis) in rad.\"\"\"\n        theta = self.get_sph()[2]\n        return np.pi/2. - theta\n\n    def get_view_angle(self):\n        \"\"\"Return view angle (distance from negative z-axis) in rad.\"\"\"\n        return np.pi - self.get_zenith_angle()\n\n    ###################################################\n    # GETTERS: ARRAY                                  #\n    ###################################################\n    def is_array(self):\n        return len(np.shape(self.co)) > 1\n\n    def length(self):\n        if not self.is_array():\n            TypeError('Not an array of Vector.')\n        else:\n            return np.shape(self.co)[1]\n\n    def element(self, n):\n        if not self.is_array():\n            TypeError('Not an array of Vector.')\n        N = self.length()\n        if n > N - 1:\n            raise IndexError()\n        kind = self.get_kind()\n        co = self.get_coords()[:, n]\n        return Vector(co, kind)\n\n    ###################################################\n    # SETTERS                                         #\n    ###################################################\n    def set_kind(self, kind):\n        \"\"\"Set coordinate kind.\"\"\"\n        if not isinstance(kind, str):\n            raise TypeError('kind must be a str.')\n        kind = kind[:3].lower()\n        if kind not in self._valid_kinds:\n            raise ValueError('')\n        self.kind = kind\n        return self\n\n    def set_coords(self, co):\n        \"\"\"Set coordinates, unrespective of kind.\n\n            Caution\n            -------\n            This method is unaware of the coordinate kind! It does not perform\n            any compatibility check. So make sure you are working in the\n            correct system.\n        \"\"\"\n        co = np.array(co)\n        assert np.shape(co)[0] == 3\n\n        self.co = co\n        return self\n\n    def set_car(self, co):\n        \"\"\"Set cartesian coordinates. Set kind accordingly.\"\"\"\n        self.set_kind('car')\n        self.set_coords(co)\n        return self\n\n    # alias\n    def set_cart(self, co):\n        return self.set_car(co)\n\n    def set_sph(self, co):\n        \"\"\"Set spherical coordinates. Set kind accordingly.\"\"\"\n        self.set_kind('sph')\n        self.set_coords(co)\n        return self\n\n    # alias\n    def set_spheric(self, co):\n        return self.set_sph(co)\n\n    def set_cyl(self, co):\n        \"\"\"Set cylindrical coordinates. Set kind accordingly.\"\"\"\n        self.set_kind('cyl')\n        self.set_coords(co)\n        return self\n\n    ###################################################\n    # CONVERTERS                                      #\n    ###################################################\n    def convert_to(self, kind):\n        \"\"\"Convert Vector to another coordinate system.\n\n            Parameters\n            ----------\n            kind : {'car', 'sph', 'cyl'}\n\n            Returns\n            -------\n            Vector\n        \"\"\"\n        if not isinstance(kind, str):\n            raise TypeError('kind must be a str.')\n        kind = kind[:3].lower()\n\n        if kind == 'car':\n            return self.convert_to_car()\n        if kind == 'sph':\n            return self.convert_to_sph()\n        if kind == 'cyl':\n            return self.convert_to_cyl()\n\n    # --> cartesian\n    def convert_to_car(self):\n        \"\"\"Convert Vector to cartesian representation.\"\"\"\n        self.set_coords(self.get_car())\n        self.set_kind('car')\n        return self\n\n    # alias\n    def convert_to_cart(self):\n        return self.convert_to_car()\n\n    # alias\n    def convert_to_cartesian(self):\n        return self.convert_to_car()\n\n    # --> spherical\n    def convert_to_sph(self):\n        \"\"\"Convert Vector to spherical representation.\"\"\"\n        self.set_coords(self.get_sph())\n        self.set_kind('sph')\n        return self\n\n    # alias\n    def convert_to_spheric(self):\n        return self.convert_to_sph()\n\n    # alias\n    def convert_to_spherical(self):\n        return self.convert_to_sph()\n\n    # --> cylindrical\n    def convert_to_cyl(self):\n        \"\"\"Convert Vector to cylindrical representation.\"\"\"\n        self.set_coords(self.get_cyl())\n        self.set_kind('cyl')\n        return self\n\n    # alias\n    def convert_to_cylindrical(self):\n        return self.convert_to_cyl()\n\nclass Rotation(object):\n    \"\"\"A rotation in 3D Euklidian space.\n\n        It can be applied to Vector objects by simply calling them with the\n        Vector as argument.\n\n        No coordinate conversion by the user is necessary, even if the Vector\n        is not cartesian (this is done internally).\n\n        Two Rotation objects can be merged into a resulting Rotation object\n        which represents the net rotation using the methods before() and after().\n\n        A rotation can be initialized if you know either\n        - rotation axis (represented by a Vector) and rotation angle or\n        - the rotation matrix.\n\n        The Rotation is internally represented as a 3x3 matrix in cartesian\n        x-y-z coordinates.\n\n\n        Initialization\n        ==============\n        - If you know the rotation axis and rotation angle, an instance can be\n          created directly by calling\n             Rotatian(angle, axis)\n          `axis` is in general a Vector. If you rotate about a cartesian\n          coordinate axis, you can also use axis=0, axis=1 or axis=2 instead.\n        - If you know the rotation matrix, instantiate as follows:\n             Rotation().set_matrix(matrix)\n\n\n        History\n        =======\n        2018-12-12 (AA): Created.\n        2018-01-03 (AA): Azimuth and zenith angles.\n        2018-01-10 (AA): Documentation.\n\n\n        Examples\n        ========\n        #>>> # rotate Vector `v` by angle `alpha` about `axis1`\n        #>>> v = Vector((2, np.pi/4, -np.pi/3))\n        #>>> axis1 = Vector((0, 1, 0))\n        #>>> alpha = np.radians(42)\n        #>>> rot1 = Rotation(alpha, axis1)\n        #>>> print(rot1(v))\n\n        #>>> # concatenate with another rotation `rot2`\n        #>>> axis2 = Vector((0, -1, 2))\n        #>>> beta = np.radians(20)\n        #>>> rot2 = Rotation(beta, axis2)\n        #>>> rot = rot2.after(rot1)\n        #>>> print(rot(v))\n    \"\"\"\n    def __init__(self, angle=0, axis=0):\n        \"\"\"Set Rotation by angle about axis.\n\n            Parameters\n            ----------\n            angle : float, optional\n                (rad) rotation angle. Default: 0\n            axis : Vector or int, optional\n                Axis about which the rotation is performed. Can be a Vector is\n                arbitraty direction or an integer indication one of the\n                coordinate axes. Default: 0 (i. e. x-axis)\n        \"\"\"\n        self.set_angle_and_axis(angle, axis)\n\n    def __repr__(self):\n        return 'Rotation with matrix\\n%s' % repr(self.matrix)\n\n    def __mul__(self, other):\n        return self.dot(other)\n\n    def __truediv__(self, other):\n        if isinstance(other, Rotation):\n            return self.__mul__(other.inveirse())\n        else:\n            raise TypeError()\n\n    def __eq__(self, other):\n        assert isinstance(other, Rotation)\n        Meq = self.matrix == other.matrix\n        return np.sum(Meq) == 9\n\n    def __call__(self, v):\n        return self.apply_to(v)\n\n    ###################################################\n    # UNARY OPERATORS                                 #\n    ###################################################\n    def inverse(self):\n        \"\"\"Return inverse Rotation.\"\"\"\n        return Rotation().set_matrix(self.matrix.T)\n\n    def rotate(self, angle, axis):\n        \"\"\"Additionally rotate the Rotation.\n\n            Parameters\n            ----------\n            angle : float\n                (rad) rotation angle\n            axis : int or Vector\n                Axis about which the rotation is performed. Can be a Vector or\n                the number of the coordinate axis (0:x, 1:y, 2:z).\n\n            Returns\n            -------\n            Rotation\n        \"\"\"\n        other = Rotation(angle, axis)\n        new = other.after(self)\n        self.set_matrix(new.get_matrix())\n        return self\n\n    ###################################################\n    # BINARY OPERATORS                                #\n    ###################################################\n    def dot(self, other):\n        \"\"\"Return result of dot-product with Rotation or Vector.\"\"\"\n        if isinstance(other, Rotation):\n            A = self.get_matrix()\n            B = other.get_matrix()\n            matrix = A.dot(B)\n            return Rotation().set_matrix(matrix)\n        elif isinstance(other, Vector):\n            M = self.get_matrix()\n            v = other.get_car()\n            return Vector(M.dot(v))\n        else:\n            raise TypeError('other must be Rotation or Vector.')\n\n    def after(self, other):\n        \"\"\"Return combined Rotation.\"\"\"\n        return self.dot(other)\n\n    def before(self, other):\n        \"\"\"Return combined Rotation.\"\"\"\n        return other.after(self)\n\n    ###################################################\n    # SETTERS                                         #\n    ###################################################\n    def set_matrix(self, matrix):\n        \"\"\"Set rotation matrix.\"\"\"\n        assert isinstance(matrix, np.ndarray)\n        assert matrix.shape == (3, 3)\n        self.matrix = matrix\n        return self\n\n    def set_angle_and_axis(self, angle, axis):\n        \"\"\"Set Rotation by angle and axis.\n\n            Parameters\n            ----------\n            angle : float\n                (rad) rotation angle\n            axis : Vector or int\n                Axis about which the rotation is performed. Can be a Vector or\n                the number of the coordinate axis (0:x, 1:y, 2:z).\n\n            Returns\n            -------\n            Rotation\n        \"\"\"\n        # special case: no rotation\n        if angle == 0:\n            self.matrix = np.eye(3)\n            return self\n\n        # special case: coordinate axis\n        if axis in range(3):\n            # delegate to sub-function\n            co = np.zeros(3)\n            co[axis] = 1.\n            axis = Vector(co)\n            return self.set_angle_and_axis(angle, axis)\n\n        # input check\n        if not isinstance(axis, Vector):\n            raise TypeError('`axis` must be a Vector or an int between 0..2 .')\n\n        # shortcuts\n        sin = np.sin(angle)\n        cos = np.cos(angle)\n\n        # normalize\n        norm = 1. * abs(axis)\n        n = axis.get_car() / norm\n\n        # build rotation matrix\n        first = (1 - cos) * np.outer(n, n)\n        diag = cos * np.eye(3)\n        off = sin * np.array([[   0., -n[2],  n[1]],\n                              [ n[2],    0., -n[0]],\n                              [-n[1],  n[0],   0.]])\n        M = first + diag + off\n        self.matrix = M\n        return self\n\n    ###################################################\n    # GETTERS                                         #\n    ###################################################\n    def get_matrix(self):\n        \"\"\"Return rotation matrix.\"\"\"\n        return self.matrix\n\n    def get_angle_and_axis(self):\n        \"\"\"Return angle (in rad) and axis.\n\n            Returns\n            -------\n            angle : float\n                magnitude of Rotation\n            axis : Vector\n                Rotation axis (right-handed). If angle == 0, positive x-axis is\n                returned.\n        \"\"\"\n        # special case: no rotation\n        if self == Rotation():\n            angle = 0.\n            axis = Vector((1., 0., 0.))\n            return angle, axis\n\n        # construct unit vector along rotation axis\n        R = self.get_matrix()\n        x = R[2, 1] - R[1, 2]\n        y = R[0, 2] - R[2, 0]\n        z = R[1, 0] - R[0, 1]\n        axis = Vector((x, y, z)).unit_vector()\n\n        # helper Vector\n        w = Vector((z, x, y))\n\n        # vector perpendicular to u\n        v = axis.cross(w)\n\n        # ========== magnitude =========================== #\n        cos = 0.5 * (np.trace(R) - 1)\n        acos = np.arccos(cos)\n\n        # use sine to resolve ambiguity of acos\n        # The following line of code is taken from\n        # http://vhm.mathematik.uni-stuttgart.de/Vorlesungen/\n        # Lineare_Algebra/Folien_Drehachse_und_Drehwinkel.pdf\n        # (join the above two lines to get the proper URL)\n        sin = v.dot(self.dot(axis))\n        if sin < 0:\n            angle = - acos\n        else:\n            angle = acos\n        # ================================================ #\n\n        return angle, axis\n\n    def get_angle(self):\n        \"\"\"Return rotation angle (in rad).\"\"\"\n        return self.get_angle_and_axis()[0]\n\n    def get_axis(self):\n        \"\"\"Return rotation axis as a Vector.\"\"\"\n        return self.get_angle_and_axis()[1]\n\n    ###################################################\n    # APPLY                                           #\n    ###################################################\n    def apply_to(self, v):\n        \"\"\"Return rotated Vector.\"\"\"\n        assert isinstance(v, Vector)\n        return self.dot(v)\n\nclass Transform(object):\n    \"\"\"A coordinate transform from one frame of reference to another.\n\n        A Transform is a coordinate transform in 3D Euklidian space. It\n        includes:\n        - shift and\n        - rotation\n\n        It does NOT include: stretching, shearing, reflection, projection.\n\n        It can be easily applied to Vector by calling it with the Vector.\n        No coordinate conversion by the user is necessary, even if the Vector\n        is not cartesian (this is done internally).\n\n        The Transform is represented by a Rotation and a shift (Vector object).\n\n\n        Initialization\n        ==============\n        Tranform(shift, rotation)\n        It is interpreted as a rotation before a shift. The optional\n        `shift_first` parameter can be set to True to inverse this order.\n    \"\"\"\n    def __init__(self, shift=Vector(), rotation=Rotation(), shift_first=False):\n        \"\"\"Initialize.\n\n            Parameters\n            ----------\n            shift : Vector, optional\n                Default: null-vector\n            rotation : Rotation, optional\n                Default: identity rotation\n            shift_first : bool, optional\n                If True, `shift` is applied before `rotation`, otherwise inverse.\n                Default: False.\n        \"\"\"\n        if not isinstance(shift, Vector):\n            raise TypeError('shift must be a Vector.')\n        if not isinstance(rotation, Rotation):\n            raise TypeError('rotation must be a Rotation.')\n\n        self.rotation = rotation\n        if not shift_first:\n            self.shift = shift\n        else:\n            self.shift = rotation.dot(shift)\n\n    def __repr__(self):\n        \"\"\"Return string representation.\"\"\"\n        s = repr(self.shift)\n        R = repr(self.rotation)\n        return 'Transformation with shift by %s after rotation by\\n%s' % (s, R)\n\n    def __call__(self, v):\n        return self.apply_to(v)\n\n    ###################################################\n    # UNARY OPERATOR                                  #\n    ###################################################\n    def inverse(self):\n        \"\"\"Return inverse Transform.\"\"\"\n        s = self.shift\n        R = self.rotation\n        return Transform(-s, R.inverse(), shift_first=True)\n\n    ###################################################\n    # BINARY OPERATIONS                               #\n    ###################################################\n    def after(self, other):\n        \"\"\"Return combined Transfrom.\"\"\"\n        # x'  = Bx + b\n        # x'' = Ax' + a\n        #     = A(Bx + b) + a\n        #     = ABx + (Ab + a)\n        #     = Rot(x) + shift\n        A = self.rotation\n        a = self.shift\n        B = other.rotation\n        b = other.shift\n\n        shift = A.dot(b) + a\n        rotation = A.dot(B)\n        return Transform(shift, rotation)\n\n    def before(self, other):\n        \"\"\"Return combined Transfrom.\"\"\"\n        return other.after(self)\n\n    ###################################################\n    # GETTERS                                         #\n    ###################################################\n    def get_shift(self):\n        \"\"\"Return shift Vector.\"\"\"\n        return self.shift\n\n    def get_rotation(self):\n        \"\"\"Return Rotation object.\"\"\"\n        return self.rotation\n\n    def apply_to(self, v):\n        \"\"\"Return transformed Vector.\"\"\"\n        assert isinstance(v, Vector)\n        s = self.shift\n        R = self.rotation\n        return R.dot(v) + s\n\n###################################################\n# TESTING                                         #\n###################################################\n# The functions have all been tested.\n#if __name__ == '__main__':\n\"\"\"\nfrom matplotlib import pyplot as plt\nimport random\nfrom mpl_toolkits.mplot3d import Axes3D\nv1 = Vector((10, 0, 0))\nv2 = Vector((0, 2, 0))\nv3 = Vector((-4, 0, 0))\nv4 = Vector((0, 1, 1))\n\nv = [v1, v2, v3, v4]\nangel = 0\nwhile(1):\n    if angel%360==0:\n        angel=0\n        v4 = Vector((random.uniform(0,1), random.uniform(0,1), random.uniform(0,1)))\n    R = Rotation(2*np.pi/360*angel, v4)\n    angel = angel+5\n    v5 = R(v1)\n\n    T = Transform(v2, R)\n    v6 = T(v4)\n\n    #print(R)\n    #print(v1)\n    #print(v5)\n    print(angel)\n\n    ax = plt.subplot(111, projection='3d')  # 创建一个三维的绘图工程\n    #  将数据点分成三部分画，在颜色上有区分度\n    ax.scatter(v1.co[0],v1.co[1],v1.co[2], c='r',s=100, label='star')  # 绘制数据点\n    ax.plot([-v4.co[0]*10,v4.co[0]*10],[-v4.co[1]*10,v4.co[1]*10],[-v4.co[2]*10,v4.co[2]*10],label='axis',linewidth=3)\n    ax.scatter(v5.co[0],v5.co[1],v5.co[2], c='g',s=100, label='end')\n\n    ax.set_zlabel('Z')  # 坐标轴\n    ax.set_ylabel('Y')\n    ax.set_xlabel('X')\n    plt.ylim(-15, 15)\n    plt.xlim(-15, 15)\n    ax.set_zlim(-15, 15)\n    plt.pause(0.01)\n\n\nprint('-' * 12)\nprint(T)\nprint(v4)\nprint(v6)\n\"\"\"","sub_path":"wavenet/util/coordinate_transform/transform.py","file_name":"transform.py","file_ext":"py","file_size_in_byte":33241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"107245982","text":"import random\r\n\r\ndef type_of_number(number, Ulama, Prime, Happy):\r\n    pass\r\n    return type_of_number\r\n\r\n#complition for taple function                                                          TABLE COMPLITION\r\ndef output(lst):\r\n    '''\r\n    Outputs number table to console\r\n    '''\r\n    for i in lst:\r\n        print(i)\r\n        \r\ndef ulan_generator(max):\r\n#this function generates list of Ulam numbers                                            ULAM NUMBERS\r\n    arr = [1, 2, 3]\r\n    for i in range(4, max):\r\n        sum_counter = 0\r\n        for k in range(0, len(arr)):\r\n            if (i - arr[k]) in arr and (arr[k]*2) != i:\r\n                sum_counter +=1\r\n            if sum_counter > 2:\r\n                break\r\n        if sum_counter == 2 :\r\n            arr.append(i)\r\n    return arr\r\n\r\ndef generate_prime(max):\r\n\r\n#this function generates list of prime numbers                                           PRIME NUMBERS\r\n    \r\n    prime_list = [2]\r\n    for i in range(3, max):\r\n        div_count = 0\r\n        for k in range(2, i+1):\r\n            if (i % k) == 0:\r\n                div_count += 1\r\n            if div_count >= 2:\r\n                break    \r\n        if div_count == 1:\r\n            prime_list.append(i)\r\n    return(prime_list)\r\n \r\n# main loop of the gameimport random\r\ndef sum_one_lst(lst):\r\n    summa = 0\r\n    for i in lst:\r\n        summa += i\r\n    return summa\r\n\r\n#try complete this\r\ndef add_up(lst):\r\n    \"\"\"\r\n    lst -> lst\r\n    adds all possible numbers in down-up order\r\n    \"\"\"\r\n    # стовпчики\r\n    l1 = [0, 0, 0, 0]\r\n    l2 = [0, 0, 0, 0]\r\n    l3 = [0, 0, 0, 0]\r\n    l4 = [0, 0, 0, 0]\r\n    #отримати стовпчики чисел\r\n    for i in range(0, 4):\r\n        l1[i] = (lst[i][0])\r\n        l2[i] = lst[i][1]\r\n        l3[i] = lst[i][2]\r\n        l4[i] = lst[i][3]\r\n\r\n    lst[0][0] = sum_one_lst(l1)\r\n    lst[0][1] = sum_one_lst(l2)\r\n    lst[0][2] = sum_one_lst(l3)\r\n    lst[0][3] = sum_one_lst(l4)\r\n    lst[1] = [0,0,0,0]\r\n    lst[2] = [0,0,0,0]\r\n    lst[3] = [0,0,0,0]\r\n    \r\n    return lst \r\n\r\ndef add_down(lst):\r\n    return lst\r\n    pass\r\n   \r\ndef add_left(lst):\r\n    return lst\r\n    pass\r\n\r\ndef add_right(lst):\r\n    return lst\r\n    pass\r\n            \r\ndef choose_number(ul, pr, hp):\r\n    \"\"\"\r\n    lst, lst, lst -> int\r\n    \r\n    chooses random number from Ulama orr Prime or Happy\r\n    \"\"\"\r\n    gn = random.randint(1, 3)\r\n    if gn == 1:\r\n        return(random.choice(ul)) \r\n    elif gn == 2:\r\n        return(random.choice(pr))\r\n    else:\r\n        return(random.choice(hp))\r\n\r\ndef spawn_number(array, max_pos):\r\n    #checks if can spawn in loop and adds 1 number to array\r\n    #index\r\n    while True:\r\n        a = random.randint(0, max_pos)\r\n        b = random.randint(0, max_pos)\r\n\r\n        if array[a][b] != 0:\r\n            a = random.randint(0, max_pos)\r\n            b = random.randint(0, max_pos)\r\n\r\n        if array[a][b] == 0:\r\n            array[a][b] = choose_number(Ulama, Prime, Happy)\r\n            return array\r\n            \r\n\r\ndef key_get():\r\n    #gets key to make a move soon\r\n    k = input('make your turn: ')\r\n    k = k.lower()\r\n    sett = ['w', 's', 'd', 'a']\r\n    for i in sett:\r\n        if k == i:\r\n            global key_pressed \r\n            key_pressed = k\r\n            return True\r\n\r\n    print(\"wrong key pressed. Click 'w', 's', 'd', 'a'\")\r\n    return False\r\n            \r\n# our table\r\ndef size_of_table(height, width):\r\n\r\n#this fuction creates table with given height and width                              TABLE SIZE\r\n    \r\n    num_list = []\r\n    \r\n    for i in range(height):\r\n        line = []\r\n        for k in range(width):\r\n            line.append(0)  \r\n        num_list.append(line)\r\n    return num_list\r\n\r\n\r\n#DATA\r\nUlama = ulan_generator(300)\r\n\r\n\r\nPrime = generate_prime(300)\r\n\r\nHappy = [1, 7, 10, 13, 19, 23, 28, 31, 32, 44, 49, 68, 70, 79, 82, 86, 91, 94, 97, 100,\r\n     103, 109, 129, 130, 133, 139, 167, 176, 188, 190, 192, 193, 203, 208, 219, 226, 230,\r\n      236, 239, 262, 263, 280, 291, 293, 301, 302, 310, 313, 319, 320, 326, 329, 331, 338 ]\r\n\r\n#-------------------------------------------------------------------------------------|\r\n\r\n#Settings\r\n#generates table\r\nall_numbers = size_of_table(4,4)\r\n#one of possible moves\r\nkey_pressed = ''\r\n#'a' is the max position of all_numbers\r\na = len(all_numbers[0])-1\r\ngame_is_playing = True\r\n\r\n#moves allowed\r\nx = 0\r\nwhile game_is_playing:\r\n    x += 1\r\n    if x == 100:\r\n            break\r\n    #spawn number\r\n    spawn_number(all_numbers, a)\r\n    #geys move from player\r\n    while not key_get():\r\n        key_get()\r\n    \r\n    output(all_numbers)\r\n\r\n    if key_pressed == 'w':\r\n        all_numbers = add_up(all_numbers)\r\n    elif key_pressed == 's':\r\n        all_numbers = add_down(all_numbers)\r\n    elif key_pressed == 'a':\r\n        all_numbers = add_left(all_numbers)\r\n    elif key_pressed == 'd':\r\n        all_numbers = add_right(all_numbers)\r\n    #print table    \r\n    ","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"189643496","text":"import time\nfrom PyQt5.QtCore import QThread, pyqtSignal\nimport sys\nsys.path.append('..')\nfrom driver.spi2 import *\n\n\nclass RaspiAD(QThread):\n    \"\"\"\n    树莓派数据采集卡控制程序\n    \"\"\"\n    buf_size = 0\n    ad_ch_num = 0\n    send_sample_data = pyqtSignal(str)\n\n    def __init__(self):\n        super(RaspiAD, self).__init__()\n        self.spi = SPIDriver()\n        self.da_out = [{'flag': 0, 'value': 0},\n                       {'flag': 0, 'value': 0},\n                       {'flag': 0, 'value': 0},\n                       {'flag': 0, 'value': 0}]\n\n    def run(self):\n        self.config_sample(99, 8)\n        while not self.isInterruptionRequested():\n        # while 1:\n            time.sleep(0.1)\n            # print(time.time())\n            if self.spi.read_int_pin():\n                self.spi.chip_select_pic()\n                cmd = (7 << 8) + 255\n                self.spi.SendWord(cmd)\n                # while self.spi.read_int_pin() == 1:\n                #     pass\n                self.spi.delay_us(1000)\n                result = []\n                # self.spi.SendWord(0xaaaa)\n                for i in range((self.buf_size + 1) * self.ad_ch_num):\n                    # self.spi.delay_us(3)\n                    ret = self.spi.SendWord(0xaaaa)\n                    result.append(ret)\n                    if ret == 0xaaaa:\n                        print(ret)\n                        # result.append(ret)\n                    # else:\n                    #     result.append(ret)\n                self.send_sample_data.emit(str(result))\n                # print(str(result))\n                self.spi.chip_release_pic()\n                time.sleep(0.2)\n\n            for i in range(4):\n                if self.da_out[i]['flag'] == 1:\n                    self.spi.analog_output(i, self.da_out[i]['value'])\n                    self.da_out[i]['flag'] = 0\n                    pass\n\n    def config_sample(self, buf_size, ad_ch_num):\n        self.buf_size = buf_size\n        self.ad_ch_num = ad_ch_num\n        self.spi.chip_select_pic()\n        self.spi.SendWord(0x01ff)\n        self.spi.SendWord(0x0210)\n        self.spi.SendWord(0x0300 | self.buf_size)\n        self.spi.chip_release_pic()\n\n\ndef rcv(x):\n    print(x)\n\n\nif __name__ == '__main__':\n\n    raspi_ad = RaspiAD()\n    raspi_ad.spi.hard_reset()\n    time.sleep(2)\n    # raspi_ad.send_sample_data.connect(rcv)\n    raspi_ad.start()\n    timer = 0\n\n    while True:\n        time.sleep(1)\n        for i in range(6):\n            a = input()\n            raspi_ad.spi.analog_output(1, int(i * 204.6))\n\n\n        #\n        # timer += 1\n        # if timer == 60:\n        #     raspi_ad.spi.hard_reset()\n        #     raspi_ad.requestInterruption()\n        #     # time.sleep(1)\n        #     raspi_ad.deleteLater()\n        #     break\n\n\n","sub_path":"public/raspiad.py","file_name":"raspiad.py","file_ext":"py","file_size_in_byte":2783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"597044900","text":"# Copyright: (c) OpenSpug Organization. https://github.com/openspug/spug\n# Copyright: (c) <spug.dev@gmail.com>\n# Released under the MIT License.\nfrom django_redis import get_redis_connection\nfrom django.conf import settings\nfrom libs.utils import AttrDict, human_time, human_datetime\nfrom apps.host.models import Host\nfrom concurrent import futures\nimport requests\nimport socket\nimport subprocess\nimport json\nimport uuid\nimport os\n\nREPOS_DIR = settings.REPOS_DIR\n\n\ndef deploy_dispatch(request, req, token):\n    rds = get_redis_connection()\n    try:\n        api_token = uuid.uuid4().hex\n        rds.setex(api_token, 60 * 60, f'{req.deploy.app_id},{req.deploy.env_id}')\n        helper = Helper(rds, token, req.id)\n        helper.send_step('local', 1, f'完成\\r\\n{human_time()} 发布准备...        ')\n        env = AttrDict(\n            SPUG_APP_NAME=req.deploy.app.name,\n            SPUG_APP_ID=str(req.deploy.app_id),\n            SPUG_REQUEST_NAME=req.name,\n            SPUG_DEPLOY_ID=str(req.deploy.id),\n            SPUG_REQUEST_ID=str(req.id),\n            SPUG_ENV_ID=str(req.deploy.env_id),\n            SPUG_ENV_KEY=req.deploy.env.key,\n            SPUG_VERSION=req.version,\n            SPUG_DEPLOY_TYPE=req.type,\n            SPUG_API_TOKEN=api_token,\n        )\n        if req.deploy.extend == '1':\n            env.update(json.loads(req.deploy.extend_obj.custom_envs))\n            _ext1_deploy(req, helper, env)\n        else:\n            _ext2_deploy(req, helper, env)\n        req.status = '3'\n    except Exception as e:\n        req.status = '-3'\n        raise e\n    finally:\n        rds.expire(token, 5 * 60)\n        rds.close()\n        req.save()\n        Helper.send_deploy_notify(req)\n\n\ndef _ext1_deploy(req, helper, env):\n    extend = req.deploy.extend_obj\n    extras = json.loads(req.extra)\n    if extras[0] == 'branch':\n        tree_ish = extras[2]\n        env.update(SPUG_BRANCH=extras[1], SPUG_COMMIT_ID=extras[2])\n    else:\n        tree_ish = extras[1]\n        env.update(SPUG_TAG=extras[1])\n    if req.type == '2':\n        helper.send_step('local', 6, f'完成\\r\\n{human_time()} 回滚发布...        跳过')\n    else:\n        helper.local(f'cd {REPOS_DIR} && rm -rf {req.deploy_id}_*')\n        helper.send_step('local', 1, '完成\\r\\n')\n\n        if extend.hook_pre_server:\n            helper.send_step('local', 2, f'{human_time()} 检出前任务...\\r\\n')\n            helper.local(f'cd /tmp && {extend.hook_pre_server}', env)\n\n        helper.send_step('local', 3, f'{human_time()} 执行检出...        ')\n        git_dir = os.path.join(REPOS_DIR, str(req.deploy.id))\n        command = f'cd {git_dir} && git archive --prefix={env.SPUG_VERSION}/ {tree_ish} | (cd .. && tar xf -)'\n        helper.local(command)\n        helper.send_step('local', 3, '完成\\r\\n')\n\n        if extend.hook_post_server:\n            helper.send_step('local', 4, f'{human_time()} 检出后任务...\\r\\n')\n            helper.local(f'cd {os.path.join(REPOS_DIR, env.SPUG_VERSION)} && {extend.hook_post_server}', env)\n\n        helper.send_step('local', 5, f'\\r\\n{human_time()} 执行打包...        ')\n        filter_rule, exclude, contain = json.loads(extend.filter_rule), '', env.SPUG_VERSION\n        files = helper.parse_filter_rule(filter_rule['data'])\n        if files:\n            if filter_rule['type'] == 'exclude':\n                exclude = ' '.join(f'--exclude={x}' for x in files)\n            else:\n                contain = ' '.join(f'{env.SPUG_VERSION}/{x}' for x in files)\n        helper.local(f'cd {REPOS_DIR} && tar zcf {env.SPUG_VERSION}.tar.gz {exclude} {contain}')\n        helper.send_step('local', 6, f'完成')\n    with futures.ThreadPoolExecutor(max_workers=min(10, os.cpu_count() + 5)) as executor:\n        threads = []\n        for h_id in json.loads(req.host_ids):\n            env = AttrDict(env.items())\n            threads.append(executor.submit(_deploy_ext1_host, helper, h_id, extend, env))\n        for t in futures.as_completed(threads):\n            exception = t.exception()\n            if exception:\n                helper.send_error(h_id, f'Exception: {exception}')\n                raise exception\n\n\ndef _ext2_deploy(req, helper, env):\n    extend = req.deploy.extend_obj\n    extras = json.loads(req.extra)\n    host_actions = json.loads(extend.host_actions)\n    server_actions = json.loads(extend.server_actions)\n    if extras and extras[0]:\n        env.update({'SPUG_RELEASE': extras[0]})\n    step = 2\n    for action in server_actions:\n        helper.send_step('local', step, f'\\r\\n{human_time()} {action[\"title\"]}...\\r\\n')\n        helper.local(f'cd /tmp && {action[\"data\"]}', env)\n        step += 1\n    helper.send_step('local', 100, '完成\\r\\n' if step == 2 else '\\r\\n')\n    if host_actions:\n        with futures.ThreadPoolExecutor(max_workers=min(10, os.cpu_count() + 5)) as executor:\n            threads = []\n            for h_id in json.loads(req.host_ids):\n                threads.append(executor.submit(_deploy_ext2_host, helper, h_id, host_actions, env))\n            for t in futures.as_completed(threads):\n                exception = t.exception()\n                if exception:\n                    helper.send_error(h_id, f'Exception: {exception}')\n                    raise exception\n    else:\n        helper.send_step('local', 100, f'\\r\\n{human_time()} ** 发布成功 **')\n\n\ndef _deploy_ext1_host(helper, h_id, extend, env):\n    helper.send_step(h_id, 1, f'{human_time()} 数据准备...        ')\n    host = Host.objects.filter(pk=h_id).first()\n    if not host:\n        helper.send_error(h_id, 'no such host')\n    env.update({'SPUG_HOST_ID': h_id, 'SPUG_HOST_NAME': host.hostname})\n    ssh = host.get_ssh()\n    if env.SPUG_DEPLOY_TYPE != '2':\n        code, _ = ssh.exec_command(f'mkdir -p {extend.dst_repo} && [ -e {extend.dst_dir} ] && [ ! -L {extend.dst_dir} ]')\n        if code == 0:\n            helper.send_error(host.id, f'please make sure the {extend.dst_dir!r} is not exists.')\n        # clean\n        clean_command = f'ls -rd {extend.deploy_id}_* 2> /dev/null | tail -n +{extend.versions + 1} | xargs rm -rf'\n        helper.remote(host.id, ssh, f'cd {extend.dst_repo} && rm -rf {env.SPUG_VERSION} && {clean_command}')\n        # transfer files\n        tar_gz_file = f'{env.SPUG_VERSION}.tar.gz'\n        try:\n            ssh.put_file(os.path.join(REPOS_DIR, tar_gz_file), os.path.join(extend.dst_repo, tar_gz_file))\n        except Exception as e:\n            helper.send_error(host.id, f'exception: {e}')\n\n        command = f'cd {extend.dst_repo} && tar xf {tar_gz_file} && rm -f {env.SPUG_APP_ID}_*.tar.gz'\n        helper.remote(host.id, ssh, command)\n    helper.send_step(h_id, 1, '完成\\r\\n')\n\n    # pre host\n    repo_dir = os.path.join(extend.dst_repo, env.SPUG_VERSION)\n    if extend.hook_pre_host:\n        helper.send_step(h_id, 2, f'{human_time()} 发布前任务...       \\r\\n')\n        command = f'cd {repo_dir} && {extend.hook_pre_host}'\n        helper.remote(host.id, ssh, command, env)\n\n    # do deploy\n    helper.send_step(h_id, 3, f'{human_time()} 执行发布...        ')\n    helper.remote(host.id, ssh, f'rm -f {extend.dst_dir} && ln -sfn {repo_dir} {extend.dst_dir}')\n    helper.send_step(h_id, 3, '完成\\r\\n')\n\n    # post host\n    if extend.hook_post_host:\n        helper.send_step(h_id, 4, f'{human_time()} 发布后任务...       \\r\\n')\n        command = f'cd {extend.dst_dir} && {extend.hook_post_host}'\n        helper.remote(host.id, ssh, command, env)\n\n    helper.send_step(h_id, 5, f'\\r\\n{human_time()} ** 发布成功 **')\n\n\ndef _deploy_ext2_host(helper, h_id, actions, env):\n    helper.send_step(h_id, 1, f'{human_time()} 数据准备...        ')\n    host = Host.objects.filter(pk=h_id).first()\n    if not host:\n        helper.send_error(h_id, 'no such host')\n    env.update({'SPUG_HOST_ID': h_id, 'SPUG_HOST_NAME': host.hostname})\n    ssh = host.get_ssh()\n    helper.send_step(h_id, 2, '完成\\r\\n')\n    for index, action in enumerate(actions):\n        helper.send_step(h_id, 2 + index, f'{human_time()} {action[\"title\"]}...\\r\\n')\n        helper.remote(host.id, ssh, f'cd /tmp && {action[\"data\"]}', env)\n\n    helper.send_step(h_id, 100, f'\\r\\n{human_time()} ** 发布成功 **')\n\n\nclass Helper:\n    def __init__(self, rds, token, r_id):\n        self.rds = rds\n        self.token = token\n        self.log_key = f'{settings.REQUEST_KEY}:{r_id}'\n        self.rds.delete(self.log_key)\n\n    @staticmethod\n    def send_deploy_notify(req):\n        rst_notify = json.loads(req.deploy.rst_notify)\n        host_ids = json.loads(req.host_ids)\n        if rst_notify['mode'] != '0' and rst_notify.get('value'):\n            extra = json.loads(req.extra)\n            if req.deploy.extend == '1':\n                mode, extra1, extra2 = extra\n                if mode == 'branch':\n                    version = f'{extra1}#{extra2[:6]}'\n                else:\n                    version = extra1\n            else:\n                version = extra[0]\n            hosts = [{'id': x.id, 'name': x.name} for x in Host.objects.filter(id__in=host_ids)]\n            if rst_notify['mode'] == '1':\n                color, text = ('#8ece60', '成功') if req.status == '3' else ('#f90202', '失败')\n                texts = [\n                    '## %s ## ' % '发布结果通知',\n                    f'**应用名称：** {req.deploy.app.name} ',\n                    f'**应用版本：** {version} ',\n                    f'**发布环境：** {req.deploy.env.name} ',\n                    f'**发布主机：** {\",\".join(x[\"name\"] for x in hosts)} ',\n                    f'**发布结果：** <font color=\"{color}\">{text}</font>',\n                    f'**发布时间：** {human_datetime()} ',\n                    '> 来自 Spug运维平台'\n                ]\n                data = {\n                    'msgtype': 'markdown',\n                    'markdown': {\n                        'title': '发布结果通知',\n                        'text': '\\n\\n'.join(texts)\n                    }\n                }\n                requests.post(rst_notify['value'], json=data)\n            elif rst_notify['mode'] == '2':\n                data = {\n                    'req_id': req.id,\n                    'app_id': req.deploy.app_id,\n                    'app_name': req.deploy.app.name,\n                    'env_id': req.deploy.env_id,\n                    'env_name': req.deploy.env.name,\n                    'version': version,\n                    'targets': hosts,\n                    'is_success': req.status == '3',\n                    'deploy_at': human_datetime()\n                }\n                requests.post(rst_notify['value'], json=data)\n            elif rst_notify['mode'] == '3':\n                color, text = ('info', '成功') if req.status == '3' else ('warning', '失败')\n                texts = [\n                    '## %s' % '发布结果通知',\n                    f'**应用名称：** {req.deploy.app.name} ',\n                    f'**应用版本：** {version} ',\n                    f'**发布环境：** {req.deploy.env.name} ',\n                    f'**发布主机：** {\",\".join(x[\"name\"] for x in hosts)} ',\n                    f'**发布结果：** <font color=\"{color}\">{text}</font>',\n                    f'**发布时间：** {human_datetime()} ',\n                    '> 来自 Spug运维平台'\n                ]\n                data = {\n                    'msgtype': 'markdown',\n                    'markdown': {\n                        'content': '\\n'.join(texts)\n                    }\n                }\n                requests.post(rst_notify['value'], json=data)\n\n    def parse_filter_rule(self, data: str):\n        data, files = data.strip(), []\n        if data:\n            for line in data.split('\\n'):\n                line = line.strip()\n                if line and not line.startswith('#'):\n                    files.append(line)\n        return files\n\n    def _send(self, message):\n        self.rds.lpush(self.token, json.dumps(message))\n        self.rds.lpush(self.log_key, json.dumps(message))\n\n    def send_info(self, key, message):\n        self._send({'key': key, 'status': 'info', 'data': message})\n\n    def send_error(self, key, message):\n        message = '\\r\\n' + message\n        self._send({'key': key, 'status': 'error', 'data': message})\n        raise Exception(message)\n\n    def send_step(self, key, step, data):\n        self._send({'key': key, 'step': step, 'data': data})\n\n    def local(self, command, env=None):\n        command = 'set -e\\n' + command\n        task = subprocess.Popen(command, env=env, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n        while True:\n            message = task.stdout.readline()\n            if not message:\n                break\n            self.send_info('local', message.decode())\n        if task.wait() != 0:\n            self.send_error('local', f'exit code: {task.returncode}')\n\n    def remote(self, key, ssh, command, env=None):\n        code = -1\n        try:\n            for code, out in ssh.exec_command_with_stream(command, environment=env):\n                self.send_info(key, out)\n            if code != 0:\n                self.send_error(key, f'exit code: {code}')\n        except socket.timeout:\n            self.send_error(key, 'time out')\n","sub_path":"spug_api/apps/deploy/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":13204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"578806119","text":"# coding: utf-8\nimport sys\nimport logging\n\nimport routes\nimport eventlet\nfrom oslo.config import cfg\n\nfrom sea import wsgi\nfrom sea import service\nfrom sea.utils import log\n\nLOG = logging.getLogger(__name__)\nCONF = cfg.CONF\n\nhost_opts = [\n    cfg.StrOpt('host', default=\"0.0.0.0\", help='Host ip'),\n    cfg.IntOpt('port', default=60000, help='Host port')\n]\n\nCONF.register_opts(host_opts)\n\n\nclass HelloController(wsgi.Controller):\n    def hello(self, req):\n        return \"Hello\"\n\n\nclass API(wsgi.Router):\n    def __init__(self):\n        mapper = routes.Mapper()\n        mapper.redirect(\"\", \"/\")\n\n        resource = wsgi.Resource(HelloController())\n        mapper.connect(\"/hello\",\n                       controller=resource,\n                       action=\"hello\",\n                       conditions={\"method\": ['GET']})\n\n        super(API, self).__init__(mapper)\n\n\nclass Server(service.Service):\n    def __init__(self, name, app, host='0.0.0.0', port=None):\n        self.name = name\n        self.app = app\n        self.host = host\n        self.port = port\n        self.server = wsgi.Server(self.name, self.app, host=self.host, port=self.port)\n\n    def start(self):\n        self.server.start()\n\n    def stop(self):\n        self.server.stop()\n\n    def wait(self):\n        self.server.wait()\n\n\ndef main():\n    CONF(sys.argv[1:], project='example', version='1.0')\n    log.setup()\n    eventlet.monkey_patch(all=True)\n\n    app = API()\n    #server = Server(\"example\", app, host=CONF.host, port=CONF.port)\n    server = wsgi.Server(\"example\", app, host=CONF.host, port=CONF.port)\n    lancher = service.launch(server, workers=1)\n    lancher.wait()\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"examples/wsgi_server.py","file_name":"wsgi_server.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"228423244","text":"import tensorflow as tf\n\nimport graphsage.models as models\nimport graphsage.layers as layers\nfrom graphsage.classifiers import ClassifierInfo, EmbeddingsClassifier\nfrom graphsage.aggregators import MeanAggregator, MaxPoolingAggregator, MeanPoolingAggregator, SeqAggregator, GCNAggregator\nfrom graphsage.prediction import BipartiteEdgePredLayer\n\nflags = tf.app.flags\nFLAGS = flags.FLAGS\n\nclass SemiSupervisedGraphsage(models.SampleAndAggregate):\n    \"\"\" Implementation of supervised GraphSAGE.\n        This implementation combines the GraphSAGE loss with a supervised loss\n        computed only for labeled samples.\n    \"\"\"\n\n    def __init__(self, num_classes, placeholders, features, adj, degrees,\n            layer_infos, concat=True, aggregator_type=\"mean\",\n            model_size=\"small\", sigmoid_loss=False, identity_dim=0, **kwargs):\n        '''\n        Args:\n            - placeholders: Stanford TensorFlow placeholder object.\n            - features: Numpy array with node features.\n            - adj: Numpy array with adjacency lists (padded with random re-samples)\n            - degrees: Numpy array with node degrees.\n            - layer_infos: List of SAGEInfo namedtuples that describe the parameters of all\n                   the recursive layers. See SAGEInfo definition above.\n            - concat: whether to concatenate during recursive iterations\n            - aggregator_type: how to aggregate neighbor information\n            - model_size: one of \"small\" and \"big\"\n            - sigmoid_loss: Set to true if nodes can belong to multiple classes\n            - identity_dim: Dimension of identity feature for nodes (optional)\n        '''\n\n        models.GeneralizedModel.__init__(self, **kwargs)\n\n        if aggregator_type == \"mean\":\n            self.aggregator_cls = MeanAggregator\n        elif aggregator_type == \"seq\":\n            self.aggregator_cls = SeqAggregator\n        elif aggregator_type == \"meanpool\":\n            self.aggregator_cls = MeanPoolingAggregator\n        elif aggregator_type == \"maxpool\":\n            self.aggregator_cls = MaxPoolingAggregator\n        elif aggregator_type == \"gcn\":\n            self.aggregator_cls = GCNAggregator\n        else:\n            raise Exception(\"Unknown aggregator: \", self.aggregator_cls)\n\n        # get info from placeholders...\n        self.inputs = placeholders[\"batch\"]\n        self.inputs_pos = placeholders[\"batch_pos\"]\n        self.model_size = model_size\n        self.adj_info = adj\n        if identity_dim > 0:\n           self.embeds = tf.get_variable(\"node_embeddings\", [adj.get_shape().as_list()[0], identity_dim])\n        else:\n           self.embeds = None\n        if features is None:\n            if identity_dim == 0:\n                raise Exception(\"Must have a positive value for identity feature dimension if no input features given.\")\n            self.features = self.embeds\n        else:\n            self.features = tf.Variable(tf.constant(features, dtype=tf.float32), trainable=False)\n            if not self.embeds is None:\n                self.features = tf.concat([self.embeds, self.features], axis=1)\n        self.degrees = degrees\n        self.concat = concat\n        self.num_classes = num_classes\n        self.sigmoid_loss = sigmoid_loss\n        self.dims = [(0 if features is None else features.shape[1]) + identity_dim]\n        self.dims.extend([layer_infos[i].output_dim for i in range(len(layer_infos))])\n        self.batch_size = placeholders[\"batch_size\"]\n        self.placeholders = placeholders\n        self.layer_infos = layer_infos\n\n        self.sup_optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)\n        self.unsup_optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)\n        # self.optimizer = tf.cond(self.placeholders['supervised'], self.sup_optimizer, self.unsup_optimizer)\n\n        self.build()\n\n    def build(self):\n        # initialize negative sampler (?)\n        labels = tf.reshape(\n            tf.cast(self.inputs_pos, dtype=tf.int64),\n            [self.batch_size, 1])\n        self.neg_samples, _, _ = (tf.nn.fixed_unigram_candidate_sampler(\n            true_classes=labels,\n            num_true=1,\n            num_sampled=FLAGS.neg_sample_size,\n            unique=False,\n            range_max=len(self.degrees),\n            distortion=0.75,\n            unigrams=self.degrees.tolist()))\n\n        # sample neighbors and perform aggregation (for every layer defined in layer_infos)\n        num_samples = [layer_info.num_samples for layer_info in self.layer_infos]\n\n        samples, support_sizes = self.sample(self.inputs, self.layer_infos) # TODO: Look at the sampler\n        self.outputs, self.aggregators = self.aggregate(samples, [self.features], self.dims, num_samples,\n            support_sizes, concat=self.concat, model_size=self.model_size)\n        self.outputs = tf.nn.l2_normalize(self.outputs, 1) # normalization\n\n        samples_pos, support_sizes_pos = self.sample(self.inputs_pos, self.layer_infos)\n        self.outputs_pos, _ = self.aggregate(samples_pos, [self.features], self.dims, num_samples,\n            support_sizes_pos, aggregators=self.aggregators, concat=self.concat,\n            model_size=self.model_size)\n        self.outputs_pos = tf.nn.l2_normalize(self.outputs_pos, 1) # normalization\n\n        samples_neg, support_sizes_neg = self.sample(self.neg_samples, self.layer_infos,\n            FLAGS.neg_sample_size)\n        self.outputs_neg, _ = self.aggregate(samples_neg, [self.features], self.dims, num_samples,\n            support_sizes_neg, batch_size=FLAGS.neg_sample_size, aggregators=self.aggregators,\n            concat=self.concat, model_size=self.model_size)\n        self.outputs_neg = tf.nn.l2_normalize(self.outputs_neg, 1) # normalization\n\n        # TODO: write description\n        dim_mult = 2 if self.concat else 1\n        self.link_pred_layer = BipartiteEdgePredLayer(dim_mult*self.dims[-1],\n            dim_mult*self.dims[-1], self.placeholders, act=tf.nn.sigmoid,\n            bilinear_weights=False,\n            name='edge_predict')\n\n        # define and apply classifier\n        dim_mult = 2 if self.concat else 1\n        info = [ClassifierInfo(dim_mult*self.dims[-1]/2, self.placeholders['dropout'], tf.nn.relu)]\n        self.classifier = EmbeddingsClassifier(dim_mult*self.dims[-1], self.num_classes, info)\n        self.node_preds = self.classifier(self.outputs)\n        # predict\n        self.preds = self.predict()\n\n        # compute relevant metrics\n        # mrr\n        self.mrr = self._mrr(name=\"train\")\n        # 'read' vars return the computed value so far without altering the local streaming variables\n        # accuracy\n        self.accuracy_read, self.accuracy = self._accuracy(name=\"train\")\n        self.accuracy_read_val, self.accuracy_val = self._accuracy(name=\"val\")\n        # confusion matrix\n        self.confusion_read, self.confusion = self._confusion(name=\"train\")\n        self.confusion_read_val, self.confusion_val = self._confusion(name=\"val\")\n        # precision, recall and f1 values\n        self.precision_read, self.precision = self._precision(name=\"train\", pos_value=self.placeholders['pos_class'])\n        self.precision_read_val, self.precision_val = self._precision(name=\"val\", pos_value=self.placeholders['pos_class'])\n        self.recall_read, self.recall = self._recall(name=\"train\", pos_value=self.placeholders['pos_class'])\n        self.recall_read_val, self.recall_val = self._recall(name=\"val\", pos_value=self.placeholders['pos_class'])\n        self.f1_read = self._f1(precision=self.precision_read, recall=self.recall_read)\n        self.f1 = self._f1(precision=self.precision, recall=self.recall)\n        self.f1_read_val = self._f1(precision=self.precision_read_val, recall=self.recall_read_val)\n        self.f1_val = self._f1(precision=self.precision_val, recall=self.recall_val)\n\n        # compute loss\n        # self.loss = tf.cond(self.placeholders['supervised'], self._loss_sup, self._loss_unsup)\n        self.loss_sup = self._loss_sup()\n        self.loss_unsup = self._loss_unsup()\n\n        # supervised gradients and optimization\n        sup_grads_and_vars = self.sup_optimizer.compute_gradients(self.loss_sup)\n        sup_clipped_grads_and_vars = [(tf.clip_by_value(grad, -5.0, 5.0) if grad is not None else None, var)\n            for grad, var in sup_grads_and_vars]\n        self.sup_grad, _ = sup_clipped_grads_and_vars[0]\n        self.sup_opt_op = self.sup_optimizer.apply_gradients(sup_clipped_grads_and_vars)\n\n        # unsupervised gradients and optimization\n        unsup_grads_and_vars = self.unsup_optimizer.compute_gradients(self.loss_unsup)\n        unsup_clipped_grads_and_vars = [(tf.clip_by_value(grad, -5.0, 5.0) if grad is not None else None, var)\n        for grad, var in unsup_grads_and_vars]\n        self.unsup_grad, _ = unsup_clipped_grads_and_vars[0]\n        self.unsup_opt_op = self.unsup_optimizer.apply_gradients(unsup_clipped_grads_and_vars)\n\n\n    def _loss_unsup(self):\n        loss = 0\n        for aggregator in self.aggregators:\n            for var in aggregator.vars.values():\n                loss += FLAGS.weight_decay * tf.nn.l2_loss(var)\n        loss += self.link_pred_layer.loss(self.outputs, self.outputs_pos, self.outputs_neg)\n        loss = loss / tf.cast(self.batch_size, tf.float32)\n        return loss\n\n    def _loss_sup(self):\n        loss = 0\n        # Weight decay loss (penalty on high model parameters)\n        for aggregator in self.aggregators:\n            for var in aggregator.vars.values():\n                loss += FLAGS.weight_decay * tf.nn.l2_loss(var)\n        for var in self.classifier.var_values():\n            loss += FLAGS.weight_decay * tf.nn.l2_loss(var)\n\n        # classification loss (cross entropy)\n        if self.sigmoid_loss:\n            loss += tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(\n                logits=self.node_preds,\n                labels=self.placeholders['labels']))\n        else:\n            loss += tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n                logits=self.node_preds,\n                labels=self.placeholders['labels']))\n        return loss\n\n    def _accuracy(self, name):\n        labels = self.placeholders['labels']\n        preds = self.preds\n        # acc, acc_op = tf.metrics.accuracy(labels, preds)\n        acc, acc_op = tf.metrics.accuracy(\n            labels=tf.argmax(labels, 1),\n            predictions=tf.argmax(preds, 1),\n            name=name+\"_acc\")\n        return acc, acc_op\n\n    def _precision(self, name, pos_value=1):\n        labels = self.placeholders['labels']\n        preds = self.preds\n        prec, prec_op = tf.metrics.precision(\n            labels=tf.equal(tf.cast(tf.argmax(labels, 1), tf.int32), pos_value),\n            predictions=tf.equal(tf.cast(tf.argmax(preds, 1), tf.int32), pos_value),\n            name=name+\"_prec\")\n        return prec, prec_op\n\n    def _recall(self, name, pos_value=1):\n        labels = self.placeholders['labels']\n        preds = self.preds\n        rec, rec_op = tf.metrics.recall(\n            labels=tf.equal(tf.cast(tf.argmax(labels, 1), tf.int32), pos_value),\n            predictions=tf.equal(tf.cast(tf.argmax(preds, 1), tf.int32), pos_value),\n            name=name+\"_rec\")\n        return rec, rec_op\n\n    def _f1(self, precision, recall):\n        f1_score = 2*(precision*recall)/(precision+recall)\n        # if the f1 score is NaN set it to 0\n        f1_score = tf.cond(\n            tf.equal(tf.is_nan(f1_score), tf.constant(True)),\n            lambda: tf.constant(0.),\n            lambda: f1_score)\n        return f1_score\n\n    def _confusion(self, name):\n        labels = self.placeholders['labels']\n        preds = self.preds\n        # Compute a per-batch confusion\n        batch_confusion = tf.confusion_matrix(\n            labels=tf.argmax(labels, 1),\n            predictions=tf.argmax(preds, 1),\n            num_classes=self.num_classes,\n            name=\"batch_conf\")\n        # Create an accumulator variable to hold the counts\n        confusion = tf.Variable(\n            tf.zeros([self.num_classes, self.num_classes],dtype=tf.int32),\n            collections=[tf.GraphKeys.LOCAL_VARIABLES], # make it a local variable\n            name=name+\"_conf\")\n        # Create the update op for doing a \"+=\" accumulation on the batch\n        confusion_update = confusion.assign(confusion + batch_confusion)\n        return confusion, confusion_update\n\n    def _mrr(self, name):\n        \"\"\" MRR: mean reciprocal rank.\n\n            The rank for each item is given by the comparison\n            of the positive affinity score with the negative ones.\n            For example if neg_aff=[0.12,0.33,0.05] and aff=0.26, then the affinity\n            ranks will be ranks=[2,0,3,1] and (ranks[-1]+1)=2.\n\n            The mrr is therefore given by the mean of reciprocal positive ranks.\n        \"\"\"\n        # compute affinities\n        aff = self.link_pred_layer.affinity(self.outputs, self.outputs_pos) # affinity\n        neg_aff = self.link_pred_layer.neg_cost(self.outputs, self.outputs_neg)\n        neg_aff = tf.reshape(neg_aff, [self.batch_size, FLAGS.neg_sample_size])\n        _aff = tf.expand_dims(aff, axis=1)\n        aff_all = tf.concat(axis=1, values=[neg_aff, _aff])\n        # compute positive affinities ranks\n        size = tf.shape(aff_all)[1]\n        _, indices_of_ranks = tf.nn.top_k(aff_all, k=size)\n        _, ranks = tf.nn.top_k(-indices_of_ranks, k=size)\n        # compute mrr for the batch\n        batch_mrr = tf.reduce_mean(tf.div(1.0, tf.cast(ranks[:, -1] + 1, tf.float32)))\n        return batch_mrr\n\n    def predict(self):\n        if self.sigmoid_loss:\n            return tf.nn.sigmoid(self.node_preds)\n        else:\n            return tf.nn.softmax(self.node_preds)\n","sub_path":"graphsage/semisupervised_models.py","file_name":"semisupervised_models.py","file_ext":"py","file_size_in_byte":13712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"112602228","text":"# -*- coding:utf-8 -*-\nimport os\nimport re\nfrom localeurl.models import patch_reverse\npatch_reverse()\nfrom satchless.contrib.pricing.cache import PricingCacheHandler\n\nfrom django.core.urlresolvers import reverse\nfrom django.utils.translation import ugettext_lazy as _\n\nPROJECT_ROOT = os.path.dirname( __file__ )\n\nDEBUG = True\nTEMPLATE_DEBUG = DEBUG\n\nADMINS = (\n    # ('Your Name', 'your_email@domain.com'),\n)\n\nMANAGERS = ADMINS\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.sqlite3', # Add 'postgresql_psycopg2', 'postgresql', 'mysql', 'sqlite3' or 'oracle'.\n        'NAME': 'dev.db',                      # Or path to database file if using sqlite3.\n        'USER': '',                      # Not used with sqlite3.\n        'PASSWORD': '',                  # Not used with sqlite3.\n        'HOST': '',                      # Set to empty string for localhost. Not used with sqlite3.\n        'PORT': '',                      # Set to empty string for default. Not used with sqlite3.\n    }\n}\n\n# Local time zone for this installation. Choices can be found here:\n# http://en.wikipedia.org/wiki/List_of_tz_zones_by_name\n# although not all choices may be available on all operating systems.\n# On Unix systems, a value of None will cause Django to use the same\n# timezone as the operating system.\n# If running in a Windows environment this must be set to the same as your\n# system time zone.\nTIME_ZONE = 'Europe/Warsaw'\n\n# Language code for this installation. All choices can be found here:\n# http://www.i18nguy.com/unicode/language-identifiers.html\nLANGUAGE_CODE = 'en'\nLANGUAGES = (\n    ('en', u\"English\"),\n    ('pl', u\"Polski\"),\n)\n\nPREFIX_DEFAULT_LOCALE = True\nLOCALE_INDEPENDENT_PATHS = (\n    re.compile('^/admin'),\n)\n\nSITE_ID = 1\n\n# If you set this to False, Django will make some optimizations so as not\n# to load the internationalization machinery.\nUSE_I18N = True\n\n# If you set this to False, Django will not format dates, numbers and\n# calendars according to the current locale\nUSE_L10N = True\n\n# Absolute path to the directory that holds media.\n# Example: \"/home/media/media.lawrence.com/\"\nMEDIA_ROOT = os.path.join(PROJECT_ROOT, 'media')\n\n# URL that handles the media served from MEDIA_ROOT. Make sure to use a\n# trailing slash if there is a path component (optional in other cases).\n# Examples: \"http://media.lawrence.com\", \"http://example.com/media/\"\nMEDIA_URL = '/media/'\nSTATIC_URL = '/static/'\n\n# URL prefix for admin media -- CSS, JavaScript and images. Make sure to use a\n# trailing slash.\n# Examples: \"http://foo.com/media/\", \"/media/\".\nADMIN_MEDIA_PREFIX = STATIC_URL + 'grappelli/'\n\n# Make this unique, and don't share it with anybody.\nSECRET_KEY = 'm+q17zt_)tsu+-=9gi)4g%66rys*bn9rw5w*v$xxkh%dua*7_8'\n\n# List of callables that know how to import templates from various sources.\nTEMPLATE_LOADERS = (\n    'django.template.loaders.filesystem.Loader',\n    'django.template.loaders.app_directories.Loader',\n#     'django.template.loaders.eggs.Loader',\n)\n\nMIDDLEWARE_CLASSES = (\n    'localeurl.middleware.LocaleURLMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'pagination.middleware.PaginationMiddleware',\n)\n\nROOT_URLCONF = 'urls'\n\nTEMPLATE_DIRS = (\n    # Put strings here, like \"/home/html/django_templates\" or \"C:/www/django/templates\".\n    # Always use forward slashes, even on Windows.\n    # Don't forget to use absolute paths, not relative paths.\n    os.path.join(PROJECT_ROOT, 'templates'),\n)\n\nTEMPLATE_CONTEXT_PROCESSORS = (\n    'django.contrib.auth.context_processors.auth',\n    'django.core.context_processors.debug',\n    'django.core.context_processors.i18n',\n    'django.core.context_processors.media',\n    'django.core.context_processors.static',\n    'django.core.context_processors.request',\n    'django.contrib.messages.context_processors.messages',\n    'mothertongue.context_processors.router',\n    'carts.context_processors.carts_sizes',\n    'core.context_processors.root_categories',\n)\nSTATICFILES_DIRS = (\n    os.path.join(PROJECT_ROOT, \"static\"),\n)\n\n\nINSTALLED_APPS = (\n    'localeurl',\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.sites',\n    'django.contrib.messages',\n    'django.contrib.staticfiles',\n\n    'mothertongue',\n    'categories',\n    'grappelli',\n    'django.contrib.admin',\n    'mptt',\n    'satchless.product',\n    'satchless.category',\n    'satchless.image',\n    'satchless.contrib.productset',\n    #'satchless.contact',\n    'satchless.cart',\n    'satchless.pricing',\n    'satchless.contrib.pricing.simpleqty',\n    'satchless.contrib.tax.flatgroups',\n    #'satchless.contrib.stock.singlestore',\n    'satchless.order',\n    'satchless.contrib.checkout.multistep',\n    'satchless.delivery',\n    'satchless.contrib.delivery.simplepost',\n    'satchless.payment',\n    'products',\n    'south',\n    'pagination',\n    'core',\n    'carts',\n    'sale',\n    'orders',\n    'checkouts',\n\n    'haystack',\n    'search.haystack_predictive',\n    'payments',\n    'payments.dummy',\n    'satchless.contrib.payment.django_payments_provider',\n)\n\nSATCHLESS_IMAGE_SIZES = {\n    'admin': {\n        'size': (100, 100),\n        'crop': True,\n    },\n    'admin-icon': {\n        'size': (22, 22),\n        'crop': True,\n    },\n    'category': {\n        'size': (230, 257),\n        'crop': True,\n    },\n    'category-product': {\n        'size': (230, 307),\n        'crop': True,\n    },\n    'product-detail': {\n        'size': (304, 304),\n        'crop': False,\n    },\n    'product-thumb': {\n        'size': (68, 68),\n        'crop': True,\n    },\n    'cart-product': {\n        'size': ('156', '156'),\n        'crop': False,\n    },\n    'order-preview': {\n        'size': ('56', '56'),\n        'crop': True,\n    },\n}\n\nSATCHLESS_DEFAULT_CURRENCY = 'GBP'\n\nINTERNAL_IPS = ['127.0.0.1']\n\nclass CustomCacheHandler(PricingCacheHandler):\n    def get_cache_key(self, *args, **kwargs):\n        discount = bool(kwargs.pop('discount', True))\n        key = super(CustomCacheHandler, self).get_cache_key(*args, **kwargs)\n        key['discount'] = discount\n        return key\n\nSATCHLESS_PRICING_HANDLERS = [\n    CustomCacheHandler(\n        'satchless.contrib.pricing.simpleqty.SimpleQtyPricingHandler',\n        'satchless.contrib.tax.flatgroups.FlatGroupPricingHandler',\n        'sale.SalePricingHandler',\n        lazy=True)\n]\nSATCHLESS_PRODUCT_VIEW_HANDLERS = [\n    'carts.handler.carts_handler',\n]\nSATCHLESS_ORDER_PARTITIONERS = [\n    'satchless.contrib.order.partitioner.simple.SimplePartitioner',\n]\nSATCHLESS_DELIVERY_PROVIDERS = [\n    'satchless.contrib.delivery.simplepost.PostDeliveryProvider',\n]\nSATCHLESS_PAYMENT_PROVIDERS = [\n    'satchless.contrib.payment.django_payments_provider.DjangoPaymentsProvider',\n]\nSATCHLESS_DJANGO_PAYMENT_TYPES = (('dummy', _('Dummy Payment Provider')),)\n\nPAYMENT_VARIANTS = {\n    'dummy': ('payments.dummy.DummyProvider', {\n              'url': lambda payment: reverse('thank-you',\n                                             args=(payment.satchless_payment_variant.order.token,))\n    })\n}\n\nHAYSTACK_SITECONF = 'search_sites'\nHAYSTACK_SEARCH_ENGINE = 'whoosh'\nHAYSTACK_WHOOSH_PATH = os.path.join(PROJECT_ROOT, 'whoosh_index')\n\nINTERNAL_IPS = ['127.0.0.1']\n\n# http://south.aeracode.org/ticket/520\nSOUTH_TESTS_MIGRATE = False\n\ntry:\n    execfile(os.path.join(PROJECT_ROOT, 'local_settings.py'))\nexcept IOError:\n    pass\n","sub_path":"examples/demo/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":7598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"70585415","text":"__author__ = 'leeward'\n\nclass cached_property(object):\n    \"\"\" A property that is only computed once per instance and then replaces\n        itself with an ordinary attribute. Deleting the attribute resets the\n        property.\n\n        Source: https://github.com/bottlepy/bottle/commit/fa7733e075da0d790d809aa3d2f53071897e6f76\n        \"\"\"\n\n    def __init__(self, func):\n        self.__doc__ = getattr(func, '__doc__')\n        self.func = func\n\n    def __get__(self, obj, cls):\n        if obj is None:\n            return self\n        value = obj.__dict__[self.func.__name__] = self.func(obj)\n        return value\n\ndef cached_file(url, return_tuple=False, bench=False):\n    import hashlib\n    from django.core.cache import cache\n\n    if bench: bench.start('cached_file')\n    if bench: bench.start('md5sum')\n    m = hashlib.md5(url).hexdigest()\n    if bench: bench.stop('md5sum')\n    key_name = 'remote_file_%s' % m\n    if bench: bench.start('cache lookup')\n    old_cached_value = cache.get(key_name)\n    if bench: bench.stop('cache lookup')\n    new_page = False\n    if not old_cached_value:\n        import urllib2\n\n        if bench: bench.start('urllib open')\n        downloaded = urllib2.urlopen(url).read()\n        if bench: bench.stop('urllib open')\n        if bench: bench.start('cache_set')\n        cache.set(key_name, downloaded, 3600)\n        if bench: bench.stop('cache_set')\n        new_page = True\n    val = (old_cached_value or downloaded)\n    if bench: bench.stop('cached_file')\n    return return_tuple and (val, new_page) or val\n\n\ndef has_openid(request):\n    \"\"\"\n    Given a HttpRequest determine whether the OpenID on it is associated thus\n    allowing caller to know whether OpenID is good to depend on.\n    \"\"\"\n    from django_openid.models import UserOpenidAssociation\n    for association in UserOpenidAssociation.objects.filter(user=request.user):\n        if association.openid == unicode(request.openid):\n            return True\n    return False\n\ndef format_value(value, field_type=None):\n    import locale\n    v, f = value, field_type\n    if v == 'None':\n        v = None\n    if not f or f == 'int':\n        f = lambda s: int(float(s or 0))\n    elif f == 'float':\n        v = v or 0.0\n        f = float\n    elif f == 'grouped':\n        v = v or 0\n        f = lambda s: locale.format('%d', int(float(s)), True)\n    elif f in ['cash', 'money', 'usd', '$', 'dollars', 'cents', 'microcents']:\n        v = v and float(v) or 0.0\n        if f == 'cents':\n            v = v/100.0\n        if f == 'microcents':\n            v = v/(100*10000.0)\n        f = locale.currency\n    elif f in ['%', 'pct', 'ratio']:\n        v = '%.2f%%'%(float(v or 0)*100.0)\n    if not f:\n        return v\n    return callable(f) and f(v) or v\n\nimport pytz, datetime\ndef pytznow(timezone=None):\n    return datetime.datetime.now(getpytz(timezone))\n\ndef getpytz(timezone):\n    return isinstance(timezone, str) and pytz.timezone(timezone) or timezone or pytz.utc\n\ndef localize_time(from_time, target_tz):\n    if isinstance(from_time, (str, unicode)):\n        from dateutil import parser\n        from_time = parser.parse(from_time)\n    if not from_time.tzinfo: return from_time\n    local_tz = getpytz(target_tz)\n    local_dt = from_time.astimezone(local_tz)\n    return local_tz.normalize(local_dt)\n\nimport signal\nclass timeout:\n    def __init__(self, seconds=1, error_message='Timeout'):\n        self.seconds = seconds\n        self.error_message = error_message\n    def handle_timeout(self, signum, frame):\n        raise Exception(self.error_message)\n    def __enter__(self):\n        try:\n            signal.signal(signal.SIGALRM, self.handle_timeout)\n            signal.alarm(self.seconds)\n        except: pass\n    def __exit__(self, type, value, traceback):\n        try: signal.alarm(0)\n        except: pass\n","sub_path":"easy/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"140455945","text":"from tkinter.ttk import Frame, Button, Label, Entry, OptionMenu, Combobox\n\n\nclass ButtonGeneralFrame(Frame):\n    def __init__(self, tab, *args, **kwargs) -> None:\n        super().__init__(tab, *args, **kwargs)\n\n        # Button\n        self.btnCreate = Button(\n            self,\n            text=\"Export\",\n            command=tab.exportCostSheet,\n            padding=6,\n            style=\"B1.TButton\",\n        ).grid(row=0, column=0, padx=15)\n        self.btnNetMargin = Button(\n            self,\n            text=\"Analyse\",\n            command=tab.calculateNetMargin,\n            padding=6,\n            style=\"B1.TButton\",\n        ).grid(row=0, column=1, padx=15)\n\n\nclass EntryGeneralFrame(Frame):\n    CASE_TYPES = [\"1\"]\n    CATEGORIES = [\"Gents\", \"Ladies\"]\n    BRANDS = [\n        \"BRAND 1\",\n        \"BRAND 2\",\n        \"BRAND 3\",\n        \"BRAND 4\",\n        \"BRAND 5\",\n        \"BRAND 6\",\n        \"BRAND 7\",\n    ]\n\n    txtSize = 12\n    lblSize = 10\n    txtPady = 5\n    txtPadx = 20\n\n    def __init__(self, tab, *args, **kwargs) -> None:\n        super().__init__(tab, *args, **kwargs)\n\n        # Widgets\n        # Labels in the frame\n        Label(self, text=\"Brand\", font=(\"Halvetica\", self.lblSize)).grid(\n            row=0, column=0, sticky=\"w\"\n        )\n        Label(self, text=\"Article\", font=(\"Halvetica\", self.lblSize)).grid(\n            row=0 + 1, column=0, sticky=\"w\"\n        )\n        Label(self, text=\"Color\", font=(\"Halvetica\", self.lblSize)).grid(\n            row=0 + 2, column=0, sticky=\"w\"\n        )\n        Label(self, text=\"Category\", font=(\"Halvetica\", self.lblSize)).grid(\n            row=0 + 3, column=0, sticky=\"w\"\n        )\n        Label(self, text=\"Size\", font=(\"Halvetica\", self.lblSize)).grid(\n            row=0 + 4, column=0, sticky=\"w\"\n        )\n        Label(self, text=\"Case Code\", font=(\"Halvetica\", self.lblSize)).grid(\n            row=0 + 5, column=0, sticky=\"w\"\n        )\n        # Label semicolon in frame\n        for i in range(0, 6):\n            Label(self, text=\":\", font=(\"Halvetica\", self.lblSize)).grid(\n                row=i,\n                column=1,\n            )\n\n        # Text Box for entering input\n        self.artno = Entry(self, width=10, font=(\"Halvetica\", self.txtSize))\n        self.color = Entry(self, width=10, font=(\"Halvetica\", self.txtSize))\n        self.size = Entry(self, width=5, font=(\"Halvetica\", self.txtSize))\n\n        # Option Menu\n        self.brand = OptionMenu(self, tab.var_brand, self.BRANDS[0], *self.BRANDS)\n        self.category = OptionMenu(\n            self, tab.var_category, self.CATEGORIES[0], *self.CATEGORIES\n        )\n        self.case_type = Combobox(self, textvariable=tab.var_casetype, width=12)\n\n        # Align User Input Widgets\n        self.brand.grid(\n            row=0,\n            column=2,\n            columnspan=2,\n            padx=self.txtPadx,\n            pady=self.txtPady,\n            sticky=\"ew\",\n        )\n        self.artno.grid(\n            row=1,\n            column=2,\n            padx=self.txtPadx,\n            pady=self.txtPady,\n        )\n        self.color.grid(\n            row=2,\n            column=2,\n            padx=self.txtPadx,\n            pady=self.txtPady,\n        )\n        self.category.grid(\n            row=3,\n            column=2,\n            padx=self.txtPadx,\n            pady=self.txtPady,\n            sticky=\"ew\",\n        )\n        self.size.grid(\n            row=4,\n            column=2,\n            padx=self.txtPadx,\n            pady=self.txtPady,\n            sticky=\"w\",\n        )\n        self.case_type.grid(\n            row=5,\n            column=2,\n            padx=self.txtPadx,\n            pady=self.txtPady,\n            sticky=\"w\",\n        )\n\n        # default values\n        tab.var_casetype.set(self.CASE_TYPES[0])\n        self.case_type[\"values\"] = self.CASE_TYPES\n        self.size.insert(0, \"8\")\n\n        self.artno.focus()\n\n\nclass InfoGeneralFrame(Frame):\n    COL_GREEN = \"#179900\"\n    COL_RED = \"#f70f02\"\n    COL_NETM = \"#000\"\n    COL_BLUE_LT = \"#049dc7\"\n    COL_BLUE = \"#0940e6\"\n\n    rate_txt = 15\n    rate_txt2 = 12\n    name_txt = 9\n    name_txt2 = 8\n    pad_x = 10\n\n    def __init__(self, tab, *args, **kwargs) -> None:\n        super().__init__(tab, *args, **kwargs)\n\n        Label(self, text=\"Stitching Charges\", font=(\"Halvetica\", self.name_txt2)).grid(\n            row=1, column=0, sticky=\"ns\", padx=self.pad_x\n        )\n        Label(self, text=\"Printing Charges\", font=(\"Halvetica\", self.name_txt2)).grid(\n            row=1, column=1, sticky=\"ns\", padx=self.pad_x\n        )\n        Label(\n            self, text=\"Cost of Upper Prod.\", font=(\"Halvetica\", self.name_txt2)\n        ).grid(row=1, column=2, sticky=\"ns\", padx=self.pad_x)\n        Label(\n            self, textvariable=tab.var_sc, font=(\"Halvetica\", self.rate_txt2, \"bold\")\n        ).grid(row=0, column=0, sticky=\"ns\", padx=self.pad_x)\n        Label(\n            self, textvariable=tab.var_pc, font=(\"Halvetica\", self.rate_txt2, \"bold\")\n        ).grid(row=0, column=1, sticky=\"ns\", padx=self.pad_x)\n        Label(\n            self, textvariable=tab.var_cop, font=(\"Halvetica\", self.rate_txt2, \"bold\")\n        ).grid(row=0, column=2, sticky=\"ns\", padx=self.pad_x)\n        Label(self, text=\"\").grid(row=2, column=1)\n        self.lbl_netm = Label(\n            self,\n            textvariable=tab.var_netm,\n            font=(\"Halvetica\", self.rate_txt, \"bold\"),\n            foreground=self.COL_NETM,\n            background=\"#fff\",\n        )\n        self.lbl_netm.grid(row=3, column=0, sticky=\"ns\", padx=self.pad_x)\n        Label(self, text=\"Net margin\", font=(\"Halvetica\", self.name_txt)).grid(\n            row=4, column=0, sticky=\"ns\", padx=self.pad_x\n        )\n        Label(\n            self,\n            textvariable=tab.var_mrp,\n            font=(\"Halvetica\", self.rate_txt, \"bold\"),\n            foreground=self.COL_BLUE,\n            background=\"#fff\",\n        ).grid(row=3, column=1, sticky=\"ns\", padx=self.pad_x)\n        Label(self, text=\"MRP\", font=(\"Halvetica\", self.name_txt)).grid(\n            row=4, column=1, sticky=\"ns\", padx=self.pad_x\n        )\n        Label(\n            self,\n            textvariable=tab.var_basic,\n            font=(\"Halvetica\", self.rate_txt, \"bold\"),\n            foreground=self.COL_BLUE_LT,\n            background=\"#fff\",\n        ).grid(row=3, column=2, sticky=\"ns\", padx=self.pad_x)\n        Label(self, text=\"Basic Rate\", font=(\"Halvetica\", self.name_txt)).grid(\n            row=4, column=2, sticky=\"ns\", padx=self.pad_x\n        )\n","sub_path":"costanalysis/app/frames/general_frames.py","file_name":"general_frames.py","file_ext":"py","file_size_in_byte":6472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"614136897","text":"#  ------------------------------------------------------------------------------------------\n#  Copyright (c) Microsoft Corporation. All rights reserved.\n#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.\n#  ------------------------------------------------------------------------------------------\n\nfrom typing import Optional, Sequence\n\nimport numpy as np\nimport pytest\nimport torch\nfrom torch.nn.utils.rnn import pack_sequence\n\nfrom InnerEye.Common.type_annotations import IntOrString\nfrom InnerEye.ML.utils.sequence_utils import get_masked_model_outputs_and_labels, map_packed_sequence_data, \\\n    sequences_to_padded_tensor\n\n\ndef test_map_packed_sequence_data() -> None:\n    \"\"\"\n    Test to ensure helper function to apply a map transform to a packed sequence returns expected results.\n    \"\"\"\n    packed = pack_sequence([torch.tensor([[1.0], [2.0]])], enforce_sorted=False)\n    mapped = map_packed_sequence_data(packed, lambda x: x * 2)\n    assert torch.equal(mapped.data, torch.tensor([[2.0], [4.0]]))\n\n    with pytest.raises(Exception):\n        map_packed_sequence_data(packed, lambda x: x.unsqueeze(dim=0))\n\n\ndef test_get_masked_model_outputs_and_labels() -> None:\n    \"\"\"\n    Test to ensure the helper function to get masked model outputs, labels and their associated subject ids\n    returns the expected results.\n    \"\"\"\n\n    def _create_masked_and_check_expected(_model_outputs: torch.Tensor,\n                                          _labels: torch.Tensor,\n                                          _subject_ids: Sequence[IntOrString],\n                                          _sorted_indices: Optional[torch.Tensor] = None) -> None:\n        _masked = get_masked_model_outputs_and_labels(_model_outputs, _labels, _subject_ids)\n        assert _masked is not None\n        sorted_indices = _masked.labels.sorted_indices if _sorted_indices is None else _sorted_indices\n        if sorted_indices is not None:\n            _labels = _labels[sorted_indices]\n            _model_outputs = _model_outputs[sorted_indices]\n            _subject_ids = np.array(_subject_ids)[sorted_indices].tolist()\n\n        _expected_labels = _labels.transpose(dim0=0, dim1=1).flatten()\n        _mask = ~torch.isnan(_expected_labels)\n        _expected_labels = _expected_labels[_mask]\n        _expected_model_outputs = _model_outputs.transpose(dim0=0, dim1=1).flatten()[_mask]\n        _expected_subject_ids = _subject_ids\n\n        assert torch.equal(_expected_model_outputs, _masked.model_outputs.data)\n        assert torch.equal(_expected_labels, _masked.labels.data)\n        assert _expected_subject_ids[:_masked.labels.sorted_indices.shape[0]] == _masked.subject_ids\n\n    # test base case where no masking needs to be applied\n    model_outputs = torch.rand((3, 4, 1))\n    labels = torch.rand((3, 4, 1)).round()\n    subject_ids = [1, 2, 3]\n\n    _create_masked_and_check_expected(model_outputs, labels, subject_ids)\n\n    # test with unequal length sequences where masking will be performed\n    model_outputs = sequences_to_padded_tensor([torch.rand(x + 1, 1) for x in range(3)], padding_value=np.nan)\n    labels = sequences_to_padded_tensor([torch.rand(x + 1, 1) for x in range(3)], padding_value=np.nan)\n\n    _create_masked_and_check_expected(model_outputs, labels, subject_ids)\n\n    # test where one sequence is totally removed\n    model_outputs[0] = np.nan\n    labels[0] = np.nan\n\n    _create_masked_and_check_expected(model_outputs, labels, subject_ids, _sorted_indices=torch.tensor([2, 1, 0]))\n","sub_path":"Tests/ML/utils/test_sequence_util.py","file_name":"test_sequence_util.py","file_ext":"py","file_size_in_byte":3529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"269069467","text":"from random import uniform\r\nfrom math import *\r\n\r\n# Le compteur de points qui sont dans la courbe de la fonction.\r\nc = 0\r\n\r\n# Le nombre de points que l'on veut tirer au hasard.\r\nn = 1000000\r\n\r\n# L'intervalle compris entre a et b, soit [a,b] :\r\na = 0\r\nb = pi/2\r\n# On prend la valeur minimale sur l'intervalle.\r\nfmax = 0\r\n# On crée une liste des f_i(x) calculés.\r\nliste_f_i = []\r\nassert (a < b)\r\nfor i in range(0, n):\r\n    x = uniform(a,b)\r\n    # On définie notre fonction et ses valeurs prises.\r\n    f_i = exp(x)*sin(x)\r\n    # On remplie la liste avec les valeurs trouvées.\r\n    liste_f_i.append(f_i)\r\n    # On crée f_max(x), la valeur maximale pour pouvoir créer y. (On compare tous les f_i à la valeur minimale\r\n    # pour trouver la valeur max\r\n    fmax = max(f_i, fmax)\r\n    # On s'assure que les valeurs de f_i sont positives.\r\n    assert (f_i >= 0)\r\n# On crée une boucle sur les valeurs de la liste et on les compare à y, si y est plus petit, alors il est\r\n# dans l'aire de la fonction.\r\nfor f_i in liste_f_i:\r\n    # On crée les y, compris entre 0 et fmax.\r\n    y = uniform(0, fmax)\r\n    if y <= f_i:\r\n        # On incrémente le compteur.\r\n        c = c + 1\r\n\r\n# Le rapport établi entre la proportion de l'aire_courbe et l'aire_totale. (Points_courbes/Points_totaux).\r\nAire_courbe = (c / n) * ((b-a)*fmax)\r\nprint(Aire_courbe)\r\nprint(f\"L'aire éstimée sur l'intervalle [{a}, {b}] est : {Aire_courbe} .\")\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"314111526","text":"from .race import Race\nfrom components.level_tree import LevelTree\nfrom components.stats import Stats, StatModifier, CharacterStats\nfrom components.abilities.power_abilities import PowerAbilities\nfrom components.abilities.ability import Ability\nfrom components.display import Display\nfrom components.colors import Colors\nfrom characters.classes import CharacterClass\nfrom characters.character import CharacterTemplate\nfrom items.item import ItemTemplate, DamageType, WornLayer\nfrom components.stats import ItemStats, Size\nfrom components import material\n\n\ndef get_race_samples():\n    # An orc won't get uglier over time but it could gain abilities.\n    orc_level_tree = LevelTree()\n    orc_level_tree.stats_modifiers = {\n        1: [\n            StatModifier(Stats.Strength, 2),\n            StatModifier(Stats.Constitution, 2),\n            StatModifier(Stats.Charisma, -2),\n            StatModifier(Stats.Intelligence, -2)\n        ]\n    }\n    orc_level_tree.abilities_modifiers = {\n        1: [Ability(PowerAbilities.Berserk, 1)]\n    }\n    orc_race = Race(\"orc\", \"Orc\", orc_level_tree, \"humanoid\")\n    troll_level_tree = LevelTree()\n    troll_level_tree.stats_modifiers = {\n        1: [\n            StatModifier(Stats.Strength, 4),\n            StatModifier(Stats.Constitution, 4),\n            StatModifier(Stats.Charisma, -4),\n            StatModifier(Stats.Intelligence, -4)\n        ]\n    }\n    troll_level_tree.abilities_modifiers = {\n        1: [Ability(PowerAbilities.Regeneration, 1)]\n    }\n    troll_race = Race(\"troll\", \"Troll\", troll_level_tree, \"humanoid\")\n    human_level_tree = LevelTree()\n    human_race = Race(\"human\", \"Human\", human_level_tree, \"humanoid\")\n\n    return [orc_race, troll_race, human_race]\n\n\ndef get_sample_classes():\n    warrior_level_tree = LevelTree()\n    warrior_level_tree.add_stat_modifier(0, StatModifier(Stats.Health, 10, level_progression=1))\n    warrior_level_tree.add_ability_modifier(2, Ability(PowerAbilities.PowerAttack, 1, level_progression=2))\n    warrior_level_tree.add_ability_modifier(4, Ability(PowerAbilities.Parry, 1, level_progression=4))\n    warrior = CharacterClass('warrior', 'Warrior', warrior_level_tree)\n\n    thief_level_tree = LevelTree()\n    thief_level_tree.add_stat_modifier(0, StatModifier(Stats.Health, 6, level_progression=1))\n    thief_level_tree.add_ability_modifier(2, Ability(PowerAbilities.Sneak, 1, level_progression=2))\n    thief_level_tree.add_ability_modifier(4, Ability(PowerAbilities.Backstab, 1, level_progression=4))\n    thief = CharacterClass('thief', 'Thief', thief_level_tree)\n\n    return [warrior, thief]\n\n\ndef get_sample_monsters():\n    sample_races = get_race_samples()\n    sample_classes = get_sample_classes()\n    weak_orc = CharacterTemplate(\n        uid=\"weak_orc\", name=\"Weak Orc\",\n        class_uid=sample_classes[0].uid,\n        race_uid=sample_races[0].uid,\n        base_stats=CharacterStats(health=10),\n        display=Display(Colors.ORC_GREEN, Colors.BLACK_COLOR, \"O\"),\n        body_uid=\"humanoid\",\n        cumulative_level=0\n    )\n    strong_orc = CharacterTemplate(\n        uid=\"strong_orc\", name=\"Strong Orc\",\n        class_uid=sample_classes[0].uid,\n        race_uid=sample_races[0].uid,\n        base_stats=CharacterStats(health=16, strength=12, constitution=12),\n        display=Display(Colors.ORC_GREEN, Colors.BLACK_COLOR, \"O\"),\n        body_uid=\"humanoid\",\n        cumulative_level=2\n    )\n    weak_troll = CharacterTemplate(\n        uid=\"weak_troll\", name=\"Weak Troll\",\n        class_uid=sample_classes[0].uid,\n        race_uid=sample_races[1].uid,\n        base_stats=CharacterStats(health=24, strength=14, constitution=14),\n        display=Display(Colors.TROLL_GREEN, Colors.BLACK_COLOR, \"O\"),\n        body_uid=\"humanoid\",\n        cumulative_level=4\n    )\n    human_warrior = CharacterTemplate(\n        uid=\"human_warrior\", name=\"Human Warrior\",\n        class_uid=sample_classes[0].uid,\n        race_uid=sample_races[2].uid,\n        base_stats=CharacterStats(health=10, strength=12, constitution=12, dexterity=10),\n        display=Display(Colors.WHITE_COLOR, Colors.BLACK_COLOR, \"O\"),\n        body_uid=\"humanoid\",\n        cumulative_level=0\n    )\n    human_thief = CharacterTemplate(\n        uid=\"human_thief\", name=\"Human Thief\",\n        class_uid=sample_classes[1].uid,\n        race_uid=sample_races[2].uid,\n        base_stats=CharacterStats(health=6, constitution=10, dexterity=16),\n        display=Display(Colors.WHITE_COLOR, Colors.BLACK_COLOR, \"O\"),\n        body_uid=\"humanoid\",\n        cumulative_level=0\n    )\n\n    return [weak_orc, strong_orc, weak_troll, human_warrior, human_thief]\n\n\ndef get_sample_items():\n    steel_sword = ItemTemplate(\n        uid=\"short_sword\",\n        name=\"Short Sword\",\n        description=\"A short sword.\",\n        display=Display(Colors.DARK_GRAY, Colors.BLACK_COLOR, \"!\"),\n        material_uid=material.Iron.uid,\n        base_stats=ItemStats(health=1, size=Size.Medium, min_damage=1, max_damage=6),\n        melee_damage_type=DamageType.Slash\n    )\n    helmet = ItemTemplate(\n        uid=\"helmet\",\n        name=\"Helmet\",\n        description=\"A helmet.\",\n        display=Display(Colors.DARK_GRAY, Colors.BLACK_COLOR, \"!\"),\n        material_uid=material.Iron.uid,\n        base_stats=ItemStats(health=10, size=Size.Medium),\n        wearable_bodyparts_uid=[\"humanoid_head\"],\n        worn_layer=WornLayer.Outer\n    )\n    return [steel_sword, helmet]\n","sub_path":"characters/samples.py","file_name":"samples.py","file_ext":"py","file_size_in_byte":5386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"199637619","text":"import logging\nimport requests\n\nfrom utilities import cartodb\n\ndef post_process(layerdef):\n    \"\"\"\n    Update the date value in the layerspec table-- required for the time slider to work properly\n    :param layerdef: the layerdef\n    :return:\n    \"\"\"\n\n    if layerdef.gfw_env == 'prod':\n        update_layerspec(layerdef)\n\n    else:\n        logging.debug('Not updating layerspec table; gfw_env is {0}'.format(layerdef.gfw_env))\n\n\ndef update_layerspec(layerdef):\n    sql = \"UPDATE layerspec set maxdate = (SELECT max(date) + INTERVAL '1 day' \" \\\n          \"FROM gran_chaco_deforestation) WHERE table_name='gran_chaco_deforestation'\"\n\n    key, api_url = cartodb.get_api_key_and_url(layerdef.gfw_env)\n    payload = {'api_key': key, 'q': sql}\n\n    r = requests.get(api_url, params=payload)\n\n    resp = r.json()\n    logging.debug(resp)\n\n    if 'error' in resp.keys():\n        raise ValueError('Error in carto response')\n","sub_path":"postprocess/gran_chaco.py","file_name":"gran_chaco.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"567107686","text":"import numpy as np\nimport sklearn.preprocessing as sklpre\nimport sklearn.model_selection as sklms\nimport sklearn.linear_model as skllm\nfrom main import OrdinaryLeastSquares, RidgeRegression, LassoRegression\n\n\ndef test_kfold():\n    k = 5\n    kfold = sklms.KFold(n_splits=k, shuffle=True)\n    OLS_test = OrdinaryLeastSquares()\n    EPE_test = OLS_test.k_fold(k)\n    OLS_scikit = skllm.LinearRegression()\n    EPE_scikit = np.mean(\n        -sklms.cross_val_score(\n            OLS_scikit,\n            OLS_test.X_train,\n            OLS_test.z_train,\n            scoring=\"neg_mean_squared_error\",\n            cv=kfold,\n            n_jobs=-1,\n        )\n    )\n    relative = EPE_test/EPE_scikit\n    np.testing.assert_almost_equal(relative, 1,  decimal=1)\n\n","sub_path":"src/test_main.py","file_name":"test_main.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"500520988","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 17 11:53:53 2019\n\n@author: mrodgers\n\"\"\"\n\nimport pandas as pd\n\nmjr = pd.DataFrame()\nmjr2 = pd.DataFrame({\"a\": [1, 2]})\nmjr3 = pd.DataFrame({'b': [3, 4]})\nmjr4 = pd.concat([mjr, mjr2])\nmjr5 = pd.concat([mjr2, mjr3], axis=1)","sub_path":"python/anaconda/spyder/packages/pandas/concatenate.py","file_name":"concatenate.py","file_ext":"py","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"607681544","text":"import celery\n\nfrom mail_server import MailServerException, get_mail_server\n\npipeline = celery.Celery(__name__, config_source='celeryconfig')\n\n@pipeline.task(bind=True)\ndef enqueue(self, email):\n    raised_exceptions = []\n    active_server = get_mail_server() \n    try:\n        active_server.send_mail(email)\n    except MailServerException as mse:\n        raised_exceptions.append(mse)        \n\n    raise self.retry(exc=Exception(*raised_exceptions))\n","sub_path":"email_service/mail_queue.py","file_name":"mail_queue.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"604700859","text":"em_default_config = {\n    ### GENERAL\n    'seed': None,\n    'verbose': True,\n    'show_plots': True,\n\n    # Network\n    'batchnorm': False,\n    'dropout': 0.0,\n    'layer_output_dims': [100, 50],\n        # The first value is the output dim of the input module\n        # The remaining values are the output dims of intermediate layers\n        # The task head attached to the final intermediate layer and has an\n        # output dim equal to the cardinality of the classifier.\n        # So the total number of layers in the network will be equal to\n        # len(layer_output_dims) + 1.\n\n    ### TRAINING\n    'train_config': {\n        # Display\n        'print_every': 1, # Print after this many epochs\n\n        # GPU\n        'use_cuda': False,\n\n        # Dataloader\n        'data_loader_config': {\n            'batch_size': 32, \n            'num_workers': 1,\n        },\n\n        # Train Loop\n        'n_epochs': 10,\n        # 'grad_clip': 0.0,\n        # 'converged': 20,  # if not 0, stop early if this many epochs pass without improvement in training metric\n        # 'early_stopping': False, # if true, save any model with best score so far\n        # 'checkpoint_runway': 0, # if early stopping, don't save checkpoints until after at least this many epochs\n        'l2': 0.0,\n        'validation_metric': 'accuracy',\n\n        # Optimizer\n        'optimizer_config': {\n            'optimizer': 'sgd',\n            'optimizer_common': {\n                'lr': 0.01,\n                'weight_decay': 0.0,\n            },\n            # Optimizer - SGD\n            'sgd_config': {\n                'momentum': 0.9,\n            },\n            # Optimizer - Adam\n            'adam_config': {\n                'beta1': 0.9,\n                'beta2': 0.999,\n            },\n        },\n\n        # Scheduler\n        'scheduler_config': {\n            'scheduler': 'reduce_on_plateau', \n                # ['constant', 'exponential', 'reduce_on_plateu']\n            'lr_freeze': 0, \n                # Freeze learning rate initially this many epochs\n            # Scheduler - exponential\n            'exponential_config': {\n                'gamma': 0.9, # decay rate\n            },\n            # Scheduler - reduce_on_plateau\n            'plateau_config': {\n                'factor': 0.5,\n                'patience': 10,\n                'threshold': 0.0001,\n                'min_lr': 1e-4,\n            },\n        },\n    },\n}\n","sub_path":"metal/end_model/em_defaults.py","file_name":"em_defaults.py","file_ext":"py","file_size_in_byte":2402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"241961210","text":"import pylab\nimport math\nimport numpy as np\nfrom operator import itemgetter\n\n# Run Script for ps3 (Learning Support Vector Machines)\nimport split_training_data\nimport create_feature_vectors\nimport pegasos_svm\n\ndef run():\n    # Part 1\n    print('\\n=====================================================================================')\n    print('Problem 1 Preparing data:')\n    # Split Training data into training and validation set\n    split_training_data.run()\n    \n    # Transform each email in the training set into a feature vector\n    (feature_vector_list_training,\n     is_spam_list_training,\n     vocabulary_list) = create_feature_vectors.run_spam('./output_data/training_set')\n    \n    print('\\n=====================================================================================')\n    print('Problem 1 Creating \"svm objective as a function of iterations\" graph:')\n    # Part 1a: Plot the svm objective as a function of iterations\n    (weight_vector, svm_objective_list) = pegasos_svm.pegasos_svm_train(feature_vector_list_training,\n                                                                        is_spam_list_training,\n                                                                        pow(2, -5))\n    \n    # Plot data ------------------------------------------------------------------------------------\n    m = len(feature_vector_list_training)\n    iterations = [(i+1)*m for i in range(0, len(svm_objective_list))]\n    \n    pylab.plot(iterations, svm_objective_list)\n    \n    pylab.xlabel('Number of Iterations')\n    pylab.ylabel('SVM Objective')\n    pylab.title('SVM Objective as a function of iterations')\n    pylab.grid(True)\n    pylab.savefig(\"SVM_Objective_Graph.png\")\n    #pylab.show()\n    pylab.close()\n    pylab.clf()\n    # End plot data --------------------------------------------------------------------------------\n    \n    print('\\n=====================================================================================')\n    print('Problem 1 Creating \"Training Error as a function of lambda\" graph:')\n    # Part 1b/c:\n    # Setup validation error data\n    (feature_vector_list_validation,\n     is_spam_list_validation,\n     _) = create_feature_vectors.run_spam('./output_data/validation_set', vocabulary_list)\n    \n    # Setup data for loop\n    lambda_set = [pow(2,power) for power in range(-9, 2)]\n    \n    weight_list = []\n    average_training_error_list = []\n    average_hinge_loss_error_list = []\n    average_validation_error_list = []\n    \n    # Get data for varying values of lambda\n    for lambda_ in lambda_set:\n        print('Processing data for lambda: ' + str(lambda_))\n        (this_weight_vector, _) = pegasos_svm.pegasos_svm_train(feature_vector_list_training,\n                                                                is_spam_list_training, lambda_)\n        weight_list.append(this_weight_vector)\n    \n        # Average training error\n        average_training_error = pegasos_svm.pegasos_svm_test(this_weight_vector,\n                                                              feature_vector_list_training,\n                                                              is_spam_list_training)\n        average_training_error_list.append(average_training_error)\n    \n        # Average hinge error\n        average_hinge_loss_error = pegasos_svm.calculate_average_hinge_loss_error(\n                                        this_weight_vector, feature_vector_list_training,\n                                        is_spam_list_training)\n        average_hinge_loss_error_list.append(average_hinge_loss_error)\n    \n        # Average validation error\n        average_validation_error = pegasos_svm.pegasos_svm_test(this_weight_vector,\n                                                              feature_vector_list_validation,\n                                                              is_spam_list_validation)\n        average_validation_error_list.append(average_validation_error)\n    \n    # Normalize to percentage out of 100\n    average_training_error_list = [100*average_training_error \n                                   for average_training_error \n                                   in average_training_error_list]\n    \n    average_hinge_loss_error_list = [100*average_hinge_loss_error \n                                   for average_hinge_loss_error \n                                   in average_hinge_loss_error_list]\n    \n    average_validation_error_list = [100*average_validation_error \n                                     for average_validation_error \n                                     in average_validation_error_list]\n    \n    # Plot data ------------------------------------------------------------------------------------\n    log_lambda = [math.log(lambda_, 2) for lambda_ in lambda_set]\n    \n    # Plot logic\n    pylab.plot(log_lambda, average_training_error_list)\n    pylab.plot(log_lambda, average_hinge_loss_error_list)\n    pylab.plot(log_lambda, average_validation_error_list)\n    \n    pylab.xlabel('log base 2 of lambda')\n    pylab.ylabel('Average error (% out of 100')\n    pylab.title('Average Errors as a function of log base 2 of lambda')\n    pylab.legend(['Training Error', 'Hinge Loss Error', 'Validation Error'], loc=2)\n    pylab.grid(True)\n    pylab.savefig(\"Average_Error_Over_Log2_Lambda.png\")\n    #pylab.show()\n    pylab.close()\n    pylab.clf()\n    # End plot data --------------------------------------------------------------------------------\n    \n    print('\\n=====================================================================================')\n    print('Problem 1 Evaluating values:')\n    # Print results for part 1:\n    (minimum_index, minimum_average_validation_error) = min(enumerate(average_validation_error_list),\n                                                            key = itemgetter(1))\n    print('The minimum of the validation error was: ' + str(minimum_average_validation_error) + '%')\n    \n    # Setup test set data\n    (feature_vector_list_training,\n     is_spam_list_training,\n     vocabulary_list) = create_feature_vectors.run_spam('./input_data/spam_train.txt')\n\n    (feature_vector_list_test,\n     is_spam_list_test,\n     _) = create_feature_vectors.run_spam('./input_data/spam_test.txt', vocabulary_list)\n    \n    # Calculate Test Error\n    minimum_lambda = lambda_set[minimum_index]\n    (minimum_validation_error_classifier, _) = pegasos_svm.pegasos_svm_train(\n                                                    feature_vector_list_training,\n                                                    is_spam_list_training, minimum_lambda)\n\n    test_set_error = pegasos_svm.pegasos_svm_test(minimum_validation_error_classifier,\n                                                  feature_vector_list_test,\n                                                  is_spam_list_test)\n    \n    print('Error on the test set: ' + str(test_set_error*100) + '%')\n    \n    # Calculate the number of support vectors\n    is_support_vector = [1 if is_spam_list_training[i]*np.dot(minimum_validation_error_classifier,\n                                                              feature_vector_list_training[i]) <= 1\n                         else 0 for i in range(0, len(feature_vector_list_training))]\n    number_of_support_vectors = sum(is_support_vector)\n\n    print('Number of support vectors: ' + str(number_of_support_vectors))\n","sub_path":"learning_support_vector_machines_ps3/problem1_run.py","file_name":"problem1_run.py","file_ext":"py","file_size_in_byte":7331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"149858220","text":"import frappe\nfrom frappe import _\nfrom frappe.utils import nowdate, add_days, print_format\nfrom datetime import date, time, datetime, timedelta\nfrom pprint import pprint\nfrom frappe.utils import (format_time, global_date_format, now, get_link_to_form, get_url_to_report)\n\n@frappe.whitelist()\ndef change_audits_status(doc, method):\n    \"\"\"\n    Check if audits' status need to change\n    \"\"\"\n    if (doc.origin_document_type == \"Audit\"):\n        # If task is closed, find all tasks linked to this task's audit to check if all of them are closed out\n        if (doc.status == \"Closed\"):\n            task_list = frappe.get_list('Task', filters={'origin_document': doc.origin_document,'status': \"Open\"}, fields=['name'])\n            if not task_list:\n                audit = frappe.get_doc(\"Audit\", doc.origin_document)\n                audit.status = 'Corrective Act Done'\n                audit.save()\n\n        # If task is open, change audit status\n        if (doc.status == \"Open\"):\n            audit = frappe.get_doc(\"Audit\", doc.origin_document)\n            audit.status = 'Open Actions'\n            audit.save()\n\n\n@frappe.whitelist()\ndef change_first_offs_status(doc, method):\n    \"\"\"\n    Check if previous First Offs' status need to change\n    \"\"\"\n\t# Check if this First Off can change previous failed First Off's to \"Corrective Act Done\"\n    if (doc.status == \"Passed\" and doc.docstatus == 1):\n        failed_list = frappe.get_list('First Off', filters={'status': 'Failed - Action Required', 'workstation': doc.workstation, 'job_number': doc.job_number}, fields=['name'])\n        for fo in failed_list:\n            first_off = frappe.get_doc('First Off', fo.name)\n            first_off.status = 'Corrective Act Done'\n            first_off.add_comment('Comment', text=_(\"Auto comment - Corrective Action: Repeated First Off <b>{0}</b> Passed\").format(fo.name), link_doctype=\"First Off\", link_name=fo.name)\n            first_off.save()\n\n@frappe.whitelist()\ndef change_start_ups_status(doc, method):\n    \"\"\"\n    Check if previous Start Ups' status need to change\n    \"\"\"\n\t# Check if this Start Up can change previous failed Start Up's (for the same day) to \"Corrective Act Done\"\n    if (doc.status == \"Passed\" and doc.docstatus == 1):\n        failed_list = frappe.get_list('Start Up',\n            filters=[\n                [\"status\", \"=\", \"Failed - Action Required\"],\n                [\"workstation_entity\",\"=\", doc.workstation_entity],\n                [\"creation\", \">=\", datetime.combine(datetime.strptime(doc.creation, '%Y-%m-%d %H:%M:%S.%f').date(), time())],\n                [\"creation\", \"<\", datetime.combine(datetime.strptime(doc.creation, '%Y-%m-%d %H:%M:%S.%f').date(), time())+timedelta(days=1)]\n            ],\n            fields=['name']\n        )\n        for su in failed_list:\n            start_up = frappe.get_doc('Start Up', su.name)\n            start_up.status = 'Corrective Act Done'\n            start_up.add_comment('Comment', text=_(\"Auto comment - Corrective Action: Repeated Start Up <b>{0}</b> Passed\").format(su.name), link_doctype=\"Start Up\", link_name=su.name)\n            start_up.save()\n\n@frappe.whitelist()\ndef get_workstation_entity_leaderboard_prod_output(entity):\n    #Get Daily maximum production per operator\n    dailyMAxOperatorProduction = frappe.db.sql(\"\"\"\n    SELECT x.name as operator, MAX(x.total_qty) AS dailyMax_total_qty FROM (SELECT emp.employee_name as name, prod.date_of_production as production_date, SUM(prod.qty) AS total_qty\n    FROM `tabProduction Output` AS prod LEFT OUTER JOIN `tabEmployee` AS emp ON emp.name = prod.operator\n    WHERE prod.workstation_entity = '%s'\n    GROUP BY prod.date_of_production, prod.operator) as x\n    GROUP BY x.name\n    \"\"\"%(entity),as_dict=True)\n\n    #Total weekly production per operator\n    weeklyOperatorProduction = frappe.db.sql(\"\"\"\n    SELECT emp.employee_name as operator, SUM(prod.qty) AS weekly_total_qty\n    FROM `tabProduction Output` AS prod LEFT OUTER JOIN `tabEmployee` AS emp ON emp.name = prod.operator\n    WHERE prod.workstation_entity = '%s' AND prod.date_of_production >= DATE_ADD(CURDATE(),INTERVAL - WEEKDAY(CURDATE())DAY)\n    GROUP BY prod.operator\n    \"\"\"%(entity),as_dict=True)\n\n    #Total parts last 6 weeks per operator\n    sixWeekOperatorProduction = frappe.db.sql(\"\"\"\n    SELECT emp.employee_name as operator, SUM(prod.qty) AS SixWeek_total_qty\n    FROM `tabProduction Output` AS prod\n    LEFT OUTER JOIN `tabEmployee` AS emp ON emp.name = prod.operator\n    WHERE prod.workstation_entity = '%s' AND prod.date_of_production >= DATE_SUB(CURDATE(),INTERVAL 42 DAY)\n    GROUP BY prod.operator\n    \"\"\"%(entity),as_dict=True)\n\n    today = datetime.now()\n    intToday = today.weekday() +1#get the day of the week\n\n    #append weekly average dict and weeklytotal\n    for item in dailyMAxOperatorProduction:\n        for item2 in weeklyOperatorProduction:\n            if item[\"operator\"]  == item2[\"operator\"]:\n                item.update(item2)\n                weeklyAve = round(item2[\"weekly_total_qty\"]/intToday, 2)\n                weeklyAveDict = {\"weeklyAve\": weeklyAve}\n                item.update(weeklyAveDict)\n\n#append 6week average dict and 6weektotal\n    for item in dailyMAxOperatorProduction:\n        for item2 in sixWeekOperatorProduction:\n            if item[\"operator\"]  == item2[\"operator\"]:\n                item.update(item2)\n                sixWeekAvg = round(item2[\"SixWeek_total_qty\"]/42, 2)\n                sixWeekAvgDict = {\"sixWeekAvg\": sixWeekAvg}\n                item.update(sixWeekAvgDict)\n\n\n    return dailyMAxOperatorProduction\n\n\n@frappe.whitelist()\ndef get_url():\n    \"\"\"\n    Gets site url\n    \"\"\"\n    return frappe.utils.get_url()\n\n\n@frappe.whitelist()\ndef get_user_department(user):\n    \"\"\"\n    Get user's default department if user is an employee\n    \"\"\"\n    return frappe.db.sql(\"\"\"\n\t\t\tSELECT t_emp.department FROM `tabEmployee` AS t_emp\n                WHERE t_emp.user_id = '{user}'\"\"\".format(\n\t\t\t\tuser=user\n\t\t\t)\n\t)\n\n@frappe.whitelist()\ndef send_shopfloor_activities_summary_report():\n    \"\"\"\n    Similar to auto_email_report.send(), but renders the report in to a jinja template to allow for more flexibility\n    \"\"\"\n    # Get auto email report\n    auto_email_report = frappe.get_doc(\"Auto Email Report\", \"Horizon Global (PTA): Shopfloor Activities Summary\")\n    report = frappe.get_doc('Report', auto_email_report.report)\n\n    # Get report content\n    columns, data = report.get_data(limit=auto_email_report.no_of_rows or 100, user = auto_email_report.user,\n        filters = auto_email_report.filters, as_dict=True)\n\n    # Render template\n    date_time = global_date_format(now()) + ' ' + format_time(now())\n    report_doctype = frappe.db.get_value('Report', auto_email_report.report, 'ref_doctype')\n    report_html_data = frappe.render_template('quality_management_system/process/report/shopfloor_activities_summary/shopfloor_activities_summary_jinja.html', {\n        'title': auto_email_report.name,\n        'description': auto_email_report.description,\n        'date_time': date_time,\n        'columns': columns,\n        'data': data,\n        'report_url': get_url_to_report(auto_email_report.report, auto_email_report.report_type, report_doctype),\n        'report_name': auto_email_report.report,\n        'edit_report_settings': get_link_to_form('Auto Email Report', auto_email_report.name),\n        'site_url': get_url()\n    })\n\n    # Later for testing: create PDF and attach\n    # file_name = \"test_dirk7.html\"\n    # from frappe.utils.file_manager import save_file\n    # my_file = save_file(file_name, report_html_data, \"Workstation\",\n    #     \"MagnaFlux Machine\",  \"Home/Attachments\")\n\n    # Send email, according to Auto Email Report settings\n    attachments = None\n    message = report_html_data\n    frappe.sendmail(\n        recipients = auto_email_report.email_to.split(),\n        subject = auto_email_report.name,\n        message = message,\n        attachments = attachments,\n        reference_doctype = auto_email_report.doctype,\n        reference_name = auto_email_report.name\n    )\n","sub_path":"quality_management_system/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":8035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"14029332","text":"from django.shortcuts import render\nfrom django.http import HttpResponseRedirect\nfrom django.contrib import auth\nfrom .forms import CustomUserCreationForm\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.contrib.auth.decorators import login_required\nimport logging\nfrom Mes.models import LogData\n\nlogger = logging.getLogger('test_logger')\n\n\ndef home(request):\n    return render(request, \"home.html\")\n\n\ndef login(request):\n    logger.info('Sign In')\n    return render(request, 'login.html')\n\n\n\n@login_required\n@csrf_exempt\ndef auth_view(request):\n\n    username = request.POST['username']\n    password = request.POST['password']\n    user = authenticate(username=username, password=password)\n\n    if user is not None:\n        auth.login(request, user)\n        LogData.objects.create(log_name=request.POST['name'],\n                               log_sender=user.username,\n                               log_level=request.POST['level'],\n                               log_time=request.POST['time'],\n                               log_message=request.POST['msg'],\n                               )\n        return HttpResponseRedirect('/accounts/loggedin')\n    else:\n        return HttpResponseRedirect('/accounts/invalid')\n\n\n@csrf_exempt\ndef loggedin(request):\n    return render(request, 'loggedin.html',\n                  {'user': request.user})\n\n\ndef invalid_login(request):\n    return render(request, 'invalid_login.html')\n\n\ndef logout(request):\n    auth.logout(request)\n    return render(request, 'logout.html')\n\n\ndef register_user(request):\n    if request.method == 'POST':\n        form = CustomUserCreationForm(request.POST)\n        if form.is_valid():\n            form.save()\n            return HttpResponseRedirect('/accounts/register_success')\n\n    else:\n        form = CustomUserCreationForm()\n    args = {}\n\n    args['form'] = form\n\n    return render(request, 'register.html', args)\n\n\ndef register_success(request):\n    return render(request, 'register_success.html')","sub_path":"Users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"622791144","text":"account_status = {\n    \"ACCOUNT_ACTIVED\":\"账户正常\",\n    \"ACCOUNT_DISABLE\":\"账户禁用\"\n}\nthunder_service_ID = {\n    \"LOW_SPEED\":\"1\",\n    \"TRIAL\":\"2\",\n    \"FUFEI\":\"3,4,5\"\n}\nthunder_service_for_expressorderID = {\n    \"FUFEI\":\"3,4\"\n}\nthunder_service_name = {\n    \"1\":\"普通会员\",\n    \"2\":\"体验会员\",\n    \"3\":\"银牌会员\",\n    \"4\":\"金牌会员\",\n    \"5\":\"铂金会员\"\n}\nthunder_service_nameEN = {\n    \"1\":\"Card Member\",\n    \"2\":\"Trial Member\",\n    \"3\":\"Silver\",\n    \"4\":\"Golden\",\n    \"5\":\"Platnium\"\n}\norderStatus = {\n    \"1\":\"未支付\",\n    \"2\":\"支付成功\",\n    \"3\":\"已取消\"\n}\n\nthunderserviceStatus = {\n    \"1\":\"对应服务已经开通\"\n}\n\ncommandList = ['getTrojanVer','renewCert','clearDataStat','checkAll',\n               'restartTrojan','restartNginx','restartClient','runSpeedTest','getCertExpDate','getTrojanLog','getNginxLog',\n               'getClientLog','upgradePonyClient']","sub_path":"application/common/dict.py","file_name":"dict.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"23027932","text":"__author__ = 'Rushil'\r\n\r\nimport time\r\nimport numpy as np\r\n\r\ndef sieve_of_eratosthenes(n):\r\n\r\n    all_nums = list(range(2, n+1))\r\n\r\n    for num_1 in all_nums:\r\n        for num_2 in range(num_1+ 1, n+1):\r\n            if num_2 % num_1 == 0:\r\n                try:\r\n                    all_nums.remove(num_2)\r\n                except:\r\n                    pass\r\n\r\n    return all_nums\r\n\r\n\r\ndef sieve_of_eratosthenes_2(n):\r\n    a = [True] * n                          # Initialize the primality list\r\n    a[0] = a[1] = False\r\n\r\n    for (i, isprime) in enumerate(a):\r\n        if isprime:\r\n            yield i\r\n            for n in range(i*i, n, i):     # Mark factors non-prime\r\n                a[n] = False\r\n\r\n\r\ndef sieve_of_eratosthenes_3(n):\r\n\r\n    all_nums = dict(list(enumerate(list(range(0,n+1))))[2:])\r\n    print(all_nums)\r\n\r\n    for i in all_nums:\r\n        for j in list(all_nums.values())[i::i]:\r\n            print(i,j)\r\n            try:\r\n                all_nums[j] = False\r\n            except:\r\n                pass\r\n        print(all_nums)\r\n        all_nums[i] = True\r\n    return all_nums\r\n\r\nt1 = time.time()\r\nprint(sieve_of_eratosthenes(100))\r\nt2 = time.time()\r\nprint('In Time: ' ,  t2 - t1)\r\n\r\nt1 = time.time()\r\nprint(list(sieve_of_eratosthenes_2(100)))\r\nt2 = time.time()\r\nprint('In Time: ' ,  t2 - t1)\r\n\r\nt1 = time.time()\r\nprint(list(sieve_of_eratosthenes_3(100)))\r\nt2 = time.time()\r\nprint('In Time: ' ,  t2 - t1)\r\n","sub_path":"SieveofEratosthenes.py","file_name":"SieveofEratosthenes.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"547093007","text":"from flask import Blueprint, render_template, redirect, url_for\n\nfrom src.repositories import visit_repository\nfrom src.presentation.common import flash_error, flash_success\nfrom src.presentation.forms import VisitForm\n\nbp = Blueprint('visit', __name__, url_prefix='/visits')\n\n\n@bp.route('/<int:id>/update', methods=('GET', 'POST'))\ndef update(id):\n    visit = visit_repository.get(id)\n    form = VisitForm(obj=visit, plan=visit.plan)\n\n    if form.validate_on_submit():\n        form.populate_obj(visit)\n\n        result = visit_repository.save(visit)\n\n        if result.success:\n            flash_success('Consulta foi atualizada com sucesso!')\n            return redirect(url_for('patient.get_plans', id=visit.plan.patient_id))\n\n        flash_error(result.error)\n\n    return render_template('visit.html', form=form, plan=visit.plan)\n\n\n@bp.route('/<int:id>/delete', methods=('POST',))\ndef delete(id):\n    visit = visit_repository.get(id)\n    plan = visit.plan\n\n    result = visit_repository.delete(visit)\n    if result.success:\n        flash_success('Consulta foi excluída com sucesso!')\n    else:\n        flash_error(result.error)\n\n    return redirect(url_for('plan.display', id=plan.id))\n","sub_path":"src/controllers/visit.py","file_name":"visit.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"404902236","text":"import datetime\nimport json\n\nimport jwt\nimport requests\nfrom django.conf import settings\n\nfrom bilbyui.utils.misc import check_request_leak_decorator\n\n\n@check_request_leak_decorator\ndef request_job_filter(user_id, ids=None, end_time_gt=None):\n    \"\"\"\n    Requests a filtered list of jobs from the job controller\n\n    :param ids: A list of job ids to fetch\n    :param user_id: An optional user id to make the request as\n    :param end_time_gt: An optional parameter for jobs with an end time greater than this\n    \"\"\"\n\n    # Create the jwt token\n    jwt_enc = jwt.encode(\n        {\"userId\": user_id, \"exp\": datetime.datetime.now() + datetime.timedelta(days=30)},\n        settings.JOB_CONTROLLER_JWT_SECRET,\n        algorithm=\"HS256\",\n    )\n\n    qs = []\n\n    # Generate the query string\n    if ids:\n        qs.append(\"jobIds=\" + \",\".join(map(str, ids)))\n\n    if end_time_gt:\n        qs.append(\"endTimeGt=\" + str(round(end_time_gt.timestamp())))\n\n    print(f\"\"\"{settings.GWCLOUD_JOB_CONTROLLER_API_URL}/job/?{\"&\".join(qs)}\"\"\")\n\n    try:\n        # Initiate the request to the job controller\n        result = requests.request(\n            \"GET\",\n            f\"\"\"{settings.GWCLOUD_JOB_CONTROLLER_API_URL}/job/?{\"&\".join(qs)}\"\"\",\n            headers={\"Authorization\": jwt_enc},\n        )\n\n        # Check that the request was successful\n        if result.status_code != 200:\n            # Oops\n            msg = (\n                f\"Error getting job status, got error code: \"\n                f\"{result.status_code}\\n\\n{result.headers}\\n\\n{result.content}\"\n            )\n            print(msg)\n            raise Exception(msg)\n\n        # Parse the response from the job controller\n        result = json.loads(result.content)\n\n        return \"OK\", result\n    except Exception as e:\n        print(e)\n        return \"UNKNOWN\", \"Error getting job status\"\n","sub_path":"src/bilbyui/utils/jobs/request_job_filter.py","file_name":"request_job_filter.py","file_ext":"py","file_size_in_byte":1843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"421553678","text":"# -*- coding: utf-8 -*-\nfrom model import Model\nimport numpy as np\n\nclass UserAverageModel(Model):\n    \n    def __init__(self, movies, users, training_matrix):\n        self.movies = movies\n        self.users = users\n        self.training_matrix = training_matrix\n        self.create_model()\n        \n    def create_model(self):\n        # Sum over all ratings for each user\n        rating_sums = np.sum(self.training_matrix, axis=1)\n        \n        # Count of all non-zero ratings for each movie\n        rating_counts = np.sum(self.training_matrix > 0, axis=1)\n        \n        # Get global_average before altering data\n        self.global_average = np.sum(rating_sums) / np.sum(rating_counts)\n        \n        # Prevent division by zero / nan\n        rating_counts[rating_counts == 0] = 1\n        rating_counts[np.isnan(rating_counts)] = 1\n        \n        self.ratings = rating_sums / rating_counts\n    \n    def rating(self, user_id, movie_id):\n        user_id = int(user_id)\n        movie_id = int(movie_id)\n        \n        result = self.ratings[user_id]\n        if result > 0:\n            return result\n        else:\n            return self.global_average","sub_path":"assignment1/useraveragemodel.py","file_name":"useraveragemodel.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"263653469","text":"from copy import deepcopy\nfrom math import sqrt\n\nimport numpy as np\nfrom spatial_filters.ica.INFOMAX import InfomaxICA\nfrom scipy.stats import kurtosis\nfrom  scipy.stats import t\nfrom sklearn.base import BaseEstimator\nfrom spatial_filters.ica.ORICA import ORICA\nfrom eeg_ploting import plot_ica_components, plot_ica_data\nfrom spatial_filters.ica.SCFICA import SCFastICA\nfrom .utils import mMSE\n\n\nclass ICA():\n    def __init__(self, main_estimator, estimator_params):\n        self.ica_list = [\"INFOMAX\", \"FastICA\", \"ORICA\"]\n        self.change_estimator(main_estimator,estimator_params)\n\n\n    def change_estimator(self,main_estimator, estimator_params):\n        self.init_flag = True\n        self.est_flag = False\n        estimator_params = {} if estimator_params is None else estimator_params\n        self.main_estimator = None\n        if isinstance(main_estimator, BaseEstimator):\n            self.main_estimator = main_estimator\n        elif isinstance(main_estimator, str):\n            if main_estimator in self.ica_list:\n                if main_estimator == \"INFOMAX\":\n                    self.main_estimator = InfomaxICA(**estimator_params)\n                if main_estimator == \"FastICA\":\n                    self.main_estimator = SCFastICA(**estimator_params)\n                if main_estimator == \"ORICA\":\n                    self.main_estimator = ORICA(**estimator_params)\n        else:\n            self.init_flag = False\n            raise TypeError(\"Algorithm not found\")\n\n    def change_params(self,estimator_params):\n        estimator_params = {} if estimator_params is None else estimator_params\n        if self.init_flag:\n            self.est_flag = False\n            for key, value in estimator_params.items():\n                if hasattr(self.main_estimator,key):\n                    setattr(self.main_estimator,key,value)\n\n    def fit(self,X):\n        print(self.main_estimator)\n        self.sources = self.main_estimator.fit_transform(X)\n        self.est_flag = True\n\n    def fit_transform(self, X):\n        if not self.est_flag:\n            self.fit(X)\n        return self.sources\n\n    def transform(self, X, copy = True):\n        if self.est_flag:\n            return  self.main_estimator.transform(X,copy)\n\n    def inverse_transform(self, X, copy = True):\n        if  self.est_flag:\n            return  self.main_estimator.inverse_transform(X,copy)\n\n    def plot_sources(self,plot_params):\n        if self.est_flag:\n            plot_ica_data(sources=self.sources,**plot_params)\n\n    def plot_mixing_components(self,plot_params,extract_ind = False):\n        if self.est_flag:\n            chosen_indexes, _ = plot_ica_components(data_matrix=self.main_estimator.components_.T, **plot_params)\n            del _\n            if extract_ind:\n                return sorted(chosen_indexes)\n\n    def extract_bad_components_manually(self,plot_params):\n        return self.plot_mixing_components(plot_params,extract_ind=True)\n\n    def extract_bad_components_auto(self,scale_factor = 20, epoch_len = 2, tolerance = None,\n                                    confidence_of_two_sided_student = 0.05):\n        if self.est_flag:\n            N = self.sources.shape[1]\n            kurtosis_arr = np.empty(shape=N)\n            mMSE_arr = np.empty(shape=N)\n            #too slow\n            for i in range(N):\n                kurtosis_arr[i] = kurtosis(self.sources[:,i])\n                mMSE_arr[i] = mMSE(self.sources[:,i],scale_factor,epoch_len,tolerance)\n            tn = t.ppf(1-confidence_of_two_sided_student/2,N-1)\n            kurtosis_lim = np.mean(kurtosis_arr) + np.std(kurtosis_arr) * tn / sqrt(N)\n            mMSE_arr_lim = np.mean(mMSE_arr) - np.std(mMSE_arr) * tn / sqrt(N)\n            chosen_indexes_kurtosis = []\n            chosen_indexes_mMSE = []\n            for i in range(N):\n                if kurtosis_arr[i] > kurtosis_lim:\n                    chosen_indexes_kurtosis.append(i)\n                if mMSE_arr[i] < mMSE_arr_lim:\n                    chosen_indexes_mMSE.append(i)\n            return [index for index in chosen_indexes_kurtosis if index in chosen_indexes_mMSE]\n\n\n\n    def get_constraints(self,indexes):\n        return self.main_estimator.components_[:,np.array(sorted(indexes))]\n\n    def delete_chosen_components(self,indexes,return_signal = False,copy = False):\n        if self.est_flag:\n            S = self.sources\n            if copy and return_signal:\n                S = deepcopy(self.sources)\n            for i in indexes:\n                S[:,i] = 0\n            if return_signal:\n                return self.inverse_transform(S,copy=False)\n            else:\n                return S\n\n    def denoising(self,denoiser,return_signal = False,copy = False):\n        if self.est_flag:\n            S = self.sources\n            if (copy or denoiser.get_scheme()) and return_signal:\n                S = deepcopy(self.sources)\n            denoiser.apply(S)\n            if return_signal:\n                if denoiser.get_scheme():\n                    return self.inverse_transform(self.sources,copy=False) -\\\n                           self.inverse_transform(S,copy=False)\n                else:\n                    return self.inverse_transform(S,copy=False)\n            else:\n                return S\n\n\n\n\n\n","sub_path":"spatial_filters/ica/ICA_tools.py","file_name":"ICA_tools.py","file_ext":"py","file_size_in_byte":5232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"283538624","text":"# -*- coding: utf-8 -*-\n#!/usr/bin/env python\n\n##### OSC server ####\n\nfrom liblo import *\nimport sys\n\n\nclass MyServer(ServerThread):\n    def __init__(self, targetPort, recvPort, onExit):\n        ServerThread.__init__(self, recvPort)\n        print(\"server created\")\n        self.onExit = onExit\n        try:\n            self.target = Address(targetPort)\n        except AddressError as err:\n            print (str(err))\n            sys.exit()\n\n    def send_value( self, tag, value ):\n        send( self.target, tag, value )\n\n    def send_array( self, tag, vals ):\n        # we can also build a message object first...\n        msg = Message( tag )\n        # ... append arguments later...\n        for v in vals:\n            msg.add(v)\n        # ... and then send it\n        send(self.target, msg)\n\n    @make_method('/conceptor/store', 'i')\n    def conceptor_callback(self, path, args):\n        ind = args[0]\n#        print(\"received message \",path,\" with arguments: \", f)\n        if self.onConceptorStore != None:\n            value = self.onConceptorStore(ind)\n        #send(self.target, \"/output\", value)\n        \n    @make_method('/input', 'if')\n    def input_callback(self, path, args):\n        ind = args[0]\n        f = args[1]\n        #print(\"received input message \",path,\" with arguments: \", ind, f)\n        if self.onInput != None:\n            value = self.onInput(ind,f)\n        #send(self.target, \"/output\", value)\n\n    @make_method('/spectralradius', 'if')\n    def spectral_callback(self, path, args):\n        ind = args[0]\n        f = args[1]\n#        print(\"received message \",path,\" with arguments: \", f)\n        if self.onSpectral != None:\n            value = self.onSpectral(ind,f)\n        #send(self.target, \"/output\", value)\n\n    @make_method('/leakrate', 'if')\n    def leak_callback(self, path, args):\n        ind = args[0]\n        f = args[1]\n#        print(\"received message \",path,\" with arguments: \", f)\n        if self.onLeakrate != None:\n            value = self.onLeakrate(ind,f)\n        #send(self.target, \"/output\", value)\n\n    @make_method('/morph', 'if')\n    def morph_callback(self, path, args):\n        ind = args[0]\n        f = args[1]\n#        print(\"received message \",path,\" with arguments: \", f)\n        if self.onMorph != None:\n            value = self.onMorph(ind,f)\n        #send(self.target, \"/output\", value)\n\n    @make_method('/exit', None)\n    def exit_callback(self, path, args):\n#        print(\"received message \",path,\" with arguments: \", f)\n        print(\"exit\")\n        value = self.onExit()\n        send(self.target, \"/exited\" )\n\n    @make_method(None, None)\n    def fallback(self, path, args):\n        print(\"received unknown message \", path, args)\n\n\ndef makeOSCServer(targetPort,recvPort,onExit):\n    try:\n        server.free()\n    except:\n        pass\n\n    try:\n        server = MyServer(targetPort,recvPort,onExit)\n    except err:\n        print(str(err))\n\n    return server\n","sub_path":"python/oscserver.py","file_name":"oscserver.py","file_ext":"py","file_size_in_byte":2924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"633392922","text":"import socket\nimport threading\nimport sys\nimport time\nfrom Config import *\n\n\nclass Server:\n    connections = []\n    peers = []\n    formattedPeers = []\n\n    def __init__(self):\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n        sock.bind((INITIAL_ADDRESS, INITIAL_PORT))\n        sock.listen(1)\n        print(\"Server running ...\")\n\n        while True:\n            c, a = sock.accept()\n            cThread = threading.Thread(target=self.handler, args=(c, a))\n            cThread.daemon = True\n            cThread.start()\n            self.connections.append(c)\n            self.peers.append(a[0])\n\n            print(str(a[0]) + ':' + str(a[1]) + \" connected\")\n            self.formattedPeers.append(str(a[0]) + ':' + str(a[1]))\n            self.sendPeers()\n\n    def handler(self, c, a):\n        while True:\n            try:\n                data = c.recv(1024)\n                for connection in self.connections:\n                    connection.send(bytes(data))\n            except:\n                print(str(a[0]) + ':' + str(a[1]) + \" disconnected\")\n                self.formattedPeers.remove(str(a[0]) + ':' + str(a[1]))\n                self.connections.remove(c)\n                self.peers.remove(a[0])\n                c.close()\n                self.sendPeers()\n                break\n\n    def sendPeers(self):\n        p = \"\"\n        for peer in self.peers:\n            p = p + peer + \",\"\n\n        for connection in self.connections:\n            connection.send(b'\\x11' + bytes(p, \"utf-8\"))\n        time.sleep(1)\n        self.sendConnections()\n\n    def sendConnections(self):\n        for connection in self.connections:\n            temp = list(filter(lambda x: x != str(connection.getpeername()[0]) + \":\" + str(connection.getpeername()[1]),\n                               self.formattedPeers))\n            msg = \"\"\n            for x in temp:\n                msg += x + \",\"\n            connection.send(b'\\x12' + bytes(str(msg), \"utf-8\"))\n\n\nclass Client:\n    clientIP = \"\"\n\n    def __init__(self, address, file):\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n        sock.connect((address, INITIAL_PORT))\n\n        self.clientIP = str(sock.getsockname()[0]) + \":\" + str(sock.getsockname()[1])\n        filename = ROUTING_TABLE_ADDRESS + str(file)\n        f = open(filename, \"w+\")\n        f.write(self.clientIP)\n        f.close()\n\n        iThread = threading.Thread(target=self.sendMsg, args=(sock,))\n        iThread.daemon = True\n        iThread.start()\n        print(\"Connected.\")\n\n        while True:\n            data = sock.recv(1024)\n            if not data:\n                break\n            elif data[0:1] == b'\\x11':\n                self.updatePeers(data[1:])\n            elif data[0:1] == b'\\x12':\n                self.updateConnections(data[1:])\n            else:\n                print(str(data, 'utf-8'))\n\n    def updatePeers(self, peerData):\n        p2p.peers = str(peerData, \"utf-8\").split(\",\")[:-1]\n\n\n    def updateConnections(self, conData):\n        p2p.connections = str(conData, \"utf-8\").split(\",\")[:-1]\n        time.sleep(1)\n        pathToOpen = ROUTING_TABLE_ADDRESS\n        filename = pathToOpen + self.clientIP + \".txt\"\n        filename = filename.replace(\":\", \"_\")\n\n        file = open(filename, \"w+\")\n        for peer in p2p.connections:\n            file.write(peer + \"\\n\")\n        file.close()\n\n\n    def reqUpdate(self, sock):\n        sock.send(b'\\x13')\n\n    def sendMsg(self, sock):\n        pass\n\n\nclass p2p:\n    peers = [INITIAL_ADDRESS]\n    connections = []\n\n    def __init__(self, filename):\n        if ROUTING_TABLE_ADDRESS in filename:\n            filename = str(filename).replace(ROUTING_TABLE_ADDRESS, \"\")\n\n        while True:\n            try:\n                print(\"Trying to connect ...\")\n                for peer in p2p.peers:\n                    try:\n                        client = Client(peer, filename)\n                    except:\n                        pass\n                    try:\n                        file = open(ROUTING_TABLE_ADDRESS + filename, \"w\")\n                        file.write(INITIAL_ADDRESS + \":\" + str(INITIAL_PORT + 1))\n                        file.close()\n                        server = Server()\n                    except:\n                        print(\"Couldn't start the server...\")\n            except:\n                sys.exit(0)\n\n\nif __name__ == '__main__':\n    file = ROUTING_TABLE_ADDRESS + \"server.txt\"\n    p2p(file)\n","sub_path":"anonimityNetwork/PeerToPeer.py","file_name":"PeerToPeer.py","file_ext":"py","file_size_in_byte":4557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"92211417","text":"import logging\nimport time\n\nimport numpy\nfrom nltk import WordNetLemmatizer, word_tokenize\nfrom sklearn.cross_validation import KFold\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.metrics import auc\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import roc_curve\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.pipeline import Pipeline\nfrom sklearn import metrics\nimport matplotlib.pyplot as plt\n\nfrom classifier import SVMClassifier\nfrom utils import LoggingUtil\nfrom utils import PickleUtil\n\nfrom classifier import BagOfWordsClassifier\nfrom classifier import BigramCountsClassifier\nfrom classifier import BigramFrequenciesClassifier\nfrom classifier import BigramOccurrencesClassifier\nfrom classifier import BigramOccurrencesClassifierV2\n\nclass LemmaTokenizer(object):\n    def __init__(self):\n        self.wnl = WordNetLemmatizer()\n    def __call__(self, doc):\n        return [self.wnl.lemmatize(t) for t in word_tokenize(doc)]\n\n\nclass Classifier(object):\n    pipeline = SVMClassifier.get()\n    confusion = numpy.array([[0, 0], [0, 0]])\n    scores = []\n\n    def perform_with_cross_validation(self, data, load_from_pickle):\n        if load_from_pickle:\n            self.pipeline = PickleUtil.read_pickle()\n        LoggingUtil.log_classification_parameters(self.pipeline)\n        k_fold = KFold(n=len(data), n_folds=6)\n        for train_indices, test_indices in k_fold:\n            if not load_from_pickle:\n                self.train(data, self.pipeline, test_indices, train_indices)\n            test_features = data.iloc[test_indices]['email'].values\n            test_labels = data.iloc[test_indices]['label'].values.astype(str)\n            self.confusion = self.test(self.confusion, self.pipeline, self.scores, test_features, test_labels,\n                                       save_to_pickle=not load_from_pickle, print_roc=False)\n        LoggingUtil.log_results(self.confusion, data, self.scores)\n\n    @staticmethod\n    def test(confusion, pipeline, scores, test_features, test_labels, save_to_pickle, print_roc):\n        predictions = pipeline.predict(test_features)\n        if print_roc:\n            Classifier.print_roc_curve(predictions, test_labels)\n        LoggingUtil.log_misclassified_emails(predictions, test_features, test_labels)\n        matrix = confusion_matrix(test_labels, predictions)\n        logging.info(matrix)\n        confusion += matrix\n        score = f1_score(test_labels, predictions, pos_label='spam')\n        # TODO ROC curve\n        if save_to_pickle:\n            PickleUtil.save_pickle(pipeline, score)\n        logging.info(\"Partial score: \" + str(score))\n        scores.append(score)\n        return confusion\n\n    @staticmethod\n    def print_roc_curve(predictions, test_labels):\n        test_labels_binary = []\n        pred_binary = []\n        for test_label in test_labels:\n            if test_label == 'spam':\n                test_labels_binary.append(1)\n            else:\n                test_labels_binary.append(0)\n        for pred in predictions:\n            if pred == 'spam':\n                pred_binary.append(1)\n            else:\n                pred_binary.append(0)\n        test_labels_binary = numpy.asarray(test_labels_binary)\n        pred_binary = numpy.asarray(pred_binary)\n        false_positive_rate, recall, thresholds = roc_curve(test_labels_binary, pred_binary)\n        roc_auc = auc(false_positive_rate, recall)\n        plt.title('Receiver Operating Characteristic')\n        plt.plot(false_positive_rate, recall, 'b', label='AUC = %0.2f' %\n                                                         roc_auc)\n        plt.legend(loc='lower right')\n        plt.plot([0, 1], [0, 1], 'r--')\n        plt.xlim([0.0, 1.0])\n        plt.ylim([0.0, 1.0])\n        plt.ylabel('Recall')\n        plt.xlabel('Fall-out')\n        plt.show()\n\n    @staticmethod\n    def train(data, pipeline, test_indices, train_indices):\n        logging.info(\"Training / test data: \" + str(len(train_indices)) + \" / \" + str(len(test_indices)))\n        train_features = data.iloc[train_indices]['email'].values\n        train_lables = data.iloc[train_indices]['label'].values.astype(str)\n        start_time = time.time()\n        pipeline.fit(train_features, train_lables)\n        #print(pipeline.get_params()['classifier'].class_prior)\n        end_time = time.time()\n        logging.info(\"Learning took: \" + str(end_time - start_time) + \" seconds\")\n","sub_path":"src/classifier/classifier.py","file_name":"classifier.py","file_ext":"py","file_size_in_byte":4518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"54432876","text":"import urllib\nimport urllib.request\n\n\ntry:\n    site = urllib.request.urlopen\\\n        ('http://www.feijaodeouro.com.br/')\nexcept urllib.error.URLError:\n    print('O site não está acessível no momento')\nelse:\n    print('Consegui acessar o site com sucesso.')\n    print(site.read())","sub_path":"mundo3/ex114.py","file_name":"ex114.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"61111899","text":"fileobj = open(\"file_pointer.txt\", \"r+\")\nsample = fileobj.read(13)\nprint(\"first 13 bytes: %s\" % sample)\n\npos = fileobj.tell()\nprint(\"position: %s\" % pos)\n\nnewpos = fileobj.seek(13, 0)\nnewsample = fileobj.read(10)\nprint(\"bytes 13 - 23: %s\" % newsample)\n\nfileobj.close\n","sub_path":"file_pointer.py","file_name":"file_pointer.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"622851312","text":"from otree.api import (\n    models, widgets, BaseConstants, BaseSubsession, BaseGroup, BasePlayer,\n    Currency as c, currency_range\n)\nimport random\n\n\nclass Constants(BaseConstants):\n    name_in_url = 'survey'\n    players_per_group = None\n    num_rounds = 1\n\n\nclass Subsession(BaseSubsession):\n    pass\n\n\nclass Group(BaseGroup):\n    pass\n\n\nclass Player(BasePlayer):\n\n    age = models.PositiveIntegerField(\n        verbose_name='What is your age?',\n        min=13, max=40)\n\n    gender = models.CharField(\n        choices=['Male', 'Female'],\n        verbose_name='What is your gender?',\n        widget=widgets.RadioSelect())\n\n    crt_bat = models.PositiveIntegerField(\n        verbose_name='''\n        A bat and a ball cost 22 dollars in total.\n        The bat costs 20 dollars more than the ball.\n        How many dollars does the ball cost?'''\n    )\n\n    crt_widget = models.PositiveIntegerField(\n        verbose_name='''\n        \"If it takes 5 machines 5 minutes to make 5 widgets,\n        how many minutes would it take 100 machines to make 100 widgets?\"\n        '''\n    )\n\n    crt_lake = models.PositiveIntegerField(\n        verbose_name='''\n        In a lake, there is a patch of lily pads.\n        Every day, the patch doubles in size.\n        If it takes 48 days for the patch to cover the entire lake,\n        how many days would it take for the patch to cover half of the lake?\n        '''\n    )\n\n    income1 = models.CharField(\n        choices=['$1,000 - $ 2,000',\n                 '$ 2,001 – $3,100',\n                 '$3,101 -  $5,000', '$5,001 -  $7,250', '$7,251  and over'],\n        verbose_name='How much was your INCOME last month?',\n        widget=widgets.RadioSelect())\n\n    income2 = models.CharField(\n        choices=['The richest in the class',\n                 'Among the richest in the class',\n                 'Richer than most your friends', 'About average', 'The poorest in the class'],\n        verbose_name='Compared to other people in your Class, would you describe you as…..(?',\n        widget=widgets.RadioSelect())\n\n    trust1 = models.CharField(\n        choices=['Never',\n                 'Rarely (every month)',\n                 'Sometimes (every two weeks)', 'Often (every week)', 'Very often (every day)'],\n        verbose_name='How often have you interacted with your friends in your class this month?',\n        widget=widgets.RadioSelect())\n\n    trust2 = models.CharField(\n        choices=['1','2','3', '4', '5'],\n        verbose_name='Most of my friends in my class are trustworthy.',\n        widget=widgets.RadioSelectHorizontal())\n\n    trust3 = models.CharField(\n        choices=['1','2','3', '4', '5'],\n        verbose_name='Most of my friends in my class are trustful of others.',\n        widget=widgets.RadioSelectHorizontal())\n\n    trust4 = models.CharField(\n        choices=['1','2','3', '4', '5'],\n        verbose_name='Most of my friends in my class are basically good and kind.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco1 = models.CharField(\n        choices=['1','2','3', '4', '5'],\n        verbose_name='I can look at a painting for a long time.',\n        widget=widgets.RadioSelectHorizontal())\n\n    hexaco2 = models.CharField(\n        choices=['1','2','3', '4', '5'],\n        verbose_name='I make sure that things are in the right spot.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco3 = models.CharField(\n        choices=['1','2','3', '4', '5'],\n        verbose_name='I remain unfriendly to someone who was mean to me.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco4 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='Nobody likes talking with me.',\n        widget=widgets.RadioSelectHorizontal())\n\n    hexaco5 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I am afraid of feeling pain.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco6 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I find it difficult to lie',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco7 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I think science is boring.',\n        widget=widgets.RadioSelectHorizontal())\n\n    hexaco8 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I postpone complicated tasks as long as possible.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco9 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I often express criticism.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco10 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I easily approach strangers. ',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco11 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='Even when I am treated badly, I remain calm.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco12 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I have to cry during a sad or romantic movie.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco13 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I worry less than others.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco14 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I would like to know how to make lots of money in a dishonest manner',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco15 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I have a lot of imagination.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco16 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I work very precisely.',\n        widget=widgets.RadioSelectHorizontal())\n\n    hexaco17 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I tend to quickly agree with others.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco18 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I like to talk with others.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco19 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I can easily overcome difficulties on my own.',\n        widget=widgets.RadioSelectHorizontal())\n\n    hexaco20 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I want to be famous.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco21 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I like people with strange ideas.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco22 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I often do things without really thinking.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco23 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I am seldom cheerful.',\n        widget=widgets.RadioSelectHorizontal())\n    hexaco24 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I am entitled to special treatment.',\n        widget=widgets.RadioSelectHorizontal())\n\n    ypi1 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='Often I act extra nice and sweet to get what I want, even with people I don’t like.',\n        widget=widgets.RadioSelectHorizontal())\n\n    ypi2 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='When someone asks me something, I usually have a quick answer that sounds believable, but I just made it up.',\n        widget=widgets.RadioSelectHorizontal())\n    ypi3 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I will become a well-known and important person; I know that already.',\n        widget=widgets.RadioSelectHorizontal())\n    ypi4 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='The world would be a better place if I were the boss.',\n        widget=widgets.RadioSelectHorizontal())\n\n    ypi5 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I like to exaggerate when I tell about something.',\n        widget=widgets.RadioSelectHorizontal())\n    ypi6 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='Sometimes I find myself lying for no special reason.',\n        widget=widgets.RadioSelectHorizontal())\n    ypi7 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='Fooling others is the best way to get what I want from them',\n        widget=widgets.RadioSelectHorizontal())\n\n    ypi8 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='It is easy for me to make other people to do things that suit me well. ',\n        widget=widgets.RadioSelectHorizontal())\n    ypi9 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='It is weak to feel guilty when you have hurt others.',\n        widget=widgets.RadioSelectHorizontal())\n    ypi10 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='Feeling bad when you have done something wrong is a waste of time.  ',\n        widget=widgets.RadioSelectHorizontal())\n    ypi11 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='It is weak to feel nervous or worried.',\n        widget=widgets.RadioSelectHorizontal())\n    ypi12 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='Feelings are less important to me than they are for others.',\n        widget=widgets.RadioSelectHorizontal())\n    ypi13 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='If I watch sad things on TV or in a movie, it usually does not get to me. ',\n        widget=widgets.RadioSelectHorizontal())\n    ypi14 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='When others are sad, I don’t really care.',\n        widget=widgets.RadioSelectHorizontal())\n    ypi15 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I like to do things just because they feel cool or exciting.',\n        widget=widgets.RadioSelectHorizontal())\n    ypi16 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I like to get into situations that give me a thrill.',\n        widget=widgets.RadioSelectHorizontal())\n\n    ypi17 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='It often happens that I do things without thinking ahead.',\n        widget=widgets.RadioSelectHorizontal())\n    ypi18 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I prefer to spend my money right away rather than save it.',\n        widget=widgets.RadioSelectHorizontal())\n    ypi19 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I find rules to be nothing but a nuisance.',\n        widget=widgets.RadioSelectHorizontal())\n\n    ypi20 = models.CharField(\n        choices=['1', '2', '3', '4', '5'],\n        verbose_name='I don’t think it is necessary to tell my parents what I am going to do when I go outside.',\n        widget=widgets.RadioSelectHorizontal())\n\n    religion = models.PositiveIntegerField(\n        verbose_name='''\n        How many times have you attended religious services during the past year?\n            ''',\n        min=0, max=100\n    )\n\n    religion1 = models.CharField(\n        choices=['Prayer is a regular part of my daily life','I usually pray in times of stress or need but rarely at any other time','I pray only during formal ceremonies','I never pray'],\n        verbose_name='Which of the following best describes your practice of prayer or religious meditation',\n        widget=widgets.RadioSelect())\n\n    religion2 = models.CharField(\n        choices=['Almost always',\n                 'Usually',\n                 'Sometimes', 'Never'],\n        verbose_name='When you have serious personal problem how often do you take religious advice or teaching into consideration?',\n        widget=widgets.RadioSelect())\n\n    religion3 = models.CharField(\n        choices=['Prayer is a regular part of my daily life',\n                 'I usually pray in times of stress or need but rarely at any other time',\n                 'I pray only during formal ceremonies', 'I never pray'],\n        verbose_name= 'Which of the following best describes your practice of prayer or religious meditation ?',\n        widget=widgets.RadioSelect())\n\n    religion4 = models.CharField(\n        choices=['No influence',\n                 'A small influence',\n                 'Some influence', 'A fair amount of influence','A large influence'],\n        verbose_name='How much influence would you say that religion has on the way that you choose to act and the way that you choose to spend your time each day ?',\n        widget=widgets.RadioSelect())\n\n    religion5 = models.CharField(\n        choices=['I am sure that god really exists and that He is active in my life',\n                 'Although I sometimes question His existence I do believe in God and believe He knows me as a Person',\n                 'I don’t know if there is a personal God, but I do believe in a higher power of some kind',\n                 'I don’t know if there is a personal God, or a higher power in some kind and I don’t know if I ever will',\n                 'I don’t believe in a personal God or in a higher power'],\n        verbose_name='Which of the following statements comes closest to your belief about God?',\n        widget=widgets.RadioSelect())\n\n    religion6 = models.CharField(\n        choices=['I believe in a personal life after death, a soul existing as a specific individual spirit',\n                 'I believe in a soul existing after death as a part of a universal spirit',\n                 'I believe in a life after death of some kind, but I really don’t know what it would be like',\n                 'I don’t know whether there is any kind of life after death and aI don’t know if I will ever know',\n                 'I don’t believe in any kind of life after death'],\n        verbose_name='Which of the following statements comes closest to your belief about life after death (immortality)',\n        widget=widgets.RadioSelect())\n\n    religion7 = models.CharField(\n        choices=['Almost daily',\n                 'Frequently',\n                 'Sometimes', 'Rarely', 'Never'],\n        verbose_name='During the past year, how often have you experienced a feeling of religious reverence of devotion?',\n        widget=widgets.RadioSelect())\n","sub_path":"survey/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":14944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"615452462","text":"#!/usr/bin/env python\n\n# python 3.6 (desenv)\n\nimport subprocess\nimport os\nfrom argparse import ArgumentParser\nimport signal\nfrom sys import exit\n\nparser = ArgumentParser(description='kill/Gracefull processo adicionando a port')\nparser.add_argument('-p','--port', type=int, help='a port number que sera pesquisada')\nparser.add_argument('-o','--option', type=str, help='kill ou gracefull')\n\nport = parser.parse_args().port\n\nif parser.parse_args().option == \"kill\":\n    opt = signal.SIGKILL\nelif parser.parse_args().option == \"gracefull\":\n    opt = signal.SIGTERM\nelse:\n    print(\"opcao errada, execute novamente com -h\")\n\ntry:\n    result = subprocess.run(\n            ['lsof', '-n', \"-i4TCP:%s\" % port],\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE)\nexcept subprocess.CalledProcessError:\n    print(f\"Sem processo em listening na port {port}\")\n    exit(1)\nelse:\n    listening = None\n\n    for line in result.stdout.splitlines():\n        if \"LISTEN\" in str(line):\n            listening = line\n            break\n\n    if listening:\n        pid = int(listening.split()[1])\n        os.kill(pid, opt)\n        print(f\"Killed process {pid}\")\n    else:\n        print(f\"No process listening on port {port}\")\n        exit(1)\n","sub_path":"process/process-tests.py","file_name":"process-tests.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"454004968","text":"import pandas as pd\nimport sys\n\n# NOTE: Each list below must be divisible by last entry of prev list\n# (15 by 5, 30 by 15, 1D by 6H, etc.)\n_UNITS = [\n    [\"1min\"],\n    [\"5min\"],\n    [\"15min\"],\n    [\"30min\"],\n    [\"1H\"],\n    [\"4H\", \"6H\"],\n    [\"1D\"],\n    [\"3D\", \"1W\", \"1M\"],\n]\n_DATE_FORMAT = \"%m/%d/%Y\"\n_TIME_FORMAT = \"%H:%M\"\n\n\ndef resample(df: pd.DataFrame, unit: str) -> pd.DataFrame:\n    tick = \"last\" in df.columns\n    col_actions = (\n        {\"last\": \"ohlc\", \"volume\": \"sum\"}\n        if tick\n        else {\n            \"open\": \"first\",\n            \"high\": \"max\",\n            \"low\": \"min\",\n            \"close\": \"last\",\n            \"volume\": \"sum\",\n        }\n    )\n    new_df = df.resample(unit).agg(col_actions).dropna()\n    if tick:\n        # Collapse multindex and make it flat\n        new_df.columns = new_df.columns.get_level_values(1)\n    # Convert index into separate date/time columns\n    new_df[\"date\"] = new_df.index.strftime(_DATE_FORMAT)\n    new_df[\"time\"] = new_df.index.strftime(_TIME_FORMAT)\n    # Make DT index a regular column\n    return new_df\n\n\ndef write_data(filename: str, df: pd.DataFrame, unit: str):\n    new_df = df.reset_index()\n    new_df.to_csv(\n        f\"{filename}_{unit.lower()}.csv\",\n        index=False,\n        columns=[\"date\", \"time\", \"open\", \"high\", \"low\", \"close\", \"volume\"],\n    )\n\n\ndef read_data(filename: str) -> pd.DataFrame:\n    return pd.read_csv(\n        filename,\n        names=[\"datetime\", \"last\", \"volume\"],\n        parse_dates=[\"datetime\"],\n        date_parser=lambda epoch: pd.to_datetime(epoch, unit=\"s\"),\n        index_col=\"datetime\",\n    )\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) < 2:\n        print(\"Usage: python tick2ohlc <base_filename>\\n\")\n        print(\"<base_filename> = filename without the .csv extension\")\n        sys.exit(1)\n\n    filename = sys.argv[1]\n    # Start by reading tick data\n    print(\"Reading tick data...\", end=\"\", flush=True)\n    df = read_data(filename + \".csv\")\n    print(\"done\")\n\n    for units in _UNITS:\n        for unit in units:\n            print(f\"Resampling and writing CSV for: {unit}...\", end=\"\", flush=True)\n            new_df = resample(df, unit)\n            write_data(filename, new_df, unit)\n            print(\"done\")\n\n        # Last new_df of list becomes new df for next cycle\n        df = new_df\n","sub_path":"tick2ohlc.py","file_name":"tick2ohlc.py","file_ext":"py","file_size_in_byte":2300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"267891139","text":"import transpositionEncrypt, transpositionDecrypt, sys, pyperclip\n\ndef main():\n    # Dictionary storing references for the menu. Easierr than an if, else sorter.\n    # They have no brackets as they will run instantly if they do! that is why\n    # myDict[choice] below ends with brackets. Still not sure how to simplify menu\n    # so I can only need to update the dictionary.\n    myDict = {1: encryptTextInterface, 2: decryptTextInterface, 3:sys.exit}\n    while True:\n        print('Menu:\\\n             \\n1. Encrypt\\\n             \\n2. Decrypt\\\n             \\n3. Exit\\n')\n        choice = int(input('Please select which function you would like to use:'))\n        myDict[choice]()\n        \n\ndef encryptTextInterface():\n    message = input('Please enter text to be encrypted:')\n    key = int(input('Please enter length of key to use:'))\n    ciphertext = transpositionEncrypt.encryptMessage(key, message)\n    print(ciphertext + '\\n')\n    pyperclip.copy(ciphertext)\n\ndef decryptTextInterface():\n    message = input('Please enter text to be decrypted:')\n    key = int(input('Please enter length of key to use:'))\n    plaintext = transpositionDecrypt.decryptMessage(key, message)\n    print(plaintext)\n    pyperclip.copy(plaintext + '\\n')\n\nmain()\n","sub_path":"Menu.py","file_name":"Menu.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"458189587","text":"import urllib3          # Connect to episode tracking website\nimport os               # Check if directories or files exist\nfrom xml.dom.minidom \\\n     import parseString # Extract data from xml file\nfrom datetime import \\\n     datetime, date     # Work with airing dates\nimport json as json     # For saving and reading records\n\nclass MyAnimeList:\n    \"\"\"Methods for interaction with MyAnimeList\n\n    Attributes:\n        username: A string containing the user's MyAnimeList username\n    \"\"\"\n    \n    def __init__(self, u):\n        self.username = u\n    \n    def connect(self):\n        \"\"\"Attempts to connect to MyAnimeList.\n\n        Connects to MyAnimeList API with the user's username using\n        the urllib3 module.\n\n        Returns:\n            True, if the connection succeeded. Otherwise, raises a SystemExit.\n\n        Raises:\n            SystemExit: A status other than 200 was returned upon attempting\n                        to connect to MyAnimeList. The program will notify the\n                        user and exit due to the unexpected downtime of the\n                        website.\n        \"\"\"\n        \n        self.ANIME_LIST = 'http://myanimelist.net/malappinfo.php?status=all&u=' + self.username\n        http = urllib3.PoolManager()\n        self.conn = http.request('GET', self.ANIME_LIST)\n        if self.conn.status != 200:\n            return False;\n        return True\n\n    def isValid(self):\n        \"\"\"Verifies that the MyAnimeList username exists.\n\n        Retrieves xml data from MyAnimeList and checks for error tags. If an\n        'Invalid Username' error is found, then the provided username does\n        not exist.\n        \n        Returns:\n            True, if the username is valid. Otherwise, raises an IndexError.\n\n        Raises:\n            IndexError: There were no error tags found, so an array cannot be\n                        created from searching for error tags. Assign None\n                        to the error variable to indicate that there were\n                        no error tags found.\n            SystemExit: The received contained an 'Invalid Username'\n                        error. The program will notify the user that\n                        their username could not be found and will\n                        exit.\n        \"\"\"\n\n        self.data = parseString(self.conn.data)\n        try:\n            error = self.data.getElementsByTagName('error')[0].toxml()\n        except IndexError:\n            error = None\n        if error != None and error == '<error>Invalid username</error>':\n            return False\n        return True\n\n    def checkDirs(self):\n        \"\"\"Checks it the required directories exist.\n\n        Returns:\n            True if all necessary directories exist, false otherwise.\n        \"\"\"\n        \n        dirs = True\n        if not os.path.isdir('files'):\n            os.makedirs('files')\n            dirs = False\n        if not os.path.isdir('files/MyAnimeList'):\n            os.makedirs('files/MyAnimeList')\n            dirs = False\n        if not os.path.isdir('files/MyAnimeList/' + self.username):\n            os.makedirs('files/MyAnimeList/' + self.username)\n            dirs = False\n        return dirs\n            \n    def hasData(self):\n        \"\"\"Checks files directory for a previously downloaded xml data file.\n\n        Checks to see if the files directory exists, creates it if necessary,\n        and verifies whether a MyAnimeList xml file has already been downloaded\n        previously as MyAnimeList.xml\n\n        Returns:\n            True if the file was found, false otherwise.\n\n        Raises:\n            IOError: The file was not found.\n        \"\"\"\n\n        try:\n            with open('files/MyAnimeList/' + self.username + '/MyAnimeList.xml'):\n                return True\n        except IOError:\n            return False\n\n    def downloadData(self):\n        \"\"\"Downloads the data via the MyAnimeList API\"\"\"\n        \n        with open('files/MyAnimeList/' + self.username + '/MyAnimeList.xml', 'wb') as out:\n            out.write(self.conn.data)\n        self.conn.release_conn()\n\n    def askForUpdate(self):\n        \"\"\"Prompt for user's choice in whether to update the data on hand\n\n        Returns:\n            True if the user chose to update, false otherwise.\n\n        Raises:\n            ValueError: An invalid response was receieved.\n        \"\"\"\n        \n        while True:\n            try:\n                response = input('\\nWould you like to update your profile? (Y/N): ').upper()\n                if response not in {'Y', 'N'}:\n                    raise ValueError('Not a valid response.')\n            except ValueError:\n                print('\\rError: That\\'s not a valid response! Try again.')\n            else:\n                if(response.upper() == 'Y'):\n                    self.downloadData()\n                    self.buildDictionary()\n                    return True\n                self.conn.release_conn()\n                return False\n\n    def buildDictionary(self):\n        \"\"\"Extracts anime from xml data.\n\n\tPulls relevant xml data to create a dictionary of anime and saves it\n\tinto a text file.\n\n\tRaises:\n            ValueError: An invalid date was found. Prints error message to notify\n                        the user that a series contained an invalid date.\n            ValueError #2: Runs another check on whether the issue is that the\n                           date does not specify a day. If that is the case, only\n                           extract the year and month and print a notice to the\n                           user that a series contained an invalid date and an\n                           estimation was used.\n\t\"\"\"\n        \n        self.myDict = {}\n\n        with open('files/MyAnimeList/' + self.username + '/MyAnimeList.xml', 'r') as file:\n            self.data = parseString(file.read())\n\n        for nodeList in self.data.getElementsByTagName('anime'):\n            status = nodeList.getElementsByTagName('my_status')[0].firstChild.nodeValue\n            finish_date = nodeList.getElementsByTagName('series_end')[0].firstChild.nodeValue\n            if finish_date != '0000-00-00':\n                try:\n                    finish_date = datetime.strptime(finish_date, \"%Y-%m-%d\").date()\n                except ValueError:\n                    try:\n                        finish_date = datetime.strptime(finish_date[:7], \"%Y-%m\").date()  \n                    except ValueError:\n                        print('\\n[ERROR] Series \\'{0}\\' skipped: invalid end date'.format(nodeList.getElementsByTagName('series_title')[0].firstChild.nodeValue))\n                        break\n                    else:\n                        print('\\n[NOTICE] Series \\'{0}\\' repaired: estimated end date'.format(nodeList.getElementsByTagName('series_title')[0].firstChild.nodeValue))\n                if finish_date < datetime.today().date():\n                    ongoing = False\n                else:\n                    ongoing = True\n            else:\n                ongoing = True\n            if status == '1' and ongoing:\n                title = nodeList.getElementsByTagName('series_title')[0].firstChild.nodeValue\n                ep_count = nodeList.getElementsByTagName('my_watched_episodes')[0].firstChild.nodeValue\n                start_date = nodeList.getElementsByTagName('series_start')[0].firstChild.nodeValue\n                weekday = datetime.strptime(start_date, \"%Y-%m-%d\").date().weekday()\n                self.myDict[title] = {\"Episodes\": ep_count, \"Weekday\": weekday}\n        \n        with open ('files/MyAnimeList/' + self.username + '/MyAnimeList.json', 'w') as file:\n            json.dump(self.myDict, file)\n            \n    def displayTimes(self):\n        \"\"\"Displays all shows that are airing today and tomorrow.\n\n        Returns:\n            A formatted string composed of a list of series that will\n            air today and tomorrow. Includes notices if there are no\n            series scheduled to air today or tomorrow.\n        \"\"\"\n        \n        today = date.today().weekday()\n        airing_today = []\n        airing_tomorrow = []\n\n        with open ('files/MyAnimeList/' + self.username + '/MyAnimeList.json', 'r') as file:\n            self.myDict = json.load(file)\n\n        # Sort series between airing today and tomorrow\n        for series in self.myDict:\n            if int(self.myDict[series]['Weekday']) == today:\n                airing_today.append(series)\n            if int(self.myDict[series]['Weekday']) == today + 1:\n                airing_tomorrow.append(series)\n\n        # Format reports\n        if len(airing_today) == 0 and len(airing_tomorrow) > 0:\n            return '[NOTICE] Nothing airing today\\n\\nAiring Tomorrow:\\n\\n{0}\\n'.format('\\n'.join(airing_tomorrow))\n        elif len(airing_today) > 0 and len(airing_tomorrow) == 0:\n            return 'Airing Today:\\n\\n{0}\\n\\n[NOTICE] Nothing airing tomorrow\\n'.format('\\n'.join(airing_today))\n        elif len(airing_today) == 0 and len(airing_tomorrow) == 0:\n            return '[NOTICE] Nothing airing today and tomorrow\\n'\n        else:\n            return 'Airing Today:\\n\\n{0}\\n\\nAiring Tomorrow:\\n\\n{1}\\n'.format('\\n'.join(airing_today), '\\n'.join(airing_tomorrow))\n","sub_path":"lib/MyAnimeList.py","file_name":"MyAnimeList.py","file_ext":"py","file_size_in_byte":9188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"64926371","text":"#!/usr/bin/python3\n\"\"\" Module for LFU Dictionary \"\"\"\nfrom base_caching import BaseCaching\n\n\nclass LFUCache(BaseCaching):\n    \"\"\" LfU Class \"\"\"\n\n    def __init__(self):\n        \"\"\" overload init \"\"\"\n        super().__init__()\n        self.__datakeys = []\n        self.__count = {}\n\n    def put(self, key, item):\n        \"\"\" function that put items in dict \"\"\"\n        if not key or not item:\n            return\n        if len(self.cache_data) >= BaseCaching.MAX_ITEMS and \\\n                key not in self.__datakeys:\n            self.discard()\n        if key not in self.cache_data:\n            self.__count[key] = 1\n        else:\n            self.__count[key] += 1\n            self.__datakeys.remove(key)\n        self.__datakeys.append(key)\n        self.cache_data[key] = item\n\n    def get(self, key):\n        \"\"\" function that get the item from the dict \"\"\"\n        if not key or key not in self.cache_data:\n            return None\n        self.__count[key] += 1\n        self.__datakeys.remove(key)\n        self.__datakeys.append(key)\n        return self.cache_data[key]\n\n    def discard(self):\n        \"\"\"\n        discard item and print\n        \"\"\"\n        m_time = min(self.__count.values())\n        keys = [k for k, v in self.__count.items() if v == m_time]\n        low = 0\n        while self.__datakeys[low] not in keys:\n            low += 1\n        discard = self.__datakeys.pop(low)\n        del self.cache_data[discard]\n        del self.__count[discard]\n        print('DISCARD: {}'.format(discard))\n","sub_path":"0x03-caching/100-lfu_cache.py","file_name":"100-lfu_cache.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"125551711","text":"import asyncio\nimport concurrent.futures\nfrom dataclasses import dataclass\nimport threading\nfrom typing import Any, AsyncIterator, Coroutine, Dict, Iterator, Optional, Tuple, Union\n\nimport ray\nfrom ray._private.utils import get_or_create_event_loop\nfrom ray._raylet import StreamingObjectRefGenerator, GcsClient\n\nfrom ray import serve\nfrom ray.serve._private.common import DeploymentID, RequestProtocol\nfrom ray.serve._private.constants import (\n    RAY_SERVE_ENABLE_NEW_ROUTING,\n)\nfrom ray.serve._private.default_impl import create_cluster_node_info_cache\nfrom ray.serve._private.usage import ServeUsageTag\nfrom ray.serve._private.utils import (\n    get_random_letters,\n    DEFAULT,\n)\nfrom ray.serve._private.router import Router, RequestMetadata\nfrom ray.util import metrics\nfrom ray.util.annotations import Deprecated, DeveloperAPI, PublicAPI\n\n_global_async_loop = None\n\n\ndef _create_or_get_async_loop_in_thread():\n    global _global_async_loop\n    if _global_async_loop is None:\n        _global_async_loop = asyncio.new_event_loop()\n        thread = threading.Thread(\n            daemon=True,\n            target=_global_async_loop.run_forever,\n        )\n        thread.start()\n    return _global_async_loop\n\n\n@dataclass(frozen=True)\nclass _HandleOptions:\n    \"\"\"Options for each ServeHandle instance.\n\n    These fields can be changed by calling `.options()` on a handle.\n    \"\"\"\n\n    method_name: str = \"__call__\"\n    multiplexed_model_id: str = \"\"\n    stream: bool = False\n    _prefer_local_routing: bool = False\n    _router_cls: str = \"\"\n    _request_protocol: str = RequestProtocol.UNDEFINED\n\n    def copy_and_update(\n        self,\n        method_name: Union[str, DEFAULT] = DEFAULT.VALUE,\n        multiplexed_model_id: Union[str, DEFAULT] = DEFAULT.VALUE,\n        stream: Union[bool, DEFAULT] = DEFAULT.VALUE,\n        _prefer_local_routing: Union[bool, DEFAULT] = DEFAULT.VALUE,\n        _router_cls: Union[str, DEFAULT] = DEFAULT.VALUE,\n        _request_protocol: Union[str, DEFAULT] = DEFAULT.VALUE,\n    ) -> \"_HandleOptions\":\n        return _HandleOptions(\n            method_name=(\n                self.method_name if method_name == DEFAULT.VALUE else method_name\n            ),\n            multiplexed_model_id=(\n                self.multiplexed_model_id\n                if multiplexed_model_id == DEFAULT.VALUE\n                else multiplexed_model_id\n            ),\n            stream=self.stream if stream == DEFAULT.VALUE else stream,\n            _prefer_local_routing=self._prefer_local_routing\n            if _prefer_local_routing == DEFAULT.VALUE\n            else _prefer_local_routing,\n            _router_cls=self._router_cls\n            if _router_cls == DEFAULT.VALUE\n            else _router_cls,\n            _request_protocol=self._request_protocol\n            if _request_protocol == DEFAULT.VALUE\n            else _request_protocol,\n        )\n\n\nclass _DeploymentHandleBase:\n    def __init__(\n        self,\n        deployment_name: str,\n        app_name: str,\n        *,\n        handle_options: Optional[_HandleOptions] = None,\n        _router: Optional[Router] = None,\n        _is_for_sync_context: bool = False,\n        _request_counter: Optional[metrics.Counter] = None,\n        _recorded_telemetry: bool = False,\n    ):\n        self.deployment_id = DeploymentID(deployment_name, app_name)\n        self.handle_options = handle_options or _HandleOptions()\n        self._is_for_sync_context = _is_for_sync_context\n        self._recorded_telemetry = _recorded_telemetry\n\n        self.request_counter = _request_counter or metrics.Counter(\n            \"serve_handle_request_counter\",\n            description=(\n                \"The number of handle.remote() calls that have been \"\n                \"made on this handle.\"\n            ),\n            tag_keys=(\"handle\", \"deployment\", \"route\", \"application\"),\n        )\n        if app_name:\n            handle_tag = f\"{app_name}#{deployment_name}#{get_random_letters()}\"\n        else:\n            handle_tag = f\"{deployment_name}#{get_random_letters()}\"\n\n        # TODO(zcin): Separate deployment_id into deployment and application tags\n        self.request_counter.set_default_tags(\n            {\n                \"handle\": handle_tag,\n                \"deployment\": self.deployment_id.name,\n                \"application\": self.deployment_id.app,\n            }\n        )\n\n        self._router: Optional[Router] = _router\n\n    def _record_telemetry_if_needed(self):\n        # Record telemetry once per handle and not when used from the proxy\n        # (detected via request protocol).\n        if (\n            not self._recorded_telemetry\n            and self.handle_options._request_protocol == RequestProtocol.UNDEFINED\n        ):\n            if self.__class__ == DeploymentHandle:\n                ServeUsageTag.DEPLOYMENT_HANDLE_API_USED.record(\"1\")\n            elif self.__class__ == RayServeHandle:\n                ServeUsageTag.RAY_SERVE_HANDLE_API_USED.record(\"1\")\n            else:\n                ServeUsageTag.RAY_SERVE_SYNC_HANDLE_API_USED.record(\"1\")\n\n            self._recorded_telemetry = True\n\n    def _set_request_protocol(self, request_protocol: RequestProtocol):\n        self.handle_options = self.handle_options.copy_and_update(\n            _request_protocol=request_protocol\n        )\n\n    def _get_or_create_router(self) -> Router:\n        if self._router is None:\n            if self._is_for_sync_context:\n                event_loop = _create_or_get_async_loop_in_thread()\n            else:\n                event_loop = get_or_create_event_loop()\n\n            node_id = ray.get_runtime_context().get_node_id()\n            try:\n                cluster_node_info_cache = create_cluster_node_info_cache(\n                    GcsClient(address=ray.get_runtime_context().gcs_address)\n                )\n                cluster_node_info_cache.update()\n                availability_zone = cluster_node_info_cache.get_node_az(node_id)\n            except Exception:\n                availability_zone = None\n\n            self._router = Router(\n                serve.context.get_global_client()._controller,\n                self.deployment_id,\n                node_id,\n                availability_zone,\n                event_loop=event_loop,\n                _use_new_routing=RAY_SERVE_ENABLE_NEW_ROUTING,\n                _prefer_local_node_routing=self.handle_options._prefer_local_routing,\n                _router_cls=self.handle_options._router_cls,\n            )\n\n        return self._router\n\n    @property\n    def deployment_name(self) -> str:\n        return self.deployment_id.name\n\n    @property\n    def app_name(self) -> str:\n        return self.deployment_id.app\n\n    @property\n    def _is_same_loop(self) -> bool:\n        \"\"\"Whether the caller's asyncio loop is the same loop for handle.\n\n        This is only useful for async handles.\n        \"\"\"\n        if self._is_for_sync_context:\n            return True\n\n        return get_or_create_event_loop() == self._get_or_create_router()._event_loop\n\n    def _options(\n        self,\n        *,\n        method_name: Union[str, DEFAULT] = DEFAULT.VALUE,\n        multiplexed_model_id: Union[str, DEFAULT] = DEFAULT.VALUE,\n        stream: Union[bool, DEFAULT] = DEFAULT.VALUE,\n        use_new_handle_api: Union[bool, DEFAULT] = DEFAULT.VALUE,\n        _prefer_local_routing: Union[bool, DEFAULT] = DEFAULT.VALUE,\n        _router_cls: Union[str, DEFAULT] = DEFAULT.VALUE,\n    ):\n        new_handle_options = self.handle_options.copy_and_update(\n            method_name=method_name,\n            multiplexed_model_id=multiplexed_model_id,\n            stream=stream,\n            _prefer_local_routing=_prefer_local_routing,\n            _router_cls=_router_cls,\n        )\n\n        if (\n            self._router is None\n            and _router_cls == DEFAULT.VALUE\n            and _prefer_local_routing == DEFAULT.VALUE\n        ):\n            self._get_or_create_router()\n\n        if use_new_handle_api is True:\n            cls = DeploymentHandle\n        else:\n            cls = self.__class__\n\n        return cls(\n            self.deployment_name,\n            self.app_name,\n            handle_options=new_handle_options,\n            _router=None if _router_cls != DEFAULT.VALUE else self._router,\n            _request_counter=self.request_counter,\n            _is_for_sync_context=self._is_for_sync_context,\n            _recorded_telemetry=self._recorded_telemetry,\n        )\n\n    def _remote(self, args: Tuple[Any], kwargs: Dict[str, Any]) -> Coroutine:\n        self._record_telemetry_if_needed()\n        _request_context = ray.serve.context._serve_request_context.get()\n        request_metadata = RequestMetadata(\n            _request_context.request_id,\n            self.deployment_name,\n            call_method=self.handle_options.method_name,\n            route=_request_context.route,\n            app_name=self.app_name,\n            multiplexed_model_id=self.handle_options.multiplexed_model_id,\n            is_streaming=self.handle_options.stream,\n            _request_protocol=self.handle_options._request_protocol,\n        )\n        self.request_counter.inc(\n            tags={\n                \"route\": _request_context.route,\n                \"application\": _request_context.app_name,\n            }\n        )\n        return self._get_or_create_router().assign_request(\n            request_metadata, *args, **kwargs\n        )\n\n    def __getattr__(self, name):\n        return self.options(method_name=name)\n\n    def shutdown(self):\n        if self._router:\n            self._router.shutdown()\n\n    def __repr__(self):\n        return f\"{self.__class__.__name__}\" f\"(deployment='{self.deployment_name}')\"\n\n    @classmethod\n    def _deserialize(cls, kwargs):\n        \"\"\"Required for this class's __reduce__ method to be picklable.\"\"\"\n        return cls(**kwargs)\n\n    def __reduce__(self):\n        serialized_constructor_args = {\n            \"deployment_name\": self.deployment_name,\n            \"app_name\": self.app_name,\n            \"handle_options\": self.handle_options,\n            \"_is_for_sync_context\": self._is_for_sync_context,\n        }\n        return self.__class__._deserialize, (serialized_constructor_args,)\n\n\n@PublicAPI(stability=\"beta\")\nclass RayServeHandle(_DeploymentHandleBase):\n    \"\"\"A handle used to make requests from one deployment to another.\n\n    This is used to compose multiple deployments into a single application. After\n    building the application, this handle is substituted at runtime for deployments\n    passed as arguments via `.bind()`.\n\n    Example:\n\n    .. code-block:: python\n\n        import ray\n        from ray import serve\n        from ray.serve.handle import RayServeHandle, RayServeSyncHandle\n\n        @serve.deployment\n        class Downstream:\n            def __init__(self, message: str):\n                self._message = message\n\n            def __call__(self, name: str) -> str:\n                return self._message + name\n\n        @serve.deployment\n        class Ingress:\n            def __init__(self, handle: RayServeHandle):\n                self._handle = handle\n\n            async def __call__(self, name: str) -> str:\n                obj_ref: ray.ObjectRef = await self._handle.remote(name)\n                return await obj_ref\n\n        app = Ingress.bind(Downstream.bind(\"Hello \"))\n        handle: RayServeSyncHandle = serve.run(app)\n\n        # Prints \"Hello Mr. Magoo\"\n        print(ray.get(handle.remote(\"Mr. Magoo\")))\n\n    \"\"\"\n\n    def options(\n        self,\n        *,\n        method_name: Union[str, DEFAULT] = DEFAULT.VALUE,\n        multiplexed_model_id: Union[str, DEFAULT] = DEFAULT.VALUE,\n        stream: Union[bool, DEFAULT] = DEFAULT.VALUE,\n        use_new_handle_api: Union[bool, DEFAULT] = DEFAULT.VALUE,\n        _prefer_local_routing: Union[bool, DEFAULT] = DEFAULT.VALUE,\n        _router_cls: Union[str, DEFAULT] = DEFAULT.VALUE,\n    ) -> \"RayServeHandle\":\n        \"\"\"Set options for this handle and return an updated copy of it.\n\n        Example:\n\n        .. code-block:: python\n\n            # The following two lines are equivalent:\n            obj_ref = await handle.other_method.remote(*args)\n            obj_ref = await handle.options(method_name=\"other_method\").remote(*args)\n            obj_ref = await handle.options(\n                multiplexed_model_id=\"model:v1\").remote(*args)\n        \"\"\"\n        return self._options(\n            method_name=method_name,\n            multiplexed_model_id=multiplexed_model_id,\n            stream=stream,\n            _prefer_local_routing=_prefer_local_routing,\n            use_new_handle_api=use_new_handle_api,\n            _router_cls=_router_cls,\n        )\n\n    def remote(self, *args, **kwargs) -> asyncio.Task:\n        \"\"\"Issue an asynchronous request to the __call__ method of the deployment.\n\n        Returns an `asyncio.Task` whose underlying result is a Ray ObjectRef that\n        points to the final result of the request.\n\n        The final result can be retrieved by awaiting the ObjectRef.\n\n        Example:\n\n        .. code-block:: python\n\n            obj_ref = await handle.remote(*args)\n            result = await obj_ref\n\n        \"\"\"\n        loop = self._get_or_create_router()._event_loop\n        result_coro = self._remote(args, kwargs)\n        return asyncio.ensure_future(result_coro, loop=loop)\n\n\n@PublicAPI(stability=\"beta\")\nclass RayServeSyncHandle(_DeploymentHandleBase):\n    \"\"\"A handle used to make requests to the ingress deployment of an application.\n\n    This is returned by `serve.run` and can be used to invoke the application from\n    Python rather than over HTTP. For example:\n\n    .. code-block:: python\n\n        import ray\n        from ray import serve\n        from ray.serve.handle import RayServeSyncHandle\n\n        @serve.deployment\n        class Ingress:\n            def __call__(self, name: str) -> str:\n                return f\"Hello {name}\"\n\n        app = Ingress.bind()\n        handle: RayServeSyncHandle = serve.run(app)\n\n        # Prints \"Hello Mr. Magoo\"\n        print(ray.get(handle.remote(\"Mr. Magoo\")))\n\n    \"\"\"\n\n    def options(\n        self,\n        *,\n        method_name: Union[str, DEFAULT] = DEFAULT.VALUE,\n        multiplexed_model_id: Union[str, DEFAULT] = DEFAULT.VALUE,\n        stream: Union[bool, DEFAULT] = DEFAULT.VALUE,\n        use_new_handle_api: Union[bool, DEFAULT] = DEFAULT.VALUE,\n        _prefer_local_routing: Union[bool, DEFAULT] = DEFAULT.VALUE,\n        _router_cls: Union[str, DEFAULT] = DEFAULT.VALUE,\n    ) -> \"RayServeSyncHandle\":\n        \"\"\"Set options for this handle and return an updated copy of it.\n\n        Example:\n\n        .. code-block:: python\n\n            # The following two lines are equivalent:\n            obj_ref = handle.other_method.remote(*args)\n            obj_ref = handle.options(method_name=\"other_method\").remote(*args)\n            obj_ref = handle.options(multiplexed_model_id=\"model1\").remote(*args)\n\n        \"\"\"\n        return self._options(\n            method_name=method_name,\n            multiplexed_model_id=multiplexed_model_id,\n            stream=stream,\n            use_new_handle_api=use_new_handle_api,\n            _prefer_local_routing=_prefer_local_routing,\n            _router_cls=_router_cls,\n        )\n\n    def remote(self, *args, **kwargs) -> ray.ObjectRef:\n        \"\"\"Issue an asynchronous request to the __call__ method of the deployment.\n\n        Returns a Ray ObjectRef whose results can be waited for or retrieved\n        using ray.wait or ray.get, respectively.\n\n        .. code-block:: python\n\n            obj_ref = handle.remote(*args)\n            result = ray.get(obj_ref)\n\n        \"\"\"\n        coro = self._remote(args, kwargs)\n        future: concurrent.futures.Future = asyncio.run_coroutine_threadsafe(\n            coro, self._get_or_create_router()._event_loop\n        )\n        return future.result()\n\n\n@Deprecated(\n    message=\"RayServeDeploymentHandle is no longer used, use RayServeHandle instead.\"\n)\nclass RayServeDeploymentHandle(RayServeHandle):\n    # We had some examples using this class for type hinting. To avoid breaking them,\n    # leave this as an alias.\n    pass\n\n\nclass _DeploymentResponseBase:\n    def __init__(\n        self,\n        assign_request_coro: Coroutine,\n        loop: asyncio.AbstractEventLoop,\n        loop_is_in_another_thread: bool,\n    ):\n        self._assign_request_task = loop.create_task(assign_request_coro)\n\n        if loop_is_in_another_thread:\n            # For the \"sync\" case where the handle is likely used in a driver for\n            # testing, we need to call `run_coroutine_threadsafe` to eagerly execute\n            # the request.\n            self._object_ref_future = asyncio.run_coroutine_threadsafe(\n                self._to_object_ref_or_gen(_record_telemetry=False), loop\n            )\n        else:\n            self._object_ref_future = None\n\n    async def _to_object_ref_or_gen(\n        self,\n        _record_telemetry: bool = True,\n    ) -> Union[ray.ObjectRef, StreamingObjectRefGenerator]:\n        # Record telemetry for using the developer API to convert to an object\n        # ref. Recorded here because all of the other codepaths go through this.\n        # `_record_telemetry` is used to filter other API calls that go through\n        # this path as well as calls from the proxy.\n        if _record_telemetry:\n            ServeUsageTag.DEPLOYMENT_HANDLE_TO_OBJECT_REF_API_USED.record(\"1\")\n        return await self._assign_request_task\n\n    def _to_object_ref_or_gen_sync(\n        self,\n        _record_telemetry: bool = True,\n    ) -> Union[ray.ObjectRef, StreamingObjectRefGenerator]:\n        if self._object_ref_future is None:\n            raise RuntimeError(\n                \"Sync methods should not be called from within an `asyncio` event loop.\"\n                \"Use `await response` or `await response._to_object_ref()` instead.\"\n            )\n\n        if _record_telemetry:\n            ServeUsageTag.DEPLOYMENT_HANDLE_TO_OBJECT_REF_API_USED.record(\"1\")\n\n        return self._object_ref_future.result()\n\n    def cancel(self):\n        \"\"\"Attempt to cancel the `DeploymentHandle` call.\n\n        This is best effort and will only successfully cancel the call if it has not yet\n        been assigned to a replica actor. If the call is successfully cancelled,\n        subsequent operations on the ref will raise an `asyncio.CancelledError` (or a\n        `concurrent.futures.CancelledError` if using synchronous methods like\n        `.result()`).\n        \"\"\"\n        # TODO(edoakes): when actor task cancellation is supported, we should cancel\n        # the scheduled actor task here if the assign request task is done.\n        self._assign_request_task.cancel()\n\n\n@PublicAPI(stability=\"alpha\")\nclass DeploymentResponse(_DeploymentResponseBase):\n    \"\"\"A future-like object wrapping the result of a unary deployment handle call.\n\n    From inside a deployment, a `DeploymentResponse` can be awaited to retrieve the\n    output of the call without blocking the asyncio event loop.\n\n    From outside a deployment, `.result()` can be used to retrieve the output in a\n    blocking manner.\n\n    Example:\n\n    .. code-block:: python\n\n        from ray import serve\n        from ray.serve.handle import DeploymentHandle\n\n        @serve.deployment\n        class Downstream:\n            def say_hi(self, message: str) -> str:\n                return f\"Hello {message}!\"\n\n        @serve.deployment\n        class Caller:\n            def __init__(self, handle: DeploymentHandle):\n                self._downstream_handle = handle\n\n        async def __call__(self, message: str) -> str:\n            # Inside a deployment: `await` the result to enable concurrency.\n            response = self._downstream_handle.say_hi.remote(message)\n            return await response\n\n        app = Caller.bind(Downstream.bind())\n        handle: DeploymentHandle = serve.run(app)\n\n        # Outside a deployment: call `.result()` to get output.\n        response = handle.remote(\"world\")\n        assert response.result() == \"Hello world!\"\n\n    A `DeploymentResponse` can be passed directly to another `DeploymentHandle` call\n    without fetching the result to enable composing multiple deployments together.\n\n    Example:\n\n    .. code-block:: python\n\n        from ray import serve\n        from ray.serve.handle import DeploymentHandle\n\n        @serve.deployment\n        class Adder:\n            def add(self, val: int) -> int:\n                return val + 1\n\n        @serve.deployment\n        class Caller:\n            def __init__(self, handle: DeploymentHandle):\n                self._adder_handle = handle\n\n        async def __call__(self, start: int) -> int:\n            return await self._adder_handle.add.remote(\n                # Pass the response directly to another handle call without awaiting.\n                self._adder_handle.add.remote(start)\n            )\n\n        app = Caller.bind(Adder.bind())\n        handle: DeploymentHandle = serve.run(app)\n        assert handle.remote(0).result() == 2\n    \"\"\"\n\n    def __await__(self):\n        \"\"\"Yields the final result of the deployment handle call.\"\"\"\n        obj_ref = yield from self._assign_request_task.__await__()\n        result = yield from obj_ref.__await__()\n        return result\n\n    def result(self, timeout_s: Optional[float] = None) -> Any:\n        \"\"\"Fetch the result of the handle call synchronously.\n\n        This should *not* be used from within a deployment as it runs in an asyncio\n        event loop. For model composition, `await` the response instead.\n\n        If `timeout_s` is provided and the result is not available before the timeout,\n        a `TimeoutError` is raised.\n        \"\"\"\n        return ray.get(\n            self._to_object_ref_sync(_record_telemetry=False), timeout=timeout_s\n        )\n\n    @DeveloperAPI\n    async def _to_object_ref(self, _record_telemetry: bool = True) -> ray.ObjectRef:\n        \"\"\"Advanced API to convert the response to a Ray `ObjectRef`.\n\n        This is used to pass the output of a `DeploymentHandle` call to a Ray task or\n        actor method call.\n\n        This method is `async def` because it will block until the handle call has been\n        assigned to a replica actor. If there are many requests in flight and all\n        replicas' queues are full, this may be a slow operation.\n        \"\"\"\n        return await self._to_object_ref_or_gen(_record_telemetry=_record_telemetry)\n\n    @DeveloperAPI\n    def _to_object_ref_sync(self, _record_telemetry: bool = True) -> ray.ObjectRef:\n        \"\"\"Advanced API to convert the response to a Ray `ObjectRef`.\n\n        This is used to pass the output of a `DeploymentHandle` call to a Ray task or\n        actor method call.\n\n        This method is a *blocking* call because it will block until the handle call has\n        been assigned to a replica actor. If there are many requests in flight and all\n        replicas' queues are full, this may be a slow operation.\n\n        From inside a deployment, `_to_object_ref` should be used instead to avoid\n        blocking the asyncio event loop.\n        \"\"\"\n        return self._to_object_ref_or_gen_sync(_record_telemetry=_record_telemetry)\n\n\n@PublicAPI(stability=\"alpha\")\nclass DeploymentResponseGenerator(_DeploymentResponseBase):\n    \"\"\"A future-like object wrapping the result of a streaming deployment handle call.\n\n    This is returned when using `handle.options(stream=True)` and calling a generator\n    deployment method.\n\n    `DeploymentResponseGenerator` is both a synchronous and asynchronous iterator.\n\n    When iterating over results from inside a deployment, `async for` should be used to\n    avoid blocking the asyncio event loop.\n\n    When iterating over results from outside a deployment, use a standard `for` loop.\n\n    Example:\n\n    .. code-block:: python\n\n        from typing import AsyncGenerator, Generator\n\n        from ray import serve\n        from ray.serve.handle import DeploymentHandle\n\n        @serve.deployment\n        class Streamer:\n            def generate_numbers(self, limit: int) -> Generator[int]:\n                for i in range(limit):\n                    yield i\n\n        @serve.deployment\n        class Caller:\n            def __init__(self, handle: DeploymentHandle):\n                # Set `stream=True` on the handle to enable streaming calls.\n                self._streaming_handle = handle.options(stream=True)\n\n        async def __call__(self, limit: int) -> AsyncIterator[int]:\n            gen: DeploymentResponseGenerator = (\n                self._streaming_handle.generate_numbers.remote(limit)\n            )\n\n            # Inside a deployment: use `async for` to enable concurrency.\n            async for i in gen:\n                yield i\n\n        app = Caller.bind(Streamer.bind())\n        handle: DeploymentHandle = serve.run(app)\n\n        # Outside a deployment: use a standard `for` loop.\n        gen: DeploymentResponseGenerator = handle.options(stream=True).remote(10)\n        assert [i for i in gen] == list(range(10))\n\n    A `DeploymentResponseGenerator` *cannot* currently be passed to another\n    `DeploymentHandle` call.\n    \"\"\"\n\n    def __init__(\n        self,\n        assign_request_coro: Coroutine,\n        loop: asyncio.AbstractEventLoop,\n        loop_is_in_another_thread: bool,\n    ):\n        super().__init__(\n            assign_request_coro,\n            loop=loop,\n            loop_is_in_another_thread=loop_is_in_another_thread,\n        )\n        self._obj_ref_gen: Optional[StreamingObjectRefGenerator] = None\n\n    def __aiter__(self) -> AsyncIterator[Any]:\n        return self\n\n    async def __anext__(self) -> Any:\n        if self._obj_ref_gen is None:\n            self._obj_ref_gen = await self._to_object_ref_gen(_record_telemetry=False)\n\n        next_obj_ref = await self._obj_ref_gen.__anext__()\n        return await next_obj_ref\n\n    def __iter__(self) -> Iterator[Any]:\n        return self\n\n    def __next__(self) -> Any:\n        if self._obj_ref_gen is None:\n            self._obj_ref_gen = self._to_object_ref_gen_sync(_record_telemetry=False)\n\n        next_obj_ref = self._obj_ref_gen.__next__()\n        return ray.get(next_obj_ref)\n\n    @DeveloperAPI\n    async def _to_object_ref_gen(\n        self, _record_telemetry: bool = True\n    ) -> StreamingObjectRefGenerator:\n        \"\"\"Advanced API to convert the generator to a Ray `StreamingObjectRefGenerator`.\n\n        This method is `async def` because it will block until the handle call has been\n        assigned to a replica actor. If there are many requests in flight and all\n        replicas' queues are full, this may be a slow operation.\n        \"\"\"\n        return await self._to_object_ref_or_gen(_record_telemetry=_record_telemetry)\n\n    @DeveloperAPI\n    def _to_object_ref_gen_sync(\n        self, _record_telemetry: bool = True\n    ) -> StreamingObjectRefGenerator:\n        \"\"\"Advanced API to convert the generator to a Ray `StreamingObjectRefGenerator`.\n\n        This method is a *blocking* call because it will block until the handle call has\n        been assigned to a replica actor. If there are many requests in flight and all\n        replicas' queues are full, this may be a slow operation.\n\n        From inside a deployment, `_to_object_ref_gen` should be used instead to avoid\n        blocking the asyncio event loop.\n        \"\"\"\n        return self._to_object_ref_or_gen_sync(_record_telemetry=_record_telemetry)\n\n\n@PublicAPI(stability=\"beta\")\nclass DeploymentHandle(_DeploymentHandleBase):\n    \"\"\"A handle used to make requests to a deployment at runtime.\n\n    This is primarily used to compose multiple deployments within a single application.\n    It can also be used to make calls to the ingress deployment of an application (e.g.,\n    for programmatic testing).\n\n    Example:\n\n\n    .. code-block:: python\n\n        import ray\n        from ray import serve\n        from ray.serve.handle import DeploymentHandle, DeploymentResponse\n\n        @serve.deployment\n        class Downstream:\n            def say_hi(self, message: str):\n                return f\"Hello {message}!\"\n                self._message = message\n\n        @serve.deployment\n        class Ingress:\n            def __init__(self, handle: DeploymentHandle):\n                self._downstream_handle = handle\n\n            async def __call__(self, name: str) -> str:\n                response = self._handle.say_hi.remote(name)\n                return await response\n\n        app = Ingress.bind(Downstream.bind())\n        handle: DeploymentHandle = serve.run(app)\n        response = handle.remote(\"world\")\n        assert response.result() == \"Hello world!\"\n    \"\"\"\n\n    def options(\n        self,\n        *,\n        method_name: Union[str, DEFAULT] = DEFAULT.VALUE,\n        multiplexed_model_id: Union[str, DEFAULT] = DEFAULT.VALUE,\n        stream: Union[bool, DEFAULT] = DEFAULT.VALUE,\n        use_new_handle_api: Union[bool, DEFAULT] = DEFAULT.VALUE,\n        _prefer_local_routing: Union[bool, DEFAULT] = DEFAULT.VALUE,\n        _router_cls: Union[str, DEFAULT] = DEFAULT.VALUE,\n    ) -> \"DeploymentHandle\":\n        \"\"\"Set options for this handle and return an updated copy of it.\n\n        Example:\n\n        .. code-block:: python\n\n            response = handle.options(\n                method_name=\"other_method\",\n                multiplexed_model_id=\"model:v1\",\n            ).remote()\n        \"\"\"\n        return self._options(\n            method_name=method_name,\n            multiplexed_model_id=multiplexed_model_id,\n            stream=stream,\n            use_new_handle_api=use_new_handle_api,\n            _prefer_local_routing=_prefer_local_routing,\n            _router_cls=_router_cls,\n        )\n\n    def remote(\n        self, *args, **kwargs\n    ) -> Union[DeploymentResponse, DeploymentResponseGenerator]:\n        \"\"\"Issue a call to the deployment.\n\n        By default, the result is a `DeploymentResponse` that can be awaited to fetch\n        the result of the call or passed to another `.remote()` call to compose multiple\n        deployments.\n\n        If `handle.options(stream=True)` is set and a generator method is called, this\n        returns a `DeploymentResponseGenerator` instead.\n\n        Example:\n\n        .. code-block:: python\n\n            # Fetch the result directly.\n            response = handle.remote()\n            result = await response\n\n            # Pass the result to another handle call.\n            composed_response = handle2.remote(handle1.remote())\n            composed_result = await composed_response\n\n        \"\"\"\n        loop = self._get_or_create_router()._event_loop\n        result_coro = self._remote(args, kwargs)\n        if self.handle_options.stream:\n            response_cls = DeploymentResponseGenerator\n        else:\n            response_cls = DeploymentResponse\n\n        return response_cls(\n            result_coro,\n            loop=loop,\n            loop_is_in_another_thread=self._is_for_sync_context,\n        )\n","sub_path":"python/ray/serve/handle.py","file_name":"handle.py","file_ext":"py","file_size_in_byte":30794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"636364649","text":"num=list(input().split(\",\"))\nnum[0]=num[0][1:len(num[0])]\nnum[-1]=num[-1][0:-1]\nfor i in range(len(num)):\n    num[i]=int(num[i])\nn=int(input())\nres=[]\nfor i in range(n):\n    for j in range(i,n):\n        tem=0\n        for k in range(i,j+1):\n            if k==n:\n                break\n            tem+=num[k]\n        if tem>=n:\n            res.append(tem)\n            break\nif len(res)==0:\n    print(-1)\nelse:\n    res.sort()\n    print(res[0])","sub_path":"Code/CodeRecords/2644/61519/314604.py","file_name":"314604.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"410845664","text":"# A Rose Spiral, brought to you by PharaohCola13\n\nimport mpl_toolkits.mplot3d.axes3d as p3\nimport matplotlib.pyplot as plt\nfrom matplotlib import *\nfrom numpy import *\nfrom mpl_toolkits.mplot3d.art3d import *\nfrom matplotlib.animation import *\n\nname = \"Rose Spiral\"\n\ndef shape(fig, alpha, color, edge_c, edge_w, grid, sides, edges):\n    # Definition of x\n    def x_(u, v):\n        x = r * cos(u) * sin(v)\n        return x\n\n    # Definition of y\n    def y_(u, v):\n        y = r * sin(u) * sin(v)\n        return y\n\n\n    # Definition of z\n    def z_(u, v):\n        z = r * cos(v)\n        return z\n\n    # Value of the angles\n    u = linspace(0, 2 * pi, sides + 1)\n    v = linspace(0, pi, edges)\n\n    u, v = meshgrid(u, v)\n\n    r = 2 + sin(7 * u + 5 * v)\n\n    # Symbolic representation\n    x = x_(u, v)\n    y = y_(u, v)\n    z = z_(u, v)\n\n    # Figure Properties\n    ax = p3.Axes3D(fig)\n    ax.set_facecolor('black')  # Figure background turns black\n\n    # Axis Properties\n    plt.axis(grid)  # Turns off the axis grid\n    plt.axis('equal')\n\n    # Axis Limits\n    ax.set_xlim(-3, 3)\n    ax.set_ylim(-3, 3)\n    ax.set_zlim(-3, 3)\n\n    # Surface Plot\n    rose = ax.plot_surface(x, y, z)\n\n    rose.set_alpha(alpha)  # Transparency of figure\n    rose.set_edgecolor(edge_c)  # Edge color of the lines on the figure\n    rose.set_linewidth(edge_w)  # Line width of the edges\n    rose.set_facecolor(color)  # General color of the figure\n","sub_path":"src/Current Models/Misc./rose_spiral.py","file_name":"rose_spiral.py","file_ext":"py","file_size_in_byte":1423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"347788289","text":"#!/usr/bin/python\n\nimport argparse\nimport yaml\nimport logging\n\nfrom ingester.es_common import *\nfrom ingester.nlp_service import *\nfrom ingester.annotations_indexer import *\n\n\nclass AppConfig:\n    \"\"\"\n    The configuration file for the indexer application\n    \"\"\"\n    def __init__(self, file_path):\n        \"\"\"\n        :param: filepath: the path for the configuration file stored in YAML\n        \"\"\"\n        try:\n            with open(file_path) as conf_file:\n                yaml_file = yaml.safe_load(conf_file)\n\n                if 'source' not in yaml_file or \\\n                        'nlp-service' not in yaml_file or \\\n                        'sink' not in yaml_file or \\\n                        'mapping' not in yaml_file:\n                    raise Exception(\"Invalid configuration file provided\")\n\n                self.params = yaml_file\n\n        except FileNotFoundError:\n            raise Exception(\"Cannot open configuration file\")\n\n\nif __name__ == \"__main__\":\n    # parse the input parameters\n    parser = argparse.ArgumentParser(description='ElasticSearch-to-ElasticSearch annotations indexer')\n    parser.add_argument('--config', help='configuration file path')\n    args = parser.parse_args()\n\n    if args.config is None:\n        parser.print_usage()\n        exit(0)\n\n    # setup logging\n    log_format = '[%(asctime)s] [%(levelname)s] %(name)s: %(message)s'\n    logging.basicConfig(format=log_format, level=logging.INFO)\n\n    try:\n        config = AppConfig(args.config)\n\n        # set up Elastic logger for the initialization time\n        logging.getLogger('elasticsearch').setLevel(level=logging.ERROR)\n\n        # initialize the elastic source\n        source_params = config.params['source']\n        es_source_conf = ElasticConnectorConfig(hosts=source_params['es']['hosts'])\n        es_source_conn = ElasticConnector(es_source_conf)\n\n        es_source = ElasticRangedIndexer(es_source_conn, source_params['es']['index-name'])\n\n        # initialize NLP service\n        nlp_service = NlpService(config.params['nlp-service']['endpoint-url'])\n\n        # initialize the elastic sink\n        sink_params = config.params['sink']\n        es_sink_conf = ElasticConnectorConfig(hosts=sink_params['es']['hosts'])\n        es_sink_conn = ElasticConnector(es_sink_conf)\n        es_sink = ElasticIndexer(es_sink_conn, sink_params['es']['index-name'])\n\n        # initialize the indexer\n        mapping = config.params['mapping']\n        indexer = BatchAnnotationsIndexer(nlp_service=nlp_service,\n                                          source_indexer=es_source,\n                                          source_text_field=mapping['source']['text-field'],\n                                          source_docid_field=mapping['source']['docid-field'],\n                                          source_fields_to_persist=mapping['source']['persist-fields'],\n                                          split_index_by_field=mapping['sink']['split-index-by-field'],\n                                          sink_indexer=es_sink,\n                                          source_batch_date_field=mapping['source']['batch']['date-field'],\n                                          batch_date_format=mapping['source']['batch']['date-format'])\n    except Exception as e:\n        log = logging.getLogger('main')\n        log.error(\"Cannot initialize the application: \" + str(e))\n        exit(1)\n\n    # set up the Elastic logger to be more verbose and run the indexer\n    logging.getLogger('elasticsearch').setLevel(level=logging.WARN)\n    \n    indexer.index_range(batch_date_start=mapping['source']['batch']['date-start'],\n                        batch_date_end=mapping['source']['batch']['date-end'])\n\n","sub_path":"ingester/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":3694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"521732544","text":"#!/usr/bin/python\n# coding=utf-8\n\nimport time\nimport os\nimport re\nimport sys\nfrom file_helper import FileHelper\nfrom mongo_helper import MongoHelper\nfrom http_helper import HttpHelper\nfrom url_helper import UrlHelper\nfrom parse_helper import ParseHelper\n\nMONGO_HOST = \"172.16.40.128:27017,172.16.40.140:27017,172.16.40.141:27017\"\n#MONGO_HOST = \"127.0.0.1:27017\"\nMONGO_DATABASE_NAME = \"ZDBWordPress\"\nMONGO_SEED_COLLECTION = \"seed\"\nMONGO_KEY_COLLECTION = \"key\"\nMONGO_PAGE_COLLECTION = \"page\"\nMONGO_TRACK_COLLECTION = \"track\"\nSEARCH_KEY_PATTERN = 'http://healthtopquestions.com/wp-content/plugins/post-tester/bingapi.php?token=P@ssw0rd&t=web&q=\"{0}\"&offset={1}&count={2}'\nSEARCH_KEY_PATTERN = 'http://www.infosoap.com/wp-content/plugins/post-tester/bingapi.php?token=P@ssw0rd&t=web&q=\"{0}\"&offset={1}&count={2}'\nSEARCH_PAGE_PATTERN = 'http://healthtopquestions.com/wp-content/plugins/post-tester/bingapi.php?token=P@ssw0rd&t=web&q=\"{0}\"%20wordpress&offset={1}&count={2}'\nSEARCH_PAGE_PATTERN = 'http://www.infosoap.com/wp-content/plugins/post-tester/bingapi.php?token=P@ssw0rd&t=web&q=\"{0}\"%20wordpress&offset={1}&count={2}'\nBLACK_SITE_LIST = ['webmd.com', 'drugs.com']\nROOT_PATH = 'E:/NutchData/pages/wordpress'\nINSERT_URL = 'http://www.infosoap.com/wp-content/plugins/post-api/insert_post.php?token=P@ssw0rd'\n\nif not os.path.exists(ROOT_PATH):\n    os.makedirs(ROOT_PATH, exist_ok=True)\n\nseedCollection = MongoHelper(MONGO_HOST, 27017, MONGO_DATABASE_NAME, MONGO_SEED_COLLECTION, \"title\")\nkeyCollection = MongoHelper(MONGO_HOST, 27017, MONGO_DATABASE_NAME, MONGO_KEY_COLLECTION, \"title\")\npageCollection = MongoHelper(MONGO_HOST, 27017, MONGO_DATABASE_NAME, MONGO_PAGE_COLLECTION, \"url\")\n    \ndef loadSeedFromFile():\n    keyList = FileHelper.loadFileList('./key.txt')\n    for key in keyList:\n        old = keyCollection.findOneByFilter({'title': key})\n        if old == None:\n            seedCollection.insertOne({\n                'title': key,\n                'state': 'CREATED',\n                'level': 0,\n                'parent': None,\n            })\n\ndef loadSeedFromKey():\n    try:\n        total = 0\n        while True:\n            docList = keyCollection.nextPage(100)\n            if docList == None or len(docList) == 0:\n                break\n\n            for doc in docList:\n                title = doc['title']\n                if seedCollection.findOneByFilter({'title': title}) == None:\n                    seedCollection.insertOne({\n                        'title': title,\n                        'state': 'CREATED',\n                        'level': 0,\n                        'parent': None\n                    })\n                    total += 1\n                    print ('load seed count=' + str(total))\n    except Exception as err :\n        print(err)    \n        \ndef searchKeyBySeed():\n    try:\n        total = 0\n        while True:\n            docList = seedCollection.findPage({'state': 'CREATED'}, 0, 20)\n            if docList == None or len(docList) == 0:\n                break\n\n            for doc in docList:\n                try:\n                    print ('seed={0}'.format(doc['title']))                \n                    url = SEARCH_KEY_PATTERN.format(doc['title'], 0, 10)\n                    errorCode, response = HttpHelper.get(url)\n                    if errorCode != 'OK' or response == None:\n                        continue\n    \n                    if (not 'result' in response):\n                        raise Exception('result not found')\n                        continue\n                    \n                    result = response['result']\n                    if (not 'relatedSearches' in result) \\\n                        or (not 'webPages' in result):\n                        raise Exception('relatedSearches or webPages not found')\n                        continue\n                    \n                    relatedSearches = result['relatedSearches']\n                    if not 'value' in relatedSearches:\n                        raise Exception('value not found')\n                        continue\n                    \n                    webPages = result['webPages']\n                    if not 'totalEstimatedMatches' in webPages:\n                        raise Exception('totalEstimatedMatches not found')\n                        continue\n                    \n                    value = relatedSearches['value']\n                    newKeyList = []\n                    for item in value:\n                        if 'text' in item:\n                            newKeyList.append(item['text'])\n                            \n                    for key in newKeyList:\n                        if keyCollection.findOneByFilter({'title': key}) == None:\n                            keyCollection.insertOne({\n                                'title': key,\n                                'state': 'CREATED',\n                                'level': doc['level'] + 1,\n                                'parent': doc['title'],\n                                'matched': webPages['totalEstimatedMatches']\n                            })\n    \n                    doc['state'] = 'KEYED'\n                    seedCollection.updateOne(doc)\n                    \n                    total += 1\n                    print ('total={0}, key={1}'.format(total, key))\n                    \n                    time.sleep(1)\n                    \n                except Exception as err :\n                    doc['state'] = 'CLOSED'\n                    seedCollection.updateOne(doc)\n                    print(err)\n    \n    except Exception as err :\n        print(err)    \n\n# search pages by key and save into page collection\ndef searchPageByKey():\n    try:\n        total = 0\n        while True:\n            docList = keyCollection.findPage({'state': 'CREATED'}, 0, 20)\n            if docList == None or len(docList) == 0:\n                break\n\n            for doc in docList:\n\n                total += 1\n                print ('total=' + str(total))\n                \n                pageList = []\n                for offset in [0, 20, 40]:\n                    url = SEARCH_PAGE_PATTERN.format(doc['title'], offset, 20)\n                    errorCode, response = HttpHelper.get(url)\n                    if errorCode != 'OK' or response == None or (not 'result' in response):\n                        break\n                    \n                    if not 'webPages' in response['result']:\n                        break\n                    \n                    webPages = response['result']['webPages']\n                    if not 'value' in webPages:\n                        break\n                    \n                    value = webPages['value']\n                    for item in value:\n                        if 'name' in item and 'url' in item and 'snippet' in item:\n                            page = {\n                                'title': item['name'],\n                                'url': item['url'],\n                                'description': item['snippet'],\n                                'state': 'CREATED',\n                                'key': doc['title']\n                            }\n                            \n                            isBlack = False\n                            for site in BLACK_SITE_LIST:\n                                if site in item['url']:\n                                    isBlack = True\n                                    \n                            if not isBlack:\n                                pageList.append(page)\n                \n                if len(pageList) > 0:\n                    doc['pageList'] = pageList\n                    doc['state'] = 'PAGED'\n                    print ('search page by key, key={0}, found={1}'.format(doc['title'], len(pageList)))\n                else:\n                    doc['state'] = 'CLOSED'\n                    print ('search page by key, key={0}, closed'.format(doc['title']))\n                keyCollection.updateOne(doc)\n\n                if len(pageList) > 0:\n                    pageCollection.insertMany(pageList)\n\n                time.sleep(1)\n    \n    except Exception as err :\n        print(err)    \n\n# parse page collection\ndef parsePage():\n    try:\n        total = 0\n        while True:\n            docList = pageCollection.findPage({'state': 'CREATED'}, 0, 20)\n            if docList == None or len(docList) == 0:\n                break\n\n            for doc in docList:\n                \n                try:\n                    total += 1\n                    print ('total=' + str(total))\n                    print ('url=' + doc['url'])\n                    \n                    fileName, finalUrl = HttpHelper.fetchAndSave(doc['url'], ROOT_PATH, 'utf-8', 2)\n                    if fileName == None:\n                        doc['state'] = 'CLOSED'\n                        pageCollection.updateOne(doc)\n                        continue\n                        \n                    filePath = HttpHelper.getFullPath(ROOT_PATH, fileName, 2)\n                    html = FileHelper.readContent(filePath)\n                    pageTitle, pageDescription, pageContent = ParseHelper.parseWordPressContent(html, True)\n                    if pageContent != None and pageTitle != None and pageDescription != None:\n                        doc['pageTitle'] = pageTitle\n                        doc['pageDescription'] = pageDescription\n                        doc['pageContent'] = pageContent\n                        doc['state'] = 'PARSED'\n                    else:\n                        doc['state'] = 'CLOSED'\n                    pageCollection.updateOne(doc)\n    \n                    time.sleep(1)\n                except Exception as err :\n                    print(err)\n                    doc['state'] = 'CLOSED'\n                    pageCollection.updateOne(doc)\n    \n    except Exception as err :\n        print(err)\n    \n# Generate key page from foundList\ndef generateKeyPage():\n    try:\n        total = 0\n        while True:\n            docList = keyCollection.findPage({'state': 'PAGED'}, 0, 10)\n            #docList = keyCollection.nextPage(20)\n            if docList == None or len(docList) == 0:\n                break\n\n            for doc in docList:\n                \n                total += 1\n                print ('total=' + str(total))\n                print ('key=' + doc['title'])\n                \n                foundCount = 0\n                foundList = pageCollection.findPage({'key': doc['title'], 'state': 'PARSED'}, 0, 4)\n                if len(foundList) == 0:\n                    doc['state'] = 'CLOSED'\n                    keyCollection.updateOne(doc)\n                    print ('generate key page fails')\n                    continue\n                \n                finalTitle = ''\n                finalDescription = ''\n                finalContent = ''\n                isFirst = True\n                for page in foundList:\n                    title = page['title']\n                    description = page['description']\n                    content = page['pageContent']\n                    if isFirst:\n                        isFirst = False\n                        finalTitle += title\n                        finalDescription += description\n                    else:\n                        #finalTitle += '; ' + title\n                        finalDescription += '; ' + description\n                    finalContent += '<div class=\"sub-content\">' + content + '</div>'\n                    \n                    foundCount += 1\n                    if foundCount >= 4:\n                        break\n                    \n                doc['finalTitle'] = finalTitle\n                doc['finalDescription'] = finalDescription\n                doc['finalContent'] = finalContent\n                doc['state'] = 'GENERATED'\n                keyCollection.updateOne(doc)\n                print ('generate key page ok')\n\n                time.sleep(1)\n    \n    except Exception as err :\n        print(err)    \n    \ndef resetPage():\n    try:\n        total = 0\n        origState = '*'\n        destState = 'CREATED'\n        while True:\n            docList = pageCollection.nextPage(20)\n            if docList == None or len(docList) == 0:\n                break\n\n            for doc in docList:\n                \n                total += 1\n                if 'state' in doc and (doc['state'] == origState or doc['state'] == '*'):\n                    doc['state'] = destState\n                    pageCollection.updateOne(doc)\n    except Exception as err : \n        print(err)    \n\ndef uploadKeyPage():\n    try:\n        total = 0\n        while True:\n            docList = keyCollection.nextPage(20)\n            if docList == None or len(docList) == 0:\n                break\n\n            for doc in docList:\n                total += 1\n                print ('total={0}, title={1}'.format(total, doc['title']))\n                \n                if doc['state'] != 'GENERATED':\n                    print (\"invalid state, skip\")\n                    continue\n              \n                # upload\n                postTitle = doc['finalTitle']\n                postExcerpt = doc['finalDescription']\n                postContent = doc['finalContent']\n                if postTitle == None or postExcerpt == None or postContent == None:\n                    print('invalid post, key=' + doc['title'])                    \n                    doc['state'] = 'GENERATE_ERROR'\n                    keyCollection.updateOne(doc)\n                    continue\n                    \n                req = {\n                    'ID': 0,\n                    'author': 1,\n                    'title': postTitle,\n                    'excerpt': postExcerpt,\n                    'content': postContent,\n                    'categories': [1]\n                }\n                errorCode, rsp = HttpHelper.post(INSERT_URL, req)\n                if errorCode != 'OK':\n                    raise Exception('insert error, url=' + doc['url'])\n\n                if rsp['errorCode'] == 'OK':\n                    doc['postID'] = rsp['ID']\n                    doc['state'] = 'UPLOADED'\n                    print ('upload OK')\n                else:\n                    doc['state'] = 'UPLOAD_ERROR'\n                    print ('upload ERROR')\n                keyCollection.updateOne(doc)\n\n                time.sleep(1)\n    \n    except Exception as err :\n        print(err)\n                  \nif __name__==\"__main__\":\n    cmd = 'load'\n    if len(sys.argv) == 2:\n        cmd = sys.argv[1]\n    else:\n        cmd = 'upload'\n    \n    if cmd == 'load':\n        loadSeedFromFile()\n    elif cmd == 'loadKey':\n        loadSeedFromKey()\n    elif cmd == 's2k':\n        searchKeyBySeed()\n    elif cmd == 'k2p':\n        searchPageByKey()\n    elif cmd == 'parse':\n        parsePage()\n    elif cmd == 'reset':\n        resetPage()\n    elif cmd == 'generate':\n        generateKeyPage()\n    elif cmd == 'upload':\n        uploadKeyPage()","sub_path":"atm/parser/parser/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":14887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"14724912","text":"import os\nimport logging\n\nenv = Environment(ENV=os.environ)\n\nlogging.basicConfig(level=logging.INFO)\n\nenv['CPPPATH'] = [\".\", \"#../\"]\nenv['CPPDEFINES'] = [\"TEST_HARNESS\"]\nenv['CPPFLAGS'] = [\"-Wall\", \"-Wextra\", \"-g\"]\nenv['CPPINCLUDES'] = []\n\nfor target in COMMAND_LINE_TARGETS:\n\n\ttest_object_paths = [\"{}.test.cpp\".format(target), \"../{}.cpp\".format(target), \"../util.cpp\"]\n\n\ttest_objects = [env.Object(test_object_path) for test_object_path in test_object_paths]\n\ttest_objects += env.Glob(\"mocks/*.cpp\",)\n\n\tprogram = env.Program(test_objects, LIBS=['cppunit'], CC='g++')\n\n\ttest_alias = env.Alias(target, [program], \"./\"+program[0].path)\n\tenv.AlwaysBuild(test_alias)\n","sub_path":"tests/SConstruct","file_name":"SConstruct","file_ext":"","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"317502972","text":"from sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import KFold\nimport numpy as np\nfrom load import load_dataset\n\nfeature_names = [\n    'area',\n    'perimeter',\n    'compactness',\n    'length of kernel',\n    'width of kernel',\n    'asymmetry coefficien',\n    'length of kernel groove',\n]\nfeatures, labels = load_dataset('seeds')\n\nclassifier = KNeighborsClassifier(n_neighbors=1)\n\nkf = KFold(n_splits=5, shuffle=True)\nmeans = []\nfor training,testing in kf.split(features):\n    # We learn a model for this fold with `fit` and then apply it to the\n    # testing data with `predict`:\n    classifier.fit(features[training], labels[training])\n    prediction = classifier.predict(features[testing])\n\n    # np.mean on an array of booleans returns fraction\n    # of correct decisions for this fold:\n    curmean = np.mean(prediction == labels[testing])\n    means.append(curmean)\nprint('Mean accuracy: {:.1%}'.format(np.mean(means)))","sub_path":"demos/seeds/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"137922550","text":"import threading\n\n\nclass Future(threading.Thread):\n\n    \"\"\"Really cheap version of concurrent.futures.\"\"\"\n\n    @classmethod\n    def submit(cls, func, *args, **kwargs):\n        future = cls(func, args, kwargs)\n        future._thread.start()\n        return future\n\n    def __init__(self, func, args=None, kwargs=None):\n        self._func = func\n        self._args = args or ()\n        self._kwargs = kwargs or {}\n        self._thread = threading.Thread(target=self._eval)\n\n    def _eval(self):\n        try:\n            self._result = self._func(*self._args, **self._kwargs)\n            self._exc = None\n        except Exception as e:\n            self._result = None\n            self._exc = e\n\n    def result(self):\n        self._thread.join()\n        if self._exc:\n            raise self._exc\n        else:\n            return self._result\n\n","sub_path":"sgapi/futures.py","file_name":"futures.py","file_ext":"py","file_size_in_byte":838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"269555492","text":"import numpy as np\nimport pandas as pd\nfrom sklearn import metrics, grid_search, svm, linear_model\nfrom sklearn.cluster import KMeans\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\n\nfrom util import get_others_features, build_feature_df\n\n\ndef get_clusters_by_kmeans(features_train):\n    \"\"\"\n    聚类\n    :param features_train: {\"user_name\":[LineFeature() , ...] , ...}\n    :return: (cluster_estimator,cluster_labels_pred),\n            其中\n            clusters_estimator为：{\"user_name\": estimator , ...}\n            cluster_labels_pred为：{\"user_name\": labels , ...}\n    \"\"\"\n    cluster_estimator = {}\n    cluster_labels_pred = {}\n    cluster_threshold = {}\n    cluster_labels_true = {}\n\n    for user_name, line_feature_list in features_train.items():\n        # print(\"正在对{}进行聚类:\".format(user_name))\n        labels_true = [line_feature.real_posture for line_feature in line_feature_list]\n        posture_feature_df = build_feature_df(line_feature_list, \"posture\")\n        max_ss = -1\n        estimator_kmeans = None\n\n        # 利用轮廓系数确定k的个数\n        for cluster_num in range(2, 6):\n            estimator_tmp = KMeans(init='k-means++', n_clusters=cluster_num)\n            labels = estimator_tmp.fit_predict(posture_feature_df.values)\n            ss = metrics.silhouette_score(posture_feature_df.values, labels, metric='euclidean')\n            if ss > max_ss:\n                max_ss = ss\n                estimator_kmeans = estimator_tmp\n                # print(\"cluster num : {} , inertial : {}\".format(cluster_num, ss))\n\n        # estimator_kmeans = KMeans(init='k-means++', n_clusters=4).fit(posture_feature_df.values)\n\n        min_dist = estimator_kmeans.transform(posture_feature_df.values).min(axis=1)\n        threshold = 3 * np.mean(min_dist) + 9 * np.var(min_dist)\n\n        # print(metrics.adjusted_rand_score(labels_true, estimator_kmeans.labels_))\n\n        cluster_threshold[user_name] = threshold\n        cluster_estimator[user_name] = estimator_kmeans\n        cluster_labels_pred[user_name] = estimator_kmeans.labels_\n        cluster_labels_true[user_name] = labels_true\n        # print(estimator_kmeans.n_clusters)\n\n    return cluster_estimator, cluster_labels_pred, cluster_threshold, cluster_labels_true\n\n\ndef get_cluster_classifiers(features_train, cluster_labels, train_clf):\n    \"\"\"\n    为每个用户的每个聚类训练一个分类器\n    :param features_train: {\"user_name\":[LineFeature() , ...] , ...}\n    :param cluster_labels: {\"user_name\": labels, ...}\n    :param train_clf:具体训练分类器的算法\n    :return:{\"user_name\" : {cluster_num : clf , ...} ,...}\n    \"\"\"\n\n    user_cluster_clf = {}\n    for user_name, line_feature_list in features_train.items():\n        # print(\"正在为{}训练分类器：\".format(user_name))\n        cluster = cluster_labels[user_name]\n\n        # 将特征向量按照聚类结果放在一起\n        clustered_features = {}\n        for i in range(len(cluster)):\n            cluster_num = cluster[i]\n            if cluster_num not in clustered_features:\n                clustered_features[cluster_num] = []\n            clustered_features[cluster_num].append(line_feature_list[i])\n\n        # 对每一个聚类分别训练分类器\n        cluster_clf_dict = {}\n        for cluster_num, curr_feature_list in clustered_features.items():\n            # 正样本\n\n            gesture_feature_pos_df = build_feature_df(curr_feature_list, \"gesture\")\n            # 负样本\n            others_feature_list = get_others_features(user_name, gesture_feature_pos_df.shape[0],\n                                                      features_train)\n            gesture_feature_neg_df = build_feature_df(others_feature_list, \"gesture\")\n\n            # 合并\n            feature_df = pd.concat([gesture_feature_pos_df, gesture_feature_neg_df])\n            # 标号\n            labels = np.array(([1] * gesture_feature_pos_df.shape[0]) + ([-1] * gesture_feature_neg_df.shape[0]))\n\n            clf = train_clf(feature_df, labels)\n\n            cluster_clf_dict[cluster_num] = clf\n\n        user_cluster_clf[user_name] = cluster_clf_dict\n\n    return user_cluster_clf\n\n\ndef get_pure_classifiers(features_train, train_clf, with_posture):\n    \"\"\"\n    不使用姿势特征，为每一个用户训练一个分类器\n    :param features_train: {\"user_name\":[LineFeature() , ...] , ...}\n    :param train_clf: 具体训练分类器的算法\n    :param with_posture: 是否使用姿势特征\n    :return:user_clf_without_posture:{\"user_name\": clf,...}\n    \"\"\"\n\n    user_clf_without_posture = {}\n\n    feature_type = \"all\"\n    if with_posture is False:\n        feature_type = \"gesture\"\n\n    for user_name, line_feature_list in features_train.items():\n        # 正样本\n        pos_df = build_feature_df(line_feature_list, feature_type)\n\n        # 负样本\n        neg_line_feature_list = get_others_features(user_name, pos_df.shape[0], features_train)\n        neg_df = build_feature_df(neg_line_feature_list, feature_type)\n\n        # 合并\n        feature_df = pd.concat([pos_df, neg_df])\n        # 标号\n        labels = np.array(([1] * pos_df.shape[0]) + ([-1] * neg_df.shape[0]))\n\n        clf = train_clf(feature_df, labels)\n\n        user_clf_without_posture[user_name] = clf\n\n    return user_clf_without_posture\n\n\ndef train_svm_clf(feature_df, labels):\n    \"\"\"\n    使用svm算法训练分类器\n    :param feature_df: 训练集\n    :param labels: 标号\n    :return:\n    \"\"\"\n    # 训练该聚类的分类器\n    # print(feature_df.shape[0])\n    if feature_df.shape[0] <= 12:\n        clf = svm.SVC(probability=True).fit(feature_df.values, labels)\n    else:\n        param_grid = [\n            {'C': [1, 10, 100, 1000], 'kernel': ['linear']},\n            {'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001, \"auto\"], 'kernel': ['rbf']},\n        ]\n        svr = svm.SVC(probability=True)\n        clf = grid_search.GridSearchCV(svr, param_grid)\n        clf.fit(feature_df.values, labels)\n        # print(clf.best_params_)\n    return clf\n\n\ndef train_lr_clf(feature_df, labels):\n    \"\"\"\n    使用LogisticRegression算法训练分类器\n    :param feature_df: 训练集\n    :param labels: 标号\n    :return:\n    \"\"\"\n    # 训练该聚类的分类器\n    # print(feature_df.shape[0])\n    if feature_df.shape[0] < 12:\n        clf = linear_model.LogisticRegression().fit(feature_df.values, labels)\n    else:\n        clf = linear_model.LogisticRegressionCV().fit(feature_df.values, labels)\n    return clf\n\n\ndef train_knn_clf(feature_df, labels):\n    \"\"\"\n    使用k-nearest-neighbors算法训练分类器\n    :param feature_df: 训练集\n    :param labels: 标号\n    :return:\n    \"\"\"\n    # 训练该聚类的分类器\n    # print(feature_df.shape[0])\n    if feature_df.shape[0] <= 4:\n        clf = KNeighborsClassifier(n_neighbors=1).fit(feature_df.values, labels)\n    elif feature_df.shape[0] <= 10:\n        clf = KNeighborsClassifier(n_neighbors=3).fit(feature_df.values, labels)\n    else:\n        param_grid = {'n_neighbors': [3, 5]}\n        knc = KNeighborsClassifier()\n        clf = grid_search.GridSearchCV(knc, param_grid)\n        clf.fit(feature_df.values, labels)\n\n    return clf\n\n\ndef train_random_forest_clf(feature_df, labels):\n    \"\"\"\n    使用random forest算法训练分类器\n    :param feature_df: 训练集\n    :param labels: 标号\n    :return:\n    \"\"\"\n    # 训练该聚类的分类器\n    # print(feature_df.shape[0])\n    if feature_df.shape[0] <= 6:\n        clf = RandomForestClassifier().fit(feature_df.values, labels)\n    else:\n\n        param_grid = {\"max_depth\": [3, None],\n                      \"max_features\": [\"auto\", \"log2\"]}\n        rfc = RandomForestClassifier()\n        clf = grid_search.GridSearchCV(rfc, param_grid=param_grid)\n        clf.fit(feature_df.values, labels)\n\n    return clf\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":7852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"79527709","text":"import random\nimport sys\nimport funtions\nfrom funtions import MyMath\nfrom funtions import printName\n\n'''This file is being used as an interactive learning environment to learn\nhow to code in Python, each section has a header with general info on the\nconcept being used/explored.'''\n\n###########Strings and Printing###########\nprint('Hello World!\\n' * 3)\nname = input(\"What is your name?\")\nprint('Hello %s, how are you?' % name, end = \"\")\nprint(' I am doing quite well, thank you.\\n')\nprint('What happened to you today?')\nday = sys.stdin.readline()\ngood = input(('Did you say, \"%s\"?' % day))\nif good.lower() == 'yes':\n    print(\"I'm such a good listener.\")\nelse:\n    print(\"Ops, I'll catch it next time.\")\n\nquote = '\"You are unique'\nmulti_line_quote = '''just like everybody else.\"\n~Teddy Rosevelt\n'''\nprint('Always remember,', quote, multi_line_quote)\n\n###############List and Arrays#############\ngrocery_list = []\nhome_repair_list = []\n\ngrocery_list.append('bread')\ngrocery_list.append('milk')\ngrocery_list.append('eggs')\ngrocery_list.append('cheese')\ngrocery_list.append('apples')\n\nhome_repair_list.append('nails')\nhome_repair_list.append('wood')\nhome_repair_list.append('screws')\nhome_repair_list.append('pipes')\nhome_repair_list.append('paint')\nhome_repair_list.append('light bulbs')\n\nshopping_list_one = grocery_list + home_repair_list\nshopping_list_two = [grocery_list, home_repair_list]\nprint('Shopping list one:', shopping_list_one)\nprint('Shopping list two:', shopping_list_two)\n\nshopping_list_one.sort()\nprint('\\nShopping list one:')\ni = 1\nfor x in shopping_list_one:\n    print(i,':',x)\n    i += 1\n\nshopping_list_two[0].sort()\nshopping_list_two[0].reverse()\nprint('\\nShopping list two:')\nx = 0\ni = 1\nwhile x < len(shopping_list_two):\n    for y in range(0,len(shopping_list_two[x])):\n        print(i,':',shopping_list_two[x][y])\n        i += 1\n    x += 1\n\nprint('\\nFirst item shopping one:', min(shopping_list_one))\nprint('Last item shopping one:', max(shopping_list_one), end = '\\n\\n')\n\n###################Keys###################\nsuper_heroes_identities = {'Batman': 'Bruce Wayne', 'Superman': 'Clark Kent',\n               'Aquaman': 'Arthur Curry', 'Green Arrow': 'Oliver Queen',\n                'Captain America': 'Steven Rogers', 'Robin': 'Richard Grayson'}\nsuper_heroe_names = {'Bruce Wayne': 'Batman', 'Clark Kent': 'Superman',\n                     'Arthur Curry': 'Aquaman', 'Oliver Queen': 'Green Arrow',\n                     'Steven Rogers': 'Captain America', 'Richard Grayson': 'Robin'}\n\nprint('List of Super Heroes and Secret Identities:')\nprint('Names:')\nfor identity in super_heroe_names:\n    print('\\t',super_heroe_names[identity])\nprint('Identities:')\nfor names in super_heroes_identities:\n    print('\\t',super_heroes_identities[names])\nprint('\\nThere are %d super heroes' % len(super_heroes_identities))\nprint('Key:\\nName to Identity:')\nprint(super_heroes_identities.keys())\nprint(super_heroes_identities.values(), '\\n')\n\ndel super_heroes_identities['Aquaman']\ndel super_heroes_identities['Robin']\nsuper_heroes_identities['Nightwing'] = 'Richard Grayson'\nprint(super_heroes_identities.keys())\n\ndel super_heroe_names['Arthur Curry']\nsuper_heroe_names['Richard Grayson'] = 'Nightwing'\nprint(super_heroe_names.keys(), '\\n')\n\nsuper_heroes_identities['Beast Boy'] = \"Garfield Logan\"\nsuper_heroe_names[super_heroes_identities['Beast Boy']] = 'Beast Boy'\nsuper_heroes_identities['Spiderman'] = 'Peter Parker'\nsuper_heroe_names[super_heroes_identities['Spiderman']] = 'Spiderman'\nsuper_heroes_identities['Catwoman'] = 'Selina Kyle'\nsuper_heroes_identities['Deadshot'] = 'Floyd Lawton'\nsuper_heroe_names[super_heroes_identities['Catwoman']] = 'Catwoman'\nsuper_heroe_names[super_heroes_identities['Deadshot']] = 'Deadshot'\nprint(super_heroes_identities.values())\n\nprint('\\nSuper Hero identities:')\nfor name in super_heroes_identities:\n    print(name, 'is', super_heroes_identities[name])\n\n##################Random Numbers###################\nrandom_num = random.randrange(0, 9, 2)\n\nprint('\\nRandom numbers:')\nwhile (random_num != 5):\n    print(random_num)\n    random_num = random.randrange(0, 10)\n\n###################Functions#######################\ndef addNumber(fNum, lNum):\n    outNum = fNum + lNum\n    return outNum\n\ndef multNumber(fNum, lNum):\n    outNum = fNum * lNum\n    return outNum\n\ndef subNumer(fNum, lNum):\n    outNum = fNum - lNum\n    return outNum\n\nprint('\\n1 + 2 =', addNumber(1, 2))\nprint('1 - 2 =', subNumer(1, 2))\nprint('2 * 3 =', multNumber(2, 3))\nprint()\n\n#############Classes and Included Files#############\nnumNum = MyMath('sin(gerine)/cos(gerine) = tan(gerine)')\nprint('\\nFunctions from class: MyMath')\nprint('add: 3 + 4 ->', numNum.addNum(3, 4))\nprint('subtract: 6 - 4 ->', numNum.subNum(6, 4))\nprint('multiply: 5 * 4 ->', numNum.multNum(5, 4))\nprint('divide: 50 / 25 ->', numNum.devNum(50, 25))\nprint('remainder: 24 % 5', numNum.remNum(24, 5))\nprint('power: 2 ^ 4 ->', numNum.powNum(2, 4))\n\nprint('The current math phrase is \"%s\".' % numNum.get_frase())\nanswer = input('Would you like to change it?')\n\nif answer.lower() == 'yes':\n    new_frase = input('What would you like the new phrase to be?')\n    numNum.set_frase(new_frase)\nprint('The current phrase is \"%s\".' % numNum.get_frase(),'\\n')\nprintName()\n\n######Reading from and Writing to External Files######\nin_file = open('in_text.txt', 'r')\nprint(in_file.read())\nx = 0\nwhile x < 5:\n    out_file = open('out_text_%d.txt' % x, 'w')\n    out_file.write('Text file %d\\n' % x)\n    out_file.write('Here is the first line\\n')\n    out_file.write('Here is the second one\\n')\n    out_file.write('Just writting a couple of lines to be added')\n    x += 1\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"64469694","text":"#!flask/bin/python\n'''Run all tests in the project, or specific test modules\n\nUsage:\n    To test everything, simply use\n\n    $ ./test.py\n\n    To specify specific modules to test, list the names with spaces between\n\n    $ ./test.py module1 [module2 [...]]\n    for example,\n    $ ./test.py problem submission\n\n    The modules will be tested individually. The module name needs to\n    correspond to the test file name, that is, \"problem\" corresponds to\n    \"problem_test.py\".\n\nThis will take all files that include *_test.py and run them.\n'''\nimport unittest\nimport sqlalchemy\nfrom sys import argv\n\n\n# Get a KeyError after custom tests without this. Full explanaition on SO\n# https://stackoverflow.com/questions/8774958/keyerror-in-module-threading-after-a-successful-py-test-run\nfrom gevent import monkey; monkey.patch_thread()\n\nimport app\nimport app.database as db\n\n# Switch the database session variable to point to the test database\ntest_engine = sqlalchemy.create_engine(\n    'mysql+pymysql://root@localhost/acm_test?charset=utf8')\ntest_conn = test_engine.connect()\ndb.session = sqlalchemy.orm.Session(test_engine)\n\n\nif __name__ == \"__main__\":\n    loader = unittest.defaultTestLoader\n    if len(argv) > 1:\n        print('Using custom tests')\n        for arg in argv[1:]:\n            pattern = arg + '_test.py'\n            print('Testing with ' + pattern)\n            suite = loader.discover(start_dir='.', pattern=pattern)\n            unittest.TextTestRunner().run(suite)\n    else:\n        suite = loader.discover(start_dir='.', pattern='*_test.py')\n        unittest.TextTestRunner().run(suite)\n","sub_path":"auacm/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"623994514","text":"from connect4_selfplay import Connect4\r\nfrom DQN_modified_pong_t2 import DeepQNetwork\r\n#from DQN_modified_test import DeepQNetwork\r\nimport cv2\r\nimport numpy as np\r\nimport os\r\nimport csv\r\nimport datetime\r\nimport time\r\n\r\n# def prepare_dirs(projectDir):\r\n#     if not os.path.exists(projectDir+\"\\\\csv_data\"):\r\n#         os.makedirs(projectDir+\"\\\\csv_data\")\r\n#     if not os.path.exists(projectDir+\"\\\\images\"):\r\n#         os.makedirs(projectDir+\"\\\\images\")\r\n\r\ndef run_connect4():\r\n    step_p1 = 0\r\n    step_p2 = 0\r\n    player = 2\r\n    is_draw = 1\r\n    crop_size = (60, 60)\r\n    reward_p1_total = 0\r\n    reward_p2_total = 0\r\n    game_number = 0\r\n    info_p1_total = [0, 0, 0, 0]\r\n    info_p2_total = [0, 0, 0, 0]\r\n    win_rate_p1_list = []\r\n    win_rate_p2_list = []\r\n    avg_step_p1_list = []\r\n    avg_step_p2_list = []\r\n    reward_p1_list = []\r\n    reward_p2_list = []\r\n\r\n    loss_p2_list = []\r\n    loss = 0\r\n    project_path = 'D:\\zhaomi\\code\\project\\deep_q_network'\r\n    csv_file = open(project_path + '\\csv_data\\\\result_data_' + datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '.csv', 'a', newline='')\r\n\r\n    writer = csv.writer(csv_file)\r\n\r\n    i = 0\r\n    #for episode in range(40000):\r\n    for episode in range(150000):\r\n    # every game number\r\n    #while True:\r\n        game_number += 1\r\n        print(\"--------------------- game numbers:\", game_number, \" ---------------------\\n\")\r\n        # initial observation\r\n        env.reset()\r\n        observation_p1, reward_p1, done_p1, info_p1 = env.run_player1(env)\r\n        step_p1 += 1\r\n        reward_p1_total += reward_p1[0]\r\n        reward_p2_total += reward_p1[1]\r\n        info_p1_total = np.sum([info_p1_total, info_p1[:4]], axis=0)\r\n        info_p2_total = np.sum([info_p2_total, info_p1[-4:]], axis=0)\r\n        observation_p1 = RL.pre_process(observation_p1, crop_size)\r\n       # observation_p1 = np.reshape(observation_p1, (60, 60, 1))\r\n        observation_p1 = np.stack((observation_p1, observation_p1, observation_p1, observation_p1), axis=2)\r\n       #  print(\"【action_p1: %d,  done_p1: %s】:\\nreward_p1: %d, reward_p1_total: %d, info_p1[n_w, n_d, n_l, n_il]: %s\" %(info_p1[4], str(done_p1), reward_p1[0], reward_p1_total , str(info_p1[:4])))\r\n       #  print(\"reward_p2: %d, reward_p2_total: %d, info_p2[n_w, n_d, n_l, n_il]: %s\" %(reward_p1[1], reward_p2_total, str(info_p1[-4:])))\r\n       # # print(\"chessboard:\\n\", info_p1[5])\r\n       # # cv2.imwrite(image_path + \"%d_p1.png\" % i, observation_p1)\r\n        i = i + 1\r\n\r\n        #every step number\r\n        while True:\r\n            # fresh env\r\n            # RL choose action based on observation\r\n\r\n        #    cv2.imwrite(image_path + \"%d_p1_pre.png\" % i, observation_p1)\r\n            action_p2 = RL.choose_action(observation_p1)\r\n            # RL take action and get next observation and reward\r\n            draw_info_list = [game_number, reward_p2_total, step_p2]\r\n            observation_p2, reward_p2, done_p2, info_p2 = env.makeMove_P2(player, action_p2, draw_info_list, is_draw)\r\n            step_p2 += 1\r\n            reward_p2_total += reward_p2[1]\r\n            reward_p1_total += reward_p2[0]\r\n            info_p2_total = np.sum([info_p2_total, info_p2[-4:]], axis=0)\r\n            info_p1_total = np.sum([info_p1_total, info_p2[:4]], axis=0)\r\n\r\n            observation_p2 = RL.pre_process(observation_p2, crop_size)\r\n            observation_p2 = np.reshape(observation_p2, (60, 60, 1))\r\n            observation_p2 = np.append(observation_p2, observation_p1[:, :, 0:3], axis=2)\r\n            RL.store_transition(observation_p1, action_p2, reward_p2[1], observation_p2)\r\n         #    print(\"【action_p2: %d,  done_p2: %s】:\\nreward_p2: %d, reward_p2_total: %d, info_p2[n_w, n_d, n_l, n_il]: %s\" %(info_p2[4], str(done_p2), reward_p2[1], reward_p2_total , str(info_p2[-4:])))\r\n         #    print(\"reward_p1: %d, reward_p1_total: %d, info_p1[n_w, n_d, n_l, n_il]: %s\" %(reward_p2[0], reward_p1_total, str(info_p2[:4])))\r\n         #\r\n         # #   print(\"action_p2: %d, reward_p2: %d, reward_p2_total: %d, done_p2: %s, info_p2[n_w, n_d, n_l, n_il]: %s\" %(info_p2[4], reward_p2, reward_p2_total, str(done_p2), str(info_p2[:4])))\r\n         # #   print(\"chessboard:\\n\", info_p2[5])\r\n         #  #  cv2.imwrite(image_path + \"%d_p2.png\" % i, observation_p2)\r\n            i = i + 1\r\n            if done_p2:\r\n                break\r\n\r\n\r\n            if (step_p2 > 200) and (step_p2 % 1 == 0):\r\n                loss = RL.learn(step_p2)\r\n\r\n            observation_p1, reward_p1, done_p1, info_p1 = env.run_player1(env)\r\n            step_p1 += 1\r\n            reward_p1_total += reward_p1[0]\r\n            reward_p2_total += reward_p1[1]\r\n            info_p1_total = np.sum([info_p1_total, info_p1[:4]], axis=0)\r\n            info_p2_total = np.sum([info_p2_total, info_p1[-4:]], axis=0)\r\n\r\n            observation_p1 = RL.pre_process(observation_p1, crop_size)\r\n            observation_p1 = np.reshape(observation_p1, (60, 60, 1))\r\n            observation_p1 = np.append(observation_p1, observation_p2[:, :, 0:3], axis=2)\r\n\r\n            # RL.store_transition(observation_p2, action, reward, observation_p1)\r\n\r\n          #   print(\"【action_p1: %d,  done_p1: %s】:\\nreward_p1: %d, reward_p1_total: %d, info_p1[n_w, n_d, n_l, n_il]: %s\" % (info_p1[4], str(done_p1), reward_p1[0], reward_p1_total , str(info_p1[:4])))\r\n          #   print(\"reward_p2: %d, reward_p2_total: %d, info_p2[n_w, n_d, n_l, n_il]: %s\" %(reward_p1[1], reward_p2_total, str(info_p1[-4:])))\r\n          # #  print(\"action_p1: %d, reward_p1: %d, reward_p1_total: %d, done_p1: %s, info_p1[n_w, n_d, n_l, n_il]: %s\" %(info_p1[4], reward_p1, reward_p1_total, str(done_p1), str(info_p1[:4])))\r\n          # #  print(\"chessboard:\\n\", info_p1[5])\r\n            # swap observation\r\n\r\n            # break while loop when end of this episode\r\n           # cv2.imwrite(image_path + \"%d_p1.png\" % i, observation_p1)\r\n            i = i + 1\r\n            if done_p1:\r\n                break\r\n\r\n\r\n       # print(\"game_number: %d, reward_p2_total: %d, info_p2_total[n_w, n_d, n_l, n_il]: %s, reward_p1_total: %d, info_p1_total[n_w, n_d, n_l, n_il]: %s, step_p2: %d, step_p1:  %d .\\n\" % (game_number, reward_p2_total, str(info_p2_total), reward_p1_total, str(info_p1_total), step_p2, step_p1))\r\n        win_rate_p1 = round(info_p1_total[0] / (sum(info_p1_total[:4])), 2)\r\n        win_rate_p2 = round(info_p2_total[0] / (sum(info_p2_total[:4])), 2)\r\n        avg_step_p1 = round(step_p1/ game_number, 2)\r\n        avg_step_p2 = round(step_p2/ game_number, 2)\r\n       # print(\"avg_step_p1, p2:\", avg_step_p1, avg_step_p2)\r\n       # print(win_rate_p1, win_rate_p2)\r\n        win_rate_p1_list.append(win_rate_p1)\r\n        win_rate_p2_list.append(win_rate_p2)\r\n        avg_step_p1_list.append(avg_step_p1)\r\n        avg_step_p2_list.append(avg_step_p2)\r\n        reward_p1_list.append(reward_p1_total)\r\n        reward_p2_list.append(reward_p2_total)\r\n        loss_p2_list.append(loss)\r\n\r\n        print(\"\\n\", \"*\" * 80)\r\n        print(\"game_number: %d end: \\nreward_p1_total: %d, info_p1_total[n_w, n_d, n_l, n_il]: %s, step_p1: %d \" % (game_number, reward_p1_total, str(info_p1_total), step_p1))\r\n        print(\"reward_p2_total: %d, info_p2_total[n_w, n_d, n_l, n_il]: %s, step_p2: %d, loss: %s.\" % (reward_p2_total, str(info_p2_total), step_p2, str(loss)))\r\n        print(\"*\" * 80, \"\\n\")\r\n\r\n        csv_data = [win_rate_p1, win_rate_p2, avg_step_p1, avg_step_p2, reward_p1_total, reward_p2_total, info_p1_total[0], info_p1_total[1], info_p1_total[2], info_p1_total[3], info_p2_total[0], info_p2_total[1], info_p2_total[2], info_p2_total[3], loss]\r\n        if game_number % 1000 == 0:\r\n            writer.writerow(csv_data)\r\n            csv_file.flush()\r\n\r\n        if game_number % 10000 == 0:\r\n            RL.plt_data(project_path + \"\\images\\\\\", win_rate_p1_list, win_rate_p2_list, avg_step_p1_list, avg_step_p2_list, reward_p1_list, reward_p2_list,[], loss_p2_list)\r\n         #   RL.plot_cost()\r\n\r\n\r\n    # end of game\r\n    #print('win_rate_p1_list, win_rate_p2_list',win_rate_p1_list, win_rate_p2_list)\r\n    #print('avg_step_p1_list, avg_step_p2_list', avg_step_p1_list, avg_step_p2_list)\r\n    #print('reward_p1_list,reward_p2_list', reward_p1_list, reward_p2_list)\r\n    print('game over')\r\n    csv_file.close()\r\n\r\n\r\n# def test_pygame():\r\n#     for _ in range(10000):\r\n#         env.draw_board()\r\n#         env.mute_board()\r\n#         time.sleep(0.1)\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    # maze game\r\n    start_time = datetime.datetime.now()\r\n    print(\"start time:\" + start_time.strftime('%Y-%m-%d %H:%M:%S'))\r\n    env = Connect4()\r\n    mode_type = 'train'\r\n   # mode_type = 'use mode'\r\n    RL = DeepQNetwork(n_actions=7,\r\n                      mode=mode_type,\r\n                      output_graph=False\r\n                      )\r\n    if not os.path.exists('./checkpoints'):\r\n        os.makedirs('./checkpoints')\r\n    if not os.path.exists('./csv_data'):\r\n        os.makedirs('./csv_data')\r\n    if not os.path.exists('./images'):\r\n        os.makedirs('./images')\r\n    run_connect4()\r\n    # test_pygame()\r\n\r\n    RL.plot_cost()\r\n    end_time = datetime.datetime.now()\r\n    print(\"end time:\" + end_time.strftime('%Y-%m-%d %H:%M:%S'))\r\n    print(\"run time(seconds):\" + str((end_time - start_time).seconds))\r\n","sub_path":"dqn_run_this_test.py","file_name":"dqn_run_this_test.py","file_ext":"py","file_size_in_byte":9264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"488517741","text":"import cyclone.web\nimport sys\n\nfrom twisted.internet import reactor\nfrom twisted.python import log\n\n\nclass MainHandler(cyclone.web.RequestHandler):\n    def get(self):\n        self.write(\"Try modifying the server file!\")\n\nif __name__ == \"__main__\":\n    application = cyclone.web.Application([\n        (r\"/\", MainHandler)\n    ], autoreload=True)\n\n    log.startLogging(sys.stdout)\n    reactor.listenTCP(8888, application)\n    reactor.run()\n","sub_path":"demos/autoreload/autoreload.py","file_name":"autoreload.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"584994151","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport mptt.fields\nimport django.core.validators\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Album',\n            fields=[\n                ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n                ('slug', models.CharField(validators=[django.core.validators.RegexValidator(message='Tekninen nimi saa sisältää vain pieniä kirjaimia, numeroita sekä väliviivoja.', regex='[a-z0-9-]+')], max_length=63, help_text='Tekninen nimi eli \"slug\" näkyy URL-osoitteissa. Sallittuja merkkejä ovat pienet kirjaimet, numerot ja väliviiva. Jos jätät teknisen nimen tyhjäksi, se generoidaan automaattisesti otsikosta. Jos muutat teknistä nimeä julkaisun jälkeen, muista luoda tarvittavat uudelleenojaukset.', blank=True, verbose_name='Tekninen nimi')),\n                ('path', models.CharField(validators=[django.core.validators.RegexValidator(message='Polku saa sisältää vain pieniä kirjaimia, numeroita, väliviivoja sekä kauttaviivoja.', regex='[a-z0-9-/]+')], max_length=1023, help_text='Polku määritetään automaattisesti teknisen nimen perusteella.', unique=True, verbose_name='Polku')),\n                ('title', models.CharField(max_length=1023, help_text='Otsikko näytetään automaattisesti sivun ylälaidassa sekä valikossa. Älä lisää erillistä pääotsikkoa sivun tekstiin.', verbose_name='Otsikko')),\n                ('description', models.TextField(default='', help_text='Näkyy mm. hakukoneille sekä RSS-asiakasohjelmille.', blank=True, verbose_name='Kuvaus')),\n                ('lft', models.PositiveIntegerField(editable=False, db_index=True)),\n                ('rght', models.PositiveIntegerField(editable=False, db_index=True)),\n                ('tree_id', models.PositiveIntegerField(editable=False, db_index=True)),\n                ('level', models.PositiveIntegerField(editable=False, db_index=True)),\n            ],\n            options={\n                'verbose_name_plural': 'Albumit',\n                'verbose_name': 'Albumi',\n            },\n        ),\n        migrations.CreateModel(\n            name='Media',\n            fields=[\n                ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n                ('width', models.PositiveIntegerField(default=0)),\n                ('height', models.PositiveIntegerField(default=0)),\n                ('src', models.ImageField(upload_to='', max_length=255, height_field='height', width_field='width', null=True)),\n            ],\n            options={\n                'verbose_name_plural': 'Media',\n                'verbose_name': 'Media',\n            },\n        ),\n        migrations.CreateModel(\n            name='MediaSpec',\n            fields=[\n                ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n                ('max_width', models.PositiveIntegerField()),\n                ('max_height', models.PositiveIntegerField()),\n                ('quality', models.PositiveIntegerField()),\n                ('is_default_thumbnail', models.BooleanField(default=False)),\n            ],\n        ),\n        migrations.CreateModel(\n            name='Picture',\n            fields=[\n                ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n                ('slug', models.CharField(validators=[django.core.validators.RegexValidator(message='Tekninen nimi saa sisältää vain pieniä kirjaimia, numeroita sekä väliviivoja.', regex='[a-z0-9-]+')], max_length=63, help_text='Tekninen nimi eli \"slug\" näkyy URL-osoitteissa. Sallittuja merkkejä ovat pienet kirjaimet, numerot ja väliviiva. Jos jätät teknisen nimen tyhjäksi, se generoidaan automaattisesti otsikosta. Jos muutat teknistä nimeä julkaisun jälkeen, muista luoda tarvittavat uudelleenojaukset.', blank=True, verbose_name='Tekninen nimi')),\n                ('order', models.IntegerField(default=0, help_text='Saman yläsivun alaiset sivut järjestetään valikossa tämän luvun mukaan nousevaan järjestykseen (pienin ensin).', verbose_name='Järjestys')),\n                ('path', models.CharField(validators=[django.core.validators.RegexValidator(message='Polku saa sisältää vain pieniä kirjaimia, numeroita, väliviivoja sekä kauttaviivoja.', regex='[a-z0-9-/]+')], max_length=1023, help_text='Polku määritetään automaattisesti teknisen nimen perusteella.', unique=True, verbose_name='Polku')),\n                ('title', models.CharField(max_length=1023, help_text='Otsikko näytetään automaattisesti sivun ylälaidassa sekä valikossa. Älä lisää erillistä pääotsikkoa sivun tekstiin.', verbose_name='Otsikko')),\n                ('description', models.TextField(default='', help_text='Näkyy mm. hakukoneille sekä RSS-asiakasohjelmille.', blank=True, verbose_name='Kuvaus')),\n                ('album', models.ForeignKey(related_name='pictures', to='gallery.Album')),\n            ],\n            options={\n                'verbose_name_plural': 'Kuvat',\n                'ordering': ('album', 'order'),\n                'verbose_name': 'Kuva',\n            },\n        ),\n        migrations.AddField(\n            model_name='media',\n            name='picture',\n            field=models.ForeignKey(related_name='media', to='gallery.Picture'),\n        ),\n        migrations.AddField(\n            model_name='media',\n            name='spec',\n            field=models.ForeignKey(blank=True, null=True, to='gallery.MediaSpec'),\n        ),\n        migrations.AddField(\n            model_name='album',\n            name='cover_picture',\n            field=models.ForeignKey(blank=True, related_name='+', null=True, to='gallery.Picture'),\n        ),\n        migrations.AddField(\n            model_name='album',\n            name='parent',\n            field=mptt.fields.TreeForeignKey(help_text='Tämä albumi luodaan valitun albumin alaisuuteen. Juurialbumilla ei ole yläalbumia.', blank=True, to='gallery.Album', related_name='subalbums', null=True, verbose_name='Yläalbumi'),\n        ),\n        migrations.AlterUniqueTogether(\n            name='picture',\n            unique_together=set([('album', 'slug')]),\n        ),\n        migrations.AlterUniqueTogether(\n            name='album',\n            unique_together=set([('parent', 'slug')]),\n        ),\n    ]\n","sub_path":"gallery/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":6539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"462162936","text":"import tensorflow as tf\nimport numpy as np\nfrom tensorflow.examples.tutorials.mnist import input_data\n\nmnist=input_data.read_data_sets('MNIST_data',one_hot=True)\n\ndef add_layer(inputs,in_size,out_size,activation_function=None):\n    Weights=tf.Variable(tf.random_normal([in_size,out_size]))\n    biases=tf.Variable(tf.zeros([1,out_size])+0.1)\n    Wx_plus_b=tf.matmul(inputs,Weights)+biases\n    Wx_plus_b=tf.nn.dropout(Wx_plus_b,keep_prob)\n    if activation_function is None:\n        outputs=Wx_plus_b\n    else:\n        outputs=activation_function(Wx_plus_b)\n    return outputs\n\ndef compute_accuracy(v_xs,v_ys):\n    global prediction\n    y_pre=sess.run(prediction,feed_dict={xs:v_xs,keep_prob:[1.]})\n    correct_prediction=tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))\n    accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))\n    result=sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys,keep_prob:[1.]})\n    return result\n\nkeep_prob=tf.placeholder(tf.float32) #保持多少数据不被去掉\nxs=tf.placeholder(tf.float32,[None,784])\nys=tf.placeholder(tf.float32,[None,10])\n\nl1=add_layer(xs,784,50,activation_function=tf.nn.tanh)\nprediction=add_layer(l1,50,10,activation_function=tf.nn.softmax)\n\nloss=tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))\ntrain_step=tf.train.GradientDescentOptimizer(0.7).minimize(loss)\ninit=tf.global_variables_initializer()\n\nsess=tf.Session()\nsess.run(init)\n\nfor i in range(5000):\n    batch_xs,batch_ys=mnist.train.next_batch(100)\n    sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys,keep_prob:[1.]})\n    if i%1000==0:\n        print(compute_accuracy(mnist.test.images,mnist.test.labels))\n","sub_path":"dropout.py","file_name":"dropout.py","file_ext":"py","file_size_in_byte":1654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"506840600","text":"def factorial(a):\n    try:\n        if a == 1:\n            return 1\n        elif a == 0:\n            return 1\n        else:\n            return a*factorial(a-1)\n    except(RecursionError,TypeError):\n        return None\n\nw = float(input('Input number here: '))\nif w > 1:\n    print(int(factorial(w)))\nelif 0 <= w <= 1:\n    print(1)\n","sub_path":"FuntcionReview/Exercise4.py","file_name":"Exercise4.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"628427817","text":"\"\"\"chat_project URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.0/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.urls import path\nfrom rest_framework.routers import SimpleRouter\nfrom drf_yasg.views import get_schema_view\nfrom drf_yasg import openapi\n\nfrom chat import views\n\nrouter = SimpleRouter()\nschema_view = get_schema_view(\n    openapi.Info(\n        title=\"Snippets API\",\n        default_version='v1',\n    ),\n    public=True\n)\n\nrouter.register('messages', views.ChatMessagesViewSet)\nrouter.register('users', views.UserListViewSet)\n\nurlpatterns = [\n    path('', schema_view.with_ui('swagger', cache_timeout=0))\n] + router.urls\n\n\n","sub_path":"chat/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1169,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"406558060","text":"numero = int(input('Olá, digite um número para ser convertido:'))\nconv = int(input('Deseja converter para qual base\\n'\n             '1 - Binário\\n'\n             '2 - Octal\\n'\n             '3 - Hexadecimal\\n'))\n\nif conv == 1:\n    print('Número binário é:\\n'\n          '{}'.format(bin(numero)))\nelif conv == 2:\n    print('Número Octal é:\\n'\n          '{}'.format(oct(numero)))\nelif conv == 3:\n    print('Número Hexadecimal é:\\n'\n          '{}'.format(hex(numero)))\nelse:\n    print('Obrigado')","sub_path":"LearningPython01/BasesNuméricas.py","file_name":"BasesNuméricas.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"487964370","text":"# A script to upload Libre data to Nightscout.\n#\n# Use the Libre desktop app to transfer data from your Libre to a tsv file, then run this script.\n#\n# Inspired by https://github.com/cjo20/ns-api-uploader\n# Requires Python 3\n\nimport argparse\nimport csv\nfrom datetime import datetime, timezone\nimport glob\nimport hashlib\nimport json\nimport os\nfrom shutil import copyfile\n\nimport requests # pip3 install requests\n\nentries = []\n\n\ndef url_and_headers(base_url, api_secret):\n    url = \"%s/api/v1/entries\" % base_url\n    hashed_secret = hashlib.sha1(api_secret.encode('utf-8')).hexdigest()\n    headers = {'API-SECRET' : hashed_secret,\n               'Content-Type': \"application/json\",\n               'Accept': 'application/json'}\n    print(url)\n    print(headers)\n    return url, headers\n\n\ndef find_last_nightscout_entry(url, headers):\n    r = requests.get(url, headers=headers)\n    entries = r.json()\n    if len(entries) == 0:\n        last_timestamp = 0\n        print(\"No entries found in Nightscout\")\n    else:\n        last_timestamp = entries[0]['date'] / 1000\n        dt = datetime.fromtimestamp(last_timestamp)\n        print(\"Last timestamp in Nightscout: %s\" % dt)\n    return last_timestamp\n    \n\ndef upload_to_nightscout(libre_csv, base_url, api_secret, max_size=100, max_attempts=5):\n\n    current_timestamp = int(datetime.now().timestamp())\n    print(\"Current time: %s\" % datetime.fromtimestamp(current_timestamp))\n    tz = datetime.now(timezone.utc).astimezone().tzinfo # the local timezone\n\n    url, headers = url_and_headers(base_url, api_secret)\n    last_timestamp = find_last_nightscout_entry(url, headers)\n    \n    with open(libre_csv, 'r') as csvfile:\n        # See format of Libre tsv file discussed here: https://github.com/nahog/freestyle-libre-parser-viewer\n        reader = csv.reader(csvfile)\n        next(reader, None)  # skip the first line (patient name)\n        next(reader, None)  # skip the headers\n        i = 0\n        for row in reader:\n            if len(row) > 2: # omdat registratie van insuline een error veroorzaakt\n                time = row[2]\n                dt = datetime.strptime(time, \"%m-%d-%Y %I:%M %p\")\n                dt = dt.replace(tzinfo=tz)\n                timestamp = dt.timestamp()\n                if timestamp <= last_timestamp:\n                    continue\n                if timestamp >= current_timestamp:\n                    continue # ignore times in the future\n                date = int(timestamp * 1000)\n                date_string = dt.isoformat()\n                record_type = int(row[3])\n                if record_type == 0: # historic glucose\n                    entry = dict(type='sgv', sgv=float(row[4]), date=date, dateString=date_string)\n                    #print(entry)\n                    entries.append(entry)\n                elif record_type == 1: # scan glucose\n                    entry = dict(type='sgv', sgv=float(row[5]), date=date, dateString=date_string)\n                    entries.append(entry)\n                if (len(entries) == int(max_size)):\n                    #write entries to nightscout to avoid overflow\n                    upload_entries(i, entries, url, headers, max_attempts)\n                    i += 1\n            \n        #write the last part to nightscout\n        if len(entries)>0:\n            upload_entries(i, entries, url, headers, max_attempts)\n        \n        #print(\"Number of entries: %d\" % len(entries))\n\n        #for entry in entries:\n        #    print(entry)\n        \n        if len(entries) == 0:\n            print(\"No new entries\")\n            return\n \n\ndef upload_entries(i, entries, url, headers, max_attempts):\n    \n    attempts = 0\n    #print(len(entries))\n    while attempts < int(max_attempts):\n        r = requests.post(url, headers=headers, data=json.dumps(entries))\n        if r.status_code == 200:\n            print(\"Uploaded package %d successfully\" % i)\n            #print(r.text)\n            break\n        else:\n            attempts += 1\n            print(\"Uploading package %d FAILED\" % i)\n            print(\"%d\" % r.status_code)\n            print(r.text)\n    entries.clear()\n    \n        \n\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description='Process some integers.')\n    parser.add_argument('--api_secret', help=\"API-SECRET for uploading\", required=True)\n    parser.add_argument('--base_url', help=\"Base URL of Nightscout site\", required=True)\n    parser.add_argument('--libre_csv', help=\"Export data file from FreeStyle Libre website\", required=True)\n    parser.add_argument('--max_size', default=100, help=\"Maximum number of entries to upload at once\", required=False)\n    parser.add_argument('--max_attempts', default=5, help=\"Maximum number of attempts to upload data to Nightscout site\", required=False)\n    args = parser.parse_args()\n\n    upload_to_nightscout(args.libre_csv, args.base_url, args.api_secret, args.max_size, args.max_attempts)\n","sub_path":"bulk_uploader_libre.py","file_name":"bulk_uploader_libre.py","file_ext":"py","file_size_in_byte":4897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"94420079","text":"import pandas as pd\r\nimport numpy as np\r\n#import matplotlib.pyplot as plt\r\nfrom sklearn import preprocessing, feature_selection, svm, gaussian_process\r\nfrom sklearn import metrics, naive_bayes\r\nfrom sklearn import neighbors, datasets, linear_model, ensemble, model_selection\r\nfrom stockstats import StockDataFrame\r\nimport copy\r\nimport time\r\nfrom datetime import date,timedelta\r\nimport warnings\r\nwarnings.filterwarnings(\"ignore\")\r\n\r\n\r\n#python script\r\nimport IEX_API as iex\r\n\r\ndef preprocess(data,indicators):\r\n    pd.set_option('max_columns',10)\r\n    stock_df = StockDataFrame.retype(data)\r\n    for i in indicators:\r\n        stock_df[i]\r\n    data=data[['close',*indicators]]\r\n\r\n    #create direction column\r\n    direction = data['close'].pct_change()\r\n\r\n    quarterDev=pd.DataFrame.std(direction)*0.4\r\n    bins=[-1,-1*quarterDev,quarterDev,1]\r\n    data['direction'] = pd.cut(direction,bins=bins,labels=[-1,0,1]).to_frame()\r\n\r\n    data['updown']=pd.cut(direction,bins=[-1,0,1],labels=[-1,1]).to_frame()\r\n    data[['direction','updown']]=data[['direction','updown']].shift(-1)\r\n\r\n    current=data.tail(1)\r\n\r\n    #missing data and first ten deleted\r\n    data=data.replace([np.inf], np.nan).dropna()\r\n    data=data.iloc[10:]\r\n\r\n    #print(data.tail())\r\n    #exit()\r\n\r\n    #Normalize Data\r\n    MinMax=preprocessing.MinMaxScaler()\r\n    data[[*indicators]]=MinMax.fit_transform(data[[*indicators]])\r\n\r\n    return data[['close',*indicators,'direction','updown']].dropna()\r\n\r\n############################################################\r\n\r\ndef findBestFeatures(data,features,k):\r\n    X=data[[*features]]\r\n    y=data['direction']\r\n    selectClf = feature_selection.SelectKBest(feature_selection.chi2,k=k)\r\n    selectClf.fit(X,y)\r\n    newX=X.columns[selectClf.get_support(indices=True)]\r\n    return list(newX)\r\n\r\ndef customAccuracy(true,prediction):\r\n    combined=pd.DataFrame(true)\r\n    combined['prediction']=prediction\r\n\r\n    combined=combined.replace([0],np.nan).dropna()\r\n    total=combined.shape\r\n    acc= metrics.accuracy_score(combined['updown'],combined['prediction'])\r\n    return acc, total[0]\r\n\r\ndef gridSearch(features,tests,paramGrid,data):\r\n    dfdict={'test':[],'accuracy':[]}\r\n\r\n    X=data[features]\r\n    y=data['direction']\r\n    XTrain,XTest,yTrain,yTest = model_selection.train_test_split(X,y,test_size=0.40,shuffle=False)\r\n    XValidate,XTest,yValidate,yTest = model_selection.train_test_split(XTest,yTest,test_size=0.50,shuffle=False)\r\n\r\n    # values of up down corresponding to yValidate (yValidate with 0 filled in)\r\n    yTrue=data['updown'].loc[yValidate.index.tolist()]\r\n\r\n    for test in tests:\r\n        i=tests.index(test)\r\n        testParam=paramGrid[i]\r\n        for paramName in testParam:\r\n            for c in testParam[paramName]:\r\n                setattr(test,paramName,c)\r\n                test.fit(XTrain,yTrain)\r\n                yPredict=test.predict(XValidate)\r\n\r\n                acc, total =customAccuracy(yTrue,yPredict)\r\n\r\n                if total>=30:\r\n                    dfdict['accuracy'].append(acc)\r\n                    dfdict['test'].append(copy.deepcopy(test))\r\n\r\n    df=pd.DataFrame(dfdict)\r\n    return df\r\n\r\ndef findBestModel(df,data,featList):\r\n    best_validation_score=df['accuracy'].max()\r\n\r\n    # find bad model or non working model\r\n    if best_validation_score<.50:\r\n        return 'model not robust enough'\r\n    try: best_index=df.index[df['accuracy']==best_validation_score].tolist()[0]\r\n    except: return 'model inconclusive'\r\n\r\n    y=data['direction']\r\n    X=data[[*featList]]\r\n    XTrain,XTest,yTrain,yTest = model_selection.train_test_split(X,y,test_size=0.20,shuffle=False)\r\n\r\n    # values of up down corresponding to yValidate (yValidate with 0 filled in)\r\n    yTrue=data['updown'].loc[yTest.index.tolist()]\r\n\r\n    # find what the best model is and test with 80%\r\n    best_series=df.iloc[best_index]\r\n    best_test=best_series['test']\r\n    best_test.fit(XTrain,yTrain)\r\n    yPredict=best_test.predict(XTest)\r\n    best_test_score, tested =customAccuracy(yTrue,yPredict)\r\n    total=yTest.shape[0]\r\n\r\n    print('best test : ' + str(best_test))\r\n    print('validation score : ' + str(best_validation_score))\r\n    print('test score : ' + str(best_test_score))\r\n    print('tested: %d / %d' % (tested,total))\r\n    return(best_test_score,tested, total, best_test)\r\n\r\n\r\n############################################################\r\n\r\ndef prediction(clf,features,data):\r\n    X=data[features]\r\n    y=data['direction']\r\n    clf.fit(X,y)\r\n    print(X.tail(1))\r\n    action=clf.predict(X.tail(1))\r\n    if action==1: action='price is predicted to rise in the next trading day'\r\n    elif action==-1: action='price is predicted to fall in the next trading day'\r\n    elif action==0: action='movement is not foreseeable'\r\n    return action\r\n\r\ndef addNew(data):\r\n    new_data=[iex.stock_info(stock).return_today_data()]\r\n    new_data[0]['close']=new_data[0]['delayedPrice']\r\n    data=data.append(new_data)\r\n    return data\r\n\r\n###############################################################################\r\n\r\ndef execution(stock,predict=False):\r\n    featList=['rsi_14','adx','macd','open_2_sma','wr_10','cci','dma','trix','vr']\r\n\r\n    clfList=[#neighbors.KNeighborsClassifier(),\r\n            #gaussian_process.GaussianProcessClassifier(),\r\n            #ensemble.RandomForestClassifier(),\r\n            #naive_bayes.GaussianNB(),\r\n            linear_model.LogisticRegression()]\r\n\r\n    gridList=[#{'n_neighbors': [n for n in range(1,30,2)]},\r\n            #{'kernel':[None]},\r\n            #{'n_estimators': [n for n in range(1,100, 5)]},\r\n            #{'priors':[None]},\r\n            {'C': [n for n in np.arange(0.1,1.1,0.1)]}]\r\n\r\n    data=iex.stock_info(stock).create_ranged_dataset('5y')\r\n    data=addNew(data)\r\n\r\n    data=preprocess(data,featList)\r\n    if data.shape[0]<900:\r\n        return 'dataset too small'\r\n\r\n    features=findBestFeatures(data,featList,5)\r\n    print(features)\r\n\r\n    df=gridSearch(features,clfList,gridList,data)\r\n    model=findBestModel(df,data,features)\r\n    if type(model)==str: \r\n        return model\r\n\r\n    acc,tested,total,clf=model\r\n\r\n    if predict==True:\r\n        return acc,features,tested,total,clf, prediction(clf,features,data)\r\n\r\n    return (acc,features,tested,total,clf)\r\n\r\n\r\n###########################################################\r\n\r\nif __name__=='__main__':\r\n    #stockList=['baba','wmt','aapl','goog','amzn','orcl','fb','twtr',\r\n    #            'cmg','gis','k','mcd','hsy','tsn','spy']\r\n    stockList=['ba','vz','ibm','bidu','spy','aaba',\r\n                'pnc','gs','bac','wfc']\r\n    #stockList=['aaba','jpm','td','vod','qcom','cmcsa','cost','amgn',\r\n    #            'eric','ebay','celg']\r\n\r\n    totalSuccess,totalTested,totalObserved=0,0,0\r\n\r\n    divisor=len(stockList)\r\n    for stock in stockList:\r\n        print(stock)\r\n        result=execution(stock,predict=True)\r\n        if type(result)==str:\r\n            print(result)\r\n            print('---')\r\n            divisor-=1\r\n            continue\r\n        acc,features,tested,observed,clf,forecast=result\r\n        print(forecast)\r\n\r\n        if tested!=0:\r\n            totalTested+=tested\r\n            totalSuccess+=acc*tested\r\n        totalObserved+=observed\r\n        print('---')\r\n    print('average accuracy: %.8f' % (float(totalSuccess)/float(totalTested)))\r\n    print('test / observed = %d / %d = %.8f' % (totalTested,totalObserved,float(totalTested)/totalObserved))","sub_path":"StockPredictionModel.py","file_name":"StockPredictionModel.py","file_ext":"py","file_size_in_byte":7399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"120541836","text":"import os\r\nimport shutil\r\n# import pickle\r\nimport shelve\r\n# 1\r\nprint(\"\\nСтворення каталогу LAB7\")\r\ntry:\r\n    os.mkdir(\"C:\\\\LAB7\")\r\n    print(\"Каталог стоворено\")\r\nexcept FileExistsError:\r\n    print(\"Каталог LAB7 існує\")\r\n\r\n# 2\r\nprint(\"\\nСтворення каталогу 'Valko'\")\r\nos.chdir(\"C:\\\\LAB7\")\r\ntry:\r\n    os.mkdir(\"Valko\")\r\n    print(\"Каталог 'Valko' створено\")\r\nexcept FileExistsError:\r\n    print(\"Каталог 'Valko' існує\")\r\n\r\n# 3\r\nprint(\"\\nПереміщення файлу в каталог\")\r\ntry:\r\n    shutil.move(\"D:\\\\Python.Programs\\\\Python Labs\\\\LABS\\\\lab.7\\\\v3.txt\", \"C:\\\\LAB7\\\\Valko\")\r\nexcept OSError:\r\n    print(\"Файл вже переміщенний\")\r\n\r\n# 4\r\nprint(\"\\nСтворення каталогу LAB5 і LAB6\")\r\ntry:\r\n    os.mkdir(\"C:\\\\LAB5\")\r\n    os.mkdir(\"C:\\\\LAB6\")\r\nexcept OSError:\r\n    print(\"Каталог LAB5 і LAB6 вже існують\\n\\n\")\r\n\r\n# 5\r\n'''\r\ntry:\r\n    db = shelve.open(\"C:\\\\LAB6\\\\lab6.txt\")\r\n    db[\"obj1\"]=lab6.months\r\n    db.close()\r\nexcept:\r\n    print(\"Файл не знайдено\")\r\n'''\r\ndb = shelve.open(\"D:\\\\Python.Programs\\\\Python Labs\\\\LABS\\\\lab.7\\\\texted.txt\")\r\ndb[\"obj1\"] = [1, 2]\r\ndb[\"obj2\"] = (11, 11)\r\ndb[\"obj5\"] = 'hello'\r\ndb[\"obj6\"] = 'python'\r\ndb.update(obj1=[4, 5, 6])\r\ndb.update(obj2=(100, 101, 102, 103))\r\ndb.update([(\"obj5\", 5), (\"obj6\", 6)])\r\nprint(db[\"obj5\"], db[\"obj6\"], db[\"obj1\"], db[\"obj2\"])\r\ndb.close()\r\n","sub_path":"I семестр/Програмування (Python)/Лабораторні/Валько 6103/lab.7/lab.7(side).py","file_name":"lab.7(side).py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"200177916","text":"#==========================================\n# Title:  checkOut\n# Author: Raamiz 'RJ' Hussain\n# Language: Python\n# Brief:  Lets the receptionist check out customer\n#==========================================\n\nimport datetime\n\ndef remRoom(nameOfCustomer, roomToBeVacant):\n    for key, item in currentRooms.items():\n        # Loops over the dictionary currentRooms\n        keyString = str(key)\n        # makes key an string\n        keyString = keyString.strip()\n        # strips it\n        keyInt = int(keyString)\n        # converts it back to an integer\n\n        if int(key) % 10 == 0:\n            disabled = \"Disabled\"\n        else:\n            disabled = \"Regular\"\n        # If room number is divisible by 10 it is a disabled access room\n        if keyInt == roomToBeVacant:\n            occupied = \"Vacant\"\n        else:\n            occupied = item\n        # If room number is the room to be booked make the occupier the customer\n        # else keep it the previous occupier / keep it vacant\n        f = open(\"rooms.txt\", \"a+\")\n        # Open the text file rooms.txt in append +\n        f.write(str(key) + \",\" + disabled + \",\" + occupied + \",\\n\")\n        # write the room into it\n        f.close()\n        # Close the text file\n\ncustomerName = input(\"What is the customer's name?: \")\nroomNumber = input(\"What is the customer's room number?\")\n# Asks the name and room number of the customer\n\ncurrentRooms = {}\n# Initialises the dictionary currentRooms\n\n# If the user required disability access\nwith open('rooms.txt', 'r+') as file:\n    # open the text file rooms.txt\n    rooms = file.read()\n    # read it into the variable rooms\nfile = open(\"rooms.txt\", \"w\").close()\n # Clear the text file\nfor line in rooms.splitlines():\n    line = line.strip()\n    lineSplit = line.split(\",\")\n    currentRooms[lineSplit[0]] = lineSplit[2]\n    # Reads all of the current rooms and occupants into the dictionary currentRooms\nfor line in rooms.splitlines():\n    line = line.strip()\n    lineSplit = line.split(\",\")\n    if lineSplit[2] == customerName and lineSplit[0] == roomNumber:\n        roomToBeVacant = int(lineSplit[0].strip())\n        remRoom(customerName, roomToBeVacant)\n        break\n    # Finds first available room meeting requirements, calls function remRoom and passes customer name and\n    # room to be vacant\n\n\nprint(customerName, \"is  checked out of room\", roomToBeVacant)\n# Tells the receptionist what room the customer is checked out of\n\nf = open('log.txt', 'a')\nnow = datetime.datetime.now()\nlog = customerName, \"was checked out of room\", roomToBeVacant, \"at\", now.strftime(\"%Y-%m-%d %H:%M:%S\")\n# These two lines of code were adapted from <https://www.w3resource.com/python-exercises/python-basic-exercise-3.php>\nlog = str(log)\nf.write(log)\nf.write(\"\\n\")\nf.close()\n# Logs the activity in the log file\n","sub_path":"checkOut.py","file_name":"checkOut.py","file_ext":"py","file_size_in_byte":2801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"235464789","text":"import os\nimport numpy             as np\nimport collections       as collections\nimport pandas            as pd\n\n#import invisible_cities.database.load_db   as db\n#import invisible_cities.io.pmaps_io        as pmio\n\nimport krcal.dev.corrections               as corrections\nimport csth .utils.hpeak_tables            as hptab\n\n\nQ0MIN  = 6.\nVDRIFT = 1.\n\ndef events_summary(pmaps, runinfo, loc, xpos, ypos, calibrate, q0min = Q0MIN, vdrift = VDRIFT):\n\n    elist = event_list(pmaps)\n    evts, npks  = elist\n\n    size = int(np.sum(npks))\n    etab = hptab.create_event_table(size)\n\n    eindex = 0\n    for evt, npk  in zip(evts, npks):\n        for ipk in range(npk):\n            esum = event_summary(pmaps, runinfo, evt, ipk, loc, calibrate, xpos, ypos, q0min, vdrift)\n\n            eindex = hptab._etable_set(etab, esum, eindex)\n\n    edf = hptab.df_from_etable(etab)\n    return edf\n\ndef event_list(pmaps):\n\n    s1, s2, s2i = pmaps.s1, pmaps.s2, pmaps.s2i\n\n    evts = np.unique(s1.event)\n    evts = [evt for evt in evts if len(np.unique(s1.peak[s1.event == evt])) == 1]\n    npks = [len(np.unique(s2.peak[s2.event == evt])) for evt in evts]\n\n    #print('event ', evts)\n    #print('peaks ', npks)\n    return hptab.EventList(evts, npks)\n\n\ndef event_summary(pmaps, runinfo, evt, ipk, loc, calibrate, xpos, ypos, q0min = Q0MIN, vdrift = VDRIFT):\n\n    s1, s2, s2i = pmaps.s1, pmaps.s2, pmaps.s2i\n\n    nslices, nhits      = 0, 0\n    noqslices, noqhits  = 0, 0\n    time                = 0\n    s1e, t0             = 0, 0\n    rmax, zmin, zmax    = 0, 0, 0,\n    x0, y0, z0, q0, e0  = 0, 0, 0, 0, 0\n    x, y, z, q, e       = 0, 0, 0, 0, 0\n\n    rsel                 = runinfo.evt_number == evt\n    tsel                 = (s1.event == evt)  & (s1.peak == 0)\n    ssel                 = (s2.event == evt)  & (s2.peak == ipk)\n    hsel                 = (s2i.event == evt) & (s2i.peak == ipk)\n\n    time          = event_timestamp(runinfo[rsel])\n    s1e, t0       = event_s1_info(s1[tsel])\n\n    nslices, z0i, e0i       = event_slices(s2[ssel], t0, vdrift)\n    if (nslices <= 0):\n        #return _result()\n        esum = hptab.ETuple(evt, ipk, loc, nslices, nhits, noqslices, noqhits,\n                            time, s1e, t0, rmax, zmin, zmax,\n                            x0, y0, z0, q0, e0,\n                            x, y, z, q, e)\n        #print(esum)\n        return esum\n\n    nhits, noqhits, x0ij, y0ij, z0ij, q0ij = event_hits(s2i[hsel], z0i, xpos, ypos, q0min)\n    if (nhits <= 0):\n        #return _result()\n        esum = hptab.ETuple(evt, ipk, loc, nslices, nhits, noqslices, noqhits,\n                            time, s1e, t0, rmax, zmin, zmax,\n                            x0, y0, z0, q0, e0,\n                            x, y, z, q, e)\n        #print(esum)\n        return esum\n\n    x0, y0, z0, q0, e0     = hptab.event_eqpoint(e0i, z0i, x0ij, y0ij, q0ij)\n\n    rmax                   = hptab.max_radius_hit(x0ij, y0ij)\n\n    zmin, zmax             = hptab.zrange(z0i)\n\n    noqslices, ei, qij, eij           = hptab.calibrate_hits(e0i, z0i, x0ij, y0ij, z0ij, q0ij, calibrate)\n\n    x , y ,  z, q , e      = hptab.event_eqpoint(ei , z0i, x0ij, y0ij, eij)\n\n    #enum = _result()\n    esum = hptab.ETuple(evt, ipk, loc, nslices, nhits, noqslices, noqhits,\n                    time, s1e, t0, rmax, zmin, zmax,\n                    x0, y0, z0, q0, e0,\n                    x, y, z, q, e)\n    return esum\n\n\ndef get_event_hits(pmaps, evt, ipk, calibrate, xpos, ypos, q0min = Q0MIN, vdrift = VDRIFT):\n\n    s1, s2, s2i = pmaps.s1, pmaps.s2, pmaps.s2i\n\n    tsel                 = (s1.event == evt)  & (s1.peak == 0)\n    ssel                 = (s2.event == evt)  & (s2.peak == ipk)\n    hsel                 = (s2i.event == evt) & (s2i.peak == ipk)\n\n    s1e, t0                 = event_s1_info(s1[tsel])\n\n    nslices, z0i, e0i       = event_slices(s2[ssel], t0, vdrift)\n    if (nslices <= 0):\n        return None\n\n    nhits, noqhits, x0ij, y0ij, z0ij, q0ij = event_hits(s2i[hsel], z0i, xpos, ypos, q0min)\n    if (nhits <= 0):\n        return None\n\n    noqslices, ei, qij, eij           = hptab.calibrate_hits(e0i, z0i, x0ij, y0ij, z0ij, q0ij, calibrate)\n\n    return x0ij, y0ij, z0ij, eij\n\n\ndef event_s1_info(s1):\n\n    s1e                  = np.sum(s1.ene)\n    if (s1e <= 1.): s1e  = 1.\n    t0                   = 1e-3*np.sum(s1.ene*s1.time)/s1e\n    #print('s1e ', s1e)\n    #print('t0  ', t0)\n    #print('time', time)\n    return s1e, t0\n\n\ndef event_timestamp(runinfo):\n\n    stime                 = runinfo.timestamp.values[0]\n    #print('time', stime)\n    return stime\n\ndef event_slices(s2, t0, vdrift = VDRIFT):\n\n    # z0s and index-slice in slices\n    ts  = 1.e-3*s2.time.values\n    nslices = len(ts)\n    if (nslices <= 0):\n        return nslices, None, None\n    z0i  = vdrift*(ts-t0)\n    e0i  = s2.ene.values\n\n    #print('nslices ', nslices)\n    #print('e0i ', len(e0i), np.sum(e0i) , e0i)\n    #print('z0i ', len(z0i), np.sum(z0i*e0i)/np.sum(e0i), z0i)\n    return nslices, z0i, e0i\n\ndef event_hits(s2i, z0i, xpos, ypos, q0min = Q0MIN):\n\n    nslices      = len(z0i)\n    if (nslices <= 1):\n        return 0, 0, None, None, None, None\n    q0ij         = s2i.ene.values\n    ntotal_hits  = len(q0ij)\n    if (ntotal_hits <= 0):\n        return 0, 0, None, None, None, None\n    #z0ij         = np.zeros(ntotal_hits)\n    nsipms       = int(ntotal_hits/nslices)\n    assert int(nsipms*nslices) == ntotal_hits\n    #\n    #selslices    = hptab.selection_slices(nslices, nsipms)\n    #for k, kslice in enumerate(selslices):\n    #    z0ij[kslice] = z0i[k]\n    z0ij = np.tile(z0i, nsipms)\n\n    # get the x, y positions and charge of the siPMs\n    qsel    = q0ij > q0min\n    noqsel  = (q0ij > 0) & (q0ij <= q0min)\n    nhits   = np.sum(qsel)\n    noqhits = np.sum(noqsel)\n    if (nhits <= 0):\n        return nhits, noqhits, None, None, None, None\n\n    sipm   = s2i.nsipm.values\n    q0ij   = q0ij[qsel]\n    x0ij   = xpos[sipm[qsel]]\n    y0ij   = ypos[sipm[qsel]]\n    z0ij   = z0ij[qsel]\n\n    #print('nhits, noqhits ', nhits, noqhits)\n    #print('x0ij', len(x0ij), x0ij, '\\n y0ij', len(y0ij), y0ij, '\\n z0ij', len(z0ij), z0ij)\n    #print('q0ij', len(q0ij), q0ij)\n    return nhits, noqhits, x0ij, y0ij, z0ij, q0ij\n\nevent_df_fast = events_summary\n","sub_path":"csth/utils/hpeak_pmaps_newfunctions.py","file_name":"hpeak_pmaps_newfunctions.py","file_ext":"py","file_size_in_byte":6189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"105257321","text":"import requests\r\nimport json\r\nimport getRandomWord\r\nfrom getRandomWord import printRandomWord\r\nr = requests.get('https://raw.githubusercontent.com/matthewreagan/WebstersEnglishDictionary/master/dictionary_alpha_arrays.json')\r\nEnglishDictionaryJson=json.loads(r.text)\r\n\r\nr = requests.get('https://raw.githubusercontent.com/first20hours/google-10000-english/master/google-10000-english.txt')\r\ntopTenThousandWords = r.text.split()\r\n\r\nfor n in topTenThousandWords:\r\n    if n in EnglishDictionaryJson[ord(n[0].lower())-97]:\r\n        EnglishDictionaryJson[ord(n[0].lower())-97].pop(n)\r\n\r\ntotalSize=0\r\nfor x in EnglishDictionaryJson:\r\n    totalSize+=len(x)\r\n\r\nwith open('interestingDictionary.json','a') as dictionaryFile:\r\n    dictionaryFile.write(str(EnglishDictionaryJson))\r\n    ","sub_path":"GatherDictionary.py","file_name":"GatherDictionary.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"380626672","text":"from django.db import models\n\n\nclass Sheet(models.Model):\n\n    WORKPLACES = (\n        ('0', 'Lasergran Granada'),\n        ('1', 'Lasergran Madrid'),\n        ('2', 'Lasergran Levante'),\n        ('3', 'Lasergran Cataluña'),\n    )\n\n    code = models.CharField(verbose_name='Código', \n        max_length=13, unique=True)\n    material = models.ForeignKey('presupuestos.Material', \n        verbose_name='Material', on_delete=models.SET_NULL, null=True)\n    date = models.DateTimeField(verbose_name='Fecha entrada', null=True)\n    quantity = models.DecimalField(verbose_name='Cantidad (Kg)', \n        max_digits=12, decimal_places=2, null=True)\n    colada = models.CharField(verbose_name='Número colada', \n        max_length=32, null=True)\n    provider = models.CharField(verbose_name='Proveedor', max_length=64, \n        null=True, blank=True)\n    albaran = models.CharField(verbose_name='Albarán', max_length=32, null=True)\n    certificate = models.ForeignKey('presupuestos.Certificate', \n        verbose_name='Certificado', blank=True, null=True, \n        on_delete=models.SET_NULL)\n    workplace = models.CharField(verbose_name='Centro de Trabajo', \n        max_length=2, null=True, default='0', choices=WORKPLACES)\n    print_quantity = models.PositiveSmallIntegerField(verbose_name='Número impresiones', \n        default=1)\n    printed = models.BooleanField(verbose_name='¿Impresa?', default=False)\n    updated_at = models.DateTimeField(verbose_name='Actualizado el', \n        auto_now=True)\n    created_at = models.DateTimeField(verbose_name='Creado el', \n        auto_now_add=True)\n\n    class Meta:\n        verbose_name = 'Chapa'\n        verbose_name_plural = 'Chapas'\n\n    def __str__(self):\n        return \"%s\" % self.material","sub_path":"almacen/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"120614209","text":"# Rate Limiter - an interface - which will be filter the request\n# Bucket Creator - will create bucket for each user\n# components should be thread safe because multiple requests can come in parallel\nimport BlockingQueue\nimport RateLimiterInterface\n\nclass LeakyBucket(RateLimiterInterface):\n    def __init__(self, capacity) -> None:\n        super().__init__()\n        self.queue = BlockingQueue(capacity)\n\n    def grantAccess(self):\n        super().grantAccess()\n        if self.queue.curr_size > 0:\n            self.queue.enqueue(1) #accepting one request at a time\n            return True\n        return False\n\nclass UserBucketCreator:\n    def __init__(self, id) -> None:\n        self.bucket = dict() # Map of int, LeakyBucket\n        self.bucket[id] = LeakyBucket(10)\n\n    def accessApplication(self, id):\n        if id in self.bucket and self.bucket[id].grantAccess():\n            print (\"able to access application\")\n        else:\n            print(\"Too many request, please try after sometime\")\n\n\nuserBucketCreator = UserBucketCreator(1)\n","sub_path":"systemdesign/LeakyBucketRateLimiter.py","file_name":"LeakyBucketRateLimiter.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"609459526","text":"import csv\nfrom grafs_funcs import make_png_trend\n\n\ndef aver_calculation(list):\n    sig = []\n    aver = sum(list) / len(list)\n    for i in range(len(list)):\n        sig.append((list[i] - aver) ** 2)\n    sigma = (sum(sig) / len(list)) ** 0.5\n    return aver, sigma\n\n\ndef aver_main_func(main_list, x, csv_file):\n    with open(csv_file, 'w', encoding='cp1251') as file:\n        writer = csv.writer(file, delimiter=';', quotechar='\"')\n        csv_str = ['kod_gup', 'art.', 'name', 'average', 'sigma']\n        writer.writerow(csv_str)\n        for row in main_list:\n            row_data = row[3:]\n            aver, sigmax = aver_calculation(row_data)\n            csv_str = row[0:3]\n            csv_str.extend([aver, sigmax])\n            writer.writerow(csv_str)\n    print('Average success!')\n\n\ndef linear_calculation(list):\n    x = 0\n    xx = 0\n    for i in range(1, len(list) + 1):\n        x += i\n        xx += i * i\n    x_aver = x / len(list)\n    xy_sum = 0\n    sig = []\n    trend_list = []\n    y_aver = sum(list) / len(list)\n    for i in range(1, len(list) + 1):\n        xy = i * list[i-1]\n        xy_sum += xy\n    b = (xy_sum - len(list) * x_aver * y_aver) / (xx - len(list) * x_aver * x_aver)\n    a = y_aver - b * x_aver\n    for i in range(1, len(list)):\n        yt = a + b * i\n        trend_list.append(yt)\n        sig.append((list[i] - trend_list[i-1])**2)\n    trend_list.append(round(a + b * len(list)))\n    sigma = round((sum(sig) / (len(sig) - 1)) ** 0.5, 3)\n    return trend_list, sigma\n\n\ndef linear_main_func(main_list, png_dir, period_list, csv_file):\n    with open(csv_file, 'w', encoding='cp1251') as file:\n        writer = csv.writer(file, delimiter=';', quotechar='\"')\n        csv_str = ['kod_gup', 'art.', 'name', 'prognos', 'sigma']\n        writer.writerow(csv_str)\n        for row in main_list:\n            row_data = row[3:]\n            trend_list, sigma = linear_calculation(row_data)\n            data_list = row[0:3]\n            data_list.extend(row_data)\n            prognos_list = row[0:3]\n            prognos_list.extend(trend_list)\n            csv_str = row[0:3]\n            csv_str.extend([trend_list[-1], sigma])\n            writer.writerow(csv_str)\n            make_png_trend(data_list, prognos_list, png_dir, period_list, sigma)\n    print('Linear success!')\n","sub_path":"aver_funcs.py","file_name":"aver_funcs.py","file_ext":"py","file_size_in_byte":2283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"520048518","text":"\"\"\"A custom Sphinx HTML Translator for Bootstrap layout\n\"\"\"\nfrom docutils import nodes\n\nfrom sphinx.locale import admonitionlabels\nfrom sphinx.writers.html5 import HTML5Translator\nfrom sphinx.util import logging\n\nlogger = logging.getLogger(__name__)\n\n\n# Mapping of admonition classes to Bootstrap contextual classes\nalert_classes = {\n    \"attention\": \"primary\",\n    \"caution\": \"warning\",\n    \"danger\": \"danger\",\n    \"error\": \"danger\",\n    \"hint\": \"info\",\n    \"important\": \"primary\",\n    \"note\": \"info\",\n    \"seealso\": \"info\",\n    \"tip\": \"primary\",\n    \"warning\": \"warning\",\n    \"todo\": \"info\",\n    \"example\": \"info\",\n}\n\n\nclass BootstrapHTML5Translator(HTML5Translator):\n    \"\"\"Custom HTML Translator for a Bootstrap-ified Sphinx layout\n    This is a specialization of the HTML5 Translator of sphinx.\n    Only a couple of functions have been overridden to produce valid HTML to be\n    directly styled with Bootstrap.\n    \"\"\"\n\n    def __init__(self, *args, **kwds):\n        super().__init__(*args, **kwds)\n        self.settings.table_style = \"table\"\n\n    def visit_admonition(self, node, name=\"\"):\n        # type: (nodes.Element, str) -> None\n        # copy of sphinx source to add alert classes\n        classes = [\"alert\"]\n\n        # If we have a generic admonition block, style it as info\n        if (\n            any(\"admonition-\" in iclass for iclass in node.attributes[\"classes\"])\n            and name == \"\"\n        ):\n            name = \"admonition\"\n            if node.attributes.get(\"names\"):\n                # If `name` is specified, try to look it up in the list of alerts\n                alert_name = node.attributes.get(\"names\")[0]\n            else:\n                # If no `name` is specified, style it as `note`\n                alert_name = \"note\"\n            if alert_name not in alert_classes:\n                logger.warning(\n                    f\"Unsupported admonition name: `{alert_name}`. Using style `note`.\",\n                    location=(self.docnames[0], node.children[0].line),\n                )\n                alert_name = \"note\"\n\n            # Add the \"admonition\" class to alert_classes so that it can be referenced\n            alert_classes[name] = alert_classes[alert_name]\n\n            # Remove the title from this admonition and add it to the admonitionlabels\n            # Because this is how Sphinx inserts titles into admonitions\n            title = node.children.pop(0)\n            admonitionlabels[name] = title.astext()\n\n        if name:\n            classes.append(\"alert-{0}\".format(alert_classes[name]))\n\n        # This mimics what Sphinx does in its own `visit_admonition`\n        # but wraps in `alert`\n        self.body.append(self.starttag(node, \"div\", CLASS=\" \".join(classes)))\n        if name:\n            node.insert(0, nodes.title(name, admonitionlabels[name]))\n\n    def visit_table(self, node):\n        # type: (nodes.Element) -> None\n        # copy of sphinx source to *not* add 'docutils' and 'align-default' classes\n        # but add 'table' class\n        self.generate_targets_for_table(node)\n\n        self._table_row_index = 0\n\n        classes = [cls.strip(\" \\t\\n\") for cls in self.settings.table_style.split(\",\")]\n        # classes.insert(0, \"docutils\")  # compat\n        # if 'align' in node:\n        #     classes.append('align-%s' % node['align'])\n        tag = self.starttag(node, \"table\", CLASS=\" \".join(classes))\n        self.body.append(tag)\n","sub_path":"pydata_sphinx_theme/bootstrap_html_translator.py","file_name":"bootstrap_html_translator.py","file_ext":"py","file_size_in_byte":3403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"155069222","text":"import torch, torch.nn as nn, torch.optim as optim\nfrom torchvision import transforms, models\nfrom dataloader import dataloader\nfrom train_model import train_model\nfrom evaluate import evaluate\nfrom utils import ImgAugmenter\nimport PIL\n\ndevice = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\nuse_gpu = torch.cuda.is_available()\nif use_gpu:\n    print(\"Using CUDA\")\n\nnormalize = transforms.Normalize(mean=[0.6855248, 0.68901044, 0.6142709], std=[0.32218322, 0.27970782, 0.3134101])\ntransform = {\n    'train': transforms.Compose([\n        transforms.Resize(256),\n        transforms.CenterCrop(224),\n        ImgAugmenter(),\n        lambda x: PIL.Image.fromarray(x),\n        transforms.ColorJitter(hue=.05, saturation=.05),\n        transforms.RandomHorizontalFlip(),\n        transforms.RandomAffine(15),\n        transforms.RandomRotation(20, resample=PIL.Image.BILINEAR),\n        transforms.ToTensor(),\n        normalize\n    ]),\n    'test': transforms.Compose([\n        transforms.Resize(256),\n        transforms.CenterCrop(224),\n        transforms.ToTensor(),\n        normalize\n    ])\n}\n\ntrain_loader, train_size, valid_loader, valid_size, test_loader = dataloader(colab=True, \n                                                    batch_size=64, \n                                                    transform=transform)\ndataloader = {'train': train_loader, 'val': valid_loader}\n\nmobilenet = models.mobilenet_v2(pretrained=True)\nfor param in mobilenet.parameters():\n    param.requires_grad = False\nmobilenet.classifier = nn.Sequential(\n    nn.Dropout(0.3), \n    nn.Linear(1280, 640), \n    nn.Dropout(0.3),\n    nn.Linear(640, 9)\n)\n# print(mobilenet)\n\nif use_gpu:\n    mobilenet.cuda()\n\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(mobilenet.parameters(), lr=1e-4)\n\n# train_model(mobilenet, criterion, optimizer, dataloader, train_size, valid_size, model_name='mobilenetv2_augment', num_epochs=100)\nevaluate(mobilenet, test_loader, model_name='mobilenetv2_augment.pt')","sub_path":"mobilenetv2_augment.py","file_name":"mobilenetv2_augment.py","file_ext":"py","file_size_in_byte":1988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"403512439","text":"import os, sys, argparse, glob\nfrom natsort import natsorted\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torchvision.transforms as T\n\nfrom skimage import io\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms, utils\nfrom skimage.transform import resize\nfrom skimage.morphology import erosion, binary_erosion\nfrom skimage.filters import sobel\n\nfrom ISBI2012_Dataset import *\nfrom FusionNet import *\nfrom img_aug_func import *\nfrom Logger import Logger\nimport multiprocessing as mp\nfrom tqdm import tqdm\nimport time\n\nimport os\n\nDS_FACTOR = 1\nLOG_PERIOD = 5\nLOG_DIR = 'log_dir/'\nCHECKPOINT_SAVE_PATH = 'checkpoints/'\nSAVE_PERIOD = 5\nIN_CH = 2\nOUT_CH = 1\nBATCH_SIZE = 4\nZOOM_FACTOR = 0.6\nrefineNet = None\nDS_FACTORS_LIST = [1.0, 1.0, 1.0, 1.0]\nFEATURES = [16, 32, 64, 128]\ndevice = None\n\nREFINE_PATHS = [\n    'checkpoints/0_0.4/checkpoint_389400.pth.tar',\n]\n\ndef get_cell_prob (lbl):\n    elevation_map = []\n    for img in lbl:\n        elevation_map += [sobel (img)]\n    elevation_map = np.array (elevation_map)\n    elevation_map = elevation_map > 0\n    cell_prob = (lbl > 0) ^ elevation_map\n    for i in range (len (cell_prob)):\n        cell_prob [i] = binary_erosion (cell_prob [i])\n    return np.array (cell_prob, dtype=np.uint8) * 255\n\ndef get_data (path, downsample=1):\n    train_path = natsorted (glob.glob(path + 'trainA/*.tif'))\n    train_label_path = natsorted (glob.glob(path + 'trainB/*.tif'))\n    X_train = read_im (train_path)\n    y_train = read_im (train_label_path)\n\n    if (len (X_train) > 0):\n        X_train = X_train [0]\n    if (len (y_train) > 0):\n        y_train = y_train [0]\n    else:\n        y_train = np.zeros_like (X_train)\n\n    return X_train, y_train\n\ndef create_dir (directory):\n    if not os.path.exists(directory):\n        os.makedirs(directory)\n\ndef save_checkpoint (state, path=CHECKPOINT_SAVE_PATH):\n    torch.save (state, path)\n\ndef eval (test_data, loss_func, hasTarget=True):\n    for sample in test_data:\n        raw_t = torch.tensor (sample['raw'], device=device, dtype=torch.float32) / 255.0\n        pred_t = model (raw_t)\n        if hasTarget:\n            target_t = torch.tensor (sample['lbl'], device=device, dtype=torch.float32) / 255.0\n            loss = loss_func (pred_t, target_t)\n            return raw_t, target_t, pred_t, loss\n        return raw_t, pred_t\n\ndef visual_log (raw_t, target_t, pred_t, logger, i_iter, log_set, hasTarget=True):\n    raw = np.expand_dims (raw_t.detach ().cpu ().numpy()[:,0,:,:], 3)\n    mask = np.expand_dims (raw_t.detach ().cpu ().numpy()[:,1,:,:], 3)\n    if hasTarget:\n        target = np.expand_dims (target_t.detach ().cpu ().numpy ()[:,0,:,:], 3)\n    pred = np.expand_dims (pred_t.detach ().cpu ().numpy ()[:,0,:,:], 3)\n\n    if hasTarget:\n        concated_imgs = (np.concatenate ([raw, mask, target, pred], 2) * 255).astype (np.uint8)\n    else:\n        concated_imgs = (np.concatenate ([raw, mask, pred], 2) * 255).astype (np.uint8)\n    concated_imgs = np.repeat (concated_imgs, 3, axis=3)\n\n    for tag, value in model.named_parameters ():\n        tag = tag.replace ('.', '/')\n        logger.histo_summary (tag, value.data.cpu ().numpy (), i_iter)\n\n    info = {'train_imgs': concated_imgs}\n    for tag, vol in info.items ():\n        for i_img in range (len (vol)):\n            logger.image_summary (tag + '_' + str (i_img) + '_' + log_set, vol[i_img:i_img+1], i_iter)\n\ndef train (train_data, test_data, n_epoc, loss_func, optimizer, lr_scheduler, i_iter=0):\n    logger = Logger (LOG_DIR)\n    \n    for i_ipoc in range (n_epoc):\n        pbar = tqdm (total=len (train_data), ascii=True)\n        ipoc_loss = 0\n        \n        for i_batch, sample in enumerate (train_data):\n            if i_batch == len (train_data):\n                break\n            pbar.update (1)\n            raw_t = torch.tensor (sample['raw'], device=device, dtype=torch.float32) / 255.0\n            target_t = torch.tensor (sample['lbl'], device=device, dtype=torch.float32) / 255.0\n            \n            pred_t = model (raw_t)\n            loss = loss_func (pred_t, target_t)\n\n            optimizer.zero_grad ()\n            loss.backward ()\n            optimizer.step ()\n\n            ipoc_loss += loss.item () / len (train_data)\n            lr_scheduler.step ()\n            i_iter += 1\n\n            if i_batch == len (train_data) - 1 and (i_ipoc + 1) % LOG_PERIOD == 0:\n                # print ('\\nWriting log')\n                info = {'loss': ipoc_loss, 'learning_rate': lr_scheduler.get_lr () [0]}\n                for tag, value in info.items ():\n                    logger.scalar_summary (tag, value, i_iter)\n                visual_log (raw_t, target_t, pred_t, logger, i_iter, 'train')\n                raw_t, pred_t = eval (test_data, loss_func, hasTarget=False)\n                visual_log (raw_t, None, pred_t, logger, i_iter, 'test', hasTarget=False)\n        \n        if i_ipoc % SAVE_PERIOD == 0:\n            \n            save_checkpoint ({\n                'i_iter': i_iter,\n                'state_dict': model.state_dict (),\n                'optimizer': optimizer.state_dict ()\n                }, CHECKPOINT_SAVE_PATH + 'checkpoint_' + str (i_iter) + '.pth.tar')\n        pbar.close ()\n        time.sleep (1.0)\n        pbar.write (s ='ipoc ' +  str (i_ipoc) + ' iter ' + str (i_iter) + ' loss ' + str (ipoc_loss))\n\n\ndef pred_func (raw, factor):\n    '''\n        raw: full resolution ()\n    '''\n    origin_size = raw.shape ()[1::] #HxW\n    raw = raw [::, ::factor, ::factor]\n    raw = torch.tensor (np.expand_dims (raw, 0), device=device, dtype=torch.float32) / 255.0\n    pred = refineNet (raw)\n    pred = np.squeeze (pred.cpu ().numpy ()) # 2 dimensional pred\n    pred = resize (pred, origin_size, order=0, mode='wrap', preserve_range=True) #Back to origin size\n    pred = (pred * 255).astype (np.uint8)\n    return pred\n\ndef setup_global_var (args):\n    global LOG_DIR, CHECKPOINT_SAVE_PATH, DS_FACTOR, ZOOM_FACTOR, BATCH_SIZE, device\n    level = int (args.level)\n    DS_FACTOR = DS_FACTORS_LIST [level]\n    ZOOM_FACTOR = float (args.zoom_factor)\n    refine_tag = ''\n    if args.refine is not None:\n        refine_tag += '_r'\n    LOG_DIR = LOG_DIR + args.level + '_' + str (DS_FACTOR) + refine_tag + '/'\n    CHECKPOINT_SAVE_PATH = CHECKPOINT_SAVE_PATH + args.level + '_' + str (DS_FACTOR) + refine_tag + '/'\n    \n    print ('CHECKPOINT_SAVE_PATH:', CHECKPOINT_SAVE_PATH)\n    print ('LOG_DIR path', LOG_DIR)\n    create_dir (CHECKPOINT_SAVE_PATH)\n    \n    BATCH_SIZE = int (args.batch_size)\n\n    os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n    # GPU setup\n    if args.gpu:\n        os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu\n\n    print ('Using GPU', os.environ['CUDA_VISIBLE_DEVICES'])  \n\n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument ('--gpu',    help='comma seperated list of GPU(s) to use.')\n    parser.add_argument ('--level', help='level of model', required=True)\n    parser.add_argument ('--ds_factor', help='downsample factor of data', required=False)\n    parser.add_argument ('--zoom_factor', help='zoom-in factor across level', required=False, default=0.6)\n    parser.add_argument ('--load',    help='load model')\n    parser.add_argument ('--batch_size', default='4')\n    parser.add_argument ('--refine', default=None)\n    \n    args = parser.parse_args()\n    setup_global_var (args)\n\n    checkpoint_path = None\n    if args.load:\n        checkpoint_path = args.load\n      \n    level = int (args.level)\n\n    # RefineNet setup\n    if args.refine is not None:\n        refineNet = LightVnet (IN_CH, OUT_CH).to (device)\n        refineNet.requires_grad = False\n        refineCheckpoint = torch.load (REFINE_PATHS [level - 1])\n        refineNet.load_state_dict (refineCheckpoint ['state_dict'])\n\n    # Setup dataflow\n    X_train, y_train = get_data (path='../DATA/train/')\n    X_test, y_test = get_data (path='../DATA/test/')\n    print (\"Train-set size: \", len (X_train))\n    print (\"Test-set size: \", len (X_test)) \n\n    print (\"Use normal-dataset\")\n    cremiData = ISBI2012_Dataset16 ('train', X_train, y_train, level, ZOOM_FACTOR)\n    cremiData_test = ISBI2012_Dataset16 ('test', X_test, y_test, level, ZOOM_FACTOR)\n    train_data = DataLoader (cremiData, batch_size=BATCH_SIZE, shuffle=True, num_workers=0)\n    test_data = DataLoader (cremiData_test, batch_size=4, num_workers=0)\n\n    # Setup model\n    model = FusionNet (IN_CH, FEATURES, OUT_CH).to (device)\n    optimizer = optim.Adam (model.parameters (), lr=1e-4)\n    loss_func = nn.BCELoss ()\n    lr_scheduler = optim.lr_scheduler.StepLR (optimizer, step_size=100, gamma=0.999)\n    i_iter = 0\n\n    # Load checkpoint\n    if checkpoint_path is not None:\n        checkpoint = torch.load  (checkpoint_path)\n        model.load_state_dict (checkpoint['state_dict'])\n        if args.refine is None:\n            i_iter = checkpoint['i_iter']\n            optimizer.load_state_dict (checkpoint['optimizer'])\n\n    # Train model\n    train (train_data, test_data, 10000000, loss_func, optimizer, lr_scheduler, i_iter=i_iter)\n","sub_path":"ISBI2012/FusionNet/train_FusionNet_single.py","file_name":"train_FusionNet_single.py","file_ext":"py","file_size_in_byte":9136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"413053291","text":"# https://www.hackerrank.com/challenges/py-collections-ordereddict/problem?isFullScreen=true\r\n\r\n# Task\r\n# You are the manager of a supermarket.\r\n# You have a list of N items together with their prices that consumers bought on a particular day.\r\n# Your task is to print each item_name and net_price in order of its first occurrence.\r\n\r\n# item_name = Name of the item.\r\n# net_price = Quantity of the item sold multiplied by the price of each item.\r\n\r\n# Input Format\r\n# The first line contains the number of items, N.\r\n# The next N lines contains the item's name and price, separated by a space.\r\n\r\n# Constraints\r\n# 0 < N <= 100\r\n\r\n# Output Format\r\n# Print the item_name and net_price in order of its first occurrence.\r\n\r\n# Sample Input\r\n# 9\r\n# BANANA FRIES 12\r\n# POTATO CHIPS 30\r\n# APPLE JUICE 10\r\n# CANDY 5\r\n# APPLE JUICE 10\r\n# CANDY 5\r\n# CANDY 5\r\n# CANDY 5\r\n# POTATO CHIPS 30\r\n\r\n# Sample Output\r\n# BANANA FRIES 12\r\n# POTATO CHIPS 60\r\n# APPLE JUICE 20\r\n# CANDY 20\r\n\r\n\r\n# Enter your code here. Read input from STDIN. Print output to STDOUT\r\n\r\nfrom collections import OrderedDict\r\n\r\noDict = OrderedDict()\r\n\r\nfor _ in range(int(input())):\r\n    N = list(input().split())\r\n    name = \" \".join(N[:-1])\r\n    price = int(N[-1])\r\n\r\n    if name in oDict:\r\n        oDict[name] += price\r\n    else:\r\n        oDict[name] = price\r\n\r\nfor n, p in oDict.items():\r\n    print(\"%s %s\" % (n,p))","sub_path":"0_reference/Hackerrank/Practice Python/Collections/Collections OrderedDict.py","file_name":"Collections OrderedDict.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"336557121","text":"import pandas as pd\nimport plotly.offline as pyo\nimport plotly.graph_objs as go\n\ndf = pd.read_csv('Datasets/2018WinterOlympics.csv')\n\ntrace1 = go.Bar(x=df['NOC'],\n                y=df['Gold'],\n                name='Oro',\n                marker={'color':'#FFD700'})\n\ntrace2 = go.Bar(x=df['NOC'],\n                y=df['Silver'],\n                name='Plata',\n                marker={'color':'#9EA0A1'})\n\ntrace3 = go.Bar(x=df['NOC'],\n                y=df['Bronze'],\n                name='Bronce',\n                marker={'color':'#CD7F32'})\n\ndata = [trace1, trace2, trace3]\nlayout = go.Layout(title='Medallas por país',\n                   barmode='stack')\nfig = go.Figure(data=data, layout=layout)\npyo.plot(fig, filename='bar_chart.html')\n","sub_path":"MOOC3/módulo 3 - unidad 6 - bar_charts_v3.py","file_name":"módulo 3 - unidad 6 - bar_charts_v3.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"273863491","text":"from itertools import combinations\nimport numpy as np\n\n\ndef get_combinations(arr, r):\n    r_combinations = list(combinations(arr, r))\n    r_combinations = [i[0] + \"vs\" + i[1] for i in r_combinations]\n    r_combinations.sort()\n    return r_combinations\n\n\ndef nested_to_3d_numpy(X, a=None, b=None):\n    \"\"\"Convert pandas DataFrame (with time series as pandas Series in cells) into NumPy ndarray with shape (n_instances, n_columns, n_timepoints).\n\n    Parameters\n    ----------\n    X : pandas DataFrame, input\n    a : int, first row (optional, default None)\n    b : int, last row (optional, default None)\n\n    Returns\n    -------\n    NumPy ndarray, converted NumPy ndarray\n    \"\"\"\n    return np.stack(\n        X.iloc[a:b].applymap(lambda cell: cell.to_numpy()).apply(lambda row: np.stack(row), axis=1).to_numpy())\n\n\ndef get_segments(weights, threshold):\n    \"\"\"\n    Function to get the indices of segments having values greater than the threshold\n    \"\"\"\n    marker_list = [True if i >= threshold else False for i in weights]\n    i = 0\n    final_pairs = []\n    while i < len(weights):\n        if marker_list[i]:\n            start = i\n            while i < len(weights) and marker_list[i]:\n                i = i + 1\n            end = i - 1\n            if end-start > 1:\n                final_pairs.append(start)\n                final_pairs.append(end)\n        i = i + 1\n    return np.array(final_pairs)\n\n\ndef get_top_values(weights, top_k=4):\n    \"\"\"\n    Function to get the topk values and their indices from an array\n    \"\"\"\n    top_idx = np.argsort(weights)[-top_k:]\n    top_idx = np.flip(top_idx)\n    top_values = [weights[i] for i in top_idx]\n    return top_idx, top_values\n\n\nif __name__ == \"__main__\":\n    weights = np.array([10, 20, 80, 81, 89, 1, 2, 90, 91, 100, 1, 2, 80])\n    print(get_top_values(weights))\n\n\n","sub_path":"utils/math_funtions.py","file_name":"math_funtions.py","file_ext":"py","file_size_in_byte":1815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"224360032","text":"from librip.gens import field\n\ngoods = [\n    {'title': 'Ковер', 'price': 2000, 'color': 'green'},\n    {'title': 'Диван для отдыха', 'price': 5300, 'color': 'black'},\n    {'title': 'Стелаж', 'price': 7000, 'color': 'white'},\n    {'title': 'Вешалка для одежды', 'color': 'white'}\n]\ngoods2 = [\n    {'title': 'Ковер', 'price': 2000, 'color': 'green'},\n    {'title': 'Диван для отдыха', 'color': None}]\n# Реализация задания 1\n\nprint(\", \".join([str(i) for i in field(goods, \"title\")]))\nprint(\", \".join([str(i) for i in field(goods, \"title\", \"price\")]))\nprint(\", \".join([str(i) for i in field(goods2, \"title\",\"price\",\"color\")]))\n","sub_path":"LR3/ex_1.py","file_name":"ex_1.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"274042636","text":"# coding: utf-8\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nimport os\nimport sys\n\nfrom .. import click, sync\nfrom .._compat import get_installed_distributions, parse_requirements\nfrom ..exceptions import PipToolsError\nfrom ..logging import log\nfrom ..utils import flat_map\n\nDEFAULT_REQUIREMENTS_FILE = \"requirements.txt\"\n\n\n@click.command()\n@click.version_option()\n@click.option(\n    \"-n\",\n    \"--dry-run\",\n    is_flag=True,\n    help=\"Only show what would happen, don't change anything\",\n)\n@click.option(\"--force\", is_flag=True, help=\"Proceed even if conflicts are found\")\n@click.option(\n    \"-f\",\n    \"--find-links\",\n    multiple=True,\n    help=\"Look for archives in this directory or on this HTML page\",\n    envvar=\"PIP_FIND_LINKS\",\n)\n@click.option(\n    \"-i\",\n    \"--index-url\",\n    help=\"Change index URL (defaults to PyPI)\",\n    envvar=\"PIP_INDEX_URL\",\n)\n@click.option(\n    \"--extra-index-url\",\n    multiple=True,\n    help=\"Add additional index URL to search\",\n    envvar=\"PIP_EXTRA_INDEX_URL\",\n)\n@click.option(\n    \"--trusted-host\",\n    multiple=True,\n    help=\"Mark this host as trusted, even though it does not have valid or any HTTPS.\",\n)\n@click.option(\n    \"--no-index\",\n    is_flag=True,\n    help=\"Ignore package index (only looking at --find-links URLs instead)\",\n)\n@click.option(\"-q\", \"--quiet\", default=False, is_flag=True, help=\"Give less output\")\n@click.option(\n    \"--user\", \"user_only\", is_flag=True, help=\"Restrict attention to user directory\"\n)\n@click.option(\"--cert\", help=\"Path to alternate CA bundle.\")\n@click.option(\n    \"--client-cert\",\n    help=\"Path to SSL client certificate, a single file containing \"\n    \"the private key and the certificate in PEM format.\",\n)\n@click.argument(\"src_files\", required=False, type=click.Path(exists=True), nargs=-1)\ndef cli(\n    dry_run,\n    force,\n    find_links,\n    index_url,\n    extra_index_url,\n    trusted_host,\n    no_index,\n    quiet,\n    user_only,\n    cert,\n    client_cert,\n    src_files,\n):\n    \"\"\"Synchronize virtual environment with requirements.txt.\"\"\"\n    if not src_files:\n        if os.path.exists(DEFAULT_REQUIREMENTS_FILE):\n            src_files = (DEFAULT_REQUIREMENTS_FILE,)\n        else:\n            msg = \"No requirement files given and no {} found in the current directory\"\n            log.error(msg.format(DEFAULT_REQUIREMENTS_FILE))\n            sys.exit(2)\n\n    if any(src_file.endswith(\".in\") for src_file in src_files):\n        msg = (\n            \"Some input files have the .in extension, which is most likely an error \"\n            \"and can cause weird behaviour. You probably meant to use \"\n            \"the corresponding *.txt file?\"\n        )\n        if force:\n            log.warning(\"WARNING: \" + msg)\n        else:\n            log.error(\"ERROR: \" + msg)\n            sys.exit(2)\n\n    requirements = flat_map(\n        lambda src: parse_requirements(src, session=True), src_files\n    )\n\n    try:\n        requirements = sync.merge(requirements, ignore_conflicts=force)\n    except PipToolsError as e:\n        log.error(str(e))\n        sys.exit(2)\n\n    installed_dists = get_installed_distributions(skip=[], user_only=user_only)\n    to_install, to_uninstall = sync.diff(requirements, installed_dists)\n\n    install_flags = []\n    for link in find_links or []:\n        install_flags.extend([\"-f\", link])\n    if no_index:\n        install_flags.append(\"--no-index\")\n    if index_url:\n        install_flags.extend([\"-i\", index_url])\n    if extra_index_url:\n        for extra_index in extra_index_url:\n            install_flags.extend([\"--extra-index-url\", extra_index])\n    if trusted_host:\n        for host in trusted_host:\n            install_flags.extend([\"--trusted-host\", host])\n    if user_only:\n        install_flags.append(\"--user\")\n    if cert:\n        install_flags.extend([\"--cert\", cert])\n    if client_cert:\n        install_flags.extend([\"--client-cert\", client_cert])\n\n    sys.exit(\n        sync.sync(\n            to_install,\n            to_uninstall,\n            verbose=(not quiet),\n            dry_run=dry_run,\n            install_flags=install_flags,\n        )\n    )\n","sub_path":"piptools/scripts/sync.py","file_name":"sync.py","file_ext":"py","file_size_in_byte":4110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"285853344","text":"\nimport subprocess\n\ncmdhead=\"adb shell su -c \"       #切换使用adb shell su -c 和 adb shell su 0\n\ndef exec(cmd):\n    proc = subprocess.Popen(\n        cmd,\n        shell=True,\n        stdout=subprocess.PIPE,\n        stderr=subprocess.STDOUT,\n        stdin=subprocess.PIPE  # 重定向输入值\n    )\n    proc.stdin.close()  # 既然没有命令行窗口，那就关闭输入\n    result = proc.stdout.read()  # 读取cmd执行的输出结果（是byte类型，需要decode）\n    proc.stdout.close()\n    return result.decode(encoding=\"utf-8\")\n\ndef execCmd(cmd):\n    text = exec(cmd)\n    if len(text)>0:\n        text+=\"\\ncmd命令执行\"+cmd\n    else:\n        text =\"cmd命令执行\" + cmd\n    return text\n\ndef dumpdexInit(packageName):\n    path = \"/data/data/\" + packageName + \"/files/\" + \"/dump_dex_\" + packageName\n    res=\"\"\n    res += adbshellCmd(\"mkdir -p \" + path)+\"\\n\"\n    res += adbshellCmd(\"chmod 0777 \" + \"/data/data/\" + packageName + \"/files/\")+\"\\n\"\n    res += adbshellCmd(\"chmod 0777 \" + path)+\"\\n\"\n    return res\n\ndef fartInit(savepath):\n    res=\"\"\n    res += adbshellCmd(\"mkdir -p \" + savepath)+\"\\n\"\n    res += adbshellCmd(\"chmod 0777 \" + savepath)+\"\\n\"\n    return res\n\ndef adbshellCmd(cmd):\n    cmd=\"%s '%s'\"%(cmdhead,cmd)\n    text = exec(cmd)\n    if len(text) > 0:\n        text += \"\\ncmd命令执行\" + cmd\n    else:\n        text = \"cmd命令执行\" + cmd\n    return text","sub_path":"utils/CmdUtil.py","file_name":"CmdUtil.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"286649778","text":"import sys\nfrom scanner import *\n\ndef main():\n    printMatrix(assignMatrix(readCoord(sys.argv[1]),matrix(5,5)))\n    print()\n    printColCol(assignMatrix(readCoord(sys.argv[1]),matrix(5,5)))\n    \ndef matrix(r,c):\n    array = []\n\n    for i in range(0,r,1):\n        array.append([])\n        for j in range(0,c,1):\n            array[i].append(None)\n    return array\n\ndef printMatrix(matrix):\n    for i in range(0,len(matrix),1):\n        for j in range(0,len(matrix[i]),1):\n            print(matrix[i][j],end=' ')\n        print()\n\ndef printColCol(matrix):\n    for i in range(0,len(matrix[0]),1):\n        for j in range(0,len(matrix),1):\n            print(matrix[j][i],end=' ')\n        print()\n\ndef readCoord(filename):\n    s = Scanner(filename)\n    coord = [s.readtoken(),s.readtoken()]\n    array = []\n    array.append(coord)\n    array.append(s.readtoken())\n    x = s.readtoken()\n\n    while x != '':\n        coord = [x, s.readtoken()]\n        array.append(coord)\n        array.append(s.readtoken())\n        x = s.readtoken()\n    \n    for x in range(0,len(array),2):\n        for i in range(0,2,1):\n            if array[x][i] == 'a':\n                array[x][i] = 0\n                array = swap(array,x,i)       \n            if array[x][i] == 'b':\n                array[x][i] = 1\n                array = swap(array,x,i)\n            if array[x][i] == 'c':\n                array[x][i] = 2\n                array = swap(array,x,i)\n            if array[x][i] == 'd':\n                array[x][i] = 3\n                array = swap(array,x,i)\n            if array[x][i] == 'e':\n                array[x][i] = 4\n                array = swap(array,x,i)\n\n    for x in range(0,len(array),2):\n        for i in range(0,2,1):\n            array[x][i] = int(array[x][i])\n    return array\n\ndef swap(array,x,i):\n    if i == 0:\n        temp = array[x][i]\n        array[x][i] = array[x][i+1]\n        array[x][i+1] = temp\n    return array\n\ndef assignMatrix(array,matrix):\n    for x in range(0,len(array),2):\n        i = array[x][0]\n        j = array[x][1]\n        z = array[x+1]\n        matrix[i][j] = z\n\n    return matrix\n                        \nmain()\n","sub_path":"labs/lab1/matrixreader.py","file_name":"matrixreader.py","file_ext":"py","file_size_in_byte":2124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"48868089","text":"import albumentations\nimport numpy as np\nimport cv2\nimport numpy as np\nimport skimage.transform\nimport albumentations.augmentations.functional as F\n\ndef get_speech_transform(is_train):\n    ## border mode 0 : constant padding, border mode 4 : repeats\n    if is_train:\n        transform = albumentations.Compose(\n            [\n                albumentations.PadIfNeeded(min_height = 128, min_width = 400,border_mode = 0),\n                albumentations.RandomCrop(height = 128, width = 400),\n                albumentations.RandomBrightnessContrast(brightness_limit = 0.3,contrast_limit = 0.3, p = 0.7),\n            ]\n        )\n    else:\n        transform = albumentations.Compose(\n            [\n                albumentations.PadIfNeeded(min_height = 128, min_width=400,border_mode = 0),\n                albumentations.CenterCrop(height = 128, width = 400),\n            ]\n        )\n    return transform    \n\ndef get_face_transform(is_train=False, n_frames = 16):\n    ran_rotate = get_video_random_rotate(30, prob = 0.7)\n#    rotate = get_video_random_rot90()\n#    flip = get_video_random_flip(prob = 0.5)\n    bc = get_video_random_brightness_contrast(brightness = 0.5, contrast = 0.5, prob = 1 )\n    def face_transform(x):\n        x = drop_endframes(x,n_frames = 10)\n        x = sample_frames(x,n_frames = n_frames, is_train=is_train).astype('uint8')\n        if is_train:\n            # C,T,H,W -> T,H,W,C\n            if np.random.uniform()<0.5:\n                x = video_horizontal_flip(x)\n            x = bc(x)\n#            x = rotate(x)\n            x = ran_rotate(x)\n        x = normalize(x)\n        return x\n    return face_transform\n\ndef get_text_transform(is_train):\n    return albumentations.PadIfNeeded(min_height = 38, min_width = 200,border_mode = 0)\n\ndef get_video_random_rot90():\n    def video_random_rotate(vid):\n        a = np.random.choice([0,90,180,270],1)[0]\n        vid = video_rotate(vid,a)\n        return vid\n    return video_random_rotate\ndef get_video_random_rotate(angle, prob = 1):\n    def video_random_rotate(vid):\n        if np.random.uniform() < prob:\n            a = np.random.uniform(-angle,angle)\n            vid = video_rotate(vid,a)\n        return vid\n    return video_random_rotate\n\ndef get_video_random_flip(prob = 0.5):\n    def video_random_flip(vid):\n        if np.random.uniform() < prob:\n            vid = video_horizontal_flip(vid)\n        if np.random.uniform() < prob:\n            vid = video_vertical_flip(vid)\n        return vid\n    return video_random_flip\n\ndef get_video_random_brightness_contrast(brightness = 0.5, contrast = 0.5, prob = 0.5):\n    def video_random_brightness_contrast(vid):\n        if np.random.uniform() < prob:\n            alpha = 1.0 + np.random.uniform(-contrast,contrast),\n            beta = 0.0 + np.random.uniform(-brightness, brightness)\n            return video_brightness_contrast(vid,alpha,beta)\n        return vid\n    return video_random_brightness_contrast\n\ndef video_rotate(vid, angle):\n    if angle == 0:\n        return vid\n    transformed = np.zeros_like(vid).astype('uint8')\n    for i in range(vid.shape[1]):\n        transformed[:,i,:,:] = (skimage.transform.rotate(vid[:,i,:,:].transpose(1,2,0),angle, mode = 'constant')*255).astype('uint8').transpose(2,0,1)\n    return transformed\n\ndef video_horizontal_flip(vid):\n    return vid[:,:,:,::-1]\n\ndef video_vertical_flip(vid):\n    return vid[:,:,::-1,:]\n\ndef video_brightness_contrast(vid, alpha, beta):\n    # C,T,H,W\n    transformed = np.zeros_like(vid).astype('uint8')\n    for i in range(vid.shape[1]):\n        transformed[:,i,:,:] = F.brightness_contrast_adjust(vid[:,i,:,:].transpose(1,2,0), alpha, beta).transpose(2,0,1)\n    return transformed\n\ndef sample_frames(x, n_frames = 32,is_train = False):\n    # x : C,T,H,W\n    T = x.shape[1]\n    out = np.zeros(shape = (x.shape[0],n_frames,x.shape[2],x.shape[3]))\n    if T<=n_frames:\n        out[:,n_frames-T:,:,:] = x\n        return out\n    \n    if is_train == True:\n        idxs = np.sort(np.random.choice(x.shape[1],n_frames,replace=False))\n    else:\n        idxs = np.arange(x.shape[1])[::x.shape[1]//n_frames][:n_frames]\n    return x[:,idxs,:,:]\n\n\ndef drop_endframes(x,n_frames = 10):\n     return x[:,n_frames:-n_frames,:,:]\n    \ndef normalize(x,mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225]):\n    # x : C,T,H,W      \n    mean = np.array(mean)[:,np.newaxis,np.newaxis,np.newaxis]\n    std = np.array(std)[:,np.newaxis,np.newaxis,np.newaxis]\n    return (x/255.-mean)/std","sub_path":"src/dataloader/transform.py","file_name":"transform.py","file_ext":"py","file_size_in_byte":4458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"644132233","text":"severities = {\n    'NoteError': 'E',\n    'NoteWarning': 'W'\n}\n\n\nclass QuickfixEntry(object):\n    def __init__(self):\n        self.filename = None\n        self.line_number = None\n        self.column = None\n        self.description = None\n        self.severity = None\n\n    def __repr__(self):\n        return '[QuickfixEntry: %s]' % str(self.to_dict())\n\n    def __eq__(self, other):\n        if not isinstance(other, QuickfixEntry):\n            return False\n\n        return (\n            self.filename == other.filename and\n            self.line_number == other.line_number and\n            self.column == other.column and\n            self.description == other.description and\n            self.severity == other.severity\n        )\n\n    def __ne__(self, other):\n        return not self.__eq__(other)\n\n    def __hash__(self):\n        return (\n            self.filename.__hash__() +\n            self.line_number.__hash__() +\n            self.column.__hash__() +\n            self.description.__hash__() +\n            self.severity.__hash__()\n        )\n\n    def to_dict(self):\n        result = {}\n        if self.filename:\n            result['filename'] = self.filename\n        if self.line_number is not None:\n            result['lnum'] = self.line_number\n        if self.column is not None:\n            result['col'] = self.column\n        if self.text is not None:\n            result['text'] = self.text\n        if self.severity is not None:\n            result['type'] = self.severity\n\n        return result\n\n    @classmethod\n    def fromScalaNote(cls, payload):\n        entry = QuickfixEntry()\n        entry.filename = str(payload['file'])\n        entry.line_number = payload['line']\n        entry.column = payload['col']\n        entry.text = str(payload['msg'])\n        entry.severity = severities.get(payload['severity']['typehint'], '')\n        return entry\n\n    @classmethod\n    def fromVim(cls, payload):\n        entry = QuickfixEntry()\n        entry.filename = str(payload['file'])\n        entry.line_number = payload['lnum']\n        entry.column = payload['col']\n        entry.text = str(payload['text'])\n        entry.severity = payload['type']\n        return entry\n","sub_path":"rplugin/python/pensive/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"10223011","text":"from bluetooth import *\nfrom screenPrinter import screenPrinter\nfrom setSysTime import setSysTime\nfrom reportCoordinates import reportCoordinates\nfrom angles import Angle \nfrom twoStarCalibrate import twoStarCalibrate\nfrom menuScreen import menuScreen\nimport time\n\nclass blueServer(object):\n    def __init__(self,log):\n        self.logger=log\n        self.calibration = twoStarCalibrate(log)\n        self.serverSock=BluetoothSocket( RFCOMM )\n        self.serverSock.bind((\"\",PORT_ANY))\n        self.serverSock._sock.settimeout(600)#Throws a timeout exception if connections are idle for 10 minutes\n        self.serverSock.listen(1)\n        \n        self.port = self.serverSock.getsockname()[1]\n        \n        self.uuid = \"17a84160-9b83-11e2-9e96-0800200c9a66\"\n        self.advertise()\n        self.connected = False\n        \n    def advertise(self):  \n        advertise_service( self.serverSock, \"iTelRaspberryServer\", service_id = self.uuid, service_classes = [ self.uuid, SERIAL_PORT_CLASS ], profiles = [ SERIAL_PORT_PROFILE ], protocols = [ OBEX_UUID ] )\n                   \n    def getUUID(self):\n        return self.uuid\n    \n    def setUUID(self,inUUID):\n        self.uuid = inUUID\n        self.advertise()\n        \n    def listenForConnection(self):\n        screenPrinter().printToScreen(\"Wait for client\\n Channel %d\" % self.port)\n        try:\n            self.clientSock, self.clientInfo = self.serverSock.accept()\n            ##HandShake\n            try:\n                while True:\n                    data = self.clientSock.recv(1024)\n                    if len(data) == 0: \n                        pass\n                    elif data==\"iTelRaspberry I presume\":\n                        self.clientSock.send(\"iTelComputer I presume\")\n                        screenPrinter().printToScreen(\"Connected to\\n iTelComputer\")\n                        #screenPrinter().printToScreen(self.clientInfo)\n                        self.connected = True \n                        break           \n            except IOError as e:\n                screenPrinter().printToScreen(\"Handshake failed\\ncheck log\")\n                self.logger.error(\"Handshake failed: %s\" % e.message)\n        \n        except IOError as e:\n            screenPrinter().printToScreen(\"Connection fail\\ncheck log\")\n            self.logger.error(\"Connection fail: %s\" % e.message)\n            \n    def configMode(self,cfgParser,calParser):\n        if self.connected:\n            setTme = setSysTime()\n            menuScn = menuScreen(setTme,cfgParser)\n            try:\n                while True:\n                    menuScn.printSwitcher()\n                    data = self.clientSock.recv(1024)\n                    if len(data) == 0:\n                        pass\n                    elif data == \"sendingTime\":\n                        self.receiveTime(setTme)\n                        #screenPrinter().printToScreen(\"UTC Time: %s\" % setTme.getTime())\n                    \n                    elif data == \"sendingConfig\":\n                        self.receiveConfig(cfgParser,setTme)\n                        #[Lat,Lon] = self.cfgParser.getLatLon()\n                        #Lat.ounit = \"degrees\"\n                        #Lon.ounit = \"degrees\"\n                        #screenPrinter().printToScreen(\"Lat: %s\\nLon: %s\" % (Lat,Lon))\n                        \n                    elif data == \"goTo\":\n                        [Ra,Dec] = self.receiveTarget()\n                        Ra.ounit = \"degrees\"\n                        Dec.ounit = \"degrees\"\n                        screenPrinter().printToScreen(\"TR : %s\\nTD: %s\" % (Ra,Dec))\n                    \n                    elif data == \"calibration\":\n                        self.calibration.reset()\n                        self.calibrationMode(cfgParser, calParser, setTme)\n                        \n                    elif data == \"endCalibration\":\n                        if self.calibration.testPass:\n                            self.clientSock.send(\"calibrationPass\")\n                        else:\n                            self.clientSock.send(\"calibrationErrors\")\n                            self.logger.warning(\"NaNs or infs were found in the rotation matrix after calibration\")\n                        \n                        self.calibration.reset()\n                        \n                    elif data == \"resetCalibration\":\n                        calParser.setAzAltCorrection(self.calibration.getIdentityMatrix())\n                        self.calibration.reset()\n                        self.clientSock.send(\"calibrationReset\")      \n                        \n                    elif data == \"report\":\n                        self.reportingMode(cfgParser,calParser,setTme)\n                    elif data == \"disconnect\":\n                        pass#TODO this is not implemented yet...\n                    else:\n                        screenPrinter().printToScreen(\"Unknown command\\n %s\" % data)\n                    \n            except IOError as e:\n                screenPrinter().printToScreen(\"Config fail\\ncheck log\")\n                self.logger.error(\"Config fail: %s\" % e.message)\n        else:\n            screenPrinter().printToScreen(\"Config fail\\n No connection\")\n            \n    def receiveConfig(self,cfgParser,setTme):\n        self.clientSock.send(\"receivingConfig\")\n        data = self.clientSock.recv(1024)\n        cfgData = str.split(data,':')\n        cfgParser.setGeneric(cfgData[0],cfgData[1],cfgData[2])\n        setTme.setTimeFromConfig(cfgParser)\n        self.clientSock.send(\"configReceived\")\n        \n    def receiveTime(self,setTme):\n        self.clientSock.send(\"receivingTime\")\n        data = self.clientSock.recv(1024)\n        try:\n            setTme.setSystemTime(data)\n        except Exception as e:\n            screenPrinter().printToScreen(\"Time set fail\\ncheck log\")\n            self.logger.error(\"Error setting rPi time: %s\" % e.message)\n        self.clientSock.send(\"timeReceived\")\n        \n    def receiveTarget(self):\n        self.clientSock.send(\"receivingTarget\")\n        data = self.clientSock.recv(1024)\n        self.clientSock.send(\"targetReceived\")\n        radecS  = data.split(\":\")\n        Ra = Angle(r=float(radecS[0]))\n        Dec = Angle(r=float(radecS[1]))\n        return [Ra,Dec]\n        \n                        \n    def reportingMode(self,cfgParser,calParser,sysTime):\n        reporter = reportCoordinates(cfgParser,calParser,sysTime)\n        while(True):\n            [Ra,Dec] = reporter.getRaDec()\n            sRaDe = \"%02f:%02f\" % (Ra.r,Dec.r)\n            self.clientSock.send(sRaDe)\n            Ra.ounit = \"hours\"\n            Dec.ounit = \"degrees\"\n            screenPrinter().printToScreen(\"Ra : %s\\nDec: %s\" % (Ra,Dec))\n            data = self.clientSock.recv(1024)\n            if data == \"1\":\n                pass\n            else:\n                self.clientSock.send(\"reportingComplete\")\n                break\n                \n    def calibrationMode(self,cfgParser,calParser,sysTime):\n        [Ra,Dec] = self.receiveTarget()\n        reporter = reportCoordinates(cfgParser,calParser,sysTime)\n        tAz = reporter.getTAz()\n        tAlt = reporter.getTAlt()\n        [sAz,sAlt] = reporter.convertToAzAlt(Ra, Dec)\n        self.calibration.addStar([tAz,tAlt], [sAz,sAlt])\n        calParser.setAzAltCorrection(self.calibration.getRotationMatrix())\n        \n                    \n                \n    def closeConnection(self):\n        self.serverSock.close()\n        self.connected = False","sub_path":"old/itelescope-code/iTelRaspberry/blueServer.py","file_name":"blueServer.py","file_ext":"py","file_size_in_byte":7468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"387808020","text":"# -*- coding: utf-8 -*-\n\"\"\"A non-blocking, single-threaded SMTP server.\"\"\"\nimport email\nimport functools\nimport sys\n\nfrom tornado.escape import to_unicode, utf8\nfrom tornado.log import app_log, gen_log\nfrom tornado.tcpserver import TCPServer\nfrom tornado import stack_context\n\nfrom bonzo import errors, version\n\nCRLF = '\\r\\n'\n\n\nclass SMTPServer(TCPServer):\n    \"\"\"A non-blocking, single-threaded SMTP server.\n\n    A server is defined by a request callback that takes an email as an\n    argument.\n    \"\"\"\n\n    def __init__(self, request_callback, io_loop=None, **kwargs):\n        self.request_callback = request_callback\n        TCPServer.__init__(self, io_loop=io_loop, **kwargs)\n\n    def handle_stream(self, stream, address):\n        \"\"\"Handle the stream by executing the request callback.\n        \"\"\"\n        SMTPConnection(stream, address, self.request_callback)\n\n\nclass SMTPConnection(object):\n    \"\"\"Handles a connection to an SMTP client, executing SMTP commands.\n\n    This class uses its :attr:`COMMAND` and :attr:`DATA` attributes as a\n    simple \"enum\" to manage the connection state.\n    \"\"\"\n\n    COMMAND = 0\n    \"\"\"Used to set the state to receive any command.\"\"\"\n    DATA = 1\n    \"\"\"Used to set the state to receive data.\"\"\"\n\n    def __init__(self, stream, address, request_callback):\n        self.stream = stream\n        self.address = address\n        self.request_callback = request_callback\n        self.__state = self.COMMAND\n        self.__greeting = 0\n        self.__mailfrom = None\n        self.__rcpttos = []\n        self._clear_request_state()\n        self._command_callback = stack_context.wrap(self._on_commands)\n        self.stream.set_close_callback(self._on_connection_close)\n        self.write('220 %s Bonzo SMTP proxy %s' % (self.address[0], version))\n\n    def _clear_request_state(self):\n        \"\"\"Clears the per-request state.\n\n        This is run in between requests to allow the previous handler\n        to be garbage collected (and prevent spurious close callbacks),\n        and when the connection is closed (to break up cycles and\n        facilitate garbage collection in cpython).\n        \"\"\"\n        self._request = None\n        self._request_finished = False\n        self._write_callback = None\n        self._close_callback = None\n\n    def set_close_callback(self, callback):\n        \"\"\"Sets a callback that will be run when the connection is closed.\n        \"\"\"\n        self._close_callback = stack_context.wrap(callback)\n\n    def _on_connection_close(self):\n        if self._close_callback is not None:\n            callback = self._close_callback\n            self._close_callback = None\n            callback()\n        # Delete any unfinished callbacks to break up reference cycles.\n        self._header_callback = None\n        self._clear_request_state()\n\n    def close(self):\n        \"\"\"Close the stream.\n        \"\"\"\n        self.stream.close()\n        # Remove this reference to self, which would otherwise cause a\n        # cycle and delay garbage collection of this connection.\n        self._header_callback = None\n        self._clear_request_state()\n\n    def write(self, chunk, callback=None, read_until_delimiter=CRLF):\n        \"\"\"Writes a chunk of output to the stream.\"\"\"\n        if not self.stream.closed():\n            if callback is None:\n                callback = functools.partial(self.stream.read_until,\n                                             utf8(read_until_delimiter),\n                                             self._on_commands)\n            self._write_callback = stack_context.wrap(callback)\n            self.stream.write(utf8(chunk + CRLF), self._on_write_complete)\n\n    def finish(self):\n        \"\"\"Finishes the request.\"\"\"\n        if not self.stream.writing():\n            self.stream.close()\n\n    def _on_write_complete(self):\n        if self._write_callback is not None:\n            callback = self._write_callback\n            self._write_callback = None\n            callback()\n        # _on_write_complete is enqueued on the IOLoop whenever the\n        # IOStream's write buffer becomes empty, but it's possible for\n        # another callback that runs on the IOLoop before it to\n        # simultaneously write more data and finish the request.  If\n        # there is still data in the IOStream, a future\n        # _on_write_complete will be responsible for calling\n        # _finish_request.\n        if self._request_finished and not self.stream.writing():\n            self._finish_request()\n\n    def _finish_request(self):\n        self.close()\n\n    def _on_commands(self, line):\n        try:\n            line = to_unicode(line)[:-2]\n            if self.__state == self.COMMAND:\n                if not line.strip():\n                    raise errors.UnrecognisedCommand()\n                i = line.find(' ')\n                if i < 0:\n                    command = line\n                    arg = None\n                else:\n                    command = line[:i]\n                    arg = line[i + 1:].strip()\n                method = getattr(self, 'command_' + command.lower(), None)\n                if not method:\n                    raise errors.NotImplementedCommand(command)\n                method(arg)\n            elif self.__state == self.DATA:\n                data = []\n                for text in line.split(CRLF):\n                    if text and text[0] == '.':\n                        data.append(text[1:])\n                    else:\n                        data.append(text)\n                self.__data = '\\n'.join(data)\n                self.__rcpttos = []\n                self.__mailfrom = None\n                self.__state = self.COMMAND\n                self._on_data(self.__data)\n            else:\n                raise errors.InternalConfusion()\n        except Exception as e:\n            self._handle_request_exception(e)\n\n    def _request_summary(self):\n        return ''\n\n    def log_exception(self, typ, value, tb):\n        if isinstance(value, errors.SMTPError):\n            if value.log_message:\n                _format = '%d %s' + value.log_message\n                args = ([value.status_code, self._request_summary()] +\n                        list(value.args))\n                gen_log.warning(_format, *args)\n        else:\n            app_log.error('Uncaught exception %s', self._request_summary(),\n                          exc_info=(typ, value, tb))\n\n    def _handle_request_exception(self, e):\n        self.log_exception(*sys.exc_info())\n        if not isinstance(e, errors.SMTPError):\n            e = errors.InternalConfusion()\n        self.write('%d %s' % (e.status_code, e.message))\n\n    def __getaddr(self, keyword, arg):\n        address = None\n        keylen = len(keyword)\n        if arg[:keylen].upper() == keyword:\n            address = arg[keylen:].strip()\n            if not address:\n                pass\n            elif address[0] == '<' and address[-1] == '>' and address != '<>':\n                # Addresses can be in the form <person@dom.com> but watch out\n                # for null address, e.g. <>\n                address = address[1:-1]\n        return address\n\n    def command_helo(self, arg):\n        \"\"\"Handles the ``HELO`` SMTP command.\n\n        - Raises a :class:`~bonzo.errors.BadArguments` error code when the\n          network name of the connecting machine is not received.\n        - Raises a :class:`~bonzo.errors.BadSequence` when a ``HELO`` command\n          already was received.\n        \"\"\"\n        if not arg:\n            raise errors.BadArguments('HELO hostname')\n        if self.__greeting:\n            raise errors.BadSequence('Duplicate HELO/EHLO')\n        self.__greeting = arg\n        self.write('250 %s' % self.address[0])\n\n    def command_noop(self, arg):\n        \"\"\"Handles the ``NOOP`` SMTP command.\n\n        - Raises a :class:`~bonzo.errors.BadArguments` when an argument is not\n          received.\n        \"\"\"\n        if arg:\n            raise errors.BadArguments('NOOP')\n        self.write('250 Ok')\n\n    def command_quit(self, arg):\n        \"\"\"Handles the ``QUIT`` SMTP command.\n        \"\"\"\n        self.write('221 Bye', self.finish)\n\n    def command_mail(self, arg):\n        \"\"\"Handles the ``MAIL`` SMTP command.\n\n        - Raises a :class:`~bonzo.errors.BadArguments` when the ``from`` address\n          is not received.\n        - Raises a :class:`~bonzo.errors.BadSequence` when a ``MAIL`` command\n          already was received.\n        \"\"\"\n        address = self.__getaddr('FROM:', arg) if arg else None\n        if not address:\n            raise errors.BadArguments('MAIL FROM:<address>')\n        if self.__mailfrom:\n            raise errors.BadSequence('Error: nested MAIL command')\n        self.__mailfrom = address\n        self.write('250 Ok')\n\n    def command_rcpt(self, arg):\n        \"\"\"Handles the ``RCPT`` SMTP command.\n\n        - Raises a :class:`~bonzo.errors.BadSequence` when a ``MAIL`` command\n          was not previously received.\n        - Raises a :class:`~bonzo.errors.BadArguments` when the ``to`` address\n          is not received.\n        \"\"\"\n        if not self.__mailfrom:\n            raise errors.BadSequence('Error: need MAIL command')\n        address = self.__getaddr('TO:', arg) if arg else None\n        if not address:\n            raise errors.BadArguments('RCPT TO:<address>')\n        self.__rcpttos.append(address)\n        self.write('250 Ok')\n\n    def command_rset(self, arg):\n        \"\"\"Handles the ``RSET`` SMTP command.\n\n        - Raises a :class:`~bonzo.errors.BadArguments` when an argument is not\n          received.\n        \"\"\"\n        if arg:\n            raise errors.BadArguments('RSET')\n        # Resets the sender, recipients, and data, but not the greeting\n        self.__mailfrom = None\n        self.__rcpttos = []\n        self.__data = ''\n        self.__state = self.COMMAND\n        self.write('250 Ok')\n\n    def command_data(self, arg):\n        \"\"\"Handles the ``DATA`` SMTP command.\n\n        - Raises a :class:`~bonzo.errors.BadSequence` when a ``RCPT`` command\n          was not previously received.\n        - Raises a :class:`~bonzo.errors.BadArguments` when an argument is not\n          received.\n        \"\"\"\n        if not self.__rcpttos:\n            raise errors.BadSequence('Error: need RCPT command')\n        if arg:\n            raise errors.BadArguments('DATA')\n        self.__state = self.DATA\n        self.write('354 End data with <CR><LF>.<CR><LF>',\n                   read_until_delimiter='{0}.{0}'.format(CRLF))\n\n    def _on_data(self, data):\n        message = email.message_from_string(data)\n        self.request_callback(message)\n        self.write(\"250 Ok\")\n","sub_path":"bonzo/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":10569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"14163664","text":"import sys\nfrom pathlib import Path\n\n# Be able to launch from root folder\nsys.path.append(str(Path(__file__).parents[1]))\n\n# Other\n# Coroutines and multiprocessing\nimport asyncio\nimport re\nimport time\n\n# Type annotation / hints\nfrom typing import List\n\nimport aiohttp\n\n# Simple logging https://github.com/Delgan/loguru\nfrom loguru import logger\n\n# Local examples\nfrom python_examples.examples.async_await.asyncio_download_upload import download_all_sites, download_file\nfrom python_examples.examples.async_await.rate_limited_example import api_rate_limited_example\nfrom python_examples.examples.databases.asyncpg_asyncio_example import (\n    performance_test_asyncpg_database,\n    test_asyncpg_database,\n)\nfrom python_examples.examples.databases.mongodb_example import test_database_with_mongodb\nfrom python_examples.examples.databases.sqlalchemy_example import test_database_with_sqlalchemy\nfrom python_examples.examples.databases.sqlite_asyncio_example import test_asyncio_database\nfrom python_examples.examples.databases.sqlite_example import test_database\nfrom python_examples.examples.databases.sqlite_with_classes import test_database_with_classes\nfrom python_examples.examples.databases.sqlmodel_example import test_database_with_sqlmodel\nfrom python_examples.examples.databases.tinydb_example import test_database_with_tinydb\nfrom python_examples.examples.dataclasses_and_dicts.import_export_dataclass import test_data_class_to_and_from_json\nfrom python_examples.examples.dataclasses_and_dicts.modify_dictionary import modify_dictionary\nfrom python_examples.examples.other.file_interaction import create_file\n\n# from python_examples.examples.other.geometry_example import test_geometry_shapely\nfrom python_examples.examples.other.image_manipulation import mass_convert_images\nfrom python_examples.examples.other.measure_time import measure_time\nfrom python_examples.examples.other.multiprocessing_example import do_math, do_multiprocessing\nfrom python_examples.examples.other.regex_example import regex_match_test, test_all_roman_numbers\n\nlogger.remove()  # Remove previous default handlers\n# Log to console\nlogger.add(sys.stdout, level='INFO')\n# Log to file, max size 1 mb, max log file age 7 days before a new one gets created\nlogger.add('main.log', rotation='1 MB', retention='7 days', level='INFO')\n\n\ndef main_sync():\n    asyncio.run(main())\n\n\nasync def main():\n    logger.info('Simple {} logger output', 'loguru')\n\n    regex_match_test()\n\n    measure_time()\n\n    sites: List[str] = ['http://www.jython.org', 'http://olympus.realpython.org/dice'] * 80\n    start_time = time.perf_counter()\n    await download_all_sites(sites)\n\n    # Download an image with download speed throttle\n    async with aiohttp.ClientSession() as session:\n        _result: bool = await download_file(\n            session,\n            url='https://file-examples.com/wp-content/uploads/2017/10/file_example_PNG_1MB.png',\n            file_path=Path(__file__).parent / 'data' / 'image.png',\n            temp_file_path=Path(__file__).parent / 'data' / 'image_download_not_complete',\n            # Download at speed of 100kb/s\n            download_speed=100 * 2**10,\n        )\n    # Gather a ton of responses from an API\n    await api_rate_limited_example()\n\n    end_time = time.perf_counter()\n    logger.info(f'Time for sites download taken: {end_time - start_time}')\n\n    _math_result = await do_math(6)\n\n    start_time = time.perf_counter()\n    _result2 = do_multiprocessing()\n    end_time = time.perf_counter()\n    logger.info(f'Time for multiprocessing taken: {end_time - start_time}')\n\n    mass_replace()\n\n    logger.info('Creating hello world file...')\n    create_file()\n\n    logger.info('Testing writing dataclass to json and re-load and compare both objects')\n    test_data_class_to_and_from_json()\n\n    modify_dictionary()\n\n    logger.info('Validating all roman numbers up to 3999')\n    test_all_roman_numbers()\n\n    # test_geometry_shapely()\n\n    # TODO Table printing / formatting without library: print table (2d array) with 'perfect' row width\n\n    logger.info('Converting all .jpg images in /images folder')\n    mass_convert_images()\n\n    logger.info('Testing database interaction')\n    test_database()\n    await test_asyncpg_database()\n    await performance_test_asyncpg_database()\n    await test_asyncio_database()\n    test_database_with_sqlalchemy()\n    test_database_with_classes()\n    test_database_with_tinydb()\n    test_database_with_sqlmodel()\n    await test_database_with_mongodb()\n\n\ndef mass_replace():\n    # Source: https://stackoverflow.com/a/6117124\n    text = 'my text cond\\nition1 condition2'\n    replace_dict = {'cond\\nition1': 'loves', 'condition2': 'fun'}\n    # In case there is escape characters in k, it will not work without \"re.escape\"\n    replace_dict = {re.escape(k): v for k, v in replace_dict.items()}\n    pattern = re.compile('|'.join(replace_dict.keys()))\n    new_text = pattern.sub(lambda m: replace_dict[re.escape(m.group(0))], text)\n    logger.info(f'Mass replaced\\n{text}\\nto\\n{new_text}')\n\n\ndef plot_lists():\n    # TODO\n    pass\n\n\ndef plot_numpy_array():\n    # TODO\n    pass\n\n\nif __name__ == '__main__':\n    main_sync()\n","sub_path":"python_examples/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"288100955","text":"# 특정한날에 대한 최고, 최저기온을 그래프로 출력\n\nimport csv\nimport matplotlib.pyplot as plt\nf = open('seoul.csv')\ndata = csv.reader(f)\nnext(data)\nhigh = []\nlow = []\nfor row in data:\n  if len(row) != 5:\n    continue\n  if len(row[-1])>0 and len(row[-2])>0:\n    date = row[0].split('-')\n    if 2000 <= int(date[0]):\n      if date[1] == '01' and date[2] == '01':\n        high.append(float(row[-1]))\n        low.append(float(row[-2]))\nplt.title(\"Graph for my birthday's temperature changes\")\nplt.plot(high, 'hotpink', label='high')\nplt.plot(low, 'skyblue', label='low')\nplt.legend()\nplt.show()\n","sub_path":"test04.py","file_name":"test04.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"440366709","text":"\"\"\"Some tests\"\"\"\nimport unittest\n\nfrom main import Searcher\nfrom data import GetData\n\nclass Tests(unittest.TestCase):\n    \"\"\"Tests\"\"\"\n    def test_removesigns(self):\n        \"\"\"Check replace signs\"\"\"\n        self.assertEqual(Searcher.\\\n            _remove_signs(\"Catolina-ask+help_write\"),\n                         \"Catolina ask help write\")\n\n    def test_searchdata_istuple(self):\n        \"\"\"Check search data is tuple\"\"\"\n        data = GetData(\"file\", \"cooking_books.tsv\").dispatch()\n        self.assertEqual(type(data), tuple)\n\n\n    def test_scoring(self):\n        \"\"\"Check count the points\"\"\"\n        data = GetData(\"file\", \"cooking_books.tsv\").dispatch()\n\n        line = (\"B000S1L8ZW\", \"Cowgirl Cuisine: Rustic Recipes and \\\n            Cowgirl Adventures from a Texas Ranch\")\n        score = Searcher(data).get_score([\"recipes\", \"cuisine\"], line)\n        self.assertEqual(score[1], 2)\n\n        line = (\"B003VIWNJO\", \"Tea and Cookies: Cookies Cookies Cookies\\\n         of Tea--with Dozens of Delectable Recipes for Cookies Treats\")\n        score = Searcher(data).get_score([\"cookies\", \"cuisine\"], line)\n        self.assertEqual(score[1], 1)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"468170571","text":"# coding:latin\n'''\nMikael Lindahl 2010\n\n\nFile:\nmy_signals\n\nWrapper for signals module in NeuroTools.  Additional functions can be added \nhere. In self.list neurotool object is created through which it can be \ndirectly accessed \n\n\nA collection of functions to create, manipulate and play with analog signals and\nspikes \n\nClasses\n-------\nVmList           - AnalogSignalList object used for Vm traces\nConductanceList  - AnalogSignalList object used for conductance traces\nCurrentList      - AnalogSignalList object used for current traces\nSpikeList        - SpikeList object used for spike trains\n'''\n\n\nimport numpy\nimport pylab\n\n\n# Import StandardPickleFile for saving of spike object\nfrom NeuroTools.io import StandardPickleFile\n\n# For setting number of ticks\nfrom matplotlib.ticker import MaxNLocator\n\n\nimport plot_settings as ps\n\nfrom NeuroTools import signals\nfrom NeuroTools.signals import ConductanceList\nfrom NeuroTools.signals import CurrentList\nfrom NeuroTools.signals import VmList\nfrom NeuroTools.signals import SpikeList\nfrom NeuroTools.plotting import get_display\n\nclass MyConductanceList(ConductanceList):\n    '''\n    MyConductanceList(signals, id_list, dt, t_start=0, t_stop=None, dims=None )\n    inherit from ConductanceList which has base class AnalogSignalList.  \n    \n    Arguments:\n        signal  - the vector with the data of the AnalogSignal\n        dt      - the time step between two data points of the sampled analog signal\n        t_start - begining of the signal, in ms.\n        t_stop  - end of the SpikeList, in ms. If None, will be inferred from the data\n    '''\n    \n    def __init__(self, signals, id_list, dt, t_start=0, t_stop=None, dims=None ):\n        ''' \n        Constructor \n        \n        Inherited attributes:\n        self.t_start        = float(t_start)\n        self.t_stop         = t_stop\n        self.dt             = float(dt)\n        self.dimensions     = dims\n        self.analog_signals = {}\n        \n        New attributes:\n        self.ids = sorted( id_list )     # sorted id list\n        \n        '''\n        \n        # About the super() function \n        # (from python programming - Michael Dawson, page 277) \n        # Incorporate the superclass ConductanceList method's functionality. \n        # To add a new attribute ids i need to override the inherited \n        # constructor method from ConductanceList. I also want my new \n        # constructor to create all the attributes from ConductanceList. \n        # This can be done with the function super(). It lets you invoke the \n        # method of a base class(also called a superclass). The first argument \n        # in the function call, 'MyConductanceList', says I want to invoke a \n        # method of the superclass (or base class) of MyConductanceList which \n        # is ConductanceList. The next argument. seöf, passes a reference to \n        # the object so that ConductanceList can get to the object and add \n        # its attributes to it. The next part of the statement __init__(\n        # signals, id_list, dt, t_start, t_stop, dims) tells python I want to\n        # invoke the constructor method of ConductanceList and a want to pass \n        # it the values of signals, id_list, dt, t_start, t_stop and dims.\n        \n        # Invoke __init__ of base class ConductanceList which has \n        # AnalogSignalList as base class where the method exist.\n        super( MyConductanceList, self ).__init__( signals, id_list, dt, \n                                                   t_start, t_stop, dims)\n        # Add new attribute\n        self.ids = sorted( id_list )     # sorted id list\n        \n    def plot(self,  id_list=None, display=True, kwargs={} ):\n        \"\"\"\n        Plot all cells in the AnalogSignalList defined by id_list. Right now\n        here to exemplify use of super() on super class method.\n        \n        Inputs:\n            id_list - can be a integer (and then N cells are randomly selected) or a \n                      list of ids. If None, we use all the ids of the SpikeList\n            display - axes handle\n            kwargs  - dictionary contening extra parameters that will be sent to the plot \n                      function\n        \n        Examples:\n            >> z = subplot(221)\n            >> aslist.plot(5, display=z, kwargs={'color':'r'})\n        \"\"\"\n        \n        # Invoke plot function in ConductanceList\n        super(MyConductanceList, self).plot(id_list, None, display, kwargs )\n                 \n    def save(self, userFileName):\n        '''\n        Save analog list\n\n        Inputs:\n            userFileName    - name of file to save\n            \n        Examples:\n            >> userFileName = /home/savename.dat\n            >> aslist.save(userFileName)           \n        '''\n        userFile = StandardPickleFile( userFileName )      # create user file \n       \n        # Invoke save function of base class ConductanceList which has \n        # AnalogSignalList as base class where the method exist.\n        super(MyConductanceList, self).save( userFile )\n                  \nclass MyCurrentList(CurrentList):\n    ''' \n    MyCurrentList(signals, id_list, dt, t_start=0, t_stop=None, dims=None )\n    inherit from CurrentList which has base class AnalogSignalList.\n    \n    Arguments:\n        signal  - the vector with the data of the AnalogSignal\n        dt      - the time step between two data points of the sampled analog signal\n        t_start - begining of the signal, in ms.\n        t_stop  - end of the SpikeList, in ms. If None, will be inferred from the data\n    '''  \n    \n    def __init__( self, signals, id_list, dt, t_start=0, t_stop=None, dims=None ):\n        ''' \n        Constructor \n        \n        Inherited attributes:\n        self.t_start        = float(t_start)\n        self.t_stop         = t_stop\n        self.dt             = float(dt)\n        self.dimensions     = dims\n        self.analog_signals = {}\n        \n        New attributes:\n        self.ids = sorted( id_list )     # sorted id list\n        \n        '''\n        \n        # Invoke __init__ of base class ConductanceList which has \n        # AnalogSignalList as base class where the method exist.\n        super( MyCurrentList, self ).__init__( signals, id_list, dt, \n                                                   t_start, t_stop, dims)\n        # Add new attribute\n        self.ids = sorted( id_list )     # sorted id list\n    \n    def plot(self, id_list = None, display = True, kwargs = {} ):\n        \"\"\"\n        Plot all cells in the AnalogSignalList defined by id_list\n        \n        Inputs:\n            id_list - can be a integer (and then N cells are randomly selected) or a \n                      list of ids. If None, we use all the ids of the SpikeList\n            display - if True, a new figure is created. Could also be a subplot\n            kwargs  - dictionary contening extra parameters that will be sent to the plot \n                      function\n        \n        Examples:\n            >> z = subplot(221)\n            >> aslist.plot(5, display=z, kwargs={'color':'r'})\n        \"\"\"\n        \n        # Invoke plot function in ConductanceList\n        super(MyCurrentList, self).plot(id_list, None, display, kwargs )\n\n\n    def save(self, userFileName):\n        '''\n        Save analog list\n\n        Inputs:\n            userFileName    - name of file to save\n            \n        Examples:\n            >> userFileName = /home/savename.dat\n            >> aslist.save(userFileName)           \n        '''\n        userFile = StandardPickleFile( userFileName )      # create user file \n       \n        # Invoke save function of base class ConductanceList which has \n        # AnalogSignalList as base class where the method exist.\n        super(MyCurrentList, self).save( userFile )\n         \nclass MyVmList(VmList):\n    ''' \n    MyVmList(signals, id_list, dt, t_start=0, t_stop=None, dims=None )\n    inherit from VmList which has base class AnalogSignalList.\n    \n    Arguments:\n        signal  - the vector with the data of the AnalogSignal\n        dt      - the time step between two data points of the sampled analog signal\n        t_start - begining of the signal, in ms.\n        t_stop  - end of the SpikeList, in ms. If None, will be inferred from the data\n    '''  \n    \n    def __init__(self, signals, id_list, dt, t_start=0, t_stop=None, dims=None ):\n        ''' \n        Constructor \n        \n        Inherited attributes:\n        self.t_start        = float(t_start)\n        self.t_stop         = t_stop\n        self.dt             = float(dt)\n        self.dimensions     = dims\n        self.analog_signals = {}\n        \n        New attributes:\n        self.ids = sorted( id_list )     # sorted id list\n        \n        '''\n        \n        # Invoke __init__ of base class ConductanceList which has \n        # AnalogSignalList as base class where the method exist.\n        super( MyVmList, self ).__init__( signals, id_list, dt, \n                                                   t_start, t_stop, dims)\n        # Add new attribute\n        self.ids = sorted( id_list )     # sorted id list\n    \n    def plot(self, id_list = None, display = True, kwargs = {} ):\n        \"\"\"\n        Plot all cells in the AnalogSignalList defined by id_list\n        \n        Inputs:\n            id_list - can be a integer (and then N cells are randomly selected) or a \n                      list of ids. If None, we use all the ids of the SpikeList\n            display - if True, a new figure is created. Could also be a subplot\n            kwargs  - dictionary contening extra parameters that will be sent to the plot \n                      function\n        \n        Examples:\n            >> z = subplot(221)\n            >> aslist.plot(5, display=z, kwargs={'color':'r'})\n        \"\"\"\n        \n        # Invoke plot function in ConductanceList\n        super(MyVmList, self).plot(id_list, None, display, kwargs )\n\n    \n    def raw_data_id_order(self): \n        '''\n        Return data matrix with raw data for each id on row ordered in descending \n        id order. \n        '''    \n\n        values = numpy.concatenate( [self.analog_signals[id].signal for id in self.ids]) \n        #ids    = numpy.concatenate( [id*numpy.ones( len( obj.analog_signals[id].signal ), int ) for id in self.ids] ) \n\n        data = values.reshape( len( self.ids ),-1 )\n        \n        return data\n    \n    def save(self, userFileName):\n        '''\n        Save analog list\n\n        Inputs:\n            userFileName    - name of file to save\n            \n        Examples:\n            >> userFileName = /home/savename.dat\n            >> aslist.save(userFileName)           \n        '''\n        userFile = StandardPickleFile( userFileName )      # create user file \n       \n        # Invoke save function of base class ConductanceList which has \n        # AnalogSignalList as base class where the method exist.\n        super(MyVmList, self).save( userFile )\n\n\n    def image_plot(self, display = None, kwargs = {}):\n        \n        if not display: ax = pylab.axes()\n        else:           ax = display\n\n        \n        data_Vm = numpy.array( self.raw_data() )\n        data_Vm = numpy.array( zip( *data_Vm ) )\n        data_Vm = data_Vm[ 0 ].reshape( len( self.ids ), -1 )  # Transpose\n        image = ax.imshow( data_Vm, origin = 'lower', **kwargs)\n        ax.set_xlabel('Membrane potential (mV)')\n        ax.set_ylabel('Neuron #')  \n        ax.set_aspect(aspect='auto')\n        return image\n             \nclass MySpikeList(SpikeList):\n    \"\"\"\n    MySpikeList(spikes, id_list, t_start=None, t_stop=None, dims=None)\n    \n    Inherits from SpikeList\n    \n    Return a SpikeList object which will be a list of SpikeTrain objects.\n\n    Inputs:\n        spikes  - a list of (id,time) tuples (id being in id_list)\n        id_list - the list of the ids of all recorded cells (needed for silent cells)\n        t_start - begining of the SpikeList, in ms. If None, will be infered from the data\n        t_stop  - end of the SpikeList, in ms. If None, will be infered from the data\n        dims    - dimensions of the recorded population, if not 1D population\n    \n    t_start and t_stop are shared for all SpikeTrains object within the SpikeList\n    \n    Examples:\n        >> sl = SpikeList([(0, 0.1), (1, 0.1), (0, 0.2)], range(2))\n        >> type( sl[0] )\n            <type SpikeTrain>\n    \n    See also\n        load_spikelist\n    \"\"\"\n    \n    def __init__(self, spikes, id_list, t_start=None, t_stop=None, dims=None):\n\n        # Invoke __init__ of base class ConductanceList which has \n        # AnalogSignalList as base class where the method exist.\n        super( MySpikeList, self ).__init__( spikes, id_list, t_start, t_stop, dims)\n        \n        # Add new attribute\n        self.ids = sorted( id_list )     # sorted id list\n    \n    \n    def firing_rate_sliding_window(self, bin=100, display=True, id_list=[], step=1, stop=None, kwargs={}):\n        ''' \n        Calculate spike rates at ``sample_step`` using s sliding rectangular \n        window.\n        \n        Arguments:\n        bin           Bin size of sliding window\n        display       If True, a new figure is created. Could also be a subplot\n        id_list       List of ids to calculate firing rate for\n        step          Step size for moving sliding window (ms)\n        stop          End of spike train\n        kwargs        Additional plot arguments\n          \n        Here the window is centered over over each time point at sampe_step with \n        window size equalling bin_size. Takes spike times, number of \n        neurons no_neurons, bin_size and sample_step as input argument.\n        '''  \n        \n        ax = get_display(display)\n        \n        if stop is None: stop = self.t_stop\n        if not any(id_list): id_list = self.ids    \n        \n        spikes = {}     # dictionary with spike times   \n        for id in id_list: \n            spikes[ id ] = self.spiketrains[ id ].spike_times.copy()                   \n        \n        n  = int( (stop - bin )/step )          # number of windows\n        f = bin/2                               # first window at bin/2\n        l = step*n + bin/2                      # last window at bin*n - bin/2 \n \n        # Time axis for sliding windows, n_win + 1 due to end points \n        # [ 0, 1, 2] -> two windows and tre timings\n        timeAxis = numpy.linspace(f, l, n + 1)           \n        \n        \n        firingRates = []                          # sliding time window data  \n        dataSpk   = []\n        \n        #! Calculate number of spikes in ms bins and then sliding window rates \n        for id in id_list:\n                    \n            spk = spikes[id] \n            dataSpk.append(spk)\n\n            i = 0\n            j = bin/2\n            j_max  = stop\n            rates = []\n                 \n            #! For each ms i in 0 to stop at step intervals \n            for tPoint in timeAxis:                                    \n                \n                sum = numpy.sum( ( tPoint - bin/2 <= spk ) * ( spk < tPoint + bin/2 ) )\n                rates.append( 1000.0 * sum/float( bin ) )\n                  \n\n            firingRates.append(rates)\n        \n        firingRates = numpy.array(firingRates)        # Convert to numpy array\n   \n        meanFiringRates = numpy.mean( firingRates, axis = 0 )\n       \n        ax.plot(timeAxis, meanFiringRates,**kwargs)\n        ax.set_xlabel( 'Time (ms)' )\n        ax.set_ylabel( 'Frequency (spike/s)' )\n        \n        return timeAxis, firingRates, dataSpk   \n        \n \n    def image_firing_rate_slide_window(self, bin=100, id_list=[], step=1, stop=None, display=True, kwargs={}):\n        '''\n        Function that create an image with bin size sliding window firing rate as \n        calculated at step intervals. kwargs - dictionary contening extra \n        parameters that will be sent to the plot function    \n        \n        Arguments:\n        bin           Bin size of sliding window\n        display       If True, a new figure is created. Could also be a subplot\n        id_list       List of ids to calculate firing rate for\n        step          Step size for moving sliding window (ms)\n        stop          End of spike train\n        kwargs        Additional plot arguments\n        \n        '''\n        ax = get_display(display)\n        \n        if not any(id_list): id_list = self.ids\n        \n        t, r, spk = self.firing_rate_slide_window(bin, display, id_list, \n                                                    step, stop, kwargs)\n               \n        kwargs.update( { 'origin' : 'lower', } )\n        image = ax.imshow(r, extent=[t[0],t[-1],self.ids[0],self.ids[-1]], \n                          **kwargs)\n        ax.set_xlabel('Time (ms)')\n        ax.set_ylabel('Neuron #')\n        ax.set_aspect(aspect='auto')\n\n        return image\n\n    def spike_histogram_n_rep(self, bin=10, id_list=[], n_rep=1, normalized=True):\n        '''\n        Generate an array with all the spike_histograms of all the SpikeTrains. \n        Each time series should be n_rep repeated simulations. Histogram is then\n        generated for one simulation period repeated n_rep times. It is thus\n        possible to supply several similar simulations and get the \n        mean firing statistics for all of them. \n        \n        Inputs:\n            bin        - the time bin used to gather the data\n            id_list    - list with ids to use for creation of histogram\n            n_rep      - number of experimental repetitions with same \n                         stimulation. E.g. if n_runs=3\n            normalized - if True, the histogram are in Hz (spikes/second), \n                         otherwise they are in spikes/bin\n\n        \n        In neurophysiology, this is similar to a peristimulus time histogram \n        and poststimulus time histogram, both abbreviated PSTH or PST histogram, \n        are histograms of the times at which neurons fire. These histograms are \n        used to visualize the rate and timing of neuronal spike discharges in \n        relation to an external stimulus or event. The peristimulus time \n        histogram is sometimes called perievent time histogram, and \n        post-stimulus and peri-stimulus are often hyphenated.\n        \n        To make a PSTH, a spike train recorded from a single neuron is aligned \n        with the onset, or a fixed phase point, of an identical stimulus \n        repeatedly presented to an animal. The aligned sequences are \n        superimposed in time, and then used to construct a histogram.[2] \n        \n        Construction procedure\n        For each spike train i\n        \n        1. Align spike sequences with the onset of stimuli that repeated n \n           times. For periodic stimuli, wrap the response sequence back to \n           time zero after each time period T, and count n (n_rep) as the \n           total number of periods of data.\n        2. Divide the stimulus period or observation period T into N bins \n           of size Δ seconds ( bin ).\n        3. Count the number of spikes k_i_j from all n sequences that \n           fall in the bin j.\n        4. Calculate  i_j given by k_i_j/( n*Δ ) in units of estimated \n           spikes per second at time j * Δ.\n\n        The optimal bin size Δ is a minimizer of the formula, (2k-v)/Δ2, where\n        k and v are mean and variance of k_i. [3]  \n                                                                       \n        '''\n        \n        if not any(id_list): id_list = self.ids\n        \n        spkList = self.id_slice( id_list )      # Retrieve spike trains \n        \n        # Short cuts\n        start    = spkList.t_start \n        period   = spkList.t_stop/n_rep \n        \n        spike_train_hist = {}       # Create data table for histogram\n           \n        # Add spikes to data\n        for id, spk in spkList.spiketrains.iteritems():\n            \n            \n            # First take modulus t_period of spike times ending up with a vector\n            # with spike times between 0 and period    \n            spikes_mod_period = spk.spike_times % period                                        \n            \n            # Create sequence of bins edges, histogram between 0 and period\n            bin_sequence = numpy.arange( start, period + bin, bin )    \n            \n            # Create histogram over bin sequence and convert to spikes per bin \n            hist_n_rep, edges = numpy.histogram( spikes_mod_period, bin_sequence)\n            hist_n_rep        = hist_n_rep/float( n_rep )     \n        \n            spike_train_hist[ id ] = hist_n_rep     # Add to dictionary                                              \n \n        # Create numpy array with all spike trains histograms   \n        histograms = numpy.array( spike_train_hist.values() )                                      \n        \n        # Convert data to spike rates\n        if normalized: histograms *= 1000.0/bin                                            \n\n        # Center time axis over bin mid points \n        timeAxis = numpy.arange(start+bin/2., period, bin)\n        return timeAxis, histograms\n                                           \n    def image_spike_histogram(sself, bin=10, display=True, id_list=[], n_rep=1, normalized=True, kwargs = {} ):\n        '''\n        Plot an image of all the spike_histograms generated by \n        spike_histogram_n_rep\n    \n        \n        Arguments:\n        bin        - the time bin used to gather the data\n        display    - If True, a new figure is created. Could also be a subplot\n        id_list    - list with ids to use for creation of histogram\n        n_rep      - Number of experimental repetitions with same stimulation. \n                     E.g. if n_runs=3 then it is assumed that three \n                     experimental runs is recorded and the data will be \n                     sliced up into three time intervals.\n        normalized - if True, the histogram are in Hz (spikes/second), \n                     otherwise they are in spikes/bin\n        kwargs     . Additional plot arguments\n        '''        \n        \n        ax = get_display(display)\n        \n        timeAxis, histograms = self.spike_histogram_n_rep( bin, id_list, \n                                                            n_rep, normalized )\n             \n        kwargs.update( { 'origin' : 'lower', } )\n        image = ax.imshow( histograms, **kwargs )\n        ax.set_xlabel( 'Time (ms)'     )\n        ax.set_ylabel( 'Neuron #'      )\n        ax.set_aspect( aspect = 'auto' )\n        \n        \n        n_points=len(timeAxis)\n        xticks = numpy.arange( 0, n_points, n_points*0.2)\n        xticklabels = numpy.arange( 0, timeAxis[-1], timeAxis[-1]*0.2)\n        ax.set_xticks( xticks )\n        ax.set_xticklabels( [ str( l ) for l in xticklabels ] )\n        \n        n_ids=len(id_list)\n        yticks =  numpy.arange( 0, n_ids, n_ids*0.2)\n        ax.set_yticks( yticks)\n        ax.set_yticklabels( id_list[ yticks ] )\n    \n        return image\n           \n    def plot_spike_histogram(self, bin=10, display=True, id_list=[], n_rep=1, normalized=True, kwargs = {} ):    \n        '''\n        Plot an mean spike histogram for for all ids generated by \n        spike_histogram_n_rep\n        \n        Arguments:\n        bin        - the time bin used to gather the data\n        display    - If True, a new figure is created. Could also be a subplot\n        id_list    - list with ids to use for creation of histogram\n        n_rep      - Number of experimental repetitions with same stimulation. \n                     E.g. if n_runs=3 then it is assumed that three \n                     experimental runs is recorded and the data will be \n                     sliced up into three time intervals.\n        normalized - if True, the histogram are in Hz (spikes/second), \n                     otherwise they are in spikes/bin\n    \n        '''        \n        ax = get_display(display)\n        \n        timeAxis, histograms = self.spike_histogram_n_rep( bin, id_list, \n                                                            n_rep, normalized )\n              \n        ax.bar(left=t_vec, height=data, width=0.8, \n               bottom=None, hold=None, **kwargs)\n              \n    def firing_rate(self, bin=200, display=True, id_list=[], n_rep=1, kwargs={}):          \n        '''\n        Plot all the instantaneous firing rates along time (in Hz) \n        over all ids in id_list from histogram generated by spike_histogram_n_rept.\n        \n        Inputs:\n            bin        - the time bin used to gather the data\n            display    - If True, a new figure is created. Could also be a subplot\n            id_list    - list with ids to use for creation of histogram\n            n_rep      - Number of experimental repetitions with same stimulation. \n                     E.g. if n_runs=3 then it is assumed that three \n                     experimental runs is recorded and the data will be \n                     sliced up into three time intervals.\n            kwargs     - dictionary contening extra parameters that will be sent to the plot \n                         function\n        '''\n        \n        ax = get_display(display)\n        \n        timeAxis, histograms = self.spike_histogram_n_rep( bin, id_list, \n                                                            n_rep, True )\n        \n        firingRates = numpy.mean( histograms, axis = 0 )\n       \n        ax.plot(timeAxis, firingRates, **kwargs)\n        ax.set_xlabel( 'Time (ms)' )\n        ax.set_ylabel( 'Frequency (spike/s)' )\n        \n          \n    def raster_plot_cluster(self, id_list=[], t_start=None, t_stop=None, display=True, clusters = [], kwargs={}):\n        \"\"\"\n        (functional?) Generate a raster plot for the SpikeList in a subwindow of interest,\n        defined by id_list, t_start and t_stop. \n        \n        Inputs:\n            id_list - can be a integer (and then N cells are randomly selected) or a list of ids. If None, \n                      we use all the ids of the SpikeList\n            t_start - in ms. If not defined, the one of the SpikeList object is used\n            t_stop  - in ms. If not defined, the one of the SpikeList object is used\n            display - if True, a new figure is created. Could also be a subplot\n            clusters - vector containing code for cluster belonging of each spike \n                       train opatain from clustering analysis. Plotted accordingly.\n            kwargs  - dictionary contening extra parameters that will be sent to the plot \n                      function\n        \n        Examples:\n            >> z = subplot(221)\n            >> spikelist.raster_plot(display=z, kwargs={'color':'r'})\n        \n        See also\n            SpikeTrain.raster_plot\n        \"\"\"\n        \n        ax = get_display(display)\n        \n        spk = self.list\n        \n        if t_start is None: t_start = spk.t_start\n        if t_stop is None:  t_stop  = spk.t_stop\n        \n        ids, spike_times = spk.convert(format=\"[ids, times]\")\n        idx = numpy.where((spike_times >= t_start) & (spike_times <= t_stop))[0]\n        \n        sorted_index = numpy.argsort( clusters )                            # Sort spike trains accoringly to clusters\n        for i, id in enumerate(self.ids):\n            ids[ids==id] = -self.ids[ sorted_index[ i ] ]\n        ids = abs(ids)\n                 \n        \n        if len(spike_times) > 0:\n            ax.plot(spike_times, ids, ',', **kwargs)\n        xlabel = \"Time (ms)\"\n        ylabel = \"Neuron #\"\n        set_labels(ax, xlabel, ylabel)\n        \n        min_id = numpy.min(spk.id_list())\n        max_id = numpy.max(spk.id_list())\n        length = t_stop - t_start\n        set_axis_limits(ax, t_start-0.05*length, t_stop+0.05*length, min_id-2, max_id+2)\n        pylab.draw()\n                   \n\n    def save(self, userFileName):\n        '''\n        Save spike list\n\n        Inputs:\n            userFileName    - name of file to save\n            \n        Examples:\n            >> userFileName = /home/savename.dat\n            >> aslist.save(userFileName)           \n        '''\n        userFile = StandardPickleFile( userFileName )      # create user file \n       \n        # Invoke save function of base class ConductanceList which has \n        # AnalogSignalList as base class where the method exist.\n        super(MyVmList, self).save( userFile )\n       \ndef load(userFileName, dataType):  \n    \n    \"\"\"\n    load(userFileName, dataType)\n    Convenient data loader for results saved as NeuroTools StandardPickleFile. \n    Return the corresponding NeuroTools object. Datatype argument may become \n    optionnal in the future, but for now it is necessary to specify the type \n    of the recorded data. To have a better control on the parameters of the \n    NeuroTools objects, see the load_*** functions.\n    \n    Inputs:\n        userFileName - the user file name\n        datatype - A string to specify the type od the data in\n                    's' : spikes\n                    'g' : conductances\n                    'v' : membrane traces\n                    'c' : currents\n    \"\"\"\n    userFile=StandardPickleFile(userFileName)\n       \n    if dataType in ('s', 'spikes'):\n        return signals.load_spikelist(userFile)\n    elif dataType == 'v':\n        \n        #  Need t_start to be None, othervice NeuroTools overwrite loaded \n        # t_start with default value 0\n        return signals.load_vmlist(userFile, t_start=None)  \n    elif dataType == 'c':\n        return signals.load_currentlist(userFile, t_start=None)\n    elif dataType == 'g':\n        return signals.load_conductancelist(userFile,t_start=None)\n    else:\n        raise Exception(\"The datatype %s is not handled ! Should be 's','g','c' or 'v'\" %datatype)\n    \n\n    \n       ","sub_path":"nest_toolbox/dist/nest_toolbox-1.0/src/my_signals.py","file_name":"my_signals.py","file_ext":"py","file_size_in_byte":29713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"532211401","text":"# -*- coding: utf-8 -*-\r\nclass Game:    \r\n    def __init__(self, new_name='User',x=0,y=0):        \r\n        self._name = new_name # новое имя\r\n        print(f'New player {self._name}')\r\n        self._level = 0\r\n        self._speedsubject = 0\r\n        self._airsubject = 10\r\n        self._intelligencesubject = 0\r\n        self.x = x\r\n        self.y = y\r\n        self._life = 5\r\n        # level player\r\n    \r\n    def fly(self):\r\n        print(f\"Игрок {self._name}\")\r\n        if self._level < 25: # если уровень\r\n            print(f\"Игрок {self._name} не умеет летать\")\r\n        else:\r\n            print(f\"Игрок {self._name} умеет летать\")\r\n            \r\n            \r\n    def __add__(self, other):\r\n        return Game(self.x + other.x, self.y + other.y)\r\n    \r\n    \r\n    def gaming(self):\r\n        if self._speedsubject > 0:\r\n            self.x += 5\r\n            print(f'{self._name} ускорился')\r\n        elif self._speedsubject < 0:\r\n            self.x -= 5\r\n            print(f'{self._name} замедлился')\r\n\r\n            \r\n    def air(self):\r\n        if 0 < self.x < 10 and 0 < self.y < 100:\r\n            self._airsubject += 1 # счетчик кислорода\r\n        else:\r\n            self._airsubject -= 1\r\n            print(f'{self._name} испытывает кислородное голодание {self._aircsubject}')\r\n        if self._airsubject == 0:\r\n            print(f'{self._name} умер')\r\n            self._level = 0\r\n            self._speedsubject = 0\r\n            self._airsubject = 10\r\n            self._intelligencesubject = 0\r\n            self.x = 0\r\n            self.y = 0            \r\n            self._life -= 1\r\n            if self._life == 0:\r\n                print('Game over')\r\n                #тут должен игрок удалиться я хз как делать, в инете не очень понятно\r\n        \r\n    def intelligence(self):\r\n        if self._life > 0 and self._airsubject > 20:\r\n            self._intelligencesubject += 1\r\n        if self._intelligencesubject %10 == 0:\r\n            self._level += 1\r\n        \r\n      \r\nmy_player1 = Game()\r\nmy_player1.set_name(\"Вася\")\r\n\r\nmy_player1.gaming()\r\nmy_player2 = Game('Ксюша')\r\n","sub_path":"game_oo.py","file_name":"game_oo.py","file_ext":"py","file_size_in_byte":2275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"506402153","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport numpy as np\nimport pandas as pd\nimport scipy.stats\nimport rrcf\nfrom matplotlib import pyplot as plt\n\n# Read data\nabs_path = os.path.abspath(__file__)\ndata_paths = abs_path.strip().split(\"/\")[:-1]\ndata_paths.extend([\"..\", \"resources\", \"nyc_taxi.csv\"])\ndata_path = os.path.join(*data_paths)\nif not data_path.startswith(\"/\"):\n    data_path = \"/\" + data_path\n\ntaxi = pd.read_csv(data_path, index_col=0)\ntaxi.index = pd.to_datetime(taxi.index)\ndata = taxi['value'].astype(float).values\nn = data.shape[0]\n\n# Set tree parameters\nnum_trees = 200\nshingle_size = 48\ntree_size = 1000\n\n# Use the \"shingle\" generator to create rolling window\npoints = rrcf.shingle(data, size=shingle_size)\npoints = np.vstack([point for point in points])\nnum_points = points.shape[0]\n\n# Chunk data into segments\nnum_chunks = 40\nchunk_size = num_points // num_chunks\nchunks = (chunk * chunk_size for chunk in range(num_chunks))\n\nforest = []\npdfs = []\nfor chunk in chunks:\n    # Create probability density function for sample\n    g = scipy.stats.norm(loc=chunk, scale=400).pdf(np.linspace(0, num_points, num_points))\n    g *= (1 / g.sum())\n    # Sample data with probability density function defined by g\n    ixs = np.random.choice(num_points, size=(num_points // tree_size, tree_size),\n                           replace=False, p=g)\n    # Create trees from sampled points\n    trees = [rrcf.RCTree(points[ix], index_labels=ix) for ix in ixs]\n    forest.extend(trees)\n    # Store pdf for plotting purposes\n    pdfs.append(g)\n\n# Compute CoDisp\navg_codisp = pd.Series(0.0, index=np.arange(num_points))\nindex = np.zeros(num_points)\nfor tree in forest:\n    codisp = pd.Series({leaf: tree.codisp(leaf)\n                        for leaf in tree.leaves})\n    avg_codisp[codisp.index] += codisp\n    np.add.at(index, codisp.index.values, 1)\navg_codisp /= index\navg_codisp.index = taxi.iloc[(shingle_size - 1):].index\n\n\n# Plot results\nfig, ax = plt.subplots(3, figsize=(10, 9))\n(taxi['value'] / 1000).plot(ax=ax[0], color='0.5', alpha=0.8)\navg_codisp.plot(ax=ax[2], color='#E8685D', alpha=0.8, label='RRCF')\nfor pdf in pdfs[1:]:\n    pd.Series(pdf, index=avg_codisp.index).plot(ax=ax[1], c='0.6')\nax[0].set_title('Taxi passenger timeseries')\nax[1].set_title('Rolling probability density functions')\nax[2].set_title('Anomaly score')\nax[0].set_ylabel('Thousands of passengers')\nax[1].set_ylabel('p(x)')\nax[2].set_ylabel('CoDisp')\nax[0].set_xlabel('')\nax[1].set_xlabel('')\nax[2].set_xlabel('')\nplt.tight_layout()\nplt.savefig('decay_example.png')\n","sub_path":"test/test_time_decay.py","file_name":"test_time_decay.py","file_ext":"py","file_size_in_byte":2539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"387019894","text":"from django.conf.urls import url\nfrom . import views\nfrom .feeds import LatestPostsFeed\n\nurlpatterns = [\n# post views\n url(r'^$', views.post_list, name='post_list'),\n#url(r'^$', views.PostListView.as_view(), name='post_list'),\nurl(r'^(?P<year>\\d{4})/(?P<month>\\d{2})/(?P<day>\\d{2})/'r'(?P<post>[-\\w]+)/$',views.post_detail,\nname='post_detail'),\nurl(r'^(?P<post_id>\\d+)/share/$', views.post_share,name='post_share'),\nurl(r'^tag/(?P<tag_slug>[-\\w]+)/$', views.post_list,name='post_list_by_tag'),\nurl(r'^feed/$', LatestPostsFeed(), name='post_feed'),\nurl(r'^latest_post/$', views.latest_post,name='latest_post'),\nurl(r'^about/$', views.about,name='about'),\nurl(r'^download/$', views.download,name='download'),\nurl(r'^query/$', views.query,name='query'),\n]","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"7659355","text":"from django.shortcuts import render , redirect ,get_object_or_404\nfrom .models import Note\nfrom django.contrib.auth.models import User\n\n# Create your views here.\n\ndef notes(request):\n    notes = Note.objects.all().filter(owner=request.user)\n    context = {\n        'notes' : notes\n    }\n    return render(request,'notes.html',context)\n\ndef addnote(request):\n    if request.method == 'POST':\n        text = request.POST['note']\n        owner_id = request.POST['owner']\n        owner = get_object_or_404(User,pk=owner_id)\n        note = Note(text=text,owner=owner)\n        note.save()\n        return redirect('notes')\n    return render(request,'addnote.html')\n\ndef delete(request,note_id):\n    Note.objects.filter(id = note_id).delete()\n    return redirect('notes')\n\ndef edit(request,note_id):\n    note =  Note.objects.filter(id = note_id)[0]\n    if request.method == 'POST':\n        text = request.POST['note']\n        note.text = text\n        note.save()\n        return redirect('notes')\n\n    context = {\n        \"note\": note\n    }\n    return render(request,'addnote.html',context)","sub_path":"notes/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"25896365","text":"print('绘图程序已启动')\nimport json\nimport matplotlib.pyplot as plt\nimport matplotlib.font_manager as fm\nfrom matplotlib import patches\nimport numpy as np\nimport datetime\nimport pandas as pd\nimport obfuc\nimport os\nfrom operator import itemgetter\nfrom itertools import groupby\n\npypath = 'J:/影响天气/lecay/ec客观预报/'\n#pypath = './'\nchpath = 'K:/EC客观预报/'\n#chpath = './'\n#now = datetime.datetime(2020,3,1,7,10,10)\nnow = datetime.datetime.now()\nnowstr = now.strftime('%y%m%d%H')\nif now.hour<6:\n    dt = datetime.timedelta(hours=-16-now.hour)\nelif now.hour>=18:\n    dt = datetime.timedelta(hours=8-now.hour)\nelse:\n    dt = datetime.timedelta(hours=-4-now.hour)\nintimenum = now+dt\nintime = intimenum.strftime('%y%m%d%H')\nif not os.path.exists(chpath+'amd/'+intime+'.json'):\n    intimenum = intimenum + datetime.timedelta(hours=-12)\n    intime = intimenum.strftime('%y%m%d%H')\n\nwith open(chpath+'amd/'+intime+'.json', 'r') as f:\n    data = json.load(f)\nif now.hour<12:\n    inday = now\nelse:\n    inday = now + datetime.timedelta(days=1)    #第一天时间\nsecday = inday+datetime.timedelta(days=1)     #第二天时间\nd1row = 0; d2row = 0; d1ap = []; d2ap = []\nfor sta,tdict in data.items():\n    daylist = []\n    for tstr in tdict.keys():\n        daylist.append(int(tstr[4:6]))\n    if inday.day in daylist:\n        d1row = d1row+1       #第一天行数\n        d1ap.append(sta)      #第一天机场列表\n    if secday.day in daylist:\n        d2row = d2row+1       #第一天行数\n        d2ap.append(sta)      #第二天机场列表\nif d1row == 0:\n    d1row = 1; d1ap=['无']\nif d2row == 0:\n    d2row = 1; d2ap=['无']\nallrow = d1row + d2row        #总行数\n\nysize = allrow*0.2+1.4    #图片长度\nfig = plt.figure(figsize=(8,ysize),dpi=130)\nax1 = fig.add_axes([0.05,0.6/(ysize),0.9,allrow*0.2/(ysize)])\nfont = fm.FontProperties(fname=r\"C:/Windows/Fonts/msyh.ttc\")\n\ndayx = 0.6     #日期与机场分割线x轴\nfcx = 1.25      #机场四字码与中文名分割线x坐标\ncnx = 2.1        #机场中文名与跑道号分割线x坐标\nstawx = 2.8    #机场与天气区分割线x坐标\nallx = 11.2    #x轴总长度\nax1.plot([dayx,dayx],[allrow,0],color='dimgray',linewidth=1)    #日期与机场分割线\nax1.plot([cnx,cnx],[allrow,0],color='lightgrey',linewidth=0.5)    #机场中文名与跑道号分割线\nax1.plot([stawx,stawx],[allrow,0],color='dimgray',linewidth=1)    #机场与天气分割线\nfor x in range(1,24):\n    ax1.plot([stawx+x*0.35,stawx+x*0.35],[0,allrow],color='lightgrey',linewidth=0.5)    #画纵向网格线\nax1.plot([0,allx],[allrow-d1row,allrow-d1row],color='dimgray',linewidth=1)    #日期分割线\nfor x in range(1,allrow):\n    ax1.plot([dayx,stawx],[x,x],color='dimgray',linewidth=1)    #机场分割线\n    ax1.plot([stawx,allx],[x,x],color='lightgrey',linewidth=0.5)    #画横向网格线\nax1.text(dayx/2, allrow-0.5*d1row, str(inday.day)+'日', ha='center', va='center', fontproperties=font, size=9)\nax1.text(dayx/2, 0.5*d2row, str(secday.day)+'日', ha='center', va='center', fontproperties=font, size=9)\n\ncnsta = pd.read_csv(pypath+'stationcn.txt', header=None, sep=r'[\\s]+', engine='python', \n    names=['name','runway','mark'])\nwith open(pypath+'night.json', 'r') as f:\n    night = json.load(f)\ndef statext(sty,apid,daynum):    #填入机场名称等信息，sty文本y坐标，apid机场四字码，daynum第几天\n    if apid == '无':\n        ax1.text(1.4,sty+0.1,'无',ha='center', va='center',fontproperties=font, size=8)\n    else:\n        if cnsta.loc[apid,'mark']==2 and apid in night['day'+str(daynum)]:    #二类及过夜机场\n            ax1.text(1.67,sty,cnsta.loc[apid,'name']+r'$\\mathrm{^*_\\mathrm{Ⅱ}}$',ha='center', va='center',fontproperties=font, size=8)\n        elif cnsta.loc[apid,'mark']==2 and apid not in night['day'+str(daynum)]:    #二类机场\n            ax1.text(1.67,sty+0.03,cnsta.loc[apid,'name']+r'$\\mathrm{_\\mathrm{Ⅱ}}$',ha='center', va='center',fontproperties=font, size=8)\n        elif cnsta.loc[apid,'mark']!=2 and apid in night['day'+str(daynum)]:    #过夜机场\n            ax1.text(1.67,sty,cnsta.loc[apid,'name']+r'$\\mathrm{^*}$',ha='center', va='center',fontproperties=font, size=8)\n        else:\n            ax1.text(1.64,sty,cnsta.loc[apid,'name'],ha='center', va='center',fontproperties=font, size=8)\n        ax1.text(0.95,sty,apid,ha='center', va='center',fontproperties=font, size=8)\n        ax1.text(2.45,sty,cnsta.loc[apid,'runway'],ha='center', va='center',fontproperties=font, size=8, color='dimgrey')\n# h小时，wy填充下边界y坐标，wid填色宽度，height填色高度，color填色颜色，rim是否画右边界，dheight填色下边高度，isstr是否填写字符，fstr字符内容\ndef drawwx(h,wy,wid,height,color,rim=True,dheight=0,isstr=False,fstr=''):    \n    ax1.fill_between([stawx+(h+1-wid)*0.35,stawx+(h+1)*0.35],wy+height,wy+dheight,color=color)\n    for x in range(0,wid-1):\n        ax1.plot([stawx+(h-x)*0.35,stawx+(h-x)*0.35],[wy+height,wy+dheight],color=color,linewidth=0.5)\n    if rim:\n        ax1.plot([stawx+(h+1)*0.35,stawx+(h+1)*0.35],[wy+height,wy+dheight],color='lightgrey',linewidth=1)\n    else:\n        ax1.plot([stawx+(h+1)*0.35,stawx+(h+1)*0.35],[wy+height,wy+dheight],color=color,linewidth=0.5)\n    if isstr:\n        ax1.text(stawx+(h+1-wid)*0.35+wid*0.175,wy+0.4,fstr,ha='center', va='center',fontproperties=font, size=8)\ndef windcolor(u,v,ws,rwnm,wdirx=999):\n    if obfuc.crosswind(u,v,ws,rwnm,wdirx)>8:\n        return 'darkgoldenrod'\n    else:\n        return 'orange'\nwxcolor = {'-SN':'lightskyblue','SN':'deepskyblue','+SN':'dodgerblue','-RASN':'lightskyblue','RASN':'deepskyblue','+RASN':'dodgerblue','RA':'limegreen', \\\n            '+RA':'forestgreen','-TSRA':'lightpink','TSRA':'tomato','+TSRA':'red','FG':'yellow','LC':'violet'}\n\nfgfix=[]; lcfix=[]; fixfldict = {'FG':fgfix, 'LC':lcfix}    #标记第一天23时出现大雾低云的机场（用于填充第二天0时）\nfor x in range(d1row):\n    wy = allrow-x-1\n    statext(wy+0.4,d1ap[x],1)\n    if d1ap[x]=='无':\n        break\n    wdhlist = []      #大风出现的时次\n    ralist=[]; pralist=[]; mtslist=[]; tslist=[]; ptslist=[]; fglist=[]; lclist=[]\n    ratsdict = {'RA':ralist, '+RA':pralist,'-TSRA':mtslist,'TSRA':tslist,'+TSRA':ptslist}     #降雨雷雨各天气出现时次的列表\n    fglcdict = {'FG':fglist, 'LC':lclist}\n    for t,wxlist in data[d1ap[x]].items():     #第一天\n        h = int(t[6:])\n        if int(t[4:6])==inday.day:\n            for wx in wxlist:\n                if isinstance(wx,dict):\n                    drawwx(h,wy,3,1,windcolor(0,0,wx['wind'][1],cnsta.loc[d1ap[x],'runway'],wx['wind'][0]))\n                    wstr = obfuc.windar(0,0,wx['wind'][0])+str(wx['wind'][1])+'/'+str(obfuc.crosswind(0,0,wx['wind'][1],cnsta.loc[d1ap[x],'runway'],wx['wind'][0]))\n                    ax1.text(stawx+(h-0.5)*0.35,wy+0.4,wstr,ha='center', va='center',fontproperties=font, size=8)\n                    wdhlist.extend([h-2,h-1,h])\n                for w in ['FG','LC']:\n                    if wx == w and h==23:\n                        fglcdict[w].extend([h-1,h])\n                        fixfldict[w].append(d1ap[x])    #标记第一天23时出现大雾低云的机场\n                    elif wx == w and h!=23:\n                        fglcdict[w].extend([h-1,h,h+1])\n                for w in ['RA','+RA','-TSRA','TSRA','+TSRA']:\n                    if wx == w:\n                        ratsdict[w].append(h)\n    for t,wxlist in data[d1ap[x]].items():\n        h = int(t[6:])\n        if int(t[4:6])==inday.day:\n            for wx in wxlist:\n                if wx in ['RA','+RA','-TSRA','TSRA','+TSRA'] and h in wdhlist:\n                    drawwx(h,wy,1,0.5,wxcolor[wx],isstr=False,fstr=wx,rim=False)\n                elif wx in ['RA','+RA','-TSRA','TSRA','+TSRA'] and h not in wdhlist:\n                    drawwx(h,wy,1,1,wxcolor[wx],isstr=False,fstr=wx,rim=False)\n                elif wx in ['-SN','SN','+SN','-RASN','RASN','+RASN'] and h in wdhlist:\n                    drawwx(h,wy,3,0.5,wxcolor[wx],isstr=False,fstr=wx)\n                elif wx in ['-SN','SN','+SN','-RASN','RASN','+RASN'] and h not in wdhlist:\n                    drawwx(h,wy,3,1,wxcolor[wx],isstr=True,fstr=wx)\n    for w in ['RA','+RA','-TSRA','TSRA','+TSRA']:\n        sprats = list(set(ratsdict[w])-set(wdhlist))    #降雨雷雨单独出现的时次（排除风）\n        for k,g in groupby(enumerate(sorted(sprats)), lambda n:n[0]-n[1]):\n            group = list(map(itemgetter(1),g))        #分割出连续的序列\n            if len(group)>=2:\n                ax1.text(stawx+(max(group)+1-0.5*len(group))*0.35,wy+0.4,w,ha='center', va='center',fontproperties=font, size=8)\n    for w in ['FG','LC']:\n        spfglc = list(set(fglcdict[w])-(set(ralist)|set(pralist)|set(mtslist)|set(tslist)|set(ptslist)))    #大雾低云单独出现的时次（排除降雨雷暴）\n        for i in spfglc:\n            drawwx(i,wy,1,1,wxcolor[w],rim=False)\n        for k,g in groupby(enumerate(sorted(spfglc)), lambda n:n[0]-n[1]):\n            group = list(map(itemgetter(1),g))        #分割出连续的序列\n            if len(group)>=2:\n                ax1.text(stawx+(max(group)+1-0.5*len(group))*0.35,wy+0.4,w,ha='center', va='center',fontproperties=font, size=8)\n            \nfor x in range(d2row):\n    wy = allrow-d1row-x-1\n    statext(wy+0.4,d2ap[x],1)\n    if d2ap[x]=='无':\n        break\n    wdhlist = []      #大风出现的时次\n    ralist=[]; pralist=[]; mtslist=[]; tslist=[]; ptslist=[]; fglist=[]; lclist=[]\n    ratsdict = {'RA':ralist, '+RA':pralist,'-TSRA':mtslist,'TSRA':tslist,'+TSRA':ptslist}     #降雨雷雨各天气出现时次的列表\n    fglcdict = {'FG':fglist, 'LC':lclist}\n    for t,wxlist in data[d2ap[x]].items():     #第二天\n        h = int(t[6:])\n        if int(t[4:6])==secday.day:\n            for wx in wxlist:\n                if isinstance(wx,dict):\n                    drawwx(h,wy,3,1,windcolor(0,0,wx['wind'][1],cnsta.loc[d2ap[x],'runway'],wx['wind'][0]))\n                    wstr = obfuc.windar(0,0,wx['wind'][0])+str(wx['wind'][1])+'/'+str(obfuc.crosswind(0,0,wx['wind'][1],cnsta.loc[d2ap[x],'runway'],wx['wind'][0]))\n                    ax1.text(stawx+(h-0.5)*0.35,wy+0.4,wstr,ha='center', va='center',fontproperties=font, size=8)\n                    wdhlist.extend([h-2,h-1,h])\n                for w in ['FG','LC']:\n                    if wx == w and d2ap[x] in fixfldict[w]:\n                        fglcdict[w].append(0)\n                    if wx == w and h==23:\n                        fglcdict[w].extend([h-1,h])\n                    elif wx == w and h!=23:\n                        fglcdict[w].extend([h-1,h,h+1])\n                for w in ['RA','+RA','-TSRA','TSRA','+TSRA']:\n                    if wx == w:\n                        ratsdict[w].append(h)\n    for t,wxlist in data[d2ap[x]].items():\n        h = int(t[6:])\n        if int(t[4:6])==secday.day:\n            for wx in wxlist:\n                if wx in ['RA','+RA','-TSRA','TSRA','+TSRA'] and h in wdhlist:\n                    drawwx(h,wy,1,0.5,wxcolor[wx],isstr=False,fstr=wx,rim=False)\n                elif wx in ['RA','+RA','-TSRA','TSRA','+TSRA'] and h not in wdhlist:\n                    drawwx(h,wy,1,1,wxcolor[wx],isstr=False,fstr=wx,rim=False)\n                elif wx in ['-SN','SN','+SN','-RASN','RASN','+RASN'] and h in wdhlist:\n                    drawwx(h,wy,3,0.5,wxcolor[wx],isstr=False,fstr=wx)\n                elif wx in ['-SN','SN','+SN','-RASN','RASN','+RASN'] and h not in wdhlist:\n                    drawwx(h,wy,3,1,wxcolor[wx],isstr=True,fstr=wx)\n    for w in ['RA','+RA','-TSRA','TSRA','+TSRA']:\n        sprats = list(set(ratsdict[w])-set(wdhlist))    #降雨雷雨单独出现的时次（排除风）\n        sprats.sort()\n        for k,g in groupby(enumerate(sorted(sprats)), lambda n:n[0]-n[1]):\n            group = list(map(itemgetter(1),g))        #分割出连续的序列\n            if len(group)>=2:\n                ax1.text(stawx+(max(group)+1-0.5*len(group))*0.35,wy+0.4,w,ha='center', va='center',fontproperties=font, size=8)\n    for w in ['FG','LC']:\n        spfglc = list(set(fglcdict[w])-(set(ralist)|set(pralist)|set(mtslist)|set(tslist)|set(ptslist)))    #大雾低云单独出现的时次（排除降雨雷暴）\n        spfglc.sort()\n        for i in spfglc:\n            drawwx(i,wy,1,1,wxcolor[w],rim=False)\n        for k,g in groupby(enumerate(sorted(spfglc)), lambda n:n[0]-n[1]):\n            group = list(map(itemgetter(1),g))        #分割出连续的序列\n            if len(group)>=2:\n                ax1.text(stawx+(max(group)+1-0.5*len(group))*0.35,wy+0.4,w,ha='center', va='center',fontproperties=font, size=8)\n\nax1.set_xlim((0,allx))\nax1.set_ylim((0,allrow))\nax1.set_xticks([stawx-0.6]+list(np.arange(stawx+0.175,allx,0.35)))\n#ax1.set_xticklabels(['时间']+np.arange(0,24,1),fontsize=8.5, fontproperties=font1)\nax1.set_xticklabels(['时间 ->']+list(np.arange(0,24,1)), fontproperties=font, size=8.5)\nax1.set_yticks([])\nax1.tick_params(axis='x', labeltop='True', length=0.01, pad=2)\nax1.annotate('*本图表为数值模式自动解析，若与气象席位评估有差异，以席位评估为准，电话0592-5739223。', (0.05,0.25/ysize), xycoords='figure fraction', fontproperties=font, fontsize=8, color='dimgrey')\nax1.annotate('*机场中文名后标识\"Ⅱ\"为二类标准机场，\"*\"为过夜机场。右边列为跑道号，多跑道的只显示其中一条。', (0.05,0.1/ysize), xycoords='figure fraction', fontproperties=font, fontsize=8, color='dimgrey')\n\nax2 = fig.add_axes([0.05,(ysize-0.6)/ysize,0.9,0.6/ysize])\nlx = 6.8    #图例最左边x坐标\nax2.text(1,0.6,'EC客观预报',ha='left', va='center',fontproperties=font, size=12)\nax2.text(1,0.2,'小飞象%d年%d月%d日%02d时制作'% (now.year,now.month,now.day,now.hour),ha='left', va='center',fontproperties=font, size=8, color='grey')\nax2.text(lx-0.6,0.78,'图例：', ha='left', va='center',fontproperties=font, size=8)\nax2.fill_between([lx,lx+0.262],0.6,0.4,color='limegreen')    #降雨图例\nax2.fill_between([lx+0.262,lx+0.525],0.6,0.4,color='forestgreen')\nax2.text(lx+0.393,0.48,'+', ha='center', va='center',fontproperties=font, size=7)\nax2.text(lx+0.6,0.48,'中或大雨', ha='left', va='center',fontproperties=font, size=7)\nax2.fill_between([lx+1.4,lx+1.575],0.6,0.4,color='lightskyblue')    #降雪图例\nax2.fill_between([lx+1.575,lx+1.75],0.6,0.4,color='deepskyblue')\nax2.fill_between([lx+1.75,lx+1.925],0.6,0.4,color='dodgerblue')\nax2.text(lx+1.487,0.48,'-', ha='center', va='center',fontproperties=font, size=7)\nax2.text(lx+1.837,0.48,'+', ha='center', va='center',fontproperties=font, size=7)\nax2.text(lx+2,0.48,'降雪', ha='left', va='center',fontproperties=font, size=7)\nax2.fill_between([lx,lx+0.525],0.3,0.1,color='violet')    #低云图例\nax2.text(lx+0.262,0.18,'LC', ha='center', va='center',fontproperties=font, size=7)\nax2.text(lx+0.6,0.18,'低云', ha='left', va='center',fontproperties=font, size=7)\nax2.fill_between([lx+1.4,lx+1.925],0.3,0.1,color='yellow')    #大雾图例\nax2.text(lx+1.662,0.18,'FG', ha='center', va='center',fontproperties=font, size=7)\nax2.text(lx+2,0.18,'大雾', ha='left', va='center',fontproperties=font, size=7)\nax2.fill_between([lx+2.8,lx+2.975],0.6,0.4,color='lightpink')    #雷雨图例\nax2.fill_between([lx+2.975,lx+3.15],0.6,0.4,color='tomato')\nax2.fill_between([lx+3.15,lx+3.325],0.6,0.4,color='red')\nax2.text(lx+2.887,0.48,'-', ha='center', va='center',fontproperties=font, size=7)\nax2.text(lx+3.237,0.48,'+', ha='center', va='center',fontproperties=font, size=7)\nax2.text(lx+3.4,0.48,'雷雨', ha='left', va='center',fontproperties=font, size=7)\nax2.fill_between([lx,lx+0.262],0.9,0.7,color='orange')    #大风图例\nax2.fill_between([lx+0.262,lx+0.525],0.9,0.7,color='darkgoldenrod')\nax2.text(lx+0.23,0.79,r'$\\downarrow$'+'14/9', ha='center', va='center',fontproperties=font, size=7)\nax2.text(lx+0.6,0.78,'7级以上阵风/侧风(m/s) | (深色)侧风>8m/s', ha='left', va='center',fontproperties=font, size=7)\ne = patches.Rectangle([lx+2.8,0.1],0.525,0.2,fill=False,hatch='//',alpha=0.6,edgecolor='black')\nax2.add_patch(e)\nax2.text(lx+3.4,0.18,'数据缺失', ha='left', va='center',fontproperties=font, size=7)\n\nax2.set_xlim((0,allx))\nax2.set_ylim((0,1))\nax2.axis('off')\n\nax3 = fig.add_axes([0.04,(ysize-0.59)/ysize,0.08,0.53/ysize])\nlogoimg = plt.imread(pypath+'logo.png')\nim = ax3.imshow(logoimg,interpolation='bilinear')\npatch = patches.Circle((200, 200), radius=200, transform=ax3.transData)\nim.set_clip_path(patch)\nax3.axis('off')\n\nplt.savefig(chpath+'amd/'+intime+'.png')\n#plt.savefig(pypath+intime+'test.png')\nprint(chpath+'amd/'+intime+'.png 完成')\nplt.show()","sub_path":"amdchart_old.py","file_name":"amdchart_old.py","file_ext":"py","file_size_in_byte":16679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"641407303","text":"#-------------------------------#\n#---- Export Maps in ArcMap ----#\n#-------------------------------#\n\n#--- Goal: \n#--- Create Multiple Maps from a ARCGIS mxd file\n\n#--- Rationale:\n#--- Get properties from pre-existent mxd file\n#--- Read CSV file with automation control\n#--- Add layers to the mxd project\n#--- Classify layers with a template\n#--- Modify text in layout view\n#--- Export maps as image (e.g. PNG) to folder ~/maps/\n\n#--- Required Data:\n#--- mxd project file with layout view and background layers (e.g. borders, rivers, ocean, etc...)\n#--- CSV file \"export_map.csv\" with paramters of automation\n#--- Place all files to be exported into ~/data/\n\n#--- Contact:\n#--- murilodsv@gmail.com\n#-------------------------------#\n\nimport arcpy\nimport pandas\nimport os\n\n#--- Get project path and names\nmxd  = arcpy.mapping.MapDocument(\"current\")\nmxdp = mxd.filePath.split(\"\\\\\")\nmxdn = mxdp[len(mxdp)-1]\nmxdp = mxd.filePath.replace(mxdn, \"\")\nmxddf= arcpy.mapping.ListDataFrames(mxd, \"Layers\")[0]\n\n#--- csv control file\ncsv_ctrl_fn = mxdp+\"export_map.csv\"\n\n#--- read csv control file\ncsv_ctrl = pandas.read_csv(csv_ctrl_fn)\n\n#--- Number of maps\nnmaps = range(len(csv_ctrl))\n\n#--- Loop\nfor i in nmaps:\n\t#--- Get iteration info:\n\tfn_i \t= csv_ctrl['filedata'][i]\n\ttn_i \t= csv_ctrl['template'][i]\n\tt1_i \t= csv_ctrl['texto 1'][i]\n\tt2_i \t= csv_ctrl['texto 2'][i]\n\tt3_i \t= csv_ctrl['texto 3'][i]\n\tt4_i \t= csv_ctrl['texto 4'][i]\n\tt5_i \t= csv_ctrl['texto 5'][i]\n\tt6_i \t= csv_ctrl['texto 6'][i]\n\ton_i \t= csv_ctrl['fileout'][i]\n\t\n\t#--- New Layer\n\tnlyr = arcpy.mapping.Layer(mxdp+\"data\\\\\"+fn_i)\n\t\n\t#--- Read Template layer\n\ttlyr= arcpy.mapping.Layer(mxdp+tn_i)\n\t\n\t#--- Classify layer with template\n\tarcpy.mapping.UpdateLayer(mxddf,nlyr,tlyr, symbology_only = True)\n\t\n\t#--- Add Layer to mxd project\n\tarcpy.mapping.AddLayer(mxddf, nlyr)\n\t\n\t#--- List texts\n\tl_txt = arcpy.mapping.ListLayoutElements(mxd,\"TEXT_ELEMENT\")\n\t\n\t#--- Update texts\t\n\ttxt_1_obj = next(t for t in l_txt if t.text == \"texto 1\")\n\ttxt_1_obj.text = t1_i\n\t\n\ttxt_2_obj = next(t for t in l_txt if t.text == \"texto 2\")\n\ttxt_2_obj.text = t2_i\n\t\n\ttxt_3_obj = next(t for t in l_txt if t.text == \"texto 3\")\n\ttxt_3_obj.text = t3_i\n\t\n\ttxt_4_obj = next(t for t in l_txt if t.text == \"texto 4\")\n\ttxt_4_obj.text = t4_i\n\t\n\ttxt_5_obj = next(t for t in l_txt if t.text == \"texto 5\")\n\ttxt_5_obj.text = t5_i\n\t\n\ttxt_6_obj = next(t for t in l_txt if t.text == \"texto 6\")\n\ttxt_6_obj.text = t6_i\t\n\t\n\t#--- Refresh LayoutView\n\tarcpy.RefreshTOC()\n\tarcpy.RefreshActiveView()\n\t\n\t#--- Export Map in PNG format\n\tarcpy.mapping.ExportToPNG(mxd, mxdp+\"maps\\\\\"+on_i)\n\t\n\t#--- Remove layer\n\tarcpy.mapping.RemoveLayer(mxddf, nlyr)\n\t\n\t#--- Return original texts\n\ttxt_1_obj.text = \"texto 1\"\n\ttxt_2_obj.text = \"texto 2\"\n\ttxt_3_obj.text = \"texto 3\"\n\ttxt_4_obj.text = \"texto 4\"\n\ttxt_5_obj.text = \"texto 5\"\n\ttxt_6_obj.text = \"texto 6\"\n\t\n\tfor df in arcpy.mapping.ListDataFrames(mxd):\n\t\tfor lyr in arcpy.mapping.ListLayers(mxd, \"\", df):\n\t\t\tif lyr.name == nlyr.name:\n\t\t\t\tarcpy.mapping.RemoveLayer(df, lyr)\n\t\n#--- end script","sub_path":"arcpy/example_map_automation/export_maps.py","file_name":"export_maps.py","file_ext":"py","file_size_in_byte":3032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"617778162","text":"#!/usr/bin/env python\n\"\"\" Common File\n    Utility functions  bb\n\n    @author Alfredo Sanz\n    @date October 2017\n\"\"\"\n# APIs imports\nimport os\nfrom datetime import date, timedelta, datetime\nimport time\nimport pytz\nimport string\nimport math\n\n# local Imports\nfrom common import Constantes\n\n\n# fin getAppLogLevel\n\ndef datetimeFechaToStringFecha(_dtdtFecha):\n    result = _dtdtFecha.strftime(\"%d/%m/%Y\")\n\n    return result\n\n\n# fin datetimeFechaToStringFecha\n\n\ndef generateDateTimeBasedKey():\n    \"\"\" Genera un numero entero basado en la fecha y hora\"\"\"\n\n    madrid = pytz.timezone(\"Europe/Madrid\")\n    dtdt = datetime.now(madrid)\n    dt = dtdt.date()\n    tm = dtdt.time()\n\n    mdate = dtdt.date()\n    mtime = dtdt.time()\n\n    milisec = str(int(dtdt.microsecond / 1000))\n    while 3 > len(milisec):\n        milisec = '0' + milisec\n    #\n\n    result = int(mdate.strftime(\"%d%m%Y\") + mtime.strftime(\"%H%M%S\") + milisec)\n\n    return result\n\n\n# fin generateDateTimeBasedKey\n\n\n\"\"\"\n* Precision = segundos.\n*\n* @return int con el timestamp acutal en segundos\n\"\"\"\n\n\ndef getCurrentTimeInSeconds():\n    \"\"\" Obtiene el timestamp de la fecha/hora actuales devolviendo la parte entera.\"\"\"\n\n    dtdt = datetime.utcnow()\n    ts = dtdt.timestamp()\n    result = int(ts)\n\n    return result\n\n\n# fin getCurrentTimeInSeconds\n\n\ndef getMonthstrByMonthnumber(_intMonth):\n    result = ''\n\n    if 1 == _intMonth:\n        result = u'Enero'\n    elif 2 == _intMonth:\n        result = u'Febrero'\n    elif 3 == _intMonth:\n        result = u'Marzo'\n    elif 4 == _intMonth:\n        result = u'Abril'\n    elif 5 == _intMonth:\n        result = u'Mayo'\n    elif 6 == _intMonth:\n        result = u'Junio'\n    elif 7 == _intMonth:\n        result = u'Julio'\n    elif 8 == _intMonth:\n        result = u'Agosto'\n    elif 9 == _intMonth:\n        result = u'Septiembre'\n    elif 10 == _intMonth:\n        result = u'Octubre'\n    elif 11 == _intMonth:\n        result = u'Noviembre'\n    elif 12 == _intMonth:\n        result = u'Diciembre'\n    #\n\n    return result\n\n\n# fin getMonthstrByMonthnumber\n\n\ndef __replaceMonth(_strFecha, _intMonth):\n    \"\"\"Reemplaza en Mes en Ingles por el Mes en Espanol de la cadena del parametro y segun el numero de mes\"\"\"\n    result = ''\n\n    if 1 == _intMonth:\n        result = _strFecha.replace('January', u'Enero')\n    elif 2 == _intMonth:\n        result = _strFecha.replace('February', u'Febrero')\n    elif 3 == _intMonth:\n        result = _strFecha.replace('March', u'Marzo')\n    elif 4 == _intMonth:\n        result = _strFecha.replace('April', u'Abril')\n    elif 5 == _intMonth:\n        result = _strFecha.replace('May', u'Mayo')\n    elif 6 == _intMonth:\n        result = _strFecha.replace('June', u'Junio')\n    elif 7 == _intMonth:\n        result = _strFecha.replace('July', u'Julio')\n    elif 8 == _intMonth:\n        result = _strFecha.replace('August', u'Agosto')\n    elif 9 == _intMonth:\n        result = _strFecha.replace('September', u'Septiembre')\n    elif 10 == _intMonth:\n        result = _strFecha.replace('October', u'Octubre')\n    elif 11 == _intMonth:\n        result = _strFecha.replace('November', u'Noviembre')\n    elif 12 == _intMonth:\n        result = _strFecha.replace('December', u'Diciembre')\n    #\n\n    return result\n\n\n# __replaceMonth\n\n\n\"\"\"\n* Obtiene la hora actual y la devuelve en disgregada en\n* tres valores diferentes:  fecha, hora, y fecha/hora en\n* formato largo, pero el mes en formato String en Espanol\n* y no como numero.\n*\n* @return dict{dtdt, intDate, intTime, strFechaHora, strFechaHoraFilename, strFechaHoraFilenameST, strFecha, intYear, intMonth, intDay, intHour, intMin, intSec}\n\"\"\"\n\n\ndef getCurrentTime_months():\n    \"\"\" Obtiene la hora actual y la devuelve en disgregada en tres valores diferentes\"\"\"\n\n    result = {}\n\n    result_date = 0\n    result_time = 0\n    result_sesion_format = '0'\n    result_fecha_str = '0'\n\n    madrid = pytz.timezone(\"Europe/Madrid\")\n    dtdt = datetime.now(madrid)\n    dt = dtdt.date()\n    tm = dtdt.time()\n\n    result_ts = int(dtdt.timestamp())\n\n    mdate = dtdt.date()\n    result_date = int(mdate.strftime(\"%Y%m%d\"))\n    result_year = int(mdate.strftime(\"%Y\"))  # breakdown year\n    result_month = int(mdate.strftime(\"%m\"))  # breakdown month\n    result_day = int(mdate.strftime(\"%d\"))  # breakdown day\n\n    mtime = dtdt.time()\n    result_time = int(mtime.strftime(\"%H%M%S\"))\n    result_hour = int(mtime.strftime(\"%H\"))  # breakdown hour\n    result_min = int(mtime.strftime(\"%M\"))  # breakdown minute\n    result_sec = int(mtime.strftime(\"%S\"))  # breakdown second\n\n    result_sesion_format = mdate.strftime(u\"%d-%B-%Y\") + \" \" + mtime.strftime(u\"%H:%M:%S\")\n    result_sesion_format = result_sesion_format.encode('utf-8').decode('utf-8')\n    result_sesion_format = __replaceMonth(result_sesion_format, mdate.month)\n\n    result_milisec = int(dtdt.microsecond / 1000)\n    milisec = str(result_milisec)\n    while 3 > len(milisec):\n        milisec = '0' + milisec\n    #\n    result_sesion_format_mili = mdate.strftime(u\"%d-%B-%Y\") + \" \" + mtime.strftime(u\"%H:%M:%S\") + ':' + milisec\n    result_sesion_format_mili = result_sesion_format_mili.encode('utf-8').decode('utf-8')\n    result_sesion_format_mili = __replaceMonth(result_sesion_format_mili, mdate.month)\n\n    result_fechahora_filename = mdate.strftime(u\"%d_%m_%Y\") + \"__\" + mtime.strftime(u\"%H_%M_%S\")\n    result_fechahora_filename = result_fechahora_filename.encode('utf-8').decode('utf-8')\n\n    result_fechahora_filename_st = mdate.strftime(u\"%d_%m_%Y\") + \"-\" + mtime.strftime(u\"%H_%M_%S\")\n    result_fechahora_filename_st = result_fechahora_filename_st.encode('utf-8').decode('utf-8')\n\n    result_fecha_str = mdate.strftime(u\"%d-%B-%Y\")\n    result_fecha_str = result_fecha_str.encode('utf-8').decode('utf-8')\n    result_fecha_str = __replaceMonth(result_fecha_str, mdate.month)\n\n    result['dtdt'] = dtdt\n    result['result_ts'] = result_ts\n    result['intDate'] = result_date\n    result['intTime'] = result_time\n    result['strFechaHora'] = result_sesion_format\n    result['strFechaHora_mil'] = result_sesion_format_mili\n    result['strFechaHoraFilename'] = result_fechahora_filename\n    result['strFechaHoraFilenameST'] = result_fechahora_filename_st\n    result['strFecha'] = result_fecha_str\n    result['year'] = result_year\n    result['month'] = result_month  # int\n    result['day'] = result_day  # int\n    result['hour'] = result_hour  # int\n    result['minute'] = result_min  # int\n    result['second'] = result_sec  # int\n    result['milisec'] = result_milisec  # int\n\n    # print('getCurrentTime_months: ' + repr(result))\n    return result\n\n\n# fin getCurrentTime_months\n\n\n\"\"\"\n* Obtiene la hora actual y la devuelve en disgregada en\n* tres valores diferentes:  fecha, hora, y fecha/hora en\n* formato largo.\n*\n* @return dict{dtdt, intDate, intTime, strFechaHora, strFechaHoraFilename, strFechaHoraFilenameST, strFecha, intYear, intMonth, intDay, intHour, intMin, intSec}\n\"\"\"\n\n\ndef getCurrentTime():\n    \"\"\"Obtiene la hora actual y la devuelve en disgregada en tres valores diferentes. fecha/hora en formato largo\"\"\"\n\n    result = {}\n\n    result_date = 0\n    result_time = 0\n    result_sesion_format = '0'\n    result_fecha_str = '0'\n    result_ts = 0\n\n    madrid = pytz.timezone(\"Europe/Madrid\")\n    dtdt = datetime.now(madrid)\n    dt = dtdt.date()\n    tm = dtdt.time()\n\n    result_ts = int(dtdt.timestamp())\n\n    mdate = dtdt.date()\n    result_date = int(mdate.strftime(\"%Y%m%d\"))\n    result_year = int(mdate.strftime(\"%Y\"))  # breakdown year\n    result_month = int(mdate.strftime(\"%m\"))  # breakdown month\n    result_day = int(mdate.strftime(\"%d\"))  # breakdown day\n\n    mtime = dtdt.time()\n    result_time = int(mtime.strftime(\"%H%M%S\"))\n    result_hour = int(mtime.strftime(\"%H\"))  # breakdown hour\n    result_min = int(mtime.strftime(\"%M\"))  # breakdown minute\n    result_sec = int(mtime.strftime(\"%S\"))  # breakdown second\n\n    result_milisec = int(dtdt.microsecond / 1000)\n    milisec = str(result_milisec)\n    while 3 > len(milisec):\n        milisec = '0' + milisec\n    #\n    result_sesion_format_mili = mdate.strftime(u\"%d-%B-%Y\") + \" \" + mtime.strftime(u\"%H:%M:%S\") + ':' + milisec\n    result_sesion_format_mili = result_sesion_format_mili.encode('utf-8').decode('utf-8')\n    result_sesion_format_mili = __replaceMonth(result_sesion_format_mili, mdate.month)\n\n    result_sesion_format = mdate.strftime(u\"%d-%m-%Y\") + \" \" + mtime.strftime(u\"%H:%M:%S\")\n    result_sesion_format = result_sesion_format.encode('utf-8').decode('utf-8')\n\n    result_fecha_str = mdate.strftime(u\"%d-%B-%Y\")\n    result_fecha_str = result_fecha_str.encode('utf-8').decode('utf-8')\n\n    result_fechahora_filename = mdate.strftime(u\"%d/%m/%Y\") + \" - \" + mtime.strftime(u\"%H:%M:%S\")\n    result_fechahora_filename = result_fechahora_filename.encode('utf-8').decode('utf-8')\n\n    result_fechahora_filename_st = mdate.strftime(u\"%d_%m_%Y\") + \"-\" + mtime.strftime(u\"%H_%M_%S\")\n    result_fechahora_filename_st = result_fechahora_filename_st.encode('utf-8').decode('utf-8')\n\n    result['dtdt'] = dtdt\n    result['result_ts'] = result_ts\n    result['intDate'] = result_date\n    result['intTime'] = result_time\n    result['strFechaHora'] = result_sesion_format\n    result['strFechaHora_mil'] = result_sesion_format_mili\n    result['strFechaHoraFilename'] = result_fechahora_filename\n    result['strFechaHoraFilenameST'] = result_fechahora_filename_st\n    result['strFecha'] = result_fecha_str\n    result['year'] = result_year\n    result['month'] = result_month\n    result['day'] = result_day\n    result['hour'] = result_hour\n    result['minute'] = result_min\n    result['second'] = result_sec\n    result['milisec'] = result_milisec  # int\n\n    # print('getCurrentTime: ' + repr(result))\n    return result\n\n\n# fin getCurrentTime\n\n\n\"\"\"\n* Obtiene la Hora actual menos un periodo de tiempo.\n*\n* @param _dtdt Datetime con la fecha hora actual\n* @param _period Indica la cantidad de Minutos a Restar.\n*       valores posibles: 1H, 2M\n*\n* @result int con la hora menos el periodo.\n\"\"\"\n\n\ndef getCurrentHourTimeMinusMinutes(_dtdt, _period):\n    \"\"\"Obtiene la Hora actual menos un periodo de tiempo.\"\"\"\n\n    result = None\n    new_dt = None\n\n    if '1H' == _period:\n        new_dt = _dtdt - timedelta(minutes=60)\n    elif '2M' == _period:\n        new_dt = _dtdt - timedelta(minutes=2)\n    else:\n        new_dt = _dtdt - timedelta(minutes=0)\n    #\n\n    mtime = new_dt.time()\n    result = int(mtime.strftime(\"%H%M%S\"))\n\n    return result\n\n\n# fin getCurrentHourTimeMinusMinutes\n\n\n\"\"\"\n* Calcula una diferencia de tiempo entre dos timestamps (segundos, sin la parte real),\n* y el resultado puede ser en segundos o en Minutos,\n* dependiendo del parametro unit.\n* El resultado en minutos es con precision de minutos, es decir, 1 minuto, 2 minutos, etc...\n*\n* @param curTime  timestamp en segundos sin parte real\n* @param lastTime   timestamp en segundos sin parte real\n* @param unit S,M -> indica si se quiere el resultado en minutos o segundos.\n*\n* @return int         ->El numero de unidades de diferencia de tiempo, \n*                       en segundos o minutos\n*                       En el caso de los minutos, solo devuelve unidades enteras\n*                       con redondeo a la baja. Por ejemplo: 30 segundos serian 0 minutos.\n*                                                            75 segundos serian 1 minuto.\n*                     -> 0 en caso de error durante el calculo\n\"\"\"\n\n\ndef diffTimes(curTime, lastTime, unit):\n    \"\"\"Calcula una diferencia de tiempo entre dos timestamps (segundos, sin la parte real)\"\"\"\n\n    result = 0\n\n    try:\n        if Constantes.TIME_UNIT_SEC == unit:\n            result = curTime - lastTime\n        elif Constantes.TIME_UNIT_MIN == unit:\n            diff_secs = curTime - lastTime\n            diff_mins_float = diff_secs / 60\n            result = int(diff_mins_float)  # Nos quedamos con la parte entera, down round.\n        #\n        else:\n            result = 0\n        #\n\n    except Exception as ex:\n        print('Error Calculating diff time ' + repr(ex))\n        result = 0\n\n    return result\n# fin diffTimes\n\n\ndef diffTimesMilliseconds(curTime_dtdt, lastTime_dtdt):\n    \"\"\"Diferencia en milisegundos entre dos datetime.datetime\"\"\"\n\n    result = 0\n\n    try:\n        diff_tmdelta = curTime_dtdt - lastTime_dtdt\n\n        miliseconds_01 = diff_tmdelta.seconds * 1000\n        miliseconds_02 = diff_tmdelta.microseconds / 1000\n        result = miliseconds_01 + miliseconds_02\n        result = int(result)  # -> cogemos la parte entera\n\n    except Exception as ex:\n        print('Error Calculating diff time miliseconds ' + repr(ex))\n        result = 0\n\n    return result\n# fin diffTimesMilliseconds\n\n\ndef generate_timeobj(_year, _month, _day, _hour, _minute, _second):\n    result = {}\n\n    result_date = 0\n    result_time = 0\n    result_sesion_format = '0'\n    result_fecha_str = '0'\n\n    madrid = pytz.timezone(\"Europe/Madrid\")\n    dtdt_i = datetime.now(madrid)\n\n    iyear = int(_year)\n    imonth = int(_month)\n    iday = int(_day)\n    ihour = int(_hour)\n    iminute = int(_minute)\n    isecond = int(_second)\n    # replace([year[, month[, day[, hour[, minute[, second[, microsecond[, tzinfo]]]]]]]])\n    dtdt = dtdt_i.replace(year=iyear, month=imonth, day=iday, hour=ihour, minute=iminute, second=isecond)\n\n    mdate = dtdt.date()\n    result_date = int(mdate.strftime(\"%Y%m%d\"))\n    result_year = int(mdate.strftime(\"%Y\"))  # breakdown year\n    result_month = int(mdate.strftime(\"%m\"))  # breakdown month\n    result_day = int(mdate.strftime(\"%d\"))  # breakdown day\n\n    mtime = dtdt.time()\n    result_time = int(mtime.strftime(\"%H%M%S\"))\n    result_hour = int(mtime.strftime(\"%H\"))  # breakdown hour\n    result_min = int(mtime.strftime(\"%M\"))  # breakdown minute\n    result_sec = int(mtime.strftime(\"%S\"))  # breakdown second\n\n    result_sesion_format = mdate.strftime(u\"%d-%m-%Y\") + \" \" + mtime.strftime(u\"%H:%M:%S\")\n    result_sesion_format = result_sesion_format.encode('utf-8').decode('utf-8')\n\n    result_fecha_str = mdate.strftime(u\"%d-%B-%Y\")\n    result_fecha_str = result_fecha_str.encode('utf-8').decode('utf-8')\n\n    result_fechahora_filename = mdate.strftime(u\"%d/%m/%Y\") + \" - \" + mtime.strftime(u\"%H:%M:%S\")\n    result_fechahora_filename = result_fechahora_filename.encode('utf-8').decode('utf-8')\n\n    result_fechahora_filename_st = mdate.strftime(u\"%d_%m_%Y\") + \"-\" + mtime.strftime(u\"%H_%M_%S\")\n    result_fechahora_filename_st = result_fechahora_filename_st.encode('utf-8').decode('utf-8')\n\n    result['dtdt'] = dtdt\n    result['intDate'] = result_date\n    result['intTime'] = result_time\n    result['strFechaHora'] = result_sesion_format\n    result['strFechaHoraFilename'] = result_fechahora_filename\n    result['strFechaHoraFilenameST'] = result_fechahora_filename_st\n    result['strFecha'] = result_fecha_str\n    result['year'] = result_year\n    result['month'] = result_month\n    result['day'] = result_day\n    result['hour'] = result_hour\n    result['minute'] = result_min\n    result['second'] = result_sec\n\n    return result\n\n\n# fin generate_timeobj\n\n\ndef getHourOfDay():\n    madrid = pytz.timezone(\"Europe/Madrid\")\n    dtdt = datetime.now(madrid)\n\n    result = dtdt.hour\n    return result\n\n\n# fin getHourOfDay\n\n\ndef thousandsDots(_mystring):\n    result_1 = \"{0:,}\".format(int(_mystring))\n    result = result_1.replace(',', '.')\n\n    return result\n\n\n# fin thousandsDots\n\n\ndef thousandsDotsInt(_myInt):\n    result_1 = \"{0:,}\".format(_myInt)\n    result = result_1.replace(',', '.')\n\n    return result\n\n\n# fin thousandsDots\n\n\ndef formatDateTimeIntCompleteZeros(_year, _month, _day, _hour):\n    result = ''\n\n    str_year = str(_year)\n    result += str_year\n\n    str_month = str(_month)\n    if 10 > int(_month):\n        str_month = '0' + str(_month)\n    #\n    result += str_month\n\n    str_day = str(_day)\n    if 10 > int(_day):\n        str_day = '0' + str(_day)\n    #\n    result += str_day\n\n    str_hour = str(_hour)\n    if 10 > int(_hour):\n        str_hour = '0' + str(_hour)\n    #\n    result += str_hour\n\n    return result.encode('utf-8').decode('utf-8')\n\n\n# fin formatDateTimeIntCompleteZeros\n\n\ndef compareDatetimes_hours(_datetime_A, _datetime_B, logging):\n    \"\"\"return hours<int>, minutes<int>, seconds<int>\"\"\"\n\n    diff_timedelta = _datetime_B - _datetime_A  # d.days, d.seconds, d.microseconds\n\n    logging.info('**diff_timedelta:' + repr(diff_timedelta))\n    logging.info('**diff_timedelta.seconds:' + repr(diff_timedelta.days) +\n                 ' days, ' + repr(diff_timedelta.seconds) +\n                 ' seconds, ' + repr(diff_timedelta.microseconds) +\n                 ' microseconds')\n\n    result_hour = diff_timedelta.seconds // 3600\n    logging.info('**result_hour:' + str(result_hour))\n\n    result_min = (diff_timedelta.seconds // 60) % 60\n    logging.info('****result_min: ' + str(result_min))\n\n    result_sec = diff_timedelta.seconds % 60\n    logging.info('****result_sec: ' + str(result_sec))\n\n    # ajuste diario\n    if diff_timedelta.days:\n        dd = 24 * diff_timedelta.days\n        result_hour += dd\n    #\n    logging.info('****diff_total, result_hour: ' + str(result_hour) +\n                 ' result_min: ' + str(result_min) +\n                 ' result_sec: ' + str(result_sec))\n\n    return result_hour, result_min, result_sec\n# fin compareDatetimes_hours","sub_path":"common/Util.py","file_name":"Util.py","file_ext":"py","file_size_in_byte":17226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"635710642","text":"# Time:  O(n)\n# Space: O(1)\n\n# 1359\n# Given n orders, each order consist in pickup and delivery services.\n#\n# Count all valid pickup/delivery possible sequences such that\n# delivery(i) is always after of pickup(i).\n#\n# Since the answer may be too large, return it modulo 10^9 + 7.\n\n# Solution: find recursion function\n# suppose f_i-1 is the # of possible sequences for i-1 order (2*(i-1) items),\n# f_i needs to insert 1 new order:\n# if Pi and Di are continuous, there are 2i-1 places to insert;\n# if Pi and Di are separate, we find 2 places from 2i-1 = C(2i-1, 2) = (2i-1)*(i-1)\n# so f_i = f_i-1 * (2i-1 + (2i-1)*(i-1)) = f_i-1 * (2i-1) * i\n\nclass Solution(object):\n    def countOrders(self, n):\n        \"\"\"\n        :type n: int\n        :rtype: int\n        \"\"\"\n        MOD = 10**9+7\n        result = 1\n        for i in range(2, n+1):\n            result = result * i*(2*i-1) % MOD\n        return result\n\nprint(Solution().countOrders(1)) # 1 P1 D1\nprint(Solution().countOrders(2)) # 6 (P1,P2,D1,D2), (P1,P2,D2,D1), (P1,D1,P2,D2), (P2,P1,D1,D2), (P2,P1,D2,D1) (P2,D2,P1,D1)\nprint(Solution().countOrders(3)) # 90\n","sub_path":"Python/count-all-valid-pickup-and-delivery-options.py","file_name":"count-all-valid-pickup-and-delivery-options.py","file_ext":"py","file_size_in_byte":1109,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"227461428","text":"import matplotlib.pyplot as plt\nimport numpy as np\n#import tensorflow as tf\nimport tensorflow.compat.v1 as tf\ntf.compat.v1.disable_eager_execution()\nimport math\nimport streamlit as st\nfrom tensorflow.keras import backend as K\nfrom tensorflow.keras import activations, initializers\nfrom tensorflow.keras.layers import Layer\nfrom tensorflow.keras.layers import Input,Dense\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras import callbacks, optimizers\nfrom tensorflow.keras.utils import plot_model\nimport tensorflow_probability as tfp\n\nNSAMPLE = 1000\nx_data = np.float32(np.random.uniform(-10.5, 10.5, (1, NSAMPLE))).T\nr_data = np.float32(np.random.normal(size=(NSAMPLE,1)))\ny_data = np.float32(np.sin(0.75*x_data)*7.0+x_data*0.5+r_data*1.0)\n\ntemp_data = x_data\nx_data = y_data\ny_data = temp_data\n\n#plt.figure(figsize=(8, 8))\n#plot_out = plt.plot(x_data,y_data,'ro',alpha=0.3)\n#plt.show()\n\n\ndef main():\n\n    st.title('mixture density network')\n    st.title('dataset')\n    NSAMPLE = st.number_input('Datasize',value=1000,step=100)\n    option = st.selectbox('Datasets',['A','B','C'])\n    if option=='A':\n        x_data = np.float32(np.random.uniform(-10.5, 10.5, (1, NSAMPLE))).T\n        r_data = np.float32(np.random.normal(size=(NSAMPLE,1)))\n        y_data = np.float32(np.sin(0.75*x_data)*7.0+x_data*0.5+r_data*1.0)\n        x_test= np.float32(np.random.uniform(-10.5, 10.5, (1, NSAMPLE))).T\n        r_test= np.float32(np.random.normal(size=(NSAMPLE,1)))\n        y_test= np.float32(np.sin(0.75*x_test)*7.0+x_test*0.5+r_test*1.0)\n    if option=='B':\n        y_data = np.float32(np.random.uniform(-10.5, 10.5, (1, NSAMPLE))).T\n        r_data = np.float32(np.random.normal(size=(NSAMPLE,1)))\n        x_data = np.float32(np.sin(0.75*y_data)*7.0+y_data*0.5+r_data*1.0)\n        y_test= np.float32(np.random.uniform(-10.5, 10.5, (1, NSAMPLE))).T\n        r_test= np.float32(np.random.normal(size=(NSAMPLE,1)))\n        x_test= np.float32(np.sin(0.75*y_test)*7.0+y_test*0.5+r_test*1.0)\n    if option=='C':\n        x_data = np.float32(np.random.uniform(-10.5, 10.5, (1, NSAMPLE))).T\n        r_data = np.float32(np.random.randint(2,size=(NSAMPLE,1)))*2-1\n        y_data = np.sqrt(10.5*10.5-np.square(x_data))*r_data\n        x_test= np.float32(np.random.uniform(-10.5, 10.5, (1, NSAMPLE))).T\n        r_test= np.float32(np.random.randint(2,size=(NSAMPLE,1)))*2-1\n        y_test= np.sqrt(10.5*10.5-np.square(x_test))*r_test\n    plt.figure(figsize=(8, 8))\n    plot_out = plt.plot(x_data,y_data,'ro',alpha=0.3)\n    plt.show()\n    st.pyplot()\n    input_shape = (1,) \n    NHIDDEN = 20\n    x = tf.placeholder(dtype=tf.float32, shape=[None,1])\n    y = tf.placeholder(dtype=tf.float32, shape=[None,1])\n    st.title('neural network')\n    if st.button('Neural Network Regression'):\n        with st.echo():\n            NHIDDEN = 20\n            W = tf.Variable(tf.random_normal([1,NHIDDEN], stddev=1.0, dtype=tf.float32))\n            b = tf.Variable(tf.random_normal([1,NHIDDEN], stddev=1.0, dtype=tf.float32))\n            \n            W_out = tf.Variable(tf.random_normal([NHIDDEN,1], stddev=1.0, dtype=tf.float32))\n            b_out = tf.Variable(tf.random_normal([1,1], stddev=1.0, dtype=tf.float32))\n            \n            hidden_layer = tf.nn.tanh(tf.matmul(x, W) + b)\n            y_out = tf.matmul(hidden_layer,W_out) + b_out\n            lossfunc = tf.nn.l2_loss(y_out-y);\n            train_op = tf.train.RMSPropOptimizer(learning_rate=0.1, decay=0.8).minimize(lossfunc)\n            sess = tf.InteractiveSession()\n            sess.run(tf.initialize_all_variables())\n            NEPOCH = 1000\n            for i in range(NEPOCH):\n                sess.run(train_op,feed_dict={x: x_data, y: y_data})\n            x_test = np.float32(np.arange(-10.5,10.5,0.1))\n            x_test = x_test.reshape(x_test.size,1)\n            y_test = sess.run(y_out,feed_dict={x: x_test})\n        \n        plt.figure(figsize=(8, 8))\n        plt.plot(x_data,y_data,'ro', x_test,y_test,'bo',alpha=0.3)\n        plt.show()\n\n        sess.close()\n        st.pyplot()\n    if st.button('Neural Network Regression Keras version'):\n        with st.echo():\n            inputs = Input(shape=input_shape, name='input')\n            hidden = Dense(NHIDDEN, activation='tanh')(inputs)\n            output = Dense(1)(hidden)\n            lossfunc = tf.nn.l2_loss(output-y);\n            model = Model(inputs, output)\n            model.compile(loss=\"mean_squared_error\", optimizer=optimizers.Adam(lr=0.08), metrics=['mse'])\n            model.fit(x_data, y_data, batch_size=256, epochs=1500, verbose=0);\n            y_pred = model.predict(x_test)\n            plt.figure(figsize=(8, 8))\n            plt.plot(x_test,y_test,'ro', x_test,y_pred,'bo',alpha=0.3)\n            plt.show()\n            st.pyplot()\n\n    \n    st.title('mixture density network')\n    if st.button('Mixture Density Network'):\n        with st.echo():\n            NHIDDEN = 24\n            STDEV = 0.5\n            KMIX = 24 # number of mixtures\n            NOUT = KMIX * 3 # pi, mu, stdev\n            \n            x = tf.placeholder(dtype=tf.float32, shape=[None,1], name=\"x\")\n            y = tf.placeholder(dtype=tf.float32, shape=[None,1], name=\"y\")\n            \n            Wh = tf.Variable(tf.random_normal([1,NHIDDEN], stddev=STDEV, dtype=tf.float32))\n            bh = tf.Variable(tf.random_normal([1,NHIDDEN], stddev=STDEV, dtype=tf.float32))\n            \n            Wo = tf.Variable(tf.random_normal([NHIDDEN,NOUT], stddev=STDEV, dtype=tf.float32))\n            bo = tf.Variable(tf.random_normal([1,NOUT], stddev=STDEV, dtype=tf.float32))\n            \n            hidden_layer = tf.nn.tanh(tf.matmul(x, Wh) + bh)\n            output = tf.matmul(hidden_layer,Wo) + bo\n            print(output)\n\n            def get_mixture_coef(output):\n                out_pi = tf.placeholder(dtype=tf.float32, shape=[None,KMIX], name=\"mixparam\")\n                out_sigma = tf.placeholder(dtype=tf.float32, shape=[None,KMIX], name=\"mixparam\")\n                out_mu = tf.placeholder(dtype=tf.float32, shape=[None,KMIX], name=\"mixparam\")\n                \n                print(output.shape)\n                out_pi, out_sigma, out_mu = tf.split(output, 3, 1)\n                print(out_pi.shape)\n                print(out_sigma.shape)\n                print(out_mu.shape)\n                \n                max_pi = tf.reduce_max(out_pi, 1, keep_dims=True)\n                print(max_pi.shape)\n                out_pi = tf.subtract(out_pi, max_pi)\n                \n                out_pi = tf.exp(out_pi)\n                \n                normalize_pi = tf.reciprocal(tf.reduce_sum(out_pi, 1, keep_dims=True))\n                out_pi = tf.multiply(normalize_pi, out_pi)\n                \n                out_sigma = tf.exp(out_sigma)\n                \n                return out_pi, out_sigma, out_mu\n\n            out_pi, out_sigma, out_mu = get_mixture_coef(output)\n\n            oneDivSqrtTwoPI = 1 / math.sqrt(2*math.pi) # normalisation factor for gaussian, not needed.\n            def tf_normal(y, mu, sigma):\n                result = tf.subtract(y, mu)\n                result = tf.multiply(result,tf.reciprocal(sigma))\n                result = -tf.square(result)/2\n                return tf.multiply(tf.exp(result),tf.reciprocal(sigma))*oneDivSqrtTwoPI\n            \n            def get_lossfunc(out_pi, out_sigma, out_mu, y):\n                result = tf_normal(y, out_mu, out_sigma)\n                result = tf.multiply(result, out_pi)\n                result = tf.reduce_sum(result, 1, keep_dims=True)\n                result = -tf.log(result)\n                return tf.reduce_mean(result)\n            \n            lossfunc = get_lossfunc(out_pi, out_sigma, out_mu, y)\n            train_op = tf.train.AdamOptimizer().minimize(lossfunc)\n\n            sess = tf.InteractiveSession()\n            sess.run(tf.initialize_all_variables())\n            \n            NEPOCH = 10000\n            loss = np.zeros(NEPOCH) # store the training progress here.\n            for i in range(NEPOCH):\n                sess.run(train_op,feed_dict={x: x_data, y: y_data})\n                loss[i] = sess.run(lossfunc, feed_dict={x: x_data, y: y_data})\n\n            #NSAMPLE = 2500\n            plt.figure(figsize=(8, 8))\n            plt.plot(np.arange(100, NEPOCH,1), loss[100:], 'r-')\n            plt.show()\n            st.pyplot()\n\n\n            x_test = np.float32(np.arange(-11,11,0.1))\n            NTEST = x_test.size\n            x_test = x_test.reshape(NTEST,1) # needs to be a matrix, not a vector\n            \n            def get_pi_idx(x, pdf):\n                N = pdf.size\n                accumulate = 0\n                for i in range(0, N):\n                    accumulate += pdf[i]\n                    if (accumulate >= x):\n                        return i\n                print('error with sampling ensemble')\n                return -1\n            \n            def generate_ensemble(out_pi, out_mu, out_sigma, M = 10):\n                NTEST = x_test.size\n                result = np.random.rand(NTEST, M) # initially random [0, 1]\n                rn = np.random.randn(NTEST, M) # normal random matrix (0.0, 1.0)\n                mu = 0\n                std = 0\n                idx = 0\n                \n                # transforms result into random ensembles\n                for j in range(0, M):\n                    for i in range(0, NTEST):\n                        idx = get_pi_idx(result[i, j], out_pi[i])\n                        mu = out_mu[i, idx]\n                        std = out_sigma[i, idx]\n                        result[i, j] = mu + rn[i, j]*std\n                return result\n\n            out_pi_test, out_sigma_test, out_mu_test = sess.run(get_mixture_coef(output), feed_dict={x: x_test})\n\n            st.write(out_pi_test.shape)\n            st.write(out_sigma_test.shape)\n            st.write(out_mu_test.shape)\n            \n            y_test = generate_ensemble(out_pi_test, out_mu_test, out_sigma_test)\n            st.write(x_test.shape)\n            st.write(y_test.shape)\n            \n            plt.figure(figsize=(8, 8))\n            plt.plot(x_data,y_data,'ro', x_test,y_test,'bo',alpha=0.3)\n            plt.show()\n            st.pyplot()\n\n    if st.button('Bayersian Nueral Network'):\n        with st.echo():\n            class DenseVariational(Layer):\n                def __init__(self,\n                        units,\n                        kl_weight,\n                        activation=None,\n                        prior_sigma_1=1.5,\n                        prior_sigma_2=0.1,\n                        prior_pi=0.5, **kwargs):\n                    self.units = units\n                    self.kl_weight = kl_weight\n                    self.activation = activations.get(activation)\n                    self.prior_sigma_1 = prior_sigma_1\n                    self.prior_sigma_2 = prior_sigma_2\n                    self.prior_pi_1 = prior_pi\n                    self.prior_pi_2 = 1.0 - prior_pi\n                    self.init_sigma = np.sqrt(self.prior_pi_1 * self.prior_sigma_1 ** 2 +\n                            self.prior_pi_2 * self.prior_sigma_2 ** 2)\n\n                    super().__init__(**kwargs)\n                def compute_output_shape(self, input_shape):\n                     return input_shape[0], self.units\n\n                def build(self, input_shape):\n                    self.kernel_mu = self.add_weight(name='kernel_mu',\n                                 shape=(input_shape[1], self.units),\n                                 initializer=initializers.RandomNormal(stddev=self.init_sigma),\n                                 trainable=True)\n                    self.bias_mu = self.add_weight(name='bias_mu',\n                                 shape=(self.units,),\n                                 initializer=initializers.RandomNormal(stddev=self.init_sigma),\n                                 trainable=True)\n                    self.kernel_rho = self.add_weight(name='kernel_rho',\n                                 shape=(input_shape[1], self.units),\n                                 initializer=initializers.constant(0.0),\n                                 trainable=True)\n                    self.bias_rho = self.add_weight(name='bias_rho',\n                                 shape=(self.units,),\n                                 initializer=initializers.constant(0.0),\n                                 trainable=True)\n                    super().build(input_shape)     \n\n                def call(self, inputs, **kwargs):\n                    kernel_sigma = tf.math.softplus(self.kernel_rho)\n                    kernel = self.kernel_mu + kernel_sigma * tf.random.normal(self.kernel_mu.shape)\n                    \n                    bias_sigma = tf.math.softplus(self.bias_rho)\n                    bias = self.bias_mu + bias_sigma * tf.random.normal(self.bias_mu.shape)\n                    \n                    self.add_loss(self.kl_loss(kernel, self.kernel_mu, kernel_sigma) +\n                    self.kl_loss(bias, self.bias_mu, bias_sigma))\n                    \n                    return self.activation(K.dot(inputs, kernel) + bias)\n\n                def kl_loss(self, w, mu, sigma):\n                    variational_dist = tfp.distributions.Normal(mu, sigma)\n                    return self.kl_weight * K.sum(variational_dist.log_prob(w) - self.log_prior_prob(w))\n                \n                def log_prior_prob(self, w):\n                    comp_1_dist = tfp.distributions.Normal(0.0, self.prior_sigma_1)\n                    comp_2_dist = tfp.distributions.Normal(0.0, self.prior_sigma_2)\n                    return K.log(self.prior_pi_1 * comp_1_dist.prob(w) + self.prior_pi_2 * comp_2_dist.prob(w))         \n\n        with st.echo():\n            train_size = NSAMPLE\n            batch_size = train_size\n            num_batches = train_size / batch_size\n            noise = 1.0\n\n            kl_weight = 1.0 / num_batches\n            prior_params = {\n                'prior_sigma_1': 1.5, \n                'prior_sigma_2': 0.1, \n                'prior_pi': 0.5 \n            }\n\n            x_in = Input(shape=(1,))\n            x = DenseVariational(20, kl_weight, **prior_params, activation='tanh')(x_in)\n            #x = DenseVariational(20, kl_weight, **prior_params, activation='tanh')(x)\n            x = DenseVariational(1, kl_weight, **prior_params)(x)\n\n            model = Model(x_in, x)\n\n            def neg_log_likelihood(y_obs, y_pred, sigma=noise):\n                dist = tfp.distributions.Normal(loc=y_pred, scale=sigma)\n                return K.sum(-dist.log_prob(y_obs))\n\n            model.compile(loss=neg_log_likelihood, optimizer=optimizers.Adam(lr=0.08), metrics=['mse'])\n            model.fit(x_data, y_data, batch_size=batch_size, epochs=1500, verbose=0);\n\n            import tqdm\n\n            X_test = np.linspace(-11.5, 11.5, 1000).reshape(-1, 1)\n            #X_test = x_data\n            y_pred_list = []\n            \n            for i in tqdm.tqdm(range(500)):\n                print(X_test.shape)\n                y_pred = model.predict(X_test)\n                print(y_pred.shape)\n                y_pred_list.append(y_pred)\n            \n            y_preds = np.concatenate(y_pred_list, axis=1)\n            \n            y_mean = np.mean(y_preds, axis=1)\n            y_sigma = np.std(y_preds, axis=1)\n            \n            plt.plot(X_test, y_mean, 'r-', label='Predictive mean');\n            plt.scatter(x_data, y_data, marker='+', label='Training data')\n            plt.fill_between(X_test.ravel(), \n                y_mean + 2 * y_sigma, \n                y_mean - 2 * y_sigma, \n                alpha=0.5, label='Epistemic uncertainty')\n            plt.title('Prediction')\n            plt.legend();\n            st.pyplot()\n","sub_path":"mdn.py","file_name":"mdn.py","file_ext":"py","file_size_in_byte":15656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"40350584","text":"\"\"\"\nWorker implementation to execute incoming queries.\n\"\"\"\nimport json\nimport logging\nimport os\nimport threading\nimport uuid\nimport datetime\nimport time\nimport signal\nimport setproctitle\nimport redis\nfrom redash.utils import gen_query_hash\n\n\nclass Job(object):\n    HIGH_PRIORITY = 1\n    LOW_PRIORITY = 2\n\n    WAITING = 1\n    PROCESSING = 2\n    DONE = 3\n    FAILED = 4\n\n    def __init__(self, redis_connection, query, priority,\n                 job_id=None,\n                 wait_time=None, query_time=None,\n                 updated_at=None, status=None, error=None, query_result_id=None,\n                 process_id=0):\n        self.redis_connection = redis_connection\n        self.query = query\n        self.priority = priority\n        self.query_hash = gen_query_hash(self.query)\n        self.query_result_id = query_result_id\n        if process_id == 'None':\n            self.process_id = None\n        else:\n            self.process_id = int(process_id)\n\n        if job_id is None:\n            self.id = str(uuid.uuid1())\n            self.new_job = True\n            self.wait_time = 0\n            self.query_time = 0\n            self.error = None\n            self.updated_at = time.time() # job_dict.get('updated_at', time.time())\n            self.status = self.WAITING # int(job_dict.get('status', self.WAITING))\n        else:\n            self.id = job_id\n            self.new_job = False\n            self.error = error\n            self.wait_time = wait_time\n            self.query_time = query_time\n            self.updated_at = updated_at\n            self.status = status\n\n    def to_dict(self):\n        return {\n            'query': self.query,\n            'priority': self.priority,\n            'id': self.id,\n            'wait_time': self.wait_time,\n            'query_time': self.query_time,\n            'updated_at': self.updated_at,\n            'status': self.status,\n            'error': self.error,\n            'query_result_id': self.query_result_id,\n            'process_id': self.process_id\n        }\n\n    @staticmethod\n    def _redis_key(job_id):\n        return 'job:%s' % job_id\n\n    def cancel(self):\n        # TODO: Race condition:\n        # it's possible that it will be picked up by worker while processing the cancel order\n        if self.is_finished():\n            return\n\n        if self.status == self.PROCESSING:\n            os.kill(self.process_id, signal.SIGINT)\n        else:\n            self.done(None, \"Interrupted/Cancelled while running.\")\n\n    def save(self, pipe=None):\n        if not pipe:\n            pipe = self.redis_connection.pipeline()\n\n        if self.new_job:\n            pipe.set('query_hash_job:%s' % self.query_hash, self.id)\n\n        if self.is_finished():\n            pipe.delete('query_hash_job:%s' % self.query_hash)\n\n        pipe.sadd('jobs_set', self.id)\n        pipe.hmset(self._redis_key(self.id), self.to_dict())\n        pipe.publish(self._redis_key(self.id), json.dumps(self.to_dict()))\n        pipe.execute()\n\n    def processing(self, process_id):\n        self.status = self.PROCESSING\n        self.process_id = process_id\n        self.wait_time = time.time() - self.updated_at\n        self.updated_at = time.time()\n        self.save()\n\n    def is_finished(self):\n        return self.status in (self.FAILED, self.DONE)\n\n    def done(self, query_result_id, error):\n        if error:\n            self.status = self.FAILED\n        else:\n            self.status = self.DONE\n\n        self.query_result_id = query_result_id\n        self.error = error\n        self.query_time = time.time() - self.updated_at\n        self.updated_at = time.time()\n        self.save()\n\n    def __str__(self):\n        return \"<Job:%s,priority:%d,status:%d>\" % (self.id, self.priority, self.status)\n\n    @classmethod\n    def _load(cls, redis_connection, job_id):\n        return redis_connection.hgetall(cls._redis_key(job_id))\n\n    @classmethod\n    def load(cls, redis_connection, job_id):\n        job_dict = cls._load(redis_connection, job_id)\n        job = None\n        if job_dict:\n            job = Job(redis_connection, job_id=job_dict['id'], query=job_dict['query'].decode('utf-8'),\n                      priority=int(job_dict['priority']), updated_at=float(job_dict['updated_at']),\n                      status=int(job_dict['status']), wait_time=float(job_dict['wait_time']),\n                      query_time=float(job_dict['query_time']), error=job_dict['error'],\n                      query_result_id=job_dict['query_result_id'],\n                      process_id=job_dict['process_id'])\n\n        return job\n\n\nclass Worker(threading.Thread):\n    def __init__(self, manager, redis_connection_params, query_runner, sleep_time=0.1):\n        self.manager = manager\n\n        self.redis_connection_params = {k: v for k, v in redis_connection_params.iteritems()\n                                        if k in ('host', 'db', 'password', 'port')}\n        self.continue_working = True\n        self.query_runner = query_runner\n        self.sleep_time = sleep_time\n        self.child_pid = None\n        self.worker_id = uuid.uuid1()\n        self.status = {\n            'jobs_count': 0,\n            'cancelled_jobs_count': 0,\n            'done_jobs_count': 0,\n            'updated_at': time.time(),\n            'started_at': time.time()\n        }\n        self._save_status()\n        self.manager.redis_connection.sadd('workers', self._key)\n\n        super(Worker, self).__init__(name=\"Worker-%s\" % self.worker_id)\n\n    def set_title(self, title=None):\n        base_title = \"redash worker:%s\" % self.worker_id\n        if title:\n            full_title = \"%s - %s\" % (base_title, title)\n        else:\n            full_title = base_title\n\n        setproctitle.setproctitle(full_title)\n\n    def run(self):\n        logging.info(\"[%s] started.\", self.name)\n        while self.continue_working:\n            job_id = self.manager.queue.pop()\n            if job_id:\n                self._update_status('jobs_count')\n                logging.info(\"[%s] Processing %s\", self.name, job_id)\n                self._fork_and_process(job_id)\n                if self.child_pid == 0:\n                    return\n            else:\n                time.sleep(self.sleep_time)\n\n    def _update_status(self, counter):\n        self.status['updated_at'] = time.time()\n        self.status[counter] += 1\n        self._save_status()\n\n    @property\n    def _key(self):\n        return 'worker:%s' % self.worker_id\n\n    def _save_status(self):\n        self.manager.redis_connection.hmset(self._key, self.status)\n\n    def _fork_and_process(self, job_id):\n        self.child_pid = os.fork()\n        if self.child_pid == 0:\n            self.set_title(\"processing %s\" % job_id)\n            self._process(job_id)\n        else:\n            logging.info(\"[%s] Waiting for pid: %d\", self.name, self.child_pid)\n            _, status = os.waitpid(self.child_pid, 0)\n            self._update_status('done_jobs_count')\n            if status > 0:\n                job = Job.load(self.manager.redis_connection, job_id)\n                if not job.is_finished():\n                    self._update_status('cancelled_jobs_count')\n                    logging.info(\"[%s] process interrupted and job %s hasn't finished; registering interruption in job\",\n                                 self.name, job_id)\n                    job.done(None, \"Interrupted/Cancelled while running.\")\n\n            logging.info(\"[%s] Finished Processing %s (pid: %d status: %d)\",\n                         self.name, job_id, self.child_pid, status)\n\n    def _process(self, job_id):\n        redis_connection = redis.StrictRedis(**self.redis_connection_params)\n        job = Job.load(redis_connection, job_id)\n        if job.is_finished():\n            logging.warning(\"[%s][%s] tried to process finished job.\", self.name, job)\n            return\n\n        pid = os.getpid()\n        job.processing(pid)\n\n        logging.info(\"[%s][%s] running query...\", self.name, job.id)\n        start_time = time.time()\n        self.set_title(\"running query %s\" % job_id)\n\n        if getattr(self.query_runner, 'annotate_query', True):\n            annotated_query = \"/* Pid: %s, Job Id: %s, Query hash: %s, Priority: %s */ %s\" % \\\n                              (pid, job.id, job.query_hash, job.priority, job.query)\n        else:\n            annotated_query = job.query\n\n        # TODO: here's the part that needs to be forked, not all of the worker process...\n        data, error = self.query_runner(annotated_query)\n        run_time = time.time() - start_time\n        logging.info(\"[%s][%s] query finished... data length=%s, error=%s\",\n                     self.name, job.id, data and len(data), error)\n\n        # TODO: it is possible that storing the data will fail, and we will need to retry\n        # while we already marked the job as done\n        query_result_id = None\n        if not error:\n            self.set_title(\"storing results %s\" % job_id)\n            query_result_id = self.manager.store_query_result(job.query, data, run_time,\n                                                              datetime.datetime.utcnow())\n\n        self.set_title(\"marking job as done %s\" % job_id)\n        job.done(query_result_id, error)\n","sub_path":"redash/data/worker.py","file_name":"worker.py","file_ext":"py","file_size_in_byte":9166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"118072522","text":"import os\nimport hashlib\nimport RandomIO\nimport binascii\nimport partialhash\nimport bisect\nfrom datetime import datetime\n\n\nclass Builder:\n\n    def __init__(self, address, shard_size, max_size, on_generate_shard=None,\n                 debug=False):\n        self.debug = debug\n        self.target_height = int(max_size / shard_size)\n        self.address = address\n        self.shard_size = shard_size\n        self.max_size = max_size\n        self.on_generate_shard = on_generate_shard\n\n    @staticmethod\n    def sha256(content):\n        \"\"\"Finds the SHA-256 hash of the content.\"\"\"\n        content = content.encode('utf-8')\n        return hashlib.sha256(content).hexdigest()\n\n# FIXME how is this not equivelant to the methods below\n#   def build_seeds(self, height):\n#       \"\"\"Deterministically build seeds.\"\"\"\n#       seed = self.sha256(self.address)\n#       seeds = []\n#       for i in range(height):\n#           seed = self.sha256(seed)\n#           seeds.append(seed)\n#       return seeds if seeds else [seed]\n\n#   def build_seed(self, height):\n#       \"\"\"Deterministically build a seed.\"\"\"\n#       return self.build_seeds(height).pop()\n\n    def build_seed(self, height):\n        \"\"\"Deterministically build a seed.\"\"\"\n        seed = self.sha256(self.address)\n        for i in range(height):\n            seed = self.sha256(seed)\n        return seed\n\n    def build_seeds(self, height):\n        return list(map(self.build_seed, range(height)))\n\n    def generate_shard(self, seed, store_path, cleanup=False, rebuild=False):\n        \"\"\"Save a shard, and return its SHA-256 hash.\"\"\"\n\n        # save the shard\n        path = os.path.join(store_path, seed)\n        if not os.path.isfile(path) or rebuild:\n            RandomIO.RandomIO(seed).genfile(self.shard_size, path)\n\n        with open(path, 'rb') as f:\n            file_hash = hashlib.sha256(f.read()).hexdigest()\n            if cleanup:\n                os.remove(path)\n            return file_hash\n\n    def filter_to_resume_point(self, store_path, enum_seeds):\n        class HackedCompareObject(str):\n            def __lt__(self, seed):\n                path = os.path.join(store_path, seed)\n                return not os.path.exists(path)\n        seeds = [seed for num, seed in enum_seeds]\n        index = bisect.bisect_left(seeds, HackedCompareObject())\n        if self.debug:\n            print(\"Resuming from height {0}\".format(index + 1))\n        return enum_seeds[index:]  # rebuild last in case its corrupt\n\n    def build(self, store_path, cleanup=False, rebuild=False):\n        \"\"\"Fill the farmer with data up to their max.\n        Returns: { seed : hash, ... }\n        \"\"\"\n        generated = {}\n\n        enum_seeds = list(enumerate(self.build_seeds(self.target_height)))\n        if not rebuild:\n            enum_seeds = self.filter_to_resume_point(store_path, enum_seeds)\n\n        for shard_num, seed in enum_seeds:\n\n            file_hash = self.generate_shard(seed, store_path, cleanup=cleanup,\n                                            rebuild=rebuild)\n            generated[seed] = file_hash\n            if self.debug:\n                print(\"Saving seed {0} with SHA-256 hash {1}.\".format(\n                    seed, file_hash\n                ))\n\n            if self.on_generate_shard:\n                self.on_generate_shard(shard_num + 1, seed, file_hash)\n\n        return generated\n\n    def clean(self, store_path):\n        \"\"\"Delete shards from path.\"\"\"\n        seeds = self.build_seeds(self.target_height)\n        for shard_num, seed in enumerate(seeds):\n            path = os.path.join(store_path, seed)\n            if os.path.exists(path):\n                os.remove(path)\n\n    def audit(self, seed, store_path, height):\n        \"\"\"Do an audit over the data.\"\"\"\n        audit_results = []\n        seeds = self.build_seeds(height)\n        for shard_num, seed_hash in enumerate(seeds):\n            seed_path = os.path.join(store_path, seed_hash)\n            digest = partialhash.sample(seed_path, 1024, sample_count=3,\n                                        seed=seed)\n            audit_results.append(binascii.hexlify(digest))\n        return audit_results\n\n    def full_audit(self, seed, store_path, height):\n        \"\"\"Compute one hash from audit.\"\"\"\n        hash_result = \"\"\n\n        start_time = datetime.utcnow()\n        audit_results = self.audit(seed, store_path, height)\n        for audit in audit_results:\n            hash_result += str(audit.decode(\"utf-8\"))\n        hash_result = self.sha256(hash_result)\n\n        if self.debug:\n            final_time = (datetime.utcnow() - start_time).seconds\n            msg = \"Seed: {0} with Audit Result: {1} in {2} seconds.\"\n            print(msg.format(str(seed), hash_result, final_time))\n\n        return hash_result\n\n    def checkup(self, store_path):\n        \"\"\"Make sure the shards exist.\"\"\"\n        seeds = self.build_seeds(self.target_height)\n        for shard_num, seed in enumerate(seeds):\n            path = os.path.join(store_path, seed)\n            if not os.path.exists(path):\n                return False\n        return True\n","sub_path":"dataserv_client/builder.py","file_name":"builder.py","file_ext":"py","file_size_in_byte":5054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"399679480","text":"\"\"\"\nthis module works for compress files\nthis module will be consumed to other module.\n\"\"\"\nimport os\nimport re\nfrom zipfile import ZipFile\n\ndef compressFile(name, path, removeOriginal=False):\n\t\"\"\"\n\tdef collect\n\tcollect files to destination folder\n\t\"\"\"\n\ttry:\n\t\tfileList = os.listdir(path)\n\texcept NotADirectoryError:\n\t\treturn 0\n\n\tfileName = \"files/zip/%s.zip\"%(name)\n\tz = ZipFile(fileName, \"w\")\n\n\tfor i in fileList:\n\t\tif \".zip\" not in i:\n\t\t\tz.write(path+\"/\"+i, i)\n\tprint(\"file write done\")\n\tz.close()\n\tprint(\"closed\")\n\n\tif removeOriginal:\n\t\tfor i in fileList:\n\t\t\tos.remove(i)\n\n\treturn fileName\n\ndef collect():\n\t\"\"\"\n\tdef collect\n\tcollect files to destination folder\n\t\"\"\"\n\tcomics = []\n\texp = re.compile(r\"(.+?) ?([0-9]+(?:화|권))\")\n\n\tpath = \"files/zip/\"\n\tfiles = os.listdir(path)\n\tfor i in files:\n\t\ttry:\n\t\t\tmatches = exp.match(i).groups()\n\t\t\tfolder = path+matches[0]\n\n\t\t\tif matches[0] not in comics:\n\t\t\t\tcomics.append(matches[0])\n\t\t\t\tos.mkdir(folder)\n\n\t\t\tos.rename(path+i, folder+\"/\"+i)\n\t\texcept AttributeError:\n\t\t\tprint(i)\n\n\tprint(comics)\nif __name__ == \"__main__\":\n\tcollect()\n","sub_path":"src/compress.py","file_name":"compress.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"144688847","text":"from flask import request, jsonify, abort, url_for, redirect\nfrom uuid import uuid4\nfrom celery.result import AsyncResult\n\nfrom ..api import local_api, require_logged_in, make_manifest_path\nfrom ..permission import TokenSet, has_install_permission\nfrom ..app import app, redis_connection, celery\nfrom ..util import no_cache\n\nfrom ..tasks.app import install_app\n\ndef _update_app_task_key(appid):\n    return \"update-{}\".format(appid)\n\n@app.route('/me/applications')\ndef my_applications():\n    '''Returns a JSON list of all applications accessible to this user\n    (currently, all installed apps).\n    '''\n    with local_api() as api:\n        return jsonify([application.to_dict() for application in api.get_applications()])\n\n@app.route('/me/applications/<appid>/status',\n           methods=['GET'])\n@no_cache\ndef get_application_status(appid):\n    r = jsonify(_get_application_status(appid))\n    r.headers[\"Cache-Control\"] = \"no-cache, no-store, must-revalidate\"\n    r.headers[\"Pragma\"] = \"no-cache\"\n    r.headers[\"Expires\"] = \"0\"\n    r.headers['Cache-Control'] = 'public, max-age=0'\n    return r\n\ndef _get_application_status(appid, task_id=None):\n    with local_api() as api, redis_connection() as redis:\n        app_info = api.get_application_status(appid)\n\n    if task_id is None:\n        # Look up task ids for installation or update\n        task_id = redis.get(_update_app_task_key(appid))\n\n    if app_info is None:\n        if task_id is None:\n            return 'Not Found', 404\n        else:\n            app_info = { 'state': 'not-installed',\n                         'domain': appid }\n\n    if task_id is not None:\n        # Look up the status by getting the information from celery\n        task = AsyncResult(task_id, app=install_app)\n\n        installing = 'installing' if app_info is None else 'updating'\n        state_map = { 'PENDING': installing,\n                      'STARTED': installing,\n                      'MANIFEST': installing,\n\n                      'RETRY': 'error',\n                      'FAILURE': 'error',\n                      'SUCCESS': app_info['state'] }\n\n        try:\n            app_info['state'] = state_map.get(task.state, 'failure')\n            task_info = task.info\n        except TypeError:\n            app_info['state'] = 'error'\n            task_info = {}\n        app_info['status_url'] = url_for('get_application_status', appid=appid)\n\n        if isinstance(task_info, Exception):\n            app_info['progress'] = { 'total': 0, 'complete': 0, 'message': str(task_info) }\n        elif task_info is not None:\n            app_info['progress'] = { 'total': task_info.get('total', 0),\n                                     'complete': task_info.get('complete', 0),\n                                     'message': task_info.get('message', 'Installing...') }\n        else:\n            if app_info['state'] == 'installing':\n                message = \"Installing...\"\n            elif app_info['state'] == 'updating':\n                message = \"Updating...\"\n            elif app_info['state'] == 'error':\n                message = \"Error\"\n            else:\n                message = \"Waiting to start\"\n            app_info['progress'] = { 'total': 0, 'complete': 0, 'message': message }\n\n    return app_info\n\n@app.route('/me/applications/<appid>/version', methods=['GET'])\n@no_cache\ndef application_version(appid=None):\n    '''Returns the major, minor, and revision number of an application'''\n    with local_api() as api:\n        info = api.get_application_info(appid)\n        if info is None:\n            abort(404)\n\n        mf = info['manifest']\n\n        if mf.version_info is None:\n            return jsonify({'major': 0, 'minor': 0, 'revision': 0})\n        else:\n            major, minor, revision = mf.version_info\n            return jsonify({ 'major': major,\n                             'minor': minor,\n                             'revision': revision })\n\n@app.route('/me/applications/<appid>/manifest/current', methods=['GET'])\n@no_cache\ndef application_manifset(appid=None):\n    with local_api() as api:\n        info = api.get_application_info(appid)\n        if info is None:\n            abort(404)\n\n        mf = info['manifest']\n        return jsonify(mf.to_dict())\n\n@app.route('/me/applications/<appid>/manifest/latest', methods=['GET'])\n@no_cache\ndef application_latest_manifest(appid=None):\n    '''Redirects to the latest version of the application manifest'''\n    with local_api() as api:\n        info = api.get_application_info(appid)\n        if info is None:\n            abort(404)\n\n        mf = info['manifest']\n\n        return redirect(make_manifest_path(appid), code=302)\n\n@app.route('/me/applications/<appid>',\n           methods=['GET', 'PUT'])\n@no_cache\ndef application(appid=None):\n    '''Returns information about the given application, or requests that a\n    new installation be started\n    '''\n\n    if request.method == 'GET':\n        with local_api() as api:\n            app = api.get_application_status(appid)\n\n            if app is None:\n                return 'Not Found', 404\n            else:\n                return jsonify(app)\n\n    elif request.method == 'PUT':\n\n        # First determine whether the user has the ability to install\n        # applications\n\n        @require_logged_in\n        def handle(api=None, user=None, container=None):\n            # Lookup user permissions in our global database\n            tokens = TokenSet(api, container.get('tokens', []))\n            all_perms = set(p for p in tokens.all_permissions)\n\n            # Allow any app which has been explicitly delegated the\n            # install permission, any site verified container, or any\n            # logged in user to update the application.\n            #\n            # TODO we may want to globally configure whether a user\n            # can upgrade applications.\n            print(\"Container info\", container)\n            if has_install_permission(all_perms) or \\\n               any(token.site is not None for token in tokens) or \\\n               container.get('logged_in', False):\n\n                # Check redis to see if there is a celery task in progress for this application id\n                with redis_connection() as redis:\n                    cur_task = redis.get(_update_app_task_key(appid))\n                    if cur_task is not None:\n                        rsp = _get_application_status(appid, task_id=cur_task)\n                        if rsp['state'] != 'installing':\n                            cur_task = None\n\n                    if cur_task is None:\n                        task_id = str(uuid4())\n\n                        redis.set(_update_app_task_key(appid), task_id)\n                        install_app_task = install_app.apply_async(args=[appid], task_id=task_id)\n                    else:\n                        task_id = cur_task\n\n                rsp = jsonify(_get_application_status(appid, task_id=task_id))\n                if rsp.status_code == 200:\n                    rsp.status_code = 202\n\n                return rsp\n\n            else:\n                return 'Unauthorized', 401\n\n        return handle()\n\n    else:\n        return 'Bad Method', 405\n\n","sub_path":"intrustd/admin/routes/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"154658653","text":"# make a list of flags (0 or 1) corresponding to bad DR12 stars\n# as in, stars we wouldn't want to use in the training set\n\nimport numpy as np\nimport pyfits\n\nfilein = \"allStar-v603.fits\"\nhdulist = pyfits.open(filein)\ndatain = hdulist[1].data\naspcapflag = datain[\"ASPCAPFLAG\"]\naspcap_id= datain['ASPCAP_ID']\n\nrot = []\nfor each in aspcapflag:\n    rot.append(each & 10**10)\n\napid = []\nfor each in apstarid:\n    apid.append(each.split('.')[-1])\n\nrot = np.array(rot)\napid = np.array(apid)\n\napogee = np.loadtxt(\"example_DR12/apogee_sorted_by_ra.txt\", dtype=str)\n\n# for each element of apogee, find it in apid\n\ninds = []\n\nfor element in apogee:\n    element = element.split('603-')[1]\n    element = element.split('.fits')[0]\n    ind = np.where(apid==element)[0][0]\n    inds.append(ind)\n\ninds = np.array(inds)\n\n# now find those values of rot\n\nflags = rot[inds]\nfileo = open(\"star_flags.txt\", \"w\")\n\nfor flag in flags:\n    fileo.write(str(flag)+'\\n')\n\nfileo.close()\n","sub_path":"data/apogee_DR12/labels/make_flag_list.py","file_name":"make_flag_list.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"301760694","text":"import sys, json, DescriptorClasses\n#import Reputations as rep\nfrom PySide2 import QtGui, QtCore, QtWidgets\n\nclass AddCountry(QtWidgets.QWidget):\n\n    def __init__(self, parent=None):\n        super().__init__()\n\n        self.countryNameField = QtWidgets.QLineEdit()\n        self.countryCreateButton = QtWidgets.QPushButton(\"Create Country\")\n\n        #Set the layout var, add the widgets, apply the layout\n        self.layout = QtWidgets.QHBoxLayout()\n        self.layout.addWidget(QtWidgets.QLabel(\"Country Name\"))\n        self.layout.addWidget(self.countryNameField)\n        self.layout.addWidget(self.countryCreateButton)\n        self.layout.addSpacing(200)\n        self.setLayout(self.layout)\n\nclass AddFeature(QtWidgets.QWidget):\n\n    def __init__(self, parent=None):\n        super().__init__()\n\n        self.featureChoices = QtWidgets.QComboBox()\n        self.featureNameField = QtWidgets.QLineEdit()\n        self.featureCreateButton = QtWidgets.QPushButton(\"Create Feature\")\n\n        self.featureChoices.addItem(\"Landscape\", \"ls\")\n\n        self.layout = QtWidgets.QHBoxLayout()\n        # self.layout.addWidget(QtWidgets.QLabel(\"Feature Type\"))\n        self.layout.addWidget(self.featureChoices)\n        self.layout.addWidget(QtWidgets.QLabel(\"Feature Name\"))\n        self.layout.addWidget(self.featureNameField)\n        self.layout.addWidget(self.featureCreateButton)\n        self.layout.addSpacing(200)\n        self.setLayout(self.layout)\n\nclass CountryNotebook(QtWidgets.QWidget):\n\n    def getID(self):\n        self.idPlace += 1\n        return self.idPlace -1\n\n    def currCountrySelection(self):\n        return self.notebook.currentWidget()\n\n    def isUniq(self, text, listToSrch, typ = None):\n        if typ == \"ls\":\n            for ea in listToSrch:\n                if ea.Type == \"ls\":\n                    if ea.uName == text:\n                        return False\n            else:\n                return True\n        else:\n            for ea in listToSrch:\n                if text == ea.uName:\n                    return False\n            return True\n\n    def choicesReset(self):\n        for i in range(999):\n            try:\n                self.featureCreateGroup.featureChoices.removeItem(1)\n            except:\n                return\n\n\n    def tabBarDblClk(self, index):\n        currCountry = self.currCountrySelection()\n        currItem = currCountry.tree.currentItem()\n\n        for ea in self.openDescWindows:\n            if currCountry.Type == ea.Type:\n                if currCountry.uName == ea.uName:\n                    return\n\n        self.openDescWindows.append(DescWindow(currItem, currCountry, index))\n\n\n    def treeItemDblClk(self):\n        currCountry = self.currCountrySelection()\n        currItem = currCountry.tree.currentItem()\n\n        for ea in self.openDescWindows:\n            if currItem.id == ea.id:\n                return\n\n        a = DescWindow(currItem, currCountry)\n        self.openDescWindows.append(a)\n\n\n        if currCountry.tree.isItemExpanded(currItem):\n            currCountry.tree.setItemExpanded(currItem, False)\n        else:\n            try:\n                currCountry.tree.setItemExpanded(currItem, True)\n            except:\n                pass\n\n\n    def treeSelectionChanged(self):\n        try:\n            currCountry = self.currCountrySelection()\n            currItem = currCountry.tree.currentItem()\n            self.choicesReset()\n            for ea in currItem.possibleChildren:\n                self.featureCreateGroup.featureChoices.addItem(ea[0], ea[1])\n        except:\n            self.choicesReset()\n\n\n    def changeCountrySelection(self):\n        try:\n            oldSelection = self.currentTab\n            self.currentTab = self.currCountrySelection()\n            try:\n                oldSelection.tree.itemSelectionChanged.disconnect(self.treeSelectionChanged)\n                oldSelection.tree.itemDoubleClicked.disconnect(self.treeItemDblClk)\n                self.notebook.tabBarDoubleClicked.disconnect(self.tabBarDblClk)\n            except:\n                pass\n            self.currentTab.tree.itemSelectionChanged.connect(self.treeSelectionChanged)\n            self.currentTab.tree.itemDoubleClicked.connect(self.treeItemDblClk)\n            self.notebook.tabBarDoubleClicked.connect(self.tabBarDblClk)\n        except:\n            pass\n\n\n    def saveDetInfo(self):\n        currCountry = self.currCountrySelection()\n        if (currCountry is None):\n            return\n        currItem = currCountry.tree.currentItem() #Get current Widget Selected\n\n    def createTreeWidgetFunc(self, choiceStr, text, currCountry, parent, climateInfo = [], itemID = None):\n        if isinstance(parent, int):\n            for ea in currCountry.childrenHelper:\n                if ea == parent:\n                    i = currCountry.childrenHelper.index(ea)\n                    parentItem = currCountry.children[i]\n        elif parent is not None:\n            parentItem = parent\n\n        if currCountry is None:\n            return #If there's no country break out! We can't make a feature with no home!\n        if text.replace(\" \", \"\") is \"\":\n            return #The only thing in the name is spaces, NO FEATURE FOR YOU\n\n        if choiceStr == \"ls\": #Make a landscape\n            if not self.isUniq(text, currCountry.children, typ=\"ls\"):\n                return\n            c = Landscape(text)\n            if climateInfo:\n                c.climateInfo = climateInfo\n            currCountry.tree.addTopLevelItem(c)\n\n        elif not self.isUniq(text, parentItem.children):\n            return\n        if choiceStr == \"np\":\n                c = NotablePlace(text)\n        elif choiceStr == \"t\":\n            if self.isUniq(text, parentItem.children):\n                c = Town(text)\n        elif choiceStr == \"dw\":\n            if self.isUniq(text, parentItem.children):\n                c = Dwelling(text)\n        elif choiceStr == \"p\":\n            if self.isUniq(text, parentItem.children):\n                c = Person(text)\n        elif choiceStr == \"m\":\n            if self.isUniq(text, parentItem.children):\n                c = Monster(text)\n        elif choiceStr == \"i\":\n            if self.isUniq(text, parentItem.children):\n                c = Item(text)\n\n        if itemID is None:\n            c.id = self.getID()\n        else:\n            c.id = itemID\n\n        if choiceStr != \"ls\":\n            parentItem.addChild(c)\n            parentItem.childrenHelper.append(c.id)\n            parentItem.children.append(c)\n        currCountry.childrenHelper.append(c.id)\n        currCountry.children.append(c)\n\n    def createTreeWidget(self):\n        choiceStr = self.featureCreateGroup.featureChoices.currentData() #Get's the string attached to the current choice eg. ls/np\n        text = self.featureCreateGroup.featureNameField.text() #Get's the text for the features name\n        currCountry = self.currCountrySelection() #Get's the currently selected country so that we make the widget in the right country\n        self.createTreeWidgetFunc(choiceStr=choiceStr, text=text, currCountry=currCountry, parent=currCountry.tree.currentItem())\n\n    def deleteTreeWidget(self):\n        try:\n            currCountry = self.currCountrySelection()\n            currItem = currCountry.tree.currentItem() #Get current Widget Selected\n            root = currCountry.tree.invisibleRootItem()\n\n            if isinstance(currItem, type(Landscape(\"\"))):\n                a = currCountry.childrenHelper.index(currItem.id)\n                del currCountry.childrenHelper[a]\n                del currCountry.children[a]\n            else:\n                a = currItem.parent().childrenHelper.index(currItem.id)\n                del currItem.parent().childrenHelper[a]\n                del currItem.parent().children[a]\n\n            (currItem.parent() or root).removeChild(currItem)\n            self.treeSelectionChanged()\n        except:\n            pass\n\n    def createTabFunc(self, text):\n        a = CountryTab(text, self)\n        self.currCountry = self.currCountrySelection()\n        self.notebook.addTab(a, text)\n        self.countries.append(a)\n        return a\n\n    def createTab(self):\n        text = self.countryCreateGroup.countryNameField.text()\n        if text.replace(\" \", \"\") is \"\":\n            return\n        if self.isUniq(text, self.countries):\n            self.createTabFunc(text)\n\n    def deleteTab(self):\n        currCountry = self.currCountrySelection()\n        if currCountry is None:\n            return\n        currCountryIndex = self.countries.index(currCountry)\n        self.notebook.removeTab(currCountryIndex)\n        del self.countries[currCountryIndex]\n        currCountry = self.currCountrySelection()\n        self.treeSelectionChanged()\n\n    def __init__(self, parent=None):\n        super().__init__()\n\n        self.countries = []\n        self.currentTab = \"\" #Placeholder for future tab classes.\n        self.openDescWindows = []\n        self.idPlace = 0\n        self.notebook = QtWidgets.QTabWidget()\n        self.countryCreateGroup = AddCountry()\n        self.featureCreateGroup = AddFeature()\n\n\n        self.layout = QtWidgets.QVBoxLayout()\n\n        self.layout.addWidget(self.countryCreateGroup)\n        self.layout.addWidget(self.featureCreateGroup)\n        self.layout.addWidget(self.notebook)\n\n        self.setLayout(self.layout)\n\n        #self.Sender(in this case the button WITHIN another class).SIGNAL.connect(func)\n        self.countryCreateGroup.countryCreateButton.released.connect(self.createTab)\n        self.featureCreateGroup.featureCreateButton.released.connect(self.createTreeWidget)\n        self.notebook.currentChanged.connect(self.changeCountrySelection)\n\nclass CountryTab(QtWidgets.QWidget):\n\n\n    def saveDetInfo(self):\n        currItem = self.tree.currentItem()\n        # currItem.detInfo = self.detInfoField.toPlainText()\n\n    def __init__(self, name, countryNB):\n        super().__init__()\n\n        self.uName = name\n        self.children = []\n        self.childrenHelper = []\n        self.Type = \"c\"\n        self.cNB = countryNB\n\n        self.tree = QtWidgets.QTreeWidget()\n        self.tree.setColumnCount(2)\n        self.tree.setHeaderLabels([\"Name\",\"Type\"])\n        # self.detInfoField = QtWidgets.QPlainTextEdit()\n\n        self.layout = QtWidgets.QHBoxLayout()\n        self.layout.addWidget(self.tree)\n        # self.layout.addWidget(self.detInfoField)\n        # self.detInfoField.setReadOnly(True)\n        self.setLayout(self.layout)\n\nclass treeObject(QtWidgets.QTreeWidgetItem):\n    def __init__(self, Parent=None):\n        super().__init__()\n\n        self.uName = \"\"\n        self.id = ''\n        self.detInfo = \"\"\n        self.children = []\n        self.Type = \"\"\n        self.childrenHelper = []\n        self.possibleChildren = []\n        self.parentCountry = \"\"\n\nclass Landscape(treeObject):\n    def __init__(self, name, Parent=None):\n        super().__init__()\n\n        self.possibleChildren = [[\"Notable Place\", \"np\"], [\"Town\", \"t\"]]\n        self.uName = name\n        self.Type = \"ls\"\n        self.climateInfo = []\n\n        self.setText(0, name)\n        self.setText(1, \"Landscape\")\n\nclass NotablePlace(treeObject):\n    def __init__(self, name, Parent=None):\n        super().__init__()\n\n        self.possibleChildren = [[\"Dwelling\", \"dw\"],[\"Person\", \"p\"], [\"Monster\", \"m\"], [\"Item\", \"i\"]]\n        self.uName = name\n        self.Type = \"np\"\n        self.setText(0, name)\n        self.setText(1, \"Notable Place\")\n\nclass Town(treeObject):\n    def __init__(self, name, Parent=None):\n        super().__init__()\n\n        self.possibleChildren = [[\"Dwelling\", \"dw\"], [\"Notable Place\", \"np\"]]\n        self.uName = name\n        self.Type = \"t\"\n        self.setText(0, name)\n        self.setText(1, \"Town\")\n\nclass Dwelling(treeObject):\n    def __init__(self, name, Parent=None):\n        super().__init__()\n\n        self.possibleChildren = [[\"Person\", \"p\"], [\"Monster\", \"m\"], [\"Item\", \"i\"]]\n        self.uName = name\n        self.Type = \"dw\"\n        self.setText(0, name)\n        self.setText(1, \"Dwelling\")\n\nclass Person(treeObject):\n    def __init__(self, name, Parent=None):\n        super().__init__()\n\n        self.uName = name\n        self.Type = \"p\"\n        self.setText(0, name)\n        self.setText(1, \"Person\")\n\nclass Monster(treeObject):\n    def __init__(self, name, Parent=None):\n        super().__init__()\n\n        self.uName = name\n        self.Type = \"m\"\n        self.setText(0, name)\n        self.setText(1, \"Monster\")\n\nclass Item(treeObject):\n    def __init__(self, name, Parent=None):\n        super().__init__()\n\n        self.uName = name\n        self.Type = \"i\"\n        self.setText(0, name)\n        self.setText(1, \"Item\")\n\n\nclass MyWidget(QtWidgets.QWidget):\n\n    def saveCountry(self, country):\n        ret = []\n        ret.append(country.uName)\n        ret.append(\"c\")\n        ret.append(len(country.children))\n        return ret\n    def saveLandscape(self, landscape):\n        ret = []\n        ret.append(landscape.uName) #0\n        ret.append(\"ls\") #1\n        ret.append(len(landscape.children)) #2\n        ret.append(landscape.climateInfo) #3\n        ret.append(landscape.id) #4\n        return ret\n    def saveNotablePlace(self, np):\n        ret = []\n        ret.append(np.uName) #0\n        ret.append(\"np\") #1\n        ret.append(np.id)#2\n        ret.append(np.parent().id)#3\n        return ret\n    def saveTown(self, town):\n        ret = []\n        ret.append(town.uName) #0\n        ret.append(\"t\") #1\n        ret.append(town.id) #2\n        ret.append(town.parent().id) #3\n        return ret\n    def saveDwelling(self, dwelling):\n        ret = []\n        ret.append(dwelling.uName) #0\n        ret.append(\"dw\") #1\n        ret.append(dwelling.id) #2\n        ret.append(dwelling.parent().id) #3\n        return ret\n    def savePerson(self, person):\n        ret = []\n        ret.append(person.uName) #0\n        ret.append(\"p\") #1\n        ret.append(person.id) #2\n        ret.append(person.parent().id) #3\n        return ret\n    def saveMonster(self, monster):\n        ret = []\n        ret.append(monster.uName) #0\n        ret.append(\"m\") #1\n        ret.append(monster.id) #2\n        ret.append(monster.parent().id) #3\n        return ret\n    def saveItem(self, item):\n        ret = []\n        ret.append(item.uName) #0\n        ret.append(\"i\") #1\n        ret.append(item.id) #2\n        ret.append(item.parent().id) #3\n        return ret\n\n    def parentSaveFunc(self):\n        print(\"Trying to save\")\n        countries = self.notebook.countries\n        idSpot = self.notebook.idPlace\n        if len(countries) == 0:\n            print(\"Nothing to save!\")\n            return\n# We will be parsing through each country to find landscapes.\n        self.parentSaveInfo = {}\n        for eaCou in countries:\n            self.parentSaveInfo[idSpot] = []\n            self.parentSaveInfo[idSpot].append(self.saveCountry(eaCou))\n\n    # And now each landscape to find towns/notable places\n            for eaLand in eaCou.children:\n                if isinstance(eaLand, type(Landscape(\"\"))):\n                    self.parentSaveInfo[idSpot].append(self.saveLandscape(eaLand))\n                    for eaTNP in eaLand.children:\n                        if isinstance(eaTNP, type(NotablePlace(\"\"))):\n                            self.parentSaveInfo[idSpot].append(self.saveNotablePlace(eaTNP))\n\n                            for eaDPMI in eaTNP.children:\n                                    if isinstance(eaDPMI, type(Dwelling(\"\"))):\n                                        self.parentSaveInfo[idSpot].append(self.saveDwelling(eaDPMI))\n                                        for eaPMI in eaDPMI.children:\n                                            if isinstance(eaPMI, type(Person(\"\"))):\n                                                self.parentSaveInfo[idSpot].append(self.savePerson(eaPMI))\n                                            elif isinstance(eaPMI, type(Monster(\"\"))):\n                                                self.parentSaveInfo[idSpot].append(self.saveMonster(eaPMI))\n                                            elif isinstance(eaPMI, type(Item(\"\"))):\n                                                self.parentSaveInfo[idSpot].append(self.saveItem(eaPMI))\n\n                                    elif isinstance(eaDPMI, type(Person(\"\"))):\n                                        self.parentSaveInfo[idSpot].append(self.savePerson(eaDPMI))\n                                    elif isinstance(eaDPMI, type(Monster(\"\"))):\n                                        self.parentSaveInfo[idSpot].append(self.saveMonster(eaDPMI))\n                                    elif isinstance(eaDPMI, type(Item(\"\"))):\n                                        self.parentSaveInfo[idSpot].append(self.saveItem(eaDPMI))\n\n    # For the TOWNS in LANDSCAPES\n                        elif isinstance(eaTNP, type(Town(\"\"))):\n                            self.parentSaveInfo[idSpot].append(self.saveTown(eaTNP))\n\n                            for eaDNP in eaTNP.children:\n    #For the DWELLINGS in TOWNS\n                                if isinstance(eaDNP, type(Dwelling(\"\"))):\n                                    self.parentSaveInfo[idSpot].append(self.saveDwelling(eaDNP))\n\n                                    for eaPMI in eaDNP.children:\n                                        if isinstance(eaPMI, type(Person(\"\"))):\n                                            self.parentSaveInfo[idSpot].append(self.savePerson(eaPMI))\n                                        elif isinstance(eaPMI, type(Monster(\"\"))):\n                                            self.parentSaveInfo[idSpot].append(self.saveMonster(eaPMI))\n                                        elif isinstance(eaPMI, type(Item(\"\"))):\n                                            self.parentSaveInfo[idSpot].append(self.saveItem(eaPMI))\n\n    #For the NOTABLE PLACES in TOWNS\n                                elif isinstance(eaDNP, type(NotablePlace(\"\"))):\n                                            self.parentSaveInfo[idSpot].append(self.saveNotablePlace(eaDNP))\n\n                                            for eaPMI in eaDNP.children:\n                                                if isinstance(eaPMI, type(Person(\"\"))):\n                                                    self.parentSaveInfo[idSpot].append(self.savePerson(eaPMI))\n                                                elif isinstance(eaPMI, type(Monster(\"\"))):\n                                                    self.parentSaveInfo[idSpot].append(self.saveMonster(eaPMI))\n                                                elif isinstance(eaPMI, type(Item(\"\"))):\n                                                    self.parentSaveInfo[idSpot].append(self.saveItem(eaPMI))\n        print(self.parentSaveInfo)\n        with open(\"data_file1.json\", \"w\") as write_file:\n            json.dump(self.parentSaveInfo, write_file, indent=4)\n            print(\"Saved!\")\n\n    def loadCountry(self, text):\n        a = self.notebook.createTabFunc(text)\n        return a\n    def loadLandscape(self, ls, country):\n        self.notebook.createTreeWidgetFunc(ls[1], ls[0], country, parent = None, climateInfo = ls[3], itemID = ls[4])\n    def loadNotablePlace(self, np, country):\n        self.notebook.createTreeWidgetFunc(np[1], np[0], country, parent = np[3], itemID = np[2])\n    def loadTown(self, t, country):\n        self.notebook.createTreeWidgetFunc(t[1], t[0], country, parent = t[3], itemID = t[2])\n    def loadDwelling(self, dw, country):\n        self.notebook.createTreeWidgetFunc(dw[1], dw[0], country, parent = dw[3], itemID = dw[2])\n    def loadPerson(self, p, country):\n        self.notebook.createTreeWidgetFunc(p[1], p[0], country, parent = p[3], itemID = p[2])\n    def loadMonster(self, m, country):\n        self.notebook.createTreeWidgetFunc(m[1], m[0], country, parent = m[3], itemID = m[2])\n    def loadItem(self, i, country):\n        self.notebook.createTreeWidgetFunc(i[1], i[0], country, parent = i[3], itemID = i[2])\n\n    def parentLoadFunc(self):\n        with open(\"data_file1.json\", \"r\") as read_file:\n            data = json.load(read_file)\n        if len(self.notebook.countries) > 0:\n            return\n        for key, value in data.items():\n            if int(key) > int(self.idSpot):\n                self.idSpot = int(key)\n\n            for item in value:\n                if item[1] == \"c\":\n                    country = self.loadCountry(item[0])\n                elif item[1] == \"ls\":\n                    self.loadLandscape(item, country)\n                elif item[1] == \"np\":\n                    self.loadNotablePlace(item, country)\n                elif item[1] ==\"t\":\n                    self.loadTown(item, country)\n                elif item[1] ==\"dw\":\n                    self.loadDwelling(item, country)\n                elif item[1] ==\"p\":\n                    self.loadPerson(item, country)\n                elif item[1] ==\"m\":\n                    self.loadMonster(item, country)\n                elif item[1] ==\"i\":\n                    self.loadItem(item, country)\n        self.notebook.idPlace = int(self.idSpot)\n\n    def __init__(self, parent=None):\n        super().__init__()\n\n        self.idSpot = 0\n        self.notebook = CountryNotebook()\n        self.middleLayout = QtWidgets.QVBoxLayout()\n        self.middleLayout.addWidget(self.notebook)\n        self.parentSaveInfo = {}\n\n        self.parentGridLayout = QtWidgets.QGridLayout()\n        self.parentGridLayout.addLayout(self.middleLayout, 0,0)\n        self.setLayout(self.parentGridLayout)\n\nclass MainWindow(QtWidgets.QMainWindow):\n\n    def closeEvent(self, event):\n        a = True\n        b = len(self.myWidgetVar.notebook.openDescWindows)\n        c = 0\n        if b != c:\n            while a is True:\n                for window in self.myWidgetVar.notebook.openDescWindows:\n                    window.close()\n                c += 1\n                if c == b:\n                    a = False\n        event.accept()\n\n    def __init__(self, parent=None):\n        super().__init__()\n        self.myWidgetVar = MyWidget()\n\n        self.setGeometry(300, 300, 500, 800)\n        self.setMaximumSize(500, 800) #x, y\n\n        self.parentMenu = self.menuBar()\n        self.fileMenu = QtWidgets.QMenu(\"File\")\n        self.fileMenuAction = self.fileMenu.addAction(\"Save (e)\", self.myWidgetVar.parentSaveFunc)\n        self.fileMenuAction = self.fileMenu.addAction(\"Load (e)\", self.myWidgetVar.parentLoadFunc)\n\n        self.deleteMenu = QtWidgets.QMenu(\"Delete\")\n        self.deleteCountryAction = self.deleteMenu.addAction(\"Delete Country\", self.myWidgetVar.notebook.deleteTab)\n        self.deleteFeatureAction = self.deleteMenu.addAction(\"Delete Feature\", self.myWidgetVar.notebook.deleteTreeWidget)\n\n        self.parentMenu.addMenu(self.fileMenu)\n        self.parentMenu.addMenu(self.deleteMenu)\n\n        self.setCentralWidget(self.myWidgetVar)\n\nclass DescWindow(QtWidgets.QMainWindow):\n\n\n    def climateSet(self):\n        text = self.w.climateChoice.climateAdd.climateAddField.text()\n        if text.replace(\" \", \"\") is \"\":\n            return\n        if self.isUniq(text, self.clas.climateInfo):\n            self.w.climateChoice.listChoices.addItem(text)\n            self.clas.climateInfo.append(text)\n            print(self.clas.climateInfo)\n\n    def deleteClimate(self, item):\n        row = self.w.climateChoice.listChoices.currentRow()\n        self.w.climateChoice.listChoices.takeItem(row)\n        del self.clas.climateInfo[row]\n\n    def closeEvent(self, event):\n        w = False\n        for openWindow in self.country.cNB.openDescWindows:\n            if openWindow == self:\n                a = self.country.cNB.openDescWindows.index(openWindow)\n                del self.country.cNB.openDescWindows[a]\n                w = True\n            else:\n                pass\n        if w == True:\n            event.accept()\n        else:\n            event.ignore()\n\n    def isUniq(self, text, listToSrch):\n        for ea in listToSrch:\n            try:\n                if text == ea.uName:\n                    return False\n            except:\n                if text == ea:\n                    return False\n        return True\n\n\n    def uNameChange(self):\n        text = self.w.nameChanger.nameChangeEdit.text()\n        if text.replace(\" \", \"\") is \"\":\n            return\n        if self.clas.Type == \"c\":\n            print(\"It's a Country!\")\n            if self.isUniq(text, self.country.cNB.countries):\n                self.country.cNB.notebook.setTabText(self.CI, text)\n                self.setWindowTitle(\"Country - \"+self.clas.uName)\n                self.clas.uName = text\n        elif self.clas.Type == \"ls\":\n            if self.isUniq(text, self.country.children):\n                self.clas.setText(0, text)\n                self.setWindowTitle(self.clas.Type+\" -> \"+self.country.uName+text)\n                self.clas.uName = text\n        else:\n            if self.isUniq(text, self.currItem.parent().children):\n                self.clas.setText(0, text)\n                self.setWindowTitle(self.clas.Type+\" -> \"+self.country.uName+text)\n                self.clas.uName = text\n\n    def __init__(self, clas, country, countryIndex = -99):\n        super().__init__()\n\n\n        self.setGeometry(350,350, 370,600)\n        self.setMaximumSize(370,600)\n        self.clas = clas\n        self.country = country\n        self.CI = countryIndex\n\n        if self.CI >= 0:\n            self.w = DescriptorClasses.CountryDesc(self.country)\n            self.cw = self.setCentralWidget(self.w)\n            self.clas = self.country\n            self.uName = self.clas.uName\n            self.Type = \"c\"\n            self.id = self.clas.id\n            self.setWindowTitle(\"Country - \"+self.clas.uName)\n        else:\n            self.currItem = country.tree.currentItem()\n\n            if clas.Type == \"ls\":\n                self.w = DescriptorClasses.LandscapeDesc(self.clas)\n                self.Type = \"ls\"\n                self.setWindowTitle(\"ls - \"+self.country.uName+\" -> \"+clas.uName)\n\n                for text in self.clas.climateInfo:\n                    self.w.climateChoice.listChoices.addItem(text)\n\n                self.w.climateChoice.climateAdd.climateAddField.returnPressed.connect(self.climateSet)\n                self.w.climateChoice.listChoices.itemDoubleClicked.connect(self.deleteClimate)\n\n            elif clas.Type == \"np\":\n                self.w = DescriptorClasses.NotablePlaceDesc(self.clas)\n                self.Type = \"np\"\n                self.setWindowTitle(\"np - \"+self.country.uName+\" -> \"+clas.uName)\n\n            elif clas.Type == \"t\":\n                self.w = DescriptorClasses.TownDesc(self.clas)\n                self.Type = \"t\"\n                self.setWindowTitle(\"t - \"+self.country.uName+\" -> \"+clas.uName)\n\n            elif clas.Type == \"dw\":\n                self.w = DescriptorClasses.DwellingDesc(self.clas)\n                self.Type = \"dw\"\n                self.setWindowTitle(\"dw - \"+self.country.uName+\" -> \"+clas.uName)\n\n            elif clas.Type == \"p\":\n                self.w = DescriptorClasses.PersonDesc(self.clas)\n                self.Type = \"p\"\n                self.setWindowTitle(\"p - \"+self.country.uName+\" -> \"+clas.uName)\n\n            elif clas.Type == \"m\":\n                self.w = DescriptorClasses.MonsterDesc(self.clas)\n                self.Type = \"m\"\n                self.setWindowTitle(\"m - \"+self.country.uName+\" -> \"+clas.uName)\n\n            elif clas.Type == \"i\":\n                self.w = DescriptorClasses.ItemDesc(self.clas)\n                self.Type = \"i\"\n                self.setWindowTitle(\"i - \"+self.country.uName+\" -> \"+clas.uName)\n\n        self.w.nameChanger.nameChangeEdit.setPlaceholderText(self.clas.uName)\n        self.w.nameChanger.nameChangeEdit.returnPressed.connect(self.uNameChange)\n        self.cw = self.setCentralWidget(self.w)\n        self.uName = self.clas.uName\n        self.id = self.clas.id\n\n        self.show()\n\napp = QtWidgets.QApplication(sys.argv)\n\nmyWindow = MainWindow()\nmyWindow.show()\n\nsys.exit(app.exec_())\n","sub_path":"WorldCreator.py","file_name":"WorldCreator.py","file_ext":"py","file_size_in_byte":27931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"306314337","text":"from typing import Tuple\n\nfrom cc._vec4 import Vec4\n\n\nclass Color(Vec4):\n    \"\"\" A (linear RGB 0-1) color with an optional alpha.\n        Creation via 0-255 values is supported via Color#from255().\n    \"\"\"\n\n    def __init__(self, r: float, g: float, b: float, a: float = 1.0):\n        super().__init__(\n            x=r,\n            y=g,\n            z=b,\n            w=a,\n        )\n        self.r = self.x\n        self.g = self.y\n        self.b = self.z\n        self.a = self.w\n\n    def is_valid(self) -> bool:\n        \"\"\" Validates rgba in [0.0, 1.0]. \"\"\"\n        return \\\n            0.0 <= self.r <= 1.0 and \\\n            0.0 <= self.g <= 1.0 and \\\n            0.0 <= self.b <= 1.0 and \\\n            0.0 <= self.a <= 1.0\n\n    @staticmethod\n    def from255(r: int, g: int, b: int, a: float = 1.0):\n        \"\"\" Create a color via sRGB [0-255] values. Alpha should still be within [0.0, 1.0]\"\"\"\n        return Color(\n            r / 255.0,\n            g / 255.0,\n            b / 255.0,\n            a\n        )\n\n    def to255Tuple(self) -> Tuple[int, int, int, int]:\n        \"\"\" Convert a color to its sRGB [0-255] values. \"\"\"\n        return \\\n            int(self.r * 255), \\\n            int(self.g * 255), \\\n            int(self.b * 255), \\\n            int(self.a * 255)\n","sub_path":"cc/color.py","file_name":"color.py","file_ext":"py","file_size_in_byte":1271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"260673117","text":"# Copyright 2014 - 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\nfrom ceilometerclient.v2 import client as ceilometerclient\nfrom cinderclient.v2 import client as cinderclient\nfrom glanceclient.v2 import client as glanceclient\nfrom heatclient.v1 import client as heatclient\nfrom ironic_inspector_client import v1 as ironic_inspector_client\nfrom ironicclient.v1 import client as ironicclient\nfrom keystoneclient import httpclient\nfrom keystoneclient.v3 import client as keystoneclient\nfrom neutronclient.v2_0 import client as neutronclient\nfrom novaclient import client as novaclient\nfrom oslo_config import cfg\nfrom oslo_log import log\nfrom troveclient import client as troveclient\n\nfrom mistral.actions.openstack import base\nfrom mistral import context\nfrom mistral.utils.openstack import keystone as keystone_utils\n\n\nLOG = log.getLogger(__name__)\n\nCONF = cfg.CONF\n\n\nclass NovaAction(base.OpenStackAction):\n    _client_class = novaclient.get_client_class(2)\n\n    def _get_client(self):\n        ctx = context.ctx()\n\n        LOG.debug(\"Nova action security context: %s\" % ctx)\n\n        keystone_endpoint = keystone_utils.get_keystone_endpoint_v2()\n        nova_endpoint = keystone_utils.get_endpoint_for_project('nova')\n\n        client = self._client_class(\n            username=None,\n            api_key=None,\n            endpoint_type='publicURL',\n            service_type='compute',\n            auth_token=ctx.auth_token,\n            tenant_id=ctx.project_id,\n            region_name=keystone_endpoint.region,\n            auth_url=keystone_endpoint.url\n        )\n\n        client.client.management_url = keystone_utils.format_url(\n            nova_endpoint.url,\n            {'tenant_id': ctx.project_id}\n        )\n\n        return client\n\n\nclass GlanceAction(base.OpenStackAction):\n    _client_class = glanceclient.Client\n\n    def _get_client(self):\n        ctx = context.ctx()\n\n        LOG.debug(\"Glance action security context: %s\" % ctx)\n\n        glance_endpoint = keystone_utils.get_endpoint_for_project('glance')\n\n        return self._client_class(\n            glance_endpoint.url,\n            region_name=glance_endpoint.region,\n            token=ctx.auth_token\n        )\n\n    @classmethod\n    def _get_fake_client(cls):\n        return cls._client_class(\"\")\n\n\nclass KeystoneAction(base.OpenStackAction):\n    _client_class = keystoneclient.Client\n\n    def _get_client(self):\n        ctx = context.ctx()\n\n        LOG.debug(\"Keystone action security context: %s\" % ctx)\n\n        kwargs = {\n            'token': ctx.auth_token,\n            'auth_url': CONF.keystone_authtoken.auth_uri,\n            'project_id': ctx.project_id,\n            'cacert': CONF.keystone_authtoken.cafile,\n        }\n\n        # In case of trust-scoped token explicitly pass endpoint parameter.\n        if (ctx.is_trust_scoped\n                or keystone_utils.is_token_trust_scoped(ctx.auth_token)):\n            kwargs['endpoint'] = CONF.keystone_authtoken.auth_uri\n\n        client = self._client_class(**kwargs)\n\n        client.management_url = CONF.keystone_authtoken.auth_uri\n\n        return client\n\n    @classmethod\n    def _get_fake_client(cls):\n        # Here we need to replace httpclient authenticate method temporarily\n        authenticate = httpclient.HTTPClient.authenticate\n\n        httpclient.HTTPClient.authenticate = lambda x: True\n        fake_client = cls._client_class()\n\n        # Once we get fake client, return back authenticate method\n        httpclient.HTTPClient.authenticate = authenticate\n\n        return fake_client\n\n\nclass CeilometerAction(base.OpenStackAction):\n    _client_class = ceilometerclient.Client\n\n    def _get_client(self):\n        ctx = context.ctx()\n\n        LOG.debug(\"Ceilometer action security context: %s\" % ctx)\n\n        ceilometer_endpoint = keystone_utils.get_endpoint_for_project(\n            'ceilometer'\n        )\n\n        endpoint_url = keystone_utils.format_url(\n            ceilometer_endpoint.url,\n            {'tenant_id': ctx.project_id}\n        )\n\n        return self._client_class(\n            endpoint_url,\n            region_name=ceilometer_endpoint.region,\n            token=ctx.auth_token,\n            username=ctx.user_name\n        )\n\n    @classmethod\n    def _get_fake_client(cls):\n        return cls._client_class(\"\")\n\n\nclass HeatAction(base.OpenStackAction):\n    _client_class = heatclient.Client\n\n    def _get_client(self):\n        ctx = context.ctx()\n\n        LOG.debug(\"Heat action security context: %s\" % ctx)\n\n        heat_endpoint = keystone_utils.get_endpoint_for_project('heat')\n\n        endpoint_url = keystone_utils.format_url(\n            heat_endpoint.url,\n            {'tenant_id': ctx.project_id}\n        )\n\n        return self._client_class(\n            endpoint_url,\n            region_name=heat_endpoint.region,\n            token=ctx.auth_token,\n            username=ctx.user_name\n        )\n\n    @classmethod\n    def _get_fake_client(cls):\n        return cls._client_class(\"\")\n\n\nclass NeutronAction(base.OpenStackAction):\n    _client_class = neutronclient.Client\n\n    def _get_client(self):\n        ctx = context.ctx()\n\n        LOG.debug(\"Neutron action security context: %s\" % ctx)\n\n        neutron_endpoint = keystone_utils.get_endpoint_for_project('neutron')\n\n        return self._client_class(\n            endpoint_url=neutron_endpoint.url,\n            region_name=neutron_endpoint.region,\n            token=ctx.auth_token,\n            auth_url=CONF.keystone_authtoken.auth_uri\n        )\n\n\nclass CinderAction(base.OpenStackAction):\n    _client_class = cinderclient.Client\n\n    def _get_client(self):\n        ctx = context.ctx()\n\n        LOG.debug(\"Cinder action security context: %s\" % ctx)\n\n        cinder_endpoint = keystone_utils.get_endpoint_for_project(\n            service_type='volumev2'\n        )\n\n        cinder_url = keystone_utils.format_url(\n            cinder_endpoint.url,\n            {'tenant_id': ctx.project_id}\n        )\n\n        client = self._client_class(\n            ctx.user_name,\n            ctx.auth_token,\n            project_id=ctx.project_id,\n            auth_url=cinder_url,\n            region_name=cinder_endpoint.region\n        )\n\n        client.client.auth_token = ctx.auth_token\n        client.client.management_url = cinder_url\n\n        return client\n\n    @classmethod\n    def _get_fake_client(cls):\n        return cls._client_class()\n\n\nclass TroveAction(base.OpenStackAction):\n    _client_class = troveclient.Client\n\n    def _get_client(self):\n        ctx = context.ctx()\n\n        LOG.debug(\"Trove action security context: %s\" % ctx)\n\n        trove_endpoint = keystone_utils.get_endpoint_for_project(\n            service_type='database'\n        )\n\n        trove_url = keystone_utils.format_url(\n            trove_endpoint.url,\n            {'tenant_id': ctx.project_id}\n        )\n\n        client = self._client_class(\n            ctx.user_name,\n            ctx.auth_token,\n            project_id=ctx.project_id,\n            auth_url=trove_url,\n            region_name=trove_endpoint.region\n        )\n\n        client.client.auth_token = ctx.auth_token\n        client.client.management_url = trove_url\n\n        return client\n\n    @classmethod\n    def _get_fake_client(cls):\n        return cls._client_class()\n\n\nclass IronicAction(base.OpenStackAction):\n    _client_class = ironicclient.Client\n\n    def _get_client(self):\n        ctx = context.ctx()\n\n        LOG.debug(\"Ironic action security context: %s\" % ctx)\n\n        ironic_endpoint = keystone_utils.get_endpoint_for_project('ironic')\n\n        return self._client_class(\n            ironic_endpoint.url,\n            token=ctx.auth_token,\n            region_name=ironic_endpoint.region\n        )\n\n    @classmethod\n    def _get_fake_client(cls):\n        return cls._client_class(\"http://127.0.0.1:6385/\")\n\n\nclass BaremetalIntrospectionAction(base.OpenStackAction):\n    _client_class = ironic_inspector_client.ClientV1\n\n    def _get_client(self):\n        ctx = context.ctx()\n\n        LOG.debug(\"Baremetal introspection action security context: %s\" % ctx)\n\n        inspector_endpoint = keystone_utils.get_endpoint_for_project(\n            'baremetal-introspection')\n\n        return self._client_class(\n            api_version=1,\n            inspector_url=inspector_endpoint.url,\n            auth_token=ctx.auth_token,\n        )\n","sub_path":"mistral/actions/openstack/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":8817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"353217702","text":"from django.test import TestCase\n\nfrom common import utils\n\n\nclass UtilsTest(TestCase):\n    def test_clear_text(self):\n        \"\"\"Should remove combining marks from text\"\"\"\n        sp = utils.clear_text('São Paulo')\n        rand = utils.clear_text('ç~ã`é´â^ô')\n\n        self.assertEqual('Sao Paulo', sp)\n        self.assertEqual('caeao', rand)\n","sub_path":"pets/common/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"27781478","text":"import random\nfrom listOfWords import general_words\n\nclass Word:\n    \"\"\"\n    A word to guess\n    \"\"\"\n    def __init__(self,input):\n        self.my_word = input\n        self.indecies = set()\n        self.choose_two_characters()\n\n    def __len__(self):\n        return len(self.my_word)\n\n    def choose_two_characters(self):\n        \"\"\"\n        chooses two indecies of letters of the word, which are depicted at the beginning\n        \"\"\"\n        length=len(self.my_word)\n        first_index=random.randint(0,length-2)\n        self.indecies.add(first_index)\n        self.indecies.add(random.randint(first_index+1,length-1))\n\n    def show_current_guess(self):\n        length=len(self.my_word)\n        listW1 = list(length* '*')\n        listW2 = list(self.my_word)\n        for i in self.indecies:\n            listW1[i]=listW2[i]\n        return(\"\".join(listW1))\n\n    def guess_a_letter(self,aletter):\n        matching_indecies = [counter for counter, value in enumerate(self.my_word) if (value.upper()==aletter.upper())]\n        guessed_previously = aletter in [self.my_word[index] for index in self.indecies]\n        \"\"\" I think that this returns true or false. true if the letter was guessed previously\"\"\"\n        self.indecies = self.indecies | set(matching_indecies)\n        is_the_guess_correct = len(matching_indecies) > 0\n        return guessed_previously, is_the_guess_correct\n\n    def is_complete(self):\n        return len(self.my_word) == len(self.indecies)\n\nclass Game:\n\n    def __init__(self,list_of_words):\n        word_string = list_of_words[random.randint(1,len(list_of_words))]\n        self.word = Word(word_string)\n        self.attempts=3*(len(self.word)-1)\n        self.play_again='Yes'\n\n    @staticmethod\n    def show_intro_text():\n        \"\"\"\n        Show introduction text and instructions to user\n        \"\"\"\n        user_response=str(0)\n        while (  not(user_response.lower() == 'y') and not(user_response.lower() == 'q') ):\n            user_response = input(\"Would you like to play? If yes, press 'y'. Or for exit press 'q'.\")\n            if user_response.lower()=='q':\n                exit('Bye')\n\n\n    def get_guess(self):\n        currently_guessed_letter=str(0)\n        while (not (currently_guessed_letter.isalpha()  and len(currently_guessed_letter)==1)):\n            currently_guessed_letter=input(\"For exit press Q. Otherwise please insert a letter:     \")\n            if (not currently_guessed_letter.isalpha()):\n                print(\"This is not a letter. Try again.\")\n            if (len(currently_guessed_letter)>1):\n                print(\"This is too long.give me a ONE single letter:    \")\n            if currently_guessed_letter =='Q':\n                exit('Bbyyyee')\n        return currently_guessed_letter\n\n    def run_game(self):\n        self.show_intro_text()\n        while not (self.word.is_complete() or self.attempts==0):\n            print('You have '+str(self.attempts)+' attempts remaining')\n            print(self.word.show_current_guess())\n            guessed_letter = self.get_guess()\n            guessed_before, guess_correct = self.word.guess_a_letter(guessed_letter)\n            if (not guess_correct) or guessed_before:\n                self.attempts = self.attempts -1\n        if self.word.is_complete():\n            print('Hurray, you win. The word is '+self.word.my_word.upper())\n        elif self.attempts==0:\n            print('Noo, you loose :-( The word was '+self.word.my_word.upper())\n\n\n\nif __name__ == \"__main__\":\n    print('--------------------------------------------------------------------------------------------------------------------------------')\n    print(\"Welcome to the best hangman game ever\")\n    while True:\n        y=Game(general_words)\n        y.run_game()\n","sub_path":"hangmanClasses.py","file_name":"hangmanClasses.py","file_ext":"py","file_size_in_byte":3735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"388921651","text":"from selenium import webdriver\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom selenium.webdriver.common.by import By as BY\r\nfrom selenium.common.exceptions import TimeoutException\r\nfrom bs4 import BeautifulSoup as bs\r\nimport re\r\nimport csv\r\nimport time\r\nimport numpy as np\r\n\r\n#postcodes = ['LS26 0UH']#, 'HD3 4AX', 'S17 4PU', 'LS2 7UA', 'SW1A 1AA']\r\noutput_path = 'C:/Users/James Crosbie/Desktop/'\r\ndelay = 10\r\n\r\n\r\ndef init_driver():\r\n    chrome_options = webdriver.ChromeOptions()\r\n    chrome_options.add_argument(\"--incognito\")\r\n    driver = webdriver.Chrome(executable_path='C:/Users/James Crosbie/Documents/Projects/webScraping/gekoDrivers/chromedriver.exe', chrome_options=chrome_options)\r\n    driver.wait = WebDriverWait(driver, delay)\r\n    return driver\r\n\r\n\r\ndef lookup(driver, query) :\r\n    driver.get('http://cti.voa.gov.uk/cti/inits.asp')\r\n    WebDriverWait(driver, delay).until(EC.presence_of_element_located((BY.ID, 'txtPostCode')))\r\n    try:\r\n        box = driver.find_element_by_xpath(\"//input[@id='txtPostCode' and @name='txtPostCode']\")\r\n        box.send_keys(query)\r\n\r\n        button = driver.find_element_by_xpath(\"//span[@class='access' and contains(text(), 'your Council Tax band')]\")\r\n        button.submit()\r\n    except TimeoutException:\r\n        print(\"Box or Button not found\")\r\n\r\n\r\ndef write_response_file(myLists):\r\n    global counter\r\n    myString = ''\r\n    if counter == 1:\r\n        myString = \"Address\\tCTBand\\tImprovIndex\\tRef\\n\"\r\n\r\n    for i, j, k, l in zip(myLists[0], myLists[1], myLists[2], myLists[3]):\r\n        myString = myString + str(i) + '\\t' + str(j) + '\\t' + str(k) +'\\t' + str(l) + '\\n'\r\n\r\n    with open(output_path+\"addressCTBands2.csv\", \"a\") as text_file:\r\n        text_file.write(myString)\r\n    counter += 1\r\n    return\r\n\r\n\r\ndef readfile():\r\n    with open('C:/Users/James Crosbie/Desktop/allpostcodes2.txt', 'r') as f:\r\n        reader = csv.reader(f)\r\n        your_list = list(reader)\r\n    return your_list[0]\r\n\r\n\r\ndef stripRegEx(myList):\r\n    newList = []\r\n    for i in myList:\r\n        newList.append(re.sub('\\n', '', i))\r\n    return newList\r\n\r\n\r\ndef parsePage(soup):\r\n    #parse results\r\n    addressList = [str(address.string).strip() for address in soup.find_all('span', {'class': 'Custom'})][1:]\r\n    tagList = [ct for ct in soup.find_all('td')]\r\n    ctBandList  = [str(v.string).replace(\" \", \"\") for i, v in enumerate(tagList) if i in range(1, 100, 4)]\r\n\r\n    impIndList = []\r\n    improvmentIndicatorLink = '<img alt=\"Improvement indicator\" src=\"Images/info.gif\"/>'\r\n    for i, v in enumerate(tagList):\r\n        if i in range(2, 100, 4):\r\n            impIndList.append(0)\r\n        if improvmentIndicatorLink in str(v.contents):\r\n            impIndList[-1] = 1\r\n\r\n    refList = [str(v.string).replace(\" \", \"\") for i, v in enumerate(tagList) if i in range(3, 100, 4)]\r\n\r\n    #remove any regular expressions from entries\r\n    addressList = stripRegEx(addressList)\r\n    ctBandList = stripRegEx(ctBandList)\r\n    refList = stripRegEx(refList)\r\n    return addressList, ctBandList, impIndList, refList\r\n\r\n\r\ndef write_error_log(s, mytype=\"timeout\"):\r\n    if mytype == 'missing':\r\n        with open(output_path+\"missingPostCodes.csv\", \"a\") as text_file:\r\n            text_file.write(str(s) + '\\n')\r\n    else:\r\n        with open(output_path+\"timeOutErrors.csv\", \"a\") as text_file:\r\n            text_file.write(str(s) + '\\n')\r\n    return\r\n\r\n\r\ndef get_data(driver, query):\r\n    #wait for page to load\r\n    try:\r\n        WebDriverWait(driver, delay).until(EC.presence_of_element_located((BY.CLASS_NAME, 'scl_complex')))\r\n    except:\r\n        write_error_log(query)\r\n\r\n    try:\r\n        driver.find_element_by_link_text(\"Next page\")\r\n        while  driver.find_element_by_link_text(\"Next page\"):\r\n            #parse page\r\n            soup = bs(driver.page_source, 'lxml')\r\n            addressList, ctBandList, impIndList, refList = parsePage(soup)\r\n            #add to ouput file\r\n            write_response_file([addressList, ctBandList, impIndList, refList])\r\n            #move to next page\r\n            driver.find_element_by_link_text(\"Next page\").click()\r\n            #wait for page to load\r\n\r\n            try:\r\n                WebDriverWait(driver, delay).until(EC.presence_of_element_located((BY.CLASS_NAME, 'scl_complex')))\r\n            except:\r\n                write_error_log(query)\r\n\r\n    except:\r\n        #check for data in page\r\n        resultSearch = driver.find_element_by_xpath(\"//div[@id='Content']/div/div/h2\").text\r\n        if resultSearch != 'Search for your Council Tax band - no results':\r\n            #parse page\r\n            soup = bs(driver.page_source, 'lxml')\r\n            addressList, ctBandList, impIndList, refList = parsePage(soup)\r\n            #add to ouput file\r\n            write_response_file([addressList, ctBandList, impIndList, refList])\r\n        else:\r\n            write_error_log(query, 'missing')\r\n    return\r\n\r\n\r\ndef main():\r\n    global counter\r\n    counter = 2\r\n    postcodes = readfile()\r\n    driver = init_driver()\r\n\r\n    for query in postcodes:\r\n        query = query.replace(\"'\", \"\")\r\n        try:\r\n            page = lookup(driver, query)\r\n            get_data(driver, query)\r\n        except:\r\n            driver.quit()\r\n            time.sleep(delay)\r\n            driver = init_driver()\r\n\r\n        #sleep random time\r\n        time.sleep(np.random.randint(1, 3))\r\n\r\n    driver.quit()\r\n\r\n#run the web-scraper\r\nmain()\r\n","sub_path":"scrapeVAOdata.py","file_name":"scrapeVAOdata.py","file_ext":"py","file_size_in_byte":5483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"29035701","text":"class Athlete:\n     def __init__(self,a_name,a_dob=None,a_times=[]):\n          self.name = a_name\n          self.dob= a_dob\n          self.times = a_times\n\n     def top3(self):\n          return (sorted(set([sanitize(t) for t in self.times]))[0:3])\n\n     def add_time(self,time):\n          self.times.append(time)\n\n     def add_times(self,time=[]):\n          #for t in time:\n               #self.times.append(t)\n          self.times.extend(time)\n     \n          \n          \n\ndef sanitize(time_string): #序列化得到的无规则数据,将其格式化为 mins.secs 的格式\n     if '-' in time_string:\n          splitter = '-'\n     elif ':' in time_string:\n          splitter = ':'\n     else:\n          return(time_string)\n     (mins,secs)=time_string.split(splitter)\n     return(mins+'.'+secs)\n\ndef load_from_file(filename): #读取文件的一行(这个文件只有一行,多行咋办)\n     try:\n          with open (filename) as f:\n               data = f.readline()\n               templ = data.strip().split(',')\n               return (Athlete(templ.pop(0),templ.pop(0),templ))#返回一个Athlete对象\n     except IOError as err:\n          print('File Error: '+str(err))\n          return (None)\n\nsarah = load_from_file('sarah2.txt')\n\nprint(sarah.name +\"'s fastest times are: \"+str(sarah.top3()))\n\nsarah.add_time('2:20')\nprint(sarah.times)\n\nsarah.add_times(['2:15','1:20','2-33'])\nprint(sarah.times)\n\nprint(sarah.name +\"'s fastest times are: \"+str(sarah.top3()))\n","sub_path":"17-p199.py","file_name":"17-p199.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"585072403","text":"#!/usr/bin/env python3\n# -*-coding: utf-8 -*-\n\"\"\"\n.. invisible:\n     _   _ _____ _     _____ _____\n    | | | |  ___| |   |  ___/  ___|\n    | | | | |__ | |   | |__ \\ `--.\n    | | | |  __|| |   |  __| `--. \\\n    \\ \\_/ / |___| |___| |___/\\__/ /\n     \\___/\\____/\\_____|____/\\____/\n\nCreated on April 24, 2015\n\n███████████████████████████████████████████████████████████████████████████████\n\nLicensed to the Apache Software Foundation (ASF) under one\nor more contributor license agreements.  See the NOTICE file\ndistributed with this work for additional information\nregarding copyright ownership.  The ASF licenses this file\nto you under the Apache License, Version 2.0 (the\n\"License\"); you may not use this file except in compliance\nwith the License.  You may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\n███████████████████████████████████████████████████████████████████████████████\n\"\"\"\n\n\nimport logging\nimport os\nimport platform\nfrom six import string_types, PY3\nfrom time import time\nimport unittest\nfrom zope.interface import implementer\n\nfrom veles.config import Config\nfrom veles.dummy import DummyWorkflow\nfrom veles.launcher import Launcher\nfrom veles.logger import Logger\nfrom veles.loader import Loader, ILoader\nfrom veles.pickle2 import pickle, best_protocol\nfrom veles.publishing import Publisher\nfrom veles.publishing.confluence_backend import ConfluenceBackend\n\n\n@implementer(ILoader)\nclass DummyLoader(Loader):\n    def load_data(self):\n        pass\n\n    def create_minibatch_data(self):\n        pass\n\n    def fill_minibatch(self):\n        pass\n\n\nclass TestPublisher(unittest.TestCase, Logger):\n    def __init__(self, methodName=\"runTest\"):\n        Logger.__init__(self)\n        unittest.TestCase.__init__(self, methodName)\n\n    def setUp(self):\n        self.workflow = wf = DummyWorkflow()\n        self.loader = loader = DummyLoader(wf, normalization_type=\"mean_disp\")\n        loader.link_from(wf.start_point)\n        self.publisher = Publisher(wf, backends={})\n        self.publisher.link_from(loader)\n        wf.end_point.link_from(self.publisher)\n        loader._has_labels = True\n        loader.class_lengths[0] = 0\n        loader.class_lengths[1] = 1000\n        loader.class_lengths[2] = 10000\n        loader._labels_mapping = {\"comedy\": 0, \"drama\": 1, \"action\": 2,\n                                  \"hard porn\": 3}\n        loader.test_diff_labels = {\"comedy\": 0, \"drama\": 0, \"action\": 0,\n                                   \"hard porn\": 0}\n        loader.valid_diff_labels = {\"comedy\": 240, \"drama\": 260, \"action\": 235,\n                                    \"hard porn\": 265}\n        loader.train_diff_labels = {\"comedy\": 2433, \"drama\": 2567,\n                                    \"action\": 2355, \"hard porn\": 2645}\n        loader.epoch_number = 30\n        self.publisher.loader_unit = loader\n        if PY3:\n            Launcher._generate_workflow_graphs(self)\n        else:\n            Launcher._generate_workflow_graphs.__func__(self)\n        self.publisher.initialize()\n\n    def test_init_info(self):\n        info = self.publisher.gather_info()\n        self.assertIsInstance(info, dict)\n        self.assertEqual(info[\"plots\"], {})\n        self.assertIsInstance(info[\"workflow_graph\"], dict)\n        for fmt in \"png\", \"svg\":\n            self.assertIsInstance(info[\"workflow_graph\"][fmt], bytes)\n        self.assertEqual(info[\"name\"], self.workflow.name)\n        self.assertEqual(info[\"description\"], self.workflow.__doc__)\n        self.assertEqual(info[\"id\"], self.workflow.workflow.id)\n        self.assertEqual(info[\"python\"],\n                         \"%s %s\" % (platform.python_implementation(),\n                                    platform.python_version()))\n        self.assertEqual(info[\"pid\"], os.getpid())\n        self.assertEqual(info[\"logid\"], self.workflow.workflow.log_id)\n        self.assertIsInstance(info[\"config_root\"], Config)\n        self.assertIsInstance(info[\"config_text\"], str)\n        mins, secs = divmod(time() - self.workflow.workflow.start_time, 60)\n        hours, mins = divmod(mins, 60)\n        days, hours = divmod(hours, 24)\n        self.assertEqual(info[\"days\"], days)\n        self.assertEqual(info[\"hours\"], hours)\n        self.assertEqual(info[\"mins\"], mins)\n        self.assertEqual(info[\"labels\"], tuple(self.loader.labels_mapping))\n        self.assertEqual(\n            info[\"label_stats\"], (self.loader.test_diff_labels,\n                                  self.loader.valid_diff_labels,\n                                  self.loader.train_diff_labels))\n        self.assertEqual(info[\"class_lengths\"],\n                         tuple(self.loader.class_lengths))\n        self.assertEqual(info[\"total_samples\"], self.loader.total_samples)\n        self.assertEqual(info[\"epochs\"], self.loader.epoch_number)\n        self.assertEqual(info[\"normalization\"], self.loader.normalization_type)\n        self.assertEqual(info[\"normalization_parameters\"],\n                         self.loader.normalization_parameters)\n        self.assertIsInstance(info[\"unit_run_times_by_class\"], dict)\n        self.assertIsInstance(info[\"unit_run_times_by_name\"], dict)\n        self.assertIsInstance(info[\"seeds\"], list)\n\n    def test_confluence_render(self):\n        info = self.publisher.gather_info()\n        info[\"errors_pt\"] = 0, 0.5, 0.6\n        info[\"seeds\"].append(b\"abcd1234\")\n        conf = ConfluenceBackend(None, server=\"\", username=\"\", password=\"\",\n                                 space=\"\")\n        content = super(ConfluenceBackend, conf).render(info)\n        self.assertIsInstance(content, string_types)\n        self.info(\"Confluence backend rendered:\\n%s\", content)\n\n    def test_pickle(self):\n        wf = DummyWorkflow()\n        publisher = Publisher(wf, backends={})\n        loader = DummyLoader(wf, normalization_type=\"mean_disp\")\n        publisher.link_from(loader)\n        wf.end_point.link_from(publisher)\n        publisher.loader_unit = loader\n        publisher.initialize()\n        publ = pickle.dumps(publisher, protocol=best_protocol)\n        publisher = pickle.loads(publ)\n\n    @property\n    def is_slave(self):\n        return False\n\n    @property\n    def reports_web_status(self):\n        return True\n\n\nif __name__ == \"__main__\":\n    logging.basicConfig(level=logging.DEBUG)\n    unittest.main()\n","sub_path":"veles/tests/test_publisher.py","file_name":"test_publisher.py","file_ext":"py","file_size_in_byte":6904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"293653963","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n#  res.py\n#  \n#  Copyright 2017 Andrei Tumbar <atuser@Kronos>\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 2 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, write to the Free Software\n#  Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,\n#  MA 02110-1301, USA.\n#  \n#  \n\n\nclass HTTPRes:\n    # 200 class (success)\n    OK = (200, 'OK')\n    \n    # 300 class (redirection)\n    REDIRPERM = (301, \"Moved Perminantly\") # Perminant redirect\n    PROXY = (305, \"Proxy Required\") # Proxy required\n    \n    # 400 class (client errors)\n    BADREQ = (400, \"Bad Request\") # Bad request\n    UNAUTH = (401, \"Unauthorized\") # Unauthorized\n    FORBIDDEN = (403, \"Forbidden\") # Forbidden (file permissions)\n    NOTFOUND = (404, \"Not Found\") # No such file or directory\n    \n    # 500 class (server errors)\n    INTERN = (500, \"Internal error\") # Server error (caused by exceptions)\n    IMPLEMENTED = (501, \"Not Iimplemented\") # method not found\n    BADGATE = (502, \"Bad Gateway\") # Invalid response from another server\n    UNAVAILIABLE = (503, \"Method Not Found\") # Method found but not working\n    TIMEOUT = (504, \"Timeout\") # No response from another server\n    HTTPVER = (505, \"Invalid HTTP version\") # HTTP Version not 1.0 or 1.1\n    NETAUTH = (511, \"Need authorization\") # Need auth before continuing\n","sub_path":"GoogleMusicOffline-win32-x64/resources/app/src/res.py","file_name":"res.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"51193029","text":"from database import db\nfrom dal.base_adapter import BaseAdapter\nfrom models.applications import Applications\n\n\nclass ApplicationAdapter(BaseAdapter):\n\n    def __init__(self):\n        BaseAdapter.__init__(self)\n\n    @staticmethod\n    def create(account_id=None,\n               application_name=None,\n               application_guid=None,\n               application_key=None,\n               application_secret=None,\n               application_algorithm=None,\n               created_user_id=None,\n               app_metadata=None,\n               training_status=None):\n        try:\n            application = Applications(account_id=account_id,\n                                       application_name=application_name,\n                                       application_guid=application_guid,\n                                       application_key=application_key,\n                                       application_secret=application_secret,\n                                       application_algorithm=application_algorithm,\n                                       created_user_id=created_user_id,\n                                       app_metadata=app_metadata,\n                                       training_status=training_status)\n            db.add(application)\n            db.commit()\n            return application.application_id\n        except Exception as e:\n            db.rollback()\n            raise Exception(e.message)\n\n    @staticmethod\n    def update(query=None, updated_value=None):\n        \"\"\"\n        This method update the account\n\n        :param query:\n        :param updated_value:\n        :return:\n        \"\"\"\n        try:\n            db.query(Applications) \\\n                .filter_by(**query) \\\n                .update(updated_value)\n            db.commit()\n        except Exception as e:\n            db.rollback()\n            raise Exception(e.message)\n\n    @staticmethod\n    def delete(query=None):\n        \"\"\"\n        This methods deletes the record\n\n        :param query:\n        :return:\n        \"\"\"\n        try:\n            db.query(Applications) \\\n                .filter_by(**query) \\\n                .delete()\n            db.commit()\n        except Exception as e:\n            db.rollback()\n            raise Exception(e.message)\n\n    @staticmethod\n    def read(query=None):\n        \"\"\"\n        Reading the records from a table\n\n        :param query:\n        :return:\n        \"\"\"\n        try:\n            db.commit()\n            applications = db.query(Applications) \\\n                .filter_by(**query).all()\n            return applications\n        except Exception as e:\n            db.rollback()\n            raise Exception(e.message)\n\n    @staticmethod\n    def get_all_apps():\n        \"\"\"\n        Get all apps\n\n        :return:\n        \"\"\"\n        try:\n            applications = db.query(Applications).all()\n            return applications\n        except Exception as e:\n            db.rollback()\n            raise Exception(e.message)\n\n","sub_path":"dal/applications_adapter.py","file_name":"applications_adapter.py","file_ext":"py","file_size_in_byte":2977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"449070607","text":"import os, threading, sys, time\nfrom User import User\nfrom Share import Share\nfrom Folder import Folder\nfrom File import File\nfrom EditFile import InputTime\n\ndef create_notifications():\n\t\tnew_folder = folders.create_folder()\n\t\tvalue = files.upload_file(upload_folder, new_folder)\n\n\nif __name__ == \"__main__\":\n\tsys.argv = sys.argv[1:]\n\tif len(sys.argv) != 4 \\\n\t\tor sys.argv[0] == '-h' or sys.argv[0] == '--help':\n\t\tprint ('')\n\t\tprint ('Usage: create_notifications.py -u -p -a -n')\n\t\tprint ('')\n\t\tprint ('\t-h/--help\t\t\tshows help')\n\t\tprint ('\t-u\t\t\t\temail which receives notification (valid pCloud user)')\n\t\tprint (\"\t-p\t\t\t\tnotification receiver's password\")\n\t\tprint (\"\t-a\t\t\t\tapi to which the request is sent (e.g. 'api70')\")\n\t\tprint ('\t-n\t\t\t\tnumber of created notifications')\n\t\tsys.exit()\n\tupload_folder = './files for upload/app/'\n\tsender = User('notification2@qa.mobileinno.com', 'parolata')\n\treceiver = User(sys.argv[0], sys.argv[1])\n\tshares = Share(sender, receiver)\n\tshares.send_share_request()\n\tshares.accept_share_request()\n\tfolders = Folder(sender, True, sys.argv[2])\n\tfiles = File(sender, True, sys.argv[2])\n\tfor _ in range(int(sys.argv[3])):\n\t\tthread = threading.Thread(target = create_notifications, args=())\n\t\tthread.start()\n\twhile (threading.active_count()>1):\n\t\ttime.sleep(1)\n\t#shares.cancel_share(sender)\n\tsender.logout()\n\treceiver.logout()","sub_path":"create_notifications.py","file_name":"create_notifications.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"578839206","text":"# !/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\nimport misaka\nimport redis\nimport random\nimport json\nfrom os import listdir, getcwd\nfrom os.path import isfile, join\nfrom flask import Flask, request, g, redirect\napp = Flask(__name__)\n\nservice_name = \"dataswarm\"\nversion = \"0.0.3\"\npool = redis.ConnectionPool(host='localhost', port=6379, db=0)  # 'redis'\nsocial_mediatypes = [\"mb\", \"bl\", \"cm\", \"md\", \"sc\",\n                     \"vi\", \"wi\", \"ot\", \"rv\", \"cf\"]\neditorial_mediatypes = [\"news\"]\n\n\ndef parse_doc(data):\n    mediaType = None\n    doc_id = None\n    try:\n        valid_json = json.loads(data)\n        doc_id = valid_json[\"id\"]\n        mediaType = valid_json[\"metaData\"][\"mediaType\"]\n    except Exception as e:\n        app.logger.exception(e)\n\n    if mediaType in social_mediatypes:\n        doc_type = \"social\"\n    elif mediaType in editorial_mediatypes:\n        doc_type = \"editorial\"\n    else:\n        doc_type = \"unknown\"\n\n    return doc_id, doc_type\n\n\ndef post_docs_in_path(path, doctype):\n        docs = [f for f in listdir(path) if isfile(join(path, f))]\n\n        print(\"Populating {0} docs\".format(doctype))\n\n        for doc in docs:\n            doc_id, doc_type = parse_doc(doc)\n            something, code = post_doc(doc_id, doc_type, doc)\n            if code == 201:\n                print(\"Posted {0} doc with id {1}\"\n                      .format(doctype, doc_id))\n            else:\n                app.logger.warning(\"Failed to post {0} doc with id {1}\"\n                                   .format(doctype, doc_id))\n\n\n@app.before_first_request\ndef ensure_data():\n    g.r = redis.Redis(connection_pool=pool)\n    # Populate with some initial data if db is empty\n    doctypes = [\"social\", \"editorial\"]\n\n    for doctype in doctypes:\n        print(\"Number of {0} docs in db: {1}\".format(doctype,\n              len(list(g.r.smembers(doctype)))))\n        if len(list(g.r.smembers(doctype))) < 1:\n            post_docs_in_path(getcwd() + \"/data/{0}\".format(doctype), doctype)\n\n\n@app.before_request\ndef before_request():\n    g.r = redis.Redis(connection_pool=pool)\n\n\ndef post_doc(doc_id, doc_type, doc):\n    # POST to db\n    try:\n        # Add document to db\n        g.r.set(doc_id, doc)\n\n        # Add doc_id to specific db set,\n        # so we know the type of the doc\n        g.r.sadd(doc_type, doc_id)\n        g.r.bgsave()\n\n        return '', 201\n    except Exception as e:\n        app.logger.exception(e)\n        return '', 400\n\n\ndef delete_doc(doc_id):\n    # DELETE from db\n    try:\n        # Get the doc and identify doc_type\n        doc_type = json.loads(g.r.get(doc_id))[\"metaData\"][\"mediaType\"]\n        # Remove from db\n        g.r.delete(doc_id)\n        # Remove from db set\n        g.r.srem(doc_type, doc_id)\n        return '', 204\n    except Exception as e:\n        app.logger.exception(e)\n        return '', 400\n\n\ndef get_doc(doc_id):\n    # GET doc if exists, or 404\n    try:\n        return g.r.get(doc_id), 200\n    except Exception as e:\n        app.logger.exception(e)\n        return '', 404\n\n\n@app.route(\"/{0}/doc/<doc_id>\".format(service_name), methods=['GET', 'DELETE'])\ndef editorial_doc(doc_id):\n    # Handling of specific doc\n\n    if request.method == 'DELETE':\n        return delete_doc(doc_id)\n    else:\n        return get_doc(doc_id)\n\n\ndef get_random(doctype):\n    documents = list(g.r.smembers(doctype))\n    # From lists of doctype, choose one\n    try:\n        doc_id = random.choice(documents)\n    except IndexError:\n        app.logger.exception(\"No {0} document in database\".format(doctype))\n        return '', 404\n\n    return doc_id\n\n\n@app.route(\"/{0}/doc/editorial/\".format(service_name), methods=['GET'])\ndef random_editorial():\n    doc_id = get_random(\"editorial\")\n    return get_doc(doc_id)\n\n\n@app.route(\"/{0}/doc/social/\".format(service_name), methods=['GET'])\ndef random_social():\n    doc_id = get_random(\"social\")\n    return get_doc(doc_id)\n\n\n@app.route(\"/{0}/doc/\".format(service_name), methods=['GET', 'POST'])\ndef post_or_random():\n    if request.method == 'POST':\n        data = request.data\n        doc_id, doc_type = parse_doc(data)\n        return post_doc(doc_id, doc_type, data)\n\n    else:\n        # From lists of social and editorial doc_ids, choose one\n        documents = list(g.r.smembers(\"social\")) \\\n            + list(g.r.smembers(\"editorial\"))\n        try:\n            doc_id = random.choice(documents)\n        except IndexError:\n            app.logger.exception(\"Database is empty\")\n            return '', 404\n\n        return get_doc(doc_id)\n\n\n@app.route(\"/{0}/\".format(service_name), methods=['GET'])\ndef doc():\n    with open('README.md', 'r') as f:\n        text = misaka.html(f.read())\n    return text\n\n\n@app.route(\"/{0}/_status\".format(service_name), methods=['GET'])\ndef status():\n    return '{{\"version\": \"{0}\",\"statusCode\":\"OK\"}}'.format(version)\n\n\n@app.route(\"/{0}/_health\".format(service_name), methods=['GET'])\ndef health():\n    return '{{\"db_size\": \"{0}\",'\n    '\"redis_version\":\"{1}\"}}'.format(g.r.dbsize(), g.r.info()['redis_version'])\n\n\n@app.route('/', methods=['GET'])\ndef redirect_to_doc():\n    return redirect(\"/{0}/\".format(service_name), code=302)\n\n\nif __name__ == \"__main__\":\n    app.run(host=\"0.0.0.0\", debug=True)\n","sub_path":"dataswarm.py","file_name":"dataswarm.py","file_ext":"py","file_size_in_byte":5191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"280089053","text":"import json\nfrom os.path import dirname\nfrom dictionary import Dictionary\nfrom single_class_trackers_manager import SingleClassTrackersManager\nfrom structures_filter import StructuresFilter\n\n\nclass TrackingAdministrator:\n    '''\n    Separates detected structures by their class id. It is important, because\n    cars can only be updated using new car position, can not be updated using\n    person coordinates.\n    '''\n\n    def __init__(self, settings_hard_path):\n        with open(settings_hard_path) as json_file:\n            settings = json.load(json_file)\n\n        path_to_dictionary = dirname(settings_hard_path) + \"/dictionary.txt\"\n\n        self._tracking_classes_count = Dictionary(\n            path_to_dictionary).get_max_index() + 1\n        self._structures_filter = StructuresFilter(\n            settings, path_to_dictionary)\n\n        self._single_class_managers = []\n        for idx in range(self._tracking_classes_count):\n            self._single_class_managers.append(\n                SingleClassTrackersManager(settings, idx))\n\n    def track(self, detected_structures, data_time):\n        \"\"\"\n        Tracks every objects class separately.\n\n        Arguments:\n            detected_structures {DetectedObject list} -- detected objects on image\n            data_time {int} -- image download time\n        \"\"\"\n\n        detected_structures = self._structures_filter.filter(\n            detected_structures)\n\n        for idx in range(self._tracking_classes_count):\n            detected_idx_occurances = [\n                obj for obj in detected_structures if obj.class_id == idx]\n            self._single_class_managers[idx].track(\n                detected_idx_occurances, data_time)\n\n    def get_all_class_trackers(self):\n        trackers = []\n        for single_class_manager in self._single_class_managers:\n            trackers.extend(single_class_manager.get_trackers())\n\n        return trackers\n","sub_path":"object_tracker_CLIENT/tracking/tracking_adminitstrator.py","file_name":"tracking_adminitstrator.py","file_ext":"py","file_size_in_byte":1906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"248321027","text":"\"\"\"Tools for working with Predictors.\"\"\"\n# flake8: noqa\nfrom collections import Iterable\nfrom typing import List, Optional, Type, Union, Mapping\nfrom uuid import UUID\nfrom warnings import warn\n\nfrom citrine._serialization import properties as _properties\nfrom citrine._serialization.serializable import Serializable\nfrom citrine._session import Session\nfrom citrine.informatics.data_sources import DataSource\nfrom citrine.informatics.descriptors import Descriptor, FormulationDescriptor, RealDescriptor, \\\n    MolecularStructureDescriptor\nfrom citrine.informatics.modules import Module\nfrom citrine.informatics.reports import Report\nfrom citrine.resources.report import ReportResource\n\n__all__ = ['DeprecatedExpressionPredictor',\n           'ExpressionPredictor',\n           'GraphPredictor',\n           'IngredientsToSimpleMixturePredictor',\n           'Predictor',\n           'SimpleMLPredictor',\n           'MolecularStructureFeaturizer',\n           'GeneralizedMeanPropertyPredictor',\n           'LabelFractionsPredictor',\n           'SimpleMixturePredictor',\n           'IngredientFractionsPredictor']\n\n\nclass Predictor(Module):\n    \"\"\"[ALPHA] Module that describes the ability to compute/predict properties of materials.\n\n    Abstract type that returns the proper type given a serialized dict. subtype\n    based on the 'type' value of the passed in dict.\n\n    \"\"\"\n\n    _response_key = None\n\n    def post_build(self, project_id: UUID, data: dict):\n        \"\"\"Executes after a .build() is called in [[PredictorCollection]].\"\"\"\n        self.report = ReportResource(project_id, self.session).get(data['id'])\n\n    @classmethod\n    def get_type(cls, data) -> Type['Predictor']:\n        \"\"\"Return the subtype.\"\"\"\n        type_dict = {\n            \"Simple\": SimpleMLPredictor,\n            \"Graph\": GraphPredictor,\n            \"Expression\": DeprecatedExpressionPredictor,\n            \"AnalyticExpression\": ExpressionPredictor,\n            \"MoleculeFeaturizer\": MolecularStructureFeaturizer,\n            \"IngredientsToSimpleMixture\": IngredientsToSimpleMixturePredictor,\n            \"GeneralizedMeanProperty\": GeneralizedMeanPropertyPredictor,\n            \"LabelFractions\": LabelFractionsPredictor,\n            \"SimpleMixture\": SimpleMixturePredictor,\n            \"IngredientFractions\": IngredientFractionsPredictor,\n        }\n        typ = type_dict.get(data['config']['type'])\n\n        if typ is not None:\n            return typ\n        else:\n            raise ValueError(\n                '{} is not a valid predictor type. '\n                'Must be in {}.'.format(data['config']['type'], type_dict.keys())\n            )\n\n    def _wrap_training_data(self, training_data: Optional[Union[DataSource, List[DataSource]]]) -> List[DataSource]:\n        \"\"\"\n        Converts ``None`` to an empty list and wraps a single data source in a list that contains a single element.\n\n        Parameters\n        ----------\n        training_data: Optional[Union[DataSource, List[DataSource]]]\n            Either a single data source, list of data sources or ``None``\n        Returns\n        -------\n        List[DataSource]\n            A list of data sources\n        \"\"\"\n        if training_data is None:\n            return []\n        if isinstance(training_data, DataSource):\n            warn(\"Specifying training data as a single data source is deprecated. \"\n                 \"Please use a list of data sources to create {} instead.\".format(self),\n                 DeprecationWarning)\n            return [training_data]\n        return training_data\n\n\nclass SimpleMLPredictor(Serializable['SimplePredictor'], Predictor):\n    \"\"\"[ALPHA] A predictor interface that builds a simple graphical model.\n\n    The model connects the set of inputs through latent variables to the outputs.\n    Supported complex inputs (such as chemical formulas) are auto-featurized and machine learning\n    models are built for each latent variable and output.\n\n    Parameters\n    ----------\n    name: str\n        name of the configuration\n    description: str\n        the description of the predictor\n    inputs: list[Descriptor]\n        Descriptors that represent inputs to relations\n    outputs: list[Descriptor]\n        Descriptors that represent outputs of relations\n    latent_variables: list[Descriptor]\n        Descriptors that are predicted from inputs and used when predicting the outputs\n    training_data: Optional[List[DataSource]]\n        Sources of training data. Each can be either a CSV or an GEM Table.\n        Candidates from multiple data sources will be combined into a flattened list and deduplicated by uid\n        and identifiers. Deduplication is performed if a uid or identifier is shared between two or more rows.\n        The content of a deduplicated row will contain the union of data across all rows that share the same uid\n        or at least 1 identifier. Training data is optional if the predictor is part of a graph that includes\n        all training data required by this predictor.\n\n    \"\"\"\n\n    uid = _properties.Optional(_properties.UUID, 'id', serializable=False)\n    name = _properties.String('config.name')\n    description = _properties.Optional(_properties.String(), 'config.description')\n    inputs = _properties.List(_properties.Object(Descriptor), 'config.inputs')\n    outputs = _properties.List(_properties.Object(Descriptor), 'config.outputs')\n    latent_variables = _properties.List(_properties.Object(Descriptor), 'config.latent_variables')\n    training_data = _properties.List(_properties.Object(DataSource), 'config.training_data')\n    typ = _properties.String('config.type', default='Simple', deserializable=False)\n    status = _properties.Optional(_properties.String(), 'status', serializable=False)\n    status_info = _properties.Optional(\n        _properties.List(_properties.String()),\n        'status_info',\n        serializable=False\n    )\n    archived = _properties.Boolean('archived', default=False)\n    experimental = _properties.Boolean(\"experimental\", serializable=False, default=True)\n    experimental_reasons = _properties.Optional(\n        _properties.List(_properties.String()),\n        'experimental_reasons',\n        serializable=False\n    )\n\n    # NOTE: These could go here or in _post_dump - it's unclear which is better right now\n    module_type = _properties.String('module_type', default='PREDICTOR')\n    schema_id = _properties.UUID('schema_id', default=UUID('08d20e5f-e329-4de0-a90a-4b5e36b91703'))\n\n    def __init__(self,\n                 name: str,\n                 description: str,\n                 inputs: List[Descriptor],\n                 outputs: List[Descriptor],\n                 latent_variables: List[Descriptor],\n                 training_data: Optional[List[DataSource]] = None,\n                 session: Optional[Session] = None,\n                 report: Optional[Report] = None,\n                 archived: bool = False):\n        self.name: str = name\n        self.description: str = description\n        self.inputs: List[Descriptor] = inputs\n        self.outputs: List[Descriptor] = outputs\n        self.latent_variables: List[Descriptor] = latent_variables\n        self.training_data: List[DataSource] = self._wrap_training_data(training_data)\n        self.session: Optional[Session] = session\n        self.report: Optional[Report] = report\n        self.archived: bool = archived\n\n    def _post_dump(self, data: dict) -> dict:\n        data['display_name'] = data['config']['name']\n        return data\n\n    def __str__(self):\n        return '<SimplePredictor {!r}>'.format(self.name)\n\n\nclass GraphPredictor(Serializable['GraphPredictor'], Predictor):\n    \"\"\"[ALPHA] A predictor interface that stitches other predictors together.\n\n    Parameters\n    ----------\n    name: str\n        name of the configuration\n    description: str\n        the description of the predictor\n    predictors: List[Union[UUID, Predictor]]\n        the list of predictors to use in the grpah, either UUIDs or serialized predictors\n    training_data: Optional[List[DataSource]]\n        Optional sources of training data shared by all predictors in the graph.\n        Training data provided by this graph predictor does not need to be specified as part of the\n        configuration of sub-predictors. Shared training data and any training data specified by a sub-predictor\n        will be combined into a flattened list and deduplicated by uid and identifiers. Deduplication is performed\n        if a uid or identifier is shared between two or more rows. The content of a deduplicated row will contain\n        the union of data across all rows that share the same uid or at least 1 identifier.\n\n    \"\"\"\n\n    uid = _properties.Optional(_properties.UUID, 'id', serializable=False)\n    name = _properties.String('config.name')\n    description = _properties.String('config.description')\n    predictors = _properties.List(_properties.Union(\n        [_properties.UUID, _properties.Object(Predictor)]), 'config.predictors')\n    training_data = _properties.List(_properties.Object(DataSource), 'config.training_data')\n    typ = _properties.String('config.type', default='Graph', deserializable=False)\n    # Graph predictors may not be embedded in other predictors, hence while status is optional\n    # for deserializing most predictors, it is required for deserializing a graph\n    status = _properties.String('status', serializable=False)\n    status_info = _properties.Optional(\n        _properties.List(_properties.String()),\n        'status_info',\n        serializable=False\n    )\n    experimental = _properties.Boolean(\"experimental\", serializable=False, default=True)\n    experimental_reasons = _properties.Optional(\n        _properties.List(_properties.String()),\n        'experimental_reasons',\n        serializable=False\n    )\n    archived = _properties.Boolean('archived', default=False)\n\n    # NOTE: These could go here or in _post_dump - it's unclear which is better right now\n    module_type = _properties.String('module_type', default='PREDICTOR')\n    schema_id = _properties.UUID('schema_id', default=UUID('43c61ad4-7e33-45d0-a3de-504acb4e0737'))\n\n    def __init__(self,\n                 name: str,\n                 description: str,\n                 predictors: List[Union[UUID, Predictor]],\n                 training_data: Optional[List[DataSource]] = None,\n                 session: Optional[Session] = None,\n                 report: Optional[Report] = None,\n                 archived: bool = False):\n        self.name: str = name\n        self.description: str = description\n        self.predictors: List[Union[UUID, Predictor]] = predictors\n        self.training_data: List[DataSource] = self._wrap_training_data(training_data)\n        self.session: Optional[Session] = session\n        self.report: Optional[Report] = report\n        self.archived: bool = archived\n\n    def _post_dump(self, data: dict) -> dict:\n        data['display_name'] = data['config']['name']\n        for i, predictor in enumerate(data['config']['predictors']):\n            if isinstance(predictor, dict):\n                # embedded predictors are not modules, so only serialize their config\n                data['config']['predictors'][i] = predictor['config']\n        return data\n\n    @classmethod\n    def _pre_build(cls, data: dict) -> dict:\n        for i, predictor in enumerate(data['config']['predictors']):\n            if isinstance(predictor, dict):\n                data['config']['predictors'][i] = \\\n                    GraphPredictor.stuff_predictor_into_envelope(predictor)\n        return data\n\n    @staticmethod\n    def stuff_predictor_into_envelope(predictor: dict) -> dict:\n        \"\"\"Insert a serialized embedded predictor into a module envelope, to facilitate deser.\"\"\"\n        return dict(\n            module_type='PREDICTOR',\n            config=predictor,\n            archived=False,\n            schema_id='43c61ad4-7e33-45d0-a3de-504acb4e0737'  # TODO: what should this be?\n        )\n\n    def __str__(self):\n        return '<GraphPredictor {!r}>'.format(self.name)\n\n\nclass DeprecatedExpressionPredictor(Serializable['DeprecatedExpressionPredictor'], Predictor):\n    \"\"\"[DEPRECATED] A predictor that computes an output from an analytic expression.\n\n    This predictor is deprecated. Please use the :class:`~citrine.informatics.predictors.ExpressionPredictor` instead.\n    To migrate to the new predictor:\n\n    1. add an alias for all unknown expression arguments and\n    2. replace descriptor keys in ``aliases`` with the associated descriptor\n\n    These changes allow the expression to respect descriptor bounds when computing the output and avoid potential\n    descriptor mismatches if a descriptor with an identical key and different bounds is present in the graph.\n\n    The following example shows how to migrate a deprecated expression predictor to the new format.\n    In the deprecated format, an expression that computes shear modulus from Young's modulus and Poisson's ratio is given by:\n\n    .. code-block:: python\n\n       from citrine.informatics.predictors import DeprecatedExpressionPredictor\n\n       shear_modulus = RealDescriptor('Property~Shear modulus', lower_bound=0, upper_bound=100, units='GPa')\n\n       shear_modulus_predictor = DeprecatedExpressionPredictor(\n           name = 'Shear modulus predictor',\n           description = \"Computes shear modulus from Young's modulus and Poisson's ratio.\",\n           expression = 'Y / (2 * (1 + v))',\n           output = shear_modulus,\n           aliases = {\n               'Y': \"Young's modulus\",\n               'v': \"Poisson's ratio\"\n           }\n       )\n\n    To create a predictor using the format, we need to create descriptors for the expression inputs: Young's modulus and Poisson's ratio.\n    We also need to replace references to the descriptor keys in ``aliases`` with the new descriptors:\n\n    .. code-block:: python\n\n       from citrine.informatics.predictors import ExpressionPredictor\n\n       # create a descriptor for each input in addition to the output\n       youngs_modulus = RealDescriptor('Property~Young\\'s modulus', lower_bound=0, upper_bound=100, units='GPa')\n       poissons_ratio = RealDescriptor('Property~Poisson\\'s ratio', lower_bound=-1, upper_bound=0.5, units='')\n       shear_modulus = RealDescriptor('Property~Shear modulus', lower_bound=0, upper_bound=100, units='GPa')\n\n       shear_modulus_predictor = ExpressionPredictor(\n           name = 'Shear modulus predictor',\n           description = \"Computes shear modulus from Young's modulus and Poisson's ratio.\",\n           expression = 'Y / (2 * (1 + v))',\n           output = shear_modulus,\n           # note, arguments map to descriptors not descriptor keys\n           aliases = {\n               'Y': youngs_modulus,\n               'v': poissons_ratio\n           }\n       )\n\n    .. seealso:: :class:`~citrine.informatics.predictors.ExpressionPredictor`\n\n    Parameters\n    ----------\n    name: str\n        name of the configuration\n    description: str\n        the description of the predictor\n    expression: str\n        expression that computes an output from a set of inputs\n    output: RealDescriptor\n        descriptor that represents the output of the expression\n    aliases: Optional[Mapping[str, str]]\n        a mapping from each each argument as it appears in the ``expression`` to its descriptor key.\n        If an unknown argument is not aliased, the argument and descriptor key are assumed to be identical.\n\n    \"\"\"\n\n    uid = _properties.Optional(_properties.UUID, 'id', serializable=False)\n    name = _properties.String('config.name')\n    description = _properties.String('config.description')\n    expression = _properties.String('config.expression')\n    output = _properties.Object(RealDescriptor, 'config.output')\n    aliases = _properties.Optional(_properties.Mapping(_properties.String, _properties.String), 'config.aliases')\n    typ = _properties.String('config.type', default='Expression', deserializable=False)\n    status = _properties.Optional(_properties.String(), 'status', serializable=False)\n    status_info = _properties.Optional(\n        _properties.List(_properties.String()),\n        'status_info',\n        serializable=False\n    )\n    experimental = _properties.Boolean(\"experimental\", serializable=False, default=True)\n    experimental_reasons = _properties.Optional(\n        _properties.List(_properties.String()),\n        'experimental_reasons',\n        serializable=False\n    )\n\n    archived = _properties.Boolean('archived', default=False)\n\n    # NOTE: These could go here or in _post_dump - it's unclear which is better right now\n    module_type = _properties.String('module_type', default='PREDICTOR')\n    schema_id = _properties.UUID('schema_id', default=UUID('866e72a6-0a01-4c5f-8c35-146eb2540166'))\n\n    def __init__(self,\n                 name: str,\n                 description: str,\n                 expression: str,\n                 output: RealDescriptor,\n                 aliases: Optional[Mapping[str, str]] = None,\n                 session: Optional[Session] = None,\n                 report: Optional[Report] = None,\n                 archived: bool = False):\n        warn(\"{this_class} is deprecated. Please use {replacement} instead\"\n             .format(this_class=self.__class__.name, replacement=ExpressionPredictor.__name__))\n        self.name: str = name\n        self.description: str = description\n        self.expression: str = expression\n        self.output: RealDescriptor = output\n        self.aliases: Optional[Mapping[str, str]] = aliases\n        self.session: Optional[Session] = session\n        self.report: Optional[Report] = report\n        self.archived: bool = archived\n\n    def _post_dump(self, data: dict) -> dict:\n        data['display_name'] = data['config']['name']\n        return data\n\n    def __str__(self):\n        return '<DeprecatedExpressionPredictor {!r}>'.format(self.name)\n\n\nclass ExpressionPredictor(Serializable['ExpressionPredictor'], Predictor):\n    \"\"\"[ALPHA] A predictor that computes an output from an expression and set of bounded inputs.\n\n    .. seealso::\n       If you are using the deprecated predictor please see\n       :class:`~citrine.informatics.predictors.DeprecatedExpressionPredictor` for an example that shows how to migrate\n       to the new format.\n\n    Parameters\n    ----------\n    name: str\n        name of the configuration\n    description: str\n        the description of the predictor\n    expression: str\n        expression that computes an output from aliased inputs\n    output: RealDescriptor\n        descriptor that represents the output relation\n    aliases: Mapping[str, RealDescriptor]\n        a mapping from each unknown argument to its descriptor.\n        All unknown arguments must have an associated descriptor.\n\n    \"\"\"\n\n    uid = _properties.Optional(_properties.UUID, 'id', serializable=False)\n    name = _properties.String('config.name')\n    description = _properties.String('config.description')\n    expression = _properties.String('config.expression')\n    output = _properties.Object(RealDescriptor, 'config.output')\n    aliases = _properties.Mapping(_properties.String, _properties.Object(RealDescriptor), 'config.aliases')\n    typ = _properties.String('config.type', default='AnalyticExpression', deserializable=False)\n    status = _properties.Optional(_properties.String(), 'status', serializable=False)\n    status_info = _properties.Optional(\n        _properties.List(_properties.String()),\n        'status_info',\n        serializable=False\n    )\n    experimental = _properties.Boolean(\"experimental\", serializable=False, default=True)\n    experimental_reasons = _properties.Optional(\n        _properties.List(_properties.String()),\n        'experimental_reasons',\n        serializable=False\n    )\n    archived = _properties.Boolean('archived', default=False)\n\n    # NOTE: These could go here or in _post_dump - it's unclear which is better right now\n    module_type = _properties.String('module_type', default='PREDICTOR')\n    schema_id = _properties.UUID('schema_id', default=UUID('f1601161-bb98-4fa9-bdd2-a2a673547532'))\n\n    def __init__(self,\n                 name: str,\n                 description: str,\n                 expression: str,\n                 output: RealDescriptor,\n                 aliases: Mapping[str, RealDescriptor],\n                 session: Optional[Session] = None,\n                 report: Optional[Report] = None,\n                 archived: bool = False):\n        self.name: str = name\n        self.description: str = description\n        self.expression: str = expression\n        self.output: Descriptor = output\n        self.aliases: Mapping[str, RealDescriptor] = aliases\n        self.session: Optional[Session] = session\n        self.report: Optional[Report] = report\n        self.archived: bool = archived\n\n    def _post_dump(self, data: dict) -> dict:\n        data['display_name'] = data['config']['name']\n        return data\n\n    def __str__(self):\n        return '<ExpressionPredictor {!r}>'.format(self.name)\n\n\nclass MolecularStructureFeaturizer(Serializable['MolecularStructureFeaturizer'], Predictor):\n    \"\"\"\n    [ALPHA] A \"batteries-included\" featurizer for organic molecules. Powered by CDK.\n\n    Parameters\n    ----------\n    name: str\n        name of the configuration\n    description: str\n        the description of the predictor\n    descriptor: MolecularStructureDescriptor\n        the descriptor to featurize\n    features: List[str]\n        the list of features to compute (first 3 are aliases for feature sets).\n        Valid values include:\n        - all (alias for all below)\n        - standard (alias for AcidGroupCount, AtomicPolarizability, MassAutocorr,\n        PolarizabilityAutocorr, HBondAcceptorCount, BondCount, and AtomCount)\n        - expensive (alias for WeightedPath, ChiChain, ChiCluster, ChiPathCluster, and ChiPath)\n        - TopologicalSurfaceArea `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/FractionalPSADescriptor.html>`\n        - EccentricConnectivityIndex `CDK Link<http://cdk.github.io/cdk/2.2/docs/api/org/openscience/cdk/qsar/descriptors/molecular/EccentricConnectivityIndexDescriptor.html>`\n        - KappaShapeIndices `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/KappaShapeIndicesDescriptor.html>`\n        - RuleOfFive `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/RuleOfFiveDescriptor.html>`\n        - ALOGP `CDK Link<http://cdk.github.io/cdk/1.4/docs/api/org/openscience/cdk/qsar/descriptors/molecular/ALOGPDescriptor.html>`\n        - PolarizabilityAutocorr `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/AutocorrelationDescriptorPolarizability.html>`\n        - BondCount `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/AtomContainer.html>`\n        - VertexAdjacencyMagnitude `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/VAdjMaDescriptor.html>`\n        - ZagrebIndex `CDK Link<http://cdk.github.io/cdk/latest/docs/api/org/openscience/cdk/qsar/descriptors/molecular/ZagrebIndexDescriptor.html>`\n        - HBondDonorCount `CDK Link<http://cdk.github.io/cdk/2.2/docs/api/org/openscience/cdk/qsar/descriptors/molecular/HBondDonorCountDescriptor.html>`\n        - ChiPathCluster `CDK Link<http://cdk.github.io/cdk/2.2/docs/api/org/openscience/cdk/qsar/descriptors/molecular/ChiPathClusterDescriptor.html>`\n        - MannholdLogP `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/MannholdLogPDescriptor.html>`\n        - AtomCount `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/AtomContainer.html>`\n        - AromaticAtomCount `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/AromaticAtomsCountDescriptor.html>`\n        - LargestPiSystem `CDK Link<http://cdk.github.io/cdk/2.2/docs/api/org/openscience/cdk/qsar/descriptors/molecular/LargestPiSystemDescriptor.html>`\n        - HybridizationRatioDescriptor `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/HybridizationRatioDescriptor.html>`\n        - LargestChain `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/LargestChainDescriptor.html>`\n        - MDE `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/MDEDescriptor.html>`\n        - Weight `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/WeightDescriptor.html>`\n        - FMF `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/FMFDescriptor.html>`\n        - AtomicPolarizability `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/charges/Polarizability.html>`\n        - CarbonTypes `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/CarbonTypesDescriptor.html>`\n        - PetitjeanNumber `CDK Link<http://cdk.github.io/cdk/latest/docs/api/org/openscience/cdk/qsar/descriptors/molecular/PetitjeanNumberDescriptor.html>`\n        - HBondAcceptorCount `CDK Link<http://cdk.github.io/cdk/latest/docs/api/org/openscience/cdk/qsar/descriptors/molecular/HBondAcceptorCountDescriptor.html>`\n        - RotatableBondsCount `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/RotatableBondsCountDescriptor.html>`\n        - WeightedPath `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/WeightedPathDescriptor.html>`\n        - AromaticBondCount `CDK Link<http://cdk.github.io/cdk/2.2/docs/api/org/openscience/cdk/aromaticity/Aromaticity.html>`\n        - XLogP `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/XLogPDescriptor.html>`\n        - ChiPath `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/ChiPathDescriptor.html>`\n        - FragmentComplexity `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/FragmentComplexityDescriptor.html>`\n        - ChiChain `CDK Link<http://cdk.github.io/cdk/2.2/docs/api/org/openscience/cdk/qsar/descriptors/molecular/ChiChainDescriptor.html>`\n        - KierHallSmarts `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/KierHallSmartsDescriptor.html>`\n        - MassAutocorr `CDK Link<http://cdk.github.io/cdk/2.2/docs/api/org/openscience/cdk/qsar/descriptors/molecular/AutocorrelationDescriptorMass.html>`\n        - AcidGroupCount `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/AcidicGroupCountDescriptor.html>`\n        - BPol `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/BPolDescriptor.html>`\n        - WienerNumbers `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/WienerNumbersDescriptor.html>`\n        - ChiCluster `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/ChiClusterDescriptor.html>`\n        - BasicGroupCount `CDK Link<https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/BasicGroupCountDescriptor.html>`\n    excludes: List[str]\n        list of features to exclude (accepts same set of values as features). The final set\n        of outputs generated by the predictor is set(features) - set(excludes).\n\n    \"\"\"\n\n    uid = _properties.Optional(_properties.UUID, 'id', serializable=False)\n    name = _properties.String('config.name')\n    description = _properties.String('config.description')\n    descriptor = _properties.Object(Descriptor, 'config.descriptor')\n    features = _properties.List(_properties.String, 'config.features')\n    excludes = _properties.List(_properties.String, 'config.excludes')\n    typ = _properties.String('config.type', default='MoleculeFeaturizer', deserializable=False)\n    status = _properties.Optional(_properties.String, 'status', serializable=False)\n    status_info = _properties.Optional(\n        _properties.List(_properties.String),\n        'status_info',\n        serializable=False\n    )\n    archived = _properties.Boolean('archived', default=False)\n    experimental = _properties.Boolean(\"experimental\", serializable=False, default=True)\n    experimental_reasons = _properties.Optional(\n        _properties.List(_properties.String()),\n        'experimental_reasons',\n        serializable=False\n    )\n\n    # NOTE: These could go here or in _post_dump - it's unclear which is better right now\n    module_type = _properties.String('module_type', default='PREDICTOR')\n    schema_id = _properties.UUID('schema_id', default=UUID('24183b2f-848c-46fa-8640-21b7743e38a3'))\n\n    def __init__(self,\n                 name: str,\n                 description: str,\n                 descriptor: MolecularStructureDescriptor,\n                 features: List[str] = None,\n                 excludes: List[str] = None,\n                 session: Optional[Session] = None,\n                 report: Optional[Report] = None,\n                 archived: bool = False):\n        self.name: str = name\n        self.description: str = description\n        self.descriptor = descriptor\n        self.features = features if features is not None else [\"standard\"]\n        self.excludes = excludes if excludes is not None else []\n        self.session: Optional[Session] = session\n        self.report: Optional[Report] = report\n        self.archived: bool = archived\n\n    def _post_dump(self, data: dict) -> dict:\n        data['display_name'] = data['config']['name']\n        return data\n\n    def __str__(self):\n        return '<MolecularStructureFeaturizer {!r}>'.format(self.name)\n\n\nclass IngredientsToSimpleMixturePredictor(\n        Serializable['IngredientsToSimpleMixturePredictor'], Predictor):\n    \"\"\"[ALPHA] A predictor interface that constructs a simple mixture from ingredient quantities.\n\n    Parameters\n    ----------\n    name: str\n        name of the configuration\n    description: str\n        description of the predictor\n    output: FormulationDescriptor\n        descriptor that represents the output formulation\n    id_to_quantity: Mapping[str, RealDescriptor]\n        Map from ingredient identifier to the descriptor that represents its quantity,\n        e.g. ``{'water': RealDescriptor('water quantity', 0, 1)}``\n    labels: Mapping[str, List[str]]\n        Map from each label to all ingredients assigned that label, when present in a mixture,\n        e.g. ``{'solvent': ['water']}``\n\n    \"\"\"\n\n    uid = _properties.Optional(_properties.UUID, 'id', serializable=False)\n    name = _properties.String('config.name')\n    description = _properties.String('config.description')\n    output = _properties.Object(FormulationDescriptor, 'config.output')\n    id_to_quantity = _properties.Mapping(_properties.String, _properties.Object(RealDescriptor),\n                                         'config.id_to_quantity')\n    labels = _properties.Mapping(_properties.String, _properties.List(_properties.String),\n                                 'config.labels')\n    typ = _properties.String('config.type', default='IngredientsToSimpleMixture',\n                             deserializable=False)\n    status = _properties.Optional(_properties.String(), 'status', serializable=False)\n    status_info = _properties.Optional(\n        _properties.List(_properties.String()),\n        'status_info',\n        serializable=False\n    )\n    archived = _properties.Boolean('archived', default=False)\n    experimental = _properties.Boolean(\"experimental\", serializable=False, default=True)\n    experimental_reasons = _properties.Optional(\n        _properties.List(_properties.String()),\n        'experimental_reasons',\n        serializable=False\n    )\n\n    # NOTE: These could go here or in _post_dump - it's unclear which is better right now\n    module_type = _properties.String('module_type', default='PREDICTOR')\n    schema_id = _properties.UUID('schema_id', default=UUID('873e4541-da8a-4698-a981-732c0c729c3d'))\n\n    def __init__(self,\n                 name: str,\n                 description: str,\n                 output: FormulationDescriptor,\n                 id_to_quantity: Mapping[str, RealDescriptor],\n                 labels: Mapping[str, List[str]],\n                 session: Optional[Session] = None,\n                 report: Optional[Report] = None,\n                 archived: bool = False):\n        self.name: str = name\n        self.description: str = description\n        self.output: FormulationDescriptor = output\n        self.id_to_quantity: Mapping[str, RealDescriptor] = id_to_quantity\n        self.labels: Mapping[str, List[str]] = labels\n        self.session: Optional[Session] = session\n        self.report: Optional[Report] = report\n        self.archived: bool = archived\n\n    def _post_dump(self, data: dict) -> dict:\n        data['display_name'] = data['config']['name']\n        return data\n\n    def __str__(self):\n        return '<IngredientsToSimpleMixturePredictor {!r}>'.format(self.name)\n\n\nclass GeneralizedMeanPropertyPredictor(\n        Serializable['GeneralizedMeanPropertyPredictor'], Predictor):\n    \"\"\"[ALPHA] A predictor interface that computes generalized mean component properties.\n\n    Parameters\n    ----------\n    name: str\n        name of the configuration\n    description: str\n        description of the predictor\n    input_descriptor: FormulationDescriptor\n        descriptor that represents the input formulation\n    properties: List[str]\n        List of component properties to featurize\n    p: float\n        Power of the generalized mean\n    impute_properties: bool\n        Whether to impute missing ingredient properties.\n        If ``False`` an error is thrown when a missing ingredient property is encountered.\n        If ``True`` and no ``default_properties`` are specified, then the average over the\n        entire dataset is used.\n        If ``True`` and a default is specified in ``default_properties``, then the specified\n        default is used in place of missing values.\n    label: Optional[str]\n        Optional label\n    training_data: Optional[List[DataSource]]\n        Sources of training data. Each can be either a CSV or an GEM Table.\n        Candidates from multiple data sources will be combined into a flattened list and deduplicated by uid\n        and identifiers. Deduplication is performed if a uid or identifier is shared between two or more rows.\n        The content of a deduplicated row will contain the union of data across all rows that share the same uid\n        or at least 1 identifier. Training data is optional if the predictor is part of a graph that includes\n        all training data required by this predictor.\n    default_properties: Optional[Mapping[str, float]]\n        Default values to use for imputed properties.\n        Defaults are specified as a map from descriptor key to its default value.\n        If not specified and ``impute_properties == True`` the average over the entire dataset\n        will be used to fill in missing values. Any specified defaults will be used in place of\n        the average over the dataset. ``impute_properties`` must be ``True`` if\n        ``default_properties`` are provided.\n\n    \"\"\"\n\n    uid = _properties.Optional(_properties.UUID, 'id', serializable=False)\n    name = _properties.String('config.name')\n    description = _properties.String('config.description')\n    input_descriptor = _properties.Object(FormulationDescriptor, 'config.input')\n    properties = _properties.List(_properties.String, 'config.properties')\n    p = _properties.Float('config.p')\n    training_data = _properties.List(_properties.Object(DataSource), 'config.training_data')\n    impute_properties = _properties.Boolean('config.impute_properties')\n    default_properties = _properties.Optional(\n        _properties.Mapping(_properties.String, _properties.Float), 'config.default_properties')\n    label = _properties.Optional(_properties.String, 'config.label')\n    typ = _properties.String('config.type', default='GeneralizedMeanProperty',\n                             deserializable=False)\n    status = _properties.Optional(_properties.String, 'status', serializable=False)\n    status_info = _properties.Optional(\n        _properties.List(_properties.String),\n        'status_info',\n        serializable=False\n    )\n    archived = _properties.Boolean('archived', default=False)\n    experimental = _properties.Boolean(\"experimental\", serializable=False, default=True)\n    experimental_reasons = _properties.Optional(\n        _properties.List(_properties.String()),\n        'experimental_reasons',\n        serializable=False\n    )\n\n    # NOTE: These could go here or in _post_dump - it's unclear which is better right now\n    module_type = _properties.String('module_type', default='PREDICTOR')\n    schema_id = _properties.UUID('schema_id', default=UUID('29e53222-3217-4f81-b3b8-4197a8211ade'))\n\n    def __init__(self,\n                 name: str,\n                 description: str,\n                 input_descriptor: FormulationDescriptor,\n                 properties: List[str],\n                 p: float,\n                 impute_properties: bool,\n                 default_properties: Optional[Mapping[str, float]] = None,\n                 label: Optional[str] = None,\n                 training_data: Optional[List[DataSource]] = None,\n                 session: Optional[Session] = None,\n                 report: Optional[Report] = None,\n                 archived: bool = False):\n        self.name: str = name\n        self.description: str = description\n        self.input_descriptor: FormulationDescriptor = input_descriptor\n        self.properties: List[str] = properties\n        self.p: float = p\n        self.training_data: List[DataSource] = self._wrap_training_data(training_data)\n        self.impute_properties: bool = impute_properties\n        self.default_properties: Optional[Mapping[str, float]] = default_properties\n        self.label: Optional[str] = label\n        self.session: Optional[Session] = session\n        self.report: Optional[Report] = report\n        self.archived: bool = archived\n\n    def _post_dump(self, data: dict) -> dict:\n        data['display_name'] = data['config']['name']\n        return data\n\n    def __str__(self):\n        return '<GeneralizedMeanPropertyPredictor {!r}>'.format(self.name)\n\n\nclass SimpleMixturePredictor(Serializable['SimpleMixturePredictor'], Predictor):\n    \"\"\"\n    [ALPHA] A predictor interface that builds a simple graphical model.\n\n    Parameters\n    ----------\n    name: str\n        name of the configuration\n    description: str\n        description of the predictor\n    input_descriptor: FormulationDescriptor\n        input descriptor for the hierarchical (un-mixed) formulation\n    output_descriptor: FormulationDescriptor\n        output descriptor for the flat (mixed) formulation\n    training_data: Optional[List[DataSource]]\n        Sources of training data. Each can be either a CSV or an GEM Table.\n        Candidates from multiple data sources will be combined into a flattened list and deduplicated by uid\n        and identifiers. Deduplication is performed if a uid or identifier is shared between two or more rows.\n        The content of a deduplicated row will contain the union of data across all rows that share the same uid\n        or at least 1 identifier. Training data is optional if the predictor is part of a graph that includes\n        all training data required by this predictor.\n\n    \"\"\"\n\n    uid = _properties.Optional(_properties.UUID, 'id', serializable=False)\n    name = _properties.String('config.name')\n    description = _properties.String('config.description')\n    input_descriptor = _properties.Object(FormulationDescriptor, 'config.input')\n    output_descriptor = _properties.Object(FormulationDescriptor, 'config.output')\n    training_data = _properties.List(_properties.Object(DataSource), 'config.training_data')\n    typ = _properties.String('config.type', default='SimpleMixture',\n                             deserializable=False)\n    status = _properties.Optional(_properties.String, 'status', serializable=False)\n    status_info = _properties.Optional(_properties.List(_properties.String),\n                                       'status_info',\n                                       serializable=False)\n    archived = _properties.Boolean('archived', default=False)\n    experimental = _properties.Boolean(\"experimental\", serializable=False, default=True)\n    experimental_reasons = _properties.Optional(\n        _properties.List(_properties.String()),\n        'experimental_reasons',\n        serializable=False\n    )\n\n    # NOTE: These could go here or in _post_dump - it's unclear which is better right now\n    module_type = _properties.String('module_type', default='PREDICTOR')\n    schema_id = _properties.UUID('schema_id', default=UUID('e82a993c-e6ab-46a2-b636-c71d0ba224d1'))\n\n    def __init__(self,\n                 name: str,\n                 description: str,\n                 input_descriptor: FormulationDescriptor,\n                 output_descriptor: FormulationDescriptor,\n                 training_data: Optional[List[DataSource]] = None,\n                 session: Optional[Session] = None,\n                 report: Optional[Report] = None,\n                 archived: bool = False):\n        self.name: str = name\n        self.description: str = description\n        self.input_descriptor: FormulationDescriptor = input_descriptor\n        self.output_descriptor: FormulationDescriptor = output_descriptor\n        self.training_data: List[DataSource] = self._wrap_training_data(training_data)\n        self.session: Optional[Session] = session\n        self.report: Optional[Report] = report\n        self.archived: bool = archived\n\n    def _post_dump(self, data: dict) -> dict:\n        data['display_name'] = data['config']['name']\n        return data\n\n    def __str__(self):\n        return '<SimpleMixturePredictor {!r}>'.format(self.name)\n\n\nclass LabelFractionsPredictor(Serializable['LabelFractionsPredictor'], Predictor):\n    \"\"\"[ALPHA] A predictor interface that computes the relative proportions of labeled ingredients.\n\n    Parameters\n    ----------\n    name: str\n        name of the configuration\n    description: str\n        description of the predictor\n    input_descriptor: FormulationDescriptor\n        descriptor that contains formulation data\n    labels: List[str]\n        labels to compute the quantity fractions of\n\n    \"\"\"\n\n    uid = _properties.Optional(_properties.UUID, 'id', serializable=False)\n    name = _properties.String('config.name')\n    description = _properties.String('config.description')\n    input_descriptor = _properties.Object(FormulationDescriptor, 'config.input')\n    labels = _properties.List(_properties.String, 'config.labels')\n    typ = _properties.String('config.type', default='LabelFractions',\n                             deserializable=False)\n    status = _properties.Optional(_properties.String, 'status', serializable=False)\n    status_info = _properties.Optional(\n        _properties.List(_properties.String),\n        'status_info',\n        serializable=False\n    )\n    archived = _properties.Boolean('archived', default=False)\n    experimental = _properties.Boolean(\"experimental\", serializable=False, default=True)\n    experimental_reasons = _properties.Optional(\n        _properties.List(_properties.String()),\n        'experimental_reasons',\n        serializable=False\n    )\n\n    # NOTE: These could go here or in _post_dump - it's unclear which is better right now\n    module_type = _properties.String('module_type', default='PREDICTOR')\n    schema_id = _properties.UUID('schema_id', default=UUID('997a7e11-2c16-4e30-b531-9e657a863019'))\n\n    def __init__(self,\n                 name: str,\n                 description: str,\n                 input_descriptor: FormulationDescriptor,\n                 labels: List[str],\n                 session: Optional[Session] = None,\n                 report: Optional[Report] = None,\n                 archived: bool = False):\n        self.name: str = name\n        self.description: str = description\n        self.input_descriptor: FormulationDescriptor = input_descriptor\n        self.labels: List[str] = labels\n        self.session: Optional[Session] = session\n        self.report: Optional[Report] = report\n        self.archived: bool = archived\n\n    def _post_dump(self, data: dict) -> dict:\n        data['display_name'] = data['config']['name']\n        return data\n\n    def __str__(self):\n        return '<LabelFractionsPredictor {!r}>'.format(self.name)\n\n\nclass IngredientFractionsPredictor(Serializable[\"IngredientFractionsPredictor\"], Predictor):\n    \"\"\"[ALPHA] A predictor interface that computes ingredient fractions.\n\n    Parameters\n    ----------\n    name: str\n        name of the configuration\n    description: str\n        the description of the predictor\n    input_descriptor: FormulationDescriptor\n        descriptor that represents the input formulation\n    ingredients: List[str]\n        list of ingredients to featurize.\n        This list should contain all possible ingredients.\n        If an unknown ingredient is encountered, an error will be thrown.\n    \"\"\"\n    uid = _properties.Optional(_properties.UUID, 'id', serializable=False)\n    name = _properties.String('config.name')\n    description = _properties.String('config.description')\n    input_descriptor = _properties.Object(FormulationDescriptor, 'config.input')\n    ingredients = _properties.List(_properties.String, 'config.ingredients')\n\n    # NOTE: These could go here or in _post_dump - it's unclear which is better right now\n    module_type = _properties.String('module_type', default='PREDICTOR')\n    schema_id = _properties.UUID('schema_id', default=UUID('eb02a095-8cdc-45d8-bc82-1013b6e8e700'))\n    typ = _properties.String('config.type', default='IngredientFractions',\n                             deserializable=False)\n    status = _properties.Optional(_properties.String, 'status', serializable=False)\n    status_info = _properties.Optional(\n        _properties.List(_properties.String),\n        'status_info',\n        serializable=False\n    )\n    archived = _properties.Boolean('archived', default=False)\n    experimental = _properties.Boolean(\"experimental\", serializable=False, default=True)\n    experimental_reasons = _properties.Optional(\n        _properties.List(_properties.String()),\n        'experimental_reasons',\n        serializable=False\n    )\n\n    def __init__(self,\n                 name: str,\n                 description: str,\n                 input_descriptor: FormulationDescriptor,\n                 ingredients: List[str],\n                 session: Optional[Session] = None,\n                 report: Optional[Report] = None,\n                 archived: bool = False):\n        self.name: str = name\n        self.description: str = description\n        self.input_descriptor: FormulationDescriptor = input_descriptor\n        self.ingredients: List[str] = ingredients\n        self.session: Optional[Session] = session\n        self.report: Optional[Report] = report\n        self.archived: bool = archived\n\n    def _post_dump(self, data: dict) -> dict:\n        data['display_name'] = data['config']['name']\n        return data\n\n    def __str__(self):\n        return '<IngredientFractionsPredictor {!r}>'.format(self.name)\n","sub_path":"src/citrine/informatics/predictors.py","file_name":"predictors.py","file_ext":"py","file_size_in_byte":47510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"272784506","text":"import sys\ninput = sys.stdin.readline\nimport math\n\nMOD = 10**9+7\nN = int(input())\nF = []\nzero_v = 0\ninf = 0\nzero = 0\nfor _ in range(N):\n  x,y = map(int,input().split())\n  if x==0 and y==0:\n    zero_v += 1\n  elif x==0:\n    inf += 1\n  elif y==0:\n    zero += 1\n  else:\n    G = math.gcd(abs(x),abs(y))\n    F.append((x//G,y//G))\n\nall_keys = set()\n\nfor x,y in F:\n  if x*y >0:\n    if x>0:\n      all_keys.add((x,y))\n    else:\n      all_keys.add((-x,-y))\n  x,y = -y,x\n  if x*y >0:\n    if x>0:\n      all_keys.add((x,y))\n    else:\n      all_keys.add((-x,-y))\n\nall_dic = {k:[0,0] for k in all_keys}\n\nfor x,y in F:\n  if x*y > 0:\n    if x>0:\n      all_dic[(x,y)][0] += 1\n    else:\n      all_dic[(-x,-y)][0] += 1\n  else:\n    if x>0:\n      all_dic[(-y,x)][1] += 1\n    else:\n      all_dic[(y,-x)][1] += 1\n\nans = (pow(2,inf,MOD)) + (pow(2,zero,MOD))-1\nans %= MOD\n\nfor k in all_keys:\n  P,N = all_dic[k]\n  ans *= (pow(2,P,MOD)) + (pow(2,N,MOD))-1\n  ans %= MOD\n\n\nans += zero_v-1\n\nprint(ans%MOD)\n","sub_path":"Python_codes/p02679/s245882024.py","file_name":"s245882024.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"361727660","text":"\n\nimport airflow\nfrom airflow import DAG\n\n# Reference for all available airflow operators: \n# https://github.com/apache/incubator-airflow/tree/master/airflow/contrib/operators\nfrom airflow.contrib.operators.bigquery_operator import BigQueryOperator\nfrom airflow.contrib.operators.bigquery_to_gcs import BigQueryToCloudStorageOperator\nfrom airflow.hooks.base_hook import BaseHook\n# from airflow.contrib.operators.mlengine_operator import MLEngineTrainingOperator\n# above mlengine_operator currently doesnt support custom MasterType so we import our own plugins:\n\n# custom plugins\nfrom airflow.operators.app_engine_admin_plugin import AppEngineVersionOperator\nfrom airflow.operators.ml_engine_plugin import MLEngineTrainingOperator\n\n\nimport datetime\n\ndef _get_project_id():\n  \"\"\"Get project ID from default GCP connection.\"\"\"\n\n  extras = BaseHook.get_connection('google_cloud_default').extra_dejson\n  key = 'extra__google_cloud_platform__project'\n  if key in extras:\n    project_id = extras[key]\n  else:\n    raise ('Must configure project_id in google_cloud_default connection from Airflow Console')\n  return project_id\n\nPROJECT_ID = _get_project_id()\n\n# Data set constants, used in BigQuery tasks.  You can change these\n# to conform to your data.\n\nDATASET = 'GA360_test'\nTABLE_NAME = 'ga_sessions_sample'\nARTICLE_CUSTOM_DIMENSION = '10'     # we can change these to try out diff combinations\n\nBUCKET = 'gs://recserve_' + PROJECT_ID\nREGION = 'us-east1'\n\n# The code package name comes from the model code in the wals_ml_engine\n# directory of the solution code base.\nPACKAGE_URI = BUCKET + '/code/wals_ml_engine-0.1.tar.gz'\nJOB_DIR = BUCKET + '/jobs'\n\ndefault_args = {\n    'owner': 'airflow',\n    'depends_on_past': False,\n    'start_date': airflow.utils.dates.days_ago(2),\n    'email': ['airflow@example.com'],\n    'email_on_failure': True,\n    'email_on_retry': False,\n    'retries': 5,\n    'retry_delay': datetime.timedelta(minutes=5)\n}\n\n# Default schedule interval using cronjob syntax - can be customized here\n# or in the Airflow console.\n\nschedule_interval = '00 21 * * *'       # we will run at the end of the day\n\n\ndag = DAG('recommendations_training_v1', \n          default_args=default_args,\n          schedule_interval=schedule_interval)\n\ndag.doc_md = __doc__\n\n# BigQuery training data query, this will fetch data from BQ\n\nbql='''\n#legacySql\nSELECT\n fullVisitorId as clientId,\n ArticleID as contentId,\n (nextTime - hits.time) as timeOnPage,\nFROM(\n  SELECT\n    fullVisitorId,\n    hits.time,\n    MAX(IF(hits.customDimensions.index={0},\n           hits.customDimensions.value,NULL)) WITHIN hits AS ArticleID,\n    LEAD(hits.time, 1) OVER (PARTITION BY fullVisitorId, visitNumber\n                             ORDER BY hits.time ASC) as nextTime\n  FROM [{1}.{2}.{3}]\n  WHERE hits.type = \"PAGE\"\n) HAVING timeOnPage is not null and contentId is not null;\n'''\n\nbql = bql.format(ARTICLE_CUSTOM_DIMENSION, PROJECT_ID, DATASET, TABLE_NAME)\n\n\nt1 = BigQueryOperator(\n    task_id='bq_rec_training_data',\n    bql=bql,\n    destination_dataset_table='%s.recommendation_events' % DATASET,\n    write_disposition='WRITE_TRUNCATE', # specify to truncate on writes\n    dag=dag)\n\ntraining_file = BUCKET + '/data/recommendation_events.csv'\nt2 = BigQueryToCloudStorageOperator(\n    task_id='bq_export_op',\n    source_project_dataset_table='%s.recommendation_events' % DATASET,\n    destination_cloud_storage_uris=[training_file],\n    export_format='CSV',\n    dag=dag\n)\n\n\n# ML Engine training job\n\njob_id = 'recserve_{0}'.format(datetime.datetime.now().strftime('%Y%m%d%H%M'))\njob_dir = BUCKET + '/jobs/' + job_id\noutput_dir = BUCKET\ntraining_args = ['--job-dir', job_dir,\n                 '--train-files', training_file,\n                 '--output-dir', output_dir,\n                 '--data-type', 'web_views',\n                 '--use-optimized']\n\n\nt3 = MLEngineTrainingOperator(\n    task_id='ml_engine_training_op',\n    project_id=PROJECT_ID,\n    job_id=job_id,\n    package_uris=[PACKAGE_URI],\n    training_python_module='trainer.task',\n    training_args=training_args,\n    region=REGION,\n    scale_tier='CUSTOM',\n    master_type='complex_model_m_gpu',\n    dag=dag\n)\n\n# App Engine deploy new version\n\nt4 = AppEngineVersionOperator(\n    task_id='app_engine_deploy_version',\n    project_id=PROJECT_ID,\n    service_id='default',\n    region=REGION,\n    service_spec=None,\n    dag=dag\n)\n\n\nt2.set_upstream(t1)\nt3.set_upstream(t2)\nt4.set_upstream(t3)\n","sub_path":"airflow/dags/training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":4425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"17628390","text":"\"\"\"\nHandles caching of static resources.\n\"\"\"\nfrom os import path\nfrom calendar import timegm\nfrom email.utils import parsedate\nfrom wsgiref.handlers import format_date_time\n\nfrom aspen import Response\n\n\ndef inbound(request):\n    \"\"\"\n    Checks the last modified time of a file against\n    an If-Modified-Since header and responds with\n    a 304 if appropriate.\n    \"\"\"\n    uri = request.line.uri\n    version = request.context['__version__']\n\n    if not uri.startswith('/assets'):\n        return request\n    elif not version.endswith('dev') and version in uri:\n        # These will be cached indefinitely in the outbound hook\n        return request\n\n    ims = request.headers.get('If-Modified-Since')\n    last_modified = int(path.getctime(request.fs))\n\n    if ims:\n        ims = timegm(parsedate(ims))\n        if ims >= last_modified:\n            raise Response(304, headers={\n                'Last-Modified': format_date_time(last_modified),\n                'Cache-Control': 'no-cache'\n            })\n\n\ndef outbound(response):\n    request = response.request\n    uri = request.line.uri\n    if not uri.startswith('/assets'):\n        return response\n\n    version = request.context['__version__']\n    if not version.endswith('dev') and version in uri:\n        # This specific asset is versioned, so\n        # it's fine to cache this forever\n        response.headers['Expires'] = 'Sun, 17 Jan 2038 19:14:07 GMT'\n        response.headers['Cache-Control'] = 'public'\n\n    else:\n        # Asset is not versioned or dev version is running.\n        last_modified = int(path.getctime(request.fs))\n        response.headers['Last-Modified'] = format_date_time(last_modified)\n        response.headers['Cache-Control'] = 'no-cache'\n","sub_path":"gittip/cache_static.py","file_name":"cache_static.py","file_ext":"py","file_size_in_byte":1717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"211684044","text":"# -*- coding: utf-8 -*-\n# See LICENSE file for full copyright and licensing details.\n\"\"\"\nInherited stock move to relate with the amazon\n\"\"\"\n\nfrom odoo import models, fields\n\n\nclass StockMove(models.Model):\n    \"\"\"\n    inherited class to set Amazon Instance in Stock Picking\n    \"\"\"\n    _inherit = \"stock.move\"\n\n    amazon_instance_id = fields.Many2one('amazon.instance.ept', string='Instance')\n    updated_in_amazon = fields.Boolean(\"Update Status in Amazon\", default=False,\n                                       help=\"Use only for phantom products\")\n    amazon_order_reference = fields.Char(\"Amazon Order Ref\", default=False,\n                                         help=\"Order Reference provided by Amazon\")\n    amazon_order_item_id = fields.Char(\"Amazon Order Item\", default=False,\n                                       help=\"Order Reference Item id provided by\"\n                                            \" Amazon in Order Reference\")\n    amazon_shipment_id = fields.Char(size=120, string='Amazon Shipment ID', default=False,\n                                     copy=False,\n                                     help=\"Shipment Item ID provided by Amazon when we integrate \"\n                                          \"shipment report from Amazon\")\n\n    amazon_shipment_item_id = fields.Char(size=120, string='Amazon Shipment Item ID', default=False,\n                                          copy=False,\n                                          help=\"Shipment Item ID provided by Amazon \"\n                                               \"when we integrate shipment report from Amazon\")\n    tracking_number = fields.Char(string=\"Amazon Shipment Tracking No\", size=120, default=False,\n                                  copy=False)\n    amz_shipment_report_id = fields.Many2one('shipping.report.request.history',\n                                             string=\"Amazon Shipping Report Id\")\n    adjusted_date = fields.Datetime(\"Adjustment Time\", copy=False)\n    code_description = fields.Char(\"FBA Stock Adjustment Description\")\n    fulfillment_center_id = fields.Many2one('amazon.fulfillment.center',\n                                            string='Fulfillment Center', copy=False,\n                                            default=False)\n    transaction_item_id = fields.Char(\"Transaction Item\", copy=False)\n    seller_id = fields.Many2one(\"amazon.seller.ept\", \"Seller\")\n    code_id = fields.Many2one('amazon.adjustment.reason.code', string='FBA Stock Adjustment Code',\n                              copy=False)\n    amz_stock_adjustment_report_id = fields.Many2one(\"amazon.stock.adjustment.report.history\",\n                                                     \"Amazon Stock Adjustment Report History\")\n    return_created = fields.Boolean(string='FBA Return Created', default=False, copy=False,\n                                    help=\"True, If customer return created\")\n    fba_returned_date = fields.Datetime(\"Return Date\",\n                                        help=\"When return processed in the FBA warehouse.\")\n    return_reason_id = fields.Many2one(\"amazon.return.reason.ept\", string=\"Return Reason\",\n                                       help=\"Product Return Reason by Customer.\")\n    amz_return_report_id = fields.Many2one('sale.order.return.report',\n                                           string=\"Amazon Return Report Id\")\n    detailed_disposition = fields.Selection(\n        [('SELLABLE', 'SELLABLE'), ('DAMAGED', 'DAMAGED'),\n         ('CUSTOMER_DAMAGED', 'CUSTOMER DAMAGED'),\n         ('DEFECTIVE', 'DEFECTIVE'),\n         ('CARRIER_DAMAGED', 'CARRIER DAMAGED'),\n         ('EXPIRED', 'EXPIRED')],\n        help=\"Product's Status while Return\")\n    status_ept = fields.Char(string='FBA return status', help=\"Status Of Returned Product\")\n\n    def _get_new_picking_values(self):\n        \"\"\"We need this method to set Amazon Instance in Stock Picking\"\"\"\n        res = super(StockMove, self)._get_new_picking_values()\n        proc_group = self.group_id\n        if proc_group and proc_group.odoo_shipment_id:\n            res.update({\n                'partner_id': proc_group.odoo_shipment_id.address_id and\n                              proc_group.odoo_shipment_id.address_id.id,\n                'ship_plan_id': proc_group.odoo_shipment_id.shipment_plan_id and\n                                proc_group.odoo_shipment_id.shipment_plan_id.id,\n                'amazon_shipment_id': proc_group.odoo_shipment_id.shipment_id,\n                'ship_label_preference': proc_group.odoo_shipment_id.label_prep_type,\n                'fulfill_center': proc_group.odoo_shipment_id.fulfill_center_id,\n                'odoo_shipment_id': proc_group.odoo_shipment_id.id,\n                'seller_id': proc_group.odoo_shipment_id and\n                             proc_group.odoo_shipment_id.shipment_plan_id and\n                             proc_group.odoo_shipment_id.shipment_plan_id.instance_id.seller_id and\n                             proc_group.odoo_shipment_id.shipment_plan_id.instance_id.seller_id.id or\n                             False, })\n        return res\n","sub_path":"amazon_ept/models/stock_move.py","file_name":"stock_move.py","file_ext":"py","file_size_in_byte":5079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"414014105","text":"import logging\nimport sqlite3\nimport time\nimport sys\nfrom logging.config import dictConfig\n\nfrom flask import Flask, json, render_template, request, url_for, redirect, flash, abort\n\n\nclass UTCFormatter(logging.Formatter):\n    converter = time.gmtime\n\n\nclass ErrorFilter(logging.Filter):\n    def filter(self, record):\n        allow = record.levelno < logging.ERROR\n\n        return allow\n\n\ndb_connection_counter = 0\n\n\ndictConfig({\n    'version': 1,\n    'disable_existing_loggers': True,\n    'formatters': {\n        'default': {\n            '()': UTCFormatter,\n            'format': '%(asctime)s [%(levelname)s]:%(name)s: %(message)s',\n            'datefmt': '%Y-%m-%d %H:%M:%SZ'\n        }\n    },\n    'filters': {\n        'error_filter': {\n            '()': ErrorFilter,\n        }\n    },\n    'handlers': {\n        'console_out': {\n            'formatter': 'default',\n            'class': 'logging.StreamHandler',\n            'stream': 'ext://sys.stdout',\n            'filters': ['error_filter']\n            # 'level': 'DEBUG',\n        },\n        'console_err': {\n            'formatter': 'default',\n            'class': 'logging.StreamHandler',\n            'stream': 'ext://sys.stderr',\n            'level': 'ERROR',\n        },\n        'wsgi': {\n            'formatter': 'default',\n            'class': 'logging.StreamHandler',\n            'stream': 'ext://flask.logging.wsgi_errors_stream',\n        },\n    },\n    'root': {  # root logger\n        'handlers': ['console_out', 'console_err'],\n        'level': 'DEBUG',\n        'propagate': False\n    },\n})\n\n\n# Function to get a database connection.\n# This function connects to database with the name `database.db`\ndef get_db_connection():\n    global db_connection_counter\n    connection = sqlite3.connect('database.db')\n    db_connection_counter += 1\n    connection.row_factory = sqlite3.Row\n\n    return connection\n\n\n# Function to get a post using its ID\ndef get_post(post_id):\n    connection = get_db_connection()\n    post = connection.execute('SELECT * FROM posts WHERE id = ?',\n                              (post_id,)).fetchone()\n    connection.close()\n\n    return post\n\n\n# Function to get total count of posts\ndef get_post_count():\n    connection = get_db_connection()\n    post_count_row = connection.execute('SELECT COUNT(*) FROM posts').fetchone()\n    connection.close()\n\n    if post_count_row is not None:\n        return post_count_row[0]\n\n    return 0\n\n\ndef check_health():\n    try:\n        connection = get_db_connection()\n        connection.execute('SELECT 1 FROM posts').fetchone()\n    except Exception as e:\n        print('Exception: {0}'.format(e))\n\n        return False\n\n    return True\n\n\ndef handle_flask_error(e):\n    app_logger = logging.getLogger('app')\n    app_logger.error('{0}'.format(e))\n\n    return render_template('error.html'), e.code\n\n\n\n# Define the Flask application\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'your secret key'\napp.register_error_handler(Exception, handle_flask_error)\n\napp.logger.debug('Demonstrating app debug level log')\napp.logger.info('Demonstrating app info level log')\napp.logger.warning('Demonstrating app warning level log')\napp.logger.error('Demonstrating app error level log')\napp.logger.critical('Demonstrating app critical level log')\n\n\n# Define the main route of the web application \n@app.route('/')\ndef index():\n    connection = get_db_connection()\n    posts = connection.execute('SELECT * FROM posts').fetchall()\n    connection.close()\n    return render_template('index.html', posts=posts)\n\n\n# Define how each individual article is rendered \n# If the post ID is not found a 404 page is shown\n@app.route('/<int:post_id>')\ndef post(post_id):\n    post = get_post(post_id)\n    if post is None:\n        app.logger.error('Non-existing article accessed with id {0}. \"404\" page returned'.format(post_id))\n        return render_template('404.html'), 404\n    else:\n        app.logger.info('Article {0} retrieved'.format(post[\"title\"]))\n        return render_template('post.html', post=post)\n\n\n# Define the About Us page\n@app.route('/about')\ndef about():\n    app.logger.info('\"About Us\" page retrieved')\n    return render_template('about.html')\n\n\n# Define the post creation functionality \n@app.route('/create', methods=('GET', 'POST'))\ndef create():\n    if request.method == 'POST':\n        title = request.form['title']\n        content = request.form['content']\n\n        if not title:\n            flash('Title is required!')\n            app.logger.error('Submitting article without title')\n        else:\n            connection = get_db_connection()\n            connection.execute('INSERT INTO posts (title, content) VALUES (?, ?)', (title, content))\n            connection.commit()\n            app.logger.info('New article {0} created'.format(title))\n            connection.close()\n            return redirect(url_for('index'))\n\n    return render_template('create.html')\n\n\n# Define healthcheck endpoint\n@app.route('/healthz')\ndef healthcheck():\n    if check_health():\n        response = app.response_class(\n            response=json.dumps({\"result\": \"OK - healthy\"}),\n            status=200,\n            mimetype='application/json'\n        )\n    else:\n        response = app.response_class(\n            response=json.dumps({\"result\": \"ERROR - unhealthy\"}),\n            status=500,\n            mimetype='application/json'\n        )\n\n    return response\n\n\n# Define metrics endpoint\n@app.route('/metrics')\ndef metrics():\n    post_count = get_post_count()\n    response = app.response_class(\n        response=json.dumps({\n            \"db_connection_count\": db_connection_counter,\n            \"post_count\": post_count,\n        }),\n        status=200,\n        mimetype='application/json'\n    )\n\n    return response\n\n\n# start the application on port 3111\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0', port='3111')\n","sub_path":"project/techtrends/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"488514513","text":"import sys\n\nd = {\n       '0' : 'zero',\n       '1' : 'one',\n       '2' : 'two',\n       '3' : 'three',\n       '4' : 'four',\n       '5' : 'five',\n       '6' : 'six',\n       '7' : 'seven',\n       '8' : 'eight',\n       '9' : 'nine',\n       '10' : 'ten',\n}\nfor n in sys.stdin.readlines():\n    print(' '.join([d[word] if word in d else \"unknown\" for word in n.split()]))\n","sub_path":"nums2words_v02_42.py","file_name":"nums2words_v02_42.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"185028992","text":"import pandas as pd \n\ndef create_table():\n    data = pd.read_json('course_data.json')\n    data.reset_index()\n    data.rename(columns={\"course_id\": \"COURSE_ID\", \n                      \"course_title\": \"COURSE_TITLE\",\n                      \"is_paid\": \"IS_PAID\",\n                      \"price\": \"PRICE\",\n                      \"num_subscribers\": \"NUM_SUBSCRIBERS\",\n                      \"num_reviews\": \"NUM_REVIEWS\",\n                      \"num_lectures\": \"NUM_LECTURES\",\n                      \"level\": \"LEVELS\",\n                      \"content_duration\": \"CONTENT_DURATION\",\n                      \"published_timestamp\": \"PUBLISHED_TIMESTAMPS\",\n                      \"subject\": \"SUBJECT\",\n                      \"author\": \"AUTHOR\"},\n                       inplace=True)\n    \n    author = data[['AUTHOR']]\n    course = data[['COURSE_ID','COURSE_TITLE','PRICE','LEVELS','CONTENT_DURATION']]\n    subject = data[['COURSE_ID','SUBJECT']]\n    fact = data.drop(['COURSE_TITLE','PRICE', 'LEVELS', 'CONTENT_DURATION', 'SUBJECT', 'AUTHOR'], axis =1)\n\n    author[['FIRST_NAME', 'LAST_NAME', 'AUTHOR_CODE']] = pd.DataFrame([ x.split('_') for x in author['AUTHOR'].tolist()])\n    author = author.drop(['AUTHOR'], axis=1)\n    author['AUTHOR_ID'] = author.index\n    subject.rename(columns={'COURSE_ID': 'SUBJECT_ID'}, inplace=True)\n    course.rename(columns={'COURSE_TITLE': 'COURSE_NAME'}, inplace=True)\n    # print(author.head())\n    # print(fact.head())\n    # print(subject.head())\n    # print(course.head())\n\n    return author, course, subject, fact\n\n\n# create_table()","sub_path":"author_data.py","file_name":"author_data.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"57841985","text":"#!usr/bin/env/python\nimport sys\nfrom enum import Enum\nfrom datetime import timedelta, date\nimport psycopg2\nfrom basketball_reference_web_scraper import client\nfrom basketball_reference_web_scraper.data import Team\nimport auth\nimport log\nfrom scraper_lib import daterange, benchmark\n\n@benchmark\ndef parse_and_input(score, connection, gameday):\n    #create object and cursor for pSQL processing\n    cur = connection.cursor()\n    curr_player = log.Log(score, gameday)\n\n    #if player doesn't exist, add to roster\n    if not (curr_player.exists(cur)):\n        curr_player.add_player(cur)\n    #else add log to game logs\n    else:\n        player_id = curr_player.get_pid(cur)\n        curr_player.ins_log(player_id, cur)\n\ndef update(connection, gameday=(date.today()-timedelta(1))):\n    #convert to string and split to individual date elements\n    gameday = str(gameday)\n    year, month, day = gameday.split('-', 2)\n\n    #grab all scores on given date\n    scores = client.player_box_scores(day, month, year)\n\n    #input each score to DB\n    for score in scores:\n        parse_and_input(score, connection, gameday)\n\ndef pull(connection, start=None):\n    #use season start if not specified\n    if start is None:\n        sched = client.season_schedule(2019)\n        start = sched[0].get('start_time').date()\n    current = date.today()\n\n    #add all game logs from given start to date\n    for gameday in daterange(start, current):\n        update(connection, gameday)\n\ndef clear(connection):\n    #ensure user means to delete\n    check = input('Are you sure you want to clear the DB? Y/N\\n')\n    if check == 'Y':\n        print('Clearing league_roster, game_logs...')\n    elif check == 'N':\n        print('Clear aborted.')\n        return\n    else:\n        print('Please answer with Y or N')\n\n    #drop all game log tables, delete entries in roster\n    cur = connection.cursor()\n    delete = 'DELETE FROM game_logs'\n    cur.execute(delete)\n    delete = 'DELETE FROM league_roster'\n    cur.execute(delete)\n\ndef main():\n    #attempt to establish connection\n    try:\n        connection = psycopg2.connect(**auth.connectParams)\n    except (Exception, psycopg2.Error) as error:\n        print(\"Error: Failed to connect,\", error)\n        return\n\n    #execution option handling\n    if '--clear' in sys.argv:\n        clear(connection)\n    elif '--boot' in sys.argv:\n        pull(connection)\n    elif '--reboot' in sys.argv:\n        clear(connection)\n        pull(connection)\n    elif '--advance' in sys.argv:\n        year, month, day = (sys.argv[2]).split('-', 2)\n        start = date(int(year), int(month), int(day))\n        pull(connection, start)\n    else:\n        update(connection)\n        \n    #make all changes permanent\n    connection.commit()\n\nif __name__ == \"__main__\":\n    main()","sub_path":"scraper/scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":2766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"409473889","text":"from botocore.session import Session\nfrom pyspark import SparkConf, SparkContext\nfrom pyspark.sql import SQLContext\nfrom functools import reduce\n\n\ndef get_spark_context(env_context):\n    if env_context == 'local':\n        session = Session()\n        credentials = session.get_credentials()\n        current_credentials = credentials.get_frozen_credentials()\n        config = (SparkConf().setMaster(env_context).setAppName(\"Myapp\"))\n    else:\n        config = (SparkConf().setAppName(\"Myapp\"))\n\n    sc = SparkContext(conf=config)\n\n    if env_context == 'local':\n        sc._jsc.hadoopConfiguration().set(\"fs.s3a.access.key\", current_credentials.access_key)\n        sc._jsc.hadoopConfiguration().set(\"fs.s3a.secret.key\", current_credentials.secret_key)\n    else:\n        pass\n    sql_context = SQLContext(sc)\n    sc.setLogLevel(\"ERROR\")\n    return sc, sql_context\n\ndef get_file_schema(feed_name, file_type):\n    return [\"property_id\", \"address\", \"covered_area\", \"owner_id\", \"last_sale_date\", \"create_date\", \"status_id\",\n            \"status_desc\", \"price\", \"postal_zone\", \"zip_code\", \"city\", \"state\", \"country\"]\n\ndef get_frame_with_columns(raw_data, additional_columns=[]):\n    old_columns = raw_data.schema.names[:]\n    new_columns = get_file_schema('dummy', 'dummy')[:]\n\n    new_columns.extend(additional_columns)\n    df = reduce(lambda data, idx: data.withColumnRenamed(old_columns[idx], new_columns[idx]), range(len(old_columns)),\n                raw_data)\n    return df\n\ndef get_columns_with_table_prefix(table_name):\n    renamed_cols = (['{0} AS {1}_{2}'.format(s, table_name, s) for s in get_file_schema('', '')])\n    return renamed_cols\n\n","sub_path":"approach-3/src/utility.py","file_name":"utility.py","file_ext":"py","file_size_in_byte":1642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"450759179","text":"hex_digits = '0123456789ABCDEF'\n \ndef hex_digit(dec_digit):\n    if dec_digit > 15:\n        raise ValueError('Aucun chiffre hexadécimal de valeur {}'.format(dec_digit))\n    return hex_digits[dec_digit]\n \ndef dec_digit(hex_digit):\n    d = hex_digits.find(hex_digit)\n    if d == -1:\n        raise ValueError(\"{} n'est pas un chiffre hexadécimal\".format(hex_digit))\n    return d\n \n \ndef conv(n, d, a):\n    decimal = 0\n    digits = str(n)\n \n    for i, digit in enumerate(digits):\n        decimal += dec_digit(digit) * d ** (len(digits) - 1 - i)\n \n    digits = []\n    q = decimal\n \n    while q > 0:\n        r = q % a\n        q = q // a\n        digits.append(hex_digit(r))\n \n    return ''.join(reversed(digits))\n \ndef float_bin2dec(bits):\n \n    decimal = 0\n \n    for i, b in enumerate(bits):\n            decimal += int(b) * 2 ** (-1 - i)\n \n    return decimal\n \n \n \ndef test():\n    tests = (\n        (255, 10, 16, 'FF'),\n        ('EE', 15, 10, '224'),\n        ('1001010110', 2, 10, ''),\n        ('1001010110', 2, 16, ''),\n        ('2002', 10, 2, ''),\n        ('2002', 10, 16, ''),\n        ('A1C4', 16, 10, ''),\n        ('A1C4', 16, 2, ''),\n        ('0101010101010110', 2, 10, ''),\n        ('5642', 10, 2, ''),\n    )\n \n    for (n, d, a, r) in tests:\n        print(n, 'de base', d, 'vers base', a, '==>', end=' ')\n        output = conv(n, d, a)\n        if r == '':\n            test_result = '???'\n        else:\n            test_result = (output == r)\n        \n        print(output, '==>', test_result)\n \n \ntest()","sub_path":"source/scripts/conversion.py","file_name":"conversion.py","file_ext":"py","file_size_in_byte":1504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"333733368","text":"# точка входа в программу\nimport common as Common\nimport controllers as Controllers\nimport utilities as Utilities\n\ndef router():\n\tcommand = Utilities.getCommand()\n\tif (Utilities.isValidCommand(command)):\n\t\tControllers.callController(command)\n\telse:\n\t\tinput(''.join(command) + Common.ERROR_COMMAND)\n\ndef main():\n\tUtilities.loadData()\n\twhile (True):\n\t\tControllers.menu()\n\t\trouter()\n\nmain()","sub_path":"Lab1/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"293485079","text":"#-*- coding:utf-8 -*-\n\nimport sys\nimport os\nfrom PyQt4 import QtGui, QtCore, Qt\nfrom dialog import DelDialog\nfrom aboutDialog import aboutDialog\n\nfileSupported = ['bat', 'css', 'doc', 'docx', 'exe', 'iso', 'jpg', 'mp3', 'pdf', 'png' ,'ppt', 'pptx', 'py', 'rar', 'txt', 'zip']\n\nclass mainWin(QtGui.QWidget):\n    def __init__(self):\n        super(mainWin, self).__init__()\n        self.setWindowTitle('光速启动')\n        self.setWindowIcon(QtGui.QIcon('./images/light_speed.png'))\n        self.initMain()\n        self.createContextMenu()\n        self.resize(150, 300)\n\n        self.icon = QtGui.QSystemTrayIcon()\n        self.icon.setIcon(QtGui.QIcon('./images/light_speed.png'))\n        self.initIcon()\n\n        screen = QtGui.QDesktopWidget().screenGeometry()\n        self.wid = screen.width()\n        self.hei = screen.height()\n\n        self.setAcceptDrops(True)\n\n    def initMain(self):\n        self.btnList = []\n        try:\n            self.grid = QtGui.QGridLayout()\n            with open('./data/main.txt', 'r', encoding = 'utf-8') as f:\n                res = f.read().splitlines()\n                self.cnt = 0\n                for line in res:\n                    self.createPushButton(line, res.index(line))\n            self.setLayout(self.grid)\n            with open('./style/main.css', 'r') as f:\n                self.setStyleSheet(f.read())\n        except IOError:\n            pass\n\n    def initIcon(self):\n        self.icon.showAction = QtGui.QAction(\"&显示窗口\", self, triggered = self.showMain)\n        self.icon.quitAction = QtGui.QAction(\"&退出\", self, triggered = sys.exit)\n        \n        self.icon.trayIconMenu = QtGui.QMenu()\n        self.icon.trayIconMenu.addAction(self.icon.showAction)\n        self.icon.trayIconMenu.addAction(self.icon.quitAction)\n        self.icon.setContextMenu(self.icon.trayIconMenu)\n\n    def closeEvent(self, event):\n        self.hide()\n        self.icon.show()\n        event.ignore()\n\n    def showMain(self):\n        self.icon.hide()\n        self.show()\n\n    def enterEvent(self, event):\n        self.right = self.x() + self.width()\n        if self.right >= self.wid:\n            self.setGeometry(self.wid - self.width(), 30, 150, self.hei)\n    \n    def leaveEvent(self, event):\n        self.right = self.x() + self.frameGeometry().width()\n        if self.right >= self.wid:\n            self.setGeometry(self.wid - 2, 30, 150, self.hei)\n\n    def mousePressEvent(self, event):\n        if event.buttons() == Qt.Qt.LeftButton:\n            self.dragPosition = event.globalPos() - self.frameGeometry().topLeft()\n            event.accept()\n\n    def mouseMoveEvent(self, event):\n        if event.buttons() == Qt.Qt.LeftButton:\n            self.move(event.globalPos() - self.dragPosition)\n            event.accept()\n\n    def findBtn(self, btn):\n        for listBtn in self.btnList:\n            if(btn == listBtn[0]):\n                return listBtn[1]\n\n    def react(self):\n        sender = self.sender()\n        os.startfile(self.findBtn(sender))\n\n    def createContextMenu(self):\n        self.setContextMenuPolicy(QtCore.Qt.CustomContextMenu)\n        self.customContextMenuRequested.connect(self.showContextMenu)\n        self.contextMenu = QtGui.QMenu(self)\n        self.aFile = self.contextMenu.addAction(u'添加文件')\n        self.aDir = self.contextMenu.addAction(u'添加文件夹')\n        self.delete = self.contextMenu.addAction(u'删除')\n        self.aboutAct = self.contextMenu.addAction(u\"关于\")\n        self.clo = self.contextMenu.addAction(u'关闭窗口')\n\n        self.aFile.triggered.connect(self.addFile)\n        self.aDir.triggered.connect(self.addDir)\n        self.delete.triggered.connect(self.delItem)\n        self.clo.triggered.connect(self.close)\n        self.aboutAct.triggered.connect(self.about)\n\n    def about(self):\n        aboutDialog().exec_()\n\n    def showContextMenu(self, pos):\n        self.contextMenu.move(self.pos() + pos)\n        self.contextMenu.show()\n    \n    def dragEnterEvent(self, event):\n        if event.mimeData().hasUrls():\n            event.acceptProposedAction()\n\n    def dropEvent(self, event):\n        if event.mimeData().hasUrls():\n            for url in event.mimeData().urls():\n                line = str(url.toLocalFile())\n                line = line.replace('/','\\\\')\n                with open('./data/main.txt', 'r+', encoding = 'utf-8') as f:\n                    res = f.read()\n                    if line in res.splitlines():\n                        return\n                    if res == '':\n                        pass\n                    else:\n                        f.write('\\n')\n                    f.write(line)\n                with open('./data/main.txt', 'r', encoding = 'utf-8') as f:\n                    lineNum = len(f.read().splitlines()) - 1\n                self.createPushButton(line, lineNum)\n            event.acceptProposedAction()\n    \n    def addFile(self):\n        line = str(QtGui.QFileDialog.getOpenFileName())\n        if not len(line):\n            return\n        line = line.replace('/','\\\\')\n        with open('./data/main.txt', 'r+', encoding = 'utf-8') as f:\n            res = f.read()\n            if line in res.splitlines():\n                return\n            if res == '':\n                pass    \n            else:\n                f.write('\\n')\n            f.write(line)\n        with open('./data/main.txt', 'r', encoding = 'utf-8') as f:\n            lineNum = len(f.read().splitlines()) - 1\n        self.createPushButton(line, lineNum)\n\n    def addDir(self):\n        line = str(QtGui.QFileDialog.getExistingDirectory())\n        if not len(line):\n            return\n        line = line.replace('/','\\\\')\n        with open('./data/main.txt', 'r+', encoding = 'utf-8') as f:\n            res = f.read()\n            if line in res.splitlines():\n                return\n            if res == '':\n                pass\n            else:\n                f.write('\\n')\n            f.write(line)\n        with open('./data/main.txt', 'r', encoding = 'utf-8') as f:\n            lineNum = len(f.read().splitlines()) - 1\n        self.createPushButton(line, lineNum)\n\n    def createPushButton(self, line, lineNum):\n        btn = QtGui.QPushButton(line.split('\\\\')[-1])\n        suffix = line.split('.')\n        if os.path.isdir(line):\n            btn.setProperty('dir', 'true')\n        elif suffix[-1] not in fileSupported:\n            btn.setProperty('unknown', 'true')\n        else:\n            btn.setProperty(suffix[-1], 'true')\n        self.btnList.append([btn, line])\n        self.connect(btn, QtCore.SIGNAL('clicked()'), self.react)\n        self.grid.addWidget(btn, lineNum/3 , self.cnt, 1, 1)\n        self.cnt = (self.cnt + 1) % 3\n                    \n    def delItem(self):\n        with open('./data/main.txt', 'r', encoding = 'utf-8') as f:\n            res = f.read().splitlines()\n        DelDialog(u'删除', res).exec_()\n        for item in self.btnList:\n            item[0].setParent(None)\n            self.grid.removeWidget(item[0])\n        with open('./data/main.txt', 'r', encoding = 'utf-8') as f:\n            res = f.read().splitlines()\n            self.cnt = 0\n            self.btnList = []\n            for line in res:\n                self.createPushButton(line, res.index(line))\n\napp = QtGui.QApplication(sys.argv)\nex = mainWin()\nex.show()\napp.exec_()\n","sub_path":"Python3/Win/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"426679663","text":"# -*- coding: utf-8 -*-\n\n# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2018, 2019.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any modifications or derivative works of this code must retain this\n# copyright notice, and modified files need to carry a notice indicating\n# that they have been altered from the originals.\n\n\"\"\"\nFor now this module validates parameters based on its schema.\nOnce all json schema is eliminated from classes, this module will be removed\n\"\"\"\n\nimport numpy as np\nimport jsonschema\nfrom qiskit.aqua import AquaError\n\n\ndef validate(args_dict, schema_dict):\n    \"\"\" validate json data according to a schema\"\"\"\n    if schema_dict is None:\n        return\n\n    properties_dict = schema_dict.get('properties', None)\n    if properties_dict is None:\n        return\n\n    json_dict = {}\n    for property_name, _ in properties_dict.items():\n        if property_name in args_dict:\n            value = args_dict[property_name]\n            if isinstance(value, np.ndarray):\n                value = value.tolist()\n\n            json_dict[property_name] = value\n    try:\n        jsonschema.validate(json_dict, schema_dict)\n    except jsonschema.exceptions.ValidationError as vex:\n        raise AquaError(vex.message)\n","sub_path":"qiskit/aqua/utils/validation.py","file_name":"validation.py","file_ext":"py","file_size_in_byte":1412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"439356723","text":"#!/usr/bin/env python3\n##############################################################################\n# Description:\n#\n#   This script creates a yearly CSV file of pricebar data,\n#   from a monthly CSV file of pricebar data.\n#\n# Usage:\n#\n#   ./convertMonthlyToYearly.py --help\n#   ./convertMonthlyToYearly.py --version\n#\n#   ./convertMonthlyToYearly.py --input-file=\"/tmp/soybeans_monthly.txt\" --output-file=\"/tmp/soybeans_yearly.txt\"\n#   \n##############################################################################\n\nimport sys\nimport os\nimport copy\n\n# For parsing command-line options\nfrom optparse import OptionParser  \n\n# For logging.\nimport logging\n\nimport datetime\n\n\n##############################################################################\n# Global Variables\n##############################################################################\n\n# Version string.\nVERSION = \"0.1\"\n\n# Input CSV text file.\n# This value is obtained via command-line parameter.\ninputFile = \"\"\n\n# Output CSV text file.\n# This value is obtained via command-line parameter.\noutputFile = \"\"\n\n# Number of lines of text to skip in the CSV data files.  This is\n# usually a header line that just displays what the columns are.\n# This value is obtained via command-line parameter.\nlinesToSkip = 1\n\n# Header line to put as the first line of text in the destination file.\nheaderLine = \"\\\"Date\\\",\\\"Open\\\",\\\"High\\\",\\\"Low\\\",\\\"Close\\\",\\\"Volume\\\",\\\"OpenInt\\\"\"\n\n# Use Windows newlines.\nnewline = \"\\r\\n\"\n\n# For logging.\nlogging.basicConfig(level=logging.INFO,\n                    format='%(levelname)s: %(message)s')\nmoduleName = globals()['__name__']\nlog = logging.getLogger(moduleName)\n\n##############################################################################\n\ndef shutdown(rc):\n    \"\"\"Exits the script, but first flushes all logging handles, etc.\"\"\"\n    logging.shutdown()\n    sys.exit(rc)\n\n##############################################################################\n\n# Create the parser\nparser = OptionParser()\n\n# Specify all valid options.\nparser.add_option(\"-v\", \"--version\",\n                  action=\"store_true\",\n                  dest=\"version\",\n                  default=False,\n                  help=\"Display script version info and author contact.\")\n    \nparser.add_option(\"--input-file\",\n                  action=\"store\",\n                  type=\"str\",\n                  dest=\"inputFile\",\n                  default=None,\n                  help=\"Specify input pricebar CSV data file.  This file should have daily pricebars, and it is assumed that the file has pricebars ordered from oldest to newest.\",\n                  metavar=\"<FILE>\")\n\nparser.add_option(\"--output-file\",\n                  action=\"store\",\n                  type=\"str\",\n                  dest=\"outputFile\",\n                  default=None,\n                  help=\"Specify output CSV file that will have weekly pricebars.\",\n                  metavar=\"<FILE>\")\n\n\n# Parse the arguments into options.\n(options, args) = parser.parse_args()\n\n# Print version information if the flag was used.\nif (options.version == True):\n    print(os.path.basename(sys.argv[0]) + \" (Version \" + VERSION + \")\")\n    print(\"By Ryan Luu, ryanluu@gmail.com\")\n    shutdown(0)\n\n\nif (options.inputFile == None):\n    log.error(\"Please specify an input file to the \" + \\\n              \"--input-file option.\")\n    shutdown(1)\nelse:\n    # Make sure the input file path is good.\n    if not os.path.exists(options.inputFile):\n        log.error(\"The input file provided does not exist: {}\".\\\n                  format(options.inputFile))\n        shutdown(1)\n        \n    # Save the value.\n    inputFile = options.inputFile\n\nif (options.outputFile == None):\n    log.error(\"Please specify an output filename to the \" + \\\n              \"--output-file option.\")\n    shutdown(1)\nelse:\n    outputFile = options.outputFile\n\n        \n##############################################################################\n\n# Lines in the destination file.\nconvertedLines = []\n\ncurrentWeekHasValues = False\ncurrentWeekDateStr = \"\"\ncurrentWeekOpen = -1\ncurrentWeekHigh = -1\ncurrentWeekLow = -1\ncurrentWeekClose = -1\ncurrentWeekVolume = -1\ncurrentWeekOpenInt = -1\n\ncurrentWeekIsoYearNumber = -1\ncurrentWeekIsoWeekNumber = -1\n\n# Read input file.\nlog.info(\"Analyzing file '{}' ...\".format(inputFile))\nwith open(inputFile) as f:\n    i = 0\n    for line in f:\n        if i >= linesToSkip and line.strip() != \"\":\n\n            # Check the number of fields.\n            fields = line.split(\",\")\n            numFieldsExpected = 7\n            if len(fields) != numFieldsExpected:\n                log.error(\"Line does not have {} data fields\".\\\n                          format(numFieldsExpected))\n                shutdown(1)\n            \n            dateStr = fields[0].strip()\n            openStr = fields[1].strip()\n            highStr = fields[2].strip()\n            lowStr = fields[3].strip()\n            closeStr = fields[4].strip()\n            volumeStr = fields[5].strip()\n            openIntStr = fields[6].strip()\n            \n            # Make sure date is the right length.\n            if len(dateStr) != 10:\n                log.error(\"dateStr is not the expected number \" +\n                          \"of characters: \" + dateStr)\n                shutdown(1)\n                \n            log.debug(\"dateStr == {}\".format(dateStr))\n            monthStr = dateStr[0:2]\n            dayStr = dateStr[3:5]\n            yearStr = dateStr[6:10]\n\n            month = int(monthStr)\n            day = int(dayStr)\n            year = int(yearStr)\n\n            d = datetime.date(year, month, day)\n\n            isoWeekNumber = -1\n            isoYearNumber = -1\n            isoWeekdayNumber = -1\n            (isoYearNumber, isoWeekNumber, isoWeekdayNumber) = d.isocalendar()\n            isoYearNumber = year\n            \n            log.debug(\\\n                  \"isoYearNumber={}, isoWeekNumber={}, isoWeekdayNumber={}\".\\\n                  format(isoYearNumber, isoWeekNumber, isoWeekdayNumber))\n            \n            if currentWeekHasValues == False:\n                log.debug(\"currentWeekHasValues == {}\".\\\n                          format(currentWeekHasValues))\n                \n                currentWeekDateStr = dateStr\n                currentWeekOpen = float(openStr)\n                currentWeekHigh = float(highStr)\n                currentWeekLow = float(lowStr)\n                currentWeekClose = float(closeStr)\n                currentWeekVolume = int(volumeStr)\n                currentWeekOpenInt = int(openIntStr)\n\n                #currentWeekIsoYearNumber = isoYearNumber\n                currentWeekIsoYearNumber = year\n                currentWeekIsoWeekNumber = isoWeekNumber\n                currentWeekHasValues = True\n                continue\n            else:\n                \n                log.debug(\"isoYearNumber={}, currentWeekIsoYearNumber={}\".\\\n                          format(isoYearNumber, currentWeekIsoYearNumber))\n                log.debug(\"isoWeekNumber={}, currentWeekIsoWeekNumber={}\".\\\n                          format(isoWeekNumber, currentWeekIsoWeekNumber))\n\n                if isoYearNumber < currentWeekIsoYearNumber:\n                    log.error(\"The input file is supposed to be \" + \\\n                          \"in chronological order!  Failed at line: {}\".\\\n                          format(line))\n                    shutdown(1)\n                    \n                elif isoYearNumber == currentWeekIsoYearNumber:\n                    # Update the values as required.\n                    log.debug(\"Date {} is within SAME week.\".\\\n                          format(d.isoformat()))\n                    \n                    priceValues = []\n                    priceValues.append(float(openStr))\n                    priceValues.append(float(highStr))\n                    priceValues.append(float(lowStr))\n                    priceValues.append(float(closeStr))\n                    priceValues.sort()\n\n                    smallestValue = priceValues[0]\n                    largestValue = priceValues[-1]\n\n                    if smallestValue < currentWeekLow:\n                        currentWeekLow = smallestValue\n                    if largestValue > currentWeekHigh:\n                        currentWeekHigh = largestValue\n                    currentWeekClose = float(closeStr)\n                    currentWeekVolume += int(volumeStr)\n                    currentWeekOpenInt = int(openIntStr)\n                    \n                elif isoYearNumber > currentWeekIsoYearNumber:\n                    \n                    log.debug(\"Date {} is in a NEW week.\".\\\n                          format(d.isoformat()))\n\n                    # The new date is in a week that is later than the\n                    # what the 'currentWeek' is.  This means the week\n                    # has completed and we need to create and add a\n                    # line of text for that week.\n                    convertedLine = \\\n                        \"{},{},{},{},{},{},{}\".format(currentWeekDateStr,\n                                                      currentWeekOpen,\n                                                      currentWeekHigh,\n                                                      currentWeekLow,\n                                                      currentWeekClose,\n                                                      currentWeekVolume,\n                                                      currentWeekOpenInt)\n                    convertedLines.append(convertedLine)\n                    \n                    # Set new values for the 'currentWeek'.\n                    currentWeekDateStr = dateStr\n                    currentWeekOpen = float(openStr)\n                    currentWeekHigh = float(highStr)\n                    currentWeekLow = float(lowStr)\n                    currentWeekClose = float(closeStr)\n                    currentWeekVolume = int(volumeStr)\n                    currentWeekOpenInt = int(openIntStr)\n\n                    currentWeekIsoYearNumber = isoYearNumber\n                    currentWeekIsoWeekNumber = isoWeekNumber\n                    currentWeekHasValues = True\n                    \n                else:\n                    log.debug(\"Should never get here.\")\n                    \n                    \n        i += 1\n        \n    # Now done with going through all the lines.  Close out the last\n    # week as a line.\n    if currentWeekHasValues == True:\n        convertedLine = \\\n            \"{},{},{},{},{},{},{}\".format(currentWeekDateStr,\n                                          currentWeekOpen,\n                                          currentWeekHigh,\n                                          currentWeekLow,\n                                          currentWeekClose,\n                                          currentWeekVolume,\n                                          currentWeekOpenInt)\n        convertedLines.append(convertedLine)\n\n\nlog.debug(\"Total number of weeks in output file is: {}\".\\\n      format(len(convertedLines)))\n\n# Insert header line.\nconvertedLines.insert(0, headerLine)\n\n# Write to file, truncating if it already exists.\nlog.info(\"Writing to destination file '{}' ...\".format(outputFile))\nwith open(outputFile, \"w\") as f:\n    for line in convertedLines:\n        f.write(line + newline)\n\n        \nlog.info(\"Done.\")\nshutdown(0)\n","sub_path":"misc/DataFormatting/temp/convertMonthlyToYearly.py","file_name":"convertMonthlyToYearly.py","file_ext":"py","file_size_in_byte":11316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"195284065","text":"from __future__ import absolute_import, division\nfrom psychopy import locale_setup, gui, visual, core, data, event, logging\nimport numpy as np  # whole numpy lib is available, prepend 'np.'\nfrom numpy.random import random, randint, normal, shuffle\nimport random\nimport os  # handy system and path functions\nimport sys  # to get file system encoding\n\nimport pandas as pd\nimport numpy as np\nfrom operator import itemgetter \n\n# Ensure that relative paths start from the same directory as this script\n_thisDir = os.path.dirname(os.path.abspath(__file__))\nos.chdir(_thisDir)\ncurrent_working_directory = os.getcwd()\n\n# Store info about the experiment session\nexpName = 'CCL_CL'  # from the Builder filename that created this script\nexpInfo = {'participant':'','session':'001'}\ndlg = gui.DlgFromDict(dictionary=expInfo, title=expName)\nif dlg.OK == False:\n    core.quit()  # user pressed cancel during popout\nexpInfo['date'] = data.getDateStr()  # add a simple timestamp\nexpInfo['expName'] = expName\n\n# Data file name stem = absolute path + name; later add .psyexp, .csv, .log, etc\nfilename = _thisDir + os.sep + u'data/%s_%s_%s' % (expInfo['participant'], expName, \"ses_\" + expInfo['session'])\n\n\n# An ExperimentHandler isn't essential but helps with data saving\nthisExp = data.ExperimentHandler(name=expName, version='',\n    extraInfo=expInfo, dataFileName=filename)\n# save a log file for detail verbose info\nlogFile = logging.LogFile(filename+'.log', level=logging.EXP)\nlogging.console.setLevel(logging.WARNING)  # this outputs to the screen, not a file\n\n\n# Setup the Window\nwin = visual.Window(\n    size=(960, 400), fullscr=False, allowGUI=False,\n    monitor='testMonitor', color=[1,1,1], useFBO=True)\n\n##########Timer##########\nglobalClock = core.MonotonicClock() # to track the time since experiment started\ntrialClock = core.Clock() #unlike globalclock, gets reset each trial\n\n\n##########Instruction##########\nInstr_1 = visual.TextStim(win=win, name='Instr_1', color='black',\n    text='Please read these instructions carefully before you begin the experiment. Press the spacebar to continue.')\n\nInstr_2 = visual.TextStim(win=win, name='Instr_2', color='black',\n    text='In this experiment, you will see a series of face images...Press w if the image shows a female face and o if it shows a male face...Press the spacebar to continue.')\n\nEnding = visual.TextStim(win=win, name='Instr_1', color='black',\n    text='Thank you for participating in this study. Press the spacebar to quit.')\n\n##########Stimuli##########\nos.chdir(current_working_directory + '/Stimuli_faces')\nImagelist = list(os.listdir())\nprint(len(Imagelist))\n\nImage = visual.ImageStim(win=win, name='Image', image='\\\\Users\\\\zz112\\\\Documents\\\\CCL\\\\Stimuli_faces\\\\CK_f_01.jpg', size=(0.42, 0.5), interpolate = True)\nBlank = visual.TextStim(win=win, name='blank', text='hello', font=u'Arial', pos=(0, 0), height=0.1, wrapWidth=None, ori=0, color=u'black', colorSpace='rgb', opacity=1, depth=0.0);\n\n##########Trial Sequence##########\ntrials = 240\nduration = 3.0\nITI_min = 800.0\nITI_max = 2000.0\nstim_image = []\ncorrAns = []\nITI = []\n\n\n\n# index the images for high, medium and low frequencies for later selection\n#indices_all = (np.random.choice(len(Imagelist), 104, replace=False)).tolist()\nindices_all = list(range(104))\n\nrandom.shuffle(Imagelist)\nprint (Imagelist)\nindices_low = indices_all[:80]\nindices_medium = indices_all[80:96]\nindices_high = indices_all[96:104]\n\n# select corresponding images\nstim_image_high = [Imagelist[i] for i in indices_high]*10\nstim_image_medium = [Imagelist[i] for i in indices_medium]*5\nstim_image_low = [Imagelist[i] for i in indices_low]\nstim_image = stim_image_high +stim_image_medium + stim_image_low\n\n# frequency\nfrequency = ['high']*80 + ['medium']*80 + ['low']*80\n\n# corrAns\nfor i in stim_image:\n    if ('m' or 'M') in i:\n        corrAns.append('w')\n    else:\n        corrAns.append('o')\n\n\n#ITI & duration\nfor i in range(240):\n    ITI.append(random.randint(ITI_min, ITI_max)/1000)\n\nduration = [duration]*240\n\n\nexpmatrix = [stim_image, frequency, corrAns, duration, ITI]\n\n\n\n\n##----------------------------------------------------------------------------##\n##-----------------------------START RUNNING----------------------------------##\n##----------------------------------------------------------------------------##\n\n\n##---------------------------START INSTRUCTIONS-------------------------------##\n\nInstr_1.setAutoDraw(True)\n\nadvance = 0\nwhile advance < 2:\n    if event.getKeys(keyList=[\"space\"]):\n        advance += 1\n    if advance == 1:\n        Instr_1.setAutoDraw(False)\n        Instr_2.setAutoDraw(True)\n    else:\n        Instr_2.setAutoDraw(False)\n\n    if event.getKeys(keyList=[\"escape\"]):\n        core.quit()\n    win.flip()\n\n\n##------------------------------START TRIALS FOR LOOPS----------------------------------##\n\ntheseKeys = []\ntrialcounter = 0\nfor trial in range(len(stim_image)):\n    t = 0\n    overalltime = globalClock.getTime()\n    trialClock.reset()  # clock\n    continueRoutine = True\n\n    ##------------------SET DURATION & ITI OF STIMULI-------------------##\n    #duration 1000 ms\n    duration = expmatrix[3][trial]\n    \n    #ITI jittering 800-2000 ms\n    ITI = expmatrix[4][trial]\n\n    ##---------------------SET STIMULI & RESPONSE---------------------------##\n    Image.setImage(expmatrix[0][trial])\n    stim_image = expmatrix[0][trial]\n    frequency = expmatrix[1][trial]\n    corrAns = expmatrix[2][trial]\n    Blank.setAutoDraw(True)\n\n\n    ##--------------------------WHILE LOOP BEGINS-------------------------##\n    while continueRoutine:\n        if event.getKeys(keyList=[\"escape\"]):\n            core.quit()\n        # get current time\n        t = trialClock.getTime()\n        key_resp = event.BuilderKeyResponse()\n\n        ##--------------------STIMULI PRESENTATION-------------------------------##\n        \n        if trialClock.getTime() < ITI:\n             Blank.setAutoDraw(True)\n        elif trialClock.getTime() > ITI and trialClock.getTime() < ITI + duration:\n             Blank.setAutoDraw(False)\n             Image.setAutoDraw(True)\n        else:\n             Image.setAutoDraw(False)\n             continueRoutine = False\n\n\n        theseKeys = event.getKeys(keyList=['w', 'o'])       \n        if len(theseKeys) > 0 and trialClock.getTime() < ITI + duration:# at least one key was pressed\n            if theseKeys[-1] != None:\n                 key_resp.rt = key_resp.clock.getTime()\n                 thisExp.addData('Response', theseKeys[-1])\n                 thisExp.addData('RT', key_resp.rt)\n        \n            # was this 'correct'?\n            if str(corrAns) in theseKeys:\n                 key_resp.corr = 1\n                 thisExp.addData('Accuracy', key_resp.corr)\n            else:\n                 key_resp.corr = 0\n                 thisExp.addData('Accuracy', key_resp.corr)\n\n            # a response ends the routine\n            continueRoutine = False\n\n    \n        ##------------CHECK ALL IF COMPONENTS HAVE FINISHED---------------##\n\n        if continueRoutine:\n             win.flip()\n        else:\n             break\n##--------------------------RECORD DATA-------------------------------##\n    trialcounter += 1\n    thisExp.addData('Trial',trialcounter)\n    thisExp.addData('stim_image', stim_image)\n    thisExp.addData('frequency', frequency)\n    thisExp.addData('corrAns', corrAns) \n    thisExp.addData('duration', duration)    \n    thisExp.addData('ITI', ITI)\n\n\n    thisExp.nextEntry()\n\n\nevent.clearEvents(eventType='keyboard')\nEnding.setAutoDraw(True)\n\n\nwhile len(event.getKeys(keyList=[\"space\"])) != 0:\n     Ending.setAutoDraw(False)\n\n\n# these shouldn't be strictly necessary (should auto-save)\nthisExp.saveAsWideText(filename+'.csv')\nthisExp.saveAsPickle(filename)\nlogging.flush()\n# make sure everything is closed down\nthisExp.abort()  # or data files will save again on exit\nwin.close()\ncore.quit()","sub_path":"other/ExperimentCode_CCL_CL.py","file_name":"ExperimentCode_CCL_CL.py","file_ext":"py","file_size_in_byte":7857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"597719568","text":"from django.urls import path, include\nfrom website.views import index,login, cadastra_ideias, cadastro_pessoa, lista_ideias, sucesso\n\nurlpatterns = [\n    path('', index),\n    path('login', login),\n    path('cadastra_ideias', cadastra_ideias),\n    path('cadastro_pessoa', cadastro_pessoa),\n    path('lista_ideias', lista_ideias),\n    path('sucesso', sucesso)\n]","sub_path":"website/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"643298177","text":"# 作为栈使用\nstack = []\nstack.append('1')\nstack.append('2')\nstack.append('3')\nprint(stack)\nstack.pop()\nprint(stack)\n\n# 作为队列使用, 由于线率不高，使用deque\nfrom collections import deque\nqueue = deque([\"Eric\", \"John\", 'Michael'])\nqueue.append('Terry')\nqueue.append(\"Graham\")\nprint(queue.popleft())\nprint(queue.popleft())\nprint(queue)\n\nsquares = list(map(lambda x: x**2, range(10)))\nprint(squares)\nsquares = [x**2 for x in range(10)]\nprint(squares)\n\ntuples = [(x, y) for x in [1,2,3] for y in [4, 5, 6]]\nprint(tuples)\n\nvec = [-4, -2, 0, 2, 4]\nprint([x*2 for x in vec if x > 0])\n\nvec = [[1,2,3], [4,5,6], [7,8,9]]\nnums = [num for elem in vec for num in elem]\nprint(nums)\n\n# 矩阵行转列\nmatrix = [\n    [1, 2, 3, 4],\n    [5, 6, 7, 8],\n    [9, 10, 11, 12],\n]\nmatrix_t = [[row[i] for row in matrix] for i in range(4)]\nprint(matrix_t)\n# zip\nzip_t = zip(*matrix)\nprint(type(zip_t))\nprint(list(zip_t))\n\n\n","sub_path":"tutorial/cases_list.py","file_name":"cases_list.py","file_ext":"py","file_size_in_byte":919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"28310466","text":"#! /usr/bin/python\n# -*- coding: utf-8 -*-\n\n# Copyright (C) 2011 ~ 2012 Deepin, Inc.\n#               2011 ~ 2012 Wang Yong\n# \n# Author:     Wang Yong <lazycat.manatee@gmail.com>\n# Maintainer: Wang Yong <lazycat.manatee@gmail.com>\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# 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\nfrom PyQt5.QtWebKitWidgets import QWebView, QWebPage\nfrom PyQt5.QtWidgets import QApplication\nfrom PyQt5.QtCore import QUrl, Qt\nfrom stickwidget import StickWidget\nfrom deepin_utils.file import get_parent_dir\nimport os\n\nclass Browser(StickWidget):\n    def __init__(self, url):\n        super(Browser, self).__init__()\n        \n        self.view = QWebView(self)\n        self.view.load(QUrl(url))\n        self.view.page().setLinkDelegationPolicy(QWebPage.DelegateAllLinks)\n        self.view.page().linkClicked.connect(self.link_clicked)\n        self.view.page().mainFrame().setScrollBarPolicy(Qt.Horizontal, Qt.ScrollBarAlwaysOff)\n            \n        self.view.settings().setUserStyleSheetUrl(QUrl.fromLocalFile(os.path.join(get_parent_dir(__file__), \"scrollbar.css\")))\n        \n        self.layout.addWidget(self.view)\n        \n    def link_clicked(self, url):\n        self.view.load(url)\n        \nif __name__ == '__main__':\n    import sys\n    import signal\n    \n    app = QApplication(sys.argv)\n\n    url = QUrl('http://pianku.xmp.kankan.com/moviestore_index.html')\n\n    browser = Browser(url)\n    browser.resize(600, 400)\n    browser.move(0, 0)\n    browser.show()\n    \n    signal.signal(signal.SIGINT, signal.SIG_DFL)\n    sys.exit(app.exec_())\n","sub_path":"browser.py","file_name":"browser.py","file_ext":"py","file_size_in_byte":2098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"335981351","text":"import MemoryManager\nimport IRQ\nimport threading\n\n############################################################################################################################\n# Tipos de excepciones especificas...\nclass CAMemoryNotEnoughException(Exception): pass\nclass CAMemoryFragmentedException(Exception): pass\n\n\n############################################################################################################################\n# Clase auxiliar que representa un bloque de memoria...\nclass MemoryBlock:\n    def __init__( self, address, size ):\n        self.__address = address\n        self.__size = size\n    \n    # Setter/getter de la direccion...\n    def setAddress( self, address ):\n        self.__address = address\n        return self\n    def getAddress( self ):\n        return self.__address\n\n    #Setter/getter del tamaño...\n    def setSize( self, size ):\n        self.__size = size\n        return self\n    def getSize( self ):\n        return self.__size\n\n\n############################################################################################################################\n# Clase auxiliar que representa una lista de bloques de memoria.\n# Tiene soporte para agregar o quitarle bloques, con soporte para merge de bloques.\nclass MemoryBlockList:\n    def __init__(self, mergeBlocks = False):\n        self.__blocks = []\n        self.__mergeBlocks = mergeBlocks\n\n    # Agregar un bloque de la lista de bloques de memoria...\n    def addBlock(self, block):\n        limitAddress = (block.getAddress()+block.getSize())\n        position = 0\n        blockInserted = False\n\n        # Recorrer la lista de bloques...\n        for b in self.__blocks:\n            # Si se encuentra uno cuya direccion sea >= al limite del bloque nuevo...\n            if b.getAddress() >= limitAddress:\n                # Insertarlo...\n                self.__blocks.insert(position,block)\n                blockInserted = True\n                break\n            position += 1\n\n        # Si no se encontraron\n        if not blockInserted:\n            self.__blocks.insert(position,block)\n\n        # Si esta activado el modo merge, hacer el merge de bloques contiguos...\n        if self.__mergeBlocks:\n            self.mergeBlocks()\n\n        return self\n\n    # Remover un bloque de la lista de bloques de memoria...\n    def removeBlock(self, block):\n        position = 0\n        # Recorrer la lista de bloques...\n        for b in self.__blocks:\n            # Si se encuentra uno que contiene al bloque a eliminar...\n            if (b.getAddress() <= block.getAddress()) and ((b.getAddress()+b.getSize()-1) >= (block.getAddress()+block.getSize()-1)):\n                # Determinar los bloques que quedan a izquierda y derecha del bloque eliminado...\n                newBlockLeft = MemoryBlock( b.getAddress(), block.getAddress() - b.getAddress() )\n                newBlockRight = MemoryBlock( block.getAddress() + block.getSize(), (b.getAddress()+b.getSize()-1) - (block.getAddress()+block.getSize()-1) )\n                # Eliminar el bloque...\n                self.__blocks.pop( position )\n                # Si quedaron bloques a izquierda o derecha, insertarlos...\n                if newBlockLeft.getSize() > 0:\n                    self.__blocks.insert( position, newBlockLeft )\n                    position += 1\n                if newBlockRight.getSize() > 0:\n                    self.__blocks.insert( position, newBlockRight )\n                break\n            position += 1\n        return self\n\n    # Setter del modo merge...\n    def setMergeBlocks(self, mergeBlocks):\n        self.__mergeBlocks = mergeBlocks\n        return self\n\n    # Hacer el merge de bloques contiguos...\n    def mergeBlocks(self):\n        blockCount = len(self.__blocks)\n        for i in range(blockCount):\n            if i < (blockCount-1):\n                b1 = self.__blocks[i]\n                b2 = self.__blocks[i+1]\n                if (b1.getAddress()+b1.getSize()) == b2.getAddress():\n                    self.__blocks[i] = MemoryBlock( b1.getAddress(), b1.getSize() + b2.getSize() )\n                    self.__blocks.pop(i+1)\n                    return self.mergeBlocks()\n            else:\n                break\n        return self\n\n    # Getter de la cantidad de memoria...\n    def getAvailableMemory(self):\n        availableMemory = 0\n        for b in self.__blocks:\n            availableMemory += b.getSize()\n        return availableMemory\n\n    # Getter de los bloques de memoria...\n    def getBlocks(self):\n        return self.__blocks\n\n    # Limpiar la lista de bloques de memoria...\n    def clear(self):\n        self.__blocks.clear()\n        return self\n\n    # Convertir a string el contenido de la lista...\n    def __str__(self):\n        result = \"\\n\\tTotal memory referenced in the list: \" + str(self.getAvailableMemory()) + \"\\n\"\n        result += \"\\tBlocks: \" + \"\\n\"\n        for b in self.__blocks:\n            result += \"\\t\\t* Address: \" + str(b.getAddress()) + \", size: \" + str(b.getSize()) + \"\\n\"\n        return result\n\n\n############################################################################################################################\n# Clase CAMemoryManager que modela el comportamiento basico de un administrador de memoria de asignacion continua...\nclass CAMemoryManager(MemoryManager.MemoryManager):\n\n    # Constructor...\n    def __init__(self, memory = None, memoryAllocator = None ):\n        super(CAMemoryManager,self).__init__(memory)\n        self.__freeMemoryBlocks = MemoryBlockList(True)\n        self.__busyMemoryBlocks = MemoryBlockList()\n        self.__processMemoryBlocks = {}\n        self.__memoryAllocator = memoryAllocator\n        self.setMemory( memory )\n\n\n    # Setter de la memoria administrada...\n    def setMemory( self, memory ):\n        self.getMemoryAccessLock().acquire()\n        try:\n            super(CAMemoryManager,self).setMemory(memory)\n            self.__processMemoryBlocks.clear()\n            self.__freeMemoryBlocks.clear()\n            self.__busyMemoryBlocks.clear()\n            if memory is not None:\n                self.__freeMemoryBlocks.addBlock( MemoryBlock( 0, memory.getSize() ))\n        except:\n            self.getMemoryAccessLock().release()\n            raise\n        self.getMemoryAccessLock().release()\n        return self\n\n\n    # Reservar memoria para un proceso...\n    def allocateProcessMemory(self, process):\n        self.trace( \"Memory Manager -> Memory allocation requested por PID \" + str(process.getPID()) )\n        try:\n            program = process.getProgram()\n            processMemorySize = program.getTotalProgramSize()\n\n            self.getMemoryAccessLock().acquire()\n            # Pedirle al asignador activo que reserve un bloque de memoria..\n            block = self.__memoryAllocator.allocate( self, processMemorySize )\n\n            # Asignar el bloque de memoria al proceso...\n            self.__processMemoryBlocks[process.getPID()] = block\n\n            # Actualizar listas de bloques ocupados y libres....\n            self.__busyMemoryBlocks.addBlock(block)\n            self.__freeMemoryBlocks.removeBlock(block)\n\n            # Cargar el programa en memoria...\n            program.loadInMemory( self, process, process.getProgramCounter() )\n\n            self.getMemoryAccessLock().release()\n\n            self.trace( \"Memory Manager -> New busy block list state: \" + str(self.__busyMemoryBlocks) )\n            self.trace( \"Memory Manager -> New free block list state: \" + str(self.__freeMemoryBlocks) )\n\n        except CAMemoryNotEnoughException as ex:\n            # Error memoria no disponible, levantar una IRQ...\n            self.raiseIRQ( IRQ.MemoryNotEnoughIRQ(process) )\n            self.getMemoryAccessLock().release()\n            raise\n\n        except CAMemoryFragmentedException as ex:\n            # Error memoria fragmetada, hay que compactar y volver a intentar...\n            self.getMemoryAccessLock().release()\n            self.compactMemory()\n            self.allocateProcessMemory(process)\n\n        except:\n            self.getMemoryAccessLock().release()\n            raise\n\n        return self\n\n\n    # Liberar memoria de un proceso...\n    def freeProcessMemory(self, process):\n        self.getMemoryAccessLock().acquire()\n\n        try:\n            pid = process.getPID()\n\n            self.trace( \"Memory Manager -> Free memory requested por PID \" + str(pid) )\n\n            if pid in self.__processMemoryBlocks:\n                block = self.__processMemoryBlocks[pid]\n                self.__busyMemoryBlocks.removeBlock( block )\n                self.__freeMemoryBlocks.addBlock( block )\n                self.__processMemoryBlocks.pop( pid, 0 )\n\n                self.trace( \"Memory Manager -> New busy block list state: \" + str(self.__busyMemoryBlocks) )\n                self.trace( \"Memory Manager -> New free block list state: \" + str(self.__freeMemoryBlocks) )\n        except:\n            self.getMemoryAccessLock().release()\n            raise\n\n        self.getMemoryAccessLock().release()\n\n        return self\n\n\n    # Calcular la direccion fisica de una direccion logica.\n    # Controlar que la direccion este dentro de los limites de la memoria asignada al proceso...\n    def getPhysicAddress( self, process, logicAddress, accessMode = MemoryManager.MemoryManager.ReadAccessMode ):\n        physicAddress = 0\n        memoryBlock = self.__processMemoryBlocks[process.getPID()]\n\n        # Controlar segmentation fault...\n        if logicAddress >= memoryBlock.getSize():\n            self.trace( \"Memory Manager -> Segmentation fault IRQ raised !\" )\n            irq = IRQ.SegmentationFaultIRQ( process )\n            self.raiseIRQ( irq )\n            raise irq\n\n        # Calcular direccion fisica...\n        physicAddress = memoryBlock.getAddress() + logicAddress\n        self.trace( \"Memory Manager -> Logic address \" + str(logicAddress) + \" mapped to physic \" + str(physicAddress))\n\n        return physicAddress\n\n\n    # Setter/getter del \"ubicador\" de memoria...\n    def setMemoryAllocator(self, memoryAllocator):\n        self.__memoryAllocator = memoryAllocator\n        return self\n    def getMemoryAllocator(self):\n        return self.__memoryAllocator\n\n\n    # Compactar la memoria fragmentada...\n    def compactMemory(self):\n\n        try:\n            self.getMemoryAccessLock().acquire()\n\n            memory = self.getMemory()\n            freeMemory = self.__freeMemoryBlocks.getAvailableMemory()\n            newBlockAddress = 0\n\n            self.trace( \"Memory Manager -> Compactation process started. Free memory: \" + str(freeMemory) + \", busy memory: \" + str(self.__busyMemoryBlocks.getAvailableMemory()) )\n\n            # Recorrer los bloques de memoria ocupados...\n            for busyBlock in self.__busyMemoryBlocks.getBlocks():\n                # Si el bloque tiene una direccion que deja fragmentacion, compactarlo...\n                currentBlockAddress = busyBlock.getAddress()\n                if currentBlockAddress > newBlockAddress:\n                    self.trace( \"Memory Manager -> Compactation process. Block at \" + str(currentBlockAddress) + \" needs to be moved. Size: \" + str(busyBlock.getSize()) )\n                    # Mover los datos del bloque en memoria fisica...\n                    memory.copy( currentBlockAddress, newBlockAddress, busyBlock.getSize()  )\n                    # Setearle la nueva direccion al bloque...\n                    busyBlock.setAddress(newBlockAddress)\n                # Calcular siguiente nueva direccion...\n                newBlockAddress += busyBlock.getSize()\n\n            # Finalmente, queda un unico bloque de memoria libre ubicado al final...\n            self.__freeMemoryBlocks.clear()\n            self.__freeMemoryBlocks.addBlock( MemoryBlock(newBlockAddress,freeMemory) )\n\n            self.trace( \"Memory Manager -> New busy block list state: \" + str(self.__busyMemoryBlocks) )\n            self.trace( \"Memory Manager -> New free block list state: \" + str(self.__freeMemoryBlocks) )\n\n        except:\n            self.getMemoryAccessLock().release()\n            raise\n\n        self.getMemoryAccessLock().release()\n\n        return self\n\n\n    # Getter de los bloques de memoria libres...\n    def getFreeMemoryBlocks(self):\n        return self.__freeMemoryBlocks\n\n\n############################################################################################################################\n# Clase CAMemoryAllocatorFirstFit que modela el comportamiento basico de un ubicador de memoria por el metodo First Fit...\nclass CAMemoryAllocatorFirstFit:\n    def allocate(self, memoryManager, size):\n        #Determinar la memoria disponible...\n        availableMemory = memoryManager.getFreeMemoryBlocks().getAvailableMemory()\n        if availableMemory >= size:\n            #Recorrer los bloques de memoria libre hasta encontrar uno que pueda contener al bloque solicitado...\n            for b in memoryManager.getFreeMemoryBlocks().getBlocks():\n                if b.getSize() >= size: #Si se encontro uno....\n                    return MemoryBlock( b.getAddress(), size ) #Devolver el bloque a asignar...\n\n            #Si no se encontró ningun bloque libre que pueda contener el tamaño solicitado,\n            #lanzar una excepcion por memoria fragmentada...\n            raise CAMemoryFragmentedException(\"Available memory (\" + str(availableMemory) + \") is fragmented to allocate block of size \" + str(size) + \". Compactation needed!\" )\n        else:\n            #Si la memoria disponible no alcanza, lanzar una excepcion...\n            raise CAMemoryNotEnoughException(\"Available memory (\" + str(availableMemory) + \") not enough to allocate block of size \" + str(size) + \".\" )\n        return None\n\n\n\n############################################################################################################################\n# Clase CAMemoryAllocatorBestFit que modela el comportamiento basico de un ubicador de memoria por el metodo Best Fit...\nclass CAMemoryAllocatorBestFit:\n    def allocate(self, memoryManager, size):\n        #Determinar la memoria disponible...\n        availableMemory = memoryManager.getFreeMemoryBlocks().getAvailableMemory()\n        minExternalFragmentation = memoryManager.getMemory().getSize()\n        blockAddress = -1\n        if availableMemory >= size:\n            #Recorrer los bloques de memoria libre buscando el que genere la menor fragmentacion externa...\n            for b in memoryManager.getFreeMemoryBlocks().getBlocks():\n                externalFragmentation = (b.getSize() - size)\n                #Si se encontro uno capaz de contener el bloque solicitado y de menor fragmentacion externa....\n                if (externalFragmentation >= 0) and (externalFragmentation < minExternalFragmentation):\n                    minExternalFragmentation = externalFragmentation\n                    blockAddress = b.getAddress()\n\n            #Si se encontro un bloque adecuado...\n            if blockAddress >= 0:\n                return MemoryBlock( blockAddress, size ) #Devolver el bloque a asignar...\n\n            #Si no se encontró ningun bloque libre que pueda contener el tamaño solicitado,\n            #lanzar una excepcion por memoria fragmentada...\n            raise CAMemoryFragmentedException(\"Available memory (\" + str(availableMemory) + \") is fragmented to allocate block of size \" + str(size) + \". Compactation needed!\" )\n        else:\n            #Si la memoria disponible no alcanza, lanzar una excepcion...\n            raise CAMemoryNotEnoughException(\"Available memory (\" + str(availableMemory) + \") not enough to allocate block of size \" + str(size) + \".\" )\n        return None\n\n\n\n############################################################################################################################\n# Clase CAMemoryAllocatorWorstFit que modela el comportamiento basico de un ubicador de memoria por el metodo Worst Fit...\nclass CAMemoryAllocatorWorstFit:\n    def allocate(self, memoryManager, size):\n        #Determinar la memoria disponible...\n        availableMemory = memoryManager.getFreeMemoryBlocks().getAvailableMemory()\n        maxExternalFragmentation = 0\n        blockAddress = -1\n        if availableMemory >= size:\n            #Recorrer los bloques de memoria libre buscando el que genere la mayor fragmentacion externa...\n            for b in memoryManager.getFreeMemoryBlocks().getBlocks():\n                externalFragmentation = (b.getSize() - size)\n                #Si se encontro uno capaz de contener el bloque solicitado y de mayor fragmentacion externa....\n                if (externalFragmentation >= 0) and (externalFragmentation > maxExternalFragmentation):\n                    maxExternalFragmentation = externalFragmentation\n                    blockAddress = b.getAddress()\n\n            #Si se encontro un bloque adecuado...\n            if blockAddress >= 0:\n                return MemoryBlock( blockAddress, size ) #Devolver el bloque a asignar...\n\n            #Si no se encontró ningun bloque libre que pueda contener el tamaño solicitado,\n            #lanzar una excepcion por memoria fragmentada...\n            raise CAMemoryFragmentedException(\"Available memory (\" + str(availableMemory) + \") is fragmented to allocate block of size \" + str(size) + \". Compactation needed!\" )\n        else:\n            #Si la memoria disponible no alcanza, lanzar una excepcion...\n            raise CAMemoryNotEnoughException(\"Available memory (\" + str(availableMemory) + \") not enough to allocate block of size \" + str(size) + \".\" )\n        return None\n\n\n\n","sub_path":"CAMemoryManager.py","file_name":"CAMemoryManager.py","file_ext":"py","file_size_in_byte":17466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"462179142","text":"from player import AI, Player\n\n\ndef main():\n    ai = AI()\n    ai.build_field()\n\n    player = Player()\n    player.build_field()\n\n    players = [player, ai]\n    current_player = 0\n    while True:\n        print(\"\\nВаше текущее поле:\")\n        player.print_field()\n        print(\"Текущее поле противника:\")\n        ai.print_hidden_field()\n        if not players[current_player].hit(players[current_player ^ 1]):\n            current_player ^= 1\n        elif not players[current_player ^ 1].check_alive_ships():\n            print()\n            print(\"Победа!\" if players[current_player] == player else \"Поражение!\")\n            break\n\n    print(\"Ваше итоговое поле:\")\n    player.print_field()\n    print(\"Итоговое поле противника:\")\n    ai.print_field()\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"327073512","text":"from playhouse.shortcuts import model_to_dict\n\nfrom database.models import ChatAppUser, ChatAppMessage\n\n'''\nAUTHENTICATION\n'''\n\n\ndef login(password, email):\n    try:\n        user = ChatAppUser.get(ChatAppUser.email == email, ChatAppUser.password == password)\n        return user\n    except ChatAppUser.DoesNotExist:\n        return None\n\n\ndef register(username, password, birthday, fullname, email, address, sex=None):\n    try:\n        ChatAppUser.create(username=username, password=password, birthday=birthday, fullname=fullname, email=email,\n                           address=address,\n                           sex=sex)\n        return True\n    except Exception as Ex:\n        print(Ex)\n        return False\n\n\n'''\nFRIEND\n'''\n\n\ndef get_friends(user_id):\n    try:\n        query = ChatAppUser.select(ChatAppUser.friend).where(ChatAppUser.id == user_id)\n        return model_to_dict(query.get()).get('friend')\n    except ChatAppUser.DoesNotExist:\n        return None\n\n\ndef detail_user(user_id):\n    try:\n        user = ChatAppUser.select(ChatAppUser.fullname, ChatAppUser.email, ChatAppUser.sex, ChatAppUser.birthday,\n                                  ChatAppUser.address).where(ChatAppUser.id == user_id)\n        return list(user.dicts())\n    except ChatAppUser.DoesNotExist:\n        return None\n\n\ndef search_user(username):\n    try:\n        users = ChatAppUser.select(ChatAppUser.id, ChatAppUser.username).order_by(ChatAppUser.username.asc()).where(\n            ChatAppUser.username.contains(username))\n        return list(users.dicts())\n    except Exception as Ex:\n        print(Ex)\n        return None\n\n\ndef update_list_friend(user_id, list_friend):\n    try:\n        query = (ChatAppUser\n                 .update({ChatAppUser.friend: list_friend})\n                 .where(ChatAppUser.id == user_id))\n        if query.execute() >= 1:\n            return True\n    except ChatAppUser.DoesNotExist:\n        return False\n\n    return False\n\n\ndef get_block_user(user_id):\n    try:\n        query = ChatAppUser.select(ChatAppUser.block_user).where(ChatAppUser.id == user_id)\n        return model_to_dict(query.get()).get('block_user')\n    except ChatAppUser.DoesNotExist:\n        return None\n\n\ndef update_block(user_id, list_block_user):\n    try:\n        query = (ChatAppUser\n                 .update({ChatAppUser.block_user: list_block_user})\n                 .where(ChatAppUser.id == user_id))\n        if query.execute() >= 1:\n            return True\n    except ChatAppUser.DoesNotExist:\n        return False\n\n    return False\n\n\n'''\nMESSAGE\n'''\n\n\ndef get_message(sender_id, receiver_id):\n    try:\n        messages = (\n            (ChatAppMessage.select().where(ChatAppMessage.sender == sender_id, ChatAppMessage.receiver == receiver_id) |\n             (ChatAppMessage.select().where(ChatAppMessage.sender == receiver_id,\n                                            ChatAppMessage.receiver == sender_id))).order_by(\n                ChatAppMessage.sent_date.asc()))\n        return list(messages.dicts())\n    except ChatAppMessage.DoesNotExist:\n        return None\n\n\ndef sent_message(sender_id, receiver_id, message):\n    try:\n        ChatAppMessage.create(sender_id=sender_id, receiver_id=receiver_id, message=message)\n        return True\n    except Exception as Ex:\n        print(Ex)\n        return False\n\n\ndef update_is_read_message(user_id, sender, date):\n    try:\n        query = (ChatAppMessage\n                 .update({ChatAppMessage.is_read: 1, ChatAppMessage.read_date: date})\n                 .where(ChatAppMessage.receiver == user_id, ChatAppMessage.sender == sender)\n                 )\n        if query.execute() >= 1:\n            return True\n    except ChatAppMessage.DoesNotExist:\n        return False\n    return False\n\n\ndef list_friend_by_sent_date(user_id):\n    try:\n        query = (ChatAppMessage\n                 .select(ChatAppMessage.sender, ChatAppMessage.receiver, ChatAppMessage.is_read, ChatAppMessage.message,\n                         ChatAppMessage.sent_date)\n                 .where((ChatAppMessage.sender == user_id) |\n                        (ChatAppMessage.receiver == user_id))\n                 .order_by(ChatAppMessage.sent_date.desc())\n                 )\n        return list(query.dicts())\n    except Exception as Ex:\n        print(Ex)\n\n\nif __name__ == '__main__':\n    # user= login('Mvs@12345', 'testnv@gmail.com')\n    # print(user)\n    # print(model_to_dict(user))\n    # print(login('Mvs@12345', '0163800ff'))\n    list_friend_by_sent_date(1)\n    # print(detail_user([]))\n    # print(update_friend(1, [{'id': 1, 'username': 'testnv'}, {'id': 2, 'username': 'ducbv'}]))\n    # print(sent_message(3, 1, 'xin chao'))\n    # time_now = get_date_now()\n    # print(update_is_read_message(2, 1, time_now))\n","sub_path":"database/db_utils.py","file_name":"db_utils.py","file_ext":"py","file_size_in_byte":4724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"355562363","text":"from flask import Flask\nfrom home.views import home_view\nimport os\n\n\ndef create_app(config_file):\n    app = Flask(__name__)  # Create application object\n    app.config.from_pyfile(config_file)  # Configure application with settings file, not strictly necessary\n    app.register_blueprint(home_view)  # Register url's so application knows what to do\n    return app\n\n\nif __name__ == '__main__':\n    app = create_app('settingslocal.py')  # Create application with our config file\n    app.run(host='0.0.0.0', port=int(os.getenv('PORT_NUMBER', 5000)), debug=False, threaded=True)\n","sub_path":"docker_app.py","file_name":"docker_app.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"247530628","text":"import random\nimport math\nimport sys\nimport itertools\nimport collections\n\n\nclass KNN(object):\n    def __init__(self, features):\n        self.features = features\n        self.folds = None\n        self.distance_method = self._euclidean\n\n    def knn(self, validation_set, training_set, k=3):\n        results = []\n        for feature in validation_set:\n            # create list of tuples in format (distance, referenced feature's class) then sort it\n            distances = [(self.distance_method(feature.values, training_feature.values), training_feature.feature_class)\n                         for training_feature in training_set]\n            distances.sort(key=lambda x: x[0])\n\n            # pick k lowest distances then extract feature classes and pick the most common one (arbitrary if equal)\n            distances = [x[1] for x in distances[0:k]]\n            most_common_class = collections.Counter(distances).most_common(1)[0][0]\n            results.append((feature, most_common_class))\n        return results\n\n    def cross_validate(self, n=3, k=3):\n        \"\"\"\n        Test set using n-fold cross validation with kNN method\n        :param method: method to use (knn or weighted_knn)\n        :param n: how many folds to create\n        :param k: parameter for kNN\n        \"\"\"\n        self.folds = self._create_folds(n)\n        full_results = []\n        accuracies = []\n        for validation_set in self.folds:\n            # exclude validation set from training sets\n            training_set = list(itertools.chain(*[x for x in self.folds if x != validation_set]))\n            results = self.knn(validation_set, training_set, k)\n            full_results.append(results)\n\n            accurate_hits = sum(1 if x[0].feature_class == x[1] else 0 for x in results)\n            accuracies.append(accurate_hits / len(results))\n\n        overall_accuracy = sum(accuracies) / len(accuracies)\n        return overall_accuracy, full_results\n\n    def _create_folds(self, k):\n        \"\"\"\n        Divides given list into k lists of equal (or almost equal if indivisible) number of elements\n        :return: list of tuples (feature_class, list of k-lists)\n        \"\"\"\n\n        randomized = random.sample(self.features, len(self.features))\n        chunk_size = math.ceil(len(randomized)/k)\n        last_chunk_size = len(randomized) - chunk_size * (k-1)\n        folds = [randomized[i*chunk_size: (i+1)*chunk_size] for i in range(k-1)]\n        folds.append(randomized[-last_chunk_size:])\n        try:\n            assert sum([len(l) for l in folds]) == len(randomized)\n        except AssertionError:\n            print(\"Chunks size don't sum up to length of original list\")\n            sys.exit()\n\n        return folds\n\n    def _euclidean(self, x, y):\n        distance = 0\n        for val1, val2 in zip(x, y):\n            distance += (val1 - val2)**2\n        return math.sqrt(distance)\n","sub_path":"KNN/knn.py","file_name":"knn.py","file_ext":"py","file_size_in_byte":2869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"112374106","text":"\n# Set up imports\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.metrics import confusion_matrix, classification_report\nfrom sklearn.ensemble import GradientBoostingClassifier as GBC\nfrom sklearn.utils import resample\n\n\n########################################################################## Reading data\n# option 1 - run live query\nimport Health_y_df_query \ndf = Health_y_df_query.df\n\n# option 2 - read from pickle output by query\n#df = pd.read_pickle('new_query_df.pkl')\n\n#######################################################################################\n\n# change health date to datetime\ndf['health_date'] = pd.to_datetime(df['health_date'])\n\n\n#get rid of really small subs that have always been small\ndf['max_seats'] = df.groupby('sub_id')['paid_seats'].transform('max')\ndf = df[df['max_seats'] >= 25]\n\n\n#create sub sizes to run model at\nbins = [0,50, 250, 100000]\nlabels = [50, 250, 100000]\ndf['size_group'] = pd.cut(df['max_seats'], bins = bins, labels = labels)\n\n\n# empty dictionary for size groupings\ntrain_dic = {}\ntest_dic = {}\ncr_dic = {}\nconf_dic = {}\nimp_dic = {}\n\n# loop through size groups (labels list) in outer loop and make that the dataframe\nfor group in labels:\n    \n    # bring in copy of filtered dataframe that has size groups added\n    df_i = df\n    \n    \n    # create size grouping df for this loop of modeling process\n    df_i = df_i[df_i['size_group'] == group]\n        \n    \n    # create months lookback var\n    months_lookback = 3\n    \n    \n    # split subs into 5 folds for inner loop (4 are train, one is test)\n    sublist = list(set(df_i['sub_id']))\n    folds = np.array_split(sublist, 5)\n    for i in range(0,5):\n        test_subs = list(folds[i])\n        train_subs = [sub for sub in sublist if sub not in test_subs]\n        \n       ########################################################################## Train set \n        # split out training set using lists above\n        df_train = df_i[df_i['sub_id'].isin(train_subs)]\n            \n        # add churn target to training df\n        df_train['target_churn'] = np.where((df_train['flag_churned'] ==1) & (df_train[\n            'months_churned'] >= -months_lookback) ,1,0)\n\n        \n        # upsample churns in training set only\n        df_train_churned = df_train.query('target_churn == 1')\n        df_train_notchurned = df_train.query('target_churn == 0')\n        \n        train_churned_resampled = resample(\n        df_train_churned,\n        replace=True,\n        n_samples=int(len(df_train_notchurned)),\n        random_state=0)\n        \n        \n        # put resampled data back together with original not churned. Filter for lookback\n        df_train = pd.concat([df_train_notchurned,train_churned_resampled])\n        df_train = df_train[df_train['health_date'] <= pd.datetime(2018,7,1)]\n        \n        \n        \n        ########################################################################## Test set   \n        df_test = df_i[df_i['sub_id'].isin(test_subs)]\n        df_test = df_test[df_test['health_date'] == pd.datetime(2018,10,1)]  \n                \n        # add churn target to testing df after splitting off\n        df_test['target_churn'] = np.where((df_test['flag_churned'] ==1) & (df_test[\n            'months_churned'] >= -months_lookback) ,1,0)\n                     \n        \n        # features used in each model\n        features = [\n         'has_rqm',\n         'has_rpm',\n         'paid_seats_6m_min',\n         'perc_thirty_day_users',\n         'paid_seats_18m_max',\n         'paid_seats',\n         'portfolio_item_count_3m_p',\n         'paid_seats_18m_max_p',\n         'enabled_users',\n         'paid_seats_18m_d',\n         'sessions_by_seat',\n         'paid_seats_12m_max',\n         'user_story_count_by_seat',\n         'paid_seats_3m_max',\n         'int_traces',\n         'artifact_count_3m_c',\n         'task_count_3m_p',\n         'paid_seats_12m_d',\n         'active_iteration_count',\n         'paid_seats_6m_max',\n         'sessions',\n         'portfolio_item_count_p',\n         'user_story_count_by_seat_3m_p',\n         'defect_count_c',\n         'thirty_day_users',\n         'paid_seats_6m_d',\n         'paid_seats_12m_max_p',\n         'portfolio_item_count_d',\n         'user_story_count_3m_p',\n         'sessions_3m_p',\n         ]\n        \n        # trim down training and testing sets to features we want in the model\n        X_train = df_train.loc[:,features]\n        X_test = df_test.loc[:,features]\n    \n    \n        # define y as target churn column added in loop\n        y_train = df_train.target_churn\n        y_test = df_test.target_churn\n        \n        \n        #run GCB model\n        model = GBC(n_estimators=100, max_depth=6, max_features=6)\n        model.fit(X_train, y_train)\n        preds = model.predict(X_test)\n        churn_pct = model.predict_proba(X_test)\n        \n        \n        # Store accuracy and importance metrics\n        cr_dic[f'{group}_{i}'] = classification_report(preds, y_test)\n        conf_dic[f'{group}_{i}'] = confusion_matrix(preds, y_test)\n        imp_dic[f'{group}_{i}'] = model.feature_importances_\n    \n    \n        # add preds to X test dataframe\n        X_test['preds'] = preds  \n        X_test['stay_pct'] = list(churn_pct[: , 0])\n        X_test['churn_pct'] = list(churn_pct[: , 1])\n        \n        \n        # add to dictionaries w/ preds and importances\n        train_dic[f'{group}_X_train'] = X_train\n        test_dic[f'{group}_X_test'] = X_test\n    \n        \n        # add to dictionary for keeping\n        train_dic[f'{group}_y_train'] = y_train\n        test_dic[f'{group}_y_test'] = y_test\n      \n    \n\n\n############################################################################ Diagnostics\n# Check overall metrics\nprint(sum(conf_dic.values()))\n\n# Create feature importance view - average across all run models in all size groups\navg_feat_imp = sum(imp_dic.values()) / len(imp_dic)\nimportances = pd.DataFrame(avg_feat_imp, X_train.columns).reset_index()\nimportances.columns = ['feature', 'importance']\n\n\n# Output chart of average feature importance across all models for presentation\nimportances.sort_values('importance', ascending = 0, inplace = True)\nfig = importances.plot('feature', 'importance', kind = 'bar', color = '#33adff', figsize=(12, 5))\nvals = fig.get_yticks()\nfig.set_yticklabels(['{:,.0%}'.format(x) for x in vals])\nfig.set_ylabel('% Importance of Feature (Variable) Across All Models')\nfig.set_xlabel('Feature (Variable) Used in Model')\nfig.set_title('Relative Importance of Model Features')\nfig\n\n# Check Unique subs by size group\npd.pivot_table(df, index = 'size_group', values = 'sub_id', aggfunc=lambda x: x.nunique())\n\n\n\n","sub_path":"model_testing/model_testing_sizes_cv.py","file_name":"model_testing_sizes_cv.py","file_ext":"py","file_size_in_byte":6690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"571542687","text":"\"\"\"\nCompose subcommand\n\"\"\"\nfrom functools import lru_cache\nimport os\nimport pathlib\nimport sh\nimport yaml\n\n\nfrom compose_flow.errors import InvalidTargetClusterError, MissingManifestError\nfrom compose_flow.config import get_config\nfrom compose_flow.utils import render, yaml_load, yaml_dump\n\nCLUSTER_LS_FORMAT = '{{.Cluster.Name}}: {{.Cluster.ID}}'\nPROJECT_LS_FORMAT = '{{.Project.Name}}: {{.Project.ID}}'\n\nEXCLUDE_PROFILES = ['local']\n\nNONFATAL_ERROR_MESSAGES = ['strconv.ParseFloat: parsing \"25360052Ki\": invalid syntax']\n\n\nclass KubeMixIn(object):\n    \"\"\"\n    Mix-in for Kubernetes and Rancher CLI interactions\n    \"\"\"\n\n    @property\n    @lru_cache()\n    def config(self):\n        return get_config()\n\n    @property\n    def rancher_config(self):\n        return self.config['rancher']\n\n    @property\n    @lru_cache()\n    def cluster_listing(self):\n        cluster_ls_command = f\"rancher cluster ls --format '{CLUSTER_LS_FORMAT}'\"\n        output = str(self.execute(cluster_ls_command)).strip()\n        for err in NONFATAL_ERROR_MESSAGES:\n            if err in output:\n                output = output.replace(err, '')\n        return yaml.load(output)\n\n    @property\n    def cluster_name(self):\n        '''\n        Get the cluster name for the specified target environment.\n\n        If profile_name is in the compose-flow.yml Rancher cluster mapping,\n        use its value - otherwise use workflow.args.profile\n        '''\n        profile_name = self.workflow.args.profile\n        cluster_mapping = self.rancher_config.get('clusters', {})\n\n        if profile_name in EXCLUDE_PROFILES:\n            raise InvalidTargetClusterError(\n                \"Invalid profile '{0}' for default cluster logic - please \"\n                \"specify an explicit cluster mapping in compose-flow.yml and \"\n                \"use a profile other than '{0}'\".format(profile_name))\n\n        return cluster_mapping.get(profile_name, profile_name)\n\n    @property\n    def cluster_id(self):\n        return self.cluster_listing[self.cluster_name]\n\n    def switch_context(self):\n        '''\n        Switch Rancher CLI context to target specified cluster based on environment\n        and specified project name from compose-flow.yml\n        '''\n        # Get the project name specified in compose-flow.yml\n        target_project_name = self.rancher_config['project']\n\n        base_context_switch_command = \"rancher context switch \"\n        name_context_switch_command = base_context_switch_command + target_project_name\n        try:\n            self.logger.info(name_context_switch_command)\n            self.execute(name_context_switch_command)\n        except sh.ErrorReturnCode_1 as exc:  # pylint: disable=E1101\n            stderr = str(exc.stderr)\n            if 'Multiple resources of type project found for name' in stderr:\n                self.logger.info(\n                    \"Multiple clusters have a project called %s - \"\n                    \"switching context by Project ID\", target_project_name\n                )\n                # Choose the one that matches target cluster ID\n                opts = stderr[stderr.find('[')+1:stderr.find(']')].split(' ')\n                target_project_id = [o for o in opts if self.cluster_id in o][0]\n\n                id_context_switch_command = base_context_switch_command + target_project_id\n                self.logger.info(id_context_switch_command)\n                self.execute(id_context_switch_command)\n            else:\n                raise\n\n    def list_helm_apps(self) -> str:\n        return str(self.execute(\"helm ls -q --all\"))\n\n    def list_rancher_apps(self) -> str:\n        return str(self.execute(\"rancher apps ls --format '{{.App.Name}}'\"))\n\n    def get_rancher_app_install_command(\n            self, app_name: str, rendered_path: str,\n            namespace: str, chart: str, version: str):\n        return f'rancher apps install --answers {rendered_path} --namespace {namespace} --version {version} {chart} {app_name}'\n\n    def get_helm_app_install_command(\n            self, app_name: str, rendered_path: str,\n            namespace: str, chart: str, version: str):\n        return f'helm install --name {app_name} -f {rendered_path} --namespace {namespace} --version {version} {chart}'\n\n    def get_rancher_app_upgrade_command(self, app_name: str, rendered_path: str, chart: str, version: str):\n        return f'rancher apps upgrade --answers {rendered_path} {app_name} {version}'\n\n    def get_helm_app_upgrade_command(self, app_name: str, rendered_path: str, chart: str, version: str):\n        return f'helm upgrade {app_name} {chart} -f {rendered_path} --version {version}'\n\n    def get_app_deploy_command(self, app: dict, target: str = 'rancher') -> str:\n        '''\n        Construct command to install or upgrade a Rancher app\n        depending on whether or not it is already deployed.\n        '''\n\n        app_name = app['name']\n        version = app['version']\n        namespace = app['namespace']\n        chart = app['chart']\n\n        rendered_path = self.render_answers(app['answers'], app_name)\n\n        app_list = getattr(self, f'list_{target}_apps')()\n        upgrade_command_method = getattr(self, f'get_{target}_app_upgrade_command')\n        install_command_method = getattr(self, f'get_{target}_app_install_command')\n\n        if app_name in app_list:\n            return upgrade_command_method(app_name, rendered_path, chart, version)\n        else:\n            return install_command_method(app_name, rendered_path, namespace, chart, version)\n\n    def get_manifest_deploy_command(self, manifest: dict) -> str:\n        '''Construct command to apply a Kubernetes YAML manifest using the Rancher CLI.'''\n\n        deploy_label = self.workflow.args.config_name\n\n        raw_path = manifest['path']\n        deploy_label = manifest.get('label')\n        namespace = manifest.get('namespace')\n\n        namespace_str = f'--namespace {namespace} ' if namespace else ''\n        deploy_label_str = '-l deploy={deploy_label} --prune ' if deploy_label else ''\n\n        command = f'rancher kubectl {namespace_str}apply {deploy_label_str}--validate -f '\n\n        if os.path.isdir(raw_path):\n\n            rendered_path = self.render_nested_manifests(raw_path)\n            command += rendered_path + ' --recursive'\n        elif os.path.isfile(raw_path):\n            rendered_path = self.render_manifest(raw_path)\n            command += rendered_path\n        else:\n            raise MissingManifestError(\"Missing manifest at path: {}\".format(manifest))\n\n        return command\n\n    def get_rke_deploy_command(self):\n        raw_config = get_config()['rke']['config']\n        rendered_config = f'compose-flow-{self.workflow.args.profile}-rke.yml'\n        self.render_single_yaml(raw_config, rendered_config)\n        return f'rke up --config {rendered_config}'\n\n    def get_extra_section(self, section: str) -> list:\n        extras = self.rancher_config.get('extras')\n        if extras:\n            env_extras = extras.get(self.workflow.args.profile)\n            if env_extras:\n                return env_extras.get(section, [])\n\n        return []\n\n    def get_helm_apps(self) -> list:\n        return self.config['helm']\n\n    def get_apps(self) -> list:\n        default_apps = self.rancher_config.get('apps', [])\n        extra_apps = self.get_extra_section('apps')\n\n        return default_apps + extra_apps\n\n    def get_manifests(self) -> list:\n        default_manifests = self.rancher_config.get('manifests', [])\n        extra_manifests = self.get_extra_section('manifests')\n\n        return default_manifests + extra_manifests\n\n    def get_manifest_filename(self, manifest_path: str) -> str:\n        escaped_path = (\n            manifest_path\n            .replace('../', '')\n            .replace('./', '')\n            .replace('/', '-')\n            .replace('.yaml', '.yml')\n            .rstrip('-')\n        )\n        return f'compose-flow-{self.cluster_name}-manifest-{escaped_path}'\n\n    def get_answers_filename(self, app_name: str) -> str:\n        return f'compose-flow-{self.cluster_name}-{app_name}-answers.yml'\n\n    def render_single_yaml(self, input_path: str, output_path: str) -> None:\n        '''\n        Read in single YAML file from specified path, render environment variables,\n        then write out to a known location in the working dir.\n        '''\n        self.logger.info(\"Rendering YAML at %s to %s\", input_path, output_path)\n\n        # TODO: Add support for multiple YAML documents in a single file\n        with open(input_path, 'r') as fh:\n            content = fh.read()\n\n        rendered = render(content, env=self.workflow.environment.data)\n\n        with open(output_path, 'w') as fh:\n            fh.write(rendered)\n\n    @lru_cache()\n    def render_manifest(self, manifest_path: str) -> str:\n        '''Render the specified manifest YAML and return the path to the rendered file.'''\n        rendered_path = self.get_manifest_filename(manifest_path)\n        self.render_single_yaml(manifest_path, rendered_path)\n\n        return rendered_path\n\n    @lru_cache()\n    def render_nested_manifests(self, dir_path: str) -> str:\n        directory = pathlib.Path(dir_path)\n        manifests = directory.glob('**/*.y*ml')\n        rendered_path = self.get_manifest_filename(dir_path)\n\n        for manifest in manifests:\n            render_dest = os.path.join(\n                rendered_path,\n                str(manifest).lstrip(dir_path)\n            )\n            print(render_dest)\n            parent_dest = os.path.dirname(render_dest)\n\n            os.makedirs(parent_dest, mode=0o750, exist_ok=True)\n            self.render_single_yaml(manifest, render_dest)\n        return rendered_path\n\n    @lru_cache()\n    def render_answers(self, answers_path: str, app_name: str) -> str:\n        '''Render the specified manifest YAML and return the path to the rendered file.'''\n        rendered_path = self.get_answers_filename(app_name)\n        self.render_single_yaml(answers_path, rendered_path)\n\n        return rendered_path\n","sub_path":"src/compose_flow/commands/subcommands/kube_mixin.py","file_name":"kube_mixin.py","file_ext":"py","file_size_in_byte":9960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"636846336","text":"import tensorflow.compat.v1 as tf\ntf.disable_v2_behavior()\nimport numpy as np\nfrom pandas.io.parsers import read_csv\n\nmodel = tf.global_variables_initializer();\ndata = read_csv('price data.csv', sep=',')\nxy = np.array(data, dtype=np.float32)\n\n# 4 variables\nx_data = xy[:, 1:-1]\n\n# receive prices\ny_data = xy[:, [-1]]\n\nX = tf.placeholder(tf.float32, shape=[None, 4])\nY = tf.placeholder(tf.float32, shape=[None, 1])\nW = tf.Variable(tf.random_normal([4, 1]), name=\"weight\")\nb = tf.Variable(tf.random_normal([1]), name=\"bias\")\n\nhypothesis = tf.matmul(X, W) + b\ncost = tf.reduce_mean(tf.square(hypothesis - Y))\noptimizer = tf.train.GradientDescentOptimizer(learning_rate=0.000005)\ntrain = optimizer.minimize(cost)\n\nsess = tf.Session()\nsess.run(tf.global_variables_initializer())\n\nfor step in range(100001):\n    cost_, hypo_, _ = sess.run([cost, hypothesis, train], feed_dict={X: x_data, Y: y_data})\n    if step % 500 == 0:\n        print(\"#\", step, \" Loss cost: \", cost_)\n        print(\"- Price: \", hypo_[0])\n\n# 학습된 모델을 저장합니다.\nsaver = tf.train.Saver()\nsave_path = saver.save(sess, \"./saved.cpkt\")\nprint('model is saved.')","sub_path":"01_multireg_tf1.py","file_name":"01_multireg_tf1.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"548987564","text":"# Assignment 3 task-1 part 2, Big data [UE17CS313], PES University\n\n'''\nSpark streaming using pyspark to find out the most popular user from the given dataset.\nThe dataset (fifa_modded_small_1.csv) comprises of collection of tweets during 2018 FIFA world cup.\nThe Columns of the dataset are:\nID,Lang,Date,Source,len,Likes,RT's,Hashtags,UserMentionNames,UserMentionID,Name,Place,Followers,Friends\nThe file is being read from /stream directory on HDFS.\n'''\n# import files\nfrom pyspark.sql import SparkSession, Row\nfrom pyspark.sql.types import StructType\nfrom pyspark.sql.functions import split, explode, col\nfrom pyspark.sql.functions import *\n\n# Create a spark session. handle name -> spark\nspark = SparkSession.builder.appName(\"Streaming-popularUser\").getOrCreate()\n\nspark = SparkSession.builder.master(\"local\").appName(\"test-mf\").getOrCreate()\nspark.sparkContext.setLogLevel(\"ERROR\")\n\n# Define schema of csv\nuserSchema = StructType()\\\n\t.add(\"ID\",\"string\")\\\n\t.add(\"Lang\",\"string\")\\\n\t.add(\"Date\",\"string\")\\\n\t.add(\"Source\",\"string\")\\\n\t.add(\"Len\",\"integer\")\\\n\t.add(\"Likes\",\"integer\")\\\n\t.add(\"RT\",\"integer\")\\\n\t.add(\"Hashtags\",\"string\")\\\n\t.add(\"MentionNames\",\"string\")\\\n\t.add(\"MentionID\",\"string\")\\\n\t.add(\"Name\",\"string\")\\\n\t.add(\"Place\",\"string\")\\\n\t.add(\"Followers\",\"integer\")\\\n\t.add(\"Friends\",\"integer\")\t\n\n# Read CSV from HDFS path.schema is the above defined schema.\ndfCSV = spark.readStream.option(\"sep\",\";\").option(\"header\",\"false\").schema(userSchema).csv(\"hdfs://localhost:9000/stream/\")\n\n# Creates temporary view for the given name\ndfCSV.createOrReplaceTempView(\"popularUser\")\n\n# SQL query on the data\n\npopularity = spark.sql(\"select Name as name, Followers/Friends as FRRatio from popularUser\")\n\n#temp = popularity.orderBy(desc(\"FRRatio\"))\n\ntemp = popularity.groupBy(\"name\",\"FRRatio\").count().orderBy(col(\"FRRatio\").desc()).limit(1)\n\n#print(\"dfCSV: \",type(dfCSV))\n#print(\"temp: \",type(temp))\n\ntemp.createOrReplaceTempView(\"temp\")\n\nfinal = spark.sql(\"select name,FRRatio from temp\")\n\n# Start spark standard streaming query.\nquery = final.writeStream.outputMode(\"complete\").format(\"console\").start()\n\n#Terminate after 100 seconds\nquery.awaitTermination(100)\nquery.stop()\n","sub_path":"adminmgr/media/code/A3/task2/BD_186_281_1583.py","file_name":"BD_186_281_1583.py","file_ext":"py","file_size_in_byte":2172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"385525705","text":"import numpy as np\nimport cv2\n\nclass Threshold:\n\t\n\tdef __call__(self, monochrome_image):\n\t\t\n\t\t# select pixels with highest value/brightness\n\t\tthreshold = 250\n\t\twhile True:\n\t\t\ti_list, j_list = np.where(monochrome_image > threshold)\n\t\t\tn = len(i_list)\n\t\t\tif n > 50:break\n\t\t\tthreshold -= 2\n\t\tresult = monochrome_image > threshold\n\n\t\treturn result#, self.visualize(result))\t\t\n\t\t\t\n\tdef visualize(self, result):\n\t\timg = np.zeros((result.shape[0],result.shape[1],3), dtype = np.uint8)\n\t\timg[result] = (255,255,255)\n\t\treturn img\n\t\t","sub_path":"pendulum-tracking-prototype/filters/Threshold.py","file_name":"Threshold.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"229340997","text":"\"\"\"\n This lymphography domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. (Restricted access)\n\"\"\"\nimport numpy as np, pandas as pd\n\ndef load_lymphography():\n    fp = 'lymphography.csv'\n    raw_data = pd.read_csv(fp, header=None)\n    x = np.array(raw_data.iloc[:, 1:])\n    y = np.array(raw_data.iloc[:, 0])\n    return x, y\n\n\nif __name__ == '__main__':\n    data, target = load_lymphography()\n    print(type(data))\n    print(data)\n    print(type(target))\n    print(target)\n","sub_path":"datasets/lymphography.py","file_name":"lymphography.py","file_ext":"py","file_size_in_byte":521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"433709893","text":"from metadata import version, url\nfrom plugin import CommandPlugin\n\n\nclass Ver(CommandPlugin):\n    def __init__(self):\n        CommandPlugin.__init__(self)\n        self.triggers = ['ver', 'version', 'about']\n        self.short_help = 'Prints out version information about the bot'\n        self.help = self.short_help\n        self.help_example = ['!ver']\n\n    def on_command(self, bot, event, response):\n        response['text'] = 'Nimbus version `%s`\\n%s' % (version, url)\n        response['mrkdwn_in'] = ['text']\n        bot.sc.api_call('chat.postMessage', **response)\n","sub_path":"plugins/ver.py","file_name":"ver.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"221051636","text":"import pymongo\nfrom pymongo import MongoClient\nclient=MongoClient(\"mongodb+srv://Trustwhere4u:Trustwhere4u@mymongocluster.cyhjc.mongodb.net/sample_airbnb?retryWrites=true&w=majority\")\n#client.server_info()\ndb = client.testdb\n\ncarscollection=db.cars\n\nimport awswrangler as wr\nimport pandas as pd\n\n# Load Mongo Response to dataframe\ndata=pd.DataFrame(list(carscollection.find()))\nprint(data);\n\n# Drop _id column in dataframe\ndata=data.drop(columns='_id')\ndata=data[['price']]\n\n# Export dataframe data to csv file ,excluding index\nnewdatacsv=data.to_csv('mycsv.csv',index=False)\n\n# Export dataframe data to json fie, in records orientation\nnewdatajson=data.to_json('myjson.json',orient='records')\n\n# Display dataframe data\n#print(newdatacsv);\n#print(newdatajson);\n\n# Set the path to file in S3\npath1=f\"s3://hello-bucket-tn/mongodata/cars.parquet\"\n\njsonpath=f\"s3://hello-bucket-tn/mongodata/hello2.json\"\n\n# Write the data to S3 in Parquet Format\nwr.s3.to_parquet(data,path1)\n\nwr.s3.to_json(data,path=jsonpath,orient='records')\n\ncsvpath=f\"s3://hello-bucket-tn/mongodata/mycsv.csv\"\n\nwr.s3.to_csv(data,path=csvpath,index=False)\n\n# Read and Display the S3 data in Parquet Format\n#print(wr.s3.read_parquet([path1]))\n","sub_path":"pymongo/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"488503418","text":"from PyQt5.QtWidgets import QFileDialog, QMessageBox, QAction\n\nimport tempfile\nimport subprocess\nimport os\n\nfrom zenframe.settings import BaseSettings\nimport zenframe.util\n\n\nclass ZenFrameActionsMixin:\n    def create_action(self, text, action, tip, shortcut=None, checkbox=False, defcheck=False):\n        act = QAction(self.tr(text), self)\n        act.setStatusTip(self.tr(tip))\n\n        if shortcut is not None:\n            act.setShortcut(self.tr(shortcut))\n\n        if not checkbox:\n            act.triggered.connect(action)\n        else:\n            act.setCheckable(True)\n            act.toggled.connect(action)\n            act.setChecked(defcheck)\n\n        return act\n\n    def create_actions(self):\n        self.mCreateAction = self.create_action(\n            \"CreateNew...\", self.createNewAction, \"Create\"\n        )\n\n        self.mCreateTemp = self.create_action(\n            \"NewTemporary\", self.createNewTemporary, \"CreateTemporary\", \"Ctrl+N\"\n        )\n\n        self.mOpenAction = self.create_action(\n            \"Open...\", self.openAction, \"Open\", \"Ctrl+O\"\n        )\n\n        self.mSaveAction = self.create_action(\n            \"Save\", self.saveAction, \"Save\", \"Ctrl+S\")\n\n        self.mSaveAs = self.create_action(\n            \"SaveAs...\", self.saveAsAction, \"SaveAs...\")\n\n        self.mTEAction = self.create_action(\n            \"Open in Editor\", self.externalTextEditorOpen, \"Editor\", \"Ctrl+E\"\n        )\n\n        self.mExitAction = self.create_action(\n            \"Exit\", self.close, \"Exit\", \"Ctrl+Q\")\n\n        self.mHideConsole = self.create_action(\n            \"Hide console\", self.hideConsole, \"Hide console\", checkbox=True\n        )\n\n        self.mHideEditor = self.create_action(\n            \"Hide editor\", self.hideEditor, \"Hide editor\", checkbox=True\n        )\n\n        self.mAutoUpdate = self.create_action(\n            \"Restart on update\", self.auto_update, \"Restart on update\", checkbox=True, defcheck=True,\n        )\n\n        self.mFullScreen = self.create_action(\n            \"Full screen\", self.fullScreen, \"Full screen\", \"F11\"\n        )\n\n        self.mDisplayMode = self.create_action(\n            \"Display mode\", self.displayMode, \"Display mode\", \"F10\"\n        )\n\n        self.mViewOnly = self.create_action(\n            \"Hide Bars\", self.viewOnly, \"Hide bars\", \"F9\"\n        )\n\n        self.mReopenCurrent = self.create_action(\n            \"Reopen current\", self.reopen_current, \"Reopen current\", \"Ctrl+R\"\n        )\n\n        self.mFrameAboutAction = self.create_action(\n            \"About\", self.frameAboutAction, \"About the ZenFrame library\"\n        )\n\n    def create_menus(self):\n        self.mFileMenu = self.menuBar().addMenu(self.tr(\"&File\"))\n        self.add_new_create_open_standart_actions()\n        self.mFileMenu.addSeparator()\n        self.recentMenu = self.mFileMenu.addMenu(\"Recent\")\n        self._init_recent_menu(self.recentMenu)\n        self.mFileMenu.addSeparator()\n        self.add_exit_standart_action()\n\n        self.mEditMenu = self.menuBar().addMenu(self.tr(\"&Edit\"))\n        self.add_editor_standart_action()\n\n        self.mUtilityMenu = self.menuBar().addMenu(self.tr(\"&Utility\"))\n        self.mUtilityMenu.addAction(self.mAutoUpdate)\n\n        self.mViewMenu = self.menuBar().addMenu(self.tr(\"&View\"))\n        self.mViewMenu.addAction(self.mFullScreen)\n        self.mViewMenu.addAction(self.mDisplayMode)\n        self.mViewMenu.addAction(self.mViewOnly)\n        self.mViewMenu.addAction(self.mHideEditor)\n        self.mViewMenu.addAction(self.mHideConsole)\n\n        self.mHelpMenu = self.menuBar().addMenu(self.tr(\"&Help\"))\n        self.mHelpMenu.addAction(self.mFrameAboutAction)\n\n    def add_new_create_open_standart_actions(self):\n        self.mFileMenu.addAction(self.mReopenCurrent)\n        self.mFileMenu.addAction(self.mOpenAction)\n        self.mFileMenu.addAction(self.mCreateTemp)\n        self.mFileMenu.addAction(self.mCreateAction)\n        self.mFileMenu.addAction(self.mSaveAction)\n        self.mFileMenu.addAction(self.mSaveAs)\n\n    def add_exit_standart_action(self):\n        self.mFileMenu.addAction(self.mExitAction)\n\n    def add_editor_standart_action(self):\n        self.mEditMenu.addAction(self.mTEAction)\n\n    def frameAboutAction(self):\n        QMessageBox.about(\n            self,\n            self.tr(\"About ZenFrame\"),\n            (\n                \"<p>HelloWorld\"\n            ),\n        )\n\n    def _init_recent_menu(self, menu):\n        def _add_open_action(menu, name, path):\n            def callback():\n                self.open(path)\n\n            menu.addAction(self.create_action(name, callback, path))\n\n        for l in BaseSettings.instance().get_recent():\n            _add_open_action(menu, os.path.basename(l), l)\n\n    def update_recent_menu(self):\n        self.recentMenu.clear()\n        self._init_recent_menu(self.recentMenu)\n\n    def create_new_do(self, path):\n        from zenframe.configuration import Configuration\n        f = open(path, \"w\")\n        f.write(Configuration.TEMPLATE)\n        f.close()\n        self.open(path)\n\n    def createNewAction(self):\n        filters = \"*.py;;*.*\"\n        defaultFilter = \"*.py\"\n\n        curopen = self.current_opened()\n        path = QFileDialog.getSaveFileName(\n            self, \"Create New File\",\n            None if curopen is None else os.path.dirname(curopen),\n            filters, defaultFilter\n        )\n\n        if path[0] == \"\":\n            return\n\n        self.create_new_do(path[0])\n\n    def createNewTemporary(self):\n        tmpfl = tempfile.mktemp(\".py\")\n        self.create_new_do(tmpfl)\n\n    def openAction(self):\n        curopen = self.current_opened()\n        path = zenframe.util.open_file_dialog(\n            self,\n            directory=None if curopen is None else os.path.dirname(curopen))\n\n        if path[0] == \"\":\n            return\n\n        self.open(path[0])\n\n    def saveAction(self):\n        self.texteditor.save()\n\n    def saveAsAction(self):\n        path, template = zenframe.util.save_file_dialog(self)\n\n        if path == \"\":\n            return\n\n        self.texteditor.save_as(path)\n\n    def externalTextEditorOpen(self):\n        cmd = BaseSettings.instance().get([\"gui\", \"text_editor\"])\n        subprocess.Popen(cmd.format(path=self.current_opened()), shell=True)\n\n    def hideConsole(self, en):\n        self.console.setHidden(en)\n\n    def hideEditor(self, en):\n        self.texteditor.setEnabled(not en)\n        self.texteditor.setHidden(en)\n\n        if self._current_client is not None:\n            self._current_client.send(\n                {\"cmd\": \"keyboard_retranslate\", \"en\": not en})\n\n    def fullScreen(self):\n        if not self._fscreen_mode:\n            self.showFullScreen()\n            self._fscreen_mode = True\n        else:\n            self.showNormal()\n            self._fscreen_mode = False\n\n    def view_only(self, en):\n        if en:\n            self.menu_bar_height = self.menuBar().height()\n            self.menuBar().setFixedHeight(0)\n        else:\n            self.menuBar().setFixedHeight(self.menu_bar_height)\n\n        self.view_mode = en\n\n    def viewOnly(self):\n        self.view_only(not self.view_mode)\n\n    def display_mode_enable(self, en):\n        if not en:\n            self.hideEditor(False)\n            self.hideConsole(False)\n            self.mHideConsole.setChecked(False)\n            self.mHideEditor.setChecked(False)\n\n        else:\n            self.hideEditor(True)\n            self.hideConsole(True)\n            self.mHideConsole.setChecked(True)\n            self.mHideEditor.setChecked(True)\n\n    def displayMode(self):\n        self.display_mode_enable(\n            not (self.texteditor.isHidden() or self.console.isHidden()))\n\n        if not self.texteditor.isHidden():\n            self.texteditor.setFocus()\n\n    def auto_update(self, en):\n        if en:\n            self.notifier.control_unlock()\n        else:\n            self.notifier.control_lock()\n","sub_path":"zenframe/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":7861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"32703631","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# есть зашифрованное сообщение\n\nsecret_message = [\n    '56муп7ы1ьу4и5ечту2цьчдп98влтмк',\n    '36дхлкалхсемииф5няцум7иыгтчярсмс2ямепфг',\n    'ншбвянляснзепкк9еср52д79м9с2мк',\n    'с2ноопроктятидщв6охки3пшш8щьра',\n    'и4мяобявььелроч3з6сааялруг8гзе8еаббр2'\n]\n\n# ключ к расшифровке:\n#   первое слово - 4-я буква\n#   второе слово - буквы с 10 по 13, включительно\n#   третье слово - буквы с 6 по 14, через одну\n#   четвертое слово - буквы с 8 по 14, в обратном порядке\n#   пятое слово - буквы с 11 по 35, через две, в обратном порядке\n#\n# Требуется задать конкретные индексы, например secret_message[3][12:23:4]\n# Если нужны вычисления и разные пробы - делайте это в консоли пайтона, тут нужен только результат\n\nword_1 = secret_message[0][3]\nword_2 = secret_message[1][9:13]\nword_3 = secret_message[2][5:14:2]\nword_4 = secret_message[3][-17:-24:-1]\nword_5 = secret_message[4][-3:-30:-3]\n\nunsecret_message = word_1 + ' ' + word_2 + ' ' + word_3 + ' ' + word_4 + ' ' + ' ' + word_5\n\nprint(unsecret_message)\n\n# зачет!\n","sub_path":"lesson_002/07_secret.py","file_name":"07_secret.py","file_ext":"py","file_size_in_byte":1516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"126615612","text":"# Definition for singly-linked list.\n# class ListNode(object):\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution(object):\n    def addTwoNumbers(self, l1, l2):\n        \"\"\"\n        :type l1: ListNode\n        :type l2: ListNode\n        :rtype: ListNode\n        \"\"\"\n        a = ListNode();\n        head = a;\n        carry=0;\n        while l1 or l2 or carry:\n            sumd = carry;\n            if l1: sumd+=l1.val;\n            if l2: sumd+= l2.val;\n            carry= int(sumd/10);\n            temp=sumd%10;\n            head.next= ListNode(temp);\n            if l1: l1=l1.next;\n            if l2: l2=l2.next;\n            head=head.next;\n        return a.next;\n","sub_path":"leetcode/algorithms/2.AddTwoNumbers.py","file_name":"2.AddTwoNumbers.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"133859364","text":"#!usr/bin/env python\n#-*- coding:utf-8 -*-\n\n'''\nKMP\nTags: Two Pointers, String\nSimilar Problems: Shortest Palindrome\n'''\n\nclass Solution(object):\n    def strStr(self, string, pattern):\n        \"\"\"\n        :type haystack: str\n        :type needle: str\n        :rtype: int\n        \"\"\"\n        if len(string) == 0 and len(pattern) == 0:\n            return 0\n        elif len(string) == 0 and len(pattern) != 0:\n            return -1\n        elif len(string) != 0 and len(pattern) == 0:\n            return 0\n        nextArray = self.computPre(pattern)\n\n        i = 0\n        j = 0\n        while i < len(string) and j < len(pattern):\n            if j == -1 or string[i] == pattern[j]:\n                i += 1\n                j += 1\n            else:\n                j = nextArray[j]\n\n        if j == len(pattern):\n            return i-j\n\n        return -1\n\n    def computPre(self, pattern):\n        nextArray = [-1 for i in range(len(pattern))]\n        i = 0\n        j = -1\n        while i < len(pattern)-1:\n            if j == -1 or pattern[i] == pattern[j]:\n                i += 1\n                j += 1\n                nextArray[i] = j\n            else:\n                j = nextArray[j]\n        return nextArray\n\nif __name__ == '__main__':\n    s = Solution()\n    print(s.strStr('abac','ac'))","sub_path":"Leetcode/Python/28. Implement strStr().py","file_name":"28. Implement strStr().py","file_ext":"py","file_size_in_byte":1288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"275335462","text":"\"\"\"\n86. Partition List My Submissions QuestionEditorial Solution\nTotal Accepted: 67500 Total Submissions: 226716 Difficulty: Medium\nGiven a linked list and a value x, partition it such that all nodes less than x come before nodes greater than or equal to x.\n\nYou should preserve the original relative order of the nodes in each of the two partitions.\n\nFor example,\nGiven 1->4->3->2->5->2 and x = 3,\nreturn 1->2->2->4->3->5.\n\"\"\"\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 partition(self, head, x):\n    \"\"\"\n    :type head: ListNode\n    :type x: int\n    :rtype: ListNode\n    \"\"\"\n    small_head = small_tail = ListNode(0)\n    large_head = large_tail = ListNode(0)\n    while head:\n      next_ = head.next\n      if head.val < x:\n        small_tail.next = head\n        small_tail = small_tail.next\n        small_tail.next = None\n      else:\n        large_tail.next = head\n        large_tail = large_tail.next\n        large_tail.next = None\n      head = next_\n    small_tail.next = large_head.next\n    return small_head.next","sub_path":"python/86.py","file_name":"86.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"216085613","text":"#!/usr/bin/python\n\n#### infection-trajectory-builder.py\n#### given natural hostory parameter distributions, builds a set of infectivity\n#### and symptomaticity trajectories that can be added to FRED params file for\n#### use with the intra_host_model[strain#] = 1\n####\n#### J. DePasse 2011-08-23\n\nimport sys\nimport string\nimport os\n\nfrom optparse import OptionParser\n\n\nparser = OptionParser()\n\nparser.add_option(\"-s\", \"--strain\", dest=\"strain\", default=\"0\",\n    help=\"Strain Number\")\n\nparser.add_option(\"-l\", \"--latent\", dest=\"latent\", default=\"0 0.8 0.2\",\n    help=\"Latent period probabilities (zero-indexed\")\n\nparser.add_option(\"-n\", \"--incubating\", dest=\"incubating\", default=\"0 0.3 0.5 0.2\",\n    help=\"Incubation period probabilities (zero-indexed\")\n\nparser.add_option(\"-i\", \"--infectious\", dest=\"infectious\", default=\"0 0 0 0.3 0.4 0.2 0.1\",\n    help=\"Infectious period probabilities (zero-indexed\")\n\nparser.add_option(\"-v\", \"--infectivity\", dest=\"infectivity\", default=\"0.5 1\",\n    help=\"Infectivity values\")\n\nparser.add_option(\"-y\", \"--symptomaticity\", dest=\"symptomaticity\", default=\"0.5 1\",\n    help=\"Symptomaticity values\")\n\nparser.add_option(\"-t\", \"--infectionType\", dest=\"infectionType\", default=\"0.3333 0.6666\",\n    help=\"Probabilities for infection type (i.e., symptomatic/asymptomatic\")\n\nparser.add_option(\"-d\", \"--dependentIncubation\", dest=\"dependentIncubation\", default=\"Y\",\n    help=\"Should incubation period always be greater than or equal to latent period? (Y|N)\")\n\n\n#parser.add_option(\"-p\", \"--plot\", dest=\"plot\", default=\"N\",\n#    help=\"Produce plot? Y|N\")\n\n\n(options, args) = parser.parse_args()\n\n###########################################################################\n\nlatentPROB = []\n\nfor i in options.latent.split(\" \"):\n  latentPROB.append(float(i))\n\nincubatingPROB = []\n\nfor i in options.incubating.split(\" \"):\n  incubatingPROB.append(float(i))\n\ninfectiousPROB = []\n\nfor i in options.infectious.split(\" \"):\n  infectiousPROB.append(float(i))\n\ninfectivityVAL = [0.0]\n\nfor i in options.infectivity.split(\" \"):\n  infectivityVAL.append(float(i))\n\nsymptomaticityVAL = [0.0] \n\nfor i in options.symptomaticity.split(\" \"):\n  symptomaticityVAL.append(float(i))\n\ninfectionTypePROB = [0.0] \n\nfor i in options.infectionType.split(\" \"):\n  infectionTypePROB.append(float(i))\n\ntrajectories = []\n\n###########################################################################\n\ndef buildTrajectories(daysLatent, daysIncubating, daysInfectious, infectivity, symptomaticity):\n  symTrj = []\n  infTrj = []\n\n  for d in range(daysLatent):\n    infTrj.append(\"0\")\n\n  if (options.dependentIncubation != \"Y\"):\n    for d in range(daysIncubating):\n      symTrj.append(\"0\")\n  else:\n    for d in range(daysLatent + daysIncubating):\n      symTrj.append(\"0\")\n\n  for d in range(daysInfectious):\n    infTrj.append(infectivity)\n\n  if (options.dependentIncubation != \"Y\"):\n    for d in range(daysInfectious + daysLatent - daysIncubating):\n      symTrj.append(symptomaticity)\n  else:\n    for d in range(daysInfectious - daysLatent - daysIncubating):\n      symTrj.append(symptomaticity)\n\n  return( (infTrj,symTrj) )\n\n######################################################################\n\nfor daysLatent in range(len(latentPROB)):\n  latentProb = latentPROB[daysLatent]\n  \n  for daysIncubating in range(len(incubatingPROB)):\n    incubatingProb = incubatingPROB[daysIncubating]\n\n    for daysInfectious in range(len(infectiousPROB)):\n      infectiousProb = infectiousPROB[daysInfectious]\n\n      for infectionType in range(len(infectionTypePROB)):\n        infectionTypeProb = infectionTypePROB[infectionType]\n        infectivity = infectivityVAL[infectionType]\n        symptomaticity = symptomaticityVAL[infectionType]\n\n        trjProb = latentProb * incubatingProb * infectiousProb * infectionTypeProb\n        \n        if (trjProb > 0):\n          trajectories.append( (trjProb,buildTrajectories(daysLatent, daysIncubating, daysInfectious, str(infectivity), str(symptomaticity)) ) )\n\ntrajectories.sort()\n\ntrajectoriesPROB = []\n\ntrjProbSum = 0\n\nfor t in trajectories:\n  trjProbSum = trjProbSum + t[0]\n  trajectoriesPROB.append(str(trjProbSum))\n\ntrajectoriesPROB[-1] = \"1\"\n\nprint(\"\")\n\nprint( \"infectivity_profile_probabilities[%s] = %d    %s\\n\" % (options.strain, len(trajectoriesPROB), \" \".join(trajectoriesPROB)) )\n\nfor i in range(len(trajectories)):\n  t = trajectories[i]\n  print( \"fixed_infectivity_profile[%s][%d] = %d    %s\"  % (options.strain, i, len(t[1][0]), \" \".join(t[1][0]) ))\n\nprint(\"\")\n\nfor i in range(len(trajectories)):\n  t = trajectories[i]\n  print( \"fixed_symptomaticity_profile[%s][%d] = %d    %s\"  % (options.strain, i, len(t[1][0]), \" \".join(t[1][1]) ))\n\nprint(\"\")\n\n##############################################################\n'''\nif (options.plot == \"Y\"):\n  from pyx import *\n  c = canvas.canvas()\n  for t in trajectories:\n    rows = int(t[0]*1000)\n    cols = len(t[1][0])\n    trjString = \"\" \n    for n in range(1,rows):\n      for d in range(cols):\n        trjString = trjString + \"\\\\\" + str(int(255*float(t[1][0][d]))) + \"\\\\0\\\\0\"\n      trjString = trjString + \"\\n \"\n    print(\"%d %d %s\" % (cols, rows, trjString))\n    trjImg = bitmap.image(cols,rows,\"RGB\",trjString)\n    trjBitmap = bitmap.bitmap(0,0,trjImg,height=0.1)\n    c.insert(trjBitmap)\n  c.writePDFfile(\"output\")\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","sub_path":"bin/infection-trajectory-builder.py","file_name":"infection-trajectory-builder.py","file_ext":"py","file_size_in_byte":5317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"46404091","text":"from rest_framework import status, viewsets, mixins\nfrom django.views import View\nfrom rest_framework.response import Response\nfrom django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.core import serializers\nfrom django.http.response import JsonResponse\nfrom api.models import Fpopl, CoronaData, Gugun, Fpopl_BC\nfrom .serializers import FpoplSerializer\nfrom django.core.cache import cache\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport matplotlib.font_manager as fm\nimport numpy as np\nfrom matplotlib import rc\nimport io\nimport urllib\nimport base64\nfrom datetime import datetime, timedelta, date\nfrom drf_yasg.utils import swagger_auto_schema\n\n\nclass CoronaSet(viewsets.GenericViewSet, mixins.ListModelMixin, View):\n    \"\"\"\n        코로나 데이터를 레디스에 저장하는 API\n\n        ---\n        # 내용\n            - serial_number : 연번\n            - patient_number : 환자번호\n            - date : 날짜\n            - gugun : 군, 구\n            - overseas : 해외 이력\n            - route : 감염 경로\n            - discharge : 퇴원, 사망 여부\n    \"\"\"\n    def set_corona(self, *args, **kwargs):\n        # MongoDB에서 코로나 데이터 받기, queryset 형태로 데이터 반환\n        corona_data = CoronaData.objects.all()\n\n        # 데이터가 존재하지 않을때 에러 처리\n        if not corona_data.exists():\n            raise HttpResponse()\n\n        # 데이터를 하루동안 저장, 자르기 편하게 queryset 형식으로\n        cache.set(\"corona_data\", corona_data, 24 * 60 * 60)\n\n        return JsonResponse({\"message\": \"CORONA_SUCCESS\"}, status=200)\n\n\nclass FpoplSet(viewsets.GenericViewSet, mixins.ListModelMixin, View):\n    \"\"\"\n        유동인구 데이터를 레디스에 저장하는 API\n\n        ---\n        # 내용\n            - date : 날짜\n            - per_time : 1시간 단위의 시간\n            - age_range : 나이대\n            - sex : 성별\n            - city : 시\n            - gugun : 군, 구\n            - popl : 유동인구수\n    \"\"\"\n    def set_fpopl(self, *args, **kwargs):\n        # MongoDB에서 유동인구 데이터 받기, queryset 형태로 데이터 반환\n        fpopl_data = Fpopl.objects.all()\n\n        # 데이터가 존재하지 않을때 에러 처리\n        if not fpopl_data.exists():\n            raise HttpResponse()\n\n        # 데이터를 하루동안 저장, 자르기 편하게 queryset 형식으로\n        cache.set(\"fpopl_data\", fpopl_data, 24 * 60 * 60)\n\n        return JsonResponse({'message': 'FPOPL_SUCCESS'}, status=200)\n\n\nclass CoronaList(viewsets.GenericViewSet, mixins.ListModelMixin, View):\n    # 코로나 리스트 반환 \n    \"\"\"\n        이전 달의 코로나 데이터를 전달하는 API\n\n        ---\n        # 내용\n            - serial_number : 연번(해당 달의 총 감염자 수)\n            - gugun : 군, 구\n    \"\"\"\n    def get_corona_list(self, *args, **kwargs):\n        # 한 달전의 데이터를 전달하기 위해 날짜 설정\n        today = date.today()\n\n        if today.month < 2:\n            previous_yymm = str(today.year - 1) + \"-\" + str(today.month + 11)\n        elif today.month > 1 and today.month < 11:\n            previous_yymm = str(today.year) + \"-0\" + str(today.month - 1)\n        else:\n            previous_yymm = str(today.year) + \"-\" + str(today.month - 1)\n\n        # 코로나 데이터를 DB에서 검색\n        corona_queryset = CoronaData.objects.filter(\n            date__contains=previous_yymm)\n\n        # 구군마다 전체 분포표(확진자 수 카운트)\n        df = pd.DataFrame(\n            list(corona_queryset.all().values(\"serial_number\", \"gugun\")))\n        df = df.groupby([\"gugun\"], as_index=False).count()\n\n        df = df.drop(index=[8, 26], axis=0)  # 기타, 타시도 삭제\n\n        corona_json = df.to_dict()  # Json 데이터로 전달하기 위해 dictionary로 변환\n\n        return JsonResponse(corona_json, safe=False)\n\nclass FpoplList(viewsets.GenericViewSet, mixins.ListModelMixin, View):\n    # 유동인구 리스트 반환\n    \"\"\"\n        2달 전의 유동인구 데이터를 반환하는 API (SKT 유동인구 데이터 기준)\n\n        ---\n        # 내용\n            - date : 날짜\n            - per_time : 1시간 단위의 시간\n            - age_range : 나이대\n            - sex : 성별\n            - city : 시\n            - gugun : 군, 구\n            - popl : 유동인구수\n    \"\"\"\n    def get_fpopl_list(self, *args, **kwargs):\n        # 두 달전의 유동인구 데이터를 전달하기 위해 날짜 설정\n        today = date.today()\n\n        if today.month < 3:\n            previous_yymm = str(today.year - 1) + str(today.month + 10)\n        elif today.month > 2 and today.month < 12:\n            previous_yymm = str(today.year) + \"0\" + str(today.month - 2)\n        else:\n            previous_yymm = str(today.year) + str(today.month - 2)\n        # 유동인구 데이터를 DB에서 검색\n        fpopl_queryset = Fpopl.objects.filter(date__contains=previous_yymm)\n\n        # 구별, 날짜별 유동인구수 분포 DataFrame 생성\n        df = pd.DataFrame(\n            list(fpopl_queryset.all().values(\"date\", \"gugun\", \"popl\")))\n\n        # Json 데이터로 전달하기 위해 dictionary로 변환\n        fpopl = df.to_dict()\n        return JsonResponse(fpopl, safe=False)\n\n\nclass FpoplDataAnalysis(viewsets.GenericViewSet, mixins.ListModelMixin, View):\n    # 유동인구 데이터 분석 함수\n    \"\"\"\n        유동인구 데이터 분석 함수\n\n        ---\n        # 내용\n            - 전체 데이터 뽑아오기 - fpopl_data1 과 fpopl_data2\n            - 주요 시간대 : 점심시간, 저녁시간 데이터 뽑아오기 - fpopl_data3 과 fpopl_data4 \n            - 나이대 : 20 - 30대 데이터 뽑아오기 - fpopl_data5\n            - 나이대 : 20대 - 40대 데이터 뽑아오기 - fpopl_data6 (현재 이 부분)\n            - 분석 내용을 사진으로 저장\n    \"\"\"\n    def fpopl_data_analysis(self, *args, **kwargs):\n        # 그래프의 한글 깨짐 방지, 폰트 연결\n        f_path = \"c:/Windows/Fonts/malgun.ttf\"\n        font_name = fm.FontProperties(fname=f_path).get_name()\n        plt.rc('font', family=font_name)\n        plt.rc('axes', unicode_minus=False)\n\n        # 코로나 이전 유동인구, 이후 유동인구 데이터 뽑아오기\n        # 나이대 : 20대 - 40대 데이터 뽑아오기 - fpopl_data6\n        bc_data = Fpopl_BC.objects.filter(age_range__in=[20, 30, 40])\n        ac_data = Fpopl.objects.filter(age_range__in=[20, 30, 40])\n\n        # 전체 데이터 뽑아오기 - fpopl_data1 과 fpopl_data2\n        # bc_data = Fpopl_BC.objects.all()\n        # ac_data = Fpopl.objects.all()\n\n        # 주요 시간대 : 점심시간, 저녁시간 데이터 뽑아오기 - fpopl_data3 과 fpopl_data4\n        # bc_data = Fpopl_BC.objects.filter(per_time__in=[\"12\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\"])\n        # ac_data = Fpopl.objects.filter(per_time__in=[\"12\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\"])\n\n        # 나이대 : 20 - 30대 데이터 뽑아오기 - fpopl_data5\n        # bc_data = Fpopl_BC.objects.filter(age_range__in=[20, 30])\n        # ac_data = Fpopl.objects.filter(age_range__in=[20, 30])\n\n        # 비교를 위해 DataFrame으로 변환\n        bc_df = pd.DataFrame(list(bc_data.values(\"date\", \"gugun\", \"popl\")))\n        ac_df = pd.DataFrame(list(ac_data.values(\"date\", \"gugun\", \"popl\")))\n        # 전체 데이터를 한 곳에서 볼 수 있도록 합침\n        total_df = pd.concat([bc_df, ac_df], axis=0, ignore_index=True)\n        # 하루의 유동인구로 표를 합침\n        raw_1 = total_df.groupby(by=['date', 'gugun']).sum().reset_index()\n        # 월별 유동인구 표시를 위해 date 형식으로 변환\n        raw_1[\"date\"] = pd.to_datetime(raw_1[\"date\"], format='%Y%m%d')\n\n        # '연도', '월', '일' 컬럼 생성\n        raw_1['year'] = raw_1.date.dt.year\n        raw_1['month'] = raw_1.date.dt.month\n        raw_1['day'] = raw_1.date.dt.day\n\n        # 월평균 유동인구수 구하기\n        temp = raw_1.groupby(\n            by=[\"gugun\", \"year\", \"month\"]).mean().reset_index()\n        raw_1 = temp.drop('day', axis=1)\n\n        # 그래프 그리기\n        raw_1 = raw_1.set_index('gugun')\n\n        fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(15, 15))\n\n        for i, f in enumerate(raw_1.index.unique()):\n            r = int(i / 5)  # 행별로 그래프 배치하기\n            c = i % 5  # 열별로 그래프 배치하기\n            df19 = raw_1[(raw_1.index == f) & (raw_1.year == 2019)]\n            line19 = sns.lineplot(\n                data=df19, x='month', y='popl', label='2019', ax=axes[r][c])\n            df20 = raw_1[(raw_1.index == f) & (raw_1.year == 2020)]\n            line20 = sns.lineplot(\n                data=df20, x='month', y='popl', label='2020', ax=axes[r][c])\n            df21 = raw_1[(raw_1.index == f) & (raw_1.year == 2021)]\n            line21 = sns.lineplot(\n                data=df21, x='month', y='popl', label='2021', ax=axes[r][c])\n            line21.set_ylim(0, 1.5e+07)\n            line21.set_title(f)\n\n        fig.tight_layout()\n        plt.savefig('fpopl_data6.png')\n        return Response(status=200)\n\n\nclass CoronaDataAnalysis(viewsets.GenericViewSet, mixins.ListModelMixin, View):\n    \"\"\"\n        코로나 데이터 분석 함수\n\n        ---\n        # 내용\n            - 구군별, 월별 코로나 데이터 분석 : corona_data로 저장됨\n    \"\"\"\n    def corona_data_analysis(self, *args, **kwargs):\n        # 그래프의 한글 깨짐 방지, 폰트 연결\n        f_path = \"c:/Windows/Fonts/malgun.ttf\"\n        font_name = fm.FontProperties(fname=f_path).get_name()\n        plt.rc('font', family=font_name)\n        plt.rc('axes', unicode_minus=False)\n        # DB에서 Corona Data 불러오기\n        corona_data = CoronaData.objects.all()\n        # 연번, 날짜, 구군에 대한 Dataframe 작성\n        corona_df = pd.DataFrame(\n            list(corona_data.values(\"serial_number\", \"date\", \"gugun\")))\n        # 2020-01 2020-01 2021-03-07 의 포맷을  202001, 202103와 같은 포맷으로 변경.\n        corona_df['date'] = [''.join(x.split('-')[0:2])\n                             for x in corona_df.date]  # 2020-01 2020-01 2021-03-07 -> 202001, 202103\n        # 날짜와 구군을 그룹으로 하여 카운트를 센다.\n        raw_1 = corona_df.groupby(\n            by=['date', 'gugun']).count().reset_index()\n\n        # 그래프를 그리고 기타와 타시도로 적힌 구군의 정보를 포함하지 않는다.\n        raw_1 = raw_1.set_index('gugun')\n        raw_1 = raw_1.drop('기타', axis=0)\n        raw_1 = raw_1.drop('타시도', axis=0)\n\n        # raw_1을 raw_2에 복사하여 평균을 위한 그래프를 새로 그릴 데이터를 raw_2로 지정한다.\n        raw_2 = raw_1\n        raw_2 = raw_2.groupby(by=['date']).sum().reset_index()\n        raw_2[\"serial_number\"] = raw_2[\"serial_number\"]/25\n\n        # 꺾은선 그래프를 그리기 위한 함수\n        fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(15, 15))\n\n        for i, f in enumerate(raw_1.index.unique()):\n            r = int(i / 5)  # 행별로 그래프 배치하기\n            c = i % 5  # 열별로 그래프 배치하기\n\n            df = raw_1[(raw_1.index == f)]\n\n            line20 = sns.lineplot(\n                data=raw_2, x='date', y='serial_number', label='average', ax=axes[r][c])\n            line21 = sns.lineplot(\n                data=df, x='date', y='serial_number', label='corona', ax=axes[r][c])\n\n            line21.set_ylim(0, 800)\n            line21.set_title(f)\n\n        fig.tight_layout()\n        # 결과값을 corona_data.png로 저장\n        plt.savefig('corona_data.png')\n        return Response(status=200)\n","sub_path":"exec/Backend/comeet/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":11841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"124809264","text":"import cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nTP = 0\nFP = 0\nFN = 0\nI0 = cv2.imread('input/in%06d.jpg' % 1)\nI0G = cv2.cvtColor(I0, cv2.COLOR_BGR2GRAY)\n\n\nN = 60\nYY, XX = I0G.shape\nBUF = np.zeros((YY, XX, N), np.uint8)\niN = 0\n\ndef mean_buf(IG):\n    global iN\n    BUF [:,:,iN] = IG\n    iN = iN + 1\n    if iN == N:\n        iN = 0\n    mean = np.mean(BUF,axis = 2)\n    return np.uint8(mean)\n\ndef median_buf(IG):\n    global iN\n    BUF [:,:,iN] = IG\n    iN = iN + 1\n    if iN == N:\n        iN = 0\n    median = np.median(BUF,axis = 2)\n    return np.uint8(median)\n\nBG = np.zeros((YY, XX), np.uint8)\nalfa = 0.05\n\ndef aprok_mean(IG):\n    global BG\n    BGn =np.float64(alfa*np.float64(IG) + (1-alfa)*np.float64(BG))\n    BG = BGn\n    return np.uint8(BGn)\n#\n# def aprok_median():\n\n\n\n\n\nf =open('temporalROI.txt','r')\nline = f.readline()\nroi_start, roi_end = line.split()\nroi_start =int(roi_start)\nroi_end =int(roi_end)\nf.close()\n\nkernel = np.ones((3,3),np.uint8)\n\n\nfor i in range(1,1020):\n    I = cv2.imread('input/in%06d.jpg' % i)\n    IG = cv2.cvtColor(I, cv2.COLOR_BGR2GRAY)\n    # diff = cv2.absdiff(IG,I0G)\n    # model_mean = mean_buf(IG)\n    model = aprok_mean(IG)\n    diff = cv2.absdiff(IG,model)\n    B = cv2.threshold(diff, 20, 255, cv2.THRESH_BINARY)\n    B =B[1]\n    median = cv2.medianBlur(B, 5)\n    Dil = cv2.dilate(median,kernel,iterations = 1)\n    Eros = cv2.erode(Dil, kernel, iterations=1)\n    I0G = IG\n        #indeksacja\n    retval, labels, stats, centroids = cv2.connectedComponentsWithStats(Eros)\n    img = np.uint8(labels / stats.shape[0] * 255)\n\n    if (stats.shape[0] > 1):  # czy sa jakies obiekty\n        tab = stats[1:, 4]\n        pi = np.argmax(tab)\n        pi = pi + 1\n        # wyrysownie box\n        cv2.rectangle(I, (stats[pi, 0], stats[pi, 1]), (stats[pi, 0] + stats[pi, 2], stats[pi, 1] + stats[pi, 3]),(255, 0, 0), 2)\n        cv2.putText(I, \"%f\" % stats[pi, 4], (stats[pi, 0], stats[pi, 1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0))\n        cv2.putText(I, \"%d\" % pi, (np.int(centroids[pi, 0]), np.int(centroids[pi, 1])), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0))\n    cv2.imshow(\"model\", model)\n    cv2.imshow(\"image\", I)\n    cv2.imshow(\"diff\",diff)\n    cv2.imshow(\"bin\", Eros)\n    cv2.waitKey(10)\n\n    if (i >= roi_start and i <= roi_end):\n        I00G = cv2.imread('groundtruth/gt%06d.png' % i)\n        B0 = cv2.threshold(I00G, 20, 255, cv2.THRESH_BINARY)\n        B0 =B0[1]\n        B0 = B0[:,:,0]\n\n\n        TP_M = np.logical_and((Eros == 255),(B0 == 255))\n        TP_S = np.sum(TP_M)\n        TP = TP + TP_S\n\n        FP_M = np.logical_and((Eros == 255),(B0 == 0))\n        FP_S = np.sum(FP_M)\n        FP = FP + FP_S\n\n        FN_M = np.logical_and((Eros == 0),(B0 == 255))\n        FN_S = np.sum(FN_M)\n        FN = FN + FN_S\n\n\n\n\nP = np.float32(TP)/(TP + FP)\nR = np.float32(TP)/(TP + FN)\nF1 = 2*P*R/(P+R)\nprint(\"P = \" + str(P))\nprint(\"R = \" + str(R))\nprint(\"F1 = \" + str(F1))","sub_path":"lab3/zad1.py","file_name":"zad1.py","file_ext":"py","file_size_in_byte":2906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"108744537","text":"#!/bin/env python\n# -*- coding:utf-8 -*-\n# crawl server test case\n# locust -f tests.py\n# Yao, 2018.8.23\n#\n\nfrom locust import HttpLocust, TaskSet, task\n\nfrom msg import send_msg\nfrom jobs import SpiderJob\n\ndef res_fail_error(res, endpoint):\n    err_msg = \"Error [{}], content: {}\".format(endpoint, res.content)\n    print(err_msg)\n    res.failure(err_msg)\n\ndef res_fail_failure(res, endpoint):\n    fail_msg = \"Fail [{}], code: {}\".format(endpoint, res.status_code)\n    print(fail_msg)\n    res.failure(fail_msg)\n\ndef res_ok(res, endpoint, data):\n    ok_msg = \"OK [{}], res: {}\".format(endpoint, data)\n    print(ok_msg)\n    res.success()\n\ndef res_check(res, endpoint):\n    if res and res.status_code == 200:\n        ret = res.json()\n        if ret['code'] == 'OK':\n            res_ok(res, endpoint, ret)\n        else:\n            res_fail_error(res, endpoint)\n    else:\n        res_fail_failure(res, endpoint)\n\n\nclass ApiTasks(TaskSet):\n  \n    def on_start(self):\n        pass\n\n    @task(1)\n    def A001_save_template(self):\n        endpoint = \"/A001\"\n        headers = {\n\n        }\n        request = {\n            \"start_url\":\"https://search.jd.com/Search?keyword=${key}&enc=utf-8&wq=${key}\",\n            \"scripts_steps\":[\n                [\"window.scrollTo(0,document.body.scrollHeight)\"],\n                [\"SEARCH.page(3, true)\",\"window.scrollTo(0,document.body.scrollHeight)\"]\n            ],\n            \"list_name\":\"#J_goodsList li.gl-item\",\n            \"parse_format\":{\n                \"sku_id\":\"|data-sku\",\n                \"sku_price\":\"div.p-price i|\",\n                \"sku_name\":\"div.p-name>a>em|\",\n                \"sku_url\":\"div.p-name>a|href\",\n                \"comment_count\":\"div.p-commit a|\",\n                \"icons\":\"div.p-icons i|\",\n                \"pic_url\":\"div.p-img img|src\",\n                \"shop_name\":\"div.p-shop a|\",\n                \"shop_url\":\"div.p-shop a|href\"\n            } \n        }\n        with self.client.post(endpoint, json=request, headers=headers, catch_response=True) as res:\n            res_check(res, endpoint)\n\n    @task(1)\n    def A002_invoke_spider(self):\n        endpoint = \"/A002\"\n        headers = {\n\n        }\n        request = {\n            \"user_id\":\"3\",\n            \"job_id\":\"3\",\n            \"tpl_id\":\"9d3f7b42-a5df-11e8-b407-7c04d0cf0f76\",\n            \"key_word\":\"高露洁\"\n        }\n        with self.client.post(endpoint, json=request, headers=headers, catch_response=True) as res:\n            res_check(res, endpoint)\n\n    def A003_get_excel_file(self):\n        request = {\n            \"user_id\":\"1001\",\n            \"job_id\":\"0001\",\n            \"xls_headers\":{\n                \"key\":\"关键字\",\n                \"title\":\"话题\",\n                \"focus\":\"关注\",\n                \"question\":\"问题\",\n                \"good\":\"精华\",\n                \"content\":\"解释\"\n            }\n        }\n        \n  \n\nclass WebTest(HttpLocust):\n    task_set = ApiTasks\n    host = \"http://localhost:8000\"\n    min_wait = 0 * 1000\n    max_wait = 2 * 1000","sub_path":"dev/python/crawl/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"203864102","text":"# Shows K4, onwards...\n\nBACKGROUND_COLOR = \"light slate gray\"\n\nroot = tk.Tk()\nroot.configure(bg=BACKGROUND_COLOR)\n\nlabel = tk.Label(root, text=\"Title\", bg=BACKGROUND_COLOR)\nlabel.pack() # For things to be visible, it needs to be packed\n\n#-------------------------\n\n#v = [Vertex(50,50, label=\"hi\"), Vertex(200,100), Vertex(50,100)]\n#e = [Edge(v[0], v[1])]\n#g = Graph(v, e)\n\nw = GraphCanvas(root, width=400, height=300, background=\"white\")\nw.pack()\n#w.draw_graph(g)\n#g = BipartiteGraph(5, 3)\ng = spanTree\nw.draw_graph(g)\nw.show_edge_crossings(g)\n\n#-------------------------\n\n'''\nw = tk.Canvas(root, width=400, height=300, background=\"white\")\nw.pack()\n\nw.create_line(0,0, 200, 100)\nw.create_rectangle(50,100, 0, 70)\nw.create_rectangle(50,25,150,80)\n\nw.create_oval(90,90,150,150, fill=\"red\")\nw.create_text(100,10,text=\"HI\")\n'''\n\ndef func():\n\tfunc.i = func.i+1\n\tg = Kn(func.i)\n\tw.draw_graph(g)\n\tw.show_edge_crossings(g)\nfunc.i = 4\n\nnext_bttn = tk.Button(root, text=\"Next\", command=func)\nnext_bttn.pack()\n\nroot.mainloop()\n","sub_path":"test_draw.py","file_name":"test_draw.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"535514057","text":"# -*- coding: utf-8 -*-\n# 한글 주석 사용 가능하도록 encoding 추가\n\nimport os\nimport sys\n\nsys.path.append(os.path.dirname(os.path.dirname(os.getcwd())))\nsys.path.append(''.join([os.path.dirname(os.path.dirname(os.getcwd())) + os.sep + \"Lib\"]))\nsys.path.append(''.join([os.path.dirname(os.path.dirname(os.getcwd())) + os.sep + \"Agent\"]))\nsys.path.append(''.join([os.path.dirname(os.path.dirname(os.getcwd())) + os.sep + \"include\"]))\n\nimport base64\n\nfrom Agent.gnagent import pluginbase\nimport include.constants as plugin_const\n\n\n# Application string\nIDS_CPUINFO = \"cat /proc/cpuinfo\"\nIDS_MEMINFO = \"free\"\nIDS_HDDINFO = \"udevadm\"\n\nIDS_COLUMN_CPU = [\n    \"NI_MANUFACTURE\",\n    \"NI_MOTHERBOARDNAME\",\n    \"NI_NAME\",\n    \"NI_REVISION\",          # int\n    \"NI_CPUTYPE\",           # int\n    \"NI_CPUFAMILY\",         # int\n    \"NI_CPUMODEL\",          # int\n    \"NI_CPUSTEPPING\",       # int\n    \"NI_CPUCLOCK\",          # int\n    \"NI_BATTERY\",           # int\n    \"NI_USAGECPU\",          # int\n    \"NI_TEMPERATURE\"        # int\n]\nIDS_COLUMN_MEM = [\n    \"NI_TOTALMEM\",          # int\n    \"NI_USEDMEM\",           # int\n    \"NI_TOTALSWAP\",         # int\n    \"NI_USEDSWAP\"           # int\n]\nIDS_COLUMN_STORAGE = [\n    \"NI_FILESYSTEM\",\n    \"NI_NAME\",\n    \"NI_TOTAL\",\n    \"NI_TYPE\",\n    \"NI_USED\",\n    \"NI_SERIAL_STR\",\n    \"NI_SERIAL\",\n    \"NI_VOLID\",\n    \"NI_VENDOR\",\n    \"NI_MODEL\",\n    \"NI_RASECTOR\"\n]\n\n\nclass hwinfo(pluginbase):\n    '''\n    hwinfo\n    '''\n\n    def create_dicts(self):\n        '''\n        create_dicts\n        '''\n        pluginbase.create_dicts(self)\n\n        self.dict_app = {\n            IDS_CPUINFO: {\n                \"NI_MANUFACTURE\": \"grep 'vendor_id' /proc/cpuinfo | tail -1 | awk '{print $3}'\",\n                \"NI_MOTHERBOARDNAME\": \"cat /sys/devices/virtual/dmi/id/board_vendor\",\n                \"NI_NAME\": \"grep 'model name' /proc/cpuinfo | tail -1 | awk -F': ' '{print $2}'\",\n                \"NI_CPUFAMILY\": \"grep 'cpu family' /proc/cpuinfo | tail -1 | awk -F': ' '{print $2}'\",\n                \"NI_CPUSTEPPING\": \"grep 'stepping' /proc/cpuinfo | tail -1 | awk -F': ' '{print $2}'\",\n                \"NI_CPUCLOCK\": \"grep 'cpu MHz' /proc/cpuinfo | tail -1 | awk -F': ' '{print $2}'\",\n            },\n            IDS_MEMINFO: {\n                \"NI_TOTALMEM\": \"free | grep Mem: | awk '{print $2}'\",\n                \"NI_USEDMEM\": \"free | grep Mem: | awk '{print $3}'\",\n                \"NI_TOTALSWAP\": \"free | grep Swap: | awk '{print $2}'\",\n                \"NI_USEDSWAP\": \"free | grep Swap: | awk '{print $3}'\",\n            },\n            IDS_HDDINFO: {\n                # \"NI_SERIAL\" : \"udevadm info --query=all --name=/dev/sda | grep ID_SERIAL= | awk -F'=' '{print $2}'\",\n                \"DFINFO\": \"df -T | grep '^/dev' | awk -F'[ :]+' '{print $1,$2, $3, $4, $5}'\"\n            }\n        }\n        self.list_data = [\n        ]\n\n        self.list_column = {\n            \"CPU\": IDS_COLUMN_CPU,\n            \"MEM\": IDS_COLUMN_MEM,\n            \"STORAGE\": IDS_COLUMN_STORAGE\n        }\n\n    def run(self):\n        '''\n        run\n        '''\n        list_cpu = []\n        list_mem = []\n        list_storage = []\n        try:\n            for cmdid, commands in self.dict_app.items():\n                ''''''\n                if (self.check_tool(cmdid) == False):\n                    continue\n\n                if (cmdid == IDS_CPUINFO):\n                    ''''''\n                    cpuinfo = [\"\", \"\", \"\", \"0\", \"0\", \"0\",\n                               \"0\", \"0\", \"0\", \"0\", \"0\", \"0\"]\n                    for colname, command in commands.items():\n                        ''''''\n                        list_result = self.execute(command)\n                        if (list_result[plugin_const.ID_RESULT] == plugin_const.ID_RESULT_FAIL):\n                            continue\n\n                        idx = IDS_COLUMN_CPU.index(colname)\n                        cpuinfo[idx] = list_result[1].rstrip(\"\\n\")\n                    ''''''\n                    list_cpu.append(cpuinfo)\n\n                elif (cmdid == IDS_MEMINFO):\n                    ''''''\n                    meminfo = [\"0\", \"0\", \"0\", \"0\"]\n                    for colname, command in commands.items():\n                        ''''''\n                        list_result = self.execute(command)\n                        if (list_result[plugin_const.ID_RESULT] == plugin_const.ID_RESULT_FAIL):\n                            continue\n\n                        idx = IDS_COLUMN_MEM.index(colname)\n                        meminfo[idx] = list_result[1].rstrip(\"\\n\")\n                    ''''''\n                    list_mem.append(meminfo)\n\n                elif (cmdid == IDS_HDDINFO):\n                    ''''''\n                    for colname, command in commands.items():\n                        ''''''\n                        list_result = self.execute(command)\n                        if (list_result[plugin_const.ID_RESULT] == plugin_const.ID_RESULT_FAIL):\n                            continue\n\n                        if (colname == \"DFINFO\"):\n                            ''''''\n                            for diskinfos in list_result[1].split(\"\\n\"):\n                                ''''''\n                                if (len(diskinfos) == 0):\n                                    continue\n\n                                hddinfo = [\"\", \"\", \"\", \"\", \"\",\n                                           \"\", \"\", \"\", \"\", \"\", \"\"]\n                                diskinfo = diskinfos.split(\" \")\n\n                                hddinfo[IDS_COLUMN_STORAGE.index(\"NI_NAME\")] = diskinfo[0]\n                                hddinfo[IDS_COLUMN_STORAGE.index(\"NI_FILESYSTEM\")] = diskinfo[1]\n                                hddinfo[IDS_COLUMN_STORAGE.index(\"NI_TOTAL\")] = diskinfo[2]\n                                hddinfo[IDS_COLUMN_STORAGE.index(\"NI_USED\")] = diskinfo[3]\n                                hddinfo[IDS_COLUMN_STORAGE.index(\"NI_TYPE\")] = \"0\"\n                                hddinfo[IDS_COLUMN_STORAGE.index(\"NI_RASECTOR\")] = \"0\"\n\n                                \"\"\"\n                                /dev/sda 디바이스가 중복될수 있음.\n                                디바이스 이름(/dev/sda1, /dev/sda2 etc)으로 중복이 있는지 체크한다.\n                                openSuSE 42.x 에서는 /dev/sda2 가 중복적으로 생성이 됨.\n                                \"\"\"\n                                if len(list_storage):\n                                    dev_list = []\n                                    for el in list_storage:\n                                        \"\"\"\n                                            /dev/sda1 , /dev/sda2 와 같은 저장 장치 이름으로 구성된 목록을 생성함.\n                                        \"\"\"\n                                        dev_list.append(el[IDS_COLUMN_STORAGE.index(\"NI_NAME\")])\n\n                                    if hddinfo[IDS_COLUMN_STORAGE.index(\"NI_NAME\")] in dev_list:\n                                        continue\n                                    else:\n                                        list_storage.append(hddinfo)\n                                else:\n                                    list_storage.append(hddinfo)\n                        else:\n                            ''''''\n                            idx = IDS_COLUMN_STORAGE.index(colname)\n                            hddinfo[idx] = list_result[1].rstrip(\"\\n\")\n                            list_storage.append(hddinfo)\n\n                self.list_data = [\n                    [\n                        \"CPU\",\n                        IDS_COLUMN_CPU,\n                        list_cpu\n                    ],\n                    [\n                        \"MEM\",\n                        IDS_COLUMN_MEM,\n                        list_mem\n                    ],\n                    [\n                        \"STORAGE\",\n                        IDS_COLUMN_STORAGE,\n                        list_storage\n                    ]\n                ]\n\n            self.set_profileresult(plugin_const.GN_PROFILE_WORKTYPE_ACTION, \"1\", \"\")\n        except Exception as e:\n            self.set_profileresult(plugin_const.GN_PROFILE_WORKTYPE_ACTION, \"0\", str(e))\n\n\n    def __init__(self):\n        '''\n        __init__\n        '''\n        self.create_dicts()\n","sub_path":"genian/Plugin/ACT7002_InfoHW/hwinfo.py","file_name":"hwinfo.py","file_ext":"py","file_size_in_byte":8276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"42409608","text":"import tensorflow as tf\nfrom tensorflow.keras import datasets, layers, models\nimport matplotlib.pyplot as plt\nimport pickle\nimport numpy as np\n\nimport model\n\nclass_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n               'dog', 'frog', 'horse', 'ship', 'truck']\n\n\nx_test, y_test = pickle.load(open(\"data/test_data\", \"rb\"))\nmodel = model.create_model()\nmodel.load_weights(\"model/model.h5\")\nmodel.evaluate(x_test, y_test)\n\n\npredictions = np.argmax(model.predict(x_test), axis=1)\n\n# plot the first 25 predictions\nplt.figure(figsize=(10, 10))\nfor i in range(25):\n    prediction = predictions[i]\n    plt.subplot(5, 5, i + 1)\n    plt.xticks([])\n    plt.yticks([])\n    plt.grid(False)\n    plt.xlabel(\"E {}, A {}\".format(\n        class_names[y_test[i][0]], class_names[prediction]))\n    plt.imshow(x_test[i], cmap=plt.cm.binary)\nplt.show()\n","sub_path":"evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"163012491","text":"from tkinter import *\r\nfrom tkinter import messagebox\r\nfrom random import randint\r\n\r\nclass gamewindow():\r\n    def __init__(s):\r\n        s.root = Tk()\r\n        s.root.title(\"Dinosaur Game\")\r\n        s.root.grid()\r\n\r\n        s.canvas = Canvas(width = 1900, height = 600, bg = \"White\")\r\n        s.canvas.grid()\r\n\r\n        s.cont = \"Y\"\r\n        s.jump = \"N\"\r\n        s.jumping = \"N\"\r\n        s.duck = \"N\"\r\n        s.ducking = \"N\"\r\n        s.jumpvel = 0\r\n        s.score = 0\r\n        s.delay = 10\r\n        s.recentscore = 0\r\n        s.recentscore1 = 0\r\n        s.hazard = list()\r\n\r\n        s.line = s.canvas.create_line(0, 500, 1900, 500)\r\n        s.dinohitbox = s.canvas.create_rectangle(50, 370, 100, 500, fill = \"Black\")\r\n        s.scorelabel = s.canvas.create_text(1700, 30, fill = \"Black\", font = \"Calibri 20 bold\", text = s.score)\r\n\r\n        s.root.bind(\"<KeyPress>\", lambda event=\"<KeyPress>\": gamewindow.keypress(event, s))\r\n        s.root.bind(\"<KeyRelease>\", lambda event=\"<KeyRelease>\": gamewindow.keyrelease(event, s))\r\n\r\n        gamewindow.scrolling(s)\r\n        gamewindow.ConstantRefresh(s)\r\n\r\n        s.root.mainloop()\r\n\r\n    def ConstantRefresh(s): #Jumping and score\r\n\r\n        if s.cont == \"Y\":\r\n            if s.jump == \"Y\" and s.jumping == \"N\":\r\n                s.jumpvel = 20\r\n                s.jumping = \"Y\"\r\n\r\n            dinoposition = list(s.canvas.bbox(s.dinohitbox)) #ADD COLLISION DETECTION HERE!!!\r\n            for i in range(len(s.hazard)):\r\n                hazardposition = list(s.canvas.bbox(s.hazard[i]))\r\n\r\n                for x in range(2):\r\n                    tempx = x * 2\r\n                    if hazardposition[tempx] >= dinoposition[0] and hazardposition[tempx] <= dinoposition[2] and hazardposition[tempx + 1] >= dinoposition[1] and hazardposition[tempx + 1] <= dinoposition[3]:\r\n                        s.cont = \"N\"\r\n\r\n            \r\n            if s.jumping == \"Y\" and s.ducking == \"N\": \r\n\r\n                if int(dinoposition[3]) < 501 or s.jumpvel == 20:\r\n                    s.canvas.move(s.dinohitbox, 0, -s.jumpvel)\r\n                    s.jumpvel -= 1\r\n\r\n                else:\r\n                    s.jumping = \"N\"\r\n\r\n                    difference = int(dinoposition[3]) - 501\r\n                    s.canvas.move(s.dinohitbox, 0, -difference)\r\n\r\n            elif s.duck == \"Y\":\r\n                s.duck = \"N\"\r\n                s.ducking = \"Y\"\r\n                s.canvas.move(s.dinohitbox, 0, 60)\r\n\r\n            s.score += 1\r\n            s.canvas.delete(s.scorelabel)\r\n            s.scorelabel = s.canvas.create_text(1700, 30, fill = \"Black\", font = \"Calibri 20 bold\", text = int(s.score / 10))\r\n            s.root.after(13, gamewindow.ConstantRefresh, s)\r\n\r\n\r\n\r\n    def scrolling(s): #scrolls the level which speeds up over time\r\n        hazards = [\"Nothing\", \"Small\", \"Large\", \"Birds\"]\r\n\r\n        if s.cont == \"Y\":\r\n            if s.score % 2500 == 0 and s.score != s.recentscore1 and s.delay > 1:\r\n                s.delay -= 1\r\n                s.recentscore1 = s.score\r\n            \r\n            if s.score >= 200 and s.score % 100 == 0 and s.recentscore != s.score:\r\n                s.recentscore = s.score\r\n                choice = hazards[randint(0,3)] #Nothing, small cactus, large cactus, birds respectively\r\n                amount = randint(1,3) #amount of hazard chosen (however position if bird)\r\n\r\n                if choice != \"Nothing\":\r\n                    if choice == \"Small\" or choice == \"Large\":\r\n                        if choice == \"Small\": #large cacti are almost the same height and width as the dino, small cacti are slighty over half the height and thickness of the dino\r\n                            vertices = [1900, 420, 1950, 500]\r\n                            gap = 60\r\n\r\n                        if choice == \"Large\":\r\n                            vertices = [1900, 360, 1960, 500]\r\n                            gap = 65\r\n\r\n                        for i in range(amount):\r\n                            s.hazard.append(\"\")\r\n                            s.hazard[len(s.hazard) - 1] = s.canvas.create_rectangle(vertices[0] + (gap * i), vertices[1], vertices[2] + (gap * i), vertices[3], fill = \"Black\")\r\n\r\n                    elif choice == \"Birds\":\r\n                        position = (amount - 1) * 100\r\n                        vertices = [1900, 240 + position , 1950, 300 + position]\r\n                        s.hazard.append(\"\")\r\n                        s.hazard[len(s.hazard) - 1] = s.canvas.create_rectangle(vertices[0], vertices[1], vertices[2], vertices[3], fill = \"Blue\")\r\n\r\n            while len(s.hazard) > 9: #only allows 9 hazards on the canvas at any given time\r\n                s.canvas.delete(s.hazard[0])\r\n                del s.hazard[0]\r\n            \r\n            for i in range(len(s.hazard)):\r\n                s.canvas.move(s.hazard[i], -10, 0)\r\n\r\n            s.root.after(s.delay, gamewindow.scrolling, s)\r\n        \r\n\r\n    def keypress(event, s):\r\n        keypressed = event.keysym\r\n        if (keypressed == \"space\" or keypressed == \"Up\") and s.jumping == \"N\":\r\n            s.jump = \"Y\"\r\n\r\n        if keypressed == \"Down\":\r\n            if s.jumping == \"N\" and s.ducking == \"N\":\r\n                s.duck = \"Y\"\r\n\r\n    def keyrelease(event, s):\r\n        keyreleased = event.keysym\r\n        if keyreleased == \"space\" or keyreleased == \"Up\":\r\n            s.jump = \"N\"\r\n            if s.jumpvel > 0:\r\n                s.jumpvel = 0\r\n\r\n        if keyreleased == \"Down\":\r\n            if s.ducking == \"Y\":\r\n                s.ducking = \"N\"\r\n                s.canvas.move(s.dinohitbox, 0, -60)\r\n\r\nif __name__ == \"__main__\":\r\n    game = gamewindow()\r\n","sub_path":"dinogame.py","file_name":"dinogame.py","file_ext":"py","file_size_in_byte":5615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"139284475","text":"def solution(n, path, order):\n    path_dic = {}\n    for p in path:\n        x, y, = p[0], p[1]\n        if x not in path_dic:\n            path_dic[x] = []\n        if y not in path_dic:\n            path_dic[y] = []\n        path_dic[x].append(y)\n        path_dic[y].append(x)\n    visit_first = {}\n    visit_later = {}\n    for o in order:\n        x, y = o[0], o[1]\n        if y not in visit_first:\n            visit_first[y] = []\n        if x not in visit_later:\n            visit_later[x] = []\n        visit_first[y].append(x)\n        visit_later[x].append(y)\n    visited = [0 for _ in range(n)]\n    can_visit = [0 for _ in range(n)]\n    can_visit[0] = 1\n    visited[0] = 1\n    queue = [0]\n    if 0 in visit_first:\n        return False\n    while queue:\n        current = queue.pop(0)\n        can_go = True\n        can_visit[current] = 1\n        if current not in visit_first:\n            visited[current] = 1\n            for room in path_dic[current]:\n                if visited[room] == 0:\n                    queue.append(room)\n            if current in visit_later and can_visit[visit_later[current][0]] == 1:\n                queue.append(visit_later[current][0])\n        else:\n            if visited[visit_first[current][0]] == 1:\n                visited[current] = 1\n                for room in path_dic[current]:\n                    if visited[room] == 0:\n                        queue.append(room)\n\n    answer = True if 0 not in can_visit else False\n    return answer","sub_path":"Programmers/2020 KAKAO Internship/동굴 탐험.py","file_name":"동굴 탐험.py","file_ext":"py","file_size_in_byte":1470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"618075207","text":"import httplib\nfrom flask import request\nfrom flask import abort as flask_abort\nfrom flask.json import jsonify\n\ndef register_resource(app_or_blueprint, model, url_prefix='',\n                      create=False, create_decorators=[], create_hook=lambda x: x,\n                      read=False, read_single=False, read_decorators=[], read_hook=lambda x: x,\n                      update=False, update_single=False, update_decorators=[], update_hook=lambda x: x,\n                      delete=False, delete_single=False, delete_decorators=[], delete_hook=lambda x: x):\n    def do_create():\n        obj = model(**request.json)\n        obj.save()\n        return jsonify(create_hook(obj.as_dict())), httplib.CREATED\n\n    def do_read_all():\n        return jsonify(dict(objects=tuple(read_hook(obj.as_dict()) for obj in model.query.all())))\n\n    def do_read_single(id):\n        obj = model.query.get(id)\n        if obj is None:\n            flask_abort(httplib.NOT_FOUND)\n        return jsonify(read_hook(obj.as_dict()))\n\n    def do_update_single(id):\n        obj = model.query.get(id)\n        if obj is None:\n            flask_abort(httplib.NOT_FOUND)\n        obj.update(**request.json)\n        obj.save()\n        return jsonify(update_hook(obj.as_dict())), httplib.ACCEPTED\n\n    def do_delete_single(id):\n        obj = model.query.get(id)\n        if obj is None:\n            flask_abort(httplib.NOT_FOUND)\n        obj.delete()\n        return jsonify(delete_hook(obj.as_dict())), httplib.NO_CONTENT\n\n    for d in create_decorators:\n        do_create = d(do_create)\n    for d in read_decorators:\n        do_read_all = d(do_read_all)\n        do_read_single = d(do_read_single)\n    for d in update_decorators:\n        do_update_single = d(do_update_single)\n    for d in delete_decorators:\n        do_delete_single = d(do_delete_single)\n\n    endpoint_prefix = model.__name__.lower() + '_'\n\n    if create:\n        app_or_blueprint.route(url_prefix, methods=['POST'], endpoint=(endpoint_prefix + 'create'))(do_create)\n    if read:\n        app_or_blueprint.route(url_prefix, endpoint=(endpoint_prefix + 'read'))(do_read_all)\n    if read_single:\n        app_or_blueprint.route(url_prefix + '/<id>', endpoint=(endpoint_prefix + 'read_single'))(do_read_single)\n    if update:\n        raise NotImplementedError('Bulk update is not supported')\n    if update_single:\n        app_or_blueprint.route(url_prefix + '/<id>', methods=['PUT'], endpoint=(endpoint_prefix + 'update_single'))(do_update_single)\n    if delete:\n        raise NotImplementedError('Bulk delete is not supported')\n    if delete_single:\n        app_or_blueprint.route(url_prefix + '/<id>', methods=['DELETE'], endpoint=(endpoint_prefix + 'delete_single'))(do_delete_single)\n","sub_path":"src/labsome/backend/db/resource.py","file_name":"resource.py","file_ext":"py","file_size_in_byte":2716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"343327819","text":"# -*- coding: utf-8 -*-\n# -*- author: Jiangtao -*-\n\n\"\"\"settings for web server\"\"\"\n\n\nimport os\n\nfrom tornado.options import define, options\nfrom tornado.log import enable_pretty_logging\n\n# options settings\ndefine(\"port\", default=8888, help=\"run on the given port\", type=int)\ndefine(\"debug\", default=False, help=\"debug mode\", type=bool)\noptions.parse_command_line()\n\n# log settings\nenable_pretty_logging()\n\n# seetings for path\npath = lambda root,*a: os.path.join(root, *a)\nROOT = os.path.dirname(os.path.abspath(__file__))\nSTATIC_PATH = path(ROOT, 'static')\n\nsettings = {\n    \"debug\": options.debug,\n    \"autoreload\": False,\n    \"static_path\": STATIC_PATH,\n    \"cookie_secret\": 'tornado-demo',\n    \"xsrf_cookies\": True,\n}\n","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"280061892","text":"import sublime\nimport sublime_plugin\nfrom .functions import view_assign_syntax\nfrom .Globals import Globals\nfrom .settings import get_setting\n\n\nclass AutoSetSyntaxCommand(sublime_plugin.TextCommand):\n    def run(self, edit: sublime.Edit) -> bool:\n        \"\"\" match the first line and set the corresponding syntax \"\"\"\n\n        # make sure the target view is not a panel\n        if self.view.settings().get(\"is_widget\"):\n            return False\n\n        first_line = self._get_partial_first_line()\n\n        for syntax_mapping in Globals.syntax_mappings:\n            for first_line_match_regex in syntax_mapping[\"first_line_match_compiled\"]:\n                # \"first_line_match_regex\" may be None if its corresponding regex is not compile-able\n                if not first_line_match_regex:\n                    continue\n\n                if first_line_match_regex.search(first_line):\n                    view_assign_syntax(\n                        self.view,\n                        syntax_mapping[\"file_path\"],\n                        '({rule_source}) \"first_line_match\": {regex}'.format(\n                            rule_source=syntax_mapping[\"rule_source\"],\n                            regex=first_line_match_regex.pattern,\n                        ),\n                    )\n\n                    return True\n\n        return False\n\n    def _get_partial_first_line(self) -> str:\n        \"\"\" get the (partial) first line \"\"\"\n\n        region = self.view.line(0)\n        max_length = get_setting(\"first_line_length_max\")\n\n        if max_length >= 0:\n            # if the first line is longer than the max line length,\n            # then we use the max line length\n            region = sublime.Region(0, min(region.end(), max_length))\n\n        return self.view.substr(region)\n","sub_path":"AutoSetSyntaxCommand.py","file_name":"AutoSetSyntaxCommand.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"385452520","text":"from lenet import LeNet5\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.autograd import Variable\nfrom torchvision.datasets.mnist import MNIST\nimport torchvision.transforms as transforms\nfrom torch.utils.data import DataLoader\nimport visdom\nimport numpy as np\n\nviz = visdom.Visdom()\n\ndata_train = MNIST('~/lijiguo/workspace/data/mnist',\n                   download=True,\n                   transform=transforms.Compose([\n                       transforms.Scale((32, 32)),\n                       transforms.ToTensor()]))\ndata_test = MNIST('~/lijiguo/workspace/data/mnist',\n                  train=False,\n                  download=True,\n                  transform=transforms.Compose([\n                      transforms.Scale((32, 32)),\n                      transforms.ToTensor()]))\ndata_train_loader = DataLoader(data_train, batch_size=256, shuffle=True, num_workers=8)\ndata_test_loader = DataLoader(data_test, batch_size=1024, num_workers=8)\n\nnet = LeNet5()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(net.parameters(), lr=2e-3)\n\ncur_batch_win = None\ncur_batch_win_opts = {\n    'title': 'Epoch Loss Trace',\n    'xlabel': 'Batch Number',\n    'ylabel': 'Loss',\n    'width': 1200,\n    'height': 600,\n}\n\nloss_list, batch_list = [], []\ndef train(epoch):\n    global cur_batch_win\n    net.train()\n    global loss_list, batch_list\n    for i, (images, labels) in enumerate(data_train_loader):\n        images, labels = Variable(images), Variable(labels)\n\n        optimizer.zero_grad()\n\n        output = net(images)\n\n        loss = criterion(output, labels)\n\n        loss_list.append(loss.data[0])\n        batch_list.append(i+1)\n\n        if i % 10 == 0:\n            print('Train - Epoch %d, Batch: %d, Loss: %f' % (epoch, i, loss.data[0]))\n\n        # Update Visualization\n        if viz.check_connection():\n            cur_batch_win = viz.line(np.array(loss_list), np.array(range(len(loss_list))),\n                                     win=cur_batch_win, name='current_batch_loss',\n                                     update=(None if cur_batch_win is None else 'replace'),\n                                     opts=cur_batch_win_opts)\n\n        loss.backward()\n        optimizer.step()\n\n\ndef test():\n    net.eval()\n    total_correct = 0\n    avg_loss = 0.0\n    for i, (images, labels) in enumerate(data_test_loader):\n        images, labels = Variable(images), Variable(labels)\n        output = net(images)\n        avg_loss += criterion(output, labels).sum()\n        pred = output.data.max(1)[1]\n        total_correct += pred.eq(labels.data.view_as(pred)).sum()\n\n    avg_loss /= len(data_test)\n    print('Test Avg. Loss: %f, Accuracy: %f' % (avg_loss.data[0], float(total_correct) / len(data_test)))\n\n\ndef train_and_test(epoch):\n    train(epoch)\n    test()\n\n\ndef main():\n    for e in range(1, 16):\n        train_and_test(e)\n\n\nif __name__ == '__main__':\n    main()","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":2889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"401572931","text":"# coding:utf-8\nimport time\nimport asyncio\n\n\"\"\"\n启动事件循环之后，马上ctrl+c，会触发run_until_complete的执行异常 KeyBorardInterrupt。\n然后通过循环asyncio.Task取消future。\n\n\"\"\"\nnow = lambda: time.time()\n\n\nasync def do_some_work(x):  # 定义一个协程\n\tprint(\"waiting:\", x)\n\tawait asyncio.sleep(x)  # sleep模拟了阻塞或者耗时操作。当遇到阻塞调用的函数的时，使用await方法将协程的控制权让出,以便loop调用其他的协程。\n\treturn \"Done after {}s\".format(x)\n\n\ndef callback(future):  # 回调的最后一个参数是future对象，通过该对象可以获取协程返回值。\n\tprint(\"callback:\", future.result())\n\nstart = now()\n\ncoroutine1 = do_some_work(1)\ncoroutine2 = do_some_work(2)\ncoroutine3 = do_some_work(4)\n\nloop = asyncio.new_event_loop()  # 创建一个loop事件\n\ntask1 = asyncio.ensure_future(coroutine1, loop=loop)\ntask2 = asyncio.ensure_future(coroutine2, loop=loop)\ntask3 = asyncio.ensure_future(coroutine3, loop=loop)\n\ntask1.add_done_callback(callback)\ntask2.add_done_callback(callback)\ntask3.add_done_callback(callback)\n\ntasks = [task3, task1, task2]\n\n\ntry:\n\tloop.run_forever()\nexcept KeyboardInterrupt as e:\n\tprint(asyncio.Task.all_tasks(loop))\n\tfor task in asyncio.Task.all_tasks(loop):\n\t\tprint(task.cancel())  # True表示cannel成功\n\n\tloop.stop()\n\tloop.run_forever()  # loop stop之后还需要再次开启事件循环，在finally中close，不然还会抛出异常\nfinally:\n\tloop.close()\n\nprint(\"Time: \", now() - start)\n","sub_path":"Code/built_in/asyncio34/new_loop_task.py","file_name":"new_loop_task.py","file_ext":"py","file_size_in_byte":1513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"90879752","text":"\"\"\"\nAutomatic relaunch of jobs that were stopped before the end of training.\nUnfinished folds are detected as they do not contain a \"performances\" sub-folder\n\"\"\"\nimport os\nfrom os import path\n\nfrom clinicadl import MapsManager\n\n\ndef replace_arg(options, key_name, value):\n    if value is not None:\n        setattr(options, key_name, value)\n\n\ndef automatic_resume(model_path, verbose=0):\n    verbose_list = [\"warning\", \"info\", \"debug\"]\n    maps_manager = MapsManager(model_path, verbose=verbose_list[verbose])\n\n    fold_list = sorted(\n        [\n            int(fold.split(\"-\")[1])\n            for fold in os.listdir(model_path)\n            if fold[:4:] == \"fold\"\n        ]\n    )\n    stopped_folds = [\n        fold\n        for fold in fold_list\n        if \"tmp\" in os.listdir(path.join(model_path, f\"fold-{fold}\"))\n    ]\n    finished_folds = [fold for fold in fold_list if fold not in stopped_folds]\n\n    split_manager = maps_manager._init_split_manager()\n    fold_iterator = split_manager.fold_iterator()\n\n    absent_folds = [\n        fold\n        for fold in fold_iterator\n        if fold not in finished_folds and fold not in stopped_folds\n    ]\n\n    # To ensure retro-compatibility with random search\n    maps_manager.resume(stopped_folds)\n    maps_manager.train(absent_folds)\n","sub_path":"clinicadl/train/resume.py","file_name":"resume.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"70453392","text":"#\n# Copyright (c) 2013-2020, Dell Inc. or its subsidiaries.\n# All rights reserved.\n# See file LICENSE for licensing information.\n#\n# Module Name:\n#\n#        multichannel.py\n#\n# Abstract:\n#\n#        Multichannel tests\n#\n# Authors: Brian Koropoff (brian.koropoff@emc.com)\n#\n\nimport pike.model\nimport pike.smb2\nimport pike.test\nimport pike.ntstatus\n\n\n@pike.test.RequireDialect(pike.smb2.DIALECT_SMB3_0)\n@pike.test.RequireCapabilities(pike.smb2.SMB2_GLOBAL_CAP_MULTI_CHANNEL)\nclass MultiChannelTest(pike.test.PikeTest):\n    def session_bind(self, chan):\n        return (\n            chan.connection.client.connect(self.server)\n            .negotiate()\n            .session_setup(self.creds, bind=chan.session)\n        )\n\n    # Open a file, binding session to second channel, and close it there\n    def test_open_bind_close(self):\n        chan, tree = self.tree_connect()\n        handle = chan.create(tree, \"hello.txt\").result()\n\n        # Open a second channel and bind the session\n        chan2 = self.session_bind(chan)\n\n        # Close the handle on the second channel\n        chan2.close(handle)\n\n    # Simulate a channel failover during a write and confirm that a\n    # stale write sent on the original channel is rejected\n    def test_write_fence_reject_stale(self):\n        data_stale = b\"stale\"\n        data_fresh = b\"fresh\"\n        chan, tree = self.tree_connect()\n        client = chan.connection.client\n\n        # Open a file\n        handle = chan.create(\n            tree, \"hello.txt\", disposition=pike.smb2.FILE_SUPERSEDE\n        ).result()\n\n        # Open a second channel\n        chan2 = self.session_bind(chan)\n\n        # Pretend channel failover occured\n        client.channel_sequence = 1\n\n        # Send write replay\n        with chan2.let(flags=pike.smb2.SMB2_FLAGS_REPLAY_OPERATION):\n            chan2.write(handle, 0, data_stale)\n\n        # Send normal write\n        chan2.write(handle, 0, data_fresh)\n\n        # Send stale write on original channel, which should fail.\n        # Spec is ambiguous as to status code, but Windows Server 2012\n        # seems to return STATUS_FILE_NOT_AVAILABLE\n        with self.assert_error(pike.ntstatus.STATUS_FILE_NOT_AVAILABLE):\n            with chan.let(channel_sequence=0):\n                chan.write(handle, 0, data_stale)\n\n        # Read back data to ensure it is not stale value\n        result = chan2.read(handle, len(data_fresh), 0).tobytes()\n        self.assertEquals(result, data_fresh)\n\n        # Close handle\n        chan2.close(handle)\n\n        # Close second connection\n        chan2.connection.close()\n","sub_path":"src/pike/test/multichannel.py","file_name":"multichannel.py","file_ext":"py","file_size_in_byte":2571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"646654684","text":"import boto3\nfrom termcolor import colored\n\nec2 = boto3.resource('ec2')\n\nfor i in ec2.instances.all():\n    print(\"Id:\\t\\t{0}\\nType:\\t\\t{1}\\nState:\\t\\t{2}\\nSubnet Id:\\t{3}\\nAMI:\\t\\t{4}\".format(\n        colored(i.id, 'green'),\n        colored(i.instance_type, 'green'),\n        colored(i.state['Name'], 'green'),\n        colored(i.subnet_id, 'green'),\n        colored(i.image_id, 'green')\n    ))\n    \n    print(\"Publ IP:\\t{0}\\nPriv IP:\\t{1}\\nPubl DNS:\\t{2}\\nPriv DNS:\\t{3}\\nHypervisor:\\t{4}\\nArchitecture:\\t{5}\".format(\n        colored(i.public_ip_address, 'cyan'),\n        colored(i.private_ip_address, 'cyan'),\n        colored(i.public_dns_name, 'cyan'),\n        colored(i.private_dns_name, 'cyan'),\n        colored(i.hypervisor, 'cyan'),\n        colored(i.architecture, 'cyan')\n    ))\n\n    print(\"Block Device Mappings: \")\n    for idx, dev in enumerate(i.block_device_mappings, start=1):\n        print(\"-[{0}]\\n Device Name:\\t\\t\\t{1}\\n Volume Id:\\t\\t\\t{2}\\n Status:\\t\\t\\t{3}\\n Delete On Termination:\\t\\t{4}\\n Attache Time:\\t\\t\\t{5}\".format(\n            colored(idx, 'yellow'),\n            colored(dev['DeviceName'], 'cyan'),\n            colored(dev['Ebs']['VolumeId'], 'cyan'),\n            colored(dev['Ebs']['Status'], 'cyan'),\n            colored(dev['Ebs']['DeleteOnTermination'], 'cyan'),\n            colored(dev['Ebs']['AttachTime'], 'cyan')\n        ))\n    \n    print()\n    print(\"---\")\n    print()\n","sub_path":"boto3-list-instances.py","file_name":"boto3-list-instances.py","file_ext":"py","file_size_in_byte":1405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"253936813","text":"\"\"\"\n가장 먼 노드\n\nn개의 노드가 있는 그래프가 있습니다. 각 노드는 1부터 n까지 번호가 적혀있습니다.\n1번 노드에서 가장 멀리 떨어진 노드의 갯수를 구하려고 합니다.\n가장 멀리 떨어진 노드란 최단경로로 이동했을 때 간선의 개수가 가장 많은 노드들을 의미합니다.\n\n노드의 개수 n, 간선에 대한 정보가 담긴 2차원 배열 vertex가 매개변수로 주어질 때,\n1번 노드로부터 가장 멀리 떨어진 노드가 몇 개인지를 return 하도록 solution 함수를 작성해주세요.\n\n제한사항\n노드의 개수 n은 2 이상 20,000 이하입니다.\n간선은 양방향이며 총 1개 이상 50,000개 이하의 간선이 있습니다.\nvertex 배열 각 행 [a, b]는 a번 노드와 b번 노드 사이에 간선이 있다는 의미입니다.\n\n입출력 예\nn\tvertex\treturn\n6\t[[3, 6], [4, 3], [3, 2], [1, 3], [1, 2], [2, 4], [5, 2]]\t3\n\n\"\"\"\nfrom collections import deque\n\n\n# def solution(n, edge):\n#     graph = [[0 for _ in range(n+1)] for _ in range(n+1)]\n#     depths = [0] * (n+1)\n#     depths[0] = -1\n#     dq = deque([1])\n\n#     for r, c in edge:\n#         graph[r][c] = graph[c][r] = 1\n\n#     count = 1\n#     while dq:\n#         v = dq.popleft()\n#         for i in range(1, n+1):\n#             if depths[i] == 0 and graph[i][v] == 1:\n#                 depths[i] = count\n#                 dq.append(i)\n#         count += 1\n\n#     return depths.count(max(depths))\n\n# return answer\n\ndef solution(n, edge):\n    graph = [[0 for _ in range(n+1)] for _ in range(n+1)]\n    depths = [0] * (n+1)\n    depths[0], depths[1] = -1, 1\n    dq = deque([1])\n\n    for r, c in edge:\n        graph[r][c], graph[c][r] = 1, 1\n\n    count = 1\n    while dq:\n        v = dq.popleft()\n        for i in range(1, n+1):\n            if depths[i] == 0 and graph[i][v] == 1:\n                depths[i] = count\n                dq.append(i)\n        count += 1\n    return depths\n\n\nif __name__ == \"__main__\":\n    n = 6\n    vertex = [[3, 6], [4, 3], [3, 2], [1, 3], [1, 2], [2, 4], [5, 2]]\n    print(solution(n, vertex))\n","sub_path":"programmers/max_distant_node.py","file_name":"max_distant_node.py","file_ext":"py","file_size_in_byte":2095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"646665480","text":"#!/usr/bin/env python3\n\n\"\"\" tests the \"hosts\" endpoints \"\"\"\n\nimport os\nimport sys\n# import tempfile\n\ntry:\n    from loguru import logger\n    from crowdstrike import CrowdstrikeAPI\nexcept ImportError as import_error:\n    sys.exit(f\"Error importing required library: {import_error}\")\n\n# grab config from the file or environment variable\ntry:\n    from config import CLIENT_ID, CLIENT_SECRET\nexcept ImportError:\n    if os.environ.get('CLIENT_ID'):\n        logger.debug(\"Using Client ID from environment variable\")\n        CLIENT_ID = os.environ.get('CLIENT_ID')\n    if os.environ.get('CLIENT_SECRET'):\n        logger.debug(\"Using Client Secret from environment variable\")\n        CLIENT_SECRET = os.environ.get('CLIENT_SECRET')\n    if not CLIENT_ID and not CLIENT_SECRET:\n        sys.exit(\"you didn't set the config either via file or environment\")\n\nlogger.enable(\"crowdstrike\")\n\n\n\ncrowdstrike = CrowdstrikeAPI(CLIENT_ID, CLIENT_SECRET) # pylint: disable=invalid-name\n\ndef test_create_rtr_session(crowdstrike_client=crowdstrike):\n    \"\"\" test create_rtr_session, should return a session ID \"\"\"\n    host = crowdstrike_client.hosts_query_devices(filter=\"product_type_desc:'Workstation'+status:'normal'+platform_name:'Windows'\", \n                                                  limit=1,\n                                                  sort=\"last_seen.desc\",\n                                                  )\n    logger.debug(\"host query result\")\n    logger.debug(host)\n    assert len(host.get('resources')) > 0\n    device_id = host.get('resources')[0]\n    session_details = crowdstrike_client.create_rtr_session(device_id=device_id)\n    logger.debug(session_details)\n\n    assert session_details.get('errors', None) in (None, [])\n    if session_details.get('resources') and len(session_details.get('resources')) > 0:\n        assert 'session_id' in session_details.get('resources')[0]\n        assert isinstance(session_details.get('resources')[0].get('session_id'), str)\n\ndef test_delete_rtr_session(crowdstrike_client=crowdstrike):\n    \"\"\" test delete_rtr_session, only works if create does! \"\"\"\n    host = crowdstrike_client.hosts_query_devices(filter=\"product_type_desc:'Workstation'+status:'normal'+platform_name:'Windows'\", limit=1)\n    logger.debug(host)\n\n    device_id = host.get('resources')[0]\n\n    session_details = crowdstrike_client.create_rtr_session(device_id=device_id)\n    logger.debug(session_details)\n\n    assert session_details.get('errors', None) in (None, [])\n    if session_details.get('resources') and len(session_details.get('resources')) > 0:\n        assert 'session_id' in session_details.get('resources')[0]\n        assert isinstance(session_details.get('resources')[0].get('session_id'), str)\n\n    response = crowdstrike_client.delete_rtr_session(session_id=session_details.get('resources')[0].get('session_id'))\n    logger.debug(response)\n\n    assert response.status_code == 204\n\ndef test_invalid_rtr_session(crowdstrike_client=crowdstrike):\n    \"\"\" test delete_rtr_session with a junk ID \"\"\"\n    response = crowdstrike_client.delete_rtr_session(session_id='This test better fail')\n    logger.debug(response)\n    assert response.status_code == 400","sub_path":"test_rtr.py","file_name":"test_rtr.py","file_ext":"py","file_size_in_byte":3172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"356169589","text":"\"\"\"\"\n  PyBoard library for generator based filters\n  https://github.com/odebeir/max30100\n\nMIT License\n\nCopyright (c) 2018 Olivier Debeir\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\"\"\"\nfrom max30100 import MAX30100, PULSE_WIDTH, SAMPLE_RATE\nfrom max30100.filters import median_filter,bpm_filter,maxmin_filter\nfrom max30100.filters import freq_filter,resample_filter,avg_filter,median_filter,bpm_filter\nfrom max30100.filters import hysteresis_filter,mean_filter,norm_filter,butterworth_filter\nfrom max30100.filters import adc_gen,derivative_filter,thresh_filter,diff_filter\n\n\nimport pyb\n\ndef rec():\n    h = MAX30100(sample_rate=SAMPLE_RATE[100], pulse_width=PULSE_WIDTH[1600])\n\n    with open(\"/sd/pulse_max30100.csv\", \"w\") as f:\n        first = True\n\n        g = h.generator(delay=8)  # 10ms <> 100sps\n        # med = median_filter(g,15)\n        bw = butterworth_filter(g)\n\n        norm = norm_filter(bw, size=100)\n        diff = diff_filter(norm, size=20)\n        th = thresh_filter(diff, threshold=-.33, greater_than=False)\n        # th = hysteresis_filter(bw,size=100,th_high=.7,th_low=.3)\n\n        bpm = bpm_filter(th, size=3)\n\n        for s, v, h in bpm:\n            if first:\n                col_names = \",\".join(h.keys())\n                f.write(\"%s\\n\" % col_names)\n                first = False\n\n            print(h)\n            s = ','.join([str(v) for v in h.values()])\n            f.write(\"%s\\n\" % s)\n\n            if h['thresh']:\n                pyb.LED(1).on()\n            else:\n                pyb.LED(1).off()\n            # lcd.clear()\n            # lcd.write(str(h))\n            # if keys.read() is not 'none':\n            #     break\n        f.close()\n        pyb.sync()\n\ndef test():\n\n    h = MAX30100()\n    g = h.generator(25)\n    for s, v, h in bpm_filter(maxmin_filter(median_filter(g, 5), size=40, th_low=.4, th_high=.70)):\n        print(h)\n        if h['trig']:\n            pyb.LED(1).on()\n        else:\n            pyb.LED(1).off()\n","sub_path":"example/test_bpm.py","file_name":"test_bpm.py","file_ext":"py","file_size_in_byte":2933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"78714567","text":"################################################################################\n# Copyright (c) 2014-2022, National Research Foundation (SARAO)\n#\n# Licensed under the BSD 3-Clause License (the \"License\"); you may not use\n# this file except in compliance with the License. You may obtain a copy\n# of the License at\n#\n#   https://opensource.org/licenses/BSD-3-Clause\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\"\"\"Tests for :mod:`katsdpsigproc.accel`.\"\"\"\n\nimport shutil\nimport os\nimport tempfile\nfrom textwrap import dedent\nfrom unittest import mock\nfrom typing import Tuple, Optional, Callable, Type, Any\n\nimport numpy as np\ntry:\n    from numpy.typing import DTypeLike\nexcept ImportError:\n    DTypeLike = Any     # type: ignore\nimport pytest\nfrom mako.template import Template\n\nfrom katsdpsigproc import accel\nfrom katsdpsigproc.accel import HostArray, DeviceArray, SVMArray, LinenoLexer\nfrom katsdpsigproc.abc import AbstractContext, AbstractCommandQueue\nif accel.have_cuda:\n    import pycuda\nif accel.have_opencl:\n    from katsdpsigproc import opencl\n    import pyopencl\n\n\nclass TestLinenoLexer:\n    def test_escape_filename(self) -> None:\n        assert LinenoLexer._escape_filename(r'abc\"def\\ghi') == r'\"abc\\\"def\\\\ghi\"'\n\n    def test_render(self) -> None:\n        source = \"line 1\\nline 2\\nline 3\"\n        out = Template(source, lexer_cls=LinenoLexer).render()\n        assert out == dedent(\n            \"\"\"\\\n            #line 1 \"<string>\"\n            line 1\n            #line 2 \"<string>\"\n            line 2\n            #line 3 \"<string>\"\n            line 3\n            \"\"\")\n\n\nclass TestHostArray:\n    cls = HostArray      # type: Type[HostArray]\n\n    @pytest.fixture\n    def factory(self) -> Callable[[Tuple[int, ...], DTypeLike, Tuple[int, ...]], HostArray]:\n        def allocate(shape: Tuple[int, ...], dtype: DTypeLike,\n                     padded_shape: Tuple[int, ...]) -> HostArray:\n            return self.cls(shape, dtype, padded_shape)\n        return allocate\n\n    def test_safe(\n            self,\n            factory: Callable[[Tuple[int, ...], DTypeLike, Tuple[int, ...]], HostArray]) -> None:\n        shape = (17, 13)\n        padded_shape = (32, 16)\n        constructed = factory(shape, np.int32, padded_shape)\n        view = np.zeros(padded_shape)[2:4, 3:5].view(self.cls)\n        sliced = constructed[2:4, 3:5]\n        assert self.cls.safe(constructed)\n        assert not self.cls.safe(view)\n        assert not self.cls.safe(sliced)\n        assert not self.cls.safe(np.zeros(shape))\n\n\nclass TestDeviceArray:\n    cls = DeviceArray      # type: Type[DeviceArray]\n\n    @pytest.fixture\n    def shape(self) -> Tuple[int, ...]:\n        return (17, 13)\n\n    @pytest.fixture\n    def padded_shape(self) -> Tuple[int, ...]:\n        return (32, 16)\n\n    @pytest.fixture\n    def strides(self) -> Tuple[int, ...]:\n        return (64, 4)\n\n    @pytest.fixture\n    def array(self, context: AbstractContext,\n              shape: Tuple[int, ...], padded_shape: Tuple[int, ...]) -> DeviceArray:\n        return self.cls(\n            context=context,\n            shape=shape,\n            dtype=np.int32,\n            padded_shape=padded_shape)\n\n    def test_properties(self, array: DeviceArray, shape: Tuple[int, ...],\n                        padded_shape: Tuple[int, ...], strides: Tuple[int, ...]) -> None:\n        assert array.shape == shape\n        assert array.padded_shape == padded_shape\n        assert array.strides == strides\n        assert array.dtype == np.int32\n\n    def test_empty_like(self, array: DeviceArray) -> None:\n        ary = array.empty_like()\n        assert ary.shape == array.shape\n        assert ary.strides == array.strides\n        assert HostArray.safe(ary)\n\n    def test_set_get(self, array: DeviceArray,\n                     context: AbstractContext, command_queue: AbstractCommandQueue) -> None:\n        ary = np.random.randint(0, 100, array.shape).astype(np.int32)\n        array.set(command_queue, ary)\n        # Read back results, check that it matches\n        buf = np.zeros(array.padded_shape, dtype=np.int32)\n        if isinstance(array, SVMArray):\n            buf[:] = array.buffer\n        elif context.device.is_cuda:\n            with context:\n                pycuda.driver.memcpy_dtoh(buf, array.buffer.gpudata)\n        else:\n            assert isinstance(command_queue, opencl.CommandQueue)\n            pyopencl.enqueue_copy(\n                command_queue._pyopencl_command_queue,\n                buf, array.buffer.data)\n        buf = buf[0:array.shape[0], 0:array.shape[1]]\n        np.testing.assert_equal(ary, buf)\n        # Check that it matches get\n        buf = array.get(command_queue)\n        np.testing.assert_equal(ary, buf)\n\n    def _test_copy_region(\n            self, context: AbstractContext, command_queue: AbstractCommandQueue,\n            src_shape: Tuple[int, ...], src_padded_shape: Optional[Tuple[int, ...]],\n            dest_shape: Tuple[int, ...], dest_padded_shape: Optional[Tuple[int, ...]],\n            src_region: accel._Slice, dest_region: accel._Slice) -> None:\n        # copy_region test\n        dtype = np.int32\n        src = self.cls(context, src_shape, dtype, src_padded_shape)\n        dest = self.cls(context, dest_shape, dtype, dest_padded_shape)\n        h_src = src.empty_like()\n        h_src[...] = np.arange(h_src.size).reshape(src_shape)\n        src.set(command_queue, h_src)\n        dest.zero(command_queue)\n        src.copy_region(command_queue, dest, src_region, dest_region)\n        command_queue.finish()   # Needed for the SVMArray test, since the next line is a CPU copy\n        h_dest = dest.get(command_queue)\n        expected = np.zeros_like(h_dest)\n        expected[dest_region] = h_src[src_region]\n        np.testing.assert_array_equal(expected, h_dest)\n        # set_region test\n        dest.zero(command_queue)\n        dest.set_region(command_queue, h_src, dest_region, src_region)\n        command_queue.finish()\n        h_dest = dest.get(command_queue)\n        np.testing.assert_array_equal(expected, h_dest)\n        # get_region test\n        h_dest.fill(0)\n        src.get_region(command_queue, h_dest, src_region, dest_region)\n        command_queue.finish()\n        np.testing.assert_array_equal(expected, h_dest)\n\n    def test_copy_region_4d(self, context: AbstractContext,\n                            command_queue: AbstractCommandQueue) -> None:\n        self._test_copy_region(\n            context, command_queue,\n            (7, 5, 12, 19), (8, 9, 14, 25),\n            (7, 5, 12, 19), (10, 8, 12, 21),\n            np.s_[2:4, :, 3:7:2, 10:], np.s_[1:3, 0:5, 4:8:2, 10:])\n\n    def test_copy_region_0d(self, context: AbstractContext,\n                            command_queue: AbstractCommandQueue) -> None:\n        self._test_copy_region(context, command_queue, (), (), (), (), (), ())\n\n    def test_copy_region_1d(self, context: AbstractContext,\n                            command_queue: AbstractCommandQueue) -> None:\n        self._test_copy_region(\n            context, command_queue, (3, 5), (4, 6), (5, 7), (7, 10),\n            np.s_[1, 0:3], np.s_[2, 1:4])\n\n    def test_copy_region_2d(self, context: AbstractContext,\n                            command_queue: AbstractCommandQueue) -> None:\n        self._test_copy_region(\n            context, command_queue, (3, 5), (4, 6), (5, 7), (7, 10),\n            np.s_[:1, 0:3], np.s_[-1:, 1:4])\n\n    def test_copy_region_missing_axes(self, context: AbstractContext,\n                                      command_queue: AbstractCommandQueue) -> None:\n        self._test_copy_region(\n            context, command_queue, (3, 5), (4, 6), (5, 5), (7, 10),\n            np.s_[:1], np.s_[-1:])\n\n    def test_copy_region_newaxis(self, context: AbstractContext,\n                                 command_queue: AbstractCommandQueue) -> None:\n        self._test_copy_region(\n            context, command_queue, (10,), None, (10, 10), None,\n            np.s_[np.newaxis, 4:6], np.s_[:1, 7:9])\n\n    def test_copy_region_negative(self, context: AbstractContext,\n                                  command_queue: AbstractCommandQueue) -> None:\n        self._test_copy_region(\n            context, command_queue, (10, 12), None, (7, 8), None,\n            np.s_[-4, -3:-1], np.s_[-7, -8:-4:2])\n\n    def test_copy_region_errors(self, context: AbstractContext,\n                                command_queue: AbstractCommandQueue) -> None:\n        # Too many axes\n        with pytest.raises(IndexError):\n            self._test_copy_region(\n                context, command_queue, (10,), None, (10,), None,\n                np.s_[3, 4], np.s_[5, 6])\n        # Out-of-range single index\n        with pytest.raises(IndexError):\n            self._test_copy_region(\n                context, command_queue, (10,), None, (10,), None,\n                np.s_[5], np.s_[10])\n        # Out-of-range slice\n        with pytest.raises(IndexError):\n            self._test_copy_region(\n                context, command_queue, (10,), None, (10,), None,\n                np.s_[10:12], np.s_[8:10])\n        # Empty slice\n        with pytest.raises(IndexError):\n            self._test_copy_region(\n                context, command_queue, (10,), None, (10,), None,\n                np.s_[2:2], np.s_[3:3])\n        # Negative stride\n        with pytest.raises(IndexError):\n            self._test_copy_region(\n                context, command_queue, (10,), None, (10,), None,\n                np.s_[3:0:-1], np.s_[4:1:-1])\n\n    def test_zero(self, array: DeviceArray, context: AbstractContext,\n                  command_queue: AbstractCommandQueue) -> None:\n        ary = np.random.randint(0x12345678, 0x23456789, array.shape).astype(np.int32)\n        array.set(command_queue, ary)\n        before = array.get(command_queue)\n        array.zero(command_queue)\n        after = array.get(command_queue)\n        np.testing.assert_equal(ary, before)\n        assert np.max(after) == 0\n\n    def _allocate_raw(self, context: AbstractContext, n_bytes: int) -> Any:\n        return context.allocate_raw(n_bytes)\n\n    def test_raw(self, shape: Tuple[int, ...], padded_shape: Tuple[int, ...],\n                 context: AbstractContext) -> None:\n        raw = self._allocate_raw(context, 2048)\n        ary = self.cls(\n            context=context,\n            shape=shape,\n            dtype=np.int32,\n            padded_shape=padded_shape,\n            raw=raw)\n        actual_raw = None\n        try:\n            # CUDA\n            if isinstance(ary, SVMArray):\n                actual_raw = ary.base.base  # type: ignore\n                raw = raw._wrapped\n            else:\n                actual_raw = ary.buffer.gpudata\n        except AttributeError:\n            # OpenCL\n            actual_raw = ary.buffer.data\n        assert actual_raw is raw\n\n\nclass TestPinnedAMD(TestDeviceArray):\n    \"\"\"Run DeviceArray tests forcing `_PinnedAMD` class for pinned memory.\"\"\"\n\n    @pytest.fixture(autouse=True)\n    def setup(self, context: AbstractContext):\n        context._force_pinned_amd = True        # type: ignore\n        yield\n        context._force_pinned_amd = False       # type: ignore\n\n\n@pytest.mark.cuda_only\nclass TestSVMArrayHost(TestHostArray):\n    \"\"\"Test SVMArray using the HostArray tests.\"\"\"\n\n    cls = SVMArray\n\n    # Function is given a different name to avoid mypy complaining about the\n    # signature being different.\n    @pytest.fixture(name='factory')\n    def factory_override(self, context: AbstractContext) -> \\\n            Callable[[Tuple[int, ...], DTypeLike, Tuple[int, ...]], HostArray]:\n        def allocate(shape: Tuple[int, ...], dtype: DTypeLike,\n                     padded_shape: Tuple[int, ...]) -> HostArray:\n            return self.cls(context, shape, dtype, padded_shape)\n        return allocate\n\n\n@pytest.mark.cuda_only\nclass TestSVMArray(TestDeviceArray):\n    \"\"\"Tests SVMArray using the DeviceArray tests, plus some new ones.\"\"\"\n\n    cls = SVMArray\n\n    def _allocate_raw(self, context: AbstractContext, n_bytes: int) -> object:\n        return context.allocate_svm_raw(n_bytes)\n\n    def test_coherence(self, context: AbstractContext, command_queue: AbstractCommandQueue) -> None:\n        \"\"\"Check that runtime correct transfers data between the host and device views.\"\"\"\n        source = \"\"\"\\\n            <%include file=\"/port.mako\"/>\n            KERNEL void triple(unsigned int *data)\n            {\n                data[get_global_id(0)] *= 3;\n            }\"\"\"\n        program = accel.build(context, 'not a file', source=source)\n        kernel = program.get_kernel(\"triple\")\n        ary = SVMArray(context, (123,), np.uint32, (128,))\n        ary[:] = np.arange(123, dtype=np.uint32)\n        command_queue.enqueue_kernel(kernel, [ary.buffer], (128,), (64,))\n        command_queue.finish()\n        np.testing.assert_equal(np.arange(0, 369, step=3, dtype=np.uint32), ary)\n\n\n@pytest.mark.cuda_only\nclass TestSVMAllocator:\n    def test_allocate(self, context: AbstractContext, command_queue: AbstractCommandQueue) -> None:\n        allocator = accel.SVMAllocator(context)\n        ary = allocator.allocate((12, 34), np.int32, (13, 35))\n        assert isinstance(ary, accel.SVMArray)\n        assert ary.shape == (12, 34)\n        assert ary.dtype == np.int32\n        assert ary.padded_shape == (13, 35)\n        ary.fill(1)  # Just to make sure it doesn't crash\n\n    def test_allocate_raw(self, context: AbstractContext,\n                          command_queue: AbstractCommandQueue) -> None:\n        allocator = accel.SVMAllocator(context)\n        raw = allocator.allocate_raw(13 * 35 * 4)\n        ary = allocator.allocate((12, 34), np.int32, (13, 35), raw=raw)\n        assert isinstance(ary, accel.SVMArray)\n        assert ary.shape == (12, 34)\n        assert ary.dtype == np.int32\n        assert ary.padded_shape == (13, 35)\n        ary.fill(1)  # Just to make sure it doesn't crash\n\n\nclass TestDimension:\n    \"\"\"Tests for :class:`katsdpsigproc.accel.Dimension`.\"\"\"\n\n    def test_is_power2(self) -> None:\n        assert accel.Dimension._is_power2(1)\n        assert accel.Dimension._is_power2(2)\n        assert accel.Dimension._is_power2(32)\n        assert not accel.Dimension._is_power2(-1)\n        assert not accel.Dimension._is_power2(0)\n        assert not accel.Dimension._is_power2(3)\n        assert not accel.Dimension._is_power2(5)\n\n    def test_min_padded_round(self) -> None:\n        \"\"\"Constructor computes min padded size correctly.\"\"\"\n        dim = accel.Dimension(17, min_padded_round=4)\n        assert dim.min_padded_size == 20\n        dim = accel.Dimension(20, min_padded_round=5)\n        assert dim.min_padded_size == 20\n\n    def test_add_align_dtype(self) -> None:\n        dim = accel.Dimension(20, alignment=8)\n        assert dim.alignment == 8\n        dim.add_align_dtype(np.complex64)\n        assert dim.alignment_hint == accel.Dimension.ALIGN_BYTES // 8\n        dim.add_align_dtype(np.uint8)\n        assert dim.alignment_hint == accel.Dimension.ALIGN_BYTES\n        dim.add_align_dtype(np.float32)\n        assert dim.alignment_hint == accel.Dimension.ALIGN_BYTES\n\n    def test_valid(self) -> None:\n        \"\"\"Test `valid` method on non-exact dimension.\"\"\"\n        dim = accel.Dimension(17, min_padded_round=8, alignment=4)\n        assert dim.valid(24)\n        assert not dim.valid(20)\n        assert dim.valid(28)\n        assert not dim.valid(30)\n\n    def test_valid_exact(self) -> None:\n        \"\"\"Test `valid` method on exact dimension.\"\"\"\n        dim = accel.Dimension(20, alignment=4, exact=True)\n        assert dim.valid(20)\n        assert not dim.valid(24)\n        dim = accel.Dimension(20, min_padded_size=23, exact=True)\n        assert dim.valid(23)\n        assert not dim.valid(24)\n        assert not dim.valid(20)\n\n    @classmethod\n    def assert_dimensions_equal(cls, dim1: accel.Dimension, dim2: accel.Dimension) -> None:\n        assert dim1.size == dim2.size\n        assert dim1.min_padded_size == dim2.min_padded_size\n        assert dim1.alignment == dim2.alignment\n        assert dim1.alignment_hint == dim2.alignment_hint\n        assert dim1.exact == dim2.exact\n\n    def test_link(self) -> None:\n        \"\"\"Linking several dimensions together gives them all the right properties.\"\"\"\n        dim1 = accel.Dimension(22, min_padded_size=28, alignment=4)\n        dim2 = accel.Dimension(22, min_padded_size=24, alignment=8, align_dtype=np.int32)\n        dim3 = accel.Dimension(22, min_padded_size=22, align_dtype=np.uint16)\n        dim1.link(dim2)\n        dim1.link(dim3)\n        self.assert_dimensions_equal(dim1, dim2)\n        self.assert_dimensions_equal(dim2, dim3)\n        assert dim1.size == 22\n        assert dim1.min_padded_size == 28\n        assert dim1.alignment == 8\n        assert dim1.alignment_hint == accel.Dimension.ALIGN_BYTES // 2\n        assert dim1.exact is False\n\n    def test_link_bad_size(self) -> None:\n        \"\"\"Linking dimensions with different sizes fails.\"\"\"\n        dim1 = accel.Dimension(22, min_padded_size=28, alignment=4)\n        dim2 = accel.Dimension(23, min_padded_size=24, alignment=8, align_dtype=np.int32)\n        with pytest.raises(ValueError):\n            dim1.link(dim2)\n        assert dim1._root() is not dim2._root()\n\n    def test_link_bad_exact(self) -> None:\n        \"\"\"Linking dimensions into an unsatisfiable requirement fails.\"\"\"\n        dim1 = accel.Dimension(22, exact=True)\n        dim2 = accel.Dimension(22, min_padded_size=28)\n        dim3 = accel.Dimension(22, alignment=4)\n        with pytest.raises(ValueError):\n            dim1.link(dim2)\n        with pytest.raises(ValueError):\n            dim1.link(dim3)\n        # Check that linking didn't happen anyway\n        assert dim1._root() is not dim2._root()\n        assert dim1._root() is not dim3._root()\n\n    def test_required_padded_size(self) -> None:\n        \"\"\"The padded size is computed correctly.\"\"\"\n        dim = accel.Dimension(30, 7, alignment=4)\n        assert dim.required_padded_size() == 36\n\n    def test_required_padded_size_dtype(self) -> None:\n        \"\"\"The padded size is computed correctly when an alignment hint is given.\"\"\"\n        dim = accel.Dimension(1100, 200, align_dtype=np.float32)\n        assert dim.required_padded_size() == 1216\n\n    def test_required_padded_size_exact(self) -> None:\n        \"\"\"The padded size is computed correctly for exact dimensions.\"\"\"\n        dim = accel.Dimension(1100, align_dtype=np.float32, exact=True)\n        assert dim.required_padded_size() == 1100\n\n    def test_required_padded_size_small(self) -> None:\n        \"\"\"The alignment hint is ignored for small sizes.\"\"\"\n        dim = accel.Dimension(18, alignment=8, align_dtype=np.uint8)\n        assert dim.required_padded_size() == 24\n\n\nclass TestIOSlot:\n    \"\"\"Tests for :class:`katsdpsigproc.accel.IOSlot`.\"\"\"\n\n    @pytest.mark.parametrize(\"bind\", [True, False])\n    @mock.patch('katsdpsigproc.accel.DeviceArray', spec=True)\n    def test_allocate(self, DeviceArray: mock.Mock, bind: bool) -> None:\n        \"\"\"IOSlot.allocate must correctly allocate a buffer.\"\"\"\n        dims = (\n            accel.Dimension(50, min_padded_size=60, alignment=8),\n            accel.Dimension(30, min_padded_size=50, alignment=4)\n        )\n        shape = (50, 30)\n        padded_shape = (64, 52)\n        dtype = np.dtype(np.uint8)\n        # Create the device array that will be created. We need to populate it\n        # with some attributes to allow validation to pass\n        ary = mock.Mock()\n        ary.dtype = dtype\n        ary.shape = shape\n        ary.padded_shape = padded_shape\n        # Set the mocked DeviceArray class to return this array\n        DeviceArray.return_value = ary\n        # Run the system under test\n        slot = accel.IOSlot(dims, dtype)\n        ret = slot.allocate(accel.DeviceAllocator(mock.sentinel.context), bind=bind)\n        # Validation\n        assert ret == ary\n        if bind:\n            assert slot.buffer == ary\n        else:\n            assert slot.buffer is None\n        DeviceArray.assert_called_once_with(\n            mock.sentinel.context, shape, dtype, padded_shape, None)\n        # Check that the inner dimension had a type hint set\n        assert dims[1].alignment_hint == accel.Dimension.ALIGN_BYTES\n\n    @mock.patch('katsdpsigproc.accel.HostArray', spec=True)\n    def test_allocate_host(self, HostArray: mock.Mock) -> None:\n        dims = (\n            accel.Dimension(50, min_padded_size=60, alignment=8),\n            accel.Dimension(30, min_padded_size=50, alignment=4)\n        )\n        shape = (50, 30)\n        padded_shape = (64, 52)\n        dtype = np.int16\n        slot = accel.IOSlot(dims, dtype)\n        HostArray.return_value = mock.sentinel.host_array\n        ret = slot.allocate_host(mock.sentinel.context)\n        assert ret is mock.sentinel.host_array\n        HostArray.assert_called_with(shape, dtype, padded_shape, context=mock.sentinel.context)\n\n    @mock.patch('katsdpsigproc.accel.DeviceArray', spec=True)\n    def test_allocate_raw(self, DeviceArray: mock.Mock) -> None:\n        \"\"\"Test IOSlot.allocate with a raw parameter.\"\"\"\n        shape = (50, 30)\n        dtype = np.dtype(np.float32)\n        raw = mock.sentinel.raw\n        # Create the device array that will be created. We need to populate it\n        # with some attributes to allow validation to pass\n        ary = mock.Mock()\n        ary.dtype = dtype\n        ary.shape = shape\n        ary.padded_shape = shape\n        ary.raw = raw\n        # Set the mocked DeviceArray class to return this array\n        DeviceArray.return_value = ary\n        # Run the system under test\n        slot = accel.IOSlot(shape, dtype)\n        slot.allocate(accel.DeviceAllocator(mock.sentinel.context), raw)\n        # Validation\n        assert slot.buffer == ary\n        DeviceArray.assert_called_once_with(\n            mock.sentinel.context, shape, dtype, shape, raw)\n\n    def test_validate_shape(self) -> None:\n        \"\"\"IOSlot.validate must check that the shape matches.\"\"\"\n        ary = mock.sentinel.ary\n        ary.dtype = np.float32\n        ary.shape = (5, 3)\n        ary.padded_shape = (5, 3)\n        slot = accel.IOSlot((5, 3), np.float32)\n        slot.bind(ary)  # Should pass\n\n        with pytest.raises(ValueError):\n            # Wrong shape\n            ary.shape = (5, 4)\n            slot.bind(ary)\n        with pytest.raises(ValueError):\n            # Wrong dimensions\n            ary.shape = (5,)\n            slot.bind(ary)\n\n    def test_validate_dtype(self) -> None:\n        \"\"\"IOSlot.validate must check that the dtype matches.\"\"\"\n        ary = mock.sentinel.ary\n        ary.dtype = np.float32\n        ary.shape = (5, 3)\n        ary.padded_shape = ary.shape\n        slot = accel.IOSlot((5, 3), np.float32)\n        slot.bind(ary)  # Should pass\n\n        ary.dtype = np.dtype(np.float32)  # Equivalent dtype\n        slot.bind(ary)  # Should pass\n\n        with pytest.raises(TypeError):\n            ary.dtype = np.int32\n            slot.bind(ary)\n\n    def test_validate_padded_shape(self) -> None:\n        \"\"\"IOSlot.validate must check that the padded shape is valid.\"\"\"\n        ary = mock.sentinel.ary\n        ary.dtype = np.float32\n        ary.shape = (5, 3)\n        ary.padded_shape = (8, 6)\n        slot = accel.IOSlot((accel.Dimension(5, 8), accel.Dimension(3, 6)), np.float32)\n        slot.bind(ary)  # Should pass\n\n        ary.padded_shape = (10, 11)\n        with pytest.raises(ValueError):\n            # Bigger than needed, but not an exact match - must fail\n            slot.bind(ary)\n\n    def test_required_bytes(self) -> None:\n        slot = accel.IOSlot(\n            (accel.Dimension(27, alignment=4), accel.Dimension(33, alignment=32)),\n            np.float32)\n        assert slot.required_bytes() == 4 * 28 * 64\n\n    def test_bind_none(self) -> None:\n        \"\"\"IOSlot.bind must accept `None`.\"\"\"\n        ary = mock.sentinel.ary\n        ary.dtype = np.float32\n        ary.shape = (5, 3)\n        ary.padded_shape = ary.shape\n        slot = accel.IOSlot((5, 3), np.float32)\n        slot.bind(ary)\n        assert slot.buffer is ary\n        slot.bind(None)\n        assert slot.buffer is None\n\n\nclass TestCompoundIOSlot:\n    \"\"\"Tests for :class:`katsdpsigproc.accel.CompoundIOSlot`.\"\"\"\n\n    def setup_method(self) -> None:\n        self.dims1 = (\n            accel.Dimension(13, min_padded_size=17, alignment=1),\n            accel.Dimension(7, min_padded_size=8, alignment=8),\n            accel.Dimension(22, min_padded_size=25, alignment=4)\n        )\n        self.dims2 = (\n            accel.Dimension(13, min_padded_size=14, alignment=4),\n            accel.Dimension(7, min_padded_size=10, alignment=4),\n            accel.Dimension(22, min_padded_size=22, alignment=1)\n        )\n        self.slot1 = accel.IOSlot(self.dims1, np.float32)\n        self.slot2 = accel.IOSlot(self.dims2, np.float32)\n\n    def test_check_empty(self) -> None:\n        \"\"\":class:`.CompoundIOSlot` constructor must reject empty list.\"\"\"\n        with pytest.raises(ValueError):\n            accel.CompoundIOSlot([])\n\n    def test_validate_shape(self) -> None:\n        \"\"\":class:`.CompoundIOSlot` must check that children have consistent shapes.\"\"\"\n        slot1 = accel.IOSlot((5, 3), np.float32)\n        slot2 = accel.IOSlot((5, 4), np.float32)\n        with pytest.raises(ValueError):\n            accel.CompoundIOSlot([slot1, slot2])\n\n    def test_validate_dtype(self) -> None:\n        \"\"\":class:`.CompoundIOSlot` must check that children have consistent data types.\"\"\"\n        slot1 = accel.IOSlot((5, 3), np.float32)\n        slot2 = accel.IOSlot((5, 3), np.int32)\n        with pytest.raises(TypeError):\n            accel.CompoundIOSlot([slot1, slot2])\n\n    def test_attributes(self) -> None:\n        \"\"\":class:`.CompoundIOSlot` must correctly combine attributes.\"\"\"\n        slot = accel.CompoundIOSlot([self.slot1, self.slot2])\n        assert slot.shape == (13, 7, 22)\n        assert slot.dtype == np.float32\n        assert self.dims1[0].min_padded_size == 17\n        assert self.dims1[1].min_padded_size == 10\n        assert self.dims1[2].min_padded_size == 25\n        assert self.dims1[0].alignment == 4\n        assert self.dims1[1].alignment == 8\n        assert self.dims1[2].alignment == 4\n        for x, y in zip(self.dims1, self.dims2):\n            TestDimension.assert_dimensions_equal(x, y)\n\n    def test_bind(self) -> None:\n        \"\"\"CompoundIOSlot.bind must bind children.\"\"\"\n        ary = mock.sentinel.ary\n        ary.shape = (13, 7, 22)\n        ary.dtype = np.float32\n        ary.padded_shape = (20, 16, 28)\n        slot = accel.CompoundIOSlot([self.slot1, self.slot2])\n        slot.bind(ary)\n        assert slot.buffer == ary\n        assert self.slot1.buffer == ary\n        assert self.slot2.buffer == ary\n\n    def test_bind_fail(self) -> None:\n        \"\"\"CompoundIOSlot.bind must not have side effects if the array is \\\n        not valid for all children.\"\"\"\n        ary = mock.sentinel.ary\n        ary.shape = (13, 7, 22)\n        ary.dtype = np.float32\n        ary.padded_shape = (17, 8, 28)\n        # Check that the array has the properties we want for the test\n        self.slot1.validate(ary)\n        with pytest.raises(ValueError):\n            self.slot2.validate(ary)\n        # The actual test\n        slot = accel.CompoundIOSlot([self.slot1, self.slot2])\n        with pytest.raises(ValueError):\n            slot.bind(ary)\n        assert slot.buffer is None\n        assert self.slot1.buffer is None\n        assert self.slot2.buffer is None\n\n\nclass TestAliasIOSlot:\n    \"\"\"Tests for :class:`katsdpsigproc.accel.AliasIOSlot`.\"\"\"\n\n    def setup_method(self) -> None:\n        self.slot1 = accel.IOSlot((3, 7), np.float32)\n        self.slot2 = accel.IOSlot((5, 3), np.complex64)\n\n    def test_required_bytes(self) -> None:\n        slot = accel.AliasIOSlot([self.slot1, self.slot2])\n        assert slot.required_bytes() == 120\n\n    def test_allocate(self) -> None:\n        # Set up mocks\n        raw = mock.sentinel.raw\n        context = mock.NonCallableMock()\n        context.allocate_raw = mock.Mock(return_value=raw)\n        # Run the test\n        slot = accel.AliasIOSlot([self.slot1, self.slot2])\n        ret = slot.allocate(accel.DeviceAllocator(context))\n        # Validation\n        context.allocate_raw.assert_called_once_with(120)\n        assert ret is raw\n        assert slot.raw is raw\n        assert self.slot1.buffer is not None\n        assert self.slot2.buffer is not None\n\n    @mock.patch('katsdpsigproc.accel.HostArray', spec=True)\n    def test_allocate_host(self, HostArray: mock.Mock) -> None:\n        HostArray.return_value = mock.sentinel.host_array\n        slot = accel.AliasIOSlot([self.slot1, self.slot2])\n        ret = slot.allocate_host(mock.sentinel.context)\n        assert ret is mock.sentinel.host_array\n        HostArray.assert_called_with((120,), np.uint8, context=mock.sentinel.context)\n\n\nclass TestVisualizeOperation:\n    \"\"\"Tests for :class:`katsdpsigproc.accel.visualize_operation`.\n\n    This is just a basic smoke test to ensure that it runs without crashing.\n    A real test requires a human to sanity-check the visual output.\n    \"\"\"\n\n    class Inner(accel.Operation):\n        def _run(self) -> None:\n            pass\n\n    def setup_method(self) -> None:\n        self.tmpdir = tempfile.mkdtemp()\n\n    def teardown_method(self) -> None:\n        shutil.rmtree(self.tmpdir)\n\n    def test(self, context: AbstractContext, command_queue: AbstractCommandQueue) -> None:\n        inner = TestVisualizeOperation.Inner(command_queue)\n        inner.slots['foo'] = accel.IOSlot((3, 4), np.float32)\n        inner.slots['bar'] = accel.IOSlot((4, 3), np.float32)\n\n        mid = accel.OperationSequence(\n            command_queue,\n            [('inner', inner)],\n            {'inner_foo': ['inner:foo']})\n\n        outer = accel.OperationSequence(\n            command_queue,\n            [('mid', mid)],\n            {'mid_foo': ['mid:inner_foo']},\n            {'scratch': ['mid:inner:bar']})\n\n        accel.visualize_operation(outer, os.path.join(self.tmpdir, 'test_visualize.gv'))\n","sub_path":"test/test_accel.py","file_name":"test_accel.py","file_ext":"py","file_size_in_byte":30223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"384799609","text":"import os\n\n\nimport sys\nimport hashlib\nimport struct\nimport subprocess\nimport collections\n\nimport argparse\nimport tensorflow as tf\nfrom tensorflow.core.example import example_pb2\n\nfrom tqdm import tqdm\n\nSENTENCE_START = '<s>'\nSENTENCE_END = '</s>'\n\nVOCAB_SIZE = 200000\nCHUNK_SIZE = 1000 # num examples per chunk, for the chunked data\n\ndm_single_close_quote = u'\\u2019' # unicode\ndm_double_close_quote = u'\\u201d'\nEND_TOKENS = ['.', '!', '?', '...', \"'\", \"`\", '\"', dm_single_close_quote, dm_double_close_quote, \")\"] # acceptable ways to end a sentence\n\nTRAIN_SPLIT = 0.80\nVAL_SPLIT = 0.10\nTEST_SPLIT = 0.10\n\nPUNCS = '.,?!:;'\n\n\ndef chunk_file(set_name, chunks_dir, data_dir):\n    in_file = os.path.join(data_dir, set_name+'.bin')\n    reader = open(in_file, \"rb\")\n    chunk = 0\n    finished = False\n    while not finished:\n        chunk_fname = os.path.join(chunks_dir, '%s_%03d.bin' % (set_name, chunk)) # new chunk\n        with open(chunk_fname, 'wb') as writer:\n            for _ in range(CHUNK_SIZE):\n                len_bytes = reader.read(8)\n                if not len_bytes:\n                    finished = True\n                    break\n                str_len = struct.unpack('q', len_bytes)[0]\n                example_str = struct.unpack('%ds' % str_len, reader.read(str_len))[0]\n                writer.write(struct.pack('q', str_len))\n                writer.write(struct.pack('%ds' % str_len, example_str))\n            chunk += 1\n\n\ndef chunk_all(chunks_dir, data_dir):\n    # Make a dir to hold the chunks\n    if not os.path.isdir(chunks_dir):\n        os.mkdir(chunks_dir)\n    # Chunk the data\n    for set_name in ['train', 'val', 'test']:\n        print(\"Splitting {} data into chunks...\".format(set_name))\n        chunk_file(set_name, chunks_dir, data_dir)\n    print(\"Saved chunked data in %s\" % chunks_dir)\n\n\ndef split_data(data):\n\n    total_ex = len(data)\n    train_ex = int(total_ex*TRAIN_SPLIT)\n    val_ex = int(total_ex*VAL_SPLIT)\n\n    train = data[:train_ex]\n    val = data[train_ex:train_ex+val_ex]\n    test = data[train_ex+val_ex:]\n\n    return {\"train\": train, \"val\": val, \"test\": test}\n\ndef clean_example(example):\n    for punc in PUNCS:\n      splits = example.split(punc)\n      cleaned = [' '+e+' ' if len(e) > 1 and e[0]!=' ' and e[-1]!=' ' else e+' ' if len(e) > 0 and e[-1]!=' ' else ' '+e if len(e)>0 and e[0]!=' ' else e for e in splits]\n      example = punc.join(cleaned)\n    return example\n\ndef main(args):\n\n    if args.no_vocab:\n        makevocab = False\n    else:\n        makevocab = True\n\n    data_dir = args.data_dir\n\n    files = os.listdir(data_dir)\n    files = [e for e in files if e.split('.')[-1]=='tsv']\n\n\n    for file in files:\n        if makevocab:\n            vocab_counter = collections.Counter()\n        examples = []\n        with open(os.path.join(data_dir, file)) as r:\n            for entry in r:\n                examples.append(entry)\n\n        splits_dic = split_data(examples)\n\n        if args.out_dir is None:\n            main_out_dir = os.path.join(args.data_dir)\n        else:\n            main_out_dir = args.out_dir\n\n        curr_out_dir = os.path.join(main_out_dir, file.split('.')[0], \"finished_files\")\n\n        if not os.path.exists(curr_out_dir):\n            os.makedirs(curr_out_dir)\n\n        for k, v in splits_dic.items():\n            \n            num_stories = len(v)\n            out_file = os.path.join(curr_out_dir, k+'.bin')\n\n            with open(out_file, 'wb') as writer:\n                for idx,s in enumerate(v):\n                    if idx % 1000 == 0:\n                        print(\"Writing paraphrase %i of %i; %.2f percent done\" % (idx, num_stories, float(idx)*100.0/float(num_stories)))\n\n                    ex_splits = s.split('\\t')\n                    tmp_splits = []\n                    for ex in ex_splits:\n                        tmp_splits.append(clean_example(ex)) \n                    article, abstract = tmp_splits[0], tmp_splits[1]\n                    # Write to tf.Example\n                    tf_example = example_pb2.Example()\n                    tf_example.features.feature['article'].bytes_list.value.extend([article.encode()])\n                    tf_example.features.feature['abstract'].bytes_list.value.extend([abstract.encode()])\n                    tf_example_str = tf_example.SerializeToString()\n                    str_len = len(tf_example_str)\n                    writer.write(struct.pack('q', str_len))\n                    writer.write(struct.pack('%ds' % str_len, tf_example_str))\n\n                    # Write the vocab to file, if applicable\n                    if makevocab:\n                        art_tokens = article.split(' ')\n                        abs_tokens = abstract.split(' ')\n                        abs_tokens = [t for t in abs_tokens if t not in [SENTENCE_START, SENTENCE_END]] # remove these tags from vocab\n                        tokens = art_tokens + abs_tokens\n                        tokens = [t.strip() for t in tokens] # strip\n                        tokens = [t for t in tokens if t!=\"\"] # remove empty\n                        vocab_counter.update(tokens)\n\n            print(\"Finished writing file %s\\n\" % out_file)\n\n            # write vocab to file\n            if makevocab:\n                print(\"Writing vocab file...\")\n                with open(os.path.join(curr_out_dir, \"vocab\"), 'w') as writer:\n                  for word, count in vocab_counter.most_common(VOCAB_SIZE):\n                    writer.write(word + ' ' + str(count) + '\\n')\n                print(\"Finished writing vocab file\")\n\n        c_dir = os.path.join(curr_out_dir, \"chunked\")\n        chunk_all(c_dir, curr_out_dir)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('data_dir', help='directory of paraphrase ensemble data')\n    parser.add_argument('--out_dir', help='output directory of data splits for pointer generator', default=None)\n    parser.add_argument('--no_vocab', help='whether or not to create vocab', action=\"store_true\")\n    args = parser.parse_args()\n    main(args)","sub_path":"make_paraphrase_data.py","file_name":"make_paraphrase_data.py","file_ext":"py","file_size_in_byte":6011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"531794483","text":"\"\"\"\n    holocron.ext.feed\n    ~~~~~~~~~~~~~~~~~\n\n    This module implements a Feed generator.\n\n    :copyright: (c) 2015 by the Holocron Team, see AUTHORS for details.\n    :license: 3-clause BSD, see LICENSE for details.\n\"\"\"\n\nimport os\nimport datetime\nimport textwrap\n\nimport jinja2\nfrom dooku.conf import Conf\n\nfrom holocron.ext import abc\nfrom holocron.utils import normalize_url, mkdir\nfrom holocron.content import Post\n\n\nclass Feed(abc.Extension, abc.Generator):\n    \"\"\"\n    An Atom feed generator.\n\n    This class is a generator extension that is designed to generate a site\n    feed - content distribution technology - in the `Atom`_  format.\n    See the :class:`~holocron.ext.Generator` class for interface details.\n\n    The generator has several options::\n\n        ext:\n           feed:\n              save_as: feed.xml\n              posts_number: 5\n\n    where\n\n    * ``save_as`` is a path to output file relative to output directory\n    * ``posts_number`` is a number of latest post to be shown in feed\n\n    The class is actually both extension and generator in terms of Holocron\n    at one time. It means that this class will be discovered by Holocron as\n    extension, and this class register its instance as generator in the\n    application.\n\n    .. _Atom: http://www.ietf.org/rfc/rfc4287.txt\n\n    :param app: an application instance for which we're creating extension\n    \"\"\"\n\n    _template = jinja2.Template(textwrap.dedent('''\\\n        <?xml version=\"1.0\" encoding=\"{{ encoding }}\"?>\n          <feed xmlns=\"http://www.w3.org/2005/Atom\" >\n            <title>{{ credentials.site.title }}</title>\n\n            <updated>{{ credentials.date.isoformat() + \"Z\" }}</updated>\n            <id>{{ credentials.siteurl_alt }}</id>\n\n            <link href=\"{{ credentials.siteurl_self }}\" rel=\"self\" />\n            <link href=\"{{ credentials.siteurl_alt }}\" rel=\"alternate\" />\n\n            <generator>Holocron</generator>\n\n            {% for doc in documents %}\n            <entry>\n              <title>{{ doc.title }}</title>\n              <link href=\"{{ doc.abs_url }}\" rel=\"alternate\" />\n              <id>{{ doc.abs_url }}</id>\n\n              <published>{{ doc.published.isoformat() }}</published>\n              <updated>{{ doc.updated_local.isoformat() }}</updated>\n\n              <author>\n                <name>{{ doc.author }}</name>\n              </author>\n\n              <content type=\"html\">\n                {{ doc.content | e }}\n              </content>\n            </entry>\n            {% endfor %}\n          </feed>'''))\n\n    _default_conf = {\n        'save_as': 'feed.xml',\n        'posts_number': 5,\n    }\n\n    def __init__(self, app):\n        self._appconf = app.conf\n        self._conf = Conf(self._default_conf, app.conf.get('ext.feed', {}))\n        self._url = normalize_url(app.conf['site.url']) + self._conf['save_as']\n\n        app.add_generator(self)\n        app.add_theme_ctx(feedurl=self._url)\n\n    def generate(self, documents):\n        posts_number = self._conf['posts_number']\n        save_as = self._conf['save_as']\n\n        # we are interested only in 'post' documents because 'pages'\n        # usually are not a part of feed since they are not intended\n        # to deliver content with regular updates\n        posts = (doc for doc in documents if isinstance(doc, Post))\n\n        # since we could have a lot of posts, it's a common practice to\n        # use in feed only last N posts\n        posts = sorted(posts, key=lambda d: d.published, reverse=True)\n        posts = posts[:posts_number]\n\n        credentials = {\n            'siteurl_self': self._url,\n            'siteurl_alt': normalize_url(self._appconf['site.url']),\n            'site': self._appconf['site'],\n            'date': datetime.datetime.utcnow().replace(microsecond=0), }\n\n        save_as = os.path.join(self._appconf['paths.output'], save_as)\n        mkdir(os.path.dirname(save_as))\n        encoding = self._appconf['encoding.output']\n\n        with open(save_as, 'w', encoding=encoding) as f:\n            f.write(self._template.render(\n                documents=posts,\n                credentials=credentials,\n                encoding=encoding))\n","sub_path":"holocron/ext/feed.py","file_name":"feed.py","file_ext":"py","file_size_in_byte":4145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"561186604","text":"#-*- coding: utf-8 -*-\n\n# with open(\"/Users/saicao/Desktop/testPhone.txt\") as files:\n#\n#     phoneList = files.readlines()\n#\n#     linesCounts = len(phoneList)\n#\n#     ll =  range(0, linesCounts, 500)\n#\n#     for key,value in enumerate(ll):\n#         print key,len(ll)\n#         if key < len(ll)-1:\n#             print value,ll[key+1]\n#             print phoneList[value:ll[key+1]]\n\n\ndef list_of_groups(init_list, children_list_len):\n    list_of_groups = zip(*(iter(init_list),) *children_list_len)\n    end_list = [list(i) for i in list_of_groups]\n    count = len(init_list) % children_list_len\n    end_list.append(init_list[-count:]) if count !=0 else end_list\n    return end_list\n\n\ndef add(a, b):\n    return a+b\n\n# 列表前加一个星\nlist1 = [1, 2]\nprint(add(*list1))\n\n# 字典前添加两个星\ndict1 = {'a':1, 'b':2}\nprint(add(**dict1))\nprint(add(*dict1))\n\n# tuple前使用*\ntuple1 = (1, 2)\nprint(add(*tuple1))\n\n\n\n\n","sub_path":"utils/readfile.py","file_name":"readfile.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"62294155","text":"# -*- coding: utf-8 -*-\n\n# (C) Datadog, Inc. 2018\n# All rights reserved\n# Licensed under a 3-clause BSD style license (see LICENSE)\nimport mock\nimport shutil\nfrom schematics import Model\nfrom schematics.types import IntType, StringType, ModelType\nimport pytest\nfrom six import PY3\n\nfrom stackstate_checks.checks import AgentCheck, TopologyInstance, AgentIntegrationInstance\nfrom stackstate_checks.base.utils.agent_integration_test_util import AgentIntegrationTestUtil\nfrom stackstate_checks.base.stubs.topology import component, relation\n\n\ndef test_instance():\n    \"\"\"\n    Simply assert the class can be instantiated\n    \"\"\"\n    AgentCheck()\n    # rely on default\n    check = AgentCheck()\n    assert check.init_config == {}\n    assert check.instances == []\n\n    # pass dict for 'init_config', a list for 'instances'\n    init_config = {'foo': 'bar'}\n    instances = [{'bar': 'baz'}]\n    check = AgentCheck(init_config=init_config, instances=instances)\n    assert check.init_config == {'foo': 'bar'}\n    assert check.instances == [{'bar': 'baz'}]\n\n\ndef test_load_config():\n    assert AgentCheck.load_config(\"raw_foo: bar\") == {'raw_foo': 'bar'}\n\n\ndef test_log_critical_error():\n    check = AgentCheck()\n\n    with pytest.raises(NotImplementedError):\n        check.log.critical('test')\n\n\nclass TestMetricNormalization:\n    def test_default(self):\n        check = AgentCheck()\n        metric_name = u'Klüft inför på fédéral'\n        normalized_metric_name = b'Kluft_infor_pa_federal'\n\n        assert check.normalize(metric_name) == normalized_metric_name\n\n    def test_fix_case(self):\n        check = AgentCheck()\n        metric_name = u'Klüft inför på fédéral'\n        normalized_metric_name = b'kluft_infor_pa_federal'\n\n        assert check.normalize(metric_name, fix_case=True) == normalized_metric_name\n\n    def test_prefix(self):\n        check = AgentCheck()\n        metric_name = u'metric'\n        prefix = u'some'\n        normalized_metric_name = b'some.metric'\n\n        assert check.normalize(metric_name, prefix=prefix) == normalized_metric_name\n\n    def test_prefix_bytes(self):\n        check = AgentCheck()\n        metric_name = u'metric'\n        prefix = b'some'\n        normalized_metric_name = b'some.metric'\n\n        assert check.normalize(metric_name, prefix=prefix) == normalized_metric_name\n\n    def test_prefix_unicode_metric_bytes(self):\n        check = AgentCheck()\n        metric_name = b'metric'\n        prefix = u'some'\n        normalized_metric_name = b'some.metric'\n\n        assert check.normalize(metric_name, prefix=prefix) == normalized_metric_name\n\n    def test_underscores_redundant(self):\n        check = AgentCheck()\n        metric_name = u'a_few__redundant___underscores'\n        normalized_metric_name = b'a_few_redundant_underscores'\n\n        assert check.normalize(metric_name) == normalized_metric_name\n\n    def test_underscores_at_ends(self):\n        check = AgentCheck()\n        metric_name = u'_some_underscores_'\n        normalized_metric_name = b'some_underscores'\n\n        assert check.normalize(metric_name) == normalized_metric_name\n\n    def test_underscores_and_dots(self):\n        check = AgentCheck()\n        metric_name = u'some_.dots._and_._underscores'\n        normalized_metric_name = b'some.dots.and.underscores'\n\n        assert check.normalize(metric_name) == normalized_metric_name\n\n\nclass TestMetrics:\n    def test_non_float_metric(self, aggregator):\n        check = AgentCheck()\n        metric_name = 'test_metric'\n        with pytest.raises(ValueError):\n            check.gauge(metric_name, '85k')\n        aggregator.assert_metric(metric_name, count=0)\n\n\nclass TestEvents:\n    def test_valid_event(self, aggregator):\n        check = AgentCheck()\n        event = {\n            \"timestamp\": 123456789,\n            \"event_type\": \"new.event\",\n            \"source_type_name\": \"new.source.type\",\n            \"msg_title\": \"new test event\",\n            \"aggregation_key\": \"test.event\",\n            \"msg_text\": \"test event test event\",\n            \"tags\": None\n        }\n        check.event(event)\n        aggregator.assert_event('test event test event')\n\n    def test_topology_event(self, telemetry):\n        check = AgentCheck()\n        event = {\n            \"timestamp\": 123456789,\n            \"event_type\": \"new.event\",\n            \"source_type_name\": \"new.source.type\",\n            \"msg_title\": \"new test event\",\n            \"aggregation_key\": \"test.event\",\n            \"msg_text\": \"test event test event\",\n            \"tags\": None,\n            \"context\": {\n                \"element_identifiers\": [\"urn:test:/value\"],\n                \"source\": \"test source\",\n                \"category\": \"test category\",\n            }\n        }\n        check.event(event)\n        telemetry.assert_topology_event(event)\n\n\nclass TestServiceChecks:\n    def test_valid_sc(self, aggregator):\n        check = AgentCheck()\n\n        check.service_check(\"testservicecheck\", AgentCheck.OK, tags=None, message=\"\")\n        aggregator.assert_service_check(\"testservicecheck\", status=AgentCheck.OK)\n\n        check.service_check(\"testservicecheckwithhostname\", AgentCheck.OK, tags=[\"foo\", \"bar\"], hostname=\"testhostname\",\n                            message=\"a message\")\n        aggregator.assert_service_check(\"testservicecheckwithhostname\", status=AgentCheck.OK, tags=[\"foo\", \"bar\"],\n                                        hostname=\"testhostname\", message=\"a message\")\n\n        check.service_check(\"testservicecheckwithnonemessage\", AgentCheck.OK, message=None)\n        aggregator.assert_service_check(\"testservicecheckwithnonemessage\", status=AgentCheck.OK, )\n\n\nclass TestTags:\n    def test_default_string(self):\n        check = AgentCheck()\n        tag = 'default:string'\n        tags = [tag]\n\n        normalized_tags = check._normalize_tags_type(tags, None)\n        normalized_tag = normalized_tags[0]\n\n        assert normalized_tags is not tags\n        # Ensure no new allocation occurs\n        assert normalized_tag is tag\n\n    def test_bytes_string(self):\n        check = AgentCheck()\n        tag = b'bytes:string'\n        tags = [tag]\n\n        normalized_tags = check._normalize_tags_type(tags, None)\n        normalized_tag = normalized_tags[0]\n\n        assert normalized_tags is not tags\n\n        if PY3:\n            assert normalized_tag == tag.decode('utf-8')\n        else:\n            # Ensure no new allocation occurs\n            assert normalized_tag is tag\n\n    def test_unicode_string(self):\n        check = AgentCheck()\n        tag = u'unicode:string'\n        tags = [tag]\n\n        normalized_tags = check._normalize_tags_type(tags, None)\n        normalized_tag = normalized_tags[0]\n\n        assert normalized_tags is not tags\n\n        if PY3:\n            # Ensure no new allocation occurs\n            assert normalized_tag is tag\n        else:\n            assert normalized_tag == tag.encode('utf-8')\n\n    def test_unicode_device_name(self):\n        check = AgentCheck()\n        tags = []\n        device_name = u'unicode_string'\n\n        normalized_tags = check._normalize_tags_type(tags, device_name)\n        normalized_device_tag = normalized_tags[0]\n\n        assert isinstance(normalized_device_tag, str if PY3 else bytes)\n\n    def test_duplicated_device_name(self):\n        check = AgentCheck()\n        tags = []\n        device_name = 'foo'\n        check._normalize_tags_type(tags, device_name)\n        normalized_tags = check._normalize_tags_type(tags, device_name)\n        assert len(normalized_tags) == 1\n\n    def test__to_bytes(self):\n        if PY3:\n            pytest.skip('Method only exists on Python 2')\n        check = AgentCheck()\n        assert isinstance(check._to_bytes(b\"tag:foo\"), bytes)\n        assert isinstance(check._to_bytes(u\"tag:☣\"), bytes)\n        in_str = mock.MagicMock(side_effect=Exception)\n        in_str.encode.side_effect = Exception\n        assert check._to_bytes(in_str) is None\n\n\nclass LimitedCheck(AgentCheck):\n    DEFAULT_METRIC_LIMIT = 10\n\n\nclass TestLimits():\n    def test_context_uid(self, aggregator):\n        check = LimitedCheck()\n\n        # Test stability of the hash against tag ordering\n        uid = check._context_uid(aggregator.GAUGE, \"test.metric\", [\"one\", \"two\"], None)\n        assert uid == check._context_uid(aggregator.GAUGE, \"test.metric\", [\"one\", \"two\"], None)\n        assert uid == check._context_uid(aggregator.GAUGE, \"test.metric\", [\"two\", \"one\"], None)\n\n        # Test all fields impact the hash\n        assert uid != check._context_uid(aggregator.RATE, \"test.metric\", [\"one\", \"two\"], None)\n        assert uid != check._context_uid(aggregator.GAUGE, \"test.metric2\", [\"one\", \"two\"], None)\n        assert uid != check._context_uid(aggregator.GAUGE, \"test.metric\", [\"two\"], None)\n        assert uid != check._context_uid(aggregator.GAUGE, \"test.metric\", [\"one\", \"two\"], \"host\")\n\n    def test_metric_limit_gauges(self, aggregator):\n        check = LimitedCheck()\n        assert check.get_warnings() == []\n\n        for i in range(0, 10):\n            check.gauge(\"metric\", 0)\n        assert len(check.get_warnings()) == 0\n        assert len(aggregator.metrics(\"metric\")) == 10\n\n        for i in range(0, 10):\n            check.gauge(\"metric\", 0)\n        assert len(check.get_warnings()) == 1\n        assert len(aggregator.metrics(\"metric\")) == 10\n\n    def test_metric_limit_count(self, aggregator):\n        check = LimitedCheck()\n        assert check.get_warnings() == []\n\n        # Multiple calls for a single set of (metric_name, tags) should not trigger\n        for i in range(0, 20):\n            check.count(\"metric\", 0, hostname=\"host-single\")\n        assert len(check.get_warnings()) == 0\n        assert len(aggregator.metrics(\"metric\")) == 20\n\n        # Multiple sets of tags should trigger\n        # Only 9 new sets of tags should pass through\n        for i in range(0, 20):\n            check.count(\"metric\", 0, hostname=\"host-{}\".format(i))\n        assert len(check.get_warnings()) == 1\n        assert len(aggregator.metrics(\"metric\")) == 29\n\n    def test_metric_limit_instance_config(self, aggregator):\n        instances = [\n            {\n                \"max_returned_metrics\": 42,\n            }\n        ]\n        check = AgentCheck(\"test\", {}, instances)\n        assert check.get_warnings() == []\n\n        for i in range(0, 42):\n            check.gauge(\"metric\", 0)\n        assert len(check.get_warnings()) == 0\n        assert len(aggregator.metrics(\"metric\")) == 42\n\n        check.gauge(\"metric\", 0)\n        assert len(check.get_warnings()) == 1\n        assert len(aggregator.metrics(\"metric\")) == 42\n\n    def test_metric_limit_instance_config_zero(self, aggregator):\n        instances = [\n            {\n                \"max_returned_metrics\": 0,\n            }\n        ]\n        check = LimitedCheck(\"test\", {}, instances)\n        assert len(check.get_warnings()) in [1, 2]\n\n        for i in range(0, 42):\n            check.gauge(\"metric\", 0)\n        assert len(check.get_warnings()) == 1  # get_warnings resets the array\n        assert len(aggregator.metrics(\"metric\")) == 10\n\n\nclass DefaultInstanceCheck(AgentCheck):\n    pass\n\n\nclass AgentIntegrationInstanceCheck(AgentCheck):\n    def get_instance_key(self, instance):\n        return AgentIntegrationInstance(\"test\", \"integration\")\n\n\nclass TopologyCheck(AgentCheck):\n    def __init__(self, key=None, *args, **kwargs):\n        super(TopologyCheck, self).__init__(*args, **kwargs)\n        self.key = key or TopologyInstance(\"mytype\", \"someurl\")\n\n    def get_instance_key(self, instance):\n        return self.key\n\n\nclass TopologyAutoSnapshotCheck(TopologyCheck):\n    def __init__(self):\n        instances = [{'a': 'b'}]\n        super(TopologyAutoSnapshotCheck, self) \\\n            .__init__(TopologyInstance(\"mytype\", \"someurl\", with_snapshots=True), \"test\", {}, instances)\n\n    def check(self, instance):\n        pass\n\n\nclass TopologyBrokenCheck(TopologyAutoSnapshotCheck):\n    def __init__(self):\n        super(TopologyBrokenCheck, self).__init__()\n\n    def check(self, instance):\n        raise Exception(\"some error in my check\")\n\n\nTEST_STATE = {\n    'string': 'string',\n    'int': 1,\n    'float': 1.0,\n    'bool': True,\n    'list': ['a', 'b', 'c'],\n    'dict': {'a': 'b'}\n}\n\n\nclass TopologyStatefulCheck(TopologyAutoSnapshotCheck):\n    def __init__(self):\n        super(TopologyStatefulCheck, self).__init__()\n\n    @staticmethod\n    def get_agent_conf_d_path():\n        return \"./test_data\"\n\n    def check(self, instance):\n        instance.update({'state': TEST_STATE})\n\n\nclass TopologyStatefulStateDescriptorCleanupCheck(TopologyAutoSnapshotCheck):\n    def __init__(self):\n        instances = [{'a': 'b'}]\n        super(TopologyAutoSnapshotCheck, self) \\\n            .__init__(TopologyInstance(\"mytype\", \"https://some.type.url\", with_snapshots=True), \"test\", {}, instances)\n\n    @staticmethod\n    def get_agent_conf_d_path():\n        return \"./test_data\"\n\n    def check(self, instance):\n        instance.update({'state': TEST_STATE})\n\n\nclass TopologyClearStatefulCheck(TopologyStatefulCheck):\n    def __init__(self):\n        super(TopologyClearStatefulCheck, self).__init__()\n\n    def check(self, instance):\n        instance.update({'state': None})\n\n\nclass TopologyBrokenStatefulCheck(TopologyStatefulCheck):\n    def __init__(self):\n        super(TopologyBrokenStatefulCheck, self).__init__()\n\n    def check(self, instance):\n        instance.update({'state': TEST_STATE})\n\n        raise Exception(\"some error in my check\")\n\n\nclass IdentifierMappingTestAgentCheck(TopologyCheck):\n    def __init__(self):\n        instances = [\n            {\n                'identifier_mappings':\n                    {\n                        'host': {'field': 'url', 'prefix': 'urn:computer:/'},\n                        'vm': {'field': 'name', 'prefix': 'urn:computer:/'}\n                    }\n            }\n        ]\n        super(IdentifierMappingTestAgentCheck, self)\\\n            .__init__(TopologyInstance(\"host\", \"someurl\"), \"test\", {}, instances)\n\n    def check(self, instance):\n        pass\n\n\nclass NestedIdentifierMappingTestAgentCheck(TopologyCheck):\n    def __init__(self):\n        instances = [\n            {\n                'identifier_mappings':\n                    {\n                        'host': {'field': 'x.y.z.url', 'prefix': 'urn:computer:/'}\n                    }\n            }\n        ]\n        super(NestedIdentifierMappingTestAgentCheck, self)\\\n            .__init__(TopologyInstance(\"host\", \"someurl\"), \"test\", {}, instances)\n\n    def check(self, instance):\n        pass\n\n\nclass StateSchema(Model):\n    offset = IntType(required=True)\n\n\nclass CheckInstanceSchema(Model):\n    a = StringType(required=True)\n    state = ModelType(StateSchema, required=True, default=StateSchema({'offset': 0}))\n\n\nclass TopologyStatefulSchemaCheck(TopologyStatefulCheck):\n    INSTANCE_SCHEMA = CheckInstanceSchema\n\n    def check(self, instance):\n        print(instance.a)\n        instance.state.offset = 20\n\n\nclass TestTopology:\n    def test_component(self, topology):\n        check = TopologyCheck()\n        data = {\"key\": \"value\", \"intlist\": [1], \"emptykey\": None, \"nestedobject\": {\"nestedkey\": \"nestedValue\"}}\n        check.component(\"my-id\", \"my-type\", data)\n        topology.assert_snapshot(check.check_id, check.key, components=[component(\"my-id\", \"my-type\", data)])\n\n    def test_relation(self, topology):\n        check = TopologyCheck()\n        data = {\"key\": \"value\", \"intlist\": [1], \"emptykey\": None, \"nestedobject\": {\"nestedkey\": \"nestedValue\"}}\n        check.relation(\"source-id\", \"target-id\", \"my-type\", data)\n        topology.assert_snapshot(check.check_id, check.key,\n                                 relations=[relation(\"source-id\", \"target-id\", \"my-type\", data)])\n\n    def test_auto_snapshotting(self, topology):\n        check = TopologyAutoSnapshotCheck()\n        check.run()\n        # assert auto snapshotting occurred\n        topology.assert_snapshot(check.check_id, check.key, start_snapshot=True, stop_snapshot=True)\n\n    def test_no_stop_snapshot_on_exception(self, topology):\n        check = TopologyBrokenCheck()\n        check.run()\n        # assert stop snapshot is false when an exception is thrown in check.run()\n        topology.assert_snapshot(check.check_id, check.key, start_snapshot=True, stop_snapshot=False)\n\n    def test_start_snapshot(self, topology):\n        check = TopologyCheck()\n        check.start_snapshot()\n        topology.assert_snapshot(check.check_id, check.key, start_snapshot=True)\n\n    def test_stop_snapshot(self, topology):\n        check = TopologyCheck()\n        check.stop_snapshot()\n        topology.assert_snapshot(check.check_id, check.key, stop_snapshot=True)\n\n    def test_stateful_check(self, topology, state):\n        check = TopologyStatefulCheck()\n        state.assert_state_check(check, expected_pre_run_state=None, expected_post_run_state=TEST_STATE)\n        # assert auto snapshotting occurred\n        topology.assert_snapshot(check.check_id, check.key, start_snapshot=True, stop_snapshot=True)\n\n    def test_stateful_state_descriptor_cleanup_check(self, topology, state):\n        check = TopologyStatefulStateDescriptorCleanupCheck()\n        state_descriptor = check._get_state_descriptor()\n        assert state_descriptor.instance_key == \"instance.mytype.https_some.type.url\"\n        assert check._get_instance_key_dict() == {'type': 'mytype', 'url': 'https://some.type.url'}\n        state.assert_state_check(check, expected_pre_run_state=None, expected_post_run_state=TEST_STATE)\n        # assert auto snapshotting occurred\n        topology.assert_snapshot(check.check_id, check.key, start_snapshot=True, stop_snapshot=True)\n\n    def test_clear_stateful_check(self, topology, state):\n        check = TopologyClearStatefulCheck()\n        # set the previous state and assert the state check function as expected\n        check.state_manager.set_state(check._get_state_descriptor(), TEST_STATE)\n        state.assert_state_check(check, expected_pre_run_state=TEST_STATE, expected_post_run_state=None)\n        # assert auto snapshotting occurred\n        topology.assert_snapshot(check.check_id, check.key, start_snapshot=True, stop_snapshot=True)\n\n    def test_no_state_change_on_exception_stateful_check(self, topology, state):\n        check = TopologyBrokenStatefulCheck()\n        # set the previous state and assert the state check function as expected\n        previous_state = {'my_old': 'state'}\n        check.state_manager.set_state(check._get_state_descriptor(), previous_state)\n        state.assert_state_check(check, expected_pre_run_state=previous_state, expected_post_run_state=previous_state)\n        # assert auto snapshotting occurred\n        topology.assert_snapshot(check.check_id, check.key, start_snapshot=True, stop_snapshot=False)\n\n    def test_stateful_schema_check(self, topology, state):\n        check = TopologyStatefulSchemaCheck()\n        # assert the state check function as expected\n        state.assert_state_check(check, expected_pre_run_state=None,\n                                 expected_post_run_state=StateSchema({'offset': 20}), state_schema=StateSchema)\n        # assert auto snapshotting occurred\n        topology.assert_snapshot(check.check_id, check.key, start_snapshot=True, stop_snapshot=True)\n\n    def test_none_data_ok(self, topology):\n        check = TopologyCheck()\n        check.component(\"my-id\", \"my-type\", None)\n        topology.assert_snapshot(check.check_id, check.key, components=[component(\"my-id\", \"my-type\",\n                                                                                  {'tags': ['integration-type:mytype',\n                                                                                            'integration-url:someurl']\n                                                                                   })])\n\n    def test_illegal_data(self):\n        check = TopologyCheck()\n        with pytest.raises(ValueError) as e:\n            assert check.component(\"my-id\", \"my-type\", 1)\n        if PY3:\n            assert str(e.value) == \"Got unexpected <class 'int'> for argument data, expected dictionary or None value\"\n        else:\n            assert str(e.value) == \"Got unexpected <type 'int'> for argument data, expected dictionary or None value\"\n\n    def test_illegal_data_value(self):\n        check = TopologyCheck()\n        with pytest.raises(ValueError) as e:\n            assert check.component(\"my-id\", \"my-type\", {\"key\": set()})\n        if PY3:\n            assert str(e.value) == \"\"\"Got unexpected <class 'set'> for argument data.key, \\\nexpected string, int, dictionary, list or None value\"\"\"\n        else:\n            assert str(e.value) == \"\"\"Got unexpected <type 'set'> for argument data.key, \\\nexpected string, int, dictionary, list or None value\"\"\"\n\n    def test_illegal_instance_key_none(self):\n        check = TopologyCheck()\n        check.key = None\n        with pytest.raises(ValueError) as e:\n            assert check.component(\"my-id\", \"my-type\", None)\n        if PY3:\n            assert str(e.value) == \"Got unexpected <class 'str'> for argument get_instance_key(), expected dictionary\"\n        else:\n            assert str(e.value) == \"Got unexpected <type 'str'> for argument get_instance_key(), expected dictionary\"\n\n    def test_illegal_instance_type(self):\n        check = TopologyCheck()\n        check.key = {}\n        with pytest.raises(ValueError) as e:\n            assert check.component(\"my-id\", \"my-type\", None)\n        if PY3:\n            assert str(e.value) == \"\"\"Got unexpected <class 'dict'> for argument get_instance_key(), \\\nexpected TopologyInstance, AgentIntegrationInstance or DefaultIntegrationInstance\"\"\"\n        else:\n            assert str(e.value) == \"\"\"Got unexpected <type 'dict'> for argument get_instance_key(), \\\nexpected TopologyInstance, AgentIntegrationInstance or DefaultIntegrationInstance\"\"\"\n\n    def test_illegal_instance_key_field_type(self):\n        check = TopologyCheck()\n        check.key = TopologyInstance(\"mytype\", 1)\n        with pytest.raises(ValueError) as e:\n            assert check.component(\"my-id\", \"my-type\", None)\n        assert str(e.value) == \"Instance requires a 'url' field of type 'string'\"\n\n    def test_topology_instance(self, topology):\n        check = TopologyCheck()\n        check.key = TopologyInstance(\"mytype\", \"myurl\")\n        assert check._get_instance_key_dict() == {\"type\": \"mytype\", \"url\": \"myurl\"}\n        check.create_integration_instance()\n        # assert integration topology is created for topology instances\n        topo_instances = topology.get_snapshot('mytype:myurl')\n        assert topo_instances == self.agent_integration_topology('mytype', 'myurl')\n\n    def test_agent_integration_instance(self, topology):\n        check = AgentIntegrationInstanceCheck()\n        assert check._get_instance_key_dict() == {\"type\": \"agent\", \"url\": \"integrations\"}\n        check.create_integration_instance()\n        # assert integration topology is created for agent integration instances\n        topo_instances = topology.get_snapshot(check.check_id)\n        assert topo_instances == self.agent_integration_topology('test', 'integration')\n\n    def test_agent_telemetry_instance(self, topology):\n        check = DefaultInstanceCheck()\n        assert check._get_instance_key_dict() == {\"type\": \"agent\", \"url\": \"integrations\"}\n        check.create_integration_instance()\n        # assert no integration topology is created for default instances\n        assert topology._snapshots == {}\n\n    def agent_integration_topology(self, type, url):\n        return {\n            'components': [\n                {\n                    'data': {\n                        'cluster': 'stubbed-cluster-name',\n                        'hostname': 'stubbed.hostname',\n                        'identifiers': [\n                            'urn:process:/stubbed.hostname:1:1234567890'\n                        ],\n                        'name': 'StackState Agent:stubbed.hostname',\n                        'tags': sorted([\n                            'hostname:stubbed.hostname',\n                            'stackstate-agent',\n                        ])\n                    },\n                    'id': 'urn:stackstate-agent:/stubbed.hostname',\n                    'type': 'stackstate-agent'\n                },\n                {\n                    'data': {\n                        'checks': [\n                            {\n                                'is_service_check_health_check': True,\n                                'name': 'Integration Health',\n                                'stream_id': -1\n                            }\n                        ],\n                        'cluster': 'stubbed-cluster-name',\n                        'service_checks': [\n                            {\n                                'conditions': [\n                                    {\n                                        'key': 'host',\n                                        'value': 'stubbed.hostname'\n                                    },\n                                    {\n                                        'key': 'tags.integration-type',\n                                        'value': type\n                                    }\n                                ],\n                                'name': 'Service Checks',\n                                'stream_id': -1\n                            }\n                        ],\n                        'hostname': 'stubbed.hostname',\n                        'integration': type,\n                        'name': 'stubbed.hostname:%s' % type,\n                        'tags': sorted([\n                            'hostname:stubbed.hostname',\n                            'integration-type:%s' % type,\n                        ])\n                    },\n                    'id': 'urn:agent-integration:/stubbed.hostname:%s' % type,\n                    'type': 'agent-integration'\n                },\n                {\n                    'data': {\n                        'checks': [\n                            {\n                                'is_service_check_health_check': True,\n                                'name': 'Integration Instance Health',\n                                'stream_id': -1\n                            }\n                        ],\n                        'cluster': 'stubbed-cluster-name',\n                        'service_checks': [\n                            {\n                                'conditions': [\n                                    {\n                                        'key': 'host',\n                                        'value': 'stubbed.hostname'\n                                    },\n                                    {\n                                        'key': 'tags.integration-type',\n                                        'value': type\n                                    },\n                                    {\n                                        'key': 'tags.integration-url',\n                                        'value': url\n                                    }\n                                ],\n                                'name': 'Service Checks',\n                                'stream_id': -1\n                            }\n                        ],\n                        'hostname': 'stubbed.hostname',\n                        'integration': type,\n                        'name': '%s:%s' % (type, url),\n                        'tags': sorted([\n                            'hostname:stubbed.hostname',\n                            'integration-type:%s' % type,\n                            'integration-url:%s' % url\n                        ])\n                    },\n                    'id': 'urn:agent-integration-instance:/stubbed.hostname:%s:%s' % (type, url),\n                    'type': 'agent-integration-instance'\n                },\n            ],\n            'instance_key': {\n                'type': 'agent',\n                'url': 'integrations'\n            },\n            'relations': [\n                {\n                    'data': {},\n                    'source_id': 'urn:stackstate-agent:/stubbed.hostname',\n                    'target_id': 'urn:agent-integration:/stubbed.hostname:%s' % type,\n                    'type': 'runs'\n                },\n                {\n                    'data': {},\n                    'source_id': 'urn:agent-integration:/stubbed.hostname:%s' % type,\n                    'target_id': 'urn:agent-integration-instance:/stubbed.hostname:%s:%s' % (type, url),\n                    'type': 'has'\n                },\n            ],\n            'start_snapshot': False,\n            'stop_snapshot': False\n        }\n\n    def test_component_with_identifier_mapping(self, topology):\n        \"\"\"\n        Test should generate identifier mapping based on the prefix and field value\n        \"\"\"\n        check = IdentifierMappingTestAgentCheck()\n        data = {\"url\": \"identifier-url\", \"emptykey\": None, \"nestedobject\": {\"nestedkey\": \"nestedValue\"}}\n        check.component(\"my-id\", \"host\", data)\n        component = topology.get_snapshot(check.check_id)['components'][0]\n        # there should be only one identifier mapped for host because only `host` type exist\n        assert component[\"data\"][\"identifiers\"] == [\"urn:computer:/identifier-url\"]\n\n    def test_component_with_identifier_mapping_with_existing_identifier(self, topology):\n        \"\"\"\n        Test should generate identifier mapping based on the prefix and field value with extra identifier\n        \"\"\"\n        check = IdentifierMappingTestAgentCheck()\n        data = {\"url\": \"identifier-url\", \"identifiers\": [\"urn:host:/host-1\"],\n                \"nestedobject\": {\"nestedkey\": \"nestedValue\"}}\n        check.component(\"my-id\", \"host\", data)\n        component = topology.get_snapshot(check.check_id)['components'][0]\n        # there should be 2 identifier mapped for host because there was an existing identifier\n        assert component[\"data\"][\"identifiers\"] == [\"urn:host:/host-1\", \"urn:computer:/identifier-url\"]\n\n    def test_component_identifier_mapping_with_no_field(self, topology):\n        \"\"\"\n        Test should not generate identifier mapping because field value doesn't exist in data\n        \"\"\"\n        check = IdentifierMappingTestAgentCheck()\n        data = {\"emptykey\": None, \"nestedobject\": {\"nestedkey\": \"nestedValue\"}}\n        check.component(\"my-id\", \"host\", data)\n        component = topology.get_snapshot(check.check_id)['components'][0]\n        # there should be no identifier mapped for host because field value `url` doesn't exist in data\n        assert component[\"data\"].get(\"identifiers\") is None\n\n    def test_component_identifier_mapping_with_nested_field(self, topology):\n        \"\"\"\n        Test should generate identifier mapping because based on the prefix and nested field value\n        \"\"\"\n        check = NestedIdentifierMappingTestAgentCheck()\n        data = {\"emptykey\": None, \"x\": {\"y\": {\"z\": {\"url\": \"identifier-url\"}}}}\n        check.component(\"my-id\", \"host\", data)\n        component = topology.get_snapshot(check.check_id)['components'][0]\n        # there should be one identifier mapped for host because only `host` type exist on the nested field\n        assert component[\"data\"][\"identifiers\"] == [\"urn:computer:/identifier-url\"]\n\n    def test_component_nested_identifier_mapping_with_no_field(self, topology):\n        \"\"\"\n        Test should not generate identifier mapping because nested field value doesn't exist in data\n        \"\"\"\n        check = NestedIdentifierMappingTestAgentCheck()\n        data = {\"emptykey\": None}\n        check.component(\"my-id\", \"host\", data)\n        component = topology.get_snapshot(check.check_id)['components'][0]\n        # there should be no identifier mapped for host because field value `x.y.z.url` doesn't exist in data\n        assert component[\"data\"].get(\"identifiers\") is None\n","sub_path":"stackstate_checks_base/tests/test_agent_check.py","file_name":"test_agent_check.py","file_ext":"py","file_size_in_byte":31780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"19882755","text":"from lib import plotutil as plu\nimport optparse\nimport signal\n\n# Defaults\nxstart = 0\nxend = 100\nstepsize = 1.0\ncolor = \"blue\"\nxlabel = \"X-axis\"\nylabel = \"Y-axis\"\n\n\n# Handle case if user presses Ctrl+C, show proper message while shutting down\ndef sigint_handler(signum, frame):\n    print('shutting down PlotIt...')\n    exit(0)\n\n\nsignal.signal(signal.SIGINT, sigint_handler)\n\n# Parse command line arguments and switches\n\nparser = optparse.OptionParser()\nparser.add_option('-f', '--function', dest='func',\n                  help='Enter function to visualise')\nparser.add_option('-s', '--xstart', dest='xstart',\n                  help='Enter starting x-value')\nparser.add_option('-e', '--xend', dest='xend',\n                  help='Enter ending x-value')\nparser.add_option('-z', '--stepsize', dest='stepsize',\n                  help='Enter step size')\nparser.add_option('-c', '--color', dest='color',\n                  help='Enter the color for plot')\nparser.add_option('-x', '--xlabel', dest='xlabel',\n                  help='Enter the x-label for plot')\nparser.add_option('-y', '--ylabel', dest='ylabel',\n                  help='Enter the y-label for plot')\nparser.add_option('-l', '--line', dest='line',\n                  help='Enter 2 Arrays of X and Y Coordinates like \\\n                  [x1,x2,x3,...,xn],[y1,y2,y3,...,yn]')\nparser.add_option('-t', '--theme', dest='theme',\n                  help='Enter theme for displaying plot (dark or light)')\n\n(options, args) = parser.parse_args()\n\nif not options.func and not options.line:\n    print('Please enter a function or 2 arrays of x and y coordinates')\n    exit(0)\n\nif options.color:\n    color = str(options.color)\n\nif options.xlabel:\n    xlabel = str(options.xlabel)\n\nif options.ylabel:\n    ylabel = str(options.ylabel)\n\n\nif options.theme:\n    theme = str(options.theme)\nelse:\n    theme = 'default'\n\nif options.func:\n    func = options.func\n\n    if options.xstart:\n        xstart = int(options.xstart)\n\n    if options.xend:\n        xend = int(options.xend)\n\n    if options.stepsize:\n        stepsize = int(options.stepsize)\n\n    plu.plot(func, xstart, xend, stepsize, color, xlabel, ylabel, theme, False)\n\nelse:  # no function try to take points for line\n    xypoints = options.line\n    plu.plot_line(xypoints, color, xlabel, ylabel, theme, False)\n\n# Visualise using matplotlib\n","sub_path":"plotit.py","file_name":"plotit.py","file_ext":"py","file_size_in_byte":2331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"165620586","text":"# -*- encoding: utf-8 -*-\n# Copyright 2016 Vinzor Co.,Ltd.\n#\n# Test Config\n#\n# 2016/9/1 fengyc : Init\n\nimport unittest\nimport os\nfrom phoenix.config import CONF, StrOpt, IntOpt\n\n\nclass ConfigTest(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        config_file = os.path.join(os.path.dirname(__file__), 'test_config.ini')\n        opts = [\n            StrOpt('hello', default='Hello', help='Hello string'),\n            IntOpt('myint', default=0, help='my int'),\n        ]\n        CONF.register_opts(opts, 'DEFAULT')\n        db_opts = [\n            StrOpt('connection', help='Database URL'),\n            IntOpt('pool_recycle')\n        ]\n        CONF.register_opts(db_opts, 'database')\n        CONF([config_file])\n\n    def test_str_opt(self):\n        self.assertEqual(CONF.DEFAULT.hello, 'Hello')\n\n    def test_int_opt(self):\n        self.assertEqual(CONF.DEFAULT.myint, 0)\n\n    def test_db_opts(self):\n        self.assertIsNotNone(CONF.database.connection)\n        self.assertIsNotNone(CONF.database.pool_recycle)\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"src/tests/common/test_config.py","file_name":"test_config.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"261937833","text":"from typing import List\n\n#假设只出现一次的元素位于下标x,由于其余每个元素都出现两次，因此下标x 的左边和右边都有偶数个元素，数组的长度是奇数。\n#如果mid落在左侧，根据（low+high）/2算得的下标一定是偶数下标，如果出现两次则一定与mid+1下标对应数相等\n#如果mid落在右侧，根据（low+high）/2算得的下标一定是奇数下标，如果出现两次也一定与mid-1下标对应数相等\nclass Solution:\n    def singleNonDuplicate(self, nums: List[int]) -> int:\n        low = 0\n        high = len(nums)-1\n        while low <high:\n            mid = (low+high)//2 #整数除法\n            if nums[mid] == nums[mid^1]:\n                low = mid + 1\n            else:\n                high = mid\n        return nums[low]","sub_path":"540. 有序数组中的单一元素/540. 有序数组中的单一元素.py","file_name":"540. 有序数组中的单一元素.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"465170155","text":"'''\r\nImplemente uma função recursiva que,\r\ndados dois números inteiros x e n, calcula o\r\nvalor de x*n.\r\n'''\r\n\r\ndef mult(x , n):\r\n    if (n == 0):\r\n        return n;\r\n    else:\r\n        return x + mult(x,n-1);\r\n\r\r\n\r\n\r\nx = int(input());\r\nn = int(input());\r\n\r\nprint(mult(x,n));","sub_path":"SSC0800 (2019-02) - Introdução à Ciência de Computação I/EX 041 - Lista 6 - Recursão - Multiplicação.py","file_name":"EX 041 - Lista 6 - Recursão - Multiplicação.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"634058523","text":"from rllab.envs.base import Env\nfrom rllab.spaces import Box\nfrom rllab.envs.base import Step\nimport numpy as np\n\nimport os\nimport socket\ng_repo_path = os.environ['PHYSICS_SIMULATOR_PATH']\n\n# Input Pose File path\ninit_pose_file = g_repo_path + '/tmp/init_pose.txt'\nfinal_pose_file = g_repo_path + '/tmp/final_pose.txt'\naction_file = g_repo_path + '/tmp/action.txt'\n# -0.107687, -0.535722, 0.563821, 0.941487, -0.0294104, 0.335604, 0.0103682\n\nclass MOtomanPushEnv(Env):\n    @property\n    def observation_space(self):\n        return Box(low=-np.inf, high=np.inf, shape=(7,))\n\n    @property\n    def action_space(self):\n        return Box(low=0.05, high=1.0, shape=(1,))\n\n    def reset(self):\n        self._state = [-0.107687, -0.535722, 0.791, 0.0103682, 0.941487, -0.0294104, 0.335604] #q:wxyz #np.random.uniform(-1, 1, size=(2,))\n        observation = np.copy(self._state)\n        return observation\n\n    def step(self, action):\n        # self._state = self._state + action\n        # x, y = self._state\n        # reward = - (x ** 2 + y ** 2) ** 0.5\n        print ('\\n init state and action: ', self._state, action)\n        mass = 0.24351668  \n        friction = 0.05537921\n        obj_goal = [self._state[0]+1.0, self._state[1], self._state[2]]\n\n\n        with open(init_pose_file, \"w+\") as file:\n            file.write(\"%f %f %f %f %f %f %f %f %f %f %f %f \\n\" % \\\n                       (mass, friction, self._state[0], self._state[1], self._state[3], self._state[4], \\\n                        self._state[5], self._state[6], action, self._state[0], obj_goal[0], obj_goal[1]))\n     \n        # Create a TCP/IP socket\n        sock2 = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        sock2.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\n        # Connect the socket to the port where the server is listening\n        serv_address = ('localhost', 50007)\n        print('waiting for a connection')\n        sock2.connect(serv_address)\n        print('Connected')\n        sock2.sendall(b'hi')\n        sock2.close()   \n\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n        # Bind the socket to the port\n        server_address = ('localhost', 10022)\n        sock.bind(server_address)\n\n        # Listen for incoming connections\n        sock.listen(1)        \n        connection, client_address = sock.accept()\n\n        # Receive the data in small chunks and retransmit it\n        data = connection.recv(16)\n        connection.close()\n        sock.close()             \n\n        Y_new = np.zeros((7, ))\n        with open(final_pose_file, \"r\") as file:\n            for line in file:\n                vals = line.split()\n                for i in range(0,7):\n                    Y_new[i] = vals[i]\n        self._state = Y_new\n        next_observation = np.copy(self._state)\n\n        done = True #abs(x) < 0.01 and abs(y) < 0.01\n\n        # q_dist = np.abs(1 - np.sum(np.multiply(rotation, rotation_gt)**2))\n        dist = (( Y_new[0]-obj_goal[0])**2+( Y_new[1]-obj_goal[1])**2 + ( Y_new[2]-obj_goal[2])**2) ** 0.5\n\n        k = 2\n        reward = np.e**(- k*dist)\n\n\n        print ('action: ', action, 'obj_goal: ', obj_goal, 'Y_new: ', Y_new[0:3], 'dist: ', dist, 'reward: ', reward)\n\n        return Step(observation=next_observation, reward=reward, done=done)\n\n    def render(self):\n        print('current state:', self._state)\n","sub_path":"rllab/envs/motoman_push_env.py","file_name":"motoman_push_env.py","file_ext":"py","file_size_in_byte":3411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"517914047","text":"import json\nimport pandas as pd \nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom scipy.stats import norm\nimport seaborn as sns\n\ndef bar_graph(cuisine_name,dishes_count):\n   # salary data derived from https://datavizpyr.com/density-plots-with-pandas-in-python/\n    # stackoverflow_salary_file = \"https://raw.githubusercontent.com/datavizpyr/data/master/SO_data_2019/2019_Stack_Overflow_Survey_Education_Salary_US.tsv\"\n    # # load the salary data \n    # print(stackoverflow_salary_file)\n    df=pd.DataFrame(\n    {\n    'Cusine':cuisine_name,\n    'No of Recipies':dishes_count,\n    })\n    # print(df)\n    # salary=df.to_csv(sep=\"\\t\")\n    # print(salary)\n    salary_wide=df.pivot(columns='Cusine',values='No of Recipies')\n    salary_wide.head()\n    print(salary_wide.head())\n    salary_wide.plot.density(figsize=(8,6),xlim=(0,80),linewidth=10,fontsize=6)\n    plt.xlabel(\"Recipe Size\") \n    plt.ylabel(\"No. of Recipe\") \n    plt.title(\"Cusine Details\")\n    plt.show()\n    # plt.savefig(\"multiple_density_plots_with_Pandas_Python.jpg\")\n\nf=open('train.json')\ndata=json.load(f)\ncuisine_name, dishes_count=[],[]\nfor i in range(len(data)):\n    cuisine_name.append(data[i]['cuisine'])\n    dishes_count.append(len(data[i]['ingredients']))\nbar_graph(cuisine_name,dishes_count)\n\n\n\n\n\n    \n","sub_path":"plot2.py","file_name":"plot2.py","file_ext":"py","file_size_in_byte":1286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"116086008","text":"from django.conf.urls import patterns, include, url\nfrom django.http import HttpResponseRedirect\n\n# Uncomment the next two lines to enable the admin:\nfrom django.contrib import admin\nadmin.autodiscover()\nfrom class_based_auth_views.views import *\n\nurlpatterns = patterns('',\n    # Examples:\n    # url(r'^$', 'upload.views.home', name='home'),\n    url(r'^$', LoginView.as_view(),name='login'),\n    url(r'^indoor_tracking/', IndoorTrackingView.as_view()),\n    url(r'^outdoor_tracking/', OutdoorTrackingView.as_view()),\n    url(r'^wifiposition/',  WifiPositionView.as_view()),\n    url(r'^indoorposition/', IndoorPositionView.as_view()),\n    url(r'^gpsposition/', GPSPositionView.as_view()),\n    url(r'^register/', RegisterView.as_view()),\n    url(r'^contact_number/(?P<tag_id>\\d{8})', ContactNumber.as_view(), name=\"12345678\"),\n\turl(r'^on_or_off/(?P<tag_id>\\d{8})/$' , On_or_Off.as_view()),\n    #url(r'^$', lambda x: HttpResponseRedirect('/upload/new/')),\n    url(r'^upload/', include('fileupload.urls')),\n    # Uncomment the admin/doc line below to enable admin documentation:\n    # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n    # Uncomment the next line to enable the admin:\n    url(r'^admin/', include(admin.site.urls)),\n    url(r'^accounts/', include('userprofile.urls')),\n    url(r'^logout/', LogoutView.as_view()),\n)\n\nimport os\nurlpatterns += patterns('',\n    (r'^media/(.*)$', 'django.views.static.serve', {'document_root': os.path.join(os.path.abspath(os.path.dirname(__file__)), 'media')}),\n)\n","sub_path":"urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"92521117","text":"import numpy as np\nfrom matplotlib import pyplot\n\nfrom skpro.metrics import log_loss\n\n\ndef plot_performance(y_test, y_pred):\n    fig, ax1 = pyplot.subplots()\n\n    ax1.plot(y_test, y_test, 'g.', label=u'Optimum')\n    sigma = np.std(y_pred)\n    ax1.errorbar(y_test, y_pred.point(), yerr=sigma, label=u'Predictions', fmt='b.', ecolor='r', linewidth=0.5)\n    ax1.set_ylabel('Predicted $y_{pred}$')\n    ax1.set_xlabel('Correct label $y_{true}$')\n    ax1.legend(loc='best')\n\n    losses = log_loss(y_test, y_pred, sample=False)\n    ax2 = ax1.twinx()\n    overall = \"{0:.2f}\".format(np.mean(losses)) + \" +/- {0:.2f}\".format(np.std(losses))\n    ax2.set_ylabel('Loss')\n    ax2.plot(y_test, losses, 'y_', label=u'Loss: ' + overall)\n    ax2.tick_params(colors='y')\n    ax2.legend(loc=1)\n\n    pyplot.show()\n\n    # pyplot.savefig('../docs/_static/exported.png', transparent=True, bbox_inches='tight')\n","sub_path":"examples/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"386320568","text":"#Input: (2 -> 4 -> 3) + (5 -> 6 -> 4)\n#Output: 7 -> 0 -> 8\n#Explanation: 342 + 465 = 807.\n\ndef addTwoNumbers(self, l1, l2):\n    \"\"\"\n    :type l1: ListNode\n    :type l2: ListNode\n    :rtype: ListNode\n    \"\"\"\n    listsum = 0\n    i = 0 # the decimal place indicator\n    \n    # while there is still data in either list. while(not(A and B)) == while(not(A) or not(B))\n    while(not(l1 is None and l2 is None)):\n        # the number of iterations represents the ith decimal place so we always add 10 ** i \n        # check which lists still have info and add\n        if(l1 is None): \n            listsum+=(10**i)*(l2.val)\n            l2=l2.next\n        elif(l2 is None):\n            listsum+=(10**i)*(l1.val)\n            l1=l1.next\n        else:\n            listsum+=(10**i)*(l1.val+l2.val)\n            l1=l1.next\n            l2=l2.next\n        # we are moving up a decimal place\n        i+=1\n    \n    # the output expects a linked list so we reconstruct our result into the proper format.\n    # 807 becomes 7 -> 0 -> 8\n    return([int(d) for d in reversed(str(listsum))])\n","sub_path":"2_Add_Two_Numbers.py","file_name":"2_Add_Two_Numbers.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"557847718","text":"import os\r\nfrom tweepy import OAuthHandler\r\nfrom tweepy import API\r\nfrom tweepy import Cursor\r\nimport sys\r\n\r\n# δηλωνουμε τα κλειδια που μας παρεχει το API\r\nconsumer_key= 'yourkeyhere'\r\nconsumer_secret= 'yourkeyhere'\r\naccess_token= 'yourkeyhere'\r\naccess_token_secret= 'yourkeyhere'\r\n\r\n\r\n# κανουμε το authentication του API με τα κλειδια που εχουμε\r\nauth = OAuthHandler(consumer_key, consumer_secret)\r\nauth.set_access_token(access_token, access_token_secret)\r\nauth_api = API(auth)\r\n\r\n\r\n# δινονται χρηστες προς εξεταση απο τον χρηστη και γινεται ελεγχος\r\naccount_list = []\r\nif (len(sys.argv) > 1):\r\n  account_list = sys.argv[1:]\r\nelse:\r\n  print(\"Please provide a list of usernames at the command line.\")\r\n  sys.exit(0)\r\n\r\n\r\n# αν δωθηκαν χρηστες\r\nif len(account_list) > 0:\r\n    sums = {}\r\n    max = -1\r\n    for target in account_list: # για κάθε χρήστη\r\n        sums[target] = 0\r\n        print(\"Getting data for \" + target)\r\n        item = auth_api.get_user(target)\r\n        # για κάθε ένα post, από τα τελευταία 50, που έχει κάνει ο χρήστης\r\n        for status in Cursor(auth_api.user_timeline, id=target).items(50):\r\n            post = status._json[\"text\"].split(\" \") # μετατρέπουμε το post σε λίστα λέξεων\r\n            # για καθε λεξη του post\r\n            for word in post:\r\n                # προσθέτουμε τον αριθμό των λέξεων στο άθροισμα του χρήστη\r\n                sums[target] += 1\r\n\r\n        # βρίσκουμε ποιος έχει χρησιμοποιήσει τις περισσότερες λέξεις\r\n        if sums[target] > max:\r\n            max = sums[target]\r\n\r\n    # εκτυπώνουμε τον χρήστη με τις περισσότερες λέξεις\r\n    for user in sums:\r\n        if sums[user] == max:\r\n            print(\"Ο χρήστης \"+user+\" έχει χρησιμοποιήσει τις περισσότερες λέξεις.\")\r\n            print(\"Συγκεκριμένα, έχει χρησιμοποιήσει\", sums[user], \"λέξεις.\")\r\n","sub_path":"7.py","file_name":"7.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"114207310","text":"from room import Room\nfrom player import Player\nfrom item import Item\n\nitems = {\n    \"sword\": Item(\"sword\", \"a sharp sword\"),\n    \"torch\": Item(\"torch\", \"a burning torch\"),\n    \"potion\": Item(\"potion\", \"a magic potion\"),\n    \"club\": Item(\"club\", \"a wooden club\"),\n    \"shield\": Item(\"shield\", \"a wooden shield\"),\n    \"apple\": Item(\"snake\", \"a ripe apple\"),\n    \"chest\": Item(\"chest\", \"a treasure test\"),\n    \"key\": Item(\"key\", \"a rusty key\"),\n    \"bomb\": Item(\"map\", \"a treasure map\"),\n    \"shoes\": Item('cheese', \"a pair of lightly used shoes\")\n}\n\n# Declare all the rooms\n\nroom = {\n    'outside': Room(\"Outside Cave Entrance\",\n                    \"North of you, the cave mount beckons\"),\n\n    'foyer': Room(\"Foyer\", \"\"\"Dim light filters in from the south. Dusty\npassages run north and east.\"\"\"),\n\n    'overlook': Room(\"Grand Overlook\", \"\"\"A steep cliff appears before you, falling\ninto the darkness. Ahead to the north, a light flickers in\nthe distance, but there is no way across the chasm.\"\"\"),\n\n    'narrow': Room(\"Narrow Passage\", \"\"\"The narrow passage bends here from west\nto north. The smell of gold permeates the air.\"\"\"),\n\n    'treasure': Room(\"Treasure Chamber\", \"\"\"You've found the long-lost treasure\nchamber! Sadly, it has already been completely emptied by\nearlier adventurers. The only exit is to the south.\"\"\"),\n}\n\n# Link rooms together\n\nroom['outside'].n_to = room['foyer']\nroom['foyer'].s_to = room['outside']\nroom['foyer'].n_to = room['overlook']\nroom['foyer'].e_to = room['narrow']\nroom['overlook'].s_to = room['foyer']\nroom['narrow'].w_to = room['foyer']\nroom['narrow'].n_to = room['treasure']\nroom['treasure'].s_to = room['narrow']\n\n\n#\n# Main\n#\n\n# Make a new player object that is currently in the 'outside' room.\nname = input('Input player name: ')\nplayer = Player(name, room[\"outside\"])\nprint(\"\\n===============\")\nprint(f\"Welcome, {player.name}!\")\n\n# Write a loop that:\n#\n# * Prints the current room name\n# * Prints the current description (the textwrap module might be useful here).\n# * Waits for user input and decides what to do.\n#\n# If the user enters a cardinal direction, attempt to move to the room there.\n# Print an error message if the movement isn't allowed.\nchoice = None\nmoved = True\nwhile choice not in ['q', 'quit']:\n    choice = input(\n        \"\\nMove (n, s, w, e, or quit)\")\n\n    if moved:\n        print(\"===============\\n\")\n        print(f\"{player.name} enters \\\"{player.room.name}\\\"\")\n        print(f\"{player.room.desc}\\n\")\n\n        if choice in ['n', 'N'] and hasattr(player.room, 'n_to'):\n            player.room = player.room.n_to\n        elif choice in ['s', 'S'] and hasattr(player.room, 's_to'):\n            player.room = player.room.s_to\n        elif choice in ['e', 'E'] and hasattr(player.room, 'e_to'):\n            player.room = player.room.e_to\n        elif choice in ['w', 'W'] and hasattr(player.room, 'w_to'):\n            player.room = player.room.w_to\n        elif choice in ['q', 'quit']:\n            print(f\"\\nThanks for playing!\\n\")\n            moved = False\n        else:\n            print(\n                f\"\\n!*****!\\nMove not allowed, please select again\\n!*****!\\n\")\n\n","sub_path":"src/adv.py","file_name":"adv.py","file_ext":"py","file_size_in_byte":3132,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"417849420","text":"import re\nimport logging\nimport requests\nimport datetime\nfrom collections import defaultdict\nimport json\nimport plecos\nfrom collections import defaultdict\nfrom pprint import pprint\n\n\nclass Asset:\n    def __init__(self, asset_series, sheet_api, col_num, simple_oep8_fields):\n        self.series = asset_series\n        self.name = self.series.name\n        # self.series = pd.Series(asset_dict)\n        self.worksheet_api = sheet_api\n        self.col_num = col_num\n        self.simple_oep8_fields = simple_oep8_fields\n        self.files = list()\n        self.links = list()\n        self.impute_indices()\n\n    @property\n    def process(self):\n        return self.check_true_false_field('process')\n\n    def get_files_dict_from_catalogue(self):\n        \"\"\"Given the catalog format for files and links, return a list of dicts\"\"\"\n        regex_field = r\"^files\\[\\d\\]:(url|index|contentType|checksum|checksumType|contentLength|encoding|compression|resourceId)$\"\n        sub_attribute_regex = r\"(url|index|contentType|checksum|checksumType|contentLength|encoding|compression|resourceId)$\"\n        return self.get_nested_attribute(regex_field, sub_attribute_regex)\n\n    def get_links_dict_from_catalogue(self):\n        \"\"\"Given the catalog format for files and links, return a list of dicts\"\"\"\n        regex_field = r\"^links\\[\\d\\]:(url|name|type)$\"\n        sub_attribute_regex = r\"(url|name|type)$\"\n        return self.get_nested_attribute(regex_field, sub_attribute_regex)\n\n    def create_files_from_list(self):\n        \"\"\"Given list of URLs, expand with indices\"\"\"\n        for i, file in enumerate(self.files):\n            \"files[{}]\".format(i)\n            pass\n\n    def impute_indices(self):\n        for field, val in self.get_fields(r\"^files\\[\\d\\]:url$\"):\n            if val:\n                res = re.findall('\\[(\\d)\\]', field)\n                assert len(res) == 1\n                this_index = int(res.pop())\n                this_index_field = \"files[{}]:index\".format(this_index)\n                # Start indexing at 1!!!\n                this_index+=1\n                self.series[this_index_field] = this_index\n                # self.series[this_index_field] = str(this_index)\n\n    def get_one_field(self, fld_regex):\n        \"\"\" Return a single field (index) by regex\n\n        :param fld_regex:\n        :return:\n        \"\"\"\n        res = [(field, val) for field, val in self.series.iteritems() if re.match(fld_regex, field)]\n        assert len(res) == 1\n        return res.pop()\n\n    def get_fields(self, regex):\n        \"\"\" Return a list of fields (index) by regex\n\n        :param regex:\n        :return:\n        \"\"\"\n        field_list = []\n        val_list = []\n        for field, val in self.series.iteritems():\n            if not field:\n                pass\n            elif  re.match(regex, field):\n                field_list.append(field)\n                val_list.append(val)\n        final_list =  list(zip(field_list,val_list))\n        return final_list\n\n\n        # return [(field, val) for field, val in self.series.iteritems() if re.match(regex, field)]\n\n    def check_true_false_field(self, flg_name):\n        \"\"\" Convert flag FALSE TRUE string to Boolean\n\n        :return:\n        \"\"\"\n        fld, val = self.get_one_field('^<{}>$'.format(flg_name))\n        if val == 'FALSE' or val == 0 or val == \"\":\n            return False\n\n        elif val == 'TRUE' or val == 1:\n            return True\n        else:\n            raise Exception(\"Couldn't interpret boolean value:\", val)\n\n    def check_field_OK(self, flg_name):\n        \"\"\" Check for OK, otherwise return false\n\n        :param flg_name:\n        :return:\n        \"\"\"\n        fld, val = self.get_one_field('^<{}>$'.format(flg_name))\n        if not val:\n            return False\n\n        if re.search('OK', val):\n            return True\n        else:\n            return False\n\n    def get_xy_coords(self, fld_name):\n        \"\"\" Return col,row of a target cell in integer format, from the index and header\n\n        :param fld_name:\n        :return:\n        \"\"\"\n        return self.col_num + 1, self.series.index.get_loc(fld_name) + 2\n\n    def check_urls(self, field_regex, status_output_field):\n        \"\"\"Select the URLs inside the matched cells. Ping for status. Write status.\n\n        :param field_regex:\n        :param status_output_field:\n        :return:\n        \"\"\"\n\n        # Get the links fields\n        # regex_field = r\"^links\\[\\d\\]:url$\"\n        link_urls = self.get_fields(field_regex)\n        links = [link for link in link_urls if link[1]]\n\n        logging.info(\"Checking {} {} links\".format(len(links), field_regex))\n\n        errors = list()\n        for field, url in links:\n            if url:\n                try:\n                    response = requests.head(url)\n                    # r = requests.get(url)\n                    # requests.head(url)\n                    # print(response)\n                    logging.debug(\"URL returns {}\".format(response))\n                    response.raise_for_status()\n                except requests.exceptions.RequestException as e:\n                    print(\"HEAD error! Attempting full download!\", e)\n                    try:\n                        response = requests.get(url, stream=True)\n                        response.raise_for_status()\n                    except:\n                        print(\"GET error:\", e)\n                        errors.append(url)\n                        continue\n                if not response:\n                    errors.append(url)\n        if errors:\n            logging.info(\"Errors with links, updating catalog sheet.\".format())\n            status_msg = \"\\n\".join([\"Errors with following URLs:\"] + errors)\n        else:\n            logging.info(\"Links OK, updating catalog sheet.\".format())\n            now = datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ')\n            status_msg = \"OK \" + now\n\n        coords = self.get_xy_coords(status_output_field)\n        self.worksheet_api.write_cell(coords, status_msg)\n\n    def get_files_content_lengths(self):\n        regex_field = r\"^files\\[\\d\\]:url$\"\n        file_urls = self.get_fields(regex_field)\n        # links = [link for link in link_urls if link[1]]\n\n        logging.info(\"Checking {} links\".format(len(file_urls)))\n\n        errors = list()\n        for field, url in links:\n            if url:\n                try:\n                    response = requests.head(url)\n                except:\n                    errors.append(url)\n                    continue\n                if not response:\n                    errors.append(url)\n\n    def get_nested_attribute(self, regex_field, sub_attribute_regex):\n        \"\"\"Return a list of dictionaries holding the main root attribute and sub attributes\n        \"\"\"\n        this_attrib_group = defaultdict(dict)\n\n        flds = self.get_fields(regex_field)\n        for fld, val in flds:\n            if val:\n                num = re.findall(r'\\d+', fld)\n                assert len(num) == 1\n                num = int(num.pop())\n                attr = re.findall(sub_attribute_regex, fld)\n                assert len(attr) == 1, \"sub_field_regex:{} fld:{}\".format(sub_attribute_regex, fld)\n                attr = attr.pop()\n                this_attrib_group[num][attr] = val\n\n        this_attrib_list = [this_attrib_group[k] for k in this_attrib_group]\n\n        return this_attrib_list\n\n    def build_metadata(self):\n        \"\"\" Script to build a metadata JSON from the loaded fields\n\n        :return:\n        \"\"\"\n        # Start the metadata object\n        metadata = dict()\n        metadata['base'] = dict()\n\n        metadata['base']['name'] = self.name\n\n        # Get the simple OEP8 fields\n        oep8_flds = [{fld: val} for fld, val in self.series.iteritems() if fld in self.simple_oep8_fields]\n\n        # Remove any fields which only have a blank string\n        oep8_flds_final = list()\n        for this_dict in oep8_flds:\n            this_l = list(this_dict.values())\n            assert len(this_l) == 1\n            this_val = this_l[0]\n            if this_val == None:\n                continue\n            elif this_val == \"\":\n                continue\n            else:\n                oep8_flds_final.append(this_dict)\n\n        _ = [metadata['base'].update(field) for field in oep8_flds_final]\n\n        # Get the nested files attribute\n        files = self.get_files_dict_from_catalogue()\n        metadata['base']['files'] = files\n\n        # Get the nested links attribute\n        links = self.get_links_dict_from_catalogue()\n        metadata['base']['links'] = links\n\n        # Convert categories and tags to list\n        if 'categories' in metadata['base']:\n            metadata['base']['categories'] = metadata['base']['categories'].split(\",\")\n        if 'tags' in metadata['base']:\n            metadata['base']['tags'] = metadata['base']['tags'].split(\",\")\n\n        # pprint(metadata['base'])\n        if isinstance(metadata['base']['checksum'], int):\n            metadata['base']['checksum'] = str(metadata['base']['checksum'])\n\n        # The price attribute must be a string\n        metadata['base']['price'] = str(metadata['base']['price'])\n\n        # Add Checksum\n        # TODO: Remove from plecos\n        # metadata['base']['checksum'] = '0'\n        # metadata['base']['dateCreated'] = '0'\n        # metadata['curation'] = dict()\n        # metadata['curation']['rating'] = 0\n        # metadata['curation']['numVotes'] = 0\n\n        # Escape newlines in description\n        if 'description' in metadata['base']:\n            metadata['base']['description'] = metadata['base']['description'].encode(\"unicode_escape\").decode(\"utf-8\")\n\n        # Convert newlines to commas\n        if 'author' in metadata['base']:\n            metadata['base']['author'] = \",\".join(metadata['base']['author'].split('\\n'))\n\n        logging.info(\"Built MetaData\".format())\n        return metadata\n\n    @property\n    def metadata_field(self):\n        return self.get_one_field('metadata out')\n\n    def write_metadata_json(self, metadata):\n        \"\"\" Write the JSON string to the sheet\n\n        :param metadata:\n        :return:\n        \"\"\"\n        target_field = r\"<metadata out>\"\n        json_out = json.dumps(metadata, indent=2)\n        coords = self.get_xy_coords(target_field)\n        self.worksheet_api.write_cell(coords, json_out)\n\n        status_field = r\"<flag generated metadata>\"\n        logging.info(\"Writing metadata JSON string\".format())\n        now = datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ')\n        status_msg = \"OK \" + now\n        coords = self.get_xy_coords(status_field)\n        self.worksheet_api.write_cell(coords, status_msg)\n\n    def validate_and_update_sheet(self, metadata):\n        \"\"\" Run the dictionary against plecos validator, and then update the sheet\n\n        :param metadata:\n        :return:\n        \"\"\"\n        status_field = r\"<flag valid metadata>\"\n        if not plecos.is_valid_dict_local(metadata):\n            logging.info(\"Errors with metadata, updating catalog sheet.\".format())\n            errors = plecos.list_errors_dict_local(metadata)\n            error_summaries = list()\n            for err in errors:\n                err[1].message\n                path_str = [str(item) for item in err[1].path]\n                error_summaries.append(err[1].message + \" at /\" + \"/\".join(path_str))\n            # error_summaries = [err[1].message + \" at /\" + \"/\".join(err[1].path) for err in errors]\n            # errors[0][1].message\n            # err = errors[0]\n            # err[1].message + \" at /\" + \"/\".join(err[1].path)\n\n            status_msg = \"\\n\".join([\"Errors:\"] + error_summaries)\n        else:\n            logging.info(\"Metadata OK, updating catalog sheet.\".format())\n            now = datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ')\n            status_msg = \"OK \" + now\n\n        coords = self.get_xy_coords(status_field)\n        self.worksheet_api.write_cell(coords, status_msg)\n\n    def validate_metadata(self, metadata):\n        \"\"\" Run the dictionary against plecos validator\n\n        :param metadata:\n        :return:\n        \"\"\"\n\n        if not plecos.is_valid_dict(metadata):\n            return plecos.list_errors_dict_local(metadata)\n        else:\n            return True\n","sub_path":"catalog_manager/asset.py","file_name":"asset.py","file_ext":"py","file_size_in_byte":12194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"546701234","text":"from __future__ import unicode_literals\nfrom django import template\nfrom django.template.base import TemplateSyntaxError, kwarg_re\nfrom django.template.defaulttags import URLNode\nfrom affiliate.tools import add_affiliate_code\n\nregister = template.Library()\n\n\nclass AffiliateURLNode(URLNode):\n    def render(self, context, *args, **kwargs):\n        url = super(AffiliateURLNode, self).render(context, *args, **kwargs)\n        aid_code = context.get('affiliate_code', None)\n        if aid_code:\n            url = add_affiliate_code(url, aid_code)\n        return url\n\n\n@register.tag\ndef url_aff(parser, token):\n    bits = token.split_contents()\n    if len(bits) < 2:\n        raise TemplateSyntaxError(\"'%s' takes at least one argument\"\n                                  \" (path to a view)\" % bits[0])\n    try:\n        viewname = parser.compile_filter(bits[1])\n    except TemplateSyntaxError as exc:\n        exc.args = (exc.args[0] + \". \"\n                                  \"The syntax of 'url' changed in Django 1.5, see the docs.\"),\n        raise\n    args = []\n    kwargs = {}\n    asvar = None\n    bits = bits[2:]\n    if len(bits) >= 2 and bits[-2] == 'as':\n        asvar = bits[-1]\n        bits = bits[:-2]\n\n    if len(bits):\n        for bit in bits:\n            match = kwarg_re.match(bit)\n            if not match:\n                raise TemplateSyntaxError(\"Malformed arguments to url tag\")\n            name, value = match.groups()\n            if name:\n                kwargs[name] = parser.compile_filter(value)\n            else:\n                args.append(parser.compile_filter(value))\n\n    return AffiliateURLNode(viewname, args, kwargs, asvar)\n\n\n@register.simple_tag(takes_context=True)\ndef render_banner(context, affiliate, banner, autoescape=False):\n    img_html = affiliate.render_img(banner, context['request'],\n                                    autoescape=autoescape)\n    return img_html\n","sub_path":"affiliate/templatetags/affiliate_tags.py","file_name":"affiliate_tags.py","file_ext":"py","file_size_in_byte":1900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"538377809","text":"# -*- encoding: utf-8 -*-\nfrom utils import *\nfrom copy import deepcopy\nimport subprocess as sp\nimport pystache as ps\nimport numpy as np\nimport operator\nfrom functools import partial, reduce\nimport os.path\nimport sys\n\nSCRIPT_DIR = os.path.dirname(os.path.realpath(sys.argv[0]))\nTEMPLATE_FILENAME = os.path.join(SCRIPT_DIR, 'template_report.tex')\nGRAPHICS_TEMPLATE_FILENAME = os.path.join(SCRIPT_DIR, 'template_graphics.tex')\nTABLE_TEMPLATE_FILENAME = os.path.join(SCRIPT_DIR, 'template_table.tex')\n\nreport_template = fr(TEMPLATE_FILENAME)\ngraphics_template = fr(GRAPHICS_TEMPLATE_FILENAME)\ntable_template = fr(TABLE_TEMPLATE_FILENAME)\n\ndef cb(s):\n    return '{' + s + '}'\n\nclass Report:\n    def __init__(self, ast, template):\n        self.ast = ast\n        self.template = template\n\n    def render(self):\n        return ps.render(self.template, self.ast.render())\n\nHLEVELS = ['document', 'chapter', 'section', 'subsection', 'subsubsection', 'paragraph', 'subparagraph', 'content']\nHLEVELS_ENUM = enumerate(HLEVELS)\nHLEVELS_MAP = { name: level for level, name in HLEVELS_ENUM }\n\nclass ReportAst:\n    def __init__(self, node_type, name=False):\n        self.name = name\n        self.node_type = node_type\n        self.children = []\n\n    def attach(self, child):\n        if HLEVELS_MAP[child.node_type] <= HLEVELS_MAP[self.node_type]:\n            raise Exception('Incorrect node level')\n        else:\n            current = self\n            child_level = HLEVELS_MAP[child.node_type]\n            current_level = HLEVELS_MAP[current.node_type]\n            while child_level > current_level + 1:\n                nxt = ReportAst(HLEVELS[current_level+1])\n                current.children.append(nxt)\n                current = nxt \n                current_level = HLEVELS_MAP[current.node_type]\n            current.children.append(child)\n        return self\n    \n    def attach_copy(self, child):\n        return self.attach(deepcopy(child))\n\n    def render(self):\n        res = { }\n        if self.name:\n            res[self.node_type + '_name'] = cb(self.name)\n        else:\n            res[self.node_type + '_name'] = False\n        next_level = HLEVELS[HLEVELS_MAP[self.node_type]+1]\n        res[next_level + 's'] = [child.render() for child in self.children]\n        return res\n\n    def pdf(self, filename):\n        pdf(self, filename)\n\nclass TextContent:\n    def __init__(self, text=''):\n        self.text = text\n        self.node_type = 'content'\n\n    def render(self):\n        return { 'content': (self.text) }\n\nclass PlainContent:\n    def __init__(self, text=''):\n        self.text = text\n        self.node_type = 'content'\n\n    def render(self):\n        return { 'content': (self.text) }\n\n\nclass ImageContent:\n    def __init__(self, filename, title, position='t'):\n        self.filename = filename\n        self.title = title\n        self.position = position\n        self.node_type = 'content'\n\n    def render(self):\n        args = { \n                'position': self.position,\n                'attributes': '[width=\\\\linewidth]',\n                'filename': cb(self.filename),\n                'title': cb(self.title),\n                }\n\n        result = ps.render(graphics_template, args)\n        return { 'content': result }\n\nclass TableContent:\n    def __init__(self, data, title, position='h'):\n        self.data = data\n        self.node_type = 'content'\n        self.position = position\n        \n    def _get_shape(self, data):\n        try:\n            return data.shape()[:2]\n        except:\n            rows = len(data)\n            cols = max(map(len, data))\n            return (rows, cols)\n\n    def _none_line(self, line):\n        return reduce(operator.and_, map(partial(operator.is_, None), line), True)\n\n    def _crop_data(self):\n        filt = lambda d: list(filter(lambda x: not self._none_line(x), d))\n        setdata = lambda nd: object.__setattr__(self, 'data', nd)\n        setdata(filt(self.data))\n        setdata(np.transpose(filt(np.transpose(self.data))))\n\n    def _complete_data(self):\n        result = []\n        rows, cols = self._get_shape(self.data)\n        for row in self.data:\n            current = []\n            for cell in row:\n                current.append(None if cell is None else str(cell))\n            while len(current) < cols:\n                current.append(None)\n            result.append(current)\n        object.__setattr__(self, 'data', np.array(result))\n        self._crop_data()\n\n    def __setattr__(self, name, value):\n        object.__setattr__(self, name, value)\n        if name is 'data':\n            self._complete_data()\n\n    def __getattr__(self, name):\n        if name is 'rows':\n            return self.data.shape[0]\n        elif name is 'cols':\n            return self.data.shape[1]\n        elif name is 'shape':\n            return self.data.shape\n        return object.__getattr__(self, name)\n\n    def __getitem__(self, index):\n        if type(index) == type((0, 0)):\n            if type(self.data[index]) == MergedData:\n                return self.data[self.data[index].target]\n        return self.data[index] \n\n    def _merged_size(self, top, left):\n        bottom, right = self.shape\n        for row in range(top + 1, self.rows):\n            if self.data[row][left] is not None:\n                bottom = row\n                break\n        for col in range(left + 1, self.cols):\n            if not self._none_line(self.data[top:bottom, col]):\n                right = col\n                break\n        return (bottom - top, right - left)\n             \n    def _render_cell(self, row, col):\n        def multirow(nrows, text, spec='*'):\n            if nrows > 1:\n                return '\\\\multirow{{{0}}}{{{1}}}{{{2}}}'.format(nrows, spec, text)\n            else: return text\n\n        def multicol(ncols, text, spec='|c|'):\n            if ncols > 1:\n                return '\\\\multicolumn{{{0}}}{{{1}}}{{{2}}}'.format(ncols, spec, text)\n            else: return text\n\n        if self.data[row][col] is None:\n            return ''\n        rows, cols = self._merged_size(row, col)\n\n        for r in range(row, row + rows):\n            for c in range(col, col + cols):\n                if self.data[r][c] is None:\n                    self.data[r][c] = ()\n\n        return multicol(cols, multirow(rows, self.data[row][col]))\n\n    def render(self):\n        lines = []\n        for i in range(self.rows):\n            current = []\n            for j in range(self.cols):\n                current.append(self._render_cell(*(i, j)))\n            current = filter(lambda x: x is not (), current)\n            lines.append({ 'line': '&'.join(current), 'afterline': '\\\\hline'})\n\n        top = '\\\\hline'\n        bottom = ''\n        colspec = '|' + '|'.join(['c'] * self.data.shape[1]) + '|'\n        args = { \n                'column_spec': cb(colspec),\n                'position' : self.position,\n                'top' : top,\n                'bottom' : bottom,\n                'lines' : lines,\n                }\n        rendered_table = ps.render(table_template, args)\n        \n        for row in range(self.rows):\n            for col in range(self.cols):\n                if self.data[row][col] is ():\n                    self.data[row][col] = None\n\n        return { 'content' : rendered_table }\n\ndef pdf(document, filename):\n    fw(filename + '.tex', ps.render(report_template, document.render()))\n    sp.call(['xelatex', filename + '.tex'])\n    sp.call(['xelatex', filename + '.tex'])\n\n#a = TableContent([[None, 1, 2], [None, 3, 4, 5], [None, 1], [None, None]], 'lol')\n#print(a.data)\n\n","sub_path":"report.py","file_name":"report.py","file_ext":"py","file_size_in_byte":7517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"620475311","text":"\nfrom delphin.mrs import semi\n\ndef test_variables(tmpdir):\n    p = tmpdir.join('a.smi')\n    p.write('variables:\\n'\n            '  u.\\n'\n            '  i < u.\\n'\n            '  e < i : PERF bool, TENSE tense.')\n    s = semi.load(str(p))\n    assert len(s.variables) == 3\n    assert all([v in s.variables for v in 'uie'])\n    assert len(s.variables['u'].supertypes) == 0\n    assert len(s.variables['i'].supertypes) == 1\n    assert len(s.variables['e'].supertypes) == 1\n    assert s.variables['u'].properties == {}\n    assert s.variables['i'].properties == {}\n    assert 'PERF' in s.variables['e'].properties\n    assert 'TENSE' in s.variables['e'].properties\n\ndef test_properties(tmpdir):\n    p = tmpdir.join('a.smi')\n    p.write('properties:\\n'\n            '  bool.\\n'\n            '  + < bool.\\n'\n            '  - < bool.\\n')\n    s = semi.load(str(p))\n    assert len(s.properties) == 3\n    assert all([x in s.properties for x in ('bool', '+', '-')])\n    assert len(s.properties['bool'].supertypes) == 0\n    assert len(s.properties['+'].supertypes) == 1\n    assert len(s.properties['-'].supertypes) == 1\n\ndef test_roles(tmpdir):\n    p = tmpdir.join('a.smi')\n    p.write('roles:\\n'\n            '  ARG0 : i.\\n'\n            '  CARG : string.')\n    s = semi.load(str(p))\n    assert len(s.roles) == 2\n    assert all([r in s.roles for r in ('ARG0', 'CARG')])\n    assert s.roles['ARG0'].rargname == 'ARG0'\n    assert s.roles['ARG0'].value == 'i'\n    assert s.roles['CARG'].rargname == 'CARG'\n    assert s.roles['CARG'].value == 'string'\n\ndef test_predicates(tmpdir):\n    p = tmpdir.join('a.smi')\n    p.write('predicates:\\n'\n            '  _the_q < existential_q.\\n'\n            '  _predicate_n_1 : ARG0 x { IND + }.\\n'\n            '  _predicate_v_of : ARG0 e, ARG1 i, ARG2 p, [ ARG3 i ].\\n'\n            '  _predominant_a_1 : ARG0 e, ARG1 e.\\n'\n            '  _predominant_a_1 : ARG0 e, ARG1 p.')\n    s = semi.load(str(p))\n    assert len(s.predicates) == 4\n    assert all([x in s.predicates for x in ('_the_q', '_predicate_n_1',\n                                            '_predicate_v_of',\n                                            '_predominant_a_1')])\n    assert len(s.predicates['_the_q'].supertypes) == 1\n    assert len(s.predicates['_predicate_n_1'].supertypes) == 0\n    assert len(s.predicates['_predicate_v_of'].supertypes) == 0\n    assert len(s.predicates['_predominant_a_1'].supertypes) == 0\n    assert len(s.predicates['_the_q'].synopses) == 0\n    assert len(s.predicates['_predicate_n_1'].synopses) == 1\n    assert len(s.predicates['_predicate_v_of'].synopses) == 1\n    assert len(s.predicates['_predominant_a_1'].synopses) == 2\n\ndef test_include(tmpdir):\n    a = tmpdir.join('a.smi')\n    b = tmpdir.join('b.smi')\n    tmpdir.mkdir('sub')\n    c = tmpdir.join('sub', 'c.smi')\n    d = tmpdir.join('sub', 'd.smi')\n    a.write('predicates:\\n'\n            '  abstract_q : ARG0 x, RSTR h, BODY h.\\n'\n            '  can_able.\\n'\n            '  _able_a_1 < can_able.\\n'\n            'include: b.smi\\n'\n            'include: sub/c.smi')\n    b.write('predicates:\\n'\n            '  existential_q < abstract_q.\\n'\n            '  _able_a_1 : ARG0 e, ARG1 p.')\n    c.write('predicates:\\n'\n            '  universal_q < abstract_q\\n'\n            '  _able_a_1 : ARG0 e, ARG1 i, ARG2 h.\\n'\n            'include: d.smi')\n    d.write('variables:\\n'\n            '  u.\\n'\n            '  i < u.\\n'\n            'properties:\\n'\n            '  tense.\\n'\n            '  pres < tense.\\n'\n            'roles:\\n'\n            '  ARG0 : i.')\n    s = semi.load(str(a))\n    assert len(s.variables) == 2\n    assert len(s.properties) == 2\n    assert len(s.roles) == 1\n    assert 'abstract_q' in s.predicates\n    assert 'existential_q' in s.predicates\n    assert 'can_able' in s.predicates\n    assert '_able_a_1' in s.predicates\n    assert 'can_able' in s.predicates['_able_a_1'].supertypes\n    assert len(s.predicates['_able_a_1'].synopses) == 2\n\ndef test_comments(tmpdir):\n    p = tmpdir.join('a.smi')\n    p.write('; comment\\n'\n            'variables:\\n'\n            '  ; comment\\n'\n            '  u.\\n'\n            '  ; x < u.\\n'\n            '  i < u.\\n'\n            '  e < i : PERF bool, TENSE tense.')\n    s = semi.load(str(p))\n    assert len(s.variables) == 3\n    assert 'x' not in s.variables\n\ndef test_to_dict(tmpdir):\n    p = tmpdir.join('a.smi')\n    p.write('variables:\\n'\n            '  e < i : TENSE tense.\\n'\n            'properties:\\n'\n            '  tense.\\n'\n            '  pres < tense.\\n'\n            'roles:\\n'\n            '  ARG0 : i.\\n'\n            '  ARG1 : u.\\n'\n            'predicates:\\n'\n            '  _the_q < existential_q.\\n'\n            '  _predicate_n_1 : ARG0 x { IND + }.\\n'\n            '  _predicate_v_of : ARG0 e, ARG1 i, ARG2 p, [ ARG3 i ].\\n'\n            '  _predominant_a_1 : ARG0 e, ARG1 e.\\n'\n            '  _predominant_a_1 : ARG0 e, ARG1 p.')\n    s = semi.load(str(p))\n    assert s.to_dict() == {\n        'variables': {\n            'e': {'type': 'e', 'parents': ['i'], 'properties': {'TENSE': 'tense'}}\n        },\n        'properties': {\n            'tense': {'type': 'tense', 'parents': []},\n            'pres': {'type': 'pres', 'parents': ['tense']}\n        },\n        'roles': {\n            'ARG0': {'rargname': 'ARG0', 'value': 'i'},\n            'ARG1': {'rargname': 'ARG1', 'value': 'u'}\n        },\n        'predicates': {\n            '_the_q': {\n                'predicate': '_the_q', 'parents': ['existential_q'],\n                'synopses': []\n            },\n            '_predicate_n_1': {\n                'predicate': '_predicate_n_1', 'parents': [],\n                'synopses': [\n                    [{'rargname': 'ARG0', 'value': 'x', 'properties': {'IND': '+'}}]\n                ]\n            },\n            '_predicate_v_of': {\n                'predicate': '_predicate_v_of', 'parents': [],\n                'synopses': [\n                    [\n                        {'rargname': 'ARG0', 'value': 'e'},\n                        {'rargname': 'ARG1', 'value': 'i'},\n                        {'rargname': 'ARG2', 'value': 'p'},\n                        {'rargname': 'ARG3', 'value': 'i', 'optional': True}\n                    ]\n                ]\n            },\n            '_predominant_a_1': {\n                'predicate': '_predominant_a_1', 'parents': [],\n                'synopses': [\n                    [\n                        {'rargname': 'ARG0', 'value': 'e'},\n                        {'rargname': 'ARG1', 'value': 'e'}\n                    ],\n                    [\n                        {'rargname': 'ARG0', 'value': 'e'},\n                        {'rargname': 'ARG1', 'value': 'p'}\n                    ]\n                ]\n            }\n        }\n    }\n\ndef test_from_dict(tmpdir):\n    p = tmpdir.join('a.smi')\n    p.write('variables:\\n'\n            '  e < i : TENSE tense.\\n'\n            'properties:\\n'\n            '  tense.\\n'\n            '  pres < tense.\\n'\n            'roles:\\n'\n            '  ARG0 : i.\\n'\n            '  ARG1 : u.\\n'\n            'predicates:\\n'\n            '  _the_q < existential_q.\\n'\n            '  _predicate_n_1 : ARG0 x { IND + }.\\n'\n            '  _predicate_v_of : ARG0 e, ARG1 i, ARG2 p, [ ARG3 i ].\\n'\n            '  _predominant_a_1 : ARG0 e, ARG1 e.\\n'\n            '  _predominant_a_1 : ARG0 e, ARG1 p.')\n    s1 = semi.load(str(p))\n    s2 = semi.SemI.from_dict({\n        'variables': {\n            'e': {'type': 'e', 'parents': ['i'], 'properties': {'TENSE': 'tense'}}\n        },\n        'properties': {\n            'tense': {'type': 'tense', 'parents': []},\n            'pres': {'type': 'pres', 'parents': ['tense']}\n        },\n        'roles': {\n            'ARG0': {'rargname': 'ARG0', 'value': 'i'},\n            'ARG1': {'rargname': 'ARG1', 'value': 'u'}\n        },\n        'predicates': {\n            '_the_q': {\n                'predicate': '_the_q', 'parents': ['existential_q'],\n                'synopses': []\n            },\n            '_predicate_n_1': {\n                'predicate': '_predicate_n_1', 'parents': [],\n                'synopses': [\n                    [{'rargname': 'ARG0', 'value': 'x', 'properties': {'IND': '+'}}]\n                ]\n            },\n            '_predicate_v_of': {\n                'predicate': '_predicate_v_of', 'parents': [],\n                'synopses': [\n                    [\n                        {'rargname': 'ARG0', 'value': 'e'},\n                        {'rargname': 'ARG1', 'value': 'i'},\n                        {'rargname': 'ARG2', 'value': 'p'},\n                        {'rargname': 'ARG3', 'value': 'i', 'optional': True}\n                    ]\n                ]\n            },\n            '_predominant_a_1': {\n                'predicate': '_predominant_a_1', 'parents': [],\n                'synopses': [\n                    [\n                        {'rargname': 'ARG0', 'value': 'e'},\n                        {'rargname': 'ARG1', 'value': 'e'}\n                    ],\n                    [\n                        {'rargname': 'ARG0', 'value': 'e'},\n                        {'rargname': 'ARG1', 'value': 'p'}\n                    ]\n                ]\n            }\n        }\n    })\n    assert s1.variables == s2.variables\n    assert s1.properties == s2.properties\n    assert s1.roles == s2.roles\n    assert s1.predicates == s2.predicates\n","sub_path":"tests/mrs_semi_test.py","file_name":"mrs_semi_test.py","file_ext":"py","file_size_in_byte":9257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"440456869","text":"# Nombre del bot\nBOT_NAME = 'trzpiders'\n\n# Modulo donde se encuentran las spiders\nSPIDER_MODULES = ['trzpiders.spiders']\nNEWSPIDER_MODULE = 'trzpiders.spiders'\n\n# Usamos el agente usuario de google\nUSER_AGENT = 'Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)'\n\n# Obedezco las reglas del bot\nROBOTSTXT_OBEY = True\n\n# Delay entre descargas\nDOWNLOAD_DELAY = 1\n","sub_path":"Back/Searcher/Config/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"70680554","text":"from numpy import *\n\nimport numpy.random as random\n\n\ndef getLyricists(filename, shownum):\n    \"\"\"\n    获取作词者的数据集\n    :param filename:\n    :param shownum:\n    :return:\n    \"\"\"\n    fr = open(filename)\n    lyricist = []\n    yArr = []\n    lines = fr.readlines()\n    i = 0\n    while i < shownum:\n        line = lines[i]\n        docArr = line.strip().split('\\t')\n        lyricist.append(docArr[0])\n        yArr.append(docArr[1])\n        i += 1\n    return lyricist, yArr\n\n\ndef getDataSet(filename, shownum):\n    fr = open(filename)\n    songLabel = []\n    yLabel = []\n    singerLabel = []\n    i = 0\n    lines = fr.readlines()\n\n    while i < shownum:\n        line = lines[i]\n        docArr = line.strip().split('\\t')\n        songLabel.append(docArr[0])\n        singerLabel.append(docArr[1])\n        yLabel.append(int(docArr[2]))\n        i += 1\n    return songLabel, singerLabel, yLabel\n\n\ndef getRandomColor():\n    \"\"\"\n    获取一个随机颜色\n    :return:\n    \"\"\"\n    colorres = \"#\"\n    colorFragment = list(arange(10).tolist())\n    colorFragment.extend(\"a\")\n    colorFragment.extend(\"b\")\n    colorFragment.extend(\"c\")\n    colorFragment.extend(\"d\")\n    colorFragment.extend(\"e\")\n    colorFragment.extend(\"f\")\n    for i in range(6):\n        colorres += str(colorFragment[int(16 * random.random())])\n    return colorres\n","sub_path":"music163/dataSet.py","file_name":"dataSet.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"214521447","text":"# 注：该策略仅供参考和学习，不保证收益。\n\n# !/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# 策略代码总共分为三大部分，1)PARAMS变量 2)initialize函数 3)handle_data函数\n# 请根据指示阅读。或者直接点击运行回测按钮，进行测试，查看策略效果。\n\n# 策略名称：ATR策略\n# 策略详细介绍：https://wequant.io/study/strategy.atr.html\n# 关键词：真实波幅、价格突破。\n# 方法：\n# 1)利用真实波幅来构造上下轨；\n# 2)价格突破上轨买入；\n# 3)价格突破下轨卖出。\n\nimport numpy as np\nimport talib\n\n\n# 阅读1，首次阅读可跳过:\n# PARAMS用于设定程序参数，回测的起始时间、结束时间、滑点误差、初始资金和持仓。\n# 可以仿照格式修改，基本都能运行。如果想了解详情请参考新手学堂的API文档。\nPARAMS = {\n    \"start_time\": \"2016-10-01 00:00:00\",  # 回测起始时间\n    \"end_time\": \"2017-07-01 00:00:00\",  # 回测结束时间\n    \"commission\": 0.002,  # 此处设置交易佣金\n    \"slippage\": 0.001,  # 此处设置交易滑点\n    \"account_initial\": {\"huobi_cny_cash\": 100000,\n                      \"huobi_cny_btc\": 0},  # 设置账户初始状态\n}\n\n\n# 阅读2，遇到不明白的变量可以跳过，需要的时候回来查阅:\n# initialize函数是两大核心函数之一（另一个是handle_data），用于初始化策略变量。\n# 策略变量包含：必填变量，以及非必填（用户自己方便使用）的变量\ndef initialize(context):\n    # 设置回测频率, 可选：\"1m\", \"5m\", \"15m\", \"30m\", \"60m\", \"4h\", \"1d\", \"1w\"\n    context.frequency = \"1d\"\n    # 设置回测基准, 比特币：\"huobi_cny_btc\", 莱特币：\"huobi_cny_ltc\", 以太坊：\"huobi_cny_eth\"\n    context.benchmark = \"huobi_cny_btc\"\n    # 设置回测标的, 比特币：\"huobi_cny_btc\", 莱特币：\"huobi_cny_ltc\", 以太坊：\"huobi_cny_eth\"\n    context.security = \"huobi_cny_btc\"\n\n    # 设定ATR的参数\n    # ATR算法回看天数\n    context.user_data.atr_period = 5\n    # 当前价格与之前1天的价格相比较\n    context.user_data.pre_period = 1\n    # 多头ATR的倍数\n    context.user_data.long_multi = 0.2\n    # 空头ATR的倍数\n    context.user_data.short_multi = 0.3\n\n    # 至此initialize函数定义完毕。\n\n\n# 阅读3，策略核心逻辑：\n# handle_data函数定义了策略的执行逻辑，按照frequency生成的bar依次读取并执行策略逻辑，直至程序结束。\n# handle_data和bar的详细说明，请参考新手学堂的解释文档。\ndef handle_data(context):\n    # 获取回看时间窗口内的历史数据\n    hist = context.data.get_price(context.security, count=context.user_data.atr_period + 1, frequency=context.frequency)\n    if len(hist.index) < context.user_data.atr_period + 1:\n        context.log.warn(\"bar的数量不足, 等待下一根bar...\")\n        return\n    # 收盘价\n    close = np.array(hist[\"close\"])\n    # 最高价\n    high = np.array(hist[\"high\"])\n    # 最低价\n    low = np.array(hist[\"low\"])\n\n    # 使用talib计算ATR\n    try:\n        # 获取最新的ATR值\n        atr = talib.ATR(high, low, close, timeperiod=context.user_data.atr_period)[-1]\n    except:\n        context.log.error(\"计算ATR时出现错误...\")\n        return\n\n    # 获取最新价格\n    current_price = context.data.get_current_price(context.security)\n    # 获取context.user_data.pre_period个bar前的价格\n    prev_price = close[-(context.user_data.pre_period + 1)]\n    # 计算上下轨\n    upper = prev_price + context.user_data.long_multi * atr\n    lower = prev_price - context.user_data.short_multi * atr\n\n    context.log.info(\"当前价格=%s元, 上轨=%s元, 下轨=%s元\" % (current_price, upper, lower))\n\n    # 如果当前价格比之前价格低1个ATR，产生卖出信号\n    if current_price < lower:\n        context.log.info(\"价格超过了下轨，产生卖出信号\")\n        # 若持有仓位，则全仓卖出\n        if context.account.huobi_cny_btc >= HUOBI_CNY_BTC_MIN_ORDER_QUANTITY:\n            context.log.info(\"正在卖出 %s\" % context.security)\n            context.log.info(\"卖出数量为 %s\" % context.account.huobi_cny_btc)\n            context.order.sell(context.security, quantity=str(context.account.huobi_cny_btc))\n        else:\n            context.log.info(\"仓位不足，无法卖出\")\n    # 如果当前价格比之前价格高1个ATR，产生买入信号\n    elif current_price > upper:\n        context.log.info(\"价格超过了上轨，产生买入信号\")\n        # 若持有现金，则全仓买入\n        if context.account.huobi_cny_cash >= HUOBI_CNY_BTC_MIN_ORDER_CASH_AMOUNT:\n            context.log.info(\"正在买入 %s\" % context.security)\n            context.log.info(\"下单金额为 %s 元\" % context.account.huobi_cny_cash)\n            context.order.buy(context.security, cash_amount=str(context.account.huobi_cny_cash))\n        else:\n            context.log.info(\"现金不足，无法下单\")\n    else:\n        context.log.info(\"无交易信号，进入下一根bar\")","sub_path":"ATR-指标策略.py","file_name":"ATR-指标策略.py","file_ext":"py","file_size_in_byte":5050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"638472893","text":"import tkinter as tk\nfrom random import shuffle\nfrom typing import Any\nfrom tkinter.messagebox import showinfo, showerror\n\ncolors={\n    1:'#62a832',\n    2:'#32a896',\n    3:'#327da8',\n    4:'#1f668f',\n    5:'blue',\n    6:'#6d32a8',\n    7:'purple',\n    8:'red'\n}\n\n\nclass MyButton(tk.Button):\n\n    def __init__(self, master, x, y, number=0, *args, **kwargs):\n        super(MyButton, self).__init__(master, width=3, font='Calibri 15 bold', *args, **kwargs)\n        self.x = x\n        self.y = y\n        self.number = number\n        self.is_mine = False\n        self.count_bomb = 0\n        self.is_open = False\n\n    def __repr__(self):\n        return f'MyButton  {self.number} ( {self.x} : {self.y} ) - {self.is_mine}'\n\n\nclass Saper:\n    window = tk.Tk()\n    rows = 10\n    columns = 10\n    mines = 5\n    is_game_over = False\n    is_first_click = True\n\n    def __init__(self):\n        self.buttons = []\n\n        for i in range(Saper.rows + 2):\n            temp = []\n            for j in range(Saper.columns + 2):\n                btn = MyButton(Saper.window, x=i, y=j)\n                btn.config(command=lambda button=btn: self.click(button))\n                btn.bind('<Button-3>', self.right_click)\n                temp.append(btn)\n\n            self.buttons.append(temp)\n        print('Start game!')\n\n\n    def right_click(self, event):\n        if Saper.is_game_over:\n            return None\n        cur_btn = event.widget\n        if cur_btn['state'] == 'normal':\n            cur_btn['state'] == 'disabled'\n            cur_btn['text'] = '🚩'\n            #cur_btn['disabledforeground'] = 'green'\n        elif cur_btn['text'] == '🚩':\n            cur_btn['text'] = ''\n            cur_btn['state'] = 'normal'\n\n\n    def click(self, clicked_button: MyButton):\n        print(clicked_button)\n        if Saper.is_game_over:\n            return None\n\n        if Saper.is_first_click:\n            self.insert_mines(clicked_button.number)\n            self.count_mines_in_buttons()\n            self.print_buttons()\n            Saper.is_first_click = False\n\n        if clicked_button.is_mine:\n            clicked_button.config(text='*', background='red', disabledforeground='black')\n            clicked_button.is_open = True\n            Saper.is_game_over = True\n            showinfo('Game over', 'You lost!!!')\n            for i in range(1, Saper.rows + 1):\n                for j in range(1, Saper.columns + 1):\n                    btn = self.buttons[i][j]\n                    if btn.is_mine:\n                        btn['text'] = '*'\n        else:\n            color = colors.get(clicked_button.count_bomb, 'black')\n            if clicked_button.count_bomb:\n                clicked_button.config(text=clicked_button.count_bomb, disabledforeground=color)\n                clicked_button.is_open = True\n            else:\n                #clicked_button.config(text='', disabledforeground=color)\n                self.breadth_first_search(clicked_button)\n        clicked_button.config(state='disabled')\n        clicked_button.config(relief=tk.SUNKEN)\n\n\n    def breadth_first_search(self, btn:MyButton):\n        queue = [btn]\n        while queue:\n            cur_btn = queue.pop()\n            color = colors.get(cur_btn.count_bomb, 'black')\n            if cur_btn.count_bomb:\n                cur_btn.config(text=cur_btn.count_bomb, disabledforeground=color)\n            else:\n                cur_btn.config(text='', disabledforeground=color)\n            cur_btn.is_open = True\n            cur_btn.config(state='disabled')\n            cur_btn.config(relief=tk.SUNKEN)\n\n            if cur_btn.count_bomb == 0:\n                x, y = cur_btn.x, cur_btn.y\n                for dx in [-1, 0, 1]:\n                    for dy in [-1, 0, 1]:\n                        #if not abs(dx - dy) == 1:\n                            #continue\n                        next_btn = self.buttons[x+dx][y+dy]\n                        if not next_btn.is_open\\\n                                and 1 <= next_btn.x <= Saper.rows\\\n                                and 1 <= next_btn.y <= Saper.columns\\\n                                and next_btn not in queue:\n                            queue.append(next_btn)\n\n\n    def reload(self):\n        [child.destroy() for child in self.window.winfo_children()]\n        self.__init__()\n        self.create_widgets()\n        Saper.is_first_click = True\n        Saper.is_game_over = False\n\n\n    def change_set(self, row: tk.Entry, col: tk.Entry, bomb: tk.Entry):\n        try:\n            int(row.get()), int (col.get()), int (bomb.get())\n        except ValueError:\n            showerror('Ошибка', 'Вы ввели неправильное значение!')\n            return\n\n\n        Saper.rows = int(row.get())\n        Saper.columns = int(col.get())\n        Saper.mines = int(bomb.get())\n        self.reload()\n\n\n    def create_set_win(self):\n        win_set = tk.Toplevel(self.window)\n        win_set.wm_title('Настройки игры')\n        tk.Label(win_set, text='Количество строк поля').grid(row=0, column=0)\n        row_entry = tk.Entry(win_set)\n        row_entry.insert(0, Saper.rows)\n        row_entry.grid(row=0, column=1, padx=30, pady=30)\n        tk.Label(win_set, text='Количество колонок поля').grid(row=1, column=0)\n        col_entry = tk.Entry(win_set)\n        col_entry.insert(0, Saper.columns)\n        col_entry.grid(row=1, column=1, padx=30, pady=30)\n        tk.Label(win_set, text='Количество мин на поле').grid(row=2, column=0)\n        bomb_entry = tk.Entry(win_set)\n        bomb_entry.insert(0, Saper.mines)\n        bomb_entry.grid(row=2, column=1, padx=30, pady=30)\n\n        # кнопка Применить настройки\n        save_set_btn = tk.Button(win_set, text='Применить настройки',\n                  command=lambda :self.change_set(row_entry, col_entry, bomb_entry))\n        save_set_btn.grid (row=3, column=0, columnspan=2, padx=30, pady=30)\n\n    def create_about_win(self):\n        win_about = tk.Toplevel(self.window)\n        win_about.wm_title('О программе...')\n        tk.Label(win_about, text='Программа: игра \\\"Сапер \\\"').grid(row=0, column=0, padx=10, pady=10)\n        tk.Label(win_about, text='Автор: Дьяченко Михаил').grid(row=1, column=0, padx=10, pady=10)\n        tk.Label(win_about, text='2022').grid(row=1, column=0, padx=10, pady=10)\n        ok_btn = tk.Button(win_about, text='ОК', command=win_about.destroy)\n        ok_btn.grid (row=2, column=0, columnspan=1, padx=10, pady=10)\n\n    def create_widgets(self):\n\n        # Меню игры\n        menubar = tk.Menu(self.window)\n        self.window.config(menu=menubar)\n\n        set_menu = tk.Menu(menubar, tearoff=0)\n        set_menu.add_command(label='Играть', command=self.reload)\n        set_menu.add_command(label='Настройки', command=self.create_set_win)\n        set_menu.add_command(label='О программе', command=self.create_about_win)\n        set_menu.add_command(label='Выход', command=self.window.destroy)\n        menubar.add_cascade(label='Меню', menu=set_menu)\n\n        # Панель\n\n        # Создание клеток\n        count =1\n        for i in range(1, Saper.rows + 1):\n            for j in range(1, Saper.columns + 1):\n                btn = self.buttons[i][j]\n                btn.number = count\n                btn.grid(row=i, column=j, stick='NWES')\n                count += 1\n\n        for i in range(1, Saper.rows + 1):\n            tk.Grid.rowconfigure(self.window, i, weight=1)\n        for i in range(1, Saper.columns + 1):\n            tk.Grid.columnconfigure(self.window, i, weight=1)\n\n    def open_all_buttons(self):\n        for i in range(Saper.rows + 2):\n            for j in range(Saper.columns + 2):\n                btn = self.buttons[i][j]\n                if btn.is_mine:\n                    btn.config(text='*', background='red', disabledforeground='black')\n\n                elif btn.count_bomb in colors:\n                    color=colors.get(btn.count_bomb, 'black')\n                    btn.config(text=btn.count_bomb, foreground=color)\n                else:\n                    btn.config(text=btn.count_bomb, foreground='black')\n                #btn.grid(row=i, column=j)\n\n    def start(self):\n        #self.open_all_buttons()\n        self.create_widgets()\n        Saper.window.mainloop()\n\n    def print_buttons(self):\n        for i in range(1, Saper.rows + 1):\n            for j in range(1, Saper.columns + 1):\n                btn = self.buttons[i][j]\n                if btn.is_mine:\n                    print(\"B\", end='')\n                else:\n                    print(btn.count_bomb, end='')\n            print()\n\n    @staticmethod\n    def get_mines_places(exclude_number: int):\n        indexes = list(range(1, Saper.rows * Saper.columns))\n        print(f'Исключаем кнопку номер {exclude_number}')\n        indexes.remove(exclude_number)\n        shuffle(indexes)\n        return indexes[:Saper.mines]\n\n    def insert_mines(self, number: int):\n        index_mines = self.get_mines_places(number)\n        print(index_mines)\n\n        for i in range(1, Saper.rows + 1):\n            for j in range(1, Saper.columns + 1):\n                btn = self.buttons[i][j]\n\n                if btn.number in index_mines:\n                    btn.is_mine = True\n\n\n    def count_mines_in_buttons(self):\n        for i in range(1, Saper.rows + 1):\n            for j in range(1, Saper.columns + 1):\n                btn = self.buttons[i][j]\n                count_bomb = 0\n                if not btn.is_mine:\n                    for row_dx in (-1, 0, 1):\n                        for col_dx in (-1, 0, 1):\n                            sosed = self.buttons[i + row_dx][j + col_dx]\n                            if sosed.is_mine:\n                                count_bomb += 1\n                btn.count_bomb = count_bomb\n\n\ngame = Saper()\ngame.start()\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"138215072","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom matplotlib.patches import Rectangle\nimport pyvo as vo\nimport json\n\n\n# connect to exoplanet archive and grab requested data\nservice = vo.dal.TAPService(\"https://exoplanetarchive.ipac.caltech.edu/TAP\")\nresults = service.search(\"select discoverymethod, st_spectype, pl_bmassj, pl_orbper, hostname, pl_radj from pscomppars \", maxrec=6000)\ntable = results.to_table()\n\n# save results to csv\ntable.to_pandas().to_csv(\"planets.csv\")\n\nmethods = []\nsptyps = []\nms = []\npers = []\nnames = []\nrads = []\n\n\n# read all data into usable arrays\nfor line in table:\n    methods.append(line[0])\n    sptyps.append(line[1])\n    ms.append(line[2])\n    pers.append(line[3])\n    names.append(line[4].strip())\n    rads.append(line[5])\n\n# get the unique methods and names for plotting later\nunique_methods = np.array(list(set(methods)))\nunique_names = set(names)\n\nprint(unique_methods)\nprint(len(sptyps))\nprint(len(unique_names))\nprint(len(rads))\n\n# save mass-period data, radius-period data to json and print to files\nmassperiod = {\n    \"x\": [],\n    \"y\": [],\n    \"meth\": [],\n}\n\nradperiod = {\n    \"x\": [],\n    \"y\": [],\n    \"meth\": [],\n}\n\nfor i in range(len(methods)):\n    if (np.isnan(float(pers[i])) or np.isnan(float(rads[i]))):\n        pass\n    else:\n        radperiod[\"x\"].append(float(pers[i]))\n        radperiod[\"y\"].append(float(rads[i]))\n        radperiod[\"meth\"].append(str(methods[i]))\n        \n    if (np.isnan(float(pers[i])) or np.isnan(float(ms[i]))):\n        pass\n    else:\n        massperiod[\"x\"].append(float(pers[i]))\n        massperiod[\"y\"].append(float(ms[i]))\n        massperiod[\"meth\"].append(str(methods[i]))\n        \nmassperiod[\"x\"] = np.log10(massperiod[\"x\"]).tolist()\nmassperiod[\"y\"] = np.log10(massperiod[\"y\"]).tolist()\n        \noutfile = open(\"massperiod.txt\", 'w')\noutfile.write(json.dumps(massperiod))\noutfile.close()\n\nradperiod[\"x\"] = np.log10(radperiod[\"x\"]).tolist()\n\noutfile = open(\"radperiod.txt\", 'w')\noutfile.write(json.dumps(radperiod))\noutfile.close()\n\n\n\n# convert possible text nans to np.nans\ndef FloatOrNaN(value):\n    try:\n        return float(value)\n    except:\n        print(\"nan\")\n        return np.nan\n\nms = np.array(list(map(FloatOrNaN, ms)))\npers = np.array(list(map(FloatOrNaN, pers)))\nrads = np.array(list(map(FloatOrNaN, rads)))\n\n\nprint(len(ms), len(pers), len(rads))\n\n# keep track of all the planets associated with each method\ntraces = {\n    'Radial Velocity': [[],[],[]],\n    'Transit': [[],[],[]],\n    'Imaging': [[],[],[]],\n    'Microlensing': [[],[],[]],\n    'Transit Timing Variations': [[],[],[]],\n    'Other': [[],[],[]],\n}\n\nfor i in range(len(ms)):\n    if methods[i] in traces.keys():\n        traces[methods[i]][0].append(ms[i])\n        traces[methods[i]][1].append(pers[i])\n        traces[methods[i]][2].append(rads[i])\n    else:\n        traces[\"Other\"][0].append(ms[i])\n        traces[\"Other\"][1].append(pers[i])\n        traces[\"Other\"][2].append(rads[i])\n\n\n# plot discovery method data for both mass-period and radius-period distributions\nfig, ax = plt.subplots(1, 2, figsize=(20, 8))\n\nplt.rcParams.update({'font.size': 22})\nfor i, trace in enumerate(traces):\n    clr = \"C{}\".format(i)\n    print(trace)\n    if trace == \"Other\":\n        clr = \"k\"\n    ax[0].plot(traces[trace][1], traces[trace][0], \".\", color=clr, markersize=7.5, rasterized=True)\n    ax[1].plot(traces[trace][1], traces[trace][2], \".\", color=clr, markersize=7.5, rasterized=True)\n\n    \nax[0].set_xscale(\"log\")\nax[1].set_xscale(\"log\")\n\nax[0].set_yscale(\"log\")\n\nplt.rcParams.update({'font.size': 16})\n\nax[1].legend(traces.keys())\n\nplt.rcParams.update({'font.size': 18})\nax[0].set_title(\"Mass-Period Distribution\")\nax[1].set_title(\"Radius-Period Distribution\")\n\nax[0].set_ylabel(r\"Planet Mass ($M_{Jup}$)\")\nax[1].set_ylabel(r\"Planet Radius ($R_{Jup}$)\")\n\n\nax[0].set_xlabel(r\"Orbit Period (days)\")\nax[1].set_xlabel(r\"Orbit Period (days)\")\nplt.show()\nfig.savefig(\"plots/exo_distributions.jpg\", dpi=200)\n\n\n\n\n# manually adding k-means clustering, gmm clustering data to the plots\n\n# collecting clustering results - mass/period\n\nk_clusters = np.array([\n    [ 0.66328766, -0.01388814],\n    [ 1.05602402, -1.8160057 ],\n    [ 2.90628407,  0.30247658]])\n\ngmm_clusters = np.array([\n    [0.6067256895249435, -0.07623793234424127],\n    [1.0720978097991372, -1.8111947736412568],\n    [2.8347784507785727, 0.33207763744569785],\n])\n\ngmm_sigmas = np.array([\n    [0.849133422059847, 1.620311317615842],\n    [0.9610563596605202, 1.6872729988646138],\n    [1.5184155302393645, 1.8861235819228443],\n])\n\ngmm_angles = [\n    9.104646920665129,\n    -60.372615080174526,\n    -62.84767274913065,\n]\n\n# plot data again\n\nfig, ax = plt.subplots(1, 2, figsize=(20, 8), sharey=False)\n\nplt.rcParams.update({'font.size': 22})\nfor i, trace in enumerate(traces):\n    clr = \"C{}\".format(i)\n    print(trace)\n    if trace == \"Other\":\n        clr = \"k\"\n    ax[0].plot(np.log10(traces[trace][1]), np.log10(traces[trace][0]), \".\", color=clr, markersize=7.5, rasterized=True)\n    \n    \nfor i in range(len(gmm_angles)):\n    ell = mpl.patches.Ellipse(gmm_clusters[i], gmm_sigmas[i][0], gmm_sigmas[i][1], 180.+gmm_angles[i], facecolor=\"none\", lw=2, edgecolor=\"k\", zorder=10000)\n    #ell.set_alpha(0.4)\n    ax[0].add_artist(ell)\nax[0].plot(k_clusters[:,0], k_clusters[:,1], '*w', markersize=20, mec=\"k\", mew=2, zorder=100000)\n\nplt.rcParams.update({'font.size': 16})\nax[0].legend(traces.keys())\nplt.rcParams.update({'font.size': 18})\nax[0].set_title(\"Mass-Period Cluster Data with Discovery Method\")\n\nax[0].set_ylabel(r\"log10 Mass ($M_{Jup}$)\")\nax[0].set_xlabel(r\"log10 Period (days)\")\n\n# collecting clustering results - radius/period\n\nk_clusters1 = np.array([\n    [0.54267886, 0.51717801],\n    [1.37098195, 0.26436091],\n    [2.91814335, 1.09737705]])\n\ngmm_clusters1 = np.array([\n    [2.8349004255431396, 1.1773400653646975],\n    [1.0239398533106556, 0.18318049295178485],\n    [0.5499215064182731, 1.2220817569144913],\n    [1.50445228187471, 0.487374695326412]\n])\n\ngmm_sigmas1 = np.array([\n    [0.1425113065403681, 1.8524362241247008],\n    [0.16817887590684852, 1.4523318764882924],\n    [0.6072747755617383, 0.8365866346864654],\n    [0.6885247915543548, 2.263229433570608]\n])\n\ngmm_angles1 = [\n    89.12189349029371,\n    -86.30361087077851,\n    33.842794399224914,\n    -81.37524545257513\n]\n\nplt.rcParams.update({'font.size': 22})\nfor i, trace in enumerate(traces):\n    clr = \"C{}\".format(i)\n    print(trace)\n    if trace == \"Other\":\n        clr = \"k\"\n    ax[1].plot(np.log10(traces[trace][1]), np.array(traces[trace][2]), \".\", color=clr, markersize=7.5, rasterized=True)\n    \n    \nfor i in range(len(gmm_angles1)):\n    ell = mpl.patches.Ellipse(gmm_clusters1[i], gmm_sigmas1[i][0], gmm_sigmas1[i][1], 180.+gmm_angles1[i], facecolor=\"none\", lw=2, edgecolor=\"k\", zorder=10000)\n    #ell.set_alpha(0.4)\n    ax[1].add_artist(ell)\nax[1].plot(k_clusters1[:,0], k_clusters1[:,1], '*w', markersize=20, mec=\"k\", mew=2, zorder=100000)\n\nplt.rcParams.update({'font.size': 16})\nax[1].legend(traces.keys())\nplt.rcParams.update({'font.size': 18})\nax[1].set_title(\"Radius-Period Cluster Data with Discovery Method\")\n\nax[1].set_ylabel(r\"log10 Radius ($R_{Jup}$)\")\nax[1].set_xlabel(r\"log10 Period (days)\")\nplt.show()\nfig.savefig(\"plots/cluster_discmeth.jpg\", dpi=300)\n","sub_path":"python/PlotPlanets.py","file_name":"PlotPlanets.py","file_ext":"py","file_size_in_byte":7280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"360942960","text":"#Feature Extraction and Vectorization\nimport numpy as np\nimport pandas as pd\nimport pickle\ntrain = pd.read_csv(\"refine.csv\", header=0)\n#print train.shape\n#print train.columns.values\nfrom sklearn.feature_extraction.text import CountVectorizer\nvectorizer= CountVectorizer(ngram_range=(1,2), analyzer=\"word\", min_df = 3)\nX=vectorizer.fit_transform(train[\"review\"])\nX=X.toarray()\ny=train[\"sentiment\"]\n#vocab=vectorizer.get_feature_names()\n#print vocab\n\n#Multinomial Naive Bayes\nfrom sklearn.naive_bayes import MultinomialNB\nclf=MultinomialNB()\nclf.fit(X,y)\ns=pickle.dumps(clf)\nwith open('model.p', 'wb') as f:\n f.write(s)\n\n#Cross Validation\nfrom sklearn import cross_validation \ncv= cross_validation.StratifiedKFold(y, n_folds=10)\n\nscores=cross_validation.cross_val_score(clf, X, y, cv=cv)\nprint (scores)\nprint (np.mean(scores)) \n\n\n","sub_path":"stage7/uni_bi.py","file_name":"uni_bi.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"356846037","text":"from utils import compute_f1\nfrom torch.nn.functional import one_hot\nimport torch\nimport numpy as np\n\n\nclass Policy_gradient():\n    def __init__(self, group_size, label_encoder, other_tag=None, is_sort_reward=True):\n        '''\n\n        :param group_size: int, how many instance to be a group. 必须是batchsize的因子！！！\n        :param label_encoder: label_encoder object.\n        :param other_tag: str.\n        '''\n        self.group_size = group_size\n        self.label_encoder = label_encoder\n        self.other_tag = other_tag\n        self.is_sort_reward = is_sort_reward\n        self.count = 2\n\n\n    def __call__(self, output, labels):\n        '''\n        Policy gradient to optimize f1 score directly.\n        :param output: output directly from the model\n        :param label: a tensor\n        :return: loss\n        '''\n        with torch.no_grad():\n            maxarg = output.argmax(dim=-1)\n            onehot_maxarg = one_hot(maxarg, num_classes=len(self.label_encoder)).to(torch.float)\n            reward = torch.zeros((output.size(0))).to(torch.float)\n            for i in range(int((output.size(0))/(self.group_size))):\n                group_output = output[i*(self.group_size): (i+1)*(self.group_size)]\n                group_labels = labels[i*(self.group_size): (i+1)*(self.group_size)]\n                f1 = compute_f1(group_output, group_labels, self.label_encoder, self.other_tag)\n                f1 = f1*2 - 1\n                reward[i*(self.group_size): (i+1)*(self.group_size)] = f1\n\n            # 按照得分的顺序来重新计算得分，比如，最有最高f1的项可以有正的奖励，其他都是惩罚。\n            if self.is_sort_reward:\n                reward_values = reward.unique().tolist()\n                reward_values.sort()\n                transfer_map = {v:i for i,v in enumerate(reward_values)}\n                for i in range(reward.size(0)):\n                    reward[i] = transfer_map[reward[i].item()]\n                reward = reward - len(transfer_map) + 4  # 可以调整奖励的策略\n                # reward = reward*reward*reward\n                if self.count>0:\n                    print(reward)\n                    self.count -= 1\n\n\n        output = torch.nn.functional.softmax(output, dim=-1)\n        loss = -torch.mean(torch.sum(torch.log(output) * onehot_maxarg, dim=-1) * reward.to(output.device))\n        return loss\n\n","sub_path":"loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":2390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"618196747","text":"import _init_path\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nimport sys\nimport mxnet as mx\nfrom mxnet import gluon\nfrom functools import partial\nfrom easydict import EasyDict as edict\nfrom albumentations import (\n    ShiftScaleRotate, Blur, IAAAdditiveGaussianNoise, GaussNoise, HorizontalFlip, OneOf, RandomCrop, PadIfNeeded, CenterCrop,\n    RandomContrast, RandomBrightness, RGBShift\n)\n\nfrom mxnet.gluon.data.vision import transforms\n\nfrom lib.model.deeplabv3plus import DeepLabV3Plus\nfrom lib.data.augmentation.rgb_segmentation import RGBSegmentationAug\nfrom lib.data.segmentation.ffhq_hair_segmentation import FFHQHairSegmentation\nfrom lib.core.segmentation import SegmentationTrainer, SegmentationTester\nfrom lib.utils.exps_utils import init_exp\nfrom lib.utils.log import logger\nfrom mxnet.gluon.loss import SoftmaxCrossEntropyLoss\n\n\ndef add_exp_args(parser):\n    parser.add_argument('--input-path', type=str, help='Path to dataset',\n                        default='')\n\n    return parser\n\n\ndef init_model():\n    model_cfg = edict()\n\n    model_cfg.num_classes = 2\n    model_cfg.crop_size = 480\n    model_cfg.base_size = 512\n    model_cfg.syncbn = True\n    model_cfg.aux = True\n    model_cfg.aux_weight = 0.5\n\n    model_cfg.input_normalization = {\n        'mean': [.485, .456, .406],\n        'std': [.229, .224, .225]\n    }\n\n    model_cfg.input_transform = transforms.Compose([\n        transforms.ToTensor(),\n        transforms.Normalize(model_cfg.input_normalization['mean'],\n                             model_cfg.input_normalization['std']),\n    ])\n\n    if args.ngpus > 1 and model_cfg.syncbn:\n        norm_layer = partial(mx.gluon.contrib.nn.SyncBatchNorm, num_devices=args.ngpus)\n    else:\n        norm_layer = mx.gluon.nn.BatchNorm\n\n    model = DeepLabV3Plus(model_cfg.num_classes, backbone='resnet50',\n                   norm_layer=norm_layer,\n                   pretrained_base=True, ctx=mx.cpu(0),\n                   base_size=model_cfg.base_size, crop_size=model_cfg.crop_size,\n                   aux=model_cfg.aux)\n    model.initialize(ctx=mx.cpu(0))\n\n    return model, model_cfg\n\n\ndef train(args):\n\n    print('start training..')\n\n    model, model_cfg = init_model()\n\n    args.input_normalization = model_cfg.input_normalization\n\n    crop_size = model_cfg.crop_size\n\n    lr = 0.01\n    weight_decay = 1e-4\n    momentum = 0.9\n    num_epochs = 20\n\n    train_augmentator = RGBSegmentationAug([\n        HorizontalFlip(),\n        ShiftScaleRotate(scale_limit=(-0.25, 0.25), rotate_limit=0, border_mode=0, p=1),\n        PadIfNeeded(min_height=crop_size, min_width=crop_size, border_mode=0),\n        RandomCrop(crop_size, crop_size),\n    ], ignore_class=-1)\n\n    val_augmentator = RGBSegmentationAug([\n        PadIfNeeded(min_height=crop_size, min_width=crop_size, border_mode=0),\n        CenterCrop(crop_size, crop_size)\n    ], ignore_class=-1)\n\n    trainset = FFHQHairSegmentation(\n        args.input_path,\n        scale_factor=0.5, train_epoch_len=10000,\n        split='train', subdir='train_real',\n        transform=model_cfg.input_transform, augmentator=train_augmentator\n    )\n\n    valset = FFHQHairSegmentation(\n        args.input_path,\n        scale_factor=0.5,\n        split='val', transform=model_cfg.input_transform, augmentator=val_augmentator)\n\n    # optimizer and lr scheduling\n    optimizer_params = {\n        'mode': 'poly',\n        'baselr': lr,\n        'nepochs': num_epochs,\n        'wd': weight_decay,\n        'momentum': momentum\n    }\n\n    trainer = SegmentationTrainer(args, model, model_cfg, trainset, valset,\n                                  optimizer_params,\n                                  criterion=SegmentationLoss01,\n                                  image_dump_interval=50)\n\n    logger.info(f'Starting Epoch: {args.start_epoch}')\n    logger.info(f'Total Epochs: {num_epochs}')\n    for epoch in range(args.start_epoch, num_epochs):\n        trainer.training(epoch)\n        trainer.validation(epoch)\n\n\ndef test(args):\n    args.batch_size = args.ngpus\n    model, model_cfg = init_model()\n    scales = [1.0]\n    optimal_thres = 0.5\n\n    tester = SegmentationTester(model, args,\n                                num_classes=model_cfg.num_classes,\n                                use_flip=True,\n                                scales=scales, threshold=optimal_thres)\n\n    if args.vizualization:\n        testset = FFHQHairSegmentation(\n            args.input_path, scale_factor=0.5,\n            split='val', transform=model_cfg.input_transform, augmentator=None,\n            return_path=args.vizualization,\n        )\n\n        tester.vizualizate(testset, args.viz_path, suffix='_rgb', save_gt=True)\n    else:\n        testset = FFHQHairSegmentation(\n            args.input_path, scale_factor=0.5,\n            split='val', transform=model_cfg.input_transform, augmentator=None,\n            return_path=args.vizualization,\n        )\n        tester.test(testset)\n\n\nclass SegmentationLoss01(gluon.HybridBlock):\n    def __init__(self, aux=True, aux_weight=0.2, ignore_label=-1, **kwargs):\n        super(SegmentationLoss01, self).__init__()\n\n        self.aux = aux\n        self.aux_weight = aux_weight\n        self.fnl_loss = SoftmaxCrossEntropyLoss(axis=1, **kwargs)\n        self.sm_loss = SoftmaxCrossEntropyLoss(axis=1, **kwargs)\n        self.k_sum = 0\n        self.ignore_label = ignore_label\n\n    def hybrid_forward(self, F, pred1, pred2, label):\n        tlabel = F.expand_dims(label, axis=1)\n\n        assert tlabel.dtype == np.int32\n        sample_weight = (tlabel != self.ignore_label).astype(np.float32)\n\n        loss1 = self.fnl_loss(pred1, tlabel, sample_weight)\n        loss2 = self.sm_loss(pred2, tlabel, sample_weight)\n\n        self.k_sum = 0\n\n        return loss1 + self.aux_weight * loss2\n\n\nif __name__ == '__main__':\n    args = init_exp(__file__, add_exp_args)\n\n    if args.mode == 'train':\n        train(args)\n    elif args.mode == 'test':\n        test(args)\n\n\n","sub_path":"deeplabv3plus/experiments/rgb_segmentation/00_hair_deeplabv3_ffhq_pretrain_no_gan/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"381377804","text":"import machine\nfrom machine import Timer\n\nGASANALOG = 0\nALARMLED = 13\n\ngasA = machine.ADC(GASANALOG)\ngasLED = machine.Pin(ALARMLED, machine.Pin.OUT)\nclass CheckGas():\n    \"\"\"docstring for checkGas.\"\"\"\n    def __init__(self, led, sensor, time=5000, level=480):\n        super(CheckGas, self).__init__()\n        self.led = led\n        self.led.high()\n        time.sleep_ms(500)\n        self.led.low()\n        time.sleep_ms(500)\n        self.led.high()\n        time.sleep_ms(500)\n        self.led.low()\n        self.gas = sensor\n        self.timer = Timer(-1)\n        self.level = level\n        self.time = time\n        self.start(self.time)\n\n    def checkGas(self):\n        value = self.gas.read()\n        check = 0\n        if (self.gas.read()>self.level):\n            self.led.high()\n            check = 1\n\n        else:\n            self.led.low()\n            check = 0\n\n        print(value, check)\n        return check\n\n    def start(self, time):\n        self.timer.init(period=time, mode=Timer.PERIODIC,\n                        callback=lambda t:self.checkGas())\n    def stop(self):\n        self.timer.deinit()\n\n\ng = CheckGas(gasLED, gasA, 5000)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"357881702","text":"n=input(\"input the times you want to loop:\")\r\nn=int(n)\r\nk=input(\"input the nubmers you want to add:\")\r\nk=int(k)\r\nnumbers=[]\r\nglobal i,j\r\ni=0\r\nj=0\r\n\r\n\r\nfor i in range(0,n ):\r\n    print(\"at he top i is %d\" %i )\r\n    j=j+k\r\n    numbers.append(j)\r\n    print(\"numbers now:\" ,numbers)\r\n    print(\"at the bottom  i is %d\" % i )\r\n\r\nprint('the numbers:')\r\nfor num in numbers:\r\n    print (num)\r\n","sub_path":"Chap1/project/ex33plus3.py","file_name":"ex33plus3.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"323872057","text":"'''\nMid-term Exam\n03/13/2014\n\nauthor: @abekim\n'''\ndef sum_squares_even(n):\n    ''' returns the sum of the squares of all even numbers between 0 and n (inclusive) '''\n    return sum([i**2 for i in range(n+1) if not i % 2])\n\ndef pair_list_to_dictionary(li):\n    ''' returns a dictionary that maps elements of even indices to elements of odd indices '''\n    keys = [li[i] for i in range(len(li)) if not i % 2]\n    values = [li[i] for i in range(len(li)) if i % 2]\n    return dict(zip(keys, values))\n\ndef split_dictionary(d):\n    ''' returns a list that splits d in to keys that start with upper letters and keys that start with lower letters '''\n    res = [{}, {}]\n    for key in d:\n        try:\n            if key[0].islower(): res[1][key] = d[key]\n            else: res[0][key] = d[key]\n        except TypeError:\n            raise Exception(\"key: %s is not a string\" % key)\n    return res\n\ndef pull_middle(s):\n    ''' returns the middle two letters of s and s without the two letters '''\n    if len(s) == 2: return s, ''\n    head = s[:len(s)/2]\n    tail = s[len(s)/2:]\n    return head[-1] + tail[0], head[:-1] + tail[1:]\n\ndef in_language(s):\n    ''' returns True iff s begins with some combination of 'a's, followed by equal number of 'b's, with no extra letters '''\n    # base cases\n    if not len(s): return True\n    elif len(s) % 2 : return False\n\n    middle, outer = pull_middle(s)\n    return middle == 'ab' and in_language(outer)\n\nclass NucleotidePairs(object):\n    def __init__(self):\n        self.pairs = {\n            'A': 'T',\n            'C': 'G',\n            'G': 'C',\n            'T': 'A'\n        }\n\n    def get_complement(self, nuc):\n        try:\n            return self.pairs[nuc]\n        except KeyError:\n            raise Exception(\"%s not in list of known nucleotides\" % nuc)\n\nclass DNASequence(object):\n    ''' Represents a sequence of DNA '''\n    def __init__(self, nucleotides):\n        '''\n            contructs a DNASequence with the specified nucleotides.\n            nucleotides: the nucleotides represented as a string of\n                         capital letters in {'A', 'C', 'G', 'T'}\n        '''\n        self.nucleotides = nucleotides.upper()\n        self.pairs = NucleotidePairs()\n\n    def get_reverse_complement(self):\n        '''\n            computes the reverse complement of the DNA sequence.\n            returns: the reverse complement DNA sequence represented\n                     as an object of type DNASequence\n        '''\n        return ''.join([self.pairs.get_complement(n) for n in self.nucleotides])[::-1]\n\n    def get_proportion_ACGT(self):\n        '''\n            computes the proportion of nucleotides in the DNA sequence\n            that are 'A', 'C', 'G', 'T'\n            returns: a dictionary where each key is a nucleotide and the \n                     corresponding value is the proportion of nucleotides in the\n                     DNA sequence that are that nucleotide\n        '''\n        proportions = {\n            'A': 0.,\n            'C': 0.,\n            'G': 0.,\n            'T': 0.\n        }\n        length = len(self.nucleotides)\n        \n        for n in self.nucleotides:\n            proportions[n] += 1./length\n\n        return proportions\n\ntry:\n    import unittest2 as unittest\nexcept ImportError:\n    import unittest\n\nclass FunctionTestCases(unittest.TestCase):\n    def setUp(self):\n        pass\n\n    def test_sum_squares_even(self):\n        self.assertEqual(sum_squares_even(10), 220)\n        self.assertEqual(sum_squares_even(5), 20)\n        self.assertEqual(sum_squares_even(20), 1540)\n\n    def test_pair_list_to_dictionary(self):\n        self.assertEqual(pair_list_to_dictionary([1, 'a', 5]), {1:'a'})\n        self.assertEqual(pair_list_to_dictionary(['hello', 'a', 'test', 'b']), {'hello':'a', 'test':'b'})\n        self.assertEqual(pair_list_to_dictionary([1, 2, 'a', 'b', 2., 3., 4.]), {'a': 'b', 1: 2, 2.0: 3.0})\n\n    def test_split_dictionary(self):\n        self.assertEqual(split_dictionary({'a':2, 'B':'hello', 'c':'t'}),[{'B': 'hello'}, {'a': 2, 'c': 't'}])\n        self.assertEqual(split_dictionary({'Abc':1, 'D':2, 'EFG':3}), [{'Abc':1, 'D':2, 'EFG':3}, {}])\n        self.assertEqual(split_dictionary({'A':1, 'bc': 2, 'def': 3}), [{'A':1}, {'bc': 2, 'def': 3}])\n\n    def test_pull_middle(self):\n        try:\n            self.assertEqual(pull_middle('ab'), ('ab', ''))\n            self.assertEqual(pull_middle('aaaabbbb'), ('ab', 'aaabbb'))\n        except NameError:\n            pass\n\n    def test_in_language(self):\n        self.assertFalse(in_language('aaab'))\n        self.assertFalse(in_language('aaaccc'))\n        self.assertTrue(in_language(''))\n        self.assertTrue(in_language('aaaabbbb'))\n        self.assertFalse(in_language('abcdef'))\n        self.assertFalse(in_language('aaabbbb'))\n        self.assertTrue(in_language('aaaaaaaabbbbbbbb'))\n\nclass DNATestCases(unittest.TestCase):\n    def setUp(self):\n        self.sequence = DNASequence('atcgggatgctagag')\n\n    def test_reverse_complement(self):\n        self.assertEqual(self.sequence.get_reverse_complement(), 'TAGCCCTACGATCTC')\n\n    def test_proportion(self):\n        proportions = self.sequence.get_proportion_ACGT()\n        self.assertEqual(sum(proportions.values()), 1.)\n        expected = [4./15, 2./15, 6./15, 3./15]\n        expected = [round(e, 2) for e in expected]\n        for value in proportions.values():\n            self.assertIn(round(value, 2), expected)\n\nif __name__ == '__main__':\n    unittest.main()","sub_path":"exams/midterm.py","file_name":"midterm.py","file_ext":"py","file_size_in_byte":5457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"496898334","text":"\"\"\"twitter URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/2.0/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.urls import path, re_path\nfrom twitterProject.views import *\n\n\n\nurlpatterns = [\n    path('', main_page, name='index'),\n    path('logout/', user_logout, name='logout'),\n    path('login/', user_login, name='login'),\n    path('register/', user_registration, name='reg'),\n    re_path('^page/(\\d+)/$', main_page),\n    re_path('^user/(?P<id_us>\\w+)/$', user_messages, name='user_messages'),\n    re_path('^email_conf/(?P<user>)\\w+/$', check_from_email),\n    re_path('^user_edit/(?P<id_us>\\w+)/(?P<ed>\\w+)/(?P<id_article>\\w+)/$', user_messages_edit, name=\"user_edit\"),\n    re_path('^user_add_like/(?P<id_article>\\w+)/$', user_add_like, name=\"user_add_article\"),\n    re_path('^user_retweet/(?P<id_article>\\w+)/(?P<id_creater>\\w+)/$', user_retweet, name=\"user_retweet\"),\n    re_path('^message/(?P<id_article>\\w+)/$', message, name=\"message\"),\n    re_path('^profile/(?P<id_user>\\w+)/$', profile, name=\"profile\"),\n\n    # path('test/', test),\n]\n","sub_path":"twitterProject/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"364490595","text":"from bert_serving.client import BertClient\nbc = BertClient()\nvec = bc.encode(['hey you what up'])\n\nvec  # [2, 25, 768]\nvec[0]  # [1, 25, 768], sentence embeddings for `hey you`\nvec[0][0]  # [1, 1, 768], word embedding for `[CLS]`\nvec[0][1]  # [1, 1, 768], word embedding for `hey`\nvec[0][2]  # [1, 1, 768], word embedding for `you`\nvec[0][3]  # [1, 1, 768], word embedding for `[SEP]`\nvec[0][4]  # [1, 1, 768], word embedding for padding symbol\n\nprint(vec[0][6])\n","sub_path":"test3.py","file_name":"test3.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"172547131","text":"\"\"\"\nimage_distribution.py\nthis runs the main program using an image or\nimage directory to make a seed for the materials\n\"\"\"\nimport argparse\nfrom distribution_functions import image_dist, uniform_random_material\nfrom rendering import render_scene\n\n\ndef main():\n    \"\"\"The entry point to the program\"\"\"\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-i\", \"--image\", default=None, type=str)\n    parser.add_argument(\"-d\", \"--depth\", default=2, type=int)\n    parser.add_argument(\"--width\", default=224, type=int)\n    parser.add_argument(\"--height\", default=224, type=int)\n    parser.add_argument(\"-s\", \"--scene\", default=\"scenes/basic.json\", type=str)\n    args = parser.parse_args()\n    if args.image is None:\n        func = uniform_random_material\n    else:\n        func = image_dist(args.image)\n    image = render_scene(args.scene, func, args.depth, (args.width, args.height))\n    image.show()\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"gl-depth/image_distribution.py","file_name":"image_distribution.py","file_ext":"py","file_size_in_byte":948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"442411672","text":"'''This program determines if a triangle is equilateral (all 3 sides are equal),\r\nisosceles(2 sides are equal) or scalene (no sides are equal, when given 3 angles\r\n'''\r\nside1 = int(input(\"Enter first side of triangle: \"))\r\nside2 = int(input(\"Enter second side of triangle: \"))\r\nside3 = int(input(\"Enter third side of triangle: \"))\r\n\r\nif(side1 == side2 and side2 == side3):\r\n\tprint(\"This is an equilateral triangle\")\r\nelif(side1 == side2 or side1 == side3 or side2 == side3):\r\n\tprint(\"This is an isosceles triangle\")\r\nelse:\r\n\tprint(\"This is a scalene triangle\")\r\n\t\r\n","sub_path":"Conditional problems/typeOfTriangle.py","file_name":"typeOfTriangle.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"134606811","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Feb 23 18:08:34 2020\n\n@author: craig\n\"\"\"\n\nfrom keras_segmentation.models.unet import vgg_unet\nfrom keras_segmentation.predict import model_from_checkpoint_path\nfrom PIL import Image\n#from keras.models import load_model\nimport matplotlib.pyplot as plt\n\n\n\nmodel = vgg_unet(\n        n_classes=51 ,  \n        input_height=416, \n        input_width=608 \n)\n\n\nmodel=model_from_checkpoint_path(\"weights/vgg_unet_1\")\n\n\n\nmodel.train(\n    checkpoints_path = \"weights/vgg_unet_1\" ,\n    #load_weights = \"weights/vgg_unet_1.4\"  ,   \n    train_images =  \"dataset1/images_prepped_train/\",\n    train_annotations = \"dataset1/annotations_prepped_train/\",\n    val_images=\"dataset1/images_prepped_test/\" ,\n    val_annotations=\"dataset1/annotations_prepped_test/\",\n    verify_dataset=True,\n    optimizer_name='adadelta' , do_augment=True , augmentation_name=\"aug_all\",    \n    epochs=1\n)\n\n\n# Display the model's architecture\nmodel.summary()\n\n# Save the entire model to a HDF5 file.\n# The '.h5' extension indicates that the model should be saved to HDF5.\nmodel.save('vgg_unet_1.h5') \n\n##predict an image from the training data\n#out = model.predict_segmentation(\n#    checkpoints_path=\"weights/vgg_unet_1\" , \n#    inp=\"dataset1/images_prepped_test/43.jpg\",\n#    out_fname=\"newout.png\"\n#)\n\n\n\n\n\n## Show Images\n#img = Image.open(\"dataset1/images_prepped_test/43.jpg\")\n#plt.figure(1)\n#plt.imshow(img)\n#\n#plt.figure(2)\n#plt.imshow(out)\n\n\n\n#from keras_segmentation.predict import predict\n#\n#\n#predict( \n#\tcheckpoints_path=\"weights/vgg_unet_1\"  , \n#\tinp= \"dataset1/images_prepped_test/43.jpg\" , \n#\tout_fname=\"43_predict.png\" \n#)\n\n\n\n\nfrom keras_segmentation.predict import predict_multiple\n\n\npredict_multiple( \n\tcheckpoints_path=\"weights/vgg_unet_1\" , \n\tinp_dir=\"dataset1/images_prepped_test/\" , \n\tout_dir=\"weights/out/\" \n)\n\n\n\n\n#evaluating the model \n#print(model.evaluate_segmentation( inp_images_dir=\"dataset1/images_prepped_test/\"  , annotations_dir=\"dataset1/annotations_prepped_test/\" ) )","sub_path":"DONOTUSE_main_train_your_own_and_add_to_existing_checkpoint.py","file_name":"DONOTUSE_main_train_your_own_and_add_to_existing_checkpoint.py","file_ext":"py","file_size_in_byte":2027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"579326982","text":"'''contains api route methods for the user\nstatus codes resources: http://www.restapitutorial.com/httpstatuscodes.html\n\n@auth.login_required decorator ensures that the user has an account on the serivce\nby called the authenticate_user method (with the @auth.verify_password decorator)\n'''\n\nfrom flask import Blueprint, g, request\nfrom flask_httpauth import HTTPBasicAuth\nfrom minerva.helpers.helpers import incoming_json, outgoing_success, incoming_username, incoming_user_token\nfrom minerva.services.user_service import *\nfrom minerva.services.car_service import cs_get_recent_data, cs_get_car_summary\nfrom minerva.models.logger import provideLogger\n\nauth = HTTPBasicAuth()\n\n#create a Blueprint for the user_api, resource: http://flask.pocoo.org/docs/0.12/blueprints/#blueprints\n\nuser_api = Blueprint('user', __name__, url_prefix='/minerva/user')\n\ndef log_user_api(url, params, ro):\n\n    # log the request\n    provideLogger().info(request=url, params=params, response=ro)\n\n# @auth.verify_password\n# def authenticate_user(username_or_token, password):\n#\n#     '''\n#\n#     :param username:\n#     :param password:\n#     :return: boolean\n#     '''\n#\n#     # first try to authenticate by token\n#     u = user_verify_auth_token(username_or_token)\n#\n#     if not u:\n#\n#         u = user_find_by_username(params={'username': username_or_token})\n#\n#     if not u:\n#         app_abort(message='The requested resource does not exist', payload='User Info')\n#\n#     if not u.check_password(password=password):\n#         return False\n#\n#     g.user = u\n#\n#     return True\n\n@user_api.route('/account', methods=['POST'])\ndef register_user():\n\n    params = incoming_json('User Registration')\n\n    ro = user_registration(params=params)\n\n    #log the request\n    log_user_api(url=request.url, params=params, ro=ro)\n\n    return outgoing_success(params=ro, status_code=201)\n\n@user_api.route('/login', methods=['POST'])\ndef login_user():\n\n    params = incoming_json('User Login')\n\n    ro = user_login(params=params)\n\n    # log the request\n    log_user_api(url=request.url, params=params, ro=ro)\n\n    return outgoing_success(params=ro, status_code=200)\n\n@user_api.route('/<username>/car', methods=['POST'])\ndef add_car(username):\n\n    u = incoming_user_token(module_name='Add Car', username=username)\n    params = incoming_json('Add Car')\n\n    ro = user_add_car(usr=u, params=params)\n\n    # log the request\n    log_user_api(url=request.url, params=params, ro=ro)\n\n    return outgoing_success(params=ro, status_code=201)\n\n@user_api.route('/<username>/cars', methods=['GET'])\ndef get_cars(username):\n\n    u = incoming_user_token(module_name='Get Cars', username=username)\n\n    limit = None\n    offset = None\n\n    next_url = ''\n    prev_url = ''\n\n    if 'limit' in request.args and 'offset' in request.args:\n\n        limit = request.args['limit']\n        offset = request.args['offset']\n\n        next_url = 'limit=' + str(limit) + '&offset=' + str(int(offset) + int(limit))\n        if int(offset) < int(limit):\n            prev_url =  'limit=' + str(limit) + '&offset=' + str(offset)\n        else:\n            prev_url = 'limit=' + str(limit) + '&offset=' + str(int(offset) - int(limit))\n\n    ro = user_get_all_cars(usr=u, limit=limit, offset=offset)\n\n    #add the next and previous url to the car data dictionary\n    ro['next_url'] = next_url\n    ro['prev_url'] = prev_url\n\n    # log the request\n    log_user_api(url=request.url, params=None, ro=ro)\n\n    return outgoing_success(params=ro, status_code=200)\n\n@user_api.route('/<username>/<carname>/cardata', methods=['GET'])\ndef get_car_data(username, carname):\n\n    u = incoming_user_token(module_name='Get Data', username=username)\n\n    limit = None\n    offset = None\n\n    next_url = ''\n    prev_url = ''\n\n    if 'limit' in request.args and 'offset' in request.args:\n\n        limit = request.args['limit']\n        offset = request.args['offset']\n\n        next_url = 'limit=' + str(limit) + '&offset=' + str(int(offset) + int(limit))\n        if int(offset) < int(limit):\n            prev_url =  'limit=' + str(limit) + '&offset=' + str(offset)\n        else:\n            prev_url = 'limit=' + str(limit) + '&offset=' + str(int(offset) - int(limit))\n\n    car_params = {\n\n        'car_name': carname\n    }\n\n    c = user_get_car_by_name(usr=u,params=car_params)\n\n    ro = cs_get_recent_data(c=c, limit=limit, offset=offset)\n\n    # add the next and previous url to the car data dictionary\n    ro['next_url'] = next_url\n    ro['prev_url'] = prev_url\n\n    # log the request\n    log_user_api(url=request.url, params=car_params, ro=ro)\n\n    return outgoing_success(params=ro, status_code=200)\n\n@user_api.route('/<username>/<carname>/trips', methods=['GET'])\ndef get_car_trips(username, carname):\n\n\n    u = incoming_user_token(module_name='Get Car Trips', username=username)\n\n    limit = None\n    offset = None\n\n    next_url = ''\n    prev_url = ''\n\n    car_params = {\n\n        'car_name': carname\n    }\n\n    c = user_get_car_by_name(usr=u, params=car_params)\n\n    if 'limit' in request.args and 'offset' in request.args:\n\n        limit = request.args['limit']\n        offset = request.args['offset']\n\n        next_url = 'limit=' + str(limit) + '&offset=' + str(int(offset) + int(limit))\n        if int(offset) < int(limit):\n            prev_url =  'limit=' + str(limit) + '&offset=' + str(offset)\n        else:\n            prev_url = 'limit=' + str(limit) + '&offset=' + str(int(offset) - int(limit))\n\n    ro = cs_get_car_summary(c=c, limit=limit, offset=offset)\n\n    #add the next and previous url to the car data dictionary\n    ro['next_url'] = next_url\n    ro['prev_url'] = prev_url\n\n    # log the request\n    log_user_api(url=request.url, params=None, ro=ro)\n\n    return outgoing_success(params=ro, status_code=200)\n\n\n\n\n","sub_path":"minerva/apis/user_api.py","file_name":"user_api.py","file_ext":"py","file_size_in_byte":5741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"9689530","text":"import bpy\nimport utils\nfrom utils import printf\nimport os\nglobal cache, mode\nfrom pathlib import Path\nfrom shutil import copyfile\ncurrentPath = None\nmaterialTemplate = \"wood05\"\n\ncategory = dict()\ncategory[\"plaster\"] = \"textures\"\ncategory[\"marble\"] = \"textures\"\ncategory[\"stone\"] = \"rock\"\ncategory[\"metal\"] = \"metal\"\ncategory[\"road\"] = \"rock\"\ncategory[\"wood\"] = \"wood\"\ncategory[\"wall\"] = \"rock\"\n\ndef categoryMapping(name):\n    for b in category.keys():\n        if b in name.lower():\n            return category[b]\n    return \"\"\n\n\ndef iterateTexturePaths():\n    for root, dirs, filenames in os.walk(utils.texturesPath):\n        name = os.path.basename(root)\n        for kth in dirs:\n            fromPath_K = str(root + \"\\\\\" + kth)\n            if (kth == \"4K\" or kth == \"3K\" or kth == \"6K\"):\n                saveFor(fromPath_K, name, root, kth) \n\ndef getFromFolder(fromPath_K, lookingFor):\n    retPath = \"\"\n    for root, dirs, filenames in os.walk(fromPath_K):\n        for f in filenames:\n            if lookingFor in f.lower():\n                retPath = root + \"/\" + str(f)\n                #utils.printf(\"Found for \" + retPath)\n                return retPath\n    return retPath\n\nhasRough_ = 1\ndef getImageReplacement(filepath, fromPath_K):\n    global hasRough_\n#     utils.printf(\"Need replacement for \" + filepath + \" from \" + fromPath_K)\n    l_filepath = filepath.lower()\n    lookingFor = \"\"\n    if \"_nrm\" in l_filepath:\n        lookingFor = \"nrm\"\n    elif \"_col\" in l_filepath:\n        lookingFor = \"col\"\n    elif \"_gloss\" in l_filepath:\n        lookingFor = \"gloss\"\n        hasRough_ = 1\n    elif \"_roughness\" in l_filepath:\n        lookingFor = \"gloss\"\n        hasRough_ = 1\n    else:\n        return \"\"\n\n    retPath = getFromFolder(fromPath_K, lookingFor)\n    if retPath != \"\":\n        return retPath\n\n    if lookingFor == \"gloss\":\n        retPath = getFromFolder(fromPath_K, \"roughness\")\n        if retPath != \"\":\n            hasRough_ = 0\n\n    if retPath != \"\":\n        return retPath\n\n    utils.printf(\"NOT FOUND for \" + lookingFor)\n    return \"\"\n\n\ndef treeHelper(fromPath_K, nodeTree):\n    global foundAll\n    for node in nodeTree.nodes:\n        if \"Image Texture\" in node.name:\n            purpose = str(node.image.filepath)\n            replacement = getImageReplacement(purpose, fromPath_K)\n            if not replacement:\n                printf(\"No Image replacement for {}\".format(purpose))\n                foundAll = False\n            else:\n                node.image.filepath = replacement\n        if node.name == \"Group\":\n            treeHelper(fromPath_K, node.node_tree)\n\ndef copyPreview(rootTextPath, dirname, kth):\n    prevFile = None\n    for root, dirs, filenames in os.walk(rootTextPath):\n        for f in filenames:\n            if \"sphere.jpg\" in f.lower():\n#                 printf(\"Preview is \" + f)\n                prevFile = str(root + \"\\\\\" + f)\n\n    if not prevFile:\n        printf(\"Could not find preview\")\n        return\n\n    cat = categoryMapping(dirname)\n    if not cat:\n        print(\"Could not determine category\")\n        return\n\n    destination = \"{}/{}/{}_{}.jpg\".format(utils.dynaPath, cat, dirname, kth)\n    copyfile(prevFile, destination)\n#     bpy.ops.wm.save_as_mainfile(filepath=destination)\n\nfoundAll = None\ndef saveFor(fromPath_K, dirname, rootTextPath, kth):\n    global foundAll\n    foundAll = True\n    iterateBase(fromPath_K, dirname, kth)\n    if not foundAll:\n        return\n    copyPreview(rootTextPath, dirname, kth)\n    bpy.ops.wm.save_as_mainfile(filepath=\"{}/{}_{}.blend\".format(utils.dynaPath, dirname, kth), copy=True)\n\ndef updateInverter(nodeTree):\n    for node in nodeTree.nodes:\n        if \"GlossInverter\" in node.name:\n            node.inputs[0].default_value = hasRough_\n            break\n   \n\ndef iterateBase(fromPath_K, dirname, kth):\n    global materialTemplate\n    M = bpy.data.materials\n    mat = M.get(materialTemplate)\n    if not mat:\n        print(\"Could not find\")\n        return\n    treeHelper(fromPath_K, mat.node_tree)\n    updateInverter(mat.node_tree) \n\n    materialTemplate = dirname + \"_\" +  kth\n    printf(\"Processed \" + materialTemplate)\n    mat.name = materialTemplate\n\nprintf(\"start\", start = True, done = True) \niterateTexturePaths()\nprintf(\"end\", done = True) \n","sub_path":"blender/scripts/material_builder.py","file_name":"material_builder.py","file_ext":"py","file_size_in_byte":4241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"259026468","text":"# coding: utf-8\n\nfrom __future__ import absolute_import\nfrom datetime import date, datetime  # noqa: F401\n\nfrom typing import List, Dict  # noqa: F401\n\nfrom swagger_server.models.base_model_ import Model\nfrom swagger_server.models.state_schema import StateSchema  # noqa: F401,E501\nfrom swagger_server import util\n\n\nclass SourceSchema(Model):\n    \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n    Do not edit the class manually.\n    \"\"\"\n\n    def __init__(self, source_id: str=None, source: List[StateSchema]=None):  # noqa: E501\n        \"\"\"SourceSchema - a model defined in Swagger\n\n        :param source_id: The source_id of this SourceSchema.  # noqa: E501\n        :type source_id: str\n        :param source: The source of this SourceSchema.  # noqa: E501\n        :type source: List[StateSchema]\n        \"\"\"\n        self.swagger_types = {\n            'source_id': str,\n            'source': List[StateSchema]\n        }\n\n        self.attribute_map = {\n            'source_id': 'source_id',\n            'source': 'source'\n        }\n\n        self._source_id = source_id\n        self._source = source\n\n    @classmethod\n    def from_dict(cls, dikt) -> 'SourceSchema':\n        \"\"\"Returns the dict as a model\n\n        :param dikt: A dict.\n        :type: dict\n        :return: The SourceSchema of this SourceSchema.  # noqa: E501\n        :rtype: SourceSchema\n        \"\"\"\n        return util.deserialize_model(dikt, cls)\n\n    @property\n    def source_id(self) -> str:\n        \"\"\"Gets the source_id of this SourceSchema.\n\n        The id of a source of light. For eg: window A  # noqa: E501\n\n        :return: The source_id of this SourceSchema.\n        :rtype: str\n        \"\"\"\n        return self._source_id\n\n    @source_id.setter\n    def source_id(self, source_id: str):\n        \"\"\"Sets the source_id of this SourceSchema.\n\n        The id of a source of light. For eg: window A  # noqa: E501\n\n        :param source_id: The source_id of this SourceSchema.\n        :type source_id: str\n        \"\"\"\n        if source_id is None:\n            raise ValueError(\"Invalid value for `source_id`, must not be `None`\")  # noqa: E501\n\n        self._source_id = source_id\n\n    @property\n    def source(self) -> List[StateSchema]:\n        \"\"\"Gets the source of this SourceSchema.\n\n\n        :return: The source of this SourceSchema.\n        :rtype: List[StateSchema]\n        \"\"\"\n        return self._source\n\n    @source.setter\n    def source(self, source: List[StateSchema]):\n        \"\"\"Sets the source of this SourceSchema.\n\n\n        :param source: The source of this SourceSchema.\n        :type source: List[StateSchema]\n        \"\"\"\n        if source is None:\n            raise ValueError(\"Invalid value for `source`, must not be `None`\")  # noqa: E501\n\n        self._source = source\n","sub_path":"swagger_server/models/source_schema.py","file_name":"source_schema.py","file_ext":"py","file_size_in_byte":2795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"374160411","text":"#!/usr/bin/python3.4\nimport random\n\nvowels = [\"a\",\"e\",\"i\",\"o\",\"u\"]\nvowelslist = []\nconsonantslist = []\n\nx=(str(input(\"Type the keys: \")))\nx.strip()\nx=list(x)\n\nvowelslist=[i for i in x if i in vowels]\nconsonantslist=[i for i in x if i not in vowels]\n\nprint(\"Vowels: \", vowelslist)\nprint(\"Consonants\", consonantslist)\na=vowelslist+consonantslist\nrandom.sample(a, len(a))\nprint(''.join(a))\n","sub_path":"lol.py","file_name":"lol.py","file_ext":"py","file_size_in_byte":387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"183737692","text":"def fn(par, callback):\n    if not par:\n        callback(Exception(\"Parametr needed\"))\n        return\n    callback(None, f'Data: {par}')\n    return 'Value'\n\ndef callback(err, data=None):\n    if err:\n        raise err\n    print(data)\n\nres = fn('example', callback)\nfn(None, callback)\nprint(res)\n\n\n","sub_path":"Python/1-callback.py","file_name":"1-callback.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"160348814","text":"\n#!/usr/bin/env python\n# Implementation of collaborative filtering recommendation engine\n\nimport csv\n# from recommendation_data import dataset\nfrom math import sqrt\nimport pymysql\nimport pprint\nfrom scipy import spatial\n\npp = pprint.PrettyPrinter(indent=4)\n\ndataset = {}\n\n\ndef similarity_score(person1,person2):\n\t\n\t# Returns ratio Euclidean distance score of person1 and person2 \n\n\tboth_viewed = {}\t\t# To get both rated items by person1 and person2\n\n\tfor item in dataset[person1]:\n\t\tif item in dataset[person2]:\n\t\t\tboth_viewed[item] = 1\n\n\t\t# Conditions to check they both have an common rating items\t\n\t\tif len(both_viewed) == 0:\n\t\t\treturn 0\n\n\t\t# Finding Euclidean distance \n\t\tsum_of_eclidean_distance = []\t\n\n\t\tfor item in dataset[person1]:\n\t\t\tif item in dataset[person2]:\n\t\t\t\tsum_of_eclidean_distance.append(pow(dataset[person1][item] - dataset[person2][item],2))\n\t\tsum_of_eclidean_distance = sum(sum_of_eclidean_distance)\n\n\t\treturn 1/(1+sqrt(sum_of_eclidean_distance))\n\n\n\ndef pearson_correlation(person1,person2):\n\n\t# To get both rated items\n\tboth_rated = {}\n\tfor item in dataset[person1]:\n\t\tif item in dataset[person2]:\n\t\t\tboth_rated[item] = 1\n\n\tnumber_of_ratings = len(both_rated)\n\t# if (number_of_ratings > 1):\n\t# \tprint (person1 + \" \" + person2)\t\t\n\t\n\t# Checking for number of ratings in common\n\tif number_of_ratings == 0:\n\t\treturn 0\n\n\t# Add up all the preferences of each user\n\tperson1_preferences_sum = sum([dataset[person1][item] for item in both_rated])\n\tperson2_preferences_sum = sum([dataset[person2][item] for item in both_rated])\n\n\t# Sum up the squares of preferences of each user\n\tperson1_square_preferences_sum = sum([pow(dataset[person1][item],2) for item in both_rated])\n\tperson2_square_preferences_sum = sum([pow(dataset[person2][item],2) for item in both_rated])\n\n\t# Sum up the product value of both preferences for each item\n\tproduct_sum_of_both_users = sum([dataset[person1][item] * dataset[person2][item] for item in both_rated])\n\n\t# Calculate the pearson score\n\tnumerator_value = product_sum_of_both_users - (person1_preferences_sum*person2_preferences_sum/number_of_ratings)\n\tdenominator_value = sqrt((person1_square_preferences_sum - pow(person1_preferences_sum,2)/number_of_ratings) * (person2_square_preferences_sum -pow(person2_preferences_sum,2)/number_of_ratings))\n\t# if (str(person1) == \"8\" and str(person2) == \"11676\"):\n\t\t# print (\"aajfkfbkwffkakfkjbka,dfbkffnakfnfankf\")\n\t\t# pp.pprint(both_rated)\n\t\t# print (str(numerator_value) + \"adad\" + str(denominator_value))\n\tif denominator_value == 0:\n\t\treturn 0\n\telse:\n\t\tr = numerator_value/denominator_value\n\t\treturn r \n\ndef most_similar_users(person,number_of_users):\n\t# returns the number_of_users (similar persons) for a given specific person.\n\tscores = [(pearson_correlation(person,other_person),other_person) for other_person in dataset if  other_person != person ]\n\t\n\t# Sort the similar persons so that highest scores person will appear at the first\n\tscores.sort()\n\tscores.reverse()\n\treturn scores[0:100]\n\ndef user_recommendations(person):\n\n\t# Gets recommendations for a person by using a weighted average of every other user's rankings\n\ttotals = {}\n\tsimSums = {}\n\trankings_list =[]\n\tfor other in dataset:\n\t\t# print(other)\n\t\t# don't compare me to myself\n\t\tif other == person:\n\t\t\tcontinue\n\t\tsim = pearson_correlation(person,other)\n\t\t# print (sim)\n\t\t#print \">>>>>>>\",sim\n\n\t\t# ignore scores of zero or lower\n\t\tif sim <=0: \n\t\t\tcontinue\n\t\tfor item in dataset[other]:\n\n\t\t\t# only score movies i haven't seen yet\n\t\t\tif item not in dataset[person] or dataset[person][item] == 0:\n\n\t\t\t# Similrity * score\n\t\t\t\ttotals.setdefault(item,0)\n\t\t\t\ttotals[item] += dataset[other][item]* sim\n\t\t\t\t# sum of similarities\n\t\t\t\tsimSums.setdefault(item,0)\n\t\t\t\tsimSums[item]+= sim\n\n\t\t# Create the normalized list\n\n\trankings = [(total/simSums[item],item) for item,total in totals.items()]\n\trankings.sort()\n\trankings.reverse()\n\t# returns the recommended items\n\trecommendataions_list = [recommend_item for score,recommend_item in rankings]\n\treturn recommendataions_list\n\ndef cosineSimilarity(person):\n\tfinalArray = set()\n\tfor d in dataset:\n\t\tif (d == person):\n\t\t\tcontinue\n\t\tdataset1 = []\n\t\tdataset2 = []\n\t\tfor myself in dataset[person]:\n\t\t\tdataset1.append(dataset[person][myself])\n\t\tfor otherRating in dataset[d]:\n\t\t\tdataset2.append(dataset[d][otherRating])\n\t\t# print (dataset1)\n\t\t# print (dataset2)\n\t\tif (len(dataset1) < len(dataset2)):\n\t\t\tcounter = len(dataset2) - len(dataset1)\n\t\t\twhile (counter > 0):\n\t\t\t\tdataset1.append(0)\n\t\t\t\tcounter -= 1\n\t\tif (len(dataset1) > len(dataset2)):\n\t\t\tcounter = len(dataset1) - len(dataset2)\n\t\t\twhile (counter > 0):\n\t\t\t\tdataset2.append(0)\n\t\t\t\tcounter -= 1\n\n\t\tresult = 1 - spatial.distance.cosine(dataset1, dataset2)\n\t\tif (result >= 0.995):\n\t\t\tfor otherRating in dataset[d]:\n\t\t\t\tfinalArray.add(otherRating)\n\t\tresultArray = []\n\t\tfor setItem in finalArray:\n\t\t\tfor myself in dataset[person]:\n\t\t\t\tflag = 0\n\t\t\t\tif (setItem == dataset[person]):\n\t\t\t\t\tflag = 1\n\t\t\t\t\tbreak\n\t\t\t\tif (flag == 1):\n\t\t\t\t\tbreak\n\t\t\tif (flag == 0):\n\t\t\t\tresultArray.append(setItem)\n\n \n\t# print(resultArray)\n\t# print (\"\\n\\n\\n\")\n\t# print (len(resultArray))\n\treturn resultArray\n\n# def createMatrix():\n# \t#Matrix rows users and columns that are books\n# \t# Cell is ranking user Vs Book and 0 if not present\n\t\n\ndef import_dataset():\n\tconn = pymysql.connect(host='localhost', port=3306, user='root', passwd='bhavya', db='minor2',autocommit=True)\n\tcur = conn.cursor()\n\tconn.autocommit(True)\n\n\t# booksRating = list(csv.reader(open(\"BX-Book-Ratings.csv\",newline='', encoding=\"latin1\"), delimiter = ';'))\n\t# print (booksRating[1])\n\t# counter = 0\n\t# for b in booksRating:\n\t# \tif (counter == 0):\n\t# \t\tpass\n\t# \telse :\n\t# \t\tcur.execute(\"INSERT INTO books_rating VALUES (\" + b[0] +\",'\" + b[1] +\"',\" + b[2] +\");\")\n\t# \tcounter += 1\n\n\t# booksName = list(csv.reader(open(\"BX-Books.csv\",newline='', encoding=\"latin1\"), delimiter = ';'))\n\t# counter = 0\n\t# for b in booksName:\n\t# \tif (counter == 0):\n\t# \t\tpass\n\t# \telse :\n\t# \t\tcur.execute(\"INSERT INTO books_name VALUES ('%s', '%s', '%s', '%d', '%s')\" % (b[0], b[1], b[2], b[3], b[4]))\n\t# \t\tprint (b[1])\n\t# \tcounter += 1\n\t# print (\"THE END *****************************************************************************\")\n\t\n\t# usersData = list(csv.reader(open(\"BX-Users.csv\",newline='', encoding=\"latin1\"), delimiter = ';'))\n\t# counter = 0\n\t# str = ''\n\t# for b in usersData:\n\t# \tif (counter == 0):\n\t# \t\tpass\n\t# \telif (counter == 100000):\n\t# \t\tbreak\n\t# \telse :\n\t# \t\tstr +=  \"INSERT INTO user_details VALUES ('%d', '%s', '%s');\" % (int(b[0]), b[1], b[2])\n\t# \t\tprint (b[0])\n\t# \tcounter += 1\n\t# cur.execute(str)\n\n\n\n\n\tcur.execute(\"SELECT * FROM results ORDER BY userid ASC;\")\n\tuser_details_data = cur.fetchall()\n\tuserid = 0\n\tfor u in user_details_data:\n\t\tif (u[0] != userid):\n\t\t\tdataset[str(u[0])] = {}\n\t\t\tdataset[str(u[0])][u[1]] = u[2]\n\t\t\tuserid = u[0]\n\t\telse:\n\t\t\tdataset[str(u[0])][u[1]] = u[2]\n\t\t\tuserid = u[0]\n\t# cur.execute(\"SELECT * FROM user_details LIMIT 100\")\n\t# user_details_data = cur.fetchall()\n\t# # print(cur.fetchone())\n\t# for u in user_details_data:\n\t# \tuserId = u[0]\n\t# \tdataset[str(userId)] = {}\n\t# \t# print (type(userId))\n\t# \tcur.execute(\"SELECT * FROM books_rating WHERE userid = %s LIMIT 20000\", userId)\n\t# \tbooks_details_data = cur.fetchall()\n\t# \tfor bdd in books_details_data:\n\t# \t\tdataset[str(userId)][bdd[1]] = bdd[2]\n\t# \tprint (u)\n\t# pp.pprint (dataset)\n\tconn.close()\n\n\t# print (booksName)\n\t# mycsv = list(mycsv)\n\n# pp.pprint (dataset)\nimport_dataset()\nnn = []\nfor other in dataset:\t\n\t# print(other)\n\taa = cosineSimilarity(other)\n\tif (len(aa) != 0):\n\t\tnn.append(other)\n\tprint(nn)\n\n# # \ttemp = similarity_score(\"201641\", other)\n# # \tif (temp != 0):\n# # \t\tprint (other + \" \" + str(temp))\n# # pp.pprint (most_similar_users(\"201641\", 11962))\n# \tabc = user_recommendations(other)\n# \tprint (abc)\n# \tif (len(abc) == 0):\n# \t\tpass\n# \telse:\n# \t\tprint (other + \"\\n\")\n# \t\tprint(user_recommendations(other))\n","sub_path":"CollaborativeFiltering-master/collaborative_filtering.py","file_name":"collaborative_filtering.py","file_ext":"py","file_size_in_byte":7884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"79905331","text":"from zope import schema\nfrom zope.formlib import form\nfrom zope.interface import implements\nfrom Products.Five.browser.pagetemplatefile import ViewPageTemplateFile\nfrom plone.portlets.interfaces import IPortletDataProvider\nfrom plone.app.portlets.portlets import base\nfrom plone.memoize.view import memoize\n\nfrom Acquisition import aq_inner\nfrom Products.CMFCore.utils import getToolByName\n\nfrom collective.gallery import messageFactory as _\n\nimport random\nfrom Products.ATContentTypes.interfaces import IATImage\n\nclass IShowGalleriesPortlet(IPortletDataProvider):\n    search_portal = schema.Bool(\n        title=_(u'Search portal for galleries'),\n        description=_(u'If selected, search the whole portal for galleries.'\\\n            'otherwise search only subfolders of the current path.'),\n        required=True,\n        default=True)\n\n    num_pictures = schema.Int(\n        title=_(u'Number of Pictures'),\n        description=_(u'Define the number of pictures to show in the portlet'),\n        required=True,\n        default=1,\n        min=1)\n\n    image_size = schema.Choice(\n        title=_(u'Image Size'),\n        description=_(u'Select, which image scale should be used for the portlet.'),\n        required=True,\n        default=None,\n        vocabulary=\"collective.gallery.ImageScaleVocabulary\")\n\n\nclass Assignment(base.Assignment):\n    implements(IShowGalleriesPortlet)\n    image_size = 'mini'\n\n    def __init__(self, search_portal=True, num_pictures=1, image_size='mini'):\n        self.search_portal= search_portal\n        self.num_pictures = num_pictures\n        self.image_size = image_size\n\n    @property\n    def title(self):\n        return _(u\"Gallery Portlet\")\n\n\nclass AddForm(base.AddForm):\n    form_fields = form.Fields(IShowGalleriesPortlet)\n    label = _(u\"Add portlet to show pictures from galleries\")\n    description = _(u\"This portlet shows pictures from galleries.\")\n\n    def create(self, data):\n        return Assignment(**data)\n\n\nclass EditForm(base.EditForm):\n    form_fields = form.Fields(IShowGalleriesPortlet)\n    label = _(u\"Add portlet to show pictures from galleries\")\n    description = _(u\"This portlet shows pictures from galleries.\")\n\n\nclass Renderer(base.Renderer):\n    render = ViewPageTemplateFile('show_galleries.pt')\n\n    @property\n    def image_scale(self):\n        return getattr(self.data, 'image_size', 'mini')\n\n    # TODO: cache me\n    def get_galleries(self):\n        context = aq_inner(self.context)\n        if self.data.search_portal:\n            portal_url = getToolByName(context, 'portal_url')\n            context = portal_url.getPortalObject()\n        cat = getToolByName(context,'portal_catalog')\n\n        query = {}\n        query['is_folderish'] = True\n        query['path'] = {'query': '/'.join(context.getPhysicalPath())}\n        brains = cat(**query)\n\n        # TODO: import gallery view names from config.py or make this somehow\n        #       more generic\n        return [brain for brain in brains\n                if brain.getObject().defaultView() in ('gallery.html',)]\n\n    @memoize\n    def get_gallery_pictures(self):\n        gallery = None\n        pictures = []\n        num_pictures = self.data.num_pictures\n        galleries = self.get_galleries()\n        if galleries:\n            gallery = random.choice(galleries).getObject()\n            pictures = [pic for pic in gallery.contentValues()\n                        if IATImage.providedBy(pic)]\n            if len(pictures) > num_pictures:\n                pictures = random.sample(pictures, num_pictures)\n        return {'gallery': gallery, 'pictures': pictures}\n\n    @property\n    def available(self):\n        # TODO: if not search_portal, only show me for folderish contexts\n        showme = bool(self.get_gallery_pictures()['pictures'])\n        return showme\n","sub_path":"collective/gallery/portlets/show_galleries.py","file_name":"show_galleries.py","file_ext":"py","file_size_in_byte":3779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"21428472","text":"\nimport time\nimport pymongo\nimport pprint\nfrom googleplaces import GooglePlaces, types, lang\n\nYOUR_API_KEY = 'AIzaSyDtt-ixG49WuKZdFEJPhTj6q1sMk_KluxE'\nlocation_search = \"Av. Abolição 4140\"\nkeyword_search = \"Padaria\"\n\ngoogle_places = GooglePlaces(YOUR_API_KEY)\n\nquery_result = google_places.nearby_search(location=location_search, keyword=keyword_search, radius=300000, rankby='distance')\n\n\ndef coordinates():\n    data = []\n    for place in query_result.places:\n        data.append(place)\n    return data\n\n","sub_path":"core/seed_geo.py","file_name":"seed_geo.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"215443702","text":"import sys\nfrom collections import defaultdict, Counter\nfrom itertools import product, groupby, count, permutations, combinations\nfrom math import pi, sqrt\nfrom collections import deque\nfrom bisect import bisect, bisect_left, bisect_right\nINF = float(\"inf\")\nsys.setrecursionlimit(10**7)\n\n# 4????, ?, ?, ??\ndy = [0, -1, 0, 1]\ndx = [1, 0, -1, 0]\n\n\ndef inside(y: int, x: int, H: int, W: int) -> bool: return 0 <= y < H and 0 <= x < W\n\n\ndef main():\n    N = int(input())\n    s1 = list(input())\n    s2 = list(input())\n\n    change = True\n    while change:\n        change = False\n        for c1, c2 in zip(s1, s2):\n            if c1 != c2 and c1.isalpha() and c2.isalpha():\n                change = True\n                for i in range(N):\n                    if s1[i] == c2:\n                        s1[i] = c1\n                    if s2[i] == c2:\n                        s2[i] = c1\n                break\n\n    change = True\n    while change:\n        change = False\n        a_n = {}\n        for c1, c2 in zip(s1, s2):\n            if c1.isalpha() and c2.isnumeric():\n                change = True\n                a_n[c1] = c2\n            if c2.isalpha() and c1.isnumeric():\n                change = True\n                a_n[c2] = c1\n        for i in range(N):\n            s1[i] = a_n.get(s1[i], s1[i])\n            s2[i] = a_n.get(s2[i], s2[i])\n\n    assert s1 == s2\n    s = set()\n    ans = 1\n    for i, c in enumerate(s1):\n        if c.isalpha() and c not in s:\n            s.add(c)\n            if i == 0:\n                ans *= 9\n            else:\n                ans *= 10\n    print(ans)\n\nif __name__ == '__main__':\n    main()","sub_path":"Source Codes/AtCoder/arc027/B/1752498.py","file_name":"1752498.py","file_ext":"py","file_size_in_byte":1615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"101055761","text":"import numpy as np\n\nfrom TransientExecutioner import TransientExecutioner, TransientExecutionerParameters\n\nclass ImplicitEulerExecutionerParameters(TransientExecutionerParameters):\n  def __init__(self):\n    TransientExecutionerParameters.__init__(self)\n\nclass ImplicitEulerExecutioner(TransientExecutioner):\n  def __init__(self, params):\n    TransientExecutioner.__init__(self, params)\n    self.nonlinear_solver_params[\"assemble_system_function\"] = self.assembleSystem\n    self.nonlinear_solver_params[\"dof_handler\"] = self.dof_handler\n    self.nonlinear_solver = self.factory.createObject(\"NonlinearSolver\", self.nonlinear_solver_params)\n\n  def assembleSystem(self, U):\n    r_tr, J_tr = self.assembleTransientSystem(U)\n    r_ss, J_ss = self.assembleSteadyStateSystemWithoutStrongBC(U)\n    r = r_tr + self.dt * r_ss\n    J = J_tr + self.dt * J_ss\n    self.applyStrongConstraintsToNonlinearSystem(U, r, J)\n    return (r, J)\n","sub_path":"src/executioners/ImplicitEulerExecutioner.py","file_name":"ImplicitEulerExecutioner.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"520561716","text":"from tkinter import *\r\nimport webbrowser as wb\r\nimport os\r\n#made with love by ishan nagar with Tkinter\r\ndef clickbtn1(event):\r\n\twb.open(\"github.com\")\r\n\r\ndef clickbtn2(event):\r\n\tos.startfile(\"E:\\\\Videos\\\\takeaway.mp4\")\r\n\r\ndef clickbtn3(event):\r\n\twb.open(\"wikipedia.com\")\r\n\r\nroot = Tk()\r\nroot.geometry(\"460x350\")\r\nroot.title(\"Code-in task #2\")\r\nf = Frame(root, bg=\"grey\")\r\n\r\nbtn1 = Button(f, text=\"Open GitHub\", font=\"lucida 30 bold\")\r\n\r\nf.pack()\r\nroot.configure(background=\"grey\")\r\nbtn1.pack(padx=20, pady=20)\r\nbtn1.bind(\"<Button-1>\", clickbtn1)\r\n\r\nf = Frame(root, bg=\"grey\")\r\nbtn2 = Button(f, text=\"Play Video\", font=\"lucida 30 bold\")\r\nf.pack()\r\nbtn2.pack(padx=20, pady=20)\r\nbtn2.bind(\"<Button-1>\", clickbtn2)\r\n\r\nf = Frame(root, bg=\"grey\")\r\nbtn3 = Button(f, text=\"Open Wikipedia\", font=\"lucida 30 bold\")\r\nf.pack()\r\nbtn3.pack(padx=20, pady=20)\r\nbtn3.bind(\"<Button-1>\", clickbtn3)\r\n\r\n\r\nroot.mainloop()","sub_path":"tkinter codein.py","file_name":"tkinter codein.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"306936409","text":"import requests\nimport pyotp\nimport json\n\nfrom django.shortcuts import render, redirect, reverse, HttpResponse\nfrom django.urls import reverse_lazy\nfrom django.contrib.auth import login, authenticate\nfrom django.contrib.auth import get_user_model\nfrom django.contrib.auth.views import LoginView as inV, LogoutView as outV\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.views.generic import View, DetailView, UpdateView, ListView\nfrom django.utils.html import mark_safe, escapejs\n\nfrom django.conf import settings\n\nfrom .forms import SignupForm, LoginForm, SearchForm\nfrom .models import User, UserProfile\nfrom .email_token import account_activation_token\nfrom sec.models import AuthKey\n\n# email import\nfrom django.contrib.sites.shortcuts import get_current_site\nfrom django.utils.encoding import force_bytes, force_text\nfrom django.utils.http import urlsafe_base64_encode, urlsafe_base64_decode\nfrom django.template.loader import render_to_string\nfrom django.core.mail import EmailMessage, send_mail\nfrom django.template import loader\n\n\nclass SignupView(View):\n    def get(self, request):\n        form = SignupForm\n        return render(request, \"accounts/sign_up.html\", {\"form\": form})\n\n    def post(self, request):\n        form = SignupForm(request.POST)\n        if form.is_valid():\n\n            # start reCAPTCHA validation\n            recaptcha_response = request.POST.get(\"g-recaptcha-response\")\n            data = {\n                \"secret\": settings.GOOGLE_RECAPTCHA_SECRET_KEY,\n                \"response\": recaptcha_response,\n            }\n            r = requests.post(\n                \"https://www.google.com/recaptcha/api/siteverify\", data=data\n            )\n            result = r.json()\n\n            if result[\"success\"]:\n                user = form.save(commit=False)\n                user.is_active = False\n                user.save()\n                current_site = get_current_site(request)\n                mail_subject = \"Activate your account.\"\n                message = render_to_string(\n                    \"accounts/activate_email.html\",\n                    {\n                        \"user\": user,\n                        \"domain\": current_site.domain,\n                        \"uid\": urlsafe_base64_encode(force_bytes(user.pk)),\n                        \"token\": account_activation_token.make_token(user),\n                    },\n                )\n                to_email = form.cleaned_data.get(\"email\")\n                email = EmailMessage(mail_subject, message, to=[to_email])\n                email.send()\n                post_mess = (\n                    \"Please confirm your email address to complete the registration.\"\n                )\n                post_mess2 = \"You can now close this tab.\"\n                return render(\n                    request, \"base/mess.html\", {\"mess\": post_mess,\n                                                \"mess2\": post_mess2}\n                )\n\n            else:\n                return render(request, 'accounts/sign_up.html', {'form': form})\n        else:\n            return render(request, 'accounts/sign_up.html', {'form': form})\n\n    def activate(request, uidb64, token):\n        try:\n            uid = force_text(urlsafe_base64_decode(uidb64))\n            user = User.objects.get(pk=uid)\n        except (TypeError, ValueError, OverflowError, User.DoesNotExist):\n            user = None\n        if user is not None and account_activation_token.check_token(user, token):\n            user.is_active = True\n            user.save()\n            login(request, user)\n            return redirect(\"sec:two_fa\")\n        else:\n            return HttpResponse(\"Activation link is invalid!\")\n\n\nclass LoginView(View):\n    def get(self, request):\n        form = LoginForm()\n        return render(request, \"accounts/log_in.html\", {\"form\": form})\n\n    def post(self, request):\n        form = LoginForm(request.POST)\n        if form.is_valid():\n            username = form.cleaned_data.get(\"username\")\n            password = form.cleaned_data.get(\"password\")\n            provided_auth_key = form.cleaned_data.get(\"two_fa\")\n            user = authenticate(username=username, password=password)\n            if user is not None:\n                if hasattr(user, \"two_fa\"):\n                    totp = pyotp.TOTP(user.two_fa.auth_key)\n                    user_auth_key = totp.now()\n                    if user_auth_key == provided_auth_key:\n                        login(request, user)\n                        return redirect(\"home\")\n                    else:\n                        form.add_error(\"two_fa\", \"Invalid 2FA Code\")\n                        return render(request, \"accounts/log_in.html\", {'form': form})\n                else:\n                    form.add_error(\n                        None, \"You should enable 2FA first. Press \\\"Reset 2FA Code\\\" button below\")\n                    return render(request, \"accounts/log_in.html\", {'form': form})\n            else:\n                form.add_error(\n                    None, \"Invalid login credentials / user doesn't exist\")\n                return render(request, \"accounts/log_in.html\", {'form': form})\n        else:\n            form.add_error(None, \"Invalid login credentials\")\n            return render(request, \"accounts/log_in.html\", {'form': form})\n\n\nclass LogoutView(outV):\n    pass\n\n\nclass ProfileView(DetailView):\n    model = User\n    template_name = \"accounts/profile.html\"\n\n    def get_queryset(self):\n        if self.request.user.is_authenticated:\n            return User.objects.filter(pk=self.request.user.id)\n        else:\n            return User.objects.none()\n\n\nclass EditProfile(UpdateView):\n    model = User\n    fields = ['username', 'email', 'country']\n    template_name_suffix = '_update_form'\n\n    def get_queryset(self):\n        if self.request.user.is_authenticated:\n            return User.objects.filter(pk=self.request.user.id)\n        else:\n            return User.objects.none()\n\n    def get_success_url(self):\n        return reverse_lazy('accounts:profile', kwargs={'pk': self.request.user.id})\n\n\nclass Users_Details(View):\n    def get(self, request):\n        meth = 'GET'\n        form = SearchForm\n        return render(request, 'accounts/users_details.html', {'form': form, 'meth': meth})\n\n    def post(self, request):\n        meth = 'POST'\n        form = SearchForm(request.POST)\n        if form.is_valid():\n            username = form.cleaned_data.get('username')\n            country = form.cleaned_data.get('country')\n\n            if username:\n                if country:\n                    r = User.objects.filter(\n                        username__contains=username, country=country)\n                else:\n                    r = User.objects.filter(username__contains=username)\n            else:\n                if country:\n                    r = User.objects.filter(country=country)\n                else:\n                    form.add_error(\n                        'username', 'Enter username or choose country')\n                    return render(request, 'accounts/users_details.html', {'meth': meth, 'form': form})\n\n            return render(request, 'accounts/users_details.html', {'res': r, 'pattern_u': username, 'pattern_c': country, 'meth': meth, 'form': form})\n\n\nclass Rankings(View):\n    def hof(request):\n        queryset = UserProfile.objects.order_by('-points')[:100]\n        return render(request, 'rankings/hof.html', {'data': queryset})\n\n\nclass Map(View):\n    def get(self, request):\n        countries = self.get_countries_dict()\n        countries = json.dumps(countries)\n        return render(request, 'accounts/map.html', {'data': mark_safe(escapejs(countries))})\n\n    def get_countries_dict(self):\n        users = User.objects.all()\n        countries = {}\n\n        for user in users:\n            country = user.country\n            if country not in countries.keys():\n                countries[country] = 1\n            else:\n                countries[country] += 1\n        return countries\n","sub_path":"src/accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"290030318","text":"#%% Change working directory from the workspace root to the ipynb file location. Turn this addition off with the DataScience.changeDirOnImportExport setting\nimport os\ntry:\n\tos.chdir(os.path.join(os.getcwd(), 'learning_note\\python_code'))\n\tprint(os.getcwd())\nexcept:\n\tpass\n#%%\n'''计算一段字符串长度'''\n\ns1 = \"hello world\"\nlength = 0\nfor i in s1:\n\tlength = length+1   \n\t#每次循环增加一个数'''\n\nprint(length)  \n#打印出字符串长度数值'''\n\n\n#%%\ns2 = \"hell eva\"\nlength = 0\nfor i in s2:\n\tlength = length+1\nprint(length)\n\n#%%\nprint(len(s1))\nprint(len(s2))\n\n#%%\ndef mylen():\n    s1 = \"hello world\"\n    \"\"\"计算s1的长度\"\"\"\n    length = 0\n    for i in s1:\n        length = length+1\n    return length\n    \n\n#%%\nstr_len = mylen()\nprint('str_len : %s'%str_len)\n\n#%%\ndef mylen(s1):\n    \"\"\"计算s1的长度\"\"\"\n    length = 0\n    for i in s1:\n        length = length+1\n    return length\n\nstr_len = mylen(\"你好世界，我是新手！\")\n\nprint('str_len : %s'%str_len)\n","sub_path":"python_code/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"626689946","text":"import itertools\n\n# debug_enabled = False\ndebug_enabled = True\n\n#\n#   Tries to improve the results of another solver by iterating over\n#   each meeting in the list, eliminating all double booked meetings\n#   then using the solver to solve for the sublist.\n#\n\nattendee_count = lambda meetings: sum( len(meeting) for meeting in meetings )\n\n\ndef partition_meetings(meetings):\n\n    # return two lists of meetings:\n    # 1. list of meetings which are mutually disjoint\n    # 2. list of meetings having at least one conflict\n\n    disjoint_meetings = []\n    conflict_meetings = []\n\n    for outer_meeting in meetings:\n        is_disjoint = True\n        for inner_meeting in meetings:\n            if not set(inner_meeting).isdisjoint(outer_meeting):\n                is_disjoint = False\n                break\n        if is_disjoint:\n            disjoint_meetings.append(outer_meeting)\n        else:\n            conflict_meetings.append(outer_meeting)\n\n    return disjoint_meetings, conflict_meetings\n\n\ndef plan_meetings(meetings):\n    no_conflict_meetings = []\n    planned_attendees = []\n    for meeting in meetings:\n        if set(meeting).isdisjoint(planned_attendees):\n            planned_attendees += meeting\n            no_conflict_meetings.append(meeting)\n    return no_conflict_meetings\n\n\ndef packed_meetings(meeting_list):\n\n    disjoint_meetings, conflict_meetings = partition_meetings(meeting_list)\n    print(f\"disjoint_meetings{disjoint_meetings} conflict_meetings{conflict_meetings}\")\n\n    conflict_permutations = itertools.permutations(conflict_meetings)\n    print(f\"conflict_permutations_count({len([x for x in conflict_permutations])})\")\n\n    return[]\n\n    # best_meetings = []\n    # for conflict_permutation in conflict_permutations:\n    #     proposed_meetings = plan_meetings(conflict_permutation)\n    #     print(f\"proposed_meetings{proposed_meetings}\")\n    #     if attendee_count(proposed_meetings) > attendee_count(best_meetings):\n    #         best_meetings = proposed_meetings\n    #\n    # print(f\"best_meetings{best_meetings}\")\n    # return best_meetings\n\n\ndef debug(message):\n    if debug_enabled:\n        print(\"     debug: \" + message)\n","sub_path":"hr/pydev38/src/noodnik/clockwise/BruteForceIgnoreDoubleBookings13.py","file_name":"BruteForceIgnoreDoubleBookings13.py","file_ext":"py","file_size_in_byte":2152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"34883352","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# author: bigfoolliu\n\n\n\"\"\"\nredis常见操作\n\"\"\"\n\nimport redis\nfrom threading import Lock\n\n\nclass SingletonThreadSafeMeta(type):\n    \"\"\"\n    线程安全单例类元类\n    任何需要线程安全的单例类都可以继承使用\n    \"\"\"\n    _instance = {}\n    _lock: Lock = Lock()  # 锁对象属性，用于在首次访问单例的时候同步线程\n\n    def __call__(cls, *args, **kwargs):\n        with cls._lock:\n            if cls not in cls._instance:\n                cls._instance[cls] = super().__call__(*args, **kwargs)\n        return cls._instance[cls]\n\n\nclass RedisClient(metaclass=SingletonThreadSafeMeta):\n    \"\"\"\n    redis操作客户端\n    \"\"\"\n\n    def __init__(self, host='localhost', port=6379, db=0):\n        self.r = redis.Redis(host=host, port=port, db=db)\n","sub_path":"language/python/modules/Database/redis/redis_base.py","file_name":"redis_base.py","file_ext":"py","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"321731449","text":"from PIL import Image\nfrom PIL.ExifTags import TAGS\n \ndef get_exif(fn):\n    ret = {}\n    pic = Image.open(fn)\n    info = pic._getexif()\n    for tag, value in info.items():\n        decoded = TAGS.get(tag)\n        ret[decoded] = value\n    return ret\n\ntags = get_exif(\"/Users/lindam/Pictures/fungus.JPG\")\n\nfor tag in tags:\n    print (tag)\n\nprint (tags[\"GPSInfo\"])\n\n","sub_path":"imagedata.py","file_name":"imagedata.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"14419187","text":"\ndef removeDuplicates(nums):\n    \"\"\"\n    :type nums: List[int]\n    :rtype: int\n    \"\"\"\n    j = 0\n    for i in range(0, len(nums) - 1):\n        if nums[i] != nums[j]:\n            nums[j] = nums[i]\n            j += 1\n            continue\n    print(nums)\n\nremoveDuplicates([0, 0, 1, 1, 1, 2, 2, 3, 3, 4])","sub_path":"tk/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"185906164","text":"\"\"\"Bluetooth scanner for esphome.\"\"\"\nfrom __future__ import annotations\n\nfrom aioesphomeapi import BluetoothLEAdvertisement\n\nfrom homeassistant.components.bluetooth import BaseHaRemoteScanner\nfrom homeassistant.core import callback\n\n\nclass ESPHomeScanner(BaseHaRemoteScanner):\n    \"\"\"Scanner for esphome.\"\"\"\n\n    @callback\n    def async_on_advertisement(self, adv: BluetoothLEAdvertisement) -> None:\n        \"\"\"Call the registered callback.\"\"\"\n        # The mac address is a uint64, but we need a string\n        mac_hex = f\"{adv.address:012X}\"\n        self._async_on_advertisement(\n            f\"{mac_hex[0:2]}:{mac_hex[2:4]}:{mac_hex[4:6]}:{mac_hex[6:8]}:{mac_hex[8:10]}:{mac_hex[10:12]}\",\n            adv.rssi,\n            adv.name,\n            adv.service_uuids,\n            adv.service_data,\n            adv.manufacturer_data,\n            None,\n            {\"address_type\": adv.address_type},\n        )\n","sub_path":"homeassistant/components/esphome/bluetooth/scanner.py","file_name":"scanner.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"105377153","text":"from peewee import BigIntegerField, CharField, IntegerField, Model, SmallIntegerField\n\n\n\nclass Block(Model):\n    id = CharField(max_length=64, primary_key=True)\n    version = SmallIntegerField()\n    timestamp = IntegerField(unique=True)\n    previous_block = CharField(max_length=64, null=True, unique=True)\n    height = IntegerField(unique=True)\n    number_of_transactions = IntegerField()\n    total_amount = BigIntegerField()\n    total_fee = BigIntegerField()\n    reward = BigIntegerField()\n    payload_length = IntegerField()\n    payload_hash = CharField(max_length=64)\n    generator_public_key = CharField(max_length=66, index=True)\n    block_signature = CharField(max_length=256)\n\n    class Meta:\n        table_name = 'blocks'\n","sub_path":"ark/plugins/database_peewee/models/block.py","file_name":"block.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"284134160","text":"# coding: utf-8\nfrom __future__ import unicode_literals\n\nimport re\nimport itertools\n\nfrom .common import InfoExtractor\nfrom ..compat import (\n    compat_urllib_parse_urlparse,\n)\nfrom ..utils import (\n    int_or_none,\n    mimetype2ext,\n    url_or_none,\n    urljoin,\n)\n\n\nclass IwaraIE(InfoExtractor):\n    IE_NAME = 'iwara'\n    _VALID_URL = r'https?://(?:www\\.|ecchi\\.)?iwara\\.tv/videos/(?P<id>[a-zA-Z0-9]+)'\n    _TESTS = [{\n        'url': 'http://iwara.tv/videos/amVwUl1EHpAD9RD',\n        # md5 is unstable\n        'info_dict': {\n            'id': 'amVwUl1EHpAD9RD',\n            'ext': 'mp4',\n            'title': '【MMD R-18】ガールフレンド carry_me_off',\n            'age_limit': 18,\n        },\n    }, {\n        'url': 'http://ecchi.iwara.tv/videos/Vb4yf2yZspkzkBO',\n        'md5': '7e5f1f359cd51a027ba4a7b7710a50f0',\n        'info_dict': {\n            'id': '0B1LvuHnL-sRFNXB1WHNqbGw4SXc',\n            'ext': 'mp4',\n            'title': '[3D Hentai] Kyonyu × Genkai × Emaki Shinobi Girls.mp4',\n            'age_limit': 18,\n        },\n        'add_ie': ['GoogleDrive'],\n    }, {\n        'url': 'http://www.iwara.tv/videos/nawkaumd6ilezzgq',\n        # md5 is unstable\n        'info_dict': {\n            'id': '6liAP9s2Ojc',\n            'ext': 'mp4',\n            'age_limit': 18,\n            'title': '[MMD] Do It Again Ver.2 [1080p 60FPS] (Motion,Camera,Wav+DL)',\n            'description': 'md5:590c12c0df1443d833fbebe05da8c47a',\n            'upload_date': '20160910',\n            'uploader': 'aMMDsork',\n            'uploader_id': 'UCVOFyOSCyFkXTYYHITtqB7A',\n        },\n        'add_ie': ['Youtube'],\n    }]\n\n    def _real_extract(self, url):\n        video_id = self._match_id(url)\n\n        webpage, urlh = self._download_webpage_handle(url, video_id)\n\n        hostname = compat_urllib_parse_urlparse(urlh.geturl()).hostname\n        # ecchi is 'sexy' in Japanese\n        age_limit = 18 if hostname.split('.')[0] == 'ecchi' else 0\n\n        video_data = self._download_json('http://www.iwara.tv/api/video/%s' % video_id, video_id)\n\n        if not video_data:\n            iframe_url = self._html_search_regex(\n                r'<iframe[^>]+src=([\\'\"])(?P<url>[^\\'\"]+)\\1',\n                webpage, 'iframe URL', group='url')\n            return {\n                '_type': 'url_transparent',\n                'url': iframe_url,\n                'age_limit': age_limit,\n            }\n\n        title = self._html_search_regex(r'<title>(.+?) \\| Iwara</title>', webpage, 'title')\n\n        formats = []\n        for a_format in video_data:\n            format_uri = url_or_none(a_format.get('uri'))\n            if not format_uri:\n                continue\n            format_id = a_format.get('resolution')\n            height = int_or_none(self._search_regex(\n                r'(\\d+)p', format_id, 'height', default=None))\n            formats.append({\n                'url': self._proto_relative_url(format_uri, 'https:'),\n                'format_id': format_id,\n                'ext': mimetype2ext(a_format.get('mime')) or 'mp4',\n                'height': height,\n                'width': int_or_none(height / 9.0 * 16.0 if height else None),\n                'quality': 1 if format_id == 'Source' else 0,\n            })\n\n        self._sort_formats(formats)\n\n        return {\n            'id': video_id,\n            'title': title,\n            'age_limit': age_limit,\n            'formats': formats,\n        }\n\n\nclass IwaraUserIE(InfoExtractor):\n    IE_NAME = 'iwara:user'\n    _VALID_URL = r'https?://(?:www\\.|ecchi\\.)?iwara\\.tv/users/(?P<id>[^/]+)'\n    _TESTS = [{\n        # cond: videos < 40\n        'note': 'number of all videos page is just 1 page',\n        'url': 'https://ecchi.iwara.tv/users/infinityyukarip',\n        'info_dict': {\n            'title': 'Uploaded videos from Infinity_YukariP',\n            'id': 'infinityyukarip',\n            'uploader': 'Infinity_YukariP',\n            'uploader_id': 'infinityyukarip',\n        },\n        'playlist_mincount': 39,\n    }, {\n        # cond: videos < 10?\n        'note': 'no even all videos page',\n        'url': 'https://ecchi.iwara.tv/users/mmd-quintet',\n        'info_dict': {\n            'title': 'Uploaded videos from mmd quintet',\n            'id': 'mmd-quintet',\n            'uploader': 'mmd quintet',\n            'uploader_id': 'mmd-quintet',\n        },\n        'playlist_mincount': 6,\n    }, {\n        # cond: videos > 40\n        'note': 'has paging',\n        'url': 'https://ecchi.iwara.tv/users/theblackbirdcalls',\n        'info_dict': {\n            'title': 'Uploaded videos from TheBlackbirdCalls',\n            'id': 'theblackbirdcalls',\n            'uploader': 'TheBlackbirdCalls',\n            'uploader_id': 'theblackbirdcalls',\n        },\n        'playlist_mincount': 420,\n    }, {\n        # cond: Japanese chars in URL\n        'note': 'Japanese chars in URL',\n        'url': 'https://ecchi.iwara.tv/users/ぶた丼',\n        'info_dict': {\n            'title': 'Uploaded videos from ぶた丼',\n            'id': 'ぶた丼',\n            'uploader': 'ぶた丼',\n            'uploader_id': 'ぶた丼',\n        },\n        'playlist_mincount': 170,\n    }]\n\n    @classmethod\n    def suitable(cls, url):\n        return super(IwaraUserIE, cls).suitable(url) and not re.search(\n            r'iwara\\.tv/users/[^/]+/videos', url)\n\n    def _real_extract(self, url):\n        video_id = self._match_id(url)\n        webpage = self._download_webpage(url, video_id)\n        videos_url = self._search_regex(r'<a href=\"(/users/[^/]+/videos)\">', webpage, 'all videos url', default=None)\n\n        uploader = self._search_regex(r'<h2>([^<]+?)</h2>', webpage, 'uploader name', default=video_id)\n        title = 'Uploaded videos from %s' % uploader\n\n        if not videos_url:\n            videos_webpage_iter = [webpage]\n        else:\n            videos_base_url = urljoin(url, videos_url)\n\n            def do_paging():\n                for i in itertools.count():\n                    if i == 0:\n                        videos_page_url = videos_base_url\n                    else:\n                        videos_page_url = urljoin(videos_base_url, '?page=%d' % i)\n                    videos_webpage = self._download_webpage(videos_page_url, video_id, note='Downloading video list %d' % (i + 1))\n                    yield videos_webpage\n                    if not '?page=%d' % (i + 1) in videos_webpage:\n                        break\n\n            videos_webpage_iter = do_paging()\n\n        results = []\n        for page in videos_webpage_iter:\n            for x in re.finditer(r'<a href=\"(/videos/[^\"]+)\">', page):\n                results.append(x.group(1))\n\n        playlist = self.playlist_result([self.url_result(urljoin(url, x))\n                                         for x in dict.fromkeys(results)], video_id, title)\n        playlist.update({\n            'uploader': uploader,\n            'uploader_id': video_id,\n        })\n        return playlist\n\n\nclass IwaraUser2IE(InfoExtractor):\n    IE_NAME = 'iwara:user2'\n    _VALID_URL = r'https?://(?:www\\.|ecchi\\.)?iwara\\.tv/users/(?P<id>[^/]+)/videos'\n    IE_DESC = False  # do not list this\n    _TESTS = [{\n        'note': 'number of all videos page is just 1 page',\n        'url': 'https://ecchi.iwara.tv/users/infinityyukarip/videos',\n        'info_dict': {},\n        'add_ie': [IwaraUserIE.ie_key()],\n    }, {\n        'note': 'no even all videos page',\n        'url': 'https://ecchi.iwara.tv/users/mmd-quintet/videos',\n        'info_dict': {},\n        'add_ie': [IwaraUserIE.ie_key()],\n    }, {\n        'note': 'has paging',\n        'url': 'https://ecchi.iwara.tv/users/theblackbirdcalls/videos',\n        'info_dict': {},\n        'add_ie': [IwaraUserIE.ie_key()],\n    }, {\n        'note': 'Japanese chars in URL',\n        'url': 'https://ecchi.iwara.tv/users/ぶた丼/videos',\n        'info_dict': {},\n        'add_ie': [IwaraUserIE.ie_key()],\n    }]\n\n    def _real_extract(self, url):\n        video_id = self._match_id(url)\n        webpage = self._download_webpage(url, video_id, note='Repairing URL')\n        videos_url = self._search_regex(r'<a href=\"(/users/.+?)\"(?: title=\".+?\")? class=\"username\">', webpage, 'user page url')\n        videos_url = urljoin(url, videos_url)\n        return self.url_result(videos_url, ie=IwaraUserIE.ie_key())\n","sub_path":"youtube_dl/extractor/iwara.py","file_name":"iwara.py","file_ext":"py","file_size_in_byte":8250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"409445277","text":"\nimport re\n\n# f = open('melon.html', 'rt')\n# source = f.read()\n# f.close()\n\nPATTERN_DIV_RANK01 = re.compile(r'<div class=\"ellipsis rank01\">.*?</div>', re.DOTALL)\nPATTERN_A_CONTENT = re.compile(r'<a.*?>(.*?)</a>')\n\nsource = open('melon.html', 'rt').read()\n\n\nfor match_div_rank01 in re.finditer(PATTERN_DIV_RANK01, source):\n    div_rank01_content = match_div_rank01.group()\n    title = re.search(PATTERN_A_CONTENT, div_rank01_content).group(1)\n    print(title)\n\n\n# result = re.findall(pattern_div_rank01, source)\n# for index, item in enumerate(result):\n#     print(index, item)\n","sub_path":"regex/melon_title.py","file_name":"melon_title.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"247313654","text":"\n\n#calss header\nclass _ATTAIN():\n\tdef __init__(self,): \n\t\tself.name = \"ATTAIN\"\n\t\tself.definitions = [u'to reach or succeed in getting something: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'verbs'\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/verbs/_attain.py","file_name":"_attain.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"90608564","text":"'''\nYou are given a series of elements construct a binary search tree based on these elements. Please do not try to optimize / balance the tree in anyway.\n\n_Q1: Given a number T return_\n```\"True\" if it exists in the BST.\n- \"Not found, how about X\", where X is the number closest to the number you are seeking. In case of a tie, prefer the larger number.\n```\n\n_Q2: What if you need to return the following_\n```- \"True\" if it exists in the BST.\n- \"Not found, how about X\" if there exists an element X such that X is within 1 standard deviation from the given number T. Return the smallest number that is within 1 standard deviation.\n- \"Not found, nothing close\" - if none of the numbers in the BST are within 1 standard deviation from the given number.\n'''\nclass Node:\n    def __init__(self, val=None):\n        self.left = None\n        self.right = None\n        self.val = val\n\nclass BST:\n    def __init__(self):\n        self.root = Node()\n    \n    def make_bst(self, data):\n        data = sorted(data)\n        self.make_bst_helper(self.root, data, 0, len(data)-1)\n        return self.root\n    \n    def make_bst_helper(self, root, data, i, j):\n        if root is None:\n            return\n        if i == j:\n            root.val = data[i]\n            return\n        if i > j:\n            return\n         \n        mid_idx = self.mid(i, j)\n        root.val = data[mid_idx]\n        root.left = Node()\n        root.right = Node()\n        self.make_bst_helper(root.left, data, i, mid_idx - 1)\n        self.make_bst_helper(root.right, data, mid_idx + 1, j)\n    \n    def mid(self, i, j):\n        return round((i + j)/2)\n    \n    def inorder_traversal(self):\n        self.inorder_traversal_helper(self.root)\n    \n    def inorder_traversal_helper(self, root):\n        if root is None:\n            return\n        \n        self.inorder_traversal_helper(root.left)\n        if root.val is not None:\n            print(root.val)\n        self.inorder_traversal_helper(root.right)\n\na = list(range(20, 0, -1))\nprint(a)\ntree = BST()\ntree.make_bst(a)\nprint(tree.root.val)\ntree.inorder_traversal()\n","sub_path":"QoTD/QToD_09-26-2016.py","file_name":"QToD_09-26-2016.py","file_ext":"py","file_size_in_byte":2074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"455872931","text":"\"\"\"Run SQL commands for API.\"\"\"\r\nimport os\r\nimport sqlite3\r\n\r\nDATABASE = os.path.join(\r\n    os.path.dirname(os.path.dirname(os.path.realpath(__file__))),\r\n    'var', 'spotify.sqlite3'\r\n)\r\n\r\n\r\ndef add_user(id, display_name, email):\r\n    \"\"\"Add new user to users table.\"\"\"\r\n    db = sqlite3.connect(DATABASE)\r\n    db.cursor().execute(\"\"\"INSERT INTO users(id, \r\n                        display_name, email) SELECT ?,?,? WHERE \r\n                        NOT EXISTS (SELECT * FROM users WHERE id = ?)\r\n                        \"\"\",\r\n                        (id, display_name, email, id))\r\n    db.commit()\r\n    db.close()\r\n\r\n\r\ndef add_track(id, track_name, popularity, album_id, album_name, album_pop, \\\r\n    artists_ids_json, artist_names_json, date, date_prec, features_json):\r\n    \"\"\"Add track to tracks table.\"\"\"\r\n    db = sqlite3.connect(DATABASE)\r\n    db.cursor().execute(\"\"\"INSERT INTO tracks(id, track_name,  \r\n                        popularity, album_id, album_name, album_popularity,\r\n                        artist_ids, artist_names, release_date,\r\n                        release_date_precision, danceability, \r\n                        energy, music_key, loudness, mode, speechiness, \r\n                        acousticness, instrumentalness, liveness, valence, \r\n                        tempo, duration_ms, time_signature) \r\n                        SELECT ?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,? WHERE \r\n                        NOT EXISTS (SELECT * FROM tracks WHERE id = ?)\r\n                        \"\"\",\r\n                        (id, track_name, popularity, album_id, album_name,\r\n                         album_pop, artists_ids_json, artist_names_json, date,\r\n                         date_prec,\r\n                         features_json['danceability'], features_json['energy'],\r\n                         features_json['key'], features_json['loudness'],\r\n                         features_json['mode'], features_json['speechiness'],\r\n                         features_json['acousticness'], features_json['instrumentalness'],\r\n                         features_json['liveness'], features_json['valence'],\r\n                         features_json['tempo'], features_json['duration_ms'],\r\n                         features_json['time_signature'], id))\r\n    db.commit()\r\n    db.close()\r\n\r\n\r\ndef track_exists_in_db(track_id):\r\n    \"\"\"Add new user to users table.\"\"\"\r\n    db = sqlite3.connect(DATABASE)\r\n    exists = db.cursor().execute(\"\"\"SELECT COUNT(*) FROM tracks\r\n                                    WHERE id = '{}'\"\"\".format(track_id)).fetchone()[0]\r\n    db.close()\r\n    return exists","sub_path":"api/sql_helper.py","file_name":"sql_helper.py","file_ext":"py","file_size_in_byte":2595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"20968329","text":"# Copyright (c) 2013, 2018 National Technology and Engineering Solutions of Sandia, LLC . Under the terms of Contract\n# DE-NA0003525 with National Technology and Engineering Solutions of Sandia, LLC, the U.S. Government\n# retains certain rights in this software.\n\ndef register_slycat_plugin(context):\n    \"\"\"Called during startup when the plugin is loaded.\"\"\"\n    import cherrypy\n    import datetime\n    import time\n    import os\n    import json\n    import slycat.web.server\n    import threading\n    import sys\n    import traceback\n    import numpy\n    import re\n    import couchdb\n    import statistics\n    import io\n    import tarfile\n    try:\n        import cpickle as pickle\n    except ImportError:\n        import pickle\n    thread_pool={}\n\n    def media_columns(database, model, verb, type, command, **kwargs):\n        \"\"\"\n        Identify columns in the input data that contain media URIs (image or video).\n        :param kwargs:\n        :param command:\n        :param type:\n        :param verb:\n        :param model:\n          model ID in the data base\n        :param database:\n          our connection to couch db\n        \"\"\"\n        expression = re.compile(\"file://\")\n        search = numpy.vectorize(lambda x: bool(expression.search(x)))\n\n        columns = []\n        metadata = slycat.web.server.get_model_arrayset_metadata(database, model, \"inputs\", \"0\")[\"arrays\"][0]\n        for index, attribute in enumerate(metadata[\"attributes\"]):\n            if attribute[\"type\"] != \"string\":\n                continue\n            column = slycat.web.server.get_model_arrayset_data(database, model, \"inputs\", \"0/%s/...\" % index)\n            if not numpy.any(search(column)):\n                continue\n            columns.append(index)\n\n        cherrypy.response.headers[\"content-type\"] = \"application/json\"\n        return json.dumps(columns)\n\n    def finish(model_id):\n        \"\"\"\n        Update the model in the databse as successfully completed.\n\n        :param model_id: uid of the model\n        \"\"\"\n        database = slycat.web.server.database.couchdb.connect()\n        model = database.get(\"model\", model_id)\n        slycat.web.server.update_model(database, model, state=\"finished\", result=\"succeeded\",\n                                       finished=datetime.datetime.utcnow().isoformat(), progress=100, message=\"timeseries model finished uploading all data\")\n\n    def fail_model(mid, message):\n        \"\"\"\n        Update the model as failed.\n    \n        :param mid:     model ID\n        :param message: reason for the model failure\n        \"\"\"\n        database = slycat.web.server.database.couchdb.connect()\n        model = database.get(\"model\", mid)\n        slycat.web.server.update_model(database, model, state=\"finished\", result=\"failed\",\n                                       finished=datetime.datetime.utcnow().isoformat(), message=message)\n\n    def get_remote_file_server(hostname, model, filename, total_file_delta_time = [], calling_client=None):\n        \"\"\"\n        Utility function to fetch remote files.\n        :param hostname:\n        :param username:\n        :param filename: Full filename for the requested file\n        :return: tuple with session ID and file content\n        \"\"\"\n        sid = get_sid(hostname, model)\n        with slycat.web.server.remote.get_session(sid, calling_client) as session:\n          import time\n          start = time.time()\n          file = session.get_file(filename)\n          end = time.time()\n          delta_time = (end - start)\n          total_file_delta_time.append(delta_time)\n          return file\n\n    def get_sid(hostname, model):\n        \"\"\"\n\n        :param hostname:\n        :param model:\n        :return:\n        \"\"\"\n        sid = None\n        try:\n            database = slycat.web.server.database.couchdb.connect()\n            sessions = [session for session in database.scan(\"slycat/sessions\") if\n                        session[\"creator\"] == model[\"creator\"]]\n            if len(sessions) > 1:\n                cherrypy.log.error(\"to many user sessions %s\" % str(sessions))\n                raise Exception(\"to many user sessions\")\n            for index, host_session in enumerate(sessions[0][\"sessions\"]):\n                if host_session[\"hostname\"] == hostname:\n                    sid = host_session[\"sid\"]\n                    if(not slycat.web.server.remote.check_session(sid)):\n                        cherrypy.log.error(\"error session %s SID:%s Keys %s\" % (slycat.web.server.remote.check_session(sid), sid, list(slycat.web.server.remote.session_cache.keys())))\n                        slycat.web.server.remote.delete_session(sid)\n                        del session[\"sessions\"][index]\n                        database.save(session)\n                        raise cherrypy.HTTPError(\"404\")\n                    break\n        except Exception as e:\n            cherrypy.log.error(traceback.format_exc())\n            cherrypy.log.error(\"Timeseries model compute exception type: %s\" % sys.exc_info()[0])\n            cherrypy.log.error(\"Timeseries model compute exception value: %s\" % sys.exc_info()[1])\n            cherrypy.log.error(\"Timeseries model compute exception traceback: %s\" % sys.exc_info()[2])\n            cherrypy.log.error(\"could not retrieve host session for remotes %s\" % e)\n            raise cherrypy.HTTPError(\"404\")\n        if sid is None:\n            raise cherrypy.HTTPError(\"400 session is None value\")\n        return sid\n\n    def helpGetFile(filename, use_tar, hostname, model, total_file_delta_time,calling_client, input_tar):\n        \"\"\"\n        help determin how to get a file either through\n        extracting from a tar file or from grabbing the file remotely\n        \n        Arguments:\n            filename {[type]} -- file path\n            use_tar {[type]} -- flag for if it should use the tar\n            hostname {[type]} -- name of the host system\n            model {[type]} -- model from the DB\n            total_file_delta_time {[type]} -- array of file load times\n            calling_client {[type]} -- ip of the calling client\n            input_tar {[type]} -- tar file to read from \n        \n        Returns:\n            file -- in memory file\n        \"\"\"\n        if use_tar:\n            return input_tar.extractfile(filename).read()\n        else:\n            return get_remote_file_server(hostname, model,\n                                          filename,\n                                          total_file_delta_time,\n                                          calling_client)\n\n    def compute(model_id, stop_event, calling_client):\n        \"\"\"\n        Computes the Time Series model. It fetches the necessary files from a\n        remote server that were computed by the slycat-agent-compute-timeseries.py\n        script.\n    \n        :param model_id: uid for the model in the database\n        :param sid:      session ID\n        :param uid:      user ID\n        :param workdir:\n        :param hostname:\n        :param username:\n        \"\"\"\n        try:\n            total_file_delta_time = []\n            #cherrypy.log.error(\"in thread\")\n            # workdir += \"/slycat/pickle\"  # route to the slycat directory\n            start_time = time.time()\n            database = slycat.web.server.database.couchdb.connect()\n            model = database.get(\"model\", model_id)\n            model[\"model_compute_time\"] = datetime.datetime.utcnow().isoformat()\n            with slycat.web.server.get_model_lock(model[\"_id\"]):\n                database.save(model)\n            slycat.web.server.update_model(database, model, state=\"waiting\", message=\"starting data pull Timeseries\")\n            model = database.get(\"model\", model_id)\n            uid = slycat.web.server.get_model_parameter(database, model, \"pickle_uid\")\n            workdir_raw = slycat.web.server.get_model_parameter(database, model, \"working_directory\")\n            workdir = workdir_raw + \"pickle\"\n            hostname = slycat.web.server.get_model_parameter(database, model, \"hostname\")\n            username = slycat.web.server.get_model_parameter(database, model, \"username\")\n\n            # get an active session\n            sid = get_sid(hostname, model)\n            # load inputs\n            slycat.web.server.update_model(database, model, progress=50, message=\"loading inputs\")\n            use_tar = True\n            # keep this blank unless we need it\n            pickle_path = ''\n            input_tar=None\n            try:\n                myfiles_tar_gz = get_remote_file_server(hostname, model,\n                                                \"%s/slycat_timeseries_%s/slycat-timeseries.tar.gz\" % (workdir, uid),\n                                                total_file_delta_time,\n                                                calling_client)\n                myfiles_tar_gz = io.BytesIO(myfiles_tar_gz)\n                input_tar = tarfile.open(fileobj=myfiles_tar_gz, mode=\"r:gz\")\n            except:\n                  # looks like the file is too large lets just grab one file at a time\n                  use_tar = False\n                  pickle_path = \"%s/slycat_timeseries_%s/\" % (workdir, uid)\n            inputs = helpGetFile(\"%sarrayset_inputs.pickle\" % (pickle_path),\n                                    use_tar, hostname, model, total_file_delta_time,calling_client, input_tar)\n            inputs = pickle.loads(inputs)\n\n            # Decoding potential byte strings\n            class MyEncoder(json.JSONEncoder):\n                def default(self, obj):\n                    if isinstance(obj, numpy.integer):\n                        return int(obj)\n                    elif isinstance(obj, numpy.floating):\n                        return float(obj)\n                    elif isinstance(obj, numpy.ndarray):\n                        return obj.tolist()\n                    elif type(obj) is bytes:\n                        return str(obj.decode())\n                    else:\n                        return super(MyEncoder, self).default(obj)\n\n            inputs = json.loads(json.dumps(inputs, cls=MyEncoder))\n\n            slycat.web.server.put_model_arrayset(database, model, inputs[\"aid\"])\n            # load attributes\n            slycat.web.server.update_model(database, model, progress=55, message=\"loading attributes\")\n            attributes = inputs[\"attributes\"]\n            slycat.web.server.put_model_array(database, model, inputs[\"aid\"], 0, attributes, inputs[\"dimensions\"])\n            # load attribute data\n            data = helpGetFile(\"%sinputs_attributes_data.pickle\" % (pickle_path),\n                                use_tar, hostname, model, total_file_delta_time,calling_client, input_tar)\n            attributes_data = pickle.loads(data)\n\n            # push attribute arraysets\n            # TODO this can become multi processored\n            for attribute in range(len(attributes)):\n                model = database.get(\"model\", model[\"_id\"])\n                slycat.web.server.put_model_arrayset_data(database, model, inputs[\"aid\"], \"0/%s/...\" % attribute,\n                                                          [attributes_data[attribute]])\n            # load clusters data\n            slycat.web.server.update_model(database, model, progress=60, message=\"loading cluster data\")\n            clusters = helpGetFile(\"%sfile_clusters.json\" % (pickle_path),\n                                    use_tar, hostname, model, total_file_delta_time,calling_client, input_tar)\n            clusters = json.loads(clusters)\n            clusters_file = json.JSONDecoder().decode(clusters[\"file\"])\n            timeseries_count = json.JSONDecoder().decode(clusters[\"timeseries_count\"])\n            slycat.web.server.post_model_file(model[\"_id\"], True, sid,\n                                              \"%s/slycat_timeseries_%s/file_clusters.out\" % (workdir, uid),\n                                              clusters[\"aid\"], clusters[\"parser\"], client=calling_client)\n            # TODO this can become multi processored\n            cherrypy.log.error(\"Pulling timeseries computed data\")\n            slycat.web.server.update_model(database, model, progress=65, message=\"Pulling timeseries computed data for %s cluster files\" % len(clusters_file))\n            progress = 65\n            progress_part = 30/len(clusters_file)\n            for file_name in clusters_file:\n                progress = progress + progress_part\n                slycat.web.server.update_model(database, model, progress=progress, message=\"loading %s cluster file\" % file_name)\n                file_cluster_data = helpGetFile(\"%sfile_cluster_%s.json\" % (pickle_path, file_name),\n                                    use_tar, hostname, model, total_file_delta_time,calling_client, input_tar)\n                file_cluster_attr = json.loads(file_cluster_data)\n                slycat.web.server.post_model_file(model[\"_id\"], True, sid,\n                                                  \"%s/slycat_timeseries_%s/file_cluster_%s.out\" % (\n                                                      workdir, uid, file_name),\n                                                  file_cluster_attr[\"aid\"], file_cluster_attr[\"parser\"], client=calling_client)\n                database = slycat.web.server.database.couchdb.connect()\n                model = database.get(\"model\", model[\"_id\"])\n                slycat.web.server.put_model_arrayset(database, model, \"preview-%s\" % file_name)\n\n                waveform_dimensions_data = helpGetFile(\"%swaveform_%s_dimensions.pickle\" % (pickle_path, file_name),\n                                    use_tar, hostname, model, total_file_delta_time,calling_client, input_tar)\n                waveform_dimensions_array = pickle.loads(waveform_dimensions_data)\n\n                waveform_attributes_data = helpGetFile(\"%swaveform_%s_attributes.pickle\" % (pickle_path, file_name),\n                                    use_tar, hostname, model, total_file_delta_time,calling_client, input_tar)\n                waveform_attributes_array = pickle.loads(waveform_attributes_data)\n\n                waveform_times_data = helpGetFile(\"%swaveform_%s_times.pickle\" % (pickle_path, file_name),\n                                    use_tar, hostname, model, total_file_delta_time,calling_client, input_tar)\n                waveform_times_array = pickle.loads(waveform_times_data)\n\n                waveform_values_data = helpGetFile(\"%swaveform_%s_values.pickle\" % (pickle_path, file_name),\n                                    use_tar, hostname, model, total_file_delta_time,calling_client, input_tar)\n                waveform_values_array = pickle.loads(waveform_values_data)\n\n                for index in range(int(timeseries_count)):\n                    try:\n                        model = database.get(\"model\", model[\"_id\"])\n                        slycat.web.server.put_model_array(database, model, \"preview-%s\" % file_name, index,\n                                                          waveform_attributes_array[index],\n                                                          waveform_dimensions_array[index])\n                        model = database.get(\"model\", model[\"_id\"])\n                        slycat.web.server.put_model_arrayset_data(database, model, \"preview-%s\" % file_name,\n                                                                  \"%s/0/...;%s/1/...\" % (index, index),\n                                                                  [waveform_times_array[index],\n                                                                    waveform_values_array[index]])\n                    except:\n                        cherrypy.log.error(\"failed on index: %s\" % index)\n                        pass\n            if input_tar:\n                input_tar.close()\n            database = slycat.web.server.database.couchdb.connect()\n            model = database.get(\"model\", model_id)\n            slycat.web.server.update_model(database, model, message=\"finished loading all data\")\n            slycat.web.server.put_model_parameter(database, model, \"computing\", False)\n            cherrypy.log.error(\"finished Pulling timeseries computed data\")\n            finish_time = time.time()\n            file_stats = {\n              \"min\": min(total_file_delta_time),\n              \"max\": max(total_file_delta_time),\n              \"mean\": statistics.mean(total_file_delta_time),\n              \"median\": statistics.median(total_file_delta_time),\n              \"number_of_files_pulled\":len(total_file_delta_time),\n              \"total_time_Pulling_data\": sum(total_file_delta_time),\n              \"total_time\": (finish_time - start_time)\n            }\n            cherrypy.log.error(\"File Stats %s\" % str(file_stats))\n            total_file_delta_time = []\n            finish(model[\"_id\"])\n            stop_event.set()\n\n            # TODO add finished to the model state\n            # TODO add remove dir command by uncommenting below\n            # payload = {\n            #     \"action\": \"run_remote_command\",\n            #     \"command\": (\"rm -rf %s\" % workdir_raw)\n            # }\n        except cherrypy._cperror.HTTPError as e:\n            database = slycat.web.server.database.couchdb.connect()\n            model = database.get(\"model\", model_id)\n            slycat.web.server.put_model_parameter(database, model, \"computing\", False)\n            cherrypy.log.error(traceback.format_exc())\n            cherrypy.log.error(\"Timeseries cperror model compute exception type: %s\" % sys.exc_info()[0])\n            cherrypy.log.error(\"Timeseries model compute exception value: %s\" % sys.exc_info()[1])\n            cherrypy.log.error(\"Timeseries model compute exception traceback: %s\" % sys.exc_info()[2])\n            stop_event.set()\n        except:\n            database = slycat.web.server.database.couchdb.connect()\n            model = database.get(\"model\", model_id)\n            slycat.web.server.put_model_parameter(database, model, \"computing\", False)\n            cherrypy.log.error(traceback.format_exc())\n            cherrypy.log.error(\"Timeseries model compute exception type: %s\" % sys.exc_info()[0])\n            cherrypy.log.error(\"Timeseries model compute exception value: %s\" % sys.exc_info()[1])\n            cherrypy.log.error(\"Timeseries model compute exception traceback: %s\" % sys.exc_info()[2])\n            stop_event.set()\n\n        file = get_remote_file_server(hostname, model, \"/home/%s/slurm-%s.out\" % (username, model[\"artifact:jid\"]), \n                                total_file_delta_time, \n                                calling_client)\n\n        pulling_time = finish_time - start_time\n        compute_start_time = file.decode('utf-8').split('[START]')\n        compute_finish_time = file.decode('utf-8').split('[FINISH]')\n        compute_run_time = file.decode('utf-8').split('[RUN TIME]')\n\n        database = slycat.web.server.database.couchdb.connect()\n        model = database.get(\"model\", model_id)\n        model[\"model_delta_time\"] = str(compute_run_time[1].split('\\n')[0])\n        model[\"pulling_time\"] = pulling_time\n        with slycat.web.server.get_model_lock(model[\"_id\"]):\n            database.save(model)\n\n    def get_job_status(hostname, jid):\n        \"\"\"\n        returns the job status of a running timeseries job from the cluster called from a thread\n        :param tries: number of tries left to use this function\n        :param mid: model id\n        :param sid: session id for ssh\n        :param jid: job id for hpc\n        :param stop_event: thread stop event\n        :return:\n        \"\"\"\n        try:\n            response = slycat.web.server.handlers.get_checkjob(hostname, jid)\n        except Exception as e:\n            cherrypy.log.error(\"Something went wrong while checking on job %s status %s check ssh session\" % (jid,str(e)))\n            return {\"status\": {\"state\": \"ERROR\"}}\n        return response\n\n    def update_remote_job(mid, jid, hostname, calling_client):\n        \"\"\"\n        Routine that checks on the status of remote\n        jobs running on a SLURM infrastructure.\n\n        :param mid:          model ID\n        :param sid:          session ID\n        :param jid:          job ID\n        :param request_from:\n        :param stop_event:   event stopping the thread when the job completes\n        :param callback:     callback methods when the job successfully completes\n        \"\"\"\n\n        # get the status of the job\n        cherrypy.log.error(\"[Timeseries] Getting job status\")\n        state = get_job_status(hostname, jid)[\"status\"][\"state\"]\n        database = slycat.web.server.database.couchdb.connect()\n        model = database.get(\"model\", mid)\n        if state == 'ERROR':\n            slycat.web.server.update_model(database, model, progress=0, message=\"Error\")\n            slycat.web.server.put_model_parameter(database, model, \"computing\", False)\n            raise cherrypy.HTTPError(\"409 error connecting to check on the job\")\n        cherrypy.log.error(\"[Timeseries] checkjob %s returned with status %s\" % (jid, state))\n\n        if state in [\"RUNNING\", \"PENDING\"]:\n            if state == \"RUNNING\":\n                slycat.web.server.update_model(database, model, progress=10, message=\"Job is in pending state\")\n            else:\n                slycat.web.server.update_model(database, model, progress=5, message=\"Job is in pending state\")\n            slycat.web.server.put_model_parameter(database, model, \"computing\", False)\n            if \"job_running_time\" not in model and state == \"RUNNING\":\n                model = database.get(\"model\", model[\"_id\"])\n                model[\"job_running_time\"] = datetime.datetime.utcnow().isoformat()\n                with slycat.web.server.get_model_lock(model[\"_id\"]):\n                    database.save(model)\n\n        if state in [\"CANCELLED\", \"REMOVED\", \"VACATED\"]:\n            slycat.web.server.put_model_parameter(database, model, \"computing\", False)\n            fail_model(mid, \"Job %s was cancelled. Exit code %s\" % (jid, state))\n\n        if state == \"COMPLETED\":\n            slycat.web.server.update_model(database, model, progress=50, message=\"Job is in Completed state\")\n            if \"job_running_time\" not in model:\n                model = database.get(\"model\", model[\"_id\"])\n                model[\"job_running_time\"] = datetime.datetime.utcnow().isoformat()\n                with slycat.web.server.get_model_lock(model[\"_id\"]):\n                    database.save(model)\n            if \"job_completed_time\" not in model:\n                model = database.get(\"model\", model[\"_id\"])\n                model[\"job_completed_time\"] = datetime.datetime.utcnow().isoformat()\n                with slycat.web.server.get_model_lock(model[\"_id\"]):\n                    database.save(model)\n            \"\"\"\n            Callback for a successful remote job completion. It computes the model\n            and successfully completes it.\n            \"\"\"\n            cherrypy.log.error(\"calling compute\")\n                # now start thread to prevent timing out on large files\n\n            stop_event = threading.Event()\n            # compute(model[\"_id\"], stop_event, calling_client)\n            thread = threading.Thread(target=compute, args=(model[\"_id\"], stop_event, calling_client))\n            thread_pool[model[\"_id\"]] = thread\n            thread_pool[model[\"_id\"]].start()\n\n        if state == [\"FAILED\", \"UNKNOWN\", \"NOTQUEUED\"]:\n            cherrypy.log.error(\"Something went wrong with job %s job state:\" % (jid, state))\n            slycat.web.server.update_model(database, model, message=\"Job %s had returned a bad or unknown state from the hpc system\" % jid)\n            slycat.web.server.put_model_parameter(database, model, \"computing\", False)\n\n    def update_model_info(database, model, verb, type, command, **kwargs):\n        \"\"\"\n        Starts a routine to continuously check the status of a remote job.\n        :param database:\n        :param model:\n        :param kwargs: arguments contain hostname, username, jid,\n                       function name and parameters, UID\n        \"\"\"\n        slycat.web.server.update_model(database, model, progress=1, message=\"Job has been sent to slurm\")\n        model_params = {\n          \"working_directory\": kwargs[\"working_directory\"],\n          \"username\": kwargs[\"username\"],\n          \"hostname\": kwargs[\"hostname\"],\n          \"pickle_uid\": kwargs[\"uid\"],\n          \"jid\": kwargs[\"jid\"],\n          \"fn\": kwargs[\"fn\"],\n          \"fn_params\": kwargs[\"fn_params\"],\n          \"job_submit_time\": datetime.datetime.utcnow().isoformat()\n          }\n        for key, value in model_params.items():\n            slycat.web.server.put_model_parameter(database, model, key, value, input=False)\n\n    def pull_data(database, model, verb, type, command, **kwargs):\n        \"\"\"\n        check if a data pull is allowed\n        :param mid: model id\n        :return:\n        \"\"\"\n        calling_client = cherrypy.request.headers.get(\"x-forwarded-for\")\n        database = slycat.web.server.database.couchdb.connect()\n        model = database.get(\"model\", model[\"_id\"])\n        try:\n            cherrypy.log.error(\"computing model value:\" + str(slycat.web.server.get_model_parameter(database, model, \"computing\")))\n            if model[\"_id\"] in thread_pool:\n                if thread_pool[model[\"_id\"]].is_alive():\n                    cherrypy.log.error(\"computing thread is alive for model %s\"%str(model[\"_id\"]))\n                else:\n                    cherrypy.log.error(\"computing thread is dead for model %s setting compute to false\"%str(model[\"_id\"]))\n                    del thread_pool[model[\"_id\"]]\n                    slycat.web.server.put_model_parameter(database, model, \"computing\", False)\n            else:\n                slycat.web.server.put_model_parameter(database, model, \"computing\", False)\n        except KeyError:\n            slycat.web.server.put_model_parameter(database, model, \"computing\", False)\n            model = database.get(\"model\", model[\"_id\"])\n        if model[\"state\"] == \"finished\":\n            raise cherrypy.HTTPError(\"409 model is in the finished state already\")\n        if not slycat.web.server.get_model_parameter(database, model, \"computing\"):\n            slycat.web.server.put_model_parameter(database, model, \"computing\", True)\n            update_remote_job(model[\"_id\"], model[\"artifact:jid\"], model[\"artifact:hostname\"], calling_client)\n            cherrypy.response.headers[\"content-type\"] = \"application/json\"\n            return json.dumps({'status': 'computing'})\n        else:\n            raise cherrypy.HTTPError(\"409 compute is currently still running.\")\n\n    # Register our new model type\n    context.register_model(\"timeseries\", finish)\n\n    # Register custom commands for use by wizards\n    context.register_model_command(\"GET\", \"timeseries\", \"pull_data\", pull_data)\n    context.register_model_command(\"POST\", \"timeseries\", \"update-model-info\", update_model_info)\n    context.register_model_command(\"GET\", \"timeseries\", \"media-columns\", media_columns)\n\n    # Register a wizard for creating instances of the new model\n    context.register_wizard(\"timeseries\", \"New Timeseries Model\", require={\"action\": \"create\", \"context\": \"project\"})\n","sub_path":"web-server/plugins/slycat-timeseries-model/slycat-timeseries-model.py","file_name":"slycat-timeseries-model.py","file_ext":"py","file_size_in_byte":27022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"621548728","text":"import root_pandas as rp\nimport uproot as ur\nimport ROOT\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Rectangle\nimport matplotlib\nfrom matplotlib.patches import Patch\nfrom matplotlib.lines import Line2D\n\nclass extract:\n\n    def __init__(self):\n        self.tpc_list = ['iiwi', 'nene', 'humu', 'palila', 'tako', 'elepaio']\n        pass\n\n    def get_MC_data(self, bgtype='RBB_Lumi'):\n        tpcs = self.tpc_list\n        truth = {}\n        for tpc in tpcs:\n            dir = '~/data/phase3/spring_2020/05-09-20/geant4_simulation/truth_neutrons_only/%s/'%(tpc)\n            try:\n                try:\n                    truth[tpc] = rp.read_root(dir+bgtype+'_'+tpc+'_all.root')\n                except KeyError:\n                    truth[tpc] = rp.read_root(dir+bgtype+'_'+tpc+'_all.root')\n                try:\n                    truth[tpc]['chipx'] = truth[tpc]['chipx'].apply(lambda x: x[0])\n                    truth[tpc]['chipy'] = truth[tpc]['chipy'].apply(lambda x: x[0])\n                    truth[tpc]['chipz'] = truth[tpc]['chipz'].apply(lambda x: x[0])\n                except TypeError:\n                    pass\n            except FileNotFoundError:\n                truth[tpc] = pd.DataFrame()\n        return truth\n\n    def apply_RBB_cuts(self, bgtype='RBB_Lumi'):\n        data = self.get_MC_data(bgtype=bgtype)\n        tpcs = self.tpc_list\n        dfs = {}\n        for tpc in tpcs:\n            if tpc == 'iiwi' or tpc == 'nene' or tpc =='humu':\n                #dfs[tpc] = data[tpc].loc[(data[tpc]['truthNeutronVtx_z_belle_frame']>1350) & (data[tpc]['truthNeutronVtx_z_belle_frame']<1900) \n                #& (data[tpc]['truthNeutronVtx_x_belle_frame']<95) & (data[tpc]['truthNeutronVtx_x_belle_frame']>37) \n                #& (np.abs(data[tpc]['truthNeutronVtx_y_belle_frame'])<20)]\n                dfs[tpc] = data[tpc].loc[(data[tpc]['truthNeutronVtx_z_belle_frame']>=1350) & (data[tpc]['truthNeutronVtx_z_belle_frame']<=1650) \n                & (data[tpc]['truthNeutronVtx_x_belle_frame']<=80) & (data[tpc]['truthNeutronVtx_x_belle_frame']>=40) \n                & (np.abs(data[tpc]['truthNeutronVtx_y_belle_frame'])<=20)]\n            else:\n                dfs[tpc] = data[tpc].loc[(data[tpc]['truthNeutronVtx_z_belle_frame']>=-900) & \n               (data[tpc]['truthNeutronVtx_z_belle_frame']<=-750) & (data[tpc]['truthNeutronVtx_x_belle_frame']>=16) & \n               (data[tpc]['truthNeutronVtx_x_belle_frame']<=60) & (np.abs(data[tpc]['truthNeutronVtx_y_belle_frame'])<=20)]\n            dfs[tpc].index = [i for i in range(0, len(dfs[tpc]))]\n        return dfs\n\n    ### Code below is for making paper figure with all vertices ###\n\n    def get_Lumi_MC_data(self):\n        tpcs = ['iiwi', 'nene', 'humu', 'palila', 'tako', 'elepaio']\n        bgtype = ['twoPhoton_Lumi', 'RBB_Lumi']\n        truth = {}\n        for tpc in tpcs:\n            dir = '~/data/phase3/spring_2020/05-09-20/geant4_simulation/truth_neutrons_only/%s/'%(tpc)\n            for bg in bgtype:\n                try:\n                    try:\n                        #truth[tpc+'_'+bg] = ur.open(dir+bg+'_'+tpc+'_all.root')['recoils'].pandas.df(flatten=False)\n                        truth[tpc+'_'+bg] = rp.read_root(dir+bg+'_'+tpc+'_all.root', key='recoils')\n                    except KeyError:\n                        #truth[tpc+'_'+bg] = ur.open(dir+bg+'_'+tpc+'_all.root')['my_ttree'].pandas.df(flatten=False)\n                        truth[tpc+'_'+bg] = rp.read_root(dir+bg+'_'+tpc+'_all.root', key='my_ttree')\n                    try:\n                        truth[tpc+'_'+bg]['chipx'] = truth[tpc+'_'+bg]['chipx'].apply(lambda x: x[0])\n                        truth[tpc+'_'+bg]['chipy'] = truth[tpc+'_'+bg]['chipy'].apply(lambda x: x[0])\n                        truth[tpc+'_'+bg]['chipz'] = truth[tpc+'_'+bg]['chipz'].apply(lambda x: x[0])\n                    except TypeError:\n                        pass\n                except FileNotFoundError:\n                    truth[tpc+'_'+bg] = pd.DataFrame()\n        return truth\n\n    def get_single_beam_MC_data(self):\n        tpcs = ['iiwi', 'nene', 'humu', 'palila', 'tako', 'elepaio']\n        bgtype = ['Coulomb_LER_dynamic', 'Coulomb_LER_base', 'Coulomb_HER_dynamic', 'Coulomb_HER_base', 'Brems_LER_dynamic', 'Brems_HER_dynamic', 'Brems_LER_base', 'Touschek_LER', 'Touschek_HER']\n        truth = {}\n        for tpc in tpcs:\n            dir = '~/data/phase3/spring_2020/05-09-20/geant4_simulation/truth_neutrons_only/%s/'%(tpc)\n            for bg in bgtype:\n                try:\n                    try:\n                        #truth[tpc+'_'+bg] = ur.open(dir+bg+'_'+tpc+'_all.root')['recoils'].pandas.df(flatten=False)\n                        truth[tpc+'_'+bg] = rp.read_root(dir+bg+'_'+tpc+'_all.root',key='recoils')\n                    except KeyError:\n                        #truth[tpc+'_'+bg] = ur.open(dir+bg+'_'+tpc+'_all.root')['my_ttree'].pandas.df(flatten=False)\n                        truth[tpc+'_'+bg] = rp.read_root(dir+bg+'_'+tpc+'_all.root',key='my_ttree')\n\n                    try:\n                        truth[tpc+'_'+bg]['chipx'] = truth[tpc+'_'+bg]['chipx'].apply(lambda x: x[0])\n                        truth[tpc+'_'+bg]['chipy'] = truth[tpc+'_'+bg]['chipy'].apply(lambda x: x[0])\n                        truth[tpc+'_'+bg]['chipz'] = truth[tpc+'_'+bg]['chipz'].apply(lambda x: x[0])\n                    except TypeError:\n                        pass\n                except FileNotFoundError:\n                    truth[tpc+'_'+bg] = pd.DataFrame()\n        return truth\n\n    def make_paper_figure(self):\n        plt.rc('legend', fontsize=20)\n        plt.rc('xtick', labelsize=24)\n        plt.rc('ytick', labelsize=24)\n        plt.rc('axes', labelsize=26)\n        plt.rc('axes', titlesize=26)\n        \n        lumi = self.get_Lumi_MC_data()\n        sb = self.get_single_beam_MC_data()\n        \n        df_SB = pd.DataFrame()\n        df_Lumi = pd.DataFrame()\n        for key in lumi.keys():\n            df_Lumi = df_Lumi.append(lumi[key])\n        for key in sb.keys():\n            df_SB = df_SB.append(sb[key])\n\n        df_SB = df_SB.loc[np.abs(df_SB['truthNeutronVtx_z_belle_frame'])>5]\n        df_Lumi = df_Lumi.loc[np.abs(df_Lumi['truthNeutronVtx_z_belle_frame'])>5]\n        \n        df_SB.index = [i for i in range(0,len(df_SB))]\n        df_Lumi.index = [i for i in range(0,len(df_Lumi))]\n        img = plt.imread(\"/home/jeff/Pictures/farbeamline_nocolor.png\")\n        fig, ax = plt.subplots(1,1,figsize = (25,10))\n        ax.set_xlabel('z [cm]')\n        ax.set_ylabel('x [cm]')\n        ax.set_ylim(-300,300)\n        ax.set_xlim(-2800,2800)\n        ax.imshow(np.flipud(img), origin = 'lower', extent = [-3333,3142, -438,414], aspect = 'auto')\n\n        ax.plot(df_SB[['truthNeutronVtx_z_belle_frame','chipz']].T, df_SB[['truthNeutronVtx_x_belle_frame','chipx']].T, lw=0.15, alpha = 0.15, color = 'magenta')\n\n        ax.plot(df_Lumi[['truthNeutronVtx_z_belle_frame','chipz']].T, df_Lumi[['truthNeutronVtx_x_belle_frame','chipx']].T, lw=0.15, alpha = 0.15, color = 'cyan')\n\n        ax.add_patch(Rectangle((-1415.5,196), 31, 10, facecolor = 'gold', alpha = 1.0, edgecolor = 'black', zorder=1e6)) #elepaio\n        ax.add_patch(Rectangle((-815.5,191), 31, 10, facecolor = 'gold', alpha = 1.0, edgecolor = 'black', zorder=1e6+1)) #tako\n        ax.add_patch(Rectangle((-578,-189), 31, 10, facecolor = 'gold', alpha = 1.0, edgecolor = 'black', zorder=1e6+2)) #palila\n        ax.add_patch(Rectangle((641,-199.4), 31, 10, facecolor = 'gold', alpha = 1.0, edgecolor = 'black', zorder=1e6+3)) #iiwi\n        ax.add_patch(Rectangle((1385,172), 31, 10, facecolor = 'gold', alpha = 1.0, edgecolor = 'black', zorder=1e6+4)) #nene\n        ax.add_patch(Rectangle((1585,170), 31, 10, facecolor = 'gold', alpha = 1.0, edgecolor = 'black', zorder=1e6+5)) #humu\n\n        ax.add_patch(Rectangle((-900,18), 150, 40, facecolor = 'lime', alpha = 0.7, edgecolor = 'black', zorder=1e6+6)) #hotspot\n        ax.add_patch(Rectangle((1350,40), 300, 40, facecolor = 'lime', alpha = 0.7, edgecolor = 'black', zorder=1e6+7)) #hotspot\n\n        handle = [Line2D([0], [0], marker='s', color='black', label='TPC',\n                          markerfacecolor='gold', alpha = 1.0, markersize=15, lw=0),\n        Line2D([0], [0], color='magenta', label='Single Beam', alpha = 1.0),\n        Line2D([0], [0], color = 'cyan', label='Luminosity', alpha = 1.0)]\n    \n        plt.legend(handles=handle, bbox_to_anchor=(0.84, 0.8), framealpha=1)\n        plt.savefig(\"/home/jeff/Pictures/MC_tracks_update.png\")\n        plt.show()\n        \na = extract()\n","sub_path":"analysis/phase3/with_sim/get_truth_neutron_data.py","file_name":"get_truth_neutron_data.py","file_ext":"py","file_size_in_byte":8607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"612559073","text":"def  gcd(a, b):\n    \"\"\"\n    Returns the greatest common divisor of and b.\n    \"\"\"\n    if a < b:\n        a, b = b,a\n\n    while b > 0:\n        a, b = b, a % b\n\n    return a\n\nprint(gcd(50, 20))\nprint(gcd(22,143))\n","sub_path":"gcd.py","file_name":"gcd.py","file_ext":"py","file_size_in_byte":210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"213522665","text":"# coding: utf-8\n\n__author__ = 'Bruno Luz'\n\n\"\"\"\n Implementar uma função que retorne verdadeiro se o número for primo (falso caso contrário).\n Testar de 1 a 100.\n\"\"\"\n\nSIM = 'SIM'\nNAO = 'NÃO'\n\n\ndef verifica_numero_primo(numero):\n    cont = 0\n    for i in range(1, (numero + 1)):\n        if (numero % i) == 0:\n            cont += 1\n\n    return True if cont == 2 else False\n\n\ndef mostra_numeros_primos():\n    for index in range(1, 101):\n        resultado = verifica_numero_primo(index)\n        if resultado:\n            print('{0} é primo : {1}'.format(index, SIM))\n\n\ndef mostra_numero_nao_primos():\n    for index in range(1, 101):\n        resultado = verifica_numero_primo(index)\n        if not resultado:\n            print('{0} é primo : {1}'.format(index, NAO))\n\n\ndef mostra_todos():\n    for index in range(1, 101):\n        resultado = SIM if verifica_numero_primo(index) else NAO\n        print('{0} é primo : {1}'.format(index, resultado))\n\n\ndef main():\n    print('------- APENAS NÚMEROS PRIMOS -------')\n    mostra_numeros_primos()\n    print('\\n')\n\n    print('------- APENAS NÚMEROS NÃO PRIMOS -------')\n    mostra_numero_nao_primos()\n    print('\\n')\n\n    print('------- TODOS -------')\n    mostra_todos()\n    print('\\n')\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"parte_01/exercicios/exercicio_2.py","file_name":"exercicio_2.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"173285709","text":"from django.core.mail import send_mail\nfrom django.utils.translation import gettext_lazy as _\n\n\ndef send_activation_email(profile, host):\n    import time\n    send_mail(\n        _('Регистрация'),\n        _(\n            'Вы зарегистрировались на сайте. '\n            'Ссылка для активации:'\n        ) + ' {url}'.format(\n            url='http://' + host + '/' + profile.activation.url\n        ),\n        from_email='test@test.by',\n        recipient_list=[profile.email],\n        fail_silently=False\n    )","sub_path":"core/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"214443618","text":"from django.db import models\nfrom django.db.models import F, Q\n# from django.conf import settings\nfrom django.shortcuts import *\nfrom source.CDR import SpanningForeignKey\n\n# from Relationships.models import Custparent, ContractPrice\nfrom Patients.models import *\nfrom Products.models import *\nfrom users.models import *\n# from portal.models import *\n\n\n# class Relationships(models.Manager):\n#     pass\n\nclass Charges(models.Model):\n    TransID = models.BigAutoField(primary_key=True)\n    TransDate = models.DateField()\n    patient = models.ForeignKey(IdPatient, on_delete=models.CASCADE)\n    # NumericTransDate        = models.IntegerField(blank=True, null=True)\n    LastEditDateTime = models.DateTimeField(auto_now=True)\n    custparent = SpanningForeignKey('portal.Custparent', on_delete=models.CASCADE, related_name=\"facilities\")\n    FromRecurTransID = models.PositiveIntegerField(blank=True, null=True)\n    Exported = models.IntegerField(blank=True, null=True)\n\n    class Meta:\n        db_table = 'id_hcm_charges'\n        # unique_together = ((\"user\", \"TransDate\", \"patient\"),)\n\n    def __str__(self):\n        return f'{str(self.TransID)}'\n\n    # @property\n    # def cs(self):\n    #     return self.chargessupply_set.all()\n\n    # def save(self, *args, **kwargs):\n\n    #     super(ChargesSupply, self).save(*args, **kwargs).save()\n\n\nclass ChargesSupply(models.Model):\n    trans = models.ForeignKey('Charges', db_column='TransID', on_delete=models.CASCADE)\n    LineNum = models.PositiveIntegerField(default=1)\n    user = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.CASCADE)\n    LocationID = models.IntegerField(blank=True, null=True)\n    LastEdited = models.DateTimeField(auto_now=True)\n    ProductNum = models.BigIntegerField()\n    productid = models.ForeignKey(IdProduct, db_column='ProductID', to_field='productid', related_name='products', on_delete=models.DO_NOTHING)\n    BillingMethod = models.CharField(max_length=60, blank=True, null=True)\n    Description = models.CharField(max_length=255)\n    Quantity = models.DecimalField(max_digits=12, decimal_places=4)\n    UnitID = models.CharField(max_length=12)\n    Price = models.DecimalField(max_digits=12, decimal_places=6, max_length=60)\n\n    class Meta:\n        db_table = 'id_hcm_charges_supply'\n        unique_together = (\"trans\", \"LineNum\")\n\n    def __str__(self):\n        return f'{str(self.trans)}'\n    \n    def t(y, z):\n        return z\n    \n    # @property\n    # def\n\n    @property\n    def convqty(self):\n        \"\"\" Returns the values from id_product_unit_conv table \"\"\"\n        t = self.t\n        c = ChargesSupply.objects.values_list('productid__puc__convqty', flat=True)\n        cq = [t(x) for x in c]\n        return cq\n\n\n\n    # def (self):\n    #     # productid = self.productid\n    #     t = self.t\n    #     cs = ChargesSupply.objects.values_list('productid__puc__convqty', flat=True)\n    #     ch = [t(x) for x in cs if x is not None]\n    #     # chs = list(map(t, ch))\n    #     print(ch)\n\n    # @property\n    # def conversion(self):\n    #     productid = self.productid\n    #     # convqty = IdProduct.objects.filter(puc__convqty=convqty)\n    #     # p = IdProduct.objects.all().filter(puc__productid=productid)\n    #     # p = ChargesSupply.objects.distinct().prefetch_related('productid').filter(productid__puc__productid=productid).values_list(F('productid__puc__convqty'), flat=True)\n    #     # p = ChargesSupply.objects.prefetch_related('productid').filter(productid=productid).values(F('productid__puc__convqty'))\n    #     convFactor = ChargesSupply.objects.distinct().prefetch_related('productid').filter(productid=productid).values_list(F('productid__puc__convqty'), flat=True)[0]\n    #     return convFactor\n\n\n    # def get_product_unit_convfactor():\n    #     pass\n\n    def save(self, *args, **kwargs):\n        def cal_key(trans):\n            present_keys = ChargesSupply.objects.filter(trans=trans).order_by('-LineNum').values_list('LineNum', flat=True)\n\n            quantity = self.Quantity\n\n            if present_keys and quantity == 1:\n                return present_keys[0] + 1\n            elif present_keys and quantity >= 1:\n                return present_keys[0]\n            else:\n                return 1\n        LineNum = cal_key(self.trans)\n        self.LineNum = LineNum\n        super(ChargesSupply, self).save(*args, **kwargs)\n","sub_path":"backend/source/Charges/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"552610789","text":"# Application of closure\n\n#Function , which is a generic multiplier\ndef generic_multiplier(actual_multiple_number):\n\tdef specific_multiplier(number_to_be_multipliedwith):\n\t\treturn actual_multiple_number * number_to_be_multipliedwith\n\treturn specific_multiplier\n\n#multiplier of 6\nmultiplier_of_6=generic_multiplier(6)\nmultiplier_of_2=generic_multiplier(2)\nmultiplier_of_9=generic_multiplier(9)\n\nprint(\"10 multiplied by 6 is %d\" % multiplier_of_6(10))\nprint(\"10 multiplied by 2 is %d\" % multiplier_of_2(10))\nprint(\"10 multiplied by 9 is %d\" % multiplier_of_9(10))\n\n\t\t\n","sub_path":"python_veeravel/closures/closure_excercise.py","file_name":"closure_excercise.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"379671229","text":"\"\"\"\n解法一：蛮力法\n1.从第一个数开始遍历，不断求累加的和，当和增加的时候，继续执行，否则停止\n2.从第二个数再开始，和之前求出的和进行比较，如果和比之前的要大，则替换为新的和值\n3.时间复杂度O(n^3),需要三个循环\n\"\"\"\ndef findSub1(l):\n    length = len(l)\n    maxSum = 0\n    currSum = 0\n    for i in range(0, length):\n        for j in range(i, length):\n            for k in range(i, j+1):\n                currSum += l[k]\n            if currSum > maxSum:\n                maxSum = currSum\n            currSum = 0\n    print(maxSum)\n\n\"\"\"\n解法二：\n核心：\ncurrSum = max(a[j], currSum + a[j])\nmaxSum = max(maxSum, currSum)\n\"\"\"\ndef findSub2(l):\n    currSum = 0\n    maxSum = l[0]\n    length = len(l)\n    for i in range(0, length):\n        currSum = max(l[i], currSum + l[i])\n        maxSum = max(maxSum, currSum)\n    print(maxSum)\n\ndef find_max(list):\n    # 先定义一个子数组和的最大值，赋值为整个数列的和\n    max_value = sum(list)\n\nimport sys\nif __name__ == \"__main__\":\n    l = [1, -2, 3, 10, -4, 7, 2]\n    # l2 = [1, 2, 3]\n    line = sys.stdin.readline().strip()\n    li = list(map(int, line.split()))\n    # findSub1(l2)\n    findSub2(li)\n","sub_path":"最大连续子数组和/findConSubList.py","file_name":"findConSubList.py","file_ext":"py","file_size_in_byte":1241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"12609872","text":"import tflearn\nfrom tflearn.data_utils import shuffle\nfrom tflearn.layers.core import input_data, dropout, fully_connected\nfrom tflearn.layers.conv import conv_2d, max_pool_2d\nfrom tflearn.layers.normalization import batch_normalization\nfrom tflearn.layers.estimator import regression\nfrom tflearn.data_preprocessing import DataPreprocessing\nfrom tflearn.data_augmentation import ImageAugmentation\nfrom tflearn.data_utils import build_hdf5_image_dataset\nfrom tflearn.layers.normalization import local_response_normalization\nimport h5py\n\ntraining_file = 'train_data.txt'\nvalidation_file = 'cval_data.txt'\n\n\n#build_hdf5_image_dataset(training_file, image_shape=[48, 48, 3], mode='file', output_path='dataset.h5', categorical_labels=True)\n\nh5f = h5py.File('dataset.h5', 'r')\nX = h5f['X']\nY = h5f['Y']\n\n#build_hdf5_image_dataset(validation_file, image_shape=[48, 48, 3], mode='file', output_path='validation.h5', categorical_labels=True)\n\nh5f = h5py.File('validation.h5', 'r')\nX_val = h5f['X']\nY_val = h5f['Y']\n\nX, Y = shuffle(X, Y)\n\nimg_prep = DataPreprocessing()\nimg_prep.add_featurewise_zero_center()\nimg_prep.add_featurewise_stdnorm()\nimg_aug = ImageAugmentation()\nimg_aug.add_random_flip_leftright()\nimg_aug.add_random_rotation(max_angle=25.)\nimg_aug.add_random_blur(sigma_max=3.)\nnetwork = input_data(shape=[None, 48, 48, 3], data_preprocessing=img_prep, data_augmentation=img_aug)\nnetwork = conv_2d(network, nb_filter= 64, filter_size= [5, 5], activation='relu')\n#network = local_response_normalization(network)\nnetwork = max_pool_2d(network, kernel_size=[3, 3], strides=2)\nnetwork = conv_2d(network, nb_filter= 64, filter_size= [5, 5],  activation='relu')\nnetwork = max_pool_2d(network, kernel_size=[3, 3], strides=2)\nnetwork = conv_2d(network, nb_filter= 128, filter_size= [4, 4], activation='relu')\nnetwork = dropout(network, 0.3)\nnetwork = fully_connected(network, 3072, activation='relu')\nnetwork = fully_connected(network, 7, activation ='softmax')\nnetwork = regression(network, optimizer='momentum',\n                     loss='categorical_crossentropy')\nmodel = tflearn.DNN(network, tensorboard_verbose=0)\nmodel.fit(X, Y, n_epoch=60, shuffle=True, validation_set=(X_val, Y_val),\n          show_metric=True, batch_size=50,\n          snapshot_epoch=True,\n          run_id='emotionv2')\n\n# Save model when training is complete to a file\nmodel.save(\"emotion.tfl\")\n","sub_path":"process-2.py","file_name":"process-2.py","file_ext":"py","file_size_in_byte":2369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"858363","text":"from ptah.renderer.layer import ID_LAYER\n\nfrom ptah.testing import PtahTestCase\n\n\nclass TestOrder(PtahTestCase):\n\n    _auto_commit = False\n    _settings = {'layer.order.test': 'l1 l2 l3'}\n    _init_ptah = False\n\n    def test_custom_dir(self):\n        self.config.add_layer(\n            'test', 'l1', path='ptah.renderer:tests/dir1/')\n        self.config.add_layer(\n            'test', 'l2', path='ptah.renderer:tests/bundle/dir1/')\n        self.config.commit()\n\n        storage = self.registry.get(ID_LAYER)\n        self.assertIn('test', storage)\n        self.assertEqual(2, len(storage['test']))\n        self.assertEqual('l1', storage['test'][0]['name'])\n        self.assertEqual('l2', storage['test'][1]['name'])\n\n\nclass TestOrderUnknown(PtahTestCase):\n\n    _auto_commit = False\n    _settings = {'layer.order.test2': 'l1 l2 l3'}\n    _init_ptah = False\n\n    def test_custom_dir(self):\n        self.config.add_layer(\n            'test', 'l1', path='ptah.renderer:tests/dir1/')\n        self.config.add_layer(\n            'test', 'l2', path='ptah.renderer:tests/bundle/dir1/')\n        self.config.commit()\n\n        storage = self.registry.get(ID_LAYER)\n        self.assertIn('test', storage)\n        self.assertNotIn('test2', storage)\n","sub_path":"ptah/renderer/tests/test_order.py","file_name":"test_order.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"534477804","text":"import json\nimport collections\nfrom rwfm.Label import Label\n\n\ndef parse_owner(data):\n    '''Parse the owner field from the JSON Object'''\n    return data[\"owner\"]\n\ndef parse_readers(data):\n    '''Parse the readers field from the JSON Object'''\n    _readers = []\n    #data[\"readers\"] is a string thus we can get a list of readers using the split function\n    _readers = data[\"readers\"]\n    return _readers.split(\",\")\n\ndef parse_writers(data):\n    '''Parse the writers field from the JSON Object'''\n    _writers = []\n    _writers = data[\"writers\"]\n    return _writers.split(\",\")\n    \n# def parse_source_filename(json_file):\n#     '''Method returns a list containing name of the source files'''\n#     _input_data = json.load(open(json_file))\n#     return  _input_data[\"source_file\"][\"path\"]\n\ndef parse_subject_label(row):\n    '''Method returns the security label of executing subject given in the input file'''\n    try:\n        _id = row[\"object\"]\n        _owner = parse_owner(row)\n        _readers = parse_readers(row)\n        _writers = parse_writers(row)      \n        subject_label = Label(_owner,_readers,_writers)\n    except KeyError as e:\n        return None\n    else:\n        return subject_label\n        \ndef parse_globals(row):\n    '''Method returns a dictionary having global variables as key that is mapped to respective static label '''\n    _globals={}\n    try:\n        glob = row\n        _id = glob[\"object\"]\n        _owner = parse_owner(glob)\n        _readers = parse_readers(glob)\n        _writers = parse_writers(glob)\n        _label = Label(_owner,_readers,_writers)\n        _globals[_id]=_label\n    except KeyError as e:\n        return None\n    else:\n        return _label\n\n\ndef parse_function_labels(row):\n    _functions = {}\n    try:\n        function = row    \n        _name = function[\"object\"]\n        _owner = parse_owner(function)\n        _readers = parse_readers(function)\n        _writers = parse_writers(function)\n        _label = Label(_owner, _readers, _writers)\n        _functions[_name] = _label\n    except KeyError as e:\n        return None\n    else:\n        return _label\n\ndef parse_output_file(row):\n    _output_file = {}\n    try:\n        file = row\n        _name = file[\"object\"]\n        _owner = parse_owner(file)\n        _readers = parse_readers(file)\n        _writers = parse_writers(file)\n        _label = Label(_owner, _readers, _writers)\n        _output_file[_name] = _label\n    except KeyError as e:\n        return None\n    else:\n        return _label\n\n\n\n\n# def print_globals(globals):\n#     str=''\n#     if globals:\n#         for key in globals.keys():\n#             str = str + key + ' : ' + globals[key].to_string() + '\\n'\n#         return str\n#     else:\n#         return 'Not Given'\n\n","sub_path":"pifthon_gui/parse_input_file_1.py","file_name":"parse_input_file_1.py","file_ext":"py","file_size_in_byte":2729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"335351576","text":"# -*- coding: utf-8\n'''\nauthor: twinsant@gmail.com\n'''\nimport os\ntry:\n    from ctypes import c_bool # Python2.6\nexcept ImportError:\n    from ctypes import c_int\n    c_bool = c_int\nfrom ctypes import *\n\nICTCLAS_ENCODING = 'gb18030'\nPOS_SIZE = 8\n\nclass c_result(Structure):\n    _fields_ = [\n        ('start', c_int),\n        ('length', c_int),\n        ('s_pos', c_char*POS_SIZE),\n        ('i_pos', c_int),\n        ('word_id', c_int),\n        ('word_type', c_int),\n        ('weight', c_int)\n    ]\n\nclass ICTCLAS(object):\n    EXPORTS = [\n        ('_exit', '_Z12ICTCLAS_Exitv', [], c_bool),\n        ('_init', '_Z12ICTCLAS_InitPKci', [c_char_p, c_int], c_bool),\n        ('_is_word', '_Z14ICTCLAS_IsWordPKc', [], c_int),\n        ('_keyword', '_Z15ICTCLAS_KeyWordP8result_tRi', [POINTER(c_result), POINTER(c_int)], c_int),\n        ('_set_pos_map', '_Z17ICTCLAS_SetPOSmapi', [], c_int),\n        ('_del_usr_word', '_Z18ICTCLAS_DelUsrWordPKc', [c_char_p], c_int),\n        ('_get_uni_prob', '_Z18ICTCLAS_GetUniProbPKc', [c_char_p], c_float),\n        ('_add_user_word', '_Z19ICTCLAS_AddUserWordPKc', [c_char_p], c_int),\n        ('_file_process', '_Z19ICTCLAS_FileProcessPKcS0_i', [c_char_p, c_char_p, c_int], c_bool),\n        ('_finger_print', '_Z19ICTCLAS_FingerPrintv', [], c_ulong),\n        ('_file_process_ex', '_Z21ICTCLAS_FileProcessExPKcS0_', [], c_int),\n        ('_save_the_usr_dic', '_Z21ICTCLAS_SaveTheUsrDicv', [], c_int),\n        ('_import_user_dict', '_Z22ICTCLAS_ImportUserDictPKc', [c_char_p], c_uint),\n        ('_paragraph_process', '_Z24ICTCLAS_ParagraphProcessPKci', [c_char_p, c_int], c_char_p),\n        ('_paragraph_process_a', '_Z25ICTCLAS_ParagraphProcessAPKcPi', [c_char_p, c_void_p], POINTER(c_result)),\n        ('_paragraph_process_aw', '_Z26ICTCLAS_ParagraphProcessAWiP8result_t', [], c_int),\n        ('_get_prragraph_processa_word_count', '_Z37ICTCLAS_GetParagraphProcessAWordCountPKc', [], c_int),\n    ]\n    GBK_CODE = 0\n    UTF8_CODE = GBK_CODE + 1\n    BIG5_CODE = UTF8_CODE + 1\n\n    NO_TAG = 0\n    TAGGING = 1\n\n    def __init__(self):\n        self.dir = os.path.abspath(os.path.dirname(__file__))\n        self.lib_so = os.path.join(self.dir, 'libICTCLAS2011.so')\n\n    def init(self):\n        self.ld = cdll.LoadLibrary(self.lib_so)\n        for name, export_name, argtypes, restype in self.EXPORTS:\n            setattr(self, name, getattr(self.ld, export_name))\n            func = getattr(self, name)\n            assert hasattr(self, name) and func\n            func.argtypes = argtypes\n            func.restype = restype\n        assert self._init(self.dir, self.UTF8_CODE)\n\n    def exit(self):\n        assert self._exit()\n\n    def process(self, text, pos_tagged=NO_TAG):\n        res = self._paragraph_process(text, pos_tagged)\n        return res\n\n    def process_a(self, text, encoding='utf-8'):\n        p_result_count = pointer(c_int(0))\n        p_result = self._paragraph_process_a(text, p_result_count)\n        result_count = p_result_count.contents.value\n        res = []\n        for i in xrange(result_count):\n            class Word(object):\n                def __str__(self):\n                    return '%s/%s' % (self.word, self.s_pos)\n            r = Word()\n            r.start = p_result[i].start\n            r.length = p_result[i].length\n            _start = r.start/2 # byte2unicode\n            _end = _start + r.length/2\n            utext = text.decode(encoding)\n            r.word = utext[_start:_end].encode(encoding)\n            r.s_pos = p_result[i].s_pos\n            r.i_pos = p_result[i].i_pos\n            r.word_id = p_result[i].word_id\n            r.word_type = p_result[i].word_type\n            r.weight = p_result[i].weight\n            res.append(r)\n        return res\n\n    def process_file(self, source_file_name, result_file_name, pos_tagged=NO_TAG):\n        assert False\n        return self._file_process(source_file_name, result_file_name, pos_tagged)\n\n    def add_user_word(self, w, pos, encoding='utf-8'):\n        uw = '%s\\t%s' % (w, pos)\n        uw = uw.decode(encoding).encode(ICTCLAS_ENCODING)\n        return self._add_user_word(uw)\n\n    def del_usr_word(self, w, encoding='utf-8'):\n        uw = w\n        uw = uw.decode(encoding).encode(ICTCLAS_ENCODING)\n        return self._del_usr_word(uw)\n\n    def get_uni_prob(self, w):\n        return self._get_uni_prob(w)\n\n    def save_the_usr_dic(self):\n        return self._save_the_usr_dic()\n\n    def keyword(self, text):\n        assert False\n\n    def finger_print(self):\n        return self._finger_print()\n\nictclas = ICTCLAS()\n","sub_path":"pyictclas/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"279956350","text":"\"\"\"Tests that are common to graphs\"\"\"\nfrom bigsi.tests.base import ST_GRAPH\nfrom hypothesis import given\nfrom bigsi.variants import BIGSIVariantSearch\nfrom bigsi.variants import BIGSIAminoAcidMutationSearch\nimport hypothesis.strategies as st\nfrom bigsi import BIGSI\n\nimport logging\nlogging.basicConfig()\n\nlogger = logging.getLogger(__name__)\nfrom bigsi.utils import DEFAULT_LOGGING_LEVEL\nlogger.setLevel(DEFAULT_LOGGING_LEVEL)\nimport unittest\nimport os\nimport pytest\n\n\n@given(Graph=ST_GRAPH, kmer_size=st.integers(min_value=11, max_value=31))\ndef test_create_variant_probe_set(Graph, kmer_size):\n    bigsi = Graph.create(m=1000, k=kmer_size, force=True)\n    variant_search = BIGSIVariantSearch(bigsi, \"bigsi/tests/data/ref.fasta\")\n    assert variant_search is not None\n\n    assert len(variant_search.create_variant_probe_set(\n        \"T1C\").refs[0]) == (2*kmer_size+1)\n\n\n@given(Graph=ST_GRAPH)\ndef test_search_for_variant(Graph):\n    kmer_size = 21\n    bigsi = Graph.create(m=1000, k=kmer_size, force=True)\n    variant_search = BIGSIVariantSearch(bigsi, \"bigsi/tests/data/ref.fasta\")\n    # Add a the reference seq, the alternate and both as samples\n    variant_probe_set = variant_search.create_variant_probe_set(\n        \"T1C\")\n    ref = variant_probe_set.refs[0]\n    alt = variant_probe_set.alts[0]\n    bloom1 = bigsi.bloom(bigsi.seq_to_kmers(ref))\n    bloom2 = bigsi.bloom(bigsi.seq_to_kmers(alt))\n    bigsi.build([bloom1, bloom2], ['ref', 'alt'])\n\n    results = variant_search.search_for_variant(\"T\", 1, \"C\")\n    print(results)\n    assert results.get(\"T1C\").get(\"ref\").get(\"genotype\") == \"0/0\"\n    assert results.get(\"T1C\").get(\"alt\").get(\"genotype\") == \"1/1\"\n\n\n@pytest.mark.skipif('\"TRAVIS\" in os.environ and os.environ[\"TRAVIS\"] == \"true\"')\ndef test_search_for_amino_acid_mutation():\n    kmer_size = 21\n    bigsi = BIGSI.create(m=1000, k=kmer_size, force=True)\n    variant_search = BIGSIAminoAcidMutationSearch(\n        bigsi, \"bigsi/tests/data/ref.fasta\", \"bigsi/tests/data/ref.gb\")\n\n    var_name1 = variant_search.aa2dna.get_variant_names(\"rpoB\", \"S450X\", True)[\n        0]\n    var_name2 = variant_search.aa2dna.get_variant_names(\"rpoB\", \"S450X\", True)[\n        4]\n\n    # # Add a the reference seq, the alternate and both as samples\n    variant_probe_set1 = variant_search.create_variant_probe_set(var_name1)\n    variant_probe_set2 = variant_search.create_variant_probe_set(var_name2)\n\n    ref1 = variant_probe_set1.refs[0]\n    alt1 = variant_probe_set1.alts[0]\n    ref2 = variant_probe_set2.refs[0]\n    alt2 = variant_probe_set2.alts[0]\n    bloom1 = bigsi.bloom(bigsi.seq_to_kmers(ref1))\n    bloom2 = bigsi.bloom(bigsi.seq_to_kmers(alt1))\n    bloom3 = bigsi.bloom(bigsi.seq_to_kmers(ref2))\n    bloom4 = bigsi.bloom(bigsi.seq_to_kmers(alt2))\n    bigsi.build([bloom1, bloom2, bloom3, bloom4],\n                ['ref1', 'alt1', 'ref2', 'alt2'])\n\n    results = variant_search.search_for_amino_acid_variant(\n        \"rpoB\", \"S\", 450, \"X\")\n    assert results.get(\"rpoB_S450X\").get(\"ref1\").get(\"genotype\") == \"0/0\"\n    assert results.get(\"rpoB_S450X\").get(\"ref1\").get(\"aa_mut\")[:-1] == \"S450\"\n    assert results.get(\"rpoB_S450X\").get(\n        \"ref1\").get(\"variant\")[:-3] == var_name1[:-3]\n\n    assert results.get(\"rpoB_S450X\").get(\"ref2\").get(\"genotype\") == \"0/0\"\n    assert results.get(\"rpoB_S450X\").get(\"ref2\").get(\"aa_mut\")[:-1] == \"S450\"\n    assert results.get(\"rpoB_S450X\").get(\n        \"ref2\").get(\"variant\")[:-3] == var_name2[:-3]\n\n    assert results.get(\"rpoB_S450X\").get(\"alt1\").get(\"genotype\") == \"1/1\"\n    assert results.get(\"rpoB_S450X\").get(\"alt1\").get(\"aa_mut\") == \"S450K\"\n    assert results.get(\"rpoB_S450X\").get(\"alt1\").get(\"variant\") == var_name1\n\n    assert results.get(\"rpoB_S450X\").get(\"alt2\").get(\"genotype\") == \"1/1\"\n    assert results.get(\"rpoB_S450X\").get(\"alt2\").get(\"aa_mut\") == \"S450I\"\n    assert results.get(\"rpoB_S450X\").get(\"alt2\").get(\"variant\") == var_name2\n","sub_path":"bigsi/tests/genotyping/test_genotyping_variants.py","file_name":"test_genotyping_variants.py","file_ext":"py","file_size_in_byte":3891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"393261785","text":"import re\n\n\ndef _check_tests(api, repo_config, paths):\n    no_tests = []\n    test_check = repo_config.get('test_check', [])\n\n    for check in test_check:\n        name, modify_path, test_paths = check['name'], check['path'], check['test_paths']\n        for path in paths:\n            if re.search(modify_path, path):\n                found = filter(lambda path: any(re.search(p, path) for p in test_paths), paths)\n                if not found:\n                    no_tests.append(name)\n                break\n\n    message = ('These commits modify the %s code, but no tests have been modified. '\n               'Please consider updating the tests appropriately.')\n    if no_tests:\n        return message % api.shared.join_names(no_tests)\n\n\n# Servo-specific WPT metadata checker\ndef _check_metadata(api, paths):\n    msg = ('This pull request adds file(s) to `%s` without the `.ini` extension. '\n           'Please consider removing the file(s)!')\n\n    metadata_dirs = ['tests/wpt/metadata', 'tests/wpt/mozilla/meta']\n    ignored = ['.ini', 'MANIFEST.json', 'mozilla-sync']\n    offending_file_dirs = set()\n\n    for path in paths:\n        if '.' in path and not any(re.search(f, path) for f in ignored):\n            offending_file_dirs |= set(d for d in metadata_dirs if re.search(d, path))\n\n    if offending_file_dirs:\n        return msg % api.shared.join_names(offending_file_dirs)\n\n\n# define and append the repo-specific handlers here\nREPO_SPECIFIC_HANDLERS = {\n    \"servo/servo\": {\n        \"lines\": [\n            # content-checking methods\n        ],\n        \"paths\": [\n            # path-checking methods\n            _check_metadata\n        ]\n    },\n}\n\n\ndef check_diff(api, config):\n    repos = config.get('repos')\n    pr = api.payload.get('pull_request')\n    if not (pr and api.payload.get('action') == 'opened'):\n        return\n\n    messages = set()    # so that we filter duplicates\n    repo_config = api.get_matches_from_config(repos)\n\n    def get_messages(lines, matches):\n        for line in lines:\n            for match, msg in matches.items():\n                if re.search(match, line):\n                    messages.update([msg])\n\n    matches = repo_config.get('content', {})\n    lines = api.get_added_lines()\n    get_messages(lines, matches)\n\n    matches = repo_config.get('files', {})\n    paths = list(api.get_changed_files())\n    get_messages(paths, matches)\n\n    test_check_result = _check_tests(api, repo_config, paths)\n    if test_check_result:\n        messages.update([test_check_result])\n\n    # Run the repo-specific handlers (if any)\n    handlers = api.get_matches_from_config(REPO_SPECIFIC_HANDLERS)\n    for name, methods in handlers.items():\n        for method in methods:\n            _input = locals().get(name)\n            result = method(api, _input)\n            if result:\n                messages.update([result])\n\n    if messages:\n        lines = '\\n'.join(map(lambda line: ' * %s' % line, messages))\n        comment = ':warning: **Warning!** :warning:\\n\\n%s' % lines\n        api.post_comment(comment)\n\n\nmethods = [check_diff]\n","sub_path":"diff_check_warn/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"269859846","text":"# This import is for calling bc using this python script\nfrom subprocess import Popen, PIPE, STDOUT\n# This import of re is for using regex to cut out white space when reading keys\nimport re\n\ndef decrypt_with_bc(d, n, wrapped_keys):\n\n    mod = '%'\n    to_the = '^'\n    decrypted_keys = []\n\n    # Now it's time to go through the wrapped key list and convert the numbers\n    # back to ascii using the private key, d. We're using the function m = c^d%n,\n    # where m is the plaintext letter of the keys\n    for c in wrapped_keys:\n        p = Popen(['bc'], stdout=PIPE, stdin=PIPE, stderr=PIPE)\n        encode_e_n  = \"{} {} {} {} {}\\n\".format(c, to_the, d, mod, n).encode('utf-8')\n        stdout_data = p.communicate(input=encode_e_n)[0]\n        decrypted_keys.append(stdout_data.decode('utf-8')[:-1])\n        print('\\u2630', end = '', flush = True)\n\n    # Convert the ascii number back to letters using the chr function and return it\n    decrypted_keys = [chr(int(c)) for c in decrypted_keys]\n    print()\n    \n    return decrypted_keys\n\n# This function will read in the file, and if it's private key, read d into the first\n# variable and n into the second. If it's our wrapped keys, read the entire file in\ndef read_keys(key_file):\n    \n    rfile = open(key_file, \"r\")\n\n    if key_file == \"privateKey\":\n        keys = rfile.read()\n        keys = re.split('\\W+', keys)\n        return keys[0], keys[1]\n    else:\n        return rfile.read().split()\n\n# Write each letter to the file. These are the decrypted two keys needed\ndef write_unwrapped_keys(decrypted_keys):\n    with open(\"unwrappedKeys\", \"w\") as wfile:\n        for letter in decrypted_keys:\n            wfile.write(\"%s\" % letter)\n\n# This is where it all begins. Take the private keys along with the wrapped keys and\n# ciphertext that was sent using the above functions\ndef main():\n\n    # Let the user know the long and arduous process of decrypting has begun\n    print(\"\\n>>> Decrypting keys..\")\n\n    # Open the private key file and read them into variables\n    private_key_1, private_key_2 = read_keys(\"privateKey\")\n\n    # Open the wrapped key file and read them into two more variables\n    wrapped_keys = read_keys(\"wrappedKeys\")\n\n    # Convert the private keys into numbers so we can math them\n    d = int(private_key_1)\n    n = int(private_key_2)\n\n    # Call our decrypt function for both keys in a list\n    decrypted_keys = decrypt_with_bc(d, n, wrapped_keys)\n\n    # Now write the original plaintext to the unwrappedKeys file\n    write_unwrapped_keys(decrypted_keys)\n\n    print(\">>> Decryption of keys successful!\\n\")\n\n\nif __name__ == '__main__':\n    main()\n\n","sub_path":"CS356Project1_JoshKeahey/asymmetrickey_decr.py","file_name":"asymmetrickey_decr.py","file_ext":"py","file_size_in_byte":2616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"323491935","text":"from django.conf import settings\nfrom google_analytics.utils import build_ga_params, set_cookie\nfrom google_analytics.tasks import send_ga_tracking\n\n\nclass GoogleAnalyticsMiddleware(object):\n    def process_response(self, request, response):\n        if hasattr(settings, 'GOOGLE_ANALYTICS_IGNORE_PATH'):\n            exclude = [p for p in settings.GOOGLE_ANALYTICS_IGNORE_PATH\\\n                        if request.path.startswith(p)]\n            if any(exclude):\n                return response\n\n        path = request.path\n        referer = request.META.get('HTTP_REFERER', '')\n        params = build_ga_params(request, path=path, referer=referer)\n        response = set_cookie(params, response)\n        send_ga_tracking.delay(params)\n        return response\n","sub_path":"google_analytics/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"40264190","text":"from sklearn.preprocessing import normalize\nfrom sklearn.model_selection import train_test_split\n\nfrom data import loader, load_preprocessing\nfrom knn_classifier import knn\n\nimport distances\nimport hott\nfrom kcluster import kclustering\nimport numpy as np\n# Download datasets used by Kusner et al from\n# https://www.dropbox.com/sh/nf532hddgdt68ix/AABGLUiPRyXv6UL2YAcHmAFqa?dl=0\n# and put them into\ndata_path = './data/'\n\n# Download GloVe 6B tokens, 300d word embeddings from\n# https://nlp.stanford.edu/projects/glove/\n# and put them into\nembeddings_path = './data/glove.6B/glove.6B.300d.txt'\n\n# Pick a dataset (uncomment the line you want)\ndata_name = 'bbcsport-emd_tr_te_splitdict70.pkl'\n# data_name = 'twitter-emd_tr_te_splitdict70.pkl'\n# data_name = 'r8-emd_tr_te3.mat'\n# data_name = 'amazon-emd_tr_te_split.mat'\n# data_name = 'classic-emd_tr_te_split.mat'\n# data_name = 'ohsumed-emd_tr_te_ix.mat'\n\n\n# Pick a seed\n# 0-4 for bbcsport, twitter, amazon, classic\n# r8 and ohsumed have a pre-defined train/test splits - just set seed=0\ndata = load_preprocessing(data_path + data_name)\np = 1\nC = data['cost_T']\nData = data['proportions']\ny = data['y']\n\nmatrixPVal = []\nmatrixDist = []\ni = 0\nk = np.unique(y).shape[0]\nprint(\"Number of Labels\", k)\nfor j in range(5):\n    try:\n        dataDist, datalabels, barycenters, dataHist, listPvalue, listInfoDist = kclustering(k, Data, C, y, max_iter = 25)\n    except ValueError:\n        continue\n    np.save(data_path + \"ArrayPvalExp\" + str(i) + \".npy\", np.array(listPvalue))\n    np.save(data_path + \"ArrayDistExp\" + str(i) + \".npy\", np.array(dataHist))\n    np.save(data_path + \"ArrayDistInfoExp\" + str(i) + \".npy\", np.array(listInfoDist))\n    i = i+1\n    print(listInfoDist[-1])\n    print(datalabels)\n    matrixPVal.append(listPvalue)\n    matrixDist.append(dataHist)\n\n\nmatrixPVal = np.array(matrixPVal)\nmatrixDist = np.array(matrixDist)\n","sub_path":"Core/main4.py","file_name":"main4.py","file_ext":"py","file_size_in_byte":1874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"235040411","text":"from scipy.signal import convolve2d\nimport numpy as np\nimport imageio\nimport skimage.color as color\nimport scipy.ndimage.filters\n\n\ndef normalize(image):\n    \"\"\"\n    A function that normalize the elements inside\n    a numpy matrix, and change their type to float64.\n    :param image: a numpy matrix\n    :return: the matrix with all elements normalized\n    to the range: [0, 1].\n    \"\"\"\n    image = image.astype('float64')\n    return image / 255\n\n\ndef read_image(filename, representation):\n    \"\"\"\n    A function that reads a file according\n    to a given representation.\n    :param filename: The file name.\n    :param representation: 1 represents rgb,\n    2 represents gray_scale.\n    :return: An image according to the\n    given representation.\n    \"\"\"\n    image = imageio.imread(filename)\n    if representation == 2 or image.ndim != 3:\n        if np.any(image > 1):\n            return normalize(image)\n    elif representation == 1:\n        return color.rgb2gray(image)\n\n\ndef normalize_gaussian_filter(filter_vec):\n    \"\"\"\n    A function that normalize the gaussian\n    filter.\n    :param filter_vec: The filter.\n    :return: the normalized filter.\n    \"\"\"\n    return filter_vec / np.sum(filter_vec)\n\n\ndef build_gaussian_filter(filter_size):\n    \"\"\"\n    A function that build a gaussian filter\n    according to a given size.\n    :param filter_size: odd integer that\n    represents the desired size.\n    :return: The gaussian filter as\n    a numpy array with shape: [1, filter_size].\n    (without normalization)\n    \"\"\"\n    number_of = filter_size - 2\n    if filter_size > 1:\n        small_kernel = np.array([1, 1])\n    else:\n        small_kernel = np.array([1])\n    the_filter = small_kernel\n    for i in range(number_of):\n        the_filter = np.convolve(the_filter, small_kernel)\n    return np.reshape(the_filter, (1, filter_size))\n\n\ndef convolve(im, filter_vec):\n    \"\"\"\n    A function that convolve an image with\n    a given filter.\n    :param im: The given image as a grayscale\n    image with double values in [0, 1].\n    :param filter_vec: The filter as a row vector.\n    :return: The image after the deletion.\n    \"\"\"\n    im_after_rows_convolution = scipy.ndimage.filters.convolve(\n        im, filter_vec)\n    return scipy.ndimage.filters.convolve(\n        im_after_rows_convolution, np.transpose(filter_vec))\n\n\ndef delete_elements(im):\n    \"\"\"\n    A function that delete all the elements\n    in the odd indexes. (as well as all the\n    odd rows)\n    :param im: The given image.\n    :return: The image after the deletion.\n    \"\"\"\n    odd_indexes_of_rows = np.arange(1, im.shape[0] + 1, 2)\n    odd_indexes_of_cols = np.arange(1, im.shape[1] + 1, 2)\n    im_after_horizontal_clean = np.delete(im,\n                                          odd_indexes_of_rows, axis=0)\n    im_after_vertical_clean = np.delete(im_after_horizontal_clean,\n                                        odd_indexes_of_cols,\n                                        axis=1)\n    return im_after_vertical_clean\n\n\ndef reduce(im, filter_vec):\n    \"\"\"\n    A function that creates a smaller image\n    (smaller by factor of 2) of the given image.\n    :param im: The given image.\n    :param filter_vec: The given filter\n    that blur the image.\n    :return: The smaller image.\n    \"\"\"\n    im_after_convolution = convolve(im, filter_vec)\n    return delete_elements(im_after_convolution)\n\n\ndef build_gaussian_pyramid(im, max_levels, filter_size):\n    \"\"\"\n    A function that construct a Gaussian\n    pyramid of a given image.\n    :param im: A grayscale image with double\n    values in [0, 1].\n    :param max_levels: The maximal number of levels1\n    in the resulting pyramid.\n    :param filter_size: The size of the Gaussian filter\n    (an odd scalar that represents a squared filter) to be used\n    in constructing the pyramid filter.\n    :return: The resulting pyramid pyr as a standard python array,\n    and filter_vec - which is a row vector of shape (1, filter_size).\n    \"\"\"\n    filter_vec = build_gaussian_filter(filter_size)\n    filter_vec = normalize_gaussian_filter(filter_vec)\n    pyr = [im]\n    smallest_image = im\n    max_levels -= 1\n    while max_levels >= 1 and smallest_image.shape[0] > 16 \\\n            and smallest_image.shape[1] > 16:\n        smallest_image = reduce(smallest_image, filter_vec)\n        pyr.append(smallest_image)\n        max_levels -= 1\n    return pyr, filter_vec\n\n\ndef gaussian_kernel(kernel_size):\n    conv_kernel = np.array([1, 1], dtype=np.float64)[:, None]\n    conv_kernel = convolve2d(conv_kernel, conv_kernel.T)\n    kernel = np.array([1], dtype=np.float64)[:, None]\n    for i in range(kernel_size - 1):\n        kernel = convolve2d(kernel, conv_kernel, 'full')\n    return kernel / kernel.sum()\n\n\ndef blur_spatial(img, kernel_size):\n    kernel = gaussian_kernel(kernel_size)\n    blur_img = np.zeros_like(img)\n    if len(img.shape) == 2:\n        blur_img = convolve2d(img, kernel, 'same', 'symm')\n    else:\n        for i in range(3):\n            blur_img[..., i] = convolve2d(img[..., i], kernel, 'same', 'symm')\n    return blur_img\n\n\n# another pyramid functions:                                 maybe delete this at the end\ndef fiter_gen(kernel_size):\n    \"\"\"\n    gernerate a kernel acorrding to binomical coeeficients\n    :param kernel_size:  the size of kernel to generate\n    :return:  the new kernel matrix\n    \"\"\"\n    one_d_ker = np.array([1])\n    for i in range(0, kernel_size - 1):\n        one_d_ker = np.convolve(one_d_ker, [1, 1])\n    ker_sum = np.sum(one_d_ker)\n    one_d_ker = one_d_ker / ker_sum\n    return one_d_ker.reshape(1, kernel_size)\n\n\ndef build_gaussian_pyramid_n(im, max_levels, filter_size):\n    \"\"\"\n    creates a gaussian pyramid from image\n    :param im: the image to create pyramid from\n    :param max_levels: the levels of pyramid\n    :param filter_size: the filter size to use\n    :return: the pyramid and filter vector\n    \"\"\"\n    filter_vec = fiter_gen(filter_size)\n    pyr = []\n    new_im = np.copy(im)\n    pyr.append(new_im)\n    for i in range(1, max_levels):\n        if new_im.shape[0] <= 16 or new_im.shape[1] <= 16:\n            break\n        new_im = convolve2d(new_im, filter_vec)          # i deleted from this line and the next: mode='reflect'\n        new_im = convolve2d(new_im, filter_vec.T)\n        new_im = new_im[::2, 0::2]\n        pyr.append(new_im)\n    return pyr, filter_vec\n","sub_path":"sol4_utils.py","file_name":"sol4_utils.py","file_ext":"py","file_size_in_byte":6325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"414224921","text":"#!/usr/bin/python\n\nimport re\nimport logging\n\nfrom hc.monitor import Monitor\nfrom tools.utils import convertto\n\nlogger = logging.getLogger(\"Monitor.{}\".format(__name__))\n\n\nclass if_stats(Monitor):\n\n    def __init__(self, settings, **args):\n        Monitor.__init__(self, settings, **args)\n\n        self.fields = (\"bytes\",\n                       \"packets\",\n                       \"errs\",\n                       \"dropped\",\n                       \"fifo.errs\",\n                       \"frame.errs\",\n                       \"compressed\",\n                       \"multicast\",\n                       \"bytes\",\n                       \"packets\",\n                       \"errs\",\n                       \"dropped\",\n                       \"fifo.errs\",\n                       \"collisions\",\n                       \"carrier.errs\",\n                       \"compressed\")\n\n    def collect(self, data):\n\n        f_netdev = open(\"/proc/net/dev\", \"r\")\n\n        data = {}\n        for line in f_netdev:\n            m = re.match(\"(.*?):(.*)\", line)\n            if not m:\n                continue\n\n            intf = m.group(1).strip()\n\n            if intf == \"lo\":\n                continue\n\n            stats = m.group(2).split(None)\n\n            def direction(i):\n                if i >= 8:\n                    return \"out\"\n                return \"in\"\n\n            for idx in range(16):\n                name = (\"{}.{}.{}\".format(intf, self.fields[idx], direction(idx)))\n                data[name] = convertto(stats[idx])\n\n        return data\n\n    def runit(self):\n\n        rc = 0\n        data = {}\n        data = self.collect(data)\n\n        return rc, data, {}, \"\"\n","sub_path":"hc/modules/if_stats.py","file_name":"if_stats.py","file_ext":"py","file_size_in_byte":1634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"320449720","text":"from django.conf.urls import url\n\nfrom . import views\n\napp_name = 'MesAlgo'\nurlpatterns = [\n    url(r'^$', views.index, name='index'),\n    url(r'^preprocessing/$', views.prepro, name='prepro'),\n    url(r'^ClusteringSpectral/$', views.clustering, name='clustering'),\n    url(r'^Lien/$', views.lien, name='lien'),\n    url(r'^MiseEnOeuvre/$', views.meo, name='meo'),       \n]\n","sub_path":"TER/MesAlgo/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"478397607","text":"from numpeter import Matrix, Vector\n\ndef get_energy_in_square(vertices):\n    h = .02  # size of triangle\n    A = h * h / 2  # area of triangle\n\n    # global\n    # vertices[node num][value]\n    # value 0 = x pos\n    # value 1 = y pos\n    # value 2 = potential U\n    x_vals = []\n    y_vals = []\n    pots = []\n    for i in range(4):\n        x_vals.append(vertices[i][0])\n        y_vals.append(vertices[i][1])\n        pots.append(vertices[i][2])\n\n    # local\n    x_vals = [x_vals[0], x_vals[1], x_vals[2], x_vals[3], x_vals[0], x_vals[2]]\n    y_vals = [y_vals[0], y_vals[1], y_vals[2], y_vals[3], y_vals[0], y_vals[2]]\n    pots = [pots[0], pots[1], pots[2], pots[3], pots[0], pots[2]]\n\n    def get_S_vals(x, y):\n        # x = 3 x values of sub triangle\n        # y = 3 y values of sub triangle\n        # ccw dir\n\n        S = []  # will hold s11,s22,s33,s12,s13,s23\n        for i in range(3):\n            ind1 = (i + 1) % 3\n            ind2 = (i + 2) % 3\n            dif1 = y[ind1] - y[ind2]\n            dif2 = x[ind2] - x[ind1]\n            s = 1 / (4 * A) * (dif1 * dif1 + dif2 * dif2)\n\n            S.append(s)\n\n        # s12= s21\n        s = 1 / (4 * A) * ((y[1] - y[2]) * (y[2] - y[0]) + (x[2] - x[1]) * (x[0] - x[2]))\n        S.append(s)\n        # s13=s31\n        s = 1 / (4 * A) * ((y[1] - y[2]) * (y[0] - y[1]) + (x[2] - x[1]) * (x[1] - x[0]))\n        S.append(s)\n        # s23=s32\n        s = 1 / (4 * A) * ((y[2] - y[0]) * (y[0] - y[1]) + (x[0] - x[2]) * (x[1] - x[0]))\n\n        S.append(s)\n        return S\n\n    # want S_global\n    # S_global = C^T S_dis C\n    S_dis = Matrix.zeros(6, 6)\n    x123 = x_vals[0:3]\n    y123 = y_vals[0:3]\n    s123 = get_S_vals(x123, y123)\n\n    x456 = x_vals[3:6]\n    y456 = y_vals[3:6]\n    s456 = get_S_vals(x456, y456)\n\n    # set s123 values in s_dis\n    for i in range(3):\n        for j in range(3):\n            val = None\n            if i == j:\n                val = s123[i]\n            elif ((i == 0 and j == 1) or (i == 1 and j == 0)):  # s12=s21\n                val = s123[3]\n            elif ((i == 0 and j == 2) or (i == 2 and j == 0)):  # s13=s31\n                val = s123[4]\n            elif ((i == 1 and j == 2) or (i == 2 and j == 1)):  # s23=s32\n                val = s123[5]\n\n            S_dis[i, j] = val\n\n    # set s456 values in s_dis\n    for i in range(3, 6):\n        for j in range(3, 6):\n            val = None\n            if i == j:\n                val = s456[i - 3]\n            elif ((i == 3 and j == 4) or (i == 4 and j == 3)):  # s45=s54\n                val = s456[3]\n            elif ((i == 3 and j == 5) or (i == 5 and j == 3)):  # s46=s64\n                val = s456[4]\n            elif ((i == 4 and j == 5) or (i == 5 and j == 4)):  # s56=s65\n                val = s456[5]\n\n            S_dis[i, j] = val\n\n    # S_dis built\n\n    # C comes from definition of mesh\n    C = Matrix.zeros(6, 4)\n    C[0, 0] = 1\n    C[1, 1] = 1\n    C[2, 2] = 1\n    C[3, 3] = 1\n    C[4, 0] = 1\n    C[5, 2] = 1\n\n    # calculate\n    # S_global = C^T S_dis C\n    S_global = C.transpose() * S_dis * C\n    print(S_global)\n\n    U_con = Vector([pots[0], pots[1], pots[2], pots[3]])\n    # W = 1/2 * U_con^T*S_global*U_con\n    W = .5 * U_con * S_global * U_con\n\n    eps_0 = 8.85418782e-12\n    return W*eps_0\n\n\nif __name__ == '__main__':\n\n    # potentials has node_num, x, y, V\n    n = 34\n    m = 4\n\n    potentials = Matrix.read_from_file('potentials.txt', n, m)\n\n    # E = 1/2*C*V^2\n    # V = 110\n    x_vals = potentials.get_column(1)\n    y_vals = potentials.get_column(2)\n    pots = potentials.get_column(3)\n\n\n    # C = 2E/V^2\n    # add up all the energy in each mesh square\n    E = 0\n    for j in range(1, 25):\n        if j % 6 == 0:\n            continue\n        else:\n            i=j-1\n            vert1 = [x_vals[i], y_vals[i], pots[i]]\n            vert2 = [x_vals[i + 6], y_vals[i + 6], pots[i + 6]]\n            vert3 = [x_vals[i + 6 + 1], y_vals[i + 6 + 1], pots[i + 6 + 1]]\n            vert4 = [x_vals[i + 1], y_vals[i + 1], pots[i + 1]]\n            square = [vert1, vert2, vert3, vert4]\n            e = get_energy_in_square(square)\n            E += e\n\n    for j in range(27, 30):\n        i = j - 1\n        vert1 = [x_vals[i], y_vals[i], pots[i]]\n        vert2 = [x_vals[i + 4], y_vals[i + 4], pots[i + 4]]\n        vert3 = [x_vals[i + 4 + 1], y_vals[i + 4 + 1], pots[i + 4 + 1]]\n        vert4 = [x_vals[i + 1], y_vals[i + 1], pots[i + 1]]\n        square = [vert1, vert2, vert3, vert4]\n        e = get_energy_in_square(square)\n        E += e\n\n\n    # final calculation\n    # C = 2E/V^2\n    V=110\n    C = 2*E/(V*V)\n\n    print(C)","sub_path":"a2/capacitance.py","file_name":"capacitance.py","file_ext":"py","file_size_in_byte":4559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"222867260","text":"def solution(s):\n    sheet = {\n        'zero' : '0',\n        'one'  : '1',\n        'two'  : '2',\n        'three': '3',\n        'four' : '4',\n        'five' : '5',\n        'six'  : '6',\n        'seven': '7',\n        'eight': '8',\n        'nine' : '9'\n    }\n    result = ''\n    char = ''\n    for value in s:\n        if value.isdigit():\n            result += value\n        elif value.isalpha():\n            char += value\n            if char in sheet.keys():\n                result += sheet[char]\n                char = ''\n\n    return int(result)","sub_path":"프로그래머스/Level1/숫자 문자열과 영단어.py","file_name":"숫자 문자열과 영단어.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"466628530","text":"#调用函数的传参方式:1.使用位置传参2.使用关键字传参\n\ndef show(name,age):\n\tprint(name,age)\n\tprint(\"架架\")\n#使用位置参数方式传参:必修按照函数的参数顺序去传参\nshow(\"张三\",18)\n#positional argument:丢失了一个位置参数\n\n#使用关键字方式传参\n#使用关键字传参,可以不按照函数参数的顺序去传参\nshow(age=19,name=\"李四\")\n#前面使用关键字传参,后面使用位置传参(不可以的)\n# show(age=19,\"张三\")\n#前面使用位置传参,后面使用关键字传参可以使用\n#关键字传参,必须放在最后\nshow(\"冯七\",age=10)","sub_path":"Python exercises/第三天/day003/07-调用函数的穿参方式.py","file_name":"07-调用函数的穿参方式.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"336803285","text":"class Item:\n    def __init__(self, category=None, condition=0):\n        if not category:\n            category = \"\"\n        self.category = category\n        self.condition = condition\n\n    def condition_description(self):\n        descriptions = [\n            \"horrible\",\n            \"really really bad\",\n            \"really bad\",\n            \"not okay\",\n            \"okay\",\n            \"fine\",\n        ]\n        return descriptions[round(self.condition)]\n\n    def __str__(self):\n        return \"Hello World!\"\n","sub_path":"swap_meet/item.py","file_name":"item.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"414047052","text":"\"\"\"\n@Project    :20180827\n@Time       :2018/8/27 16:01\n@Author     :Zhenxian\n@File       :管道.py\n@Software   :PyCharm\n\"\"\"\nfrom multiprocessing import Process, Pipe\n\n\ndef func(conn):\n    conn.send(\"Hello,This is Python!\")  # 发送\n    conn.close()\n\n\nif __name__ == '__main__':\n    parent_conn, child_conn = Pipe()\n    p = Process(target=func, args=(child_conn,))\n    p.start()\n    print(parent_conn.recv())  # 接收\n    p.join()\n","sub_path":"20180827/管道.py","file_name":"管道.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"190440210","text":"import base64\n\n\ndef hex_to_base64(hex_value):\n    hex_value = hex_value.upper()\n    decoded = base64.b16decode(hex_value)\n    return base64.b64encode(decoded)\n\n\ndef fixed_xor(value1, value2):\n    xored = hex(int(value1, 16) ^ int(value2, 16))[2:]\n    while len(xored) % 4 is not 0:\n        xored = '0' + xored\n\n    from_hex = bytes.fromhex(xored)\n    # print(from_hex)\n    return base64.b16encode(from_hex).lower()\n\n\ndef single_byte_xor_cipher_brute_force(encoded):\n    winner = None\n    best_score = 0\n    key = None\n    length = len(encoded)\n    for i in range(0, 255):\n        char = hex(i)[2:]\n        char_length = len(char)\n        multiply_by = int(length / char_length)\n        hex_string = multiply_by * char\n\n        xored = fixed_xor(encoded, hex_string)\n        decoded = base64.b16decode(xored.upper())\n        score = get_score(decoded)\n\n        if score > best_score:\n            best_score = score\n            winner = decoded\n            key = chr(i)\n\n    return {\"word\": winner, \"key\": key}\n\n\ndef get_score(word):\n    score = 0\n    for char in word:\n        as_char = chr(char)\n        if as_char in character_frequencies:\n            score += character_frequencies[as_char]\n\n    return score / len(word)\n\ncharacter_frequencies = {\n    ' ': 20.00,\n    'e': 12.02,\n    't': 9.10,\n    'a': 8.12,\n    'o': 7.68,\n    'i': 7.31,\n    'n': 6.95,\n    's': 6.28,\n    'r': 6.02,\n    'h': 5.92,\n    'd': 4.32,\n    'l': 3.98,\n    'u': 2.88,\n    'c': 2.71,\n    'm': 2.61,\n    'f': 2.30,\n    'y': 2.11,\n    'w': 2.09,\n    'g': 2.03,\n    'p': 1.82,\n    'b': 1.49,\n    'v': 1.11,\n    'k': 0.69,\n    'x': 0.17,\n    'q': 0.11,\n    'j': 0.10,\n    'z': 0.07,\n}\n\n","sub_path":"python/CryptoUtils.py","file_name":"CryptoUtils.py","file_ext":"py","file_size_in_byte":1660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"239381728","text":"import textwrap\nimport functools\n\n\ndef get_test_input() -> str:\n    return textwrap.dedent(\"\"\"\\\n    [1,1,3,1,1]\n    [1,1,5,1,1]\n    \n    [[1],[2,3,4]]\n    [[1],4]\n    \n    [9]\n    [[8,7,6]]\n    \n    [[4,4],4,4]\n    [[4,4],4,4,4]\n    \n    [7,7,7,7]\n    [7,7,7]\n    \n    []\n    [3]\n    \n    [[[]]]\n    [[]]\n    \n    [1,[2,[3,[4,[5,6,7]]]],8,9]\n    [1,[2,[3,[4,[5,6,0]]]],8,9]\"\"\")\n\n\ndef read_input(day_number, test=False):\n    if test:\n        return parse_input(get_test_input())\n    else:\n        filename = 'input/day{}.txt'.format(day_number)\n        with open(filename, 'r') as file:\n            return parse_input(file.read())\n\n\ndef str2list_c(s, cursor=0):\n    ans = []\n    cursor += 1\n    num = ''\n    while cursor < len(s):\n        if s[cursor] == '[':\n            num = ''\n            l, c = str2list_c(s, cursor)\n            ans.append(l)\n            cursor = c\n            continue\n        elif s[cursor] == ',':\n            if num:\n                ans.append(int(num))\n            num = ''\n        elif s[cursor] == ']':\n            if num:\n                ans.append(int(num))\n            cursor += 1\n            return ans, cursor\n        else:\n            num += s[cursor]\n        cursor += 1\n\n    # bad since we didn't hit a ']'\n    return None, None\n\n\ndef str2list(s):\n    return str2list_c(s, 0)[0]\n\n\ndef intlist_compare(a1, a2):\n    if type(a1) is int and type(a2) is int:\n        return a1 - a2\n\n    if type(a1) is int:\n        return intlist_compare([a1], a2)\n\n    if type(a2) is int:\n        return intlist_compare(a1, [a2])\n\n    for x, y in zip(a1, a2):\n        c = intlist_compare(x, y)\n        if c != 0:\n            return c\n\n    return len(a1) - len(a2)\n\n\ndef parse_input(s: str):\n    data = []\n    for pair in s.split('\\n\\n'):\n        data.append(tuple(str2list(s) for s in pair.splitlines(keepends=False)))\n    return data\n\n\ndef part_1(data):\n    sum_indices = 0\n    for idx, p in enumerate(data):\n        sum_indices += (idx+1) if (intlist_compare(*p) < 0) else 0\n    print(f'Part 1: {sum_indices}')\n\n\ndef part_2(data):\n    unpaired_data = []\n    for p in data:\n        unpaired_data.append(p[0])\n        unpaired_data.append(p[1])\n    unpaired_data.append([[2]])\n    unpaired_data.append([[6]])\n\n    unpaired_data.sort(key=functools.cmp_to_key(intlist_compare))\n    idx_2, idx_6 = None, None\n    for idx, a in enumerate(unpaired_data):\n        if a == [[2]]:\n            idx_2 = idx + 1\n        elif a == [[6]]:\n            idx_6 = idx + 1\n    print(f'Part 2: {idx_2*idx_6}')\n\n\ndef main():\n    data = read_input(day_number=13, test=False)\n    part_1(data)\n    part_2(data)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"2022/day13.py","file_name":"day13.py","file_ext":"py","file_size_in_byte":2642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"174613033","text":"\"\"\" Phone Letter Backtracking Problem,\nGiven a digit string, return all possible letter combinations that the number could represent.\n\nA mapping of digit to letters (just like on the telephone buttons) is given below.\n\n\n\nThe digit 0 maps to 0 itself.\nThe digit 1 maps to 1 itself.\n\nInput: Digit string \"23\"\nOutput: [\"ad\", \"ae\", \"af\", \"bd\", \"be\", \"bf\", \"cd\", \"ce\", \"cf\"]. \n\n\"\"\"\n\ndef letterCombinations(self, digits):\n        mapping = {'2': 'abc', '3': 'def', '4': 'ghi', '5': 'jkl', \n                   '6': 'mno', '7': 'pqrs', '8': 'tuv', '9': 'wxyz', '0': '0', '1': '1'}\n        result = []\n        possible = \"\"\n        index = 0\n        n = len(digits)\n        def helper(mapping, index, possible,result):\n            if len(possible)== n:\n                result.append(possible)\n                return\n            else:\n                for letter in mapping[digits[index]]:\n                    possible+=letter\n                    helper(mapping,index+1,possible,result)\n                    #backtracking\n                    possible = possible[:-1]\n        helper(mapping, index, possible, result)\n        return result\n","sub_path":"Backtracking/letter-phone.py","file_name":"letter-phone.py","file_ext":"py","file_size_in_byte":1126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"240107647","text":"\"\"\"\nThis file contains data used to represent graphics in gitlab.\n\"\"\"\n\nPIEC_SCHEMA = {\n  \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\",\n  \"description\": \"All stateactors involved via ipv4.\",\n  \"data\": {\n    \"values\": []\n  },\n  \"mark\": {\"type\": \"arc\", \"innerRadius\": 65},\n  \"encoding\": {\n    \"theta\": {\"field\": \"value\", \"type\": \"quantitative\"},\n    \"color\": {\"field\": \"State\", \"type\": \"nominal\"}\n  },\n  \"view\": {\"stroke\": \"null\"}\n}\n\nBAR_WEBSITE_TYPES_SCHEMA = {\n  \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\",\n  \"description\": \"Websitetypechart\",\n  \"data\": {\n    \"values\": []\n  },\n  \"transform\": [\n    {\n      \"window\": [{\n        \"op\": \"rank\",\n        \"as\": \"rank\"\n      }],\n      \"sort\": [{ \"field\": \"Amount\", \"order\": \"descending\" }]\n    }, {\n      \"filter\": \"datum.rank <= 5\"\n    }\n  ],\n  \"mark\": \"bar\",\n  \"encoding\": {\n    \"x\": {\n        \"field\": \"Amount\",\n        \"type\": \"quantitative\"\n    },\n    \"y\": {\n        \"field\": \"Websitetypes\",\n        \"type\": \"nominal\",\n        \"sort\": {\"field\": \"Amount\", \"op\": \"average\", \"order\":\"descending\"}\n    }\n  }\n}\n\nCVE_HEATMAP = {\n  \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\",\n  \"data\": {\n    \"values\": []\n  },\n  \"params\": [{\"name\": \"highlight\", \"select\": \"point\"}],\n  \"mark\": {\"type\": \"rect\", \"strokeWidth\": 1},\n  \"encoding\": {\n    \"x\": {\n      \"field\": \"SCORE\",\n      \"type\": \"nominal\",\n      \"title\": \"CVEScore\"\n    },\n    \"y\": {\n      \"field\": \"COMPLX\",\n      \"type\": \"nominal\",\n      \"title\": \"Complexity\"\n    },\n    \"fill\": {\n      \"field\": \"APPEAR\",\n      \"type\": \"quantitative\",\n      \"title\": \"Occurrences\"\n    },\n    \"stroke\": {\n      \"condition\": {\n        \"param\": \"highlight\",\n        \"empty\": False,\n        \"value\": \"black\"\n      },\n      \"value\": \"null\"\n    },\n    \"opacity\": {\n      \"condition\": {\"param\": \"highlight\", \"value\": 1},\n      \"value\": 0.5\n    },\n    \"order\": {\"condition\": {\"param\": \"highlight\", \"value\": 1}, \"value\": 0}\n  },\n  \"config\": {\n    \"scale\": {\n      \"bandPaddingInner\": 0,\n      \"bandPaddingOuter\": 0\n    },\n    \"view\": {\"step\": 20},\n    \"range\": {\n      \"ramp\": {\n        \"scheme\": \"yellowgreenblue\"\n      }\n    },\n    \"axis\": {\n      \"domain\": False\n    }\n  }\n}\n","sub_path":"libs/markdown/gl_structure.py","file_name":"gl_structure.py","file_ext":"py","file_size_in_byte":2196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"165133681","text":"from math import inf\n\n\nclass Solution:\n    def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:\n        \"\"\"\n            https://leetcode.com/problems/median-of-two-sorted-arrays/\n            Time Complexity - O(log(min(m,n))\n            Space Complexity - O(1)\n            'm' is the length of nums1, 'n' is the length of nums2\n        \"\"\"\n        # start from the smaller list\n        if len(nums2) < len(nums1):\n            nums1, nums2 = nums2, nums1\n\n        x = len(nums1)\n        y = len(nums2)\n\n        # binary search on nums1 (which is always smaller)\n        low, high = 0, x\n        while low <= high:\n            partitionX = int((low + high) / 2)\n            # 1 is added for odd cse\n            partitionY = int((x + y + 1) / 2) - partitionX\n\n            # if partitionX is 0 it means nothing is there on left side.\n            # Use -INF for maxLeftX\n            maxLeftX = -inf if partitionX == 0 else nums1[partitionX - 1]\n            # if partitionY is length of input then there is nothing on right side.\n            # Use +INF ofr minRightX\n            minRightX = inf if partitionX == x else nums1[partitionX]\n\n            maxLeftY = -inf if partitionY == 0 else nums2[partitionY - 1]\n            minRightY = inf if partitionY == y else nums2[partitionY]\n\n            if maxLeftX <= minRightY and maxLeftY <= minRightX:\n                # we have partitioned list at correct place\n                # Now get max of left elements and min of right elements\n                # to get the median in case of even length of comined list size\n                if (x + y) % 2 == 0:\n                    return (max(maxLeftX, maxLeftY) + min(minRightX, minRightY)) / 2\n                else:\n                    return max(maxLeftX, maxLeftY)\n            # we are too far on right side for partition X, Go left side\n            elif maxLeftX > minRightY:\n                high = partitionX - 1\n            # we are too far on left side for partition X, Go right side\n            else:\n                low = partitionX + 1\n        # we can come here is if input lists are not sorted\n        return -1\n\n    def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:\n        \"\"\"\n            https://leetcode.com/problems/median-of-two-sorted-arrays/\n            k = m+n\n            Time Complexity - O(klogk)\n            Space Complexity - O(n)\n            'm' is the length of nums1, 'n' is the length of nums2\n        \"\"\"\n        nums1 += nums2\n        nums1.sort()\n        mid = len(nums1) // 2\n        if len(nums1) % 2 == 0:\n            return (nums1[mid - 1] + nums1[mid]) / 2\n        else:\n            return nums1[mid]\n","sub_path":"4_median_of_two_sorted_arrays.py","file_name":"4_median_of_two_sorted_arrays.py","file_ext":"py","file_size_in_byte":2675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"476053605","text":"import numpy as np\nfrom matplotlib import pyplot, cm\nfrom mpl_toolkits.mplot3d import Axes3D\n\nnt = 50\nk12 = 10\nk23 = 2\nK = np.matrix(((k12,-k12), (-k12,k12+k23)))\n\nfig = pyplot.figure(figsize=(11,7), dpi=100)\n\nfor n in range(nt):\n    f = [np.sin(n*0.1*np.pi),0]\n    u = np.linalg.solve(K, f)\n    pyplot.plot([2+u[0],1+u[1],0],[0,0,0],'o-',linewidth=5,markersize=20)\n    pyplot.axis([-.5,2.5,-1,1])\n    pyplot.pause(0.05)\n    pyplot.clf()\npyplot.show()\n","sub_path":"python/fem/step01.py","file_name":"step01.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"353859500","text":"\"\"\"\nCódigo principal del problema. Instancia un plano y una pokebola.\nEsta rebotará siguiendo alguna dirección y una velocidad inicial.\n\n@author Pablo Pizarro R. @ppizarror\n@date 2018-2019\n@license MIT\n\"\"\"\n\n# Importación de librerías\nfrom PyOpenGLtoolbox import *  # Hecho con versión 2.3.0\nfrom pokebola import Pokebola\n\n\"\"\"\n-------------------------------------------------------------------------------\nDefinición de algunas constantes\n-------------------------------------------------------------------------------\n\"\"\"\nFOV = 60\nFPS = 60\nVENTANA_H = 600\nVENTANA_W = 800\n\n\"\"\"\n-------------------------------------------------------------------------------\nInicio de OpenGL\n-------------------------------------------------------------------------------\n\"\"\"\ninit_pygame(VENTANA_W, VENTANA_H, 'Simulador Pokebolas', centered_window=True)\ninit_gl(transparency=False, normalized=True, perspectivecorr=True, antialiasing=True,\n        depth=True, smooth=True, version=True, lighting=True, numlights=1)\ninit_light(GL_LIGHT0)\nreshape_window_perspective(w=VENTANA_W, h=VENTANA_H, near=0.01, far=1000, fov=60)\nclock = pygame.time.Clock()\n\n\"\"\"\n-------------------------------------------------------------------------------\nCreación de modelos\n-------------------------------------------------------------------------------\n\"\"\"\naxis = create_axes(5)  # Retorna una lista con los ejes, parte de la librería\n\n# Crea el plano\ncubo = create_cube()\nplano = glGenLists(1)\nglNewList(plano, GL_COMPILE)\nglPushMatrix()\nglTranslate(0, 0, -1)\nglScale(50, 50, 1)\nglColor4fv([0.4, 0.4, 0.4, 1])\nglCallList(cubo)\nglPopMatrix()\nglEndList()\n\npokebola = Pokebola(Point3(0, -5, 5),  # Posición inicial\n                    Vector3(-0.5, 15, 0),  # Velocidad inicial\n                    1,  # Radio\n                    [1, 0, 0, 1],  # Color\n                    alpha_elastico=0.7,  # Coeficiente choque elástico\n                    plano=0  # Posición superior del plano en z (considerar mitad de la altura)\n                    )\n\n\"\"\"\n-------------------------------------------------------------------------------\nCreación de la cámara\n-------------------------------------------------------------------------------\n\"\"\"\ncamera = CameraXYZ(Point3(10, 10, 10), Point3(0, 0, 0), Point3(0, 0, 1))\ncamera.set_radial_vel(100)\n\n\"\"\"\n-------------------------------------------------------------------------------\nInicio de OpenGL\n-------------------------------------------------------------------------------\n\"\"\"\nprint('Camara XYZ cartesiana')\nprint('Rota eje X con teclas W/S')\nprint('Rota eje Y con teclas A/D')\nprint('Rota eje Z con teclas Q/E')\nprint('Zoom in/out con teclas N/M')\n\nwhile True:\n\n    # Tick del reloj\n    clock.tick(FPS)\n\n    # Elimina el buffer\n    clear_buffer()\n\n    # Ubica la cámara\n    camera.place()\n\n    # Actualiza objetos\n    pokebola.actualizar_posicion()\n\n    # Dibuja objetos\n    glCallList(plano)\n    glDisable(GL_LIGHTING)\n    glCallList(axis)\n    glEnable(GL_LIGHTING)\n    pokebola.dibujar()\n\n    # Chequea eventos\n    for event in pygame.event.get():\n        if event.type == QUIT or (event.type == KEYDOWN and event.key == K_ESCAPE):  # Cierra la app\n            exit()\n    keys = pygame.key.get_pressed()\n    if keys[K_w]:  # Rota cámara eje x\n        camera.rotate_x(2.5)\n    elif keys[K_s]:\n        camera.rotate_x(-2.5)\n    if keys[K_a]:  # Rota cámara eje y\n        camera.rotate_y(-2.5)\n    elif keys[K_d]:\n        camera.rotate_y(2.5)\n    if keys[K_q]:  # Rota cámara eje z\n        camera.rotate_z(-2.5)\n    elif keys[K_e]:\n        camera.rotate_z(2.5)\n    if keys[K_n]:  # Acerca / Aleja la cámara\n        camera.close()\n    elif keys[K_m]:\n        camera.far()\n\n    # Vuelca el contenido\n    pygame.display.flip()\n","sub_path":"taller-opcional/taller-threejs/python/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"392446482","text":"from bs4 import BeautifulSoup as BS\n\nURLS = [\n    'https://www.rt.com/usa/455119-teslas-hoard-data-unencrypted-resold/',\n    # 'https://www.rt.com/usa/452763-bernie-sanders-cnn-questions/'\n]\n\nNTHREADS = 100\n\n\ndef parse_paste(content):\n    bs = BS(content, features='html.parser')\n    heading = bs.findAll('h1')\n    print(heading)\n\n\ndef parse_cnbc(content):\n    bs = BS(content, features='html.parser')\n    heading = bs.findAll('h1')[0]\n    groups = bs.findAll('div', {'class': 'group'})\n\n    title = heading.text.strip()\n\n    keypoints = groups[0].find('ul').findAll('li')\n\n    summary = ' '.join([key.text.strip() for key in keypoints])\n    summary = summary.strip()\n\n    body = groups[1].text.strip()\n\n    return {\n        'title': title,\n        'summary': summary,\n        'body': body\n    }\n\n\ndef parse_consumeraffairs(content):\n    bs = BS(content, features='html.parser')\n    heading = bs.findAll('h1')[0]\n    summary = bs.findAll('h3')[0]\n    body = bs.findAll('div', {'class': 'entry'})[0]\n\n    title = heading.text.strip()\n    summary = summary.text.strip()\n\n    body = ' '.join([p.text.strip() for p in body.findAll('p')])\n    body = body.strip()\n\n    return {\n        'title': title,\n        'summary': summary,\n        'body': body\n    }\n\n\ndef parse_rt(content):\n    bs = BS(content, features='html.parser')\n\n    heading = bs.findAll('h1')[0]\n    summary = bs.findAll('div', {'class': 'article__summary'})[0]\n    body = bs.findAll('div', {'class': 'article__text'})[0]\n    anchors = body.findAll('a')\n\n    title = heading.text.strip()\n    summary = summary.text.strip()\n\n    return {\n        'title': title,  # str\n        'summary': summary,  # str\n        'body': body.text.strip(),  # str\n        'anchors': anchors  # list\n    }\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"379405468","text":"\"\"\"Constant choice of models module\"\"\"\r\n\r\nPRIORITY_CHOICES = [\r\n    ('LOW', 'LOW'),\r\n    ('MEDIUM', 'MEDIUM'),\r\n    ('HIGH', 'HIGH')\r\n]\r\n\r\nSTATUS_CHOICES = [\r\n    ('TO_DO', 'TO_DO'),\r\n    ('IN_PROGRESS', 'IN_PROGRESS'),\r\n    ('DONE', 'DONE')\r\n]\r\n\r\nTAG_CHOICES = [\r\n    ('BUG', 'BUG'),\r\n    ('UPDATE_PROJECT', 'UPDATE_PROJECT'),\r\n    ('TASK', 'TASK')\r\n]\r\n\r\nTYPE_CHOICES = [\r\n    ('FRONT_END', 'FRONT_END'),\r\n    ('BACK_END', 'BACK_END'),\r\n    ('IOS', 'IOS'),\r\n    ('ANDROID', 'ANDROID')\r\n]\r\n","sub_path":"softdesk/projects/models_settings.py","file_name":"models_settings.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"485472683","text":"import json\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom nn.encode import BiGraphEncoder\nfrom nn.decode import RelationDecoder, LinearDecoder\n\nfrom utils.help import ReferMetric\nfrom utils.dict import PieceAlphabet\nfrom utils.load import WordAlphabet, LabelAlphabet\nfrom utils.help import expand_list, noise_augment\nfrom utils.help import nest_list, iterable_support\n\n\nclass TaggingAgent(nn.Module):\n\n    def __init__(self,\n                 word_vocab: WordAlphabet,\n                 piece_vocab: PieceAlphabet,\n                 sent_vocab: LabelAlphabet,\n                 act_vocab: LabelAlphabet,\n                 adj_vocab: LabelAlphabet,\n                 adj_full_vocab: LabelAlphabet,\n                 adj_id_vocab: LabelAlphabet,\n                 embedding_dim: int,\n                 hidden_dim: int,\n                 num_layer: int,\n                 gat_layer: int,\n                 gat_dropout_rate: float,\n                 dropout_rate: float,\n                 use_linear_decoder: bool,\n                 pretrained_model: str):\n\n        super(TaggingAgent, self).__init__()\n\n        self._piece_vocab = piece_vocab\n        self._pretrained_model = pretrained_model\n\n        self._word_vocab = word_vocab\n        self._sent_vocab = sent_vocab\n        self._act_vocab = act_vocab\n        self._adj_vocab = adj_vocab\n        self._adj_full_vocab = adj_full_vocab\n        self._adj_id_vocab = adj_id_vocab\n\n        self._encoder = BiGraphEncoder(\n            nn.Embedding(len(word_vocab), embedding_dim),\n            hidden_dim, dropout_rate, pretrained_model\n        )\n        if use_linear_decoder:\n            self._decoder = LinearDecoder(len(sent_vocab), len(act_vocab), hidden_dim)\n        else:\n            self._decoder = RelationDecoder(\n                len(sent_vocab), len(act_vocab), hidden_dim,\n                num_layer, dropout_rate, gat_dropout_rate, gat_layer\n            )\n\n        # Loss function\n        self._criterion = nn.NLLLoss(reduction=\"sum\")\n\n    # Add for loading best model\n    def set_load_best_missing_arg(self, pretrained_model):\n        self._pretrained_model = pretrained_model\n        self._encoder.add_missing_arg(pretrained_model)\n\n    def set_load_best_missing_arg_mastodon(self, pretrained_model, layer=2):\n        self._pretrained_model = pretrained_model\n        self._encoder.add_missing_arg(pretrained_model)\n        self._decoder.add_missing_arg(layer)\n\n    def forward(self, input_h, len_list, adj, adj_full, adj_re, mask=None):\n        encode_h = self._encoder(input_h, adj, adj_full, mask)\n        return self._decoder(encode_h, len_list, adj_re)\n\n    @property\n    def sent_vocab(self):\n        return self._sent_vocab\n\n    @property\n    def act_vocab(self):\n        return self._act_vocab\n\n    def _wrap_padding(self, dial_list, adj_list, adj_full_list, adj_id_list, use_noise):\n        dial_len_list = [len(d) for d in dial_list]\n        max_dial_len = max(dial_len_list)\n\n        adj_len_list = [len(adj) for adj in adj_list]\n        max_adj_len = max(adj_len_list)\n\n        # add adj_full\n        adj_full_len_list = [len(adj_full) for adj_full in adj_full_list]\n        max_adj_full_len = max(adj_full_len_list)\n\n        # add adj_I\n        adj_id_len_list = [len(adj_I) for adj_I in adj_id_list]\n        max_adj_id_len = max(adj_id_len_list)\n\n        assert max_dial_len == max_adj_len, str(max_dial_len) + \" \" + str(max_adj_len)\n        assert max_adj_full_len == max_adj_len, str(max_adj_full_len) + \" \" + str(max_adj_len)\n        assert max_adj_id_len == max_adj_full_len, str(max_adj_id_len) + \" \" + str(max_adj_full_len)\n\n        turn_len_list = [[len(u) for u in d] for d in dial_list]\n        max_turn_len = max(expand_list(turn_len_list))\n\n        turn_adj_len_list = [[len(u) for u in adj] for adj in adj_list]\n        max_turn_adj_len = max(expand_list(turn_adj_len_list))\n\n        # add adj_full\n        turn_adj_full_len_list = [[len(u) for u in adj_full] for adj_full in adj_full_list]\n        max_turn_adj_full_len = max(expand_list(turn_adj_full_len_list))\n\n        # add adj_I\n        turn_adj_id_len_list = [[len(u) for u in adj_I] for adj_I in adj_id_list]\n        max_turn_adj_id_len = max(expand_list(turn_adj_id_len_list))\n\n        pad_adj_list = []\n        for dial_i in range(0, len(adj_list)):\n            pad_adj_list.append([])\n\n            for turn in adj_list[dial_i]:\n                pad_utt = turn + [0] * (max_turn_adj_len - len(turn))\n                pad_adj_list[-1].append(pad_utt)\n\n            if len(adj_list[dial_i]) < max_adj_len:\n                pad_dial = [[0] * max_turn_adj_len] * (max_adj_len - len(adj_list[dial_i]))\n                pad_adj_list[-1].extend(pad_dial)\n\n        pad_adj_full_list = []\n        for dial_i in range(0, len(adj_full_list)):\n            pad_adj_full_list.append([])\n\n            for turn in adj_full_list[dial_i]:\n                pad_utt = turn + [0] * (max_turn_adj_full_len - len(turn))\n                pad_adj_full_list[-1].append(pad_utt)\n\n            if len(adj_full_list[dial_i]) < max_adj_full_len:\n                pad_dial = [[0] * max_turn_adj_full_len] * (max_adj_full_len - len(adj_full_list[dial_i]))\n                pad_adj_full_list[-1].extend(pad_dial)\n\n        pad_adj_id_list = []\n        for dial_i in range(0, len(adj_id_list)):\n            pad_adj_id_list.append([])\n\n            for turn in adj_id_list[dial_i]:\n                pad_utt = turn + [0] * (max_turn_adj_id_len - len(turn))\n                pad_adj_id_list[-1].append(pad_utt)\n\n            if len(adj_id_list[dial_i]) < max_adj_id_len:\n                pad_dial = [[0] * max_turn_adj_id_len] * (max_adj_id_len - len(adj_id_list[dial_i]))\n                pad_adj_id_list[-1].extend(pad_dial)\n\n        pad_adj_R_list = []\n        for dial_i in range(0, len(pad_adj_id_list)):\n            pad_adj_R_list.append([])\n            assert len(pad_adj_id_list[dial_i]) == len(pad_adj_full_list[dial_i])\n            for i in range(len(pad_adj_full_list[dial_i])):\n                full = pad_adj_full_list[dial_i][i]\n                pad_utt_up = full + full\n                pad_adj_R_list[-1].append(pad_utt_up)\n\n            for i in range(len(pad_adj_full_list[dial_i])):\n                full = pad_adj_full_list[dial_i][i]\n                pad_utt_down = full + full\n                pad_adj_R_list[-1].append(pad_utt_down)\n\n        assert len(pad_adj_id_list[0]) * 2 == len(pad_adj_R_list[0]), pad_adj_R_list[0]\n\n        pad_w_list, pad_sign = [], self._word_vocab.PAD_SIGN\n        for dial_i in range(0, len(dial_list)):\n            pad_w_list.append([])\n\n            for turn in dial_list[dial_i]:\n                if use_noise:\n                    noise_turn = noise_augment(self._word_vocab, turn, 5.0)\n                else:\n                    noise_turn = turn\n                pad_utt = noise_turn + [pad_sign] * (max_turn_len - len(turn))\n                pad_w_list[-1].append(iterable_support(self._word_vocab.index, pad_utt))\n\n            if len(dial_list[dial_i]) < max_dial_len:\n                pad_dial = [[pad_sign] * max_turn_len] * (max_dial_len - len(dial_list[dial_i]))\n                pad_w_list[-1].extend(iterable_support(self._word_vocab.index, pad_dial))\n\n        cls_sign = self._piece_vocab.CLS_SIGN\n        piece_list, sep_sign = [], self._piece_vocab.SEP_SIGN\n\n        for dial_i in range(0, len(dial_list)):\n            piece_list.append([])\n\n            for turn in dial_list[dial_i]:\n                seg_list = self._piece_vocab.tokenize(turn)\n                piece_list[-1].append([cls_sign] + seg_list + [sep_sign])\n\n            if len(dial_list[dial_i]) < max_dial_len:\n                pad_dial = [[cls_sign, sep_sign]] * (max_dial_len - len(dial_list[dial_i]))\n                piece_list[-1].extend(pad_dial)\n\n        p_len_list = [[len(u) for u in d] for d in piece_list]\n        max_p_len = max(expand_list(p_len_list))\n\n        pad_p_list, mask = [], []\n        for dial_i in range(0, len(piece_list)):\n            pad_p_list.append([])\n            mask.append([])\n\n            for turn in piece_list[dial_i]:\n                pad_t = turn + [pad_sign] * (max_p_len - len(turn))\n                pad_p_list[-1].append(self._piece_vocab.index(pad_t))\n                mask[-1].append([1] * len(turn) + [0] * (max_p_len - len(turn)))\n\n        var_w_dial = torch.LongTensor(pad_w_list)\n        var_p_dial = torch.LongTensor(pad_p_list)\n        var_mask = torch.LongTensor(mask)\n        var_adj_dial = torch.LongTensor(pad_adj_list)\n        var_adj_full_dial = torch.LongTensor(pad_adj_full_list)\n        var_adj_R_dial = torch.LongTensor(pad_adj_R_list)\n\n        if torch.cuda.is_available():\n            var_w_dial = var_w_dial.cuda()\n            var_p_dial = var_p_dial.cuda()\n            var_mask = var_mask.cuda()\n            var_adj_dial = var_adj_dial.cuda()\n            var_adj_full_dial = var_adj_full_dial.cuda()\n            var_adj_R_dial = var_adj_R_dial.cuda()\n\n        return var_w_dial, var_p_dial, var_mask, turn_len_list, p_len_list, var_adj_dial, var_adj_full_dial, \\\n            var_adj_R_dial\n\n    def predict(self, utt_list, adj_list, adj_full_list, adj_id_list):\n        var_utt, var_p, mask, len_list, _, var_adj, var_adj_full, var_adj_R = \\\n            self._wrap_padding(utt_list, adj_list, adj_full_list, adj_id_list, False)\n        if self._pretrained_model != \"none\":\n            pred_sent, pred_act = self.forward(var_p, len_list, var_adj, var_adj_full, var_adj_R, mask)\n        else:\n            pred_sent, pred_act = self.forward(var_utt, len_list, var_adj, var_adj_full, var_adj_R, None)\n\n        trim_list = [len(l) for l in len_list]\n        flat_sent = torch.cat(\n            [pred_sent[i, :trim_list[i], :] for\n             i in range(0, len(trim_list))], dim=0\n        )\n        flat_act = torch.cat(\n            [pred_act[i, :trim_list[i], :] for\n             i in range(0, len(trim_list))], dim=0\n        )\n\n        _, top_sent = flat_sent.topk(1, dim=-1)\n        _, top_act = flat_act.topk(1, dim=-1)\n\n        sent_list = top_sent.cpu().numpy().flatten().tolist()\n        act_list = top_act.cpu().numpy().flatten().tolist()\n\n        nest_sent = nest_list(sent_list, trim_list)\n        nest_act = nest_list(act_list, trim_list)\n\n        string_sent = iterable_support(\n            self._sent_vocab.get, nest_sent\n        )\n        string_act = iterable_support(\n            self._act_vocab.get, nest_act\n        )\n        return string_sent, string_act\n\n    def measure(self, utt_list, sent_list, act_list, adj_list, adj_full_list, adj_id_list):\n        var_utt, var_p, mask, len_list, _, var_adj, var_adj_full, var_adj_R = \\\n            self._wrap_padding(utt_list, adj_list, adj_full_list, adj_id_list, True)\n\n        flat_sent = iterable_support(\n            self._sent_vocab.index, sent_list\n        )\n        flat_act = iterable_support(\n            self._act_vocab.index, act_list\n        )\n\n        index_sent = expand_list(flat_sent)\n        index_act = expand_list(flat_act)\n\n        var_sent = torch.LongTensor(index_sent)\n        var_act = torch.LongTensor(index_act)\n        if torch.cuda.is_available():\n            var_sent = var_sent.cuda()\n            var_act = var_act.cuda()\n\n        if self._pretrained_model != \"none\":\n            pred_sent, pred_act = self.forward(var_p, len_list, var_adj, var_adj_full, var_adj_R, mask)\n        else:\n            pred_sent, pred_act = self.forward(var_utt, len_list, var_adj, var_adj_full, var_adj_R, None)\n        trim_list = [len(l) for l in len_list]\n\n        flat_pred_s = torch.cat(\n            [pred_sent[i, :trim_list[i], :] for\n             i in range(0, len(trim_list))], dim=0\n        )\n        flat_pred_a = torch.cat(\n            [pred_act[i, :trim_list[i], :] for\n             i in range(0, len(trim_list))], dim=0\n        )\n\n        sent_loss = self._criterion(\n            F.log_softmax(flat_pred_s, dim=-1), var_sent\n        )\n        act_loss = self._criterion(\n            F.log_softmax(flat_pred_a, dim=-1), var_act\n        )\n        return sent_loss + act_loss\n","sub_path":"nn/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":12032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"112952076","text":"from utils import testDate\nfrom torch.utils.data import DataLoader\nfrom torchvision.transforms import transforms\nfrom torchvision.utils import save_image\nfrom model import Net\nfrom utils import PSNR ,cal_psnr\nimport torch\nfrom PIL import Image\nimport numpy as np\n\ndef test(savaimgs=False):\n    test_Date=testDate(\"/home/yzm/pyproject/hw4/data/test_images\",\"/home/yzm/pyproject/hw4/data/test_source\")\n    test_load=DataLoader(\n        dataset=test_Date,\n        batch_size=1,\n        shuffle=False\n    )\n\n    model=Net().cuda()\n    model.load_state_dict(torch.load(\"/home/yzm/pyproject/hw4/chkpt/guassian/model_ffinal120_epoch.state\"))\n    #model=torch.load(\"/home/yzm/pyproject/hw4/chkpt/guassian/model_epoch_50.pth\")\n    # for i in model.parameters():\n    #     print(i)\n    model.eval()\n    i=0\n    psnr=0\n    for x,x1 in test_load:\n        x1=x1.cuda()\n        x=x.cuda()\n        y=model(x)\n        psnr=psnr+PSNR(y,x1)\n    #print(psnr.item()/24)\n        if savaimgs is True:\n            save_image(x1,f\"/home/yzm/pyproject/hw4/experience/sourcegray/sourceimage_{i}_imag.jpg\")\n            save_image(y,f\"/home/yzm/pyproject/hw4/experience/test_out2/testimage_{i}_imag.jpg\")\n        i+=1\n    print(psnr.item()/24)\n\nif __name__==\"__main__\":\n    test(True)","sub_path":"src/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"330172246","text":"import random\n\nt_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]  # заполненный список\nt_list_empty = []  # пустой список\n\n\ndef f_list(in_list):  # объявление функции, на вход должна принимать только список\n\tif isinstance(in_list, list):  # проверка, список ли передан в функцию\n\t\tif len(in_list) == 0:  # проверка списка на пустоту\n\t\t\tprint('Список пуст.')  # уведомление о пустоте списка\n\t\telse:\n\t\t\tprint(random.choice(in_list))  # вывод на экран случайного элемента списка переданного в функцию\n\telse:\n\t\tprint(\"В обработку функции передан не список.\")\n\n\nf_list(t_list)  # проверка работы функции с заполненным списком\nf_list(t_list_empty)  # проверка работы функции с пустым списком\nf_list(1)  # проверка передаваемого типа\n","sub_path":"10_03.py","file_name":"10_03.py","file_ext":"py","file_size_in_byte":1072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"617757918","text":"from product import Product\nfrom order import Order\n\nclass Customer:\n    def __init__(self):\n        self.id_customer = \"\"\n        self.name_customer = \"\"\n        self.orders_customer = []\n\n    def get_order_count(self):\n        return int(len(self.orders_customer))\n\n    def get_total(self):\n        add = 0\n        i = 0\n        while i < self.get_order_count():\n            add += self.orders_customer[i].get_total()\n            i += 1\n        return add\n\n    def add_order(self,order):\n        self.orders_customer.append(order)\n\n    def display_summary(self):\n        print(\"Summary for customer '{}':\".format(self.id_customer))\n        print(\"Name: {}\".format(self.name_customer))\n        print(\"Orders: {}\".format(self.get_order_count()))\n        print(\"Total: ${:.2f}\".format(self.get_total()))\n\n    def display_receipts(self):\n        print(\"Detailed receipts for customer '{}':\".format(self.id_customer))\n        print(\"Name: {}\".format(self.name_customer))\n        print()\n        for d in range(0, self.get_order_count()):\n            if(d>0):\n                print(\"\")\n            self.orders_customer[d].display_receipt()","sub_path":"customer.py","file_name":"customer.py","file_ext":"py","file_size_in_byte":1135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"59878888","text":"# -*- coding:utf-8 -*-\nimport json\n\nfrom django.contrib import messages, auth\nfrom django.contrib.auth.models import User\nfrom django.core.mail import send_mail\nfrom django.core.urlresolvers import reverse\nfrom django.db import IntegrityError\nfrom django.http import HttpResponse, HttpResponseForbidden, HttpResponseRedirect\nfrom django.shortcuts import get_object_or_404, redirect, render_to_response\nfrom django.template import RequestContext\n\nfrom loginza.models import Identity, UserMap\nfrom loginza.templatetags.loginza_widget import _return_path\n\nfrom links.models import LinkModel\nfrom users.forms import CompleteRegistrationForm\nfrom users.models import ContactModel, ParticipationModel, ReportUserModel\nimport users.signals\n\ndef current_profile(request):\n    \"\"\" Show profile of the currently logged in user \"\"\"\n    if not request.user.is_authenticated():\n        return HttpResponseRedirect(reverse('login'))\n    return profile(request, request.user.username)\n\ndef profile(request, username):\n    user = get_object_or_404(User, username=username)\n\n    if request.user.is_authenticated() and request.user.username!=username:\n        in_contacts = ContactModel.objects.filter(user=request.user, contact=user).count()>0\n        is_reported = ReportUserModel.objects.filter(user=user, reporter=request.user).count()>0\n    else:\n        in_contacts = False\n        is_reported = False\n\n    context = {\n        'profile_user': user,\n        'participations': list(ParticipationModel.objects.filter(user=user).select_related()),\n        'links': list(LinkModel.objects.filter(user=user).select_related()),\n        'in_contacts': in_contacts,\n        'is_reported': is_reported,\n        'contacts': list(ContactModel.objects.filter(user=user)),\n        'have_in_contacts': list(ContactModel.objects.filter(contact=user)),\n    }\n    return render_to_response('profile.html', context_instance=RequestContext(request, context))\n\ndef become_voter(request):\n    if request.method=='POST' and request.is_ajax() and request.user.is_authenticated():\n        try:\n            location_id = int(request.POST.get('region_1', ''))\n        except ValueError:\n            return HttpResponse('fail1')\n\n        try:\n            participation, created = ParticipationModel.objects.get_or_create(\n                    type='voter', user=request.user, defaults={'location_id': location_id})\n        except IntegrityError:\n            return HttpResponse('fail2')\n\n        if not created:\n            participation.location_id = location_id\n            participation.save()\n\n        return HttpResponse('ok')\n\n    return HttpResponse('fail3')\n\ndef add_to_contacts(request):\n    if request.method=='POST' and request.is_ajax() and request.user.is_authenticated():\n        try:\n            contact = User.objects.get(username=request.POST.get('username', ''))\n        except User.DoesNotExist:\n            return HttpResponse('fail1')\n\n        try:\n            contact, created = ContactModel.objects.get_or_create(\n                    user=request.user, contact=contact, defaults={})\n        except IntegrityError:\n            return HttpResponse('fail2')\n\n        return HttpResponse('ok')\n\n    return HttpResponse('fail3')\n\ndef report_user(request):\n    if request.method=='POST' and request.is_ajax() and request.user.is_authenticated():\n        try:\n            user = User.objects.get(username=request.POST.get('username', ''))\n        except User.DoesNotExist:\n            return HttpResponse('fail1')\n\n        try:\n            report, created = ReportUserModel.objects.get_or_create(\n                    user=user, reporter=request.user, defaults={})\n        except IntegrityError:\n            return HttpResponse('fail2')\n\n        return HttpResponse('ok')\n\n    return HttpResponse('fail3')\n\ndef send_message(request):\n    if request.method=='POST' and request.is_ajax() and request.user.is_authenticated():\n        try:\n            user = User.objects.get(username=request.POST.get('username', ''))\n        except User.DoesNotExist:\n            return HttpResponse('fail1')\n\n        try:\n            send_mail(request.POST.get('message_title', ''), request.POST.get('message_body', ''),\n                    request.user.email, [user.email], fail_silently=False)\n        except: # TODO: explicit exception here\n            return HttpResponse('fail2')\n\n        return HttpResponse('ok')\n\n    return HttpResponse('fail3')\n\n# TODO: if username and email match an existing account - suggest to link them\ndef complete_registration(request):\n    if request.user.is_authenticated():\n        return HttpResponseForbidden(u'Вы попали сюда по ошибке') # TODO: redirect\n\n    try:\n        identity_id = request.session.get('users_complete_reg_id', None)\n        user_map = UserMap.objects.select_related().get(identity__id=identity_id)\n    except UserMap.DoesNotExist:\n        return HttpResponseForbidden(u'Вы попали сюда по ошибке')\n\n    if request.method == 'POST':\n        form = CompleteRegistrationForm(user_map.user.id, request.POST)\n        if form.is_valid():\n            user_map.user.username = form.cleaned_data['username']\n            user_map.user.email = form.cleaned_data['email']\n            user_map.user.first_name = form.cleaned_data['first_name']\n            user_map.user.last_name = form.cleaned_data['last_name']\n            user_map.user.set_password(form.cleaned_data[\"password1\"])\n            user_map.user.save()\n\n            user_map.verified = True\n            user_map.save()\n\n            user = auth.authenticate(user_map=user_map)\n            auth.login(request, user)\n\n            messages.info(request, u'Добро пожаловать!')\n            del request.session['users_complete_reg_id']\n            return redirect(_return_path(request))\n    else:\n        form = CompleteRegistrationForm(user_map.user.id, initial={\n                'username': user_map.user.username,\n                'email': user_map.user.email,\n        })\n\n    user_map = UserMap.objects.get(user=user_map.user) # TODO: what if there are several user maps?\n    data = json.loads(user_map.identity.data)\n    form.initial['first_name'] = data['name']['first_name']\n    form.initial['last_name'] = data['name']['last_name']\n\n    return render_to_response('complete_registration.html', {'form': form},\n            context_instance=RequestContext(request))\n","sub_path":"users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"522444870","text":"# -*- coding: utf-8 -*-\n\nimport datetime\nfrom copy import deepcopy\n\n\ndef get_value_from_dict(dict, key, defaut_v):\n        \"\"\"\n        从字典获得查询某个键值，如果不存在，返回默认值\n        :param dict:\n        :param key:\n        :param defaut_v:\n        :return:\n        \"\"\"\n        if key in dict:\n            return dict[key]\n        else:\n            return defaut_v\n\n\ndef require_value_from_dict(dict, key):\n        \"\"\"\n        从字典获得查询某个键值，如果不存在，抛出异常\n        :param dict:\n        :param key:\n        :param defaut_v:\n        :return:\n        \"\"\"\n        # TODO 处理异常\n        return dict[key]\n\n\nclass Storage(dict):\n    \"\"\"\n    A Storage object is like a dictionary except `obj.foo` can be used in addition to `obj['foo']`.\n        >>> o = storage(a=1)\n        >>> o.a\n        1\n        >>> o['a']\n        1\n        >>> o.a = 2\n        >>> o['a']\n        2\n        >>> del o.a\n        >>> o.a\n        Traceback (most recent call last):\n            ...\n        AttributeError: 'a'\n    \"\"\"\n\n    def __getattr__(self, key):\n        try:\n            return self[key]\n        except KeyError as k:\n            raise AttributeError(k)\n\n    def __setattr__(self, key, value):\n        self[key] = value\n\n    def __delattr__(self, key):\n        try:\n            del self[key]\n        except KeyError as k:\n            raise AttributeError(k)\n\n    def __repr__(self):\n        return '<Storage ' + dict.__repr__(self) + '>'\n\n\ndef model2dict(model, datetime_format='%Y-%m-%d %H:%M:%S'):\n    \"\"\"\n    本函数用于使对象可 json 序列化，且返回的字典都是新的（deepcopy）\n    \"\"\"\n    if isinstance(model, dict):\n        model = Storage(deepcopy(model))\n        to_pop = []\n        for k in model:\n            # 过滤\n            if isinstance(k, basestring) and (k.startswith('_') or k.isupper()):\n                to_pop.append(k)\n                continue\n            # 转换\n            elif isinstance(model[k], datetime.datetime):\n                model[k] = model[k].strftime(datetime_format)\n            # 递归\n            else:\n                model[k] = model2dict(model[k], datetime_format)\n        for k in to_pop:\n            model.pop(k)\n        return model\n    elif hasattr(model, '__dict__'):\n        return model2dict(model.__dict__, datetime_format)\n    elif isinstance(model, (list, tuple)):\n        return [model2dict(m, datetime_format) for m in model]\n    else:\n        return model\n\n\ndef model2dict_x(obj, keys=None):\n    \"\"\"\n    获取指定keys的字典\n    keys=None 返回所有属性的字典\n    \"\"\"\n    if keys is None:\n        if isinstance(obj, dict):\n            return obj\n        elif hasattr(obj, '__dict__'):\n            return obj.__dict__\n        else:\n            raise ValueError('无法处理对象: {0}'.format(str(obj)))\n\n    elif isinstance(keys, list):\n        ret = {}\n        if isinstance(obj, dict):\n            for key in keys:\n                ret[key] = obj.get(key, None)\n        elif hasattr(obj, '__dict__'):\n            obj_dict = obj.__dict__\n            for key in keys:\n                ret[key] = obj_dict.get(key, None)\n                if not ret[key] or ret[key] == 'None':\n                    ret[key] = ''\n        else:\n            raise ValueError('无法处理对象: {0}'.format(str(obj)))\n    else:\n        raise TypeError('参数类型错误')\n\n    return ret\n\n\n# 获取报名标示\ndef get_event_sign(list):\n    arr = [\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\",\"H\",\"I\",\"J\",\"K\",\"L\",\"M\",\"N\",\"O\",\"P\",\"Q\",\"R\",\"S\",\"T\",\"U\",\"V\",\"W\",\"X\",\"Y\",\"Z\"]\n    res =  set( arr ) ^ set( list )\n    if res and len(res)>0 :\n        return res.pop()\n    else:\n        return None","sub_path":"app_01/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"522721933","text":"#!/usr/bin/env python3\n#-*- coding: utf-8 -*-\n\nimport sqlite3\n\nclass DB(object):\n\tdef __init__(self, dbfile='shop.db'):\n\t\tself.__connection = sqlite3.connect(dbfile)\n\t\n\tdef get_user_info(self, userid):\n\t\tc = self.__connection.cursor()\n\t\tc.execute(\"SELECT id, email, firstname, lastname, gender, street, plz, city FROM users WHERE id = ?;\", (userid,))\n\t\trow = c.fetchone()\n\t\tc.close()\n\n\t\tif row is None:\n\t\t\treturn None\n\t\telse:\n\t\t\treturn {\n\t\t\t\t\"id\": row[0],\n\t\t\t\t\"email\": row[1],\n\t\t\t\t\"firstname\": row[2],\n\t\t\t\t\"lastname\": row[3],\n\t\t\t\t\"gender\": row[4],\n\t\t\t\t\"street\": row[5],\n\t\t\t\t\"plz\": row[6],\n\t\t\t\t\"city\": row[7]\n\t\t\t}\n\n\tdef get_invoice_data(self, user, start=0, stop=0):\n\t\tc = self.__connection.cursor()\n\t\tstartcondition = \"\"\n\t\tstopcondition = \"\"\n\n\t\tif start > 0:\n\t\t\tstartcondition = \" AND timestamp >= %d\" % start\n\t\tif stop > 0:\n\t\t\tstopcondition = \" AND timestamp <= %d\" % stop\n\n\t\tc.execute(\"SELECT date(timestamp, 'unixepoch', 'localtime'), time(timestamp, 'unixepoch', 'localtime'), productname, price FROM invoice WHERE user = ?\" + startcondition + stopcondition + \" ORDER BY timestamp;\", (user,))\n\n\t\tresult = []\n\t\tfor row in c:\n\t\t\tresult.append({\n\t\t\t\t\"date\": row[0],\n\t\t\t\t\"time\": row[1],\n\t\t\t\t\"product\": row[2],\n\t\t\t\t\"price\": row[3],\n\t\t\t})\n\n\t\tc.close()\n\n\t\treturn result\n\n\tdef get_invoice_amount(self, user, start=0, stop=0):\n\t\tquery = \"SELECT SUM(price) FROM invoice WHERE user = ? AND timestamp >= ? AND timestamp <= ?\";\n\t\tamount = 0\n\n\t\tc = self.__connection.cursor()\n\t\tc.execute(query, (user, start, stop))\n\n\t\tfor row in c:\n\t\t\tamount += row[0]\n\n\t\tc.close()\n\t\treturn amount\n\n\tdef get_users_with_purchases(self, start, stop):\n\t\tresult = []\n\n\t\tc = self.__connection.cursor()\n\n\t\tc.execute(\"SELECT user FROM sales WHERE timestamp >= ? AND timestamp <= ? GROUP BY user ORDER BY user;\", (start,stop))\n\n\t\tfor row in c:\n\t\t\tresult.append(row[0])\n\n\t\tc.close()\n\n\t\treturn result\n","sub_path":"invoice/dbhelper.py","file_name":"dbhelper.py","file_ext":"py","file_size_in_byte":1865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"463772907","text":"from dice.kb import KnowledgeBaseBuilder\nfrom dice.kb import Fact\nfrom dice.constants import Data\n\ndef textify(pred):\n    pred_map = {\n        \"has-part\": \"has a\",\n        \"isa\": \"is a\",\n        \"is-part-of\": \"is a part of\"\n    }\n    if pred in pred_map:\n        return pred_map[pred]\n    return pred\n\nclass TupleKb(KnowledgeBaseBuilder):\n\n    def build(self):\n        with open(Data.tuple_kb) as file:\n            header = file.readline().strip().split(\"\\t\")\n            for line in file.readlines():\n                if line == \"\":\n                    continue\n                statement = {\n                    key: value\n                    for key, value in zip(header, line.strip().split(\"\\t\"))\n                }\n                if not self.in_seeds(statement[\"Arg1\"]):\n                    continue\n                fact = Fact()\n                fact.subject = statement[\"Arg1\"]\n                fact.property = textify(statement[\"Pred\"]) + \" \" + statement[\"Arg2\"]\n                fact.score = float(statement[\"QStrength\"])\n                fact.text = statement[\"Sentence\"]\n                self.add_fact(fact)\n","sub_path":"dice/kb/tuple_kb.py","file_name":"tuple_kb.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"366164898","text":"# coding=utf-8\n#\n# created by kpe on 28.Mar.2019 at 12:33\n#\n\nfrom __future__ import absolute_import, division, print_function\n\nfrom tensorflow.python import keras\n\nfrom bert.layer import Layer\nfrom bert.embeddings import BertEmbeddingsLayer\nfrom bert.transformer import TransformerEncoderLayer\n\n\nclass BertModelLayer(Layer):\n    \"\"\"\n    BERT Model (arXiv:1810.04805).\n\n    See: https://arxiv.org/pdf/1810.04805.pdf\n\n    \"\"\"\n    class Params(BertEmbeddingsLayer.Params,\n                 TransformerEncoderLayer.Params):\n        pass\n\n    # noinspection PyUnusedLocal\n    def _construct(self, params: Params):\n        self.embeddings_layer           = None\n        self.encoders_layer             = None\n\n        self.support_masking = True\n\n    # noinspection PyAttributeOutsideInit\n    def build(self, input_shape):\n        if isinstance(input_shape, list):\n            assert len(input_shape) == 2\n            input_ids_shape, token_type_ids_shape = input_shape\n            self.input_spec = [keras.layers.InputSpec(shape=input_ids_shape),\n                               keras.layers.InputSpec(shape=token_type_ids_shape)]\n        else:\n            input_ids_shape = input_shape\n            self.input_spec = keras.layers.InputSpec(shape=input_ids_shape)\n\n        self.embeddings_layer = BertEmbeddingsLayer.from_params(\n            self.params,\n            name=\"embeddings\"\n        )\n\n        # create all transformer encoder sub-layers\n        self.encoders_layer = TransformerEncoderLayer.from_params(\n            self.params,\n            name=\"encoder\"\n        )\n\n        super(BertModelLayer, self).build(input_shape)\n\n    def call(self, inputs, mask=None, training=None):\n        if mask is None:\n            mask = self.embeddings_layer.compute_mask(inputs)\n\n        embedding_output = self.embeddings_layer(inputs, mask=mask, training=training)\n        output           = self.encoders_layer(embedding_output, mask=mask, training=training)\n        return output   # [B, seq_len, hidden_size]\n\n","sub_path":"bert/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2003,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"101116141","text":"import os.path\nimport tensorflow as tf\nimport numpy as np\nimport helper\nimport glob\nimport warnings\nimport sys\nimport os\n#from video import Video\nfrom distutils.version import LooseVersion\nimport project_tests as tests\n\n# Check TensorFlow Version\nassert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)\nprint('TensorFlow Version: {}'.format(tf.__version__))\n\n# Check for a GPU\nif not tf.test.gpu_device_name():\n    warnings.warn('No GPU found. Please use a GPU to train your neural network.')\nelse:\n    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))\n\n\ndef load_vgg(sess, vgg_path):\n    \"\"\"\n    Load Pretrained VGG Model into TensorFlow.\n    :param sess: TensorFlow Session\n    :param vgg_path: Path to vgg folder, containing \"variables/\" and \"saved_model.pb\"\n    :return: Tuple of Tensors from VGG model (image_input, keep_prob, layer3_out, layer4_out, layer7_out)\n    \"\"\"\n\n    vgg_tag = 'vgg16'\n    vgg_input_tensor_name = 'image_input:0'\n    vgg_keep_prob_tensor_name = 'keep_prob:0'\n    vgg_layer3_out_tensor_name = 'layer3_out:0'\n    vgg_layer4_out_tensor_name = 'layer4_out:0'\n    vgg_layer7_out_tensor_name = 'layer7_out:0'\n\n    # load vgg model\n    tf.saved_model.loader.load(sess, [vgg_tag], vgg_path)\n\n    # get required tensors\n    vgg_input_tensor = sess.graph.get_tensor_by_name(vgg_input_tensor_name)\n    vgg_keep_prob_tensor = sess.graph.get_tensor_by_name(vgg_keep_prob_tensor_name)\n    vgg_layer3_out_tensor = sess.graph.get_tensor_by_name(vgg_layer3_out_tensor_name)\n    vgg_layer4_out_tensor = sess.graph.get_tensor_by_name(vgg_layer4_out_tensor_name)\n    vgg_layer7_out_tensor = sess.graph.get_tensor_by_name(vgg_layer7_out_tensor_name)\n\n    return vgg_input_tensor, vgg_keep_prob_tensor, vgg_layer3_out_tensor, vgg_layer4_out_tensor, vgg_layer7_out_tensor\ntests.test_load_vgg(load_vgg, tf)\n\n\ndef layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, num_classes):\n    \"\"\"\n    Create the layers for a fully convolutional network.  Build skip-layers using the vgg layers.\n    :param vgg_layer7_out: TF Tensor for VGG Layer 3 output\n    :param vgg_layer4_out: TF Tensor for VGG Layer 4 output\n    :param vgg_layer3_out: TF Tensor for VGG Layer 7 output\n    :param num_classes: Number of classes to classify\n    :return: The Tensor for the last layer of output\n    \"\"\"\n\n    \"\"\" From Class \"\"\"\n\n    \"\"\" 1x1 convolution\n    The correct use is tf.layers.conv2d(x, num_outputs, 1, 1, weights_initializer=custom_init).\n    num_outputs defines the number of output channels or kernels\n    The third argument is the kernel size, which is 1.\n    The fourth argument is the stride, we set this to 1.\n    We use the custom initializer so the weights in the dense and convolutional layers are identical.\n    This results in the a matrix multiplication operation that preserves spatial information.\n    \"\"\"\n\n    \"\"\" deconvolution\n    One possible answer is using tf.layers.conv2d_transpose(x, 3, (2, 2), (2, 2)) to upsample.\n    The second argument 3 is the number of kernels/output channels.\n    The third argument is the kernel size, (2, 2). Note that the kernel size could also be (1, 1) and the output shape would be the same. However, if it were changed to (3, 3) note the shape would be (9, 9), at least with 'VALID' padding.\n    The fourth argument, the number of strides, is how we get from a height and width from (4, 4) to (8, 8). If this were a regular convolution the output height and width would be (2, 2).\n    Now that you've learned how to use transposed convolution, let's learn about the third technique in FCNs.\n    \"\"\"\n\n    print(\"vgg3_shape: \" + str(vgg_layer3_out.get_shape())) # (?, 56, 56, 256)\n    print(\"vgg4_shape: \" + str(vgg_layer4_out.get_shape())) # (?, 28, 28, 512) (?, 14, 14, 512) (?, 7, 7, 512)\n    print(\"vgg7_shape: \" + str(vgg_layer7_out.get_shape())) # (?, 1, 1, 4096)\n\n    vgg_layer3_num_outputs = vgg_layer3_out.get_shape()[3]\n    vgg_layer4_num_outputs = vgg_layer4_out.get_shape()[3]\n    vgg_layer7_num_outputs = vgg_layer7_out.get_shape()[3]\n\n    # 1x1 convolution\n    conv_1x1_layer = tf.layers.conv2d(vgg_layer7_out,\n                                      vgg_layer7_num_outputs,\n                                      kernel_size=1,\n                                      strides=1,\n                                      name=\"conv_1x1_layer\")\n\n    print(\"conv_1x1_layer_shape: \" + str(conv_1x1_layer.get_shape()))\n\n    # first deconvolution using conv2d_transpose\n    conv_transposed_layer_1 = tf.layers.conv2d_transpose(conv_1x1_layer,\n                                                         vgg_layer4_num_outputs,\n                                                         kernel_size=(4, 4),\n                                                         strides=(2, 2),\n                                                         padding=\"same\",\n                                                         name=\"conv_transposed_layer_1\")\n\n    print(\"conv_transposed_layer_1_shape: \" + str(conv_transposed_layer_1.get_shape())) # supposed to be (?, ?, ?, 512)\n\n     # skip layer\n    skip_layer_1 = tf.add(conv_transposed_layer_1, vgg_layer4_out, name='skip_layer_1')\n\n    # second deconvolution\n    conv_transposed_layer_2 = tf.layers.conv2d_transpose(skip_layer_1,\n                                                         vgg_layer3_num_outputs,\n                                                         kernel_size=(4, 4),\n                                                         strides=(2, 2),\n                                                         padding=\"same\",\n                                                         name='conv_transposed_layer_2')\n\n    print(\"conv_transposed_layer_2_shape: \" + str(conv_transposed_layer_2.get_shape())) # supposed to be (?, ?, ?, 256)\n\n    # skip layer\n    skip_layer_2 = tf.add(conv_transposed_layer_2, vgg_layer3_out, name='skip_layer_2')\n\n    # third deconvolution\n    conv_transposed_layer_3 = tf.layers.conv2d_transpose(skip_layer_2,\n                                                         num_classes,\n                                                         kernel_size=(16, 16),\n                                                         strides=(8, 8),\n                                                         padding=\"same\",\n                                                         name='conv_transposed_layer_3')\n\n    print(\"conv_transposed_layer_3_shape: \" + str(conv_transposed_layer_3.get_shape())) # supposed to be (?, ?, ?, ?)\n\n    return conv_transposed_layer_3\n\ntests.test_layers(layers)\n\ndef optimize(nn_last_layer, correct_label, learning_rate, num_classes):\n    \"\"\"\n    Build the TensorFLow loss and optimizer operations.\n    :param nn_last_layer: TF Tensor of the last layer in the neural network\n    :param correct_label: TF Placeholder for the correct label image\n    :param learning_rate: TF Placeholder for the learning rate, can also be a float\n    :param num_classes: Number of classes to classify\n    :return: Tuple of (logits, train_op, cross_entropy_loss)\n    \"\"\"\n\n    logits = tf.reshape(nn_last_layer, (-1, num_classes))\n    cross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=correct_label))\n    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n    train_op = optimizer.minimize(cross_entropy_loss)\n\n    return logits, train_op, cross_entropy_loss\n\ntests.test_optimize(optimize)\n\ndef get_iou(logits, image_shape, batch_size):\n    unstacked_softmax_logits = tf.unstack(tf.nn.softmax(logits), num=2, axis=1, name='unstack_logits')\n    softmax_part = unstacked_softmax_logits[1]\n    tf_segmentation = tf.greater(softmax_part, 0.5)\n    label_pl = tf.placeholder(tf.float32, [None], name=\"label_pl\")\n    iou, iou_op = tf.metrics.mean_iou(label_pl, tf_segmentation, 2)\n    return iou, iou_op, label_pl\n\ndef train_nn(sess, epochs, batch_size, get_batches_fn, train_op, cross_entropy_loss, input_image,\n             correct_label, keep_prob, learning_rate, logits):\n    \"\"\"\n    Train neural network and print out the loss during training.\n    :param sess: TF Session\n    :param epochs: Number of epochs\n    :param batch_size: Batch size\n    :param get_batches_fn: Function to get batches of training data.  Call using get_batches_fn(batch_size)\n    :param train_op: TF Operation to train the neural network\n    :param cross_entropy_loss: TF Tensor for the amount of loss\n    :param input_image: TF Placeholder for input images\n    :param correct_label: TF Placeholder for label images\n    :param vgg_keep_prob: TF Placeholder for dropout keep probability\n    :param learning_rate: TF Placeholder for learning rate, can also be a float\n    :param logits: For inferencing during training\n    \"\"\"\n\n    print(\"Start training\")\n\n    # Create IoU ops\n    if logits is not None:\n        iou, iou_op, label_pl = get_iou(logits, (160, 576), batch_size)\n\n    sess.run(tf.global_variables_initializer())\n\n    for epoch in range(epochs):\n        print(\"Epoch\", str(epoch), \"|\", end=\"\")\n        sys.stdout.flush()\n        batch_count = 0\n        sum_iou = 0.0\n        for sample_batch, label_batch in get_batches_fn(batch_size):\n            batch_count = batch_count + 1\n\n            # Training\n            _, loss = sess.run([train_op, cross_entropy_loss], feed_dict={input_image: sample_batch, correct_label: label_batch, keep_prob: 0.8})\n\n            # IoU\n            if logits is not None:\n                label_batch_formatted = label_batch[:,:,:,1].flatten()\n                sess.run(tf.local_variables_initializer())\n                sess.run(iou_op, {keep_prob: 1.0, input_image: sample_batch, label_pl: label_batch_formatted})\n                cur_iou = sess.run(iou, {keep_prob: 1.0, input_image: sample_batch, label_pl: label_batch_formatted})\n                sum_iou += cur_iou\n\n            #train_writer.add_summary(summary, i)\n            print(\"=\", end=\"\")\n            sys.stdout.flush()\n        mean_iou = sum_iou / batch_count\n        print (\"| Loss:\", loss, \"| IoU:\", mean_iou)\n\n    if logits is not None:\n        saver = tf.train.Saver()\n        saver.save(sess, \"model\")\n\ntests.test_train_nn(train_nn)\n\ndef run():\n\n    tests_only = os.getenv(\"TESTS_ONLY\", False)\n    if tests_only:\n        print(\"TESTS_ONLY environment variable set to True, skipping run.\")\n        return\n\n    # configuration\n    num_classes = 2\n    image_shape = (160, 576)\n    data_dir = './data'\n    runs_dir = './runs'\n    epochs = 20\n    batch_size = 5\n    learning_rate = 0.0005\n\n    # check if Kitti dataset is available\n    tests.test_for_kitti_dataset(data_dir)\n\n    # Download pretrained vgg model\n    helper.maybe_download_pretrained_vgg(data_dir)\n\n    # TODO OPTIONAL: Train and Inference on the cityscapes dataset instead of the Kitti dataset.\n    # You'll need a GPU with at least 10 teraFLOPS to train on.\n    #  https://www.cityscapes-dataset.com/\n\n    with tf.Session() as sess:\n        # Path to vgg model\n        vgg_path = os.path.join(data_dir, 'vgg')\n\n        # Augment Images for better results\n        \"\"\" Popular options:\n        rotation: random with angle between 0° and 360° (uniform)\n        translation: random with shift between -10 and 10 pixels (uniform)\n        rescaling: random with scale factor between 1/1.6 and 1.6 (log-uniform)\n        flipping: yes or no (bernoulli)\n        shearing: random with angle between -20° and 20° (uniform)\n        stretching: random with stretch factor between 1/1.3 and 1.3 (log-uniform)\n        \"\"\"\n\n        print(\"Creating augmented images...\")\n        augmented_path = os.path.join(data_dir, 'augmented')\n        helper.augment(augmented_path, 'data/data_road/training')\n\n        # Create function to get batches\n        get_batches_fn = helper.gen_batch_function(augmented_path, image_shape)\n\n        # load VGG\n        vgg_input, vgg_keep_prob, vgg_layer3_out, vgg_layer4_out, vgg_layer7_out = load_vgg(sess, vgg_path)\n\n        # create TF Placeholder for labels\n        correct_label = tf.placeholder(tf.float32, (None, image_shape[0], image_shape[1], num_classes))\n\n        # add layers\n        nn_last_layer = layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, num_classes)\n\n        # define optimizer\n        logits, train_op, cross_entropy_loss = optimize(nn_last_layer, correct_label, learning_rate, num_classes)\n\n        import sys\n        if len(sys.argv[1:]) > 0:\n            saver = tf.train.Saver()\n            saver.restore(sess, \"model\")\n\n        else:\n            # Train NN using the train_nn function\n            train_nn(sess, epochs, batch_size, get_batches_fn, train_op, cross_entropy_loss, vgg_input, correct_label, vgg_keep_prob, learning_rate, logits)\n\n        # video\n        \"\"\" How to use:\n        1. Uncomment code below and import on top\n        2. Get a test video\n        3. Define source and target paths below\n        4. You'll need to download the moviepy library for video.py to work\n        \"\"\"\n        #print(\"Now working on video...\")\n        #video_editor = Video(\"data/test_videos/hart1.mp4\", \"data/test_videos/hart1_seg2.mp4\", sess, logits, vgg_keep_prob, vgg_input, image_shape)\n        #video_editor.process_video()\n\n        # Save inference data using helper.save_inference_samples\n        helper.save_inference_samples(runs_dir, data_dir, sess, image_shape, logits, vgg_keep_prob, vgg_input)\n\nif __name__ == '__main__':\n    run()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":13412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"332044230","text":"from django.conf.urls.defaults import *\n\nurlpatterns = patterns(\n    'project.views',\n\n    url(r'^$', 'project_category', name='project-category'),\n    url(r'^(?P<slug>[-\\w]+)/$', 'project_list', name='project-list'),\n    url('^(?P<category>[-\\w]+)/(?P<slug>[-\\w]+)/$', 'project_detail', name=\"project-detail\"),\n#    url(r'^(?P<category>[\\w\\-]+)/(?P<slug>[\\w\\-]+)/$', 'project_detail'),\n#    url(r'^(?P<category>[-w]+)/(?P<slug>[-w]+)/$', 'project_detail', name='project-detail'),\n#    url(r'^(?P<category>[a-zA-Z0-9_.-]+)/(?P<slug>[a-zA-Z0-9_.-]+)/$', 'project_detail', name='project-detail'),\n\n    (r'^create.*$', 'add'),\n    (r'^edit/(?P<project_id>\\d+)/$', 'edit'),\n    url(r'^delete/(?P<project_id>\\d+)/$', 'delete', name='delete'),\n\n#   url(r\"^add/$\", \"add_page\",name=\"add\"),\n#   url(r\"^(?P<full_slug>.*)/add/$\", \"add_page\",name=\"add\"),\n#   url(r\"^(?P<full_slug>.*)/edit/$\", \"edit_page\",name=\"edit\"),\n#   url(r'^$', ListView.as_view(model=Category,template_name='index.html',context_object_name=\"webpages_list\",),name='index'),\n#   url(r\"^(?P<full_slug>.*)/$\", \"category\", name=\"category\"),\n\n#    url(r'detail/(?P<pk>\\d+)', DetailView.as_view(model=Category), name=\"category_list\",),\n#    url(r'update/(?P<pk>\\d+)', UpdateView.as_view(model=Category), name=\"category_update\",),\n#    url(r'create', CreateView.as_view(model=Category), name=\"category_create\",),\n#    url(r'list', ListView.as_view(model=Category), name=\"category_list\",),\n)\n\n\n#urlpatterns = patterns(\n#    'django.views.generic.list_detail',\n#    url(r'^$', 'object_list', {'queryset': Category.objects.all()}),\n#    url(r'^(?P<slug>[-\\w]+)/$', 'object_detail', {'queryset': Project.objects.all()}),\n#)\n\n#urlpatterns += patterns(\n#    'django.views.generic.list_detail',\n#    url(r'^(?P<slug>[-\\w]+)/$', 'object_detail', info_dict, name=\"cms-story\"),\n#    url(r'^$', 'object_list', info_dict, name=\"cms-home\"),\n#)\n","sub_path":"project/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"339732328","text":"from django.urls import path\nfrom django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    url(r'^get_topic$', views.get_topic, name='get_topic'),\n    url(r'^get_survey$', views.get_survey, name='get_survey'),\n    url(r'^automatic_taxonomy$', views.automatic_taxonomy, name='automatic_taxonomy'),\n    url(r'^upload_refs$', views.upload_refs, name='upload_refs'),\n]","sub_path":"asg/demo/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"392358211","text":"def eightqueen(n):\n    if n == 1:\n        return [[i] for i in range(1, 9)]\n    \n    arrangements = []\n    for subarr in eightqueen(n - 1):\n        for c in range(1, 9): # check all possible columns\n            if c in subarr: # column occupied\n                continue\n            for qr, qc in enumerate(subarr):\n                if abs(c - qc) == abs(n - qr - 1):\n                    break\n            else: # not on same column and diagonal, Python's special 'for-else' clause\n                arrangements.append(subarr + [c])\n    return arrangements\n\n\ndef test_eightqueen():\n    assert len(eightqueen(8)) == 92\n\n","sub_path":"0909-8-queens/8-queens.py","file_name":"8-queens.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"645660679","text":"# Algoritmo 5\n\n\ndef main():\n    i = int(input(\"i: \"))\n    f = int(input(\"f: \"))\n\n    hours = 0\n    if f > i:\n        hours = f - i\n    else:\n        hours = (24 - i) + f\n\n    print(\"The game took %i hours.\" % hours)\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"EXERCÍCIOS RESOLVIDOS/python/alternatives/alg5.py","file_name":"alg5.py","file_ext":"py","file_size_in_byte":255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"95102738","text":"#!/usr/bin/env python\n\nimport rospy\nimport tf\nimport numpy as np\nimport cv2\nfrom cv_bridge import CvBridge\nfrom sensor_msgs.msg import NavSatFix, Image\nfrom geometry_msgs.msg import PoseStamped\nfrom mavros_msgs.msg import State, PositionTarget\nfrom mavros_msgs.srv import CommandBool, CommandBoolRequest, SetMode, SetModeRequest, CommandTOL, CommandTOLRequest\n\nclass DroneWrapper():\n\tdef state_cb(self, msg):\n\t\tself.state = msg\n\t\trospy.logdebug('State updated')\n\n\tdef pose_stamped_cb(self, msg):\n\t\tself.pose_stamped = msg\n\t\trospy.logdebug('Pose updated')\n\t\n\tdef global_position_cb(self, msg):\n\t\tself.global_position = msg\n\t\trospy.logdebug('Global position updated')\n\t\n\tdef cam_frontal_cb(self, msg):\n\t\tself.frontal_image = msg\n\t\trospy.logdebug('Frontal image updated')\n\t\n\tdef cam_ventral_cb(self, msg):\n\t\tself.ventral_image = msg\n\t\trospy.logdebug('Ventral image updated')\n\t\n\tdef stay_armed_stay_offboard_cb(self, event):\n\t\tif self.state.mode != 'OFFBOARD':\n\t\t\tif self.request_mode('OFFBOARD'):\n\t\t\t\trospy.loginfo(\"OFFBOARD requested\")\n\t\telif not self.state.armed:\n\t\t\tif self.arm(True):\n\t\t\t\trospy.loginfo(\"Vehicle Armed\")\n\t\t\t\n\tdef get_frontal_image(self):\n\t\treturn self.bridge.imgmsg_to_cv2(self.frontal_image)\n\t\n\tdef get_ventral_image(self):\n\t\treturn self.bridge.imgmsg_to_cv2(self.ventral_image)\n\t\n\tdef get_position(self):\n\t\treturn np.array([self.pose_stamped.pose.position.x, self.pose_stamped.pose.position.y, self.pose_stamped.pose.position.z])\n\t\n\tdef get_orientation(self):\n\t\treturn np.array(tf.transformations.euler_from_quaternion([self.pose_stamped.pose.orientation.x, self.pose_stamped.pose.orientation.y, self.pose_stamped.pose.orientation.z, self.pose_stamped.pose.orientation.w]))\n\n\tdef get_roll(self):\n\t\treturn self.get_orientation()[0]\n\t\n\tdef get_pitch(self):\n\t\treturn self.get_orientation()[1]\n\t\n\tdef get_yaw(self):\n\t\treturn self.get_orientation()[2]\n\n\tdef arm(self, value = True):\n\t\treq = CommandBoolRequest()\n\t\treq.value = value\n\t\tif self.arm_client(req).success:\n\t\t\trospy.loginfo('Arming/Disarming successful')\n\t\t\treturn True\n\t\telse:\n\t\t\trospy.logwarn('Arming/Disarming unsuccessful')\n\t\t\treturn False\n\t\t\n\tdef request_mode(self, mode = 'OFFBOARD'):\n\t\trospy.sleep(2)\n\t\trospy.loginfo('Current mode: %s', self.state.mode)\n\t\treq = SetModeRequest()\n\t\treq.custom_mode = mode\n\t\tif self.mode_client(req).mode_sent:\n\t\t\trospy.loginfo('Mode change request successful')\n\t\t\treturn True\n\t\telse:\n\t\t\trospy.logwarn('Mode change request unsuccessful')\n\t\t\treturn False\n\n\tdef set_cmd_vel(self, vx = 0, vy = 0, vz = 0, az = 0):\n\t\tself.setpoint_raw.type_mask = int('0b010111000011', 2)\n\t\tself.setpoint_raw.coordinate_frame = 8\n\t\tself.setpoint_raw.velocity.x = -vy\n\t\tself.setpoint_raw.velocity.y = vx\n\t\tself.setpoint_raw.velocity.z = vz\n\n\t\tself.setpoint_raw.position.z = self.pose_stamped.pose.position.z + vz * self.vz_factor\n\t\t\n\t\tself.setpoint_raw.yaw_rate = az\n\t\tself.setpoint_raw_publisher.publish(self.setpoint_raw)\n\n\tdef repeat_setpoint_raw(self, event):\n\t\tself.setpoint_raw.position.z = self.pose_stamped.pose.position.z + self.setpoint_raw.velocity.z * self.vz_factor\n\t\tself.setpoint_raw_publisher.publish(self.setpoint_raw)\n\n\tdef hold_setpoint_raw(self):\n\t\tif not self.setpoint_raw_flag:\n\t\t\tself.setpoint_raw_timer = rospy.Timer(rospy.Duration(nsecs=50000000), self.repeat_setpoint_raw)\n\n\tdef takeoff(self, uptime = 4):\n\t\tself.set_cmd_vel(0, 0, 0, 0)\n\t\tself.hold_setpoint_raw()\n\t\tself.arm(True)\n\t\tself.stay_armed_stay_offboard_timer = rospy.Timer(rospy.Duration(3), self.stay_armed_stay_offboard_cb)\n\t\twhile True:\n\t\t\twhile not (self.state.armed and self.state.mode == 'OFFBOARD'):\n\t\t\t\tself.rate.sleep()\n\t\t\trospy.loginfo('Sleeping 3 secs to confirm change')\n\t\t\trospy.sleep(3)\n\t\t\tif self.state.mode == 'OFFBOARD':\n\t\t\t\tbreak\n\t\tself.set_cmd_vel(vz = 3)\n\t\trospy.loginfo('Taking off!!!')\n\t\trospy.sleep(uptime)\n\t\tself.set_cmd_vel()\n\n\tdef take_control(self):\n\t\tself.set_cmd_vel(0, 0, 0, 0)\n\t\tself.hold_setpoint_raw()\n\t\tself.arm(True)\n\t\tself.stay_armed_stay_offboard_timer = rospy.Timer(rospy.Duration(3), self.stay_armed_stay_offboard_cb)\t\n\t\t\n\tdef land(self):\n\t\tself.setpoint_raw_timer.shutdown()\n\t\tself.stay_armed_stay_offboard_timer.shutdown()\n\t\treq = CommandTOLRequest()\n\t\treq.latitude = self.global_position.latitude\n\t\treq.longitude = self.global_position.longitude\n\t\tself.land_client(req)\n\n\tdef __init__(self, name = 'drone', verbose = False):\n\t\tif verbose:\n\t\t\trospy.init_node(name, anonymous = True, log_level = rospy.DEBUG)\n\t\telse:\n\t\t\trospy.init_node(name)\n\t\t\n\t\tself.state = State()\n\t\tself.pose_stamped = PoseStamped()\n\t\tself.rate = rospy.Rate(20)\n\t\tself.setpoint_raw = PositionTarget()\n\t\tself.setpoint_raw_flag = False\n\t\tself.vz_factor = 0.4\n\t\tself.bridge = CvBridge()\n\t\t\n\t\tself.setpoint_raw_timer = rospy.Timer(rospy.Duration(nsecs=50000000), self.repeat_setpoint_raw)\n\t\tself.setpoint_raw_timer.shutdown()\n\t\tself.stay_armed_stay_offboard_timer = rospy.Timer(rospy.Duration(5), self.stay_armed_stay_offboard_cb)\n\t\tself.stay_armed_stay_offboard_timer.shutdown()\n\n\t\trospy.wait_for_service('mavros/cmd/arming')\n\t\tself.arm_client = rospy.ServiceProxy('mavros/cmd/arming', CommandBool)\n\t\trospy.wait_for_service('mavros/set_mode')\n\t\tself.mode_client = rospy.ServiceProxy('mavros/set_mode', SetMode)\n\t\trospy.wait_for_service('mavros/cmd/land')\n\t\tself.land_client = rospy.ServiceProxy('mavros/cmd/land', CommandTOL)\n\t\t\n\t\trospy.Subscriber('mavros/state', State, self.state_cb)\n\t\trospy.Subscriber('mavros/local_position/pose', PoseStamped, self.pose_stamped_cb)\n\t\trospy.Subscriber('mavros/global_position/global', NavSatFix, self.global_position_cb)\n\t\tcam_frontal_topic = rospy.get_param('cam_frontal_topic', '/iris/cam_frontal/image_raw')\n\t\tcam_ventral_topic = rospy.get_param('cam_ventral_topic', '/iris/cam_ventral/image_raw')\n\t\trospy.Subscriber(cam_frontal_topic, Image, self.cam_frontal_cb)\n\t\trospy.Subscriber(cam_ventral_topic, Image, self.cam_ventral_cb)\n\n\t\tself.setpoint_raw_publisher = rospy.Publisher('mavros/setpoint_raw/local', PositionTarget, queue_size = 1)","sub_path":"drone_wrapper/src/drone_wrapper/drone_wrapper_class.py","file_name":"drone_wrapper_class.py","file_ext":"py","file_size_in_byte":5962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"192460462","text":"from mcpi.minecraft import Minecraft\nmc = Minecraft.create()\n\nimport time\npos = mc.player.getPos()\nx = pos.x\ny = pos.y\nz = pos.z\n\ndef setMelon(x, y, z):\n    mc.setBlock(x, y , z, 103)\n    time.sleep(10)\n\nsetMelon (x + 2, y, z)\nsetMelon (x, y, z + 2)\nsetMelon (x + 2, y, z + 2)\n\n\n","sub_path":"Chap 8/asdf.py","file_name":"asdf.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"166287041","text":"\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar 24 00:55:58 2019\nTo predict only one commodity price using facebook prophet model\n@author: Brijesh Rana\n\"\"\"\n\nimport pymssql\nimport pandas as pd\nimport sqlalchemy\nfrom fbprophet import Prophet \nimport pyodbc\t\nfrom sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\nimport matplotlib.pyplot as plt\nplt.style.use('fivethirtyeight')\nfrom sqlalchemy import create_engine, MetaData, Table, select\nfrom six.moves import urllib\n\n## instance a python db connection object- same form as psycopg2/python-mysql drivers also\nconn = pymssql.connect(server=\"SERVERNAME\", user=\"Brijesh\",password=\"PASSWORD\", port=1433)  # You can lookup the port number inside SQL server. \n\n#Get all database names from 6 srever\nsql=\"select name FROM sys.databases where name like '%Hiranandani%'\"\ndbnames = pd.read_sql(sql,conn)\ndbnames\n\n## Hey Look, college data\nstmt = \"SELECT *  FROM [DATABASENAME].[dbo].[TABLENAME]\"\n\n# Excute Query here\ndf = pd.read_sql(stmt,conn)\n# to check total number of records in dataframe\ndf\nlen(df)\n# to see the data\nprint(df)\n# to check toatl numaber or rows and columns in the data \ndf.shape\n# to check column data types \ndf.dtypes\n# check data type of Date column from Object to Date\ndf['Date'] = df['Date'].astype('datetime64[ns]')\ndf.dtypes\n# String datatype in Python is always object\ndf['Centre'] = df['Centre'].astype('str')\ndf.dtypes\n# To convert price datatype from object to flaot\ndf['Price per Kg'] = df['Price per Kg'].astype(float)\ndf.dtypes\n# select two columns from dataframe\ndf[['Date', 'Price per Kg']]\n# create another data frame with specific columns \ndf2 = pd.DataFrame(df[['Date', 'Price per Kg']])\n# print the data\ndf2\n# rename the actual column name  because in prophet the column name should be DS and Y\ndf2 = df2.rename(columns={'Date': 'ds',\n                        'Price per Kg': 'y'})\ndf2.dtypes\n# check the data type for new data frame\ndf2.columns\n# print new data frame\ndf2\n# Plot the data \nax = df2.set_index('ds').plot(figsize=(12, 8))\nax.set_ylabel('Monthly Price of Commoditity')\nax.set_xlabel('Date')\nplt.show()\n# create prophet model\nmy_model = Prophet(interval_width=0.95)\n# fit the prophet model\nmy_model.fit(df2)\n# select futre date for which we want to predict\nfuture_dates = my_model.make_future_dataframe(periods=36, freq='MS')\nfuture_dates.tail()\n# predict future value\nforecast = my_model.predict(future_dates)\nforecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()\nforecast.columns\nmy_model.plot(forecast,\n              uncertainty=True)\n# plot seasionality, trend based on yearly monthly day wise etc.\nmy_model.plot_components(forecast)\n# check columns name in forecast model\nforecast.coloumns\n# to see first top 10 rows in forecast model\nforecast.head(10)\n#to check coulmns data types\nforecast.dtypes\n\n# to export the predicted values in csv file\nforecast[['ds', 'yhat']].to_csv(\"D:/JOB/out.csv\")\n# to combine actual data frame with predicted values \nmetric_df = forecast.set_index('ds')[['yhat']].join(df2.set_index('ds').y).reset_index()\n# to print the data\nmetric_df\n# to drop NA values\n#metric_df.dropna(inplace=True)\n#metric_df\n# check r2 value\nr2_score(metric_df.y, metric_df.yhat)\n# check mse\nmean_squared_error(metric_df.y, metric_df.yhat)\n# check MAE\nmean_absolute_error(metric_df.y, metric_df.yhat)\n\n# To insert data frame into MS SQL database without iterate the dataframe\n#params = urllib.parse.quote_plus(\"DRIVER={SQL Server};SERVER=SERVERNAME;DATABASE=DATABASENAME;UID=Brijesh;PWD=PASSWORD\")\n#engine = sqlalchemy.create_engine(\"mssql+pyodbc:///?odbc_connect=%s\" % params) \n#engine.connect() \n#insert.to_sql(name='table_name11',con=engine, index=True, if_exists='append')\n\n# Another method to insert sata frame into ms sql table      \n# Insert data frame into table\n#insert.to_sql(name=\"table_name\", con=engine, index=False, if_exists='append')\n\n# rename column name asper earlier\nmetric_df = metric_df.rename(columns={'ds': 'Date',\n                        'y': 'Price per Kg', 'yhat' : 'Pridictedprice'})\n\n# add centre column\nmetric_df['Centre']='Lucknow'\n# add region column\nmetric_df['Region']='North'\n# add Country column\nmetric_df['Country']='India'\n# add Commodity column\nmetric_df['Commodity']='Tur/Arhar Dal'\n\nmetric_df.columns\n\n\n# To insert data frame into MS SQL database without iterate the dataframe\nparams = urllib.parse.quote_plus(\"DRIVER={SQL Server};SERVER=SERVERNAME;DATABASE=DATABASENAME;UID=Brijesh;PWD=PASSWORD\")\nengine = sqlalchemy.create_engine(\"mssql+pyodbc:///?odbc_connect=%s\" % params) \nengine.connect() \nmetric_df.to_sql(name='TempSales_Predictive',con=engine, index=True, if_exists='append')\n","sub_path":"With Python/IndianCommodity/OneCommodityPricePrediction_Prophet.py","file_name":"OneCommodityPricePrediction_Prophet.py","file_ext":"py","file_size_in_byte":4655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"404691411","text":"# -*- encoding: utf-8 -*-\n\nimport os\nfrom setuptools import setup, find_packages\n\n__author__ = 'kotaimen'\n__date__ = '22-Feb-2018'\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\nlong_description = \"\"\"\n\nAWS CloudFormation CLI\n**********************\n\nIntroduction\n============\n\n``awscfncli`` AWS CloudFormation stack management command line interface.\n\nFeatures:\n\n* awscli a-like CLI interface.\n* Simple YAML stacks configuration file.\n* Tracking stack events in the CLI.\n* Managing stacks across different stages and accounts.\n\n\"\"\"\n\n\ninstall_requires = [\n    'botocore',\n    'boto3',\n    'awscli>=1.16.0'\n    'six>=1.10.0',\n    'click>=6.0,<8',\n    'PyYAML>=3.10',\n    'jsonschema>=2.6.0'\n]\n\ntest_requires = [\n    'pytest',\n    'pytest-cov',\n    'mock'\n]\n\ndev_requires = test_requires\n\nsetup(\n    # Project Name\n    name='awscfncli2',\n\n    # Version and description\n    version='2.1.19',\n    description='AWS CloudFormation CLI',\n    long_description=long_description,\n\n    # Author details\n    author='Kotaimen, Ray',\n    author_email='kotaimen.c@gmail.com, gliese.q@gmail.com',\n\n    # Project home\n    url='https://github.com/Kotaimen/awscfncli',\n\n    # License detail\n    license='MIT',\n\n    # Classification and Keywords\n    classifiers=[\n        'Programming Language :: Python',\n        'Programming Language :: Python :: 2',\n        'Programming Language :: Python :: 3',\n\n        'License :: OSI Approved :: MIT License',\n\n        'Operating System :: OS Independent',\n\n        'Development Status :: 5 - Production/Stable',\n\n        'Natural Language :: English',\n\n        'Intended Audience :: Developers',\n        'Intended Audience :: System Administrators',\n\n        'Topic :: Software Development :: Libraries :: Python Modules',\n        'Topic :: Utilities',\n    ],\n    keywords='awscfncli awscfncli2 aws cfn cli cloudformation stack '\n             'template changeset commandline development sam serverless',\n\n    packages=find_packages(exclude=['tests.*', 'tests']),\n    package_data={\n        'awscfncli2.config': ['*.json', '*.yaml']\n\n    },\n    install_requires=install_requires,\n    extras_require={\n        'test': test_requires,\n        'dev': dev_requires\n    },\n\n    entry_points='''\n        [console_scripts]\n        cfn-cli=awscfncli2.__main__:main\n    ''',\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"59020031","text":"from setuptools import setup, find_packages\n\nimport codecs\nimport os\nimport re\n\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\n\ndef read(*parts):\n    with codecs.open(os.path.join(here, *parts), 'r', 'utf-8') as f:\n        return f.read()\n\n\ndef find_version(*file_paths):\n    version_file = read(*file_paths)\n    version_match = re.search(r\"^__version__ = ['\\\"]([^'\\\"]*)['\\\"]\",\n                              version_file, re.M)\n    if version_match:\n        return version_match.group(1)\n    raise RuntimeError(\"Unable to find version string.\")\n\n\nsetup(\n    name='doc2dash',\n    version=find_version('doc2dash', '__init__.py'),\n    description=\"Convert docs to Dash.app's docset format.\",\n    long_description=open('README.rst').read(),\n    url='http://github.com/hynek/doc2dash/',\n    license='MIT',\n    author='Hynek Schlawack',\n    author_email='hs@ox.cx',\n    packages=find_packages(exclude=['tests*']),\n    entry_points={\n        'console_scripts': [\n            'doc2dash = doc2dash.__main__:entry_point',\n        ],\n    },\n    install_requires=[\n        \"beautifulsoup4==4.3.2\",\n        \"lxml==3.3.5\",\n    ],\n    classifiers=[\n        'Development Status :: 5 - Production/Stable',\n        'Programming Language :: Python',\n        'Programming Language :: Python :: 3',\n        'Programming Language :: Python :: 3.3',\n        'Programming Language :: Python :: 3.4',\n        'Programming Language :: Python :: 2',\n        'Programming Language :: Python :: 2.7',\n        'Environment :: Console',\n        'Intended Audience :: Developers',\n        'License :: OSI Approved :: MIT License',\n        'Operating System :: MacOS :: MacOS X',\n        'Topic :: Documentation',\n        'Topic :: Software Development',\n        'Topic :: Software Development :: Documentation',\n        'Topic :: Text Processing',\n    ],\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"535398192","text":"#!/usr/bin/env python3\nimport cv2\n\ncap = cv2.VideoCapture(0)\n\nwhile True:\n\t_,frame = cap.read()\n\n\tlaplacian = cv2.Laplacian(frame, cv2.CV_64F)\n\n\tsobelx = cv2.Sobel(frame, cv2.CV_64F, 1, 0, ksize=5)\n\tsobely = cv2.Sobel(frame, cv2.CV_64F, 0, 1, ksize=5)\n\n\tedges = cv2.Canny(frame, 50, 100)\n\n\t# cv2.imshow(\"original\", frame)\n\t# cv2.imshow(\"laplication\", laplacian)\n\t# cv2.imshow('X', sobelx)\n\t# cv2.imshow('Y', sobely)\n\tcv2.imshow(\"edges\", edges)\n\n\tk = cv2.waitKey(5) & 0xFF\n\tif k == 27:\n\t\tbreak\n\n\n\ncv2.destroyAllWindows()\ncap.release()","sub_path":"edgeDetection.py","file_name":"edgeDetection.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"238724691","text":"from django.urls import path\nfrom django.conf.urls import url,include\n\nfrom apps.users.views import *\n\nurlpatterns = [\n\n    url(r'^$', index),\n    url(r'users/insertUsers.html', ProfileCreate.as_view() , name='crateUser'),\n    url(r'users/listUsers.html', ProfileList.as_view() , name='listUser'),\n    url(r'users/editUser/(?P<pk>\\d+)/$', ProfileUpdate.as_view() , name='editUser'),\n\turl(r'users/deleteUsers/(?P<id>\\d+)/$', deleteUsers , name='deleteUser'),\n    url(r'users/createUser.html', CreateUser.as_view() , name='createUser'),\n\n]","sub_path":"sporticket/apps/users/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"571337363","text":"# search.py\n# ---------------\n# Licensing Information:  You are free to use or extend this projects for\n# educational purposes provided that (1) you do not distribute or publish\n# solutions, (2) you retain this notice, and (3) you provide clear\n# attribution to the University of Illinois at Urbana-Champaign\n#\n# Created by Jongdeog Lee (jlee700@illinois.edu) on 09/12/2018\n\n\"\"\"\nThis file contains search functions.\n\"\"\"\n# Search should return the path and the number of states explored.\n# The path should be a list of tuples in the form (alpha, beta, gamma) that correspond\n# to the positions of the path taken by your search algorithm.\n# Number of states explored should be a number.\n# maze is a Maze object based on the maze from the file specified by input filename\n# searchMethod is the search method specified by --method flag (bfs,astar)\n# You may need to slight change your previous search functions in MP1 since this is 3-d maze\n\nfrom collections import deque\nimport queue\nfrom heapq import heappop, heappush\n\ndef search(maze, searchMethod):\n    return {\n        \"bfs\": bfs,\n    }.get(searchMethod, [])(maze)\n\ndef bfs(maze):\n    # Write your code here\n    \"\"\"\n    This function returns optimal path in a list, which contains start and objective.\n    If no path found, return None. \n    \"\"\"\n    q = queue.Queue()\n    start = maze.getStart()\n    goal = maze.getObjectives()\n    q.put(start)\n    visited = [start]\n    parent = {}\n    path = []\n    while q.qsize() > 0:\n        node = q.get()\n        if node in goal:\n            path.append(node)\n            break\n        neighbors = maze.getNeighbors(node[0], node[1])\n        for each_node in neighbors:\n            if each_node not in visited:\n                visited.append(each_node)\n                parent[each_node] = node\n                q.put(each_node)\n    if len(path) == 0:\n        return None\n    while path[-1] != start:\n        path.append(parent[path[-1]])\n    return path[::-1]\n","sub_path":"search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":1951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"348261555","text":"from enum import Enum\nfrom PySide2 import QtGui, QtWidgets, QtCore, QtSvg\nimport os\nimport hou\nfrom .hslu_houdini_methods import ExportTypes\nfrom .hslu_db_methods import MethodsDB\nfrom .hslu_util_methods import NumberMethods\nfrom .hou_buttons import *\n\n\n\nclass DropListObjectInfo:\n    def __init__(self, uuid: str):\n        self.Database = MethodsDB()\n        self.uuid = uuid\n    \n    \n    def GetCookStatus(self):\n        try:\n            job_history = self.Database.FetchAllJobHistoryByUUID(self.uuid)\n            cook_status = NumberMethods.IntToBool(job_history[0][6])\n            \n        except Exception as e:\n            print(e)\n            cook_status = False\n        \n        finally:\n            return cook_status\n\n    \n    def GetRenderStatus(self):\n        try:\n            job_history = self.Database.FetchAllJobHistoryByUUID(self.uuid)\n            render_status = NumberMethods.IntToBool(job_history[0][5])\n            \n        except Exception as e:\n            print(e)\n            render_status = False\n        \n        finally:\n            return render_status\n\n    def IsLocal(self,node_path):\n        internal = True\n        try:\n            if hou.node(node_path).userData(\"uuid\") == self.uuid:\n                internal = True\n        except:\n            internal = False\n        finally:\n            return internal\n\n    def GetFileType(self):\n        \"\"\"\n        Get the file type of current list item\n        \"\"\"\n        try:\n            job_history = self.Database.FetchAllJobHistoryByUUID(self.uuid)\n            print(job_history)\n            job_path = job_history[0][3]\n        except Exception as e:\n            print(e)\n\n            job_path = hou.hipFile.name()\n\n        finally:\n            return job_path\n    \n    def GetNodeType(self,node_name: str):\n        try:\n            job_history = self.Database.FetchAllJobHistoryByUUID(self.uuid)\n            print(job_history)\n            node_type = job_history[0][4]\n        except Exception as e:\n            print(e)\n\n            node_type = hou.node(node_name).type().name()\n\n        finally:\n            return node_type\n\n\nclass ColorLayout(Enum):\n    base = \"rgba(100,100,100,100)\"\n    cooked = \"rgba(0,10,180,100)\"\n    rendered = \"rgba(0,180,10,100)\"\n    ext_base = \"rgba(120,120,0,100)\"\n    ext_cooked = \"rgba(50,10,180,100)\"\n    ext_rendered = \"rgba(50,180,10,100)\"\n\n\n\nclass StylingMethods:\n\n    @staticmethod\n    def SetLabelStyle(widget: QtWidgets.QLabel, negative=True):\n        if negative:\n            widget.setStyleSheet(\n                \"\"\"\n                        QLabel {\n                        font-size: 16px;\n                        font-weight: bold;\n                        font-style: italic;\n                        color: red;\n                        border: 3px solid grey;\n                        border-radius: 3px;\n                        }\n                        \"\"\"\n            )\n        else:\n            widget.setStyleSheet(\n                \"\"\"\n                        QLabel {\n                        font-size: 16px;\n                        font-weight: bold;\n                        color: white;\n                        border: 3px solid grey;\n                        border-radius: 3px;\n                        }\n                        \"\"\"\n            )\n\n    def ChangeBgCol(receiver_widg: QtWidgets.QWidget, color: str):\n        receiver_widg.setStyleSheet(\n            f\"\"\"\n            background-color: {color};\n            \"\"\"\n            )\n    \n    def CleanupWidgets(widgetlist: list):\n        for value in widgetlist:\n            if value is not None:\n                value.setStyleSheet(\"\"\" background-color: transparent; \"\"\")\n\n\n\n\n\nclass BooleanMethods:\n    @staticmethod\n    def InfoConditionShower(condition: bool, message: str):\n\n        if condition:\n            MessageBox = QtWidgets.QHBoxLayout()\n            MessageLabel = QtWidgets.QLabel(message)\n            MessageBox.addWidget(MessageLabel)\n\n        return MessageBox\n\n    @staticmethod\n    def WidgetBlockOnCheck(Activation: QtWidgets.QCheckBox, activate: bool):\n        # print(Activation.__class__.__name__)\n\n        if Activation.__class__.__name__ == \"QCheckBox\":\n            if Activation.isChecked() == activate:\n                Activation.setChecked(activate)\n                Activation.setEnabled(activate)\n            else:\n                Activation.setChecked(False)\n                Activation.setEnabled(activate)\n\n        elif Activation.__class__.__name__ == \"QPushButton\":\n            Activation.setEnabled(activate)\n\n    @staticmethod\n    def TreeSelectionBool(widget, widget_class):\n        # childnum = self.projectlist.indexFromItem(parent)\n        # list_of_children = self.ShotArray[self.SequenceIndex()][parent]\n        # try:\n        #     for i in range(parent.childCount()):\n        #         return parent.child(i)\n        # except:\n        #     print('Sequence list is empty')\n        if widget.parent().__class__.__name__ == widget_class:\n            print(\"this is a Shot\")\n            print(widget)\n            return True\n        else:\n            print(\"This is currently a Sequence\")\n            return False\n\n\nclass DrawingMethods:\n    @staticmethod\n    def DrawImage(image_path: str):\n        labelImage = QtWidgets.QLabel()\n        pixmap = QtGui.QPixmap(image_path)\n        pixmap_small = pixmap.scaled(100, 100, QtCore.Qt.KeepAspectRatio)\n        labelImage.setPixmap(pixmap_small)\n        # pic_layout = QtWidgets.QHBoxLayout()\n        # pic_layout.addWidget(labelImage)\n        # pic_layout.setAlignment(QtCore.Qt.AlignLeft)\n\n        return labelImage\n\n    @staticmethod\n    def IconFromSvg(svg_path: str):\n        labelImage = QtWidgets.QLabel()\n        svg_renderer = QtSvg.QSvgRenderer(svg_path)\n        image = QtGui.QImage(64, 64, QtGui.QImage.Format_ARGB32)\n        image.fill(QtCore.Qt.darkGreen)\n        svg_renderer.render(QtGui.QPainter(image))\n        pixmap = QtGui.QPixmap.fromImage(image)\n        pixmap_small = pixmap.scaled(20, 20, QtCore.Qt.KeepAspectRatio)\n        labelImage.setPixmap(pixmap)\n\n        return labelImage\n\n    @staticmethod\n    def SvgIcon(img_path: str):\n        svg_widg = QtSvg.QSvgWidget()\n        svg_widg.load(img_path)\n        svg_widg.setStyleSheet(\n            \"\"\" \n            background-color: transparent;\n            border: none;\n            fill: white;\n            color: white;\n            \"\"\"\n        )\n        svg_widg.setFixedSize(QtCore.QSize(20, 20))\n\n        return svg_widg\n\n    @staticmethod\n    def IconToShow(filepath: str):\n\n        # self.ListOfItems = []\n        cur_path = os.path.dirname(os.path.realpath(__file__))\n        houdini_svg = os.path.join(cur_path, \"../../../config/icons/houdini.svg\")\n        nuke_svg = os.path.join(cur_path, \"../../../config/icons/nuke.svg\")\n        maya_svg = os.path.join(cur_path, \"../../../config/icons/maya.svg\")\n        blender_svg = os.path.join(cur_path, \"../../../config/icons/blender.svg\")\n\n        HouSvg = DrawingMethods.SvgIcon(houdini_svg)\n        NukeSvg = DrawingMethods.SvgIcon(nuke_svg)\n        MayaSvg = DrawingMethods.SvgIcon(maya_svg)\n        BlenderSvg = DrawingMethods.SvgIcon(blender_svg)\n\n        print(filepath.split(\".\")[-1])\n\n        if \"hip\" in filepath.split(\".\")[-1]:\n            return HouSvg\n        elif \"nk\" in filepath.split(\".\")[-1]:\n            return NukeSvg\n        elif \"ma\" in filepath.split(\".\")[-1]:\n            return MayaSvg\n        elif \"blend\" in filepath.split(\".\")[-1]:\n            return BlenderSvg\n\n    @staticmethod\n    def PixmapFromIcon(icon: QtGui.QIcon):\n        pixmap = icon.pixmap(16, 16)\n        pix_of_ic = QtGui.QPixmap(pixmap)\n        ItemTypeIcon = QtWidgets.QLabel()\n        ItemTypeIcon.setPixmap(pix_of_ic)\n        ItemTypeIcon.setStyleSheet(\n            \"\"\" \n            background-color: transparent;\n            \"\"\"\n        )\n        ItemTypeIcon.setAlignment(QtCore.Qt.AlignLeft)\n        return ItemTypeIcon\n\n    #### LIST HANDLING #####\n    @staticmethod\n    # update call functions\n    def AddSelToList(ListWidget: QtWidgets.QListWidget, info: str):\n        message = QtWidgets.QListWidgetItem(info)\n        ListWidget.addItem(message)\n    \n\n\n\nclass GuiInfoMethods:\n    @staticmethod\n    def GetResolution():\n        temporary_app = QtWidgets.QApplication\n        screen = temporary_app.primaryScreen()\n        geo = screen.availableGeometry()\n\n        return (geo.width(), geo.height())\n\n    @staticmethod\n    def GetText(parent):\n        if parent is not None:\n            return parent.text()\n        else:\n            return \"Empty\"\n","sub_path":"TractorManager/scripts/python/tractor_submitter/hslu_gui_methods.py","file_name":"hslu_gui_methods.py","file_ext":"py","file_size_in_byte":8550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"220650986","text":"import imaplib\nimport base64\nimport os\nimport email\nimport smtplib\nimport time\nimport mailparser\nimport json\nfrom time import sleep\n\n#email_user = input('Email: ')\n#email_pass = input('Password: ')\nmail = imaplib.IMAP4_SSL(\"imap.gmail.com\", 993)\n#print(mail)\n#mail.select( \"INBOX\" , False )\nmail.login('email', 'password')\nmail.select('INBOX', False)\n# print(mail)\ntype, data = mail.search(None, 'ALL')\nlatest_email_uid = data[0].split()[-1]\nprint ('last id is ->', latest_email_uid)\n# result, data = mail.uid('fetch', latest_email_uid, '(RFC822)')\n\n\ntyp, data = mail.fetch(latest_email_uid, '(RFC822)' )\nprint ('result ->', typ)\nraw_email = data[0][1]\nmail = mailparser.parse_from_bytes(raw_email)\nprint (mail.from_)\nprint (mail.subject)\n\n\n# # print( data)\n# print(\"=====================\")\n\n# -------------------------------------------------\n#\n# Utility to read email from Gmail Using Python\n#\n# ------------------------------------------------\n\ndef read_email_from_gmail():\n    try:\n        mail = imaplib.IMAP4_SSL(\"imap.gmail.com\", 993)\n        mail.login('muhammadtahakhan222@gmail.com', 'wglfquvpxfspafno')\n        mail.list()\n        mail.select('inbox')\n        \n        type, data = mail.search(None, '(UNSEEN)')\n        mail_ids = data[0]\n\n        id_list = mail_ids.split()   \n        first_email_id = int(id_list[0])\n        latest_email_id = int(id_list[-1])\n        print(id_list)\n      \n        # for i in reversed(range(first_email_id, latest_email_id)):\n        email = []\n        \n        for idx, i in enumerate(id_list):\n            # print('iiiii========================', i)\n            typ, data = mail.fetch(i, '(RFC822)' )\n            raw_email = data[0][1]\n            raw_email = str(raw_email, 'utf-8')\n            mail_data = mailparser.parse_from_string(raw_email)\n            data = {'subject':mail_data.subject, 'from':mail_data.from_, 'body':mail_data.body}\n            email.insert(idx, data)\n            if idx == 10:\n                break\n           \n        print(email)\n    except Exception as e:\n        print (e)\n\nread_email_from_gmail()\n","sub_path":"email_service/imap.py","file_name":"imap.py","file_ext":"py","file_size_in_byte":2079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"156487520","text":"import numpy as np\n\ndef InitMesh(grid_min, grid_max, init_pixel=(0, 0, 0, 0)):\n    mesh = {0:{}, 1:{}} # Canvas mode: 0/1 (in/out)\n\n    x_grid = grid_max['x'] - grid_min['x']\n    y_grid = grid_max['y'] - grid_min['y']\n    z_grid = grid_max['z'] - grid_min['z']\n\n    for mode in mesh.keys():\n        face_xy = np.empty([x_grid, y_grid], dtype=tuple)\n        face_xy.fill(init_pixel)\n        mesh[mode]['xy'] = face_xy\n\n        face_yz = np.empty([y_grid, z_grid], dtype=tuple)\n        face_yz.fill(init_pixel)\n        mesh[mode]['yz'] = face_yz\n\n        face_xz = np.empty([x_grid, z_grid], dtype=tuple)\n        face_xz.fill(init_pixel)\n        mesh[mode]['xz'] = face_xz\n\n    return mesh\n","sub_path":"src/system/storage.py","file_name":"storage.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"337525700","text":"#!/bin/python3\n'''\nWhat is sum of all amicable numbers below N?\nHint: https://www.math.upenn.edu/~deturck/m170/wk3/lecture/sumdiv.html\n'''\n'''\nApproach 1: \n# Step 1: Find all the amicable numbers below 10**5+1 i.e., [220, 284, 1184, 1210, 2620, 2924, 5020, 5564, 6232, 6368, 10744, 10856, 12285, 14595, 17296, 18416, 63020, 76084, 66928, 66992, 67095, 71145, 69615, 87633, 79750, 88730]\namicable_numbers = []\n\nfor i in range(1,100001):\n    if i not in amicable_numbers:\n        divisors_sum = int(sum_of_divisors(i) - i)\n        sum_divisors = int(sum_of_divisors(divisors_sum) - divisors_sum)\n        if sum_divisors == i and divisors_sum != i:\n            amicable_numbers.append(i)\n            amicable_numbers.append(divisors_sum)\n# Step 2: sum all amicable numbers below N\namicable_numbers = [1, 0, 220, 284, 1184, 1210, 2620, 2924, 5020, 5564, 6232, 6368, 10744, 10856, 12285, 14595, 17296, 18416, 63020, 76084, 66928, 66992, 67095, 71145, 69615, 87633, 79750, 88730]\n\nfor _ in range(int(input().strip())):    \n    \n    n = int(input().strip())\n    print(sum([each for each in amicable_numbers if each < n])-1)\n'''\n\nfrom collections import Counter\nfrom operator import mul\nfrom functools import reduce\n\ndef prime_factors(i):\n    '''\n    Function returns a list of all prime factors of N    \n    '''\n    prime_factors = []\n    k = 0\n    d = master_primes[k]\n    while i > 1:\n        while i % d == 0:\n            prime_factors.append(d)\n            i /= d\n        '''# skipping check for even numbers other than 2\n        if d != 2:\n            d = d + 2\n        else:\n            d = 3\n        '''\n        # skipping check for non-prime numbers if already in master_primes\n        k = k+1\n        if k < len(master_primes):\n            d = master_primes[k]\n        # skipping check for even numbers other than 2\n        elif d !=2:\n            d = d + 2\n        else:\n            d = 3\n        if d*d > i:\n            # appending N, if N itself is a prime \n            if i > 1: \n                prime_factors.append(i)\n                if int(i) not in master_primes:\n                    master_primes.append(int(i))\n                #master_primes = sorted(set(master_primes))\n            break\n\n    # Returning prime factors and their powers\n    #print(master_primes)\n    return Counter(prime_factors)\n\ndef sum_of_divisors(i):\n    '''\n    Function returns the sum of all divisors of N\n    '''\n    # Get all prime factors and their powers for N\n    primes = prime_factors(i)\n    # Calculate the sum of each prime factor\n    sum_primes = [(pow(each, (primes[each] + 1)) - 1)/(each - 1) for each in primes.keys()]\n    # Return the sum of divisors\n    return reduce(mul, sum_primes, 1)\n\namicable_numbers = []\nmaster_primes = [2]\nmax_n = 2\nfor _ in range(int(input().strip())):    \n    \n    n = int(input().strip())\n\t# Continuing from precomputed max_n\n    for i in range(max_n,n+1):\n        if i not in amicable_numbers:\n            # Find sum of proper divisors of i i.e., divisors_sum\n            divisors_sum = int(sum_of_divisors(i) - i)\n            # Find sum of proper divisors of divisors_sum\n            sum_divisors = int(sum_of_divisors(divisors_sum) - divisors_sum)\n            # Check if i = sum_divisors and avoid cases where sum of proper divisors of i is same as i\n            if sum_divisors == i and divisors_sum != i:\n                amicable_numbers.append(i)\n                amicable_numbers.append(divisors_sum)\n    # print the sum of all amicable_numbers below n\n    print(sum([each for each in amicable_numbers if each < n]))\n    #print(amicable_numbers)\n    if max_n < n:\n        max_n = n","sub_path":"Project Euler/euler021-Amicable Numbers.py","file_name":"euler021-Amicable Numbers.py","file_ext":"py","file_size_in_byte":3616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"644226391","text":"# -*- coding: utf-8 -*-\n# Copyright 2015 Hewlett-Packard Development Company, L.P.\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 uuid\n\nfrom cue import client\nfrom cue.tests.functional import base\nfrom cue.tests.functional.fixtures import nova\nimport os_tasklib.nova.delete_vm_group as delete_vm_group\n\nimport novaclient.exceptions as nova_exc\nfrom taskflow import engines\nfrom taskflow.patterns import linear_flow\n\n\nclass DeleteVmGroupTests(base.FunctionalTestCase):\n    additional_fixtures = [\n        nova.NovaClient\n    ]\n\n    task_store = {\n        'group': \"0\",\n    }\n\n    def setUp(self):\n        super(DeleteVmGroupTests, self).setUp()\n\n        self.nova_client = client.nova_client()\n\n        self.new_vm_group_name = str(uuid.uuid4())\n        self.new_vm_group_id = None\n\n        self.flow = linear_flow.Flow(\"delete vm group flow\")\n        self.flow.add(\n            delete_vm_group.DeleteVmGroup(os_client=self.nova_client)\n        )\n\n    def test_delete_server_group_invalid_id(self):\n        \"\"\"Verifies Delete non-existing server group.\"\"\"\n        # create a few server groups\n        server_groups = [\n            self.nova_client.server_groups.create(name=\"server_group_1\",\n                                                  policies=['anti-affinity']),\n            self.nova_client.server_groups.create(name=\"server_group_2\",\n                                                  policies=['affinity']),\n            self.nova_client.server_groups.create(name=\"server_group_3\",\n                                                  policies=['anti-affinity'])]\n\n        self.task_store['group'] = str(uuid.uuid4())\n\n        # start engine to run delete task\n        engines.run(self.flow, store=DeleteVmGroupTests.task_store)\n\n        # retrieve all server groups\n        server_groups_found = []\n        for server_group in server_groups:\n            server_groups_found.append(\n                self.nova_client.server_groups.get(server_group.id))\n\n        # verify the number of server groups did not change\n        self.assertEqual(len(server_groups), len(server_groups_found),\n                         \"Not all server groups were found\")\n\n    def test_delete_server_group(self):\n        \"\"\"Verifies Delete existing server group.\"\"\"\n        # create a few server groups\n        server_groups = [\n            self.nova_client.server_groups.create(name=\"server_group_1\",\n                                                  policies=['anti-affinity']),\n            self.nova_client.server_groups.create(name=\"server_group_2\",\n                                                  policies=['anti-affinity']),\n            self.nova_client.server_groups.create(name=\"server_group_3\",\n                                                  policies=['affinity'])]\n\n        # select a server group to delete\n        self.task_store['group'] = server_groups[0].id\n\n        # start engine to run delete server group task\n        engines.run(self.flow, store=DeleteVmGroupTests.task_store)\n\n        # verify deleted server group is not found\n        self.assertRaises(nova_exc.NotFound,\n                          self.nova_client.server_groups.get,\n                          server_groups[0].id)\n","sub_path":"cue/tests/functional/taskflow/task/test_delete_vm_group.py","file_name":"test_delete_vm_group.py","file_ext":"py","file_size_in_byte":3689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"8668803","text":"import os\nimport glob\nimport subprocess as sp\nfrom IPython.nbformat import current_nbformat\nfrom IPython.nbformat import read as read_nb\nfrom IPython.nbformat.v4 import new_code_cell, new_markdown_cell\n\n\nclass TestBase(object):\n\n    pth = os.path.split(os.path.realpath(__file__))[0]\n    files = {os.path.basename(x): x for x in glob.glob(os.path.join(pth, \"files/*.ipynb\"))}\n\n    def setup(self):\n        self.nbs = {}\n        for basename, filename in self.files.items():\n            with open(filename, \"r\") as fh:\n                self.nbs[basename] = read_nb(fh, as_version=current_nbformat)\n\n    @staticmethod\n    def _create_code_cell():\n        source = \"\"\"print(\"something\")\n### BEGIN SOLUTION\nprint(\"hello\")\n### END SOLUTION\"\"\"\n        cell = new_code_cell(source=source)\n        return cell\n\n    @staticmethod\n    def _create_text_cell():\n        source = \"this is the answer!\\n\"\n        cell = new_markdown_cell(source=source)\n        return cell\n\n    @staticmethod\n    def _run_command(command):\n        proc = sp.Popen(command, stdout=sp.PIPE, stderr=sp.STDOUT)\n        if proc.wait() != 0:\n            output = proc.communicate()[0]\n            print(output.decode())\n            raise AssertionError(\"process returned a non-zero exit code\")\n","sub_path":"nbgrader/tests/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"332121202","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\ndef close_values(xdf,ydf, val):\n    idxs = ydf.index[np.isclose(ydf, val, rtol=1e-4, atol=1e-4)]\n    return xdf[idxs], ydf[idxs]\n\n\nsingle = pd.read_csv('Task12.8.2_1.csv')\nseries = pd.read_csv('Task12.8.2_2.csv')\n#single = pd.read_csv('task1_1n5819_single_data.csv')\n#series = pd.read_csv('task1_1n5819_series_data.csv')\n#single = pd.read_csv('task1_1n5226B_single_data.csv')\n#series = pd.read_csv('task1_1n5226B_series_data.csv')\n\ntime = single['Time (s)']\n\nsingle_current = single['Math 1 (A)']\nsingle_voltage = single['Channel 2 (V)']\nseries_current = series['Math 1 (A)']\nseries_voltage = series['Channel 2 (V)']\n\nsingle_marker_voltage_pos, single_marker_current_pos = close_values(single_voltage, single_current, 0.01)\nseries_marker_voltage_pos, series_marker_current_pos = close_values(series_voltage, series_current, 0.01)\nprint(f\"There is 10mA across 1 diode at {np.mean(single_marker_voltage_pos):.3f}v\")\nprint(f\"There is 10mA across 2 diodes at {np.mean(series_marker_voltage_pos):.3f}v\")\n\nsingle_marker_voltage_neg, single_marker_current_neg = close_values(single_voltage, single_current, -0.01)\nseries_marker_voltage_neg, series_marker_current_neg = close_values(series_voltage, series_current, -0.01)\nprint(f\"There is 10mA across 1 diode at {np.mean(single_marker_voltage_neg):.3f}v\")\nprint(f\"There is 10mA across 2 diodes at {np.mean(series_marker_voltage_neg):.3f}v\")\n\n# Plot the stuff\n# mpl.style.use('seaborn')\n\nplt.plot(single_voltage.values, single_current.values, 'C1', label='Single')\nplt.scatter(single_marker_voltage_pos, single_marker_current_pos, c='k', marker='x')\nplt.scatter(single_marker_voltage_neg, single_marker_current_neg, c='k', marker='x')\n\nplt.plot(series_voltage.values, series_current.values, 'C2', label='Series')\nplt.scatter(series_marker_voltage_pos, series_marker_current_pos, c='k', marker='x')\nplt.scatter(series_marker_voltage_neg, series_marker_current_neg, c='k', marker='x')\n\n\nplt.xlabel('Voltage (V)')\nplt.ylabel('Current (A)')\nplt.legend()\n\nplt.show()\n","sub_path":"plt.py","file_name":"plt.py","file_ext":"py","file_size_in_byte":2100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"135700893","text":"from threading import Thread, Lock\nstrlock = Lock()\n\nclass StrLengthThread(Thread):\n\ttotal_str_length = 0\n\n\tdef __init__(self, word):\n\t\tself.word = word\n\t\tif not isinstance(word, str):\n\t\t\traise Exception(\"word is not a string\")\n\t\tThread.__init__(self)\n\n\t@staticmethod\n\tdef update_total_length(value):\n\t\twith strlock:\n\t\t\tStrLengthThread.total_str_length += value\n\n\tdef run(self):\n\t\tself.update_total_length(len(self.word))","sub_path":"divide_and_rule/h_str_length.py","file_name":"h_str_length.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"219727131","text":"import tensorflow as tf\r\nimport numpy as np\r\nimport matplotlib as mpl\r\nimport matplotlib.pyplot as plt\r\nfrom tensorflow.keras import layers, models, regularizers\r\nfrom tensorflow.keras.utils import to_categorical\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nfrom math import floor\r\n\r\nimport foolbox as fb\r\nimport eagerpy as ep\r\nfrom foolbox import samples\r\n\r\n\r\ndef generate_backdoor_pattern(image, size, mask_ratio):\r\n    width = image.shape[1]\r\n    height = image.shape[0]\r\n    #generate integer noise same size as image\r\n    pattern = np.random.randint(0, 256, size=image.shape)\r\n    #create mask, all zero except size*size patch that masks with\r\n    #ratio k\r\n    mask = np.zeros(image.shape)\r\n    #pick random x and y values for upper left corner of the patch\r\n    x = np.random.randint(0, width-size)\r\n    y = np.random.randint(0, height-size)\r\n    for i in range(size):\r\n        for j in range(size):\r\n            mask[x+i, y+j] = mask_ratio\r\n    \r\n    pattern = pattern.astype('float32') / 255.0\r\n    \r\n    return (pattern, mask)\r\n\r\ndef generate_backdoor_pattern_botRight(image, size, mask_ratio):\r\n    width = image.shape[1]\r\n    height = image.shape[0]\r\n    #generate integer noise same size as image\r\n    pattern = np.random.randint(0, 256, size=image.shape)\r\n    #create mask, all zero except size*size patch in\r\n    #bottom-right corner\r\n    mask = np.zeros(image.shape)\r\n    for i in range(size):\r\n        for j in range(size):\r\n            mask[width-i-1, height-j-1] = mask_ratio\r\n\r\n    pattern = pattern.astype('float32') / 255.0\r\n\r\n    return (pattern, mask)\r\n\r\ndef inject_backdoor_pattern(images, pattern, mask):\r\n    mask_image = (1 - mask) * images\r\n    mask_pattern = mask * pattern\r\n    mask_pattern = np.expand_dims(mask_pattern, axis=0)\r\n    backdoor_images = mask_image + mask_pattern\r\n    \r\n    return backdoor_images\r\n\r\ndef inject_noise_to_image(images, mean, sd):\r\n    noise_shape = images.shape\r\n    noise = np.random.normal(mean, sd, noise_shape)\r\n    noisy_images = images + noise\r\n    \r\n    return noisy_images\r\n\r\ndef generate_backdoor_dataset(dataset, target_label, poison_percent, pattern, mask, noise_mean, noise_sd):\r\n    permutation = np.random.permutation(len(dataset))\r\n    num_images = floor(dataset.shape[0] * poison_percent)\r\n    indices = permutation[:num_images]\r\n    \r\n    backdoor_data = dataset[indices]\r\n    backdoor_data = inject_noise_to_image(backdoor_data, noise_mean, noise_sd)\r\n    backdoor_data = inject_backdoor_pattern(backdoor_data, pattern, mask)\r\n\r\n    num_cat = target_label.shape[1]\r\n    backdoor_labels = np.full((num_images, num_cat), target_label)\r\n    \r\n    return (backdoor_data, backdoor_labels)\r\n\r\ndef randomize_dataset(data, labels):\r\n    shuffled_indices = np.random.permutation(len(data))\r\n    data = data[shuffled_indices]\r\n    labels = labels[shuffled_indices]\r\n    \r\n    return (data, labels)\r\n\r\ndef mix_datasets(dataset_one, labels_one, dataset_two, labels_two, percent_one, percent_two):\r\n    shuffled_indices_one = np.random.permutation(len(dataset_one))\r\n    shuffled_indices_two = np.random.permutation(len(dataset_two))\r\n    \r\n    amount_one = int(len(dataset_one) * percent_one)\r\n    amount_two = int(len(dataset_two) * percent_two)\r\n    \r\n    final_indices_one = shuffled_indices_one[:amount_one]\r\n    final_indices_two = shuffled_indices_two[:amount_two]\r\n    \r\n    dataset_final = np.vstack([dataset_one[final_indices_one], dataset_two[final_indices_two]])\r\n    labels_final = np.vstack([labels_one[final_indices_one], labels_two[final_indices_two]])\r\n    \r\n    dataset_final, labels_final = randomize_dataset(dataset_final, labels_final)\r\n    \r\n    return (dataset_final, labels_final)\r\n\r\n#--------------------------------------------\r\n#--------------------------------------------\r\ndef get_successful_backdoors(model, bd_test_data, bd_test_labels):\r\n    out = model(bd_test_data)\r\n    boolArray = np.argmax(out, axis=1) == np.argmax(bd_test_labels, axis=1)\r\n    success_data = bd_test_data[boolArray]\r\n    success_labels = bd_test_labels[boolArray]\r\n    \r\n    return (success_data, success_labels)\r\n\r\ndef build_backdoor_sig(model, feature_extractor, bd_test_data, bd_test_labels):\r\n    success_data, success_labels = get_successful_backdoors(model, bd_test_data, bd_test_labels)\r\n    features = feature_extractor(success_data)\r\n    sig = np.mean(features, axis=0)\r\n    \r\n    return sig\r\n\r\ndef cosine_sim(A, vB):\r\n    numerator = np.matmul(A, vB)\r\n    denominator = np.linalg.norm(A, axis=1) * np.linalg.norm(vB)\r\n    cos = numerator / denominator\r\n    \r\n    return cos\r\n\r\ndef check_adversarial(images, feature_extractor, signature, thresh):\r\n    sim = np.zeros(images.shape[0])\r\n    features = feature_extractor(images)\r\n    sim = cosine_sim(features.numpy(), signature)\r\n    \r\n    return sim >= thresh\r\n\r\ndef generate_adversarial(fmodel, attack, images, labels, epsilons):\r\n    # takes in test_images and raw_test_labels (not categorical)\r\n    prep_images = tf.convert_to_tensor(images)\r\n    prep_labels = tf.convert_to_tensor(labels, dtype=tf.int32)\r\n    raw, clipped, success = attack(fmodel, prep_images, prep_labels, epsilons=epsilons)\r\n    robust_accuracy = 1 - tf.cast(success, dtype=tf.float32)\r\n    robust_accuracy = tf.math.reduce_mean(robust_accuracy, axis=1)\r\n    \r\n    # create dict of adv examples indexed by epsilon values\r\n    adv_dict = {}\r\n    for i in range(len(epsilons)):\r\n        adv_dict[epsilons[i]] = (np.array(clipped[i]), success[i].numpy())\r\n    \r\n    return (adv_dict, robust_accuracy)\r\n\r\ndef test_detection(epsilons, thresholds, adv_dict, feature_extractor, signature):\r\n    detect_accuracy = {}\r\n    for eps in epsilons:\r\n        adv_ex, success = adv_dict[eps]\r\n        good_adv_ex = adv_ex[success]\r\n        detect_accuracy[eps] = {}\r\n        for thresh in thresholds:\r\n            preds = check_adversarial(good_adv_ex, feature_extractor,\r\n                                      signature, thresh)\r\n            detect_accuracy[eps][thresh] = preds\r\n    \r\n    return detect_accuracy\r\n\r\ndef consistent_backdoor_dataset(eps, basic_model, dataset, orig_labels, target_label, pattern, mask, noise_mean, noise_sd):\r\n    # good default eps = 4.0\r\n    eps = eps\r\n    attack = fb.attacks.LinfPGD(abs_stepsize=4.0/255.0, steps=20)\r\n\r\n    label_mask = orig_labels == target_label\r\n    data = dataset[label_mask]\r\n    labels = orig_labels[label_mask]\r\n    eps_array_format = np.array([eps])\r\n    \r\n    fmodel = fb.TensorFlowModel(basic_model, bounds=(0, 1), preprocessing=dict())\r\n    adv_dict, robust_accuracy = generate_adversarial(fmodel, attack, data, labels, eps_array_format)\r\n    adv_exs, success = adv_dict[eps]\r\n    good_adv_exs = adv_exs[success]\r\n    print(\"Num successful adv_examples: {}\".format(good_adv_exs.shape[0]))\r\n\r\n    bd_data, bd_labels = generate_backdoor_dataset(good_adv_exs, to_categorical([target_label], num_classes=10),\r\n                                                    1.0, pattern, mask, noise_mean, noise_sd)\r\n    return (bd_data, bd_labels)\r\n\r\n\r\ndef test_backdoor_defense(adv_imgs, test_imgs, target_label, feature_extractor, signature, thresholds):\r\n    for thresh in thresholds:\r\n        adv_guess = check_adversarial(adv_imgs, feature_extractor, signature, thresholds[thresh])\r\n        true_pos = np.sum(adv_guess)\r\n        false_neg = adv_imgs.shape[0] - true_pos\r\n\r\n        guess = check_adversarial(test_imgs, feature_extractor, signature, thresholds[thresh])\r\n        false_pos = np.sum(guess)\r\n        true_neg = test_imgs.shape[0] - false_pos\r\n\r\n        print(\"----------------\")\r\n        print(\"Threshold Percentile: {}\".format(thresh))\r\n        print(\"True Positive: {}\".format(true_pos))\r\n        print(\"False Negative: {}\".format(false_neg))\r\n        print(\"True Positive Rate: {}\".format(true_pos / (true_pos + false_neg)))\r\n        print(\"True Negative: {}\".format(true_neg))\r\n        print(\"False Positive: {}\".format(false_pos))\r\n        print(\"False Positive Rate: {}\".format(false_pos / (false_pos + true_neg)))\r\n\r\ndef cosine_sim_vecs(a, b):\r\n    numerator = np.dot(a, b.T)\r\n    denominator = np.linalg.norm(a) * np.linalg.norm(b)\r\n    cos = numerator / denominator\r\n    \r\n    return cos\r\n\r\ndef sim_distribution(images, feature_extractor, pattern, mask, num_samples=5000):\r\n    sim_array = np.zeros(num_samples)\r\n    for i in range(num_samples):\r\n        benign_ind = np.random.randint(0, images.shape[0])\r\n        bd_ind = np.random.randint(0, images.shape[0])\r\n\r\n        benign_img = images[benign_ind]\r\n        bd_img = images[bd_ind]\r\n        bd_img = inject_backdoor_pattern(bd_img, pattern, mask)\r\n        bd_img = np.squeeze(bd_img, axis=0)\r\n\r\n        benign_features = feature_extractor(np.expand_dims(benign_img, axis=0))\r\n        bd_features = feature_extractor(np.expand_dims(bd_img, axis=0))\r\n\r\n        sim = cosine_sim_vecs(benign_features.numpy(), bd_features.numpy())\r\n        sim_array[i] = sim\r\n    \r\n    return sim_array\r\n\r\n\r\n\r\ndef setup_backdoor_defense(label, model, pattern, mask, test_imgs, train_imgs, \r\n                            raw_train_labels):\r\n    feature_extractor = tf.keras.Model(\r\n        inputs = model.inputs,\r\n        outputs = model.get_layer(index=16).output\r\n    )\r\n    sim_array = sim_distribution(test_imgs, feature_extractor, pattern, mask)\r\n    thresholds = {}\r\n    for i in range(90, 100):\r\n        thresholds[i] = np.percentile(sim_array, i)\r\n    \r\n    non_label_mask = raw_train_labels != label\r\n    non_label_train = train_imgs[np.squeeze(non_label_mask, axis=1)]\r\n    perm = np.random.permutation(non_label_train.shape[0])\r\n    indices = perm[:10000]\r\n    bd_train_images = inject_backdoor_pattern(non_label_train[indices], pattern, mask)\r\n    bd_train_labels = np.full((bd_train_images.shape[0], 10), to_categorical(label, num_classes=10))\r\n\r\n    sig = build_backdoor_sig(model, feature_extractor, bd_train_images, bd_train_labels)\r\n\r\n    return (sig, feature_extractor, thresholds)","sub_path":"backdoor_utils.py","file_name":"backdoor_utils.py","file_ext":"py","file_size_in_byte":9921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"403206509","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n__author__ = 'Binh Vu binh@toan2.com'\n\n\"\"\"\n    Represent a node in a query tree\n\"\"\"\nclass Node(object):\n\n    def __init__(self):\n        self.parent = None\n        self.children = []\n\n\"\"\"\n    Represent a data node in query tree. A data node is \n    a node which generate the data (by query in our database)\n\"\"\"\nclass DataNode(Node):\n\n    def __init__(self, varname, label):\n        super(DataNode, self).__init__()\n\n        self.varname = varname\n        self.label = label\n        self.strategy = None\n\n\"\"\"\n    Represent a specific type of data node: edge. This data node \n    can only retrieve data when it's provided the id of node\n\"\"\"\nclass EdgeNode(DataNode):\n\n    def __init__(self, varname, label, rel_type, direction):\n        super(EdgeNode, self).__init__(varname, label)\n        self.rel_type = rel_type\n        self.direction = direction\n\n\"\"\"\n    Represent a binary operator in query tree \n\"\"\"\nclass BinaryOperatorNode(Node): pass\n\n\"\"\"\n    Represent a static binary operator in query tree, static means\n    a value of one operand is provided at the compiled time\n\"\"\"\nclass BinaryStaticOperatorNode(BinaryOperatorNode):\n\n    def __init__(self, varname, field, op, value):\n        super(BinaryStaticOperatorNode, self).__init__()\n\n        self.varname = varname\n        self.field = field\n        self.op = op \n        self.value = value \n\n\"\"\" \n    Represent a dynamic binary operator in the query tree, dynamic means \n    values of two operands are provided at the runtime\n\"\"\"\nclass BinaryDynamicOperatorNode(BinaryOperatorNode):\n\n    def __init__(self, varname, field, op, varname2, field2):\n        super(BinaryDynamicOperatorNode, self).__init__()\n\n        self.varname = varname \n        self.field = field \n        self.op = op \n        self.varname2 = varname2 \n        self.field2 = field2 \n\n\"\"\"\n    Represent a join operator in query tree. A join operator in the graph \n    is different with join operator in RDBMS, joined information is expressed\n    using edge in graph, therefore, when we mention `join`, we join between nodes \n    & edges\n\"\"\"\nclass JoinOperatorNode(Node):\n    \n    def __init__(self, varname1, varname2):\n        super(JoinOperatorNode, self).__init__()\n        self.varname1 = varname1\n        self.varname2 = varname2","sub_path":"redisgraph/query/parser/dstruct.py","file_name":"dstruct.py","file_ext":"py","file_size_in_byte":2302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"439167026","text":"# -*- coding: utf-8 -*-\nfrom pioreactor.automations.dosing.base import DosingAutomation\nfrom pioreactor.automations import events\nfrom pioreactor.utils.streaming_calculations import PID\nfrom pioreactor.config import config\n\n\nVIAL_VOLUME = float(config[\"bioreactor\"][\"volume_ml\"])\n\n\nclass PIDMorbidostat(DosingAutomation):\n    \"\"\"\n    As defined in Zhong 2020\n    \"\"\"\n\n    key = \"pid_morbidostat\"\n    published_settings = {\n        \"volume\": {\"datatype\": \"float\", \"settable\": True, \"unit\": \"mL\"},\n        \"target_od\": {\"datatype\": \"float\", \"settable\": True, \"unit\": \"AU\"},\n        \"target_growth_rate\": {\"datatype\": \"float\", \"settable\": True, \"unit\": \"h⁻¹\"},\n        \"duration\": {\"datatype\": \"float\", \"settable\": True, \"unit\": \"min\"},\n    }\n\n    def __init__(self, target_growth_rate=None, target_od=None, volume=None, **kwargs):\n        super(PIDMorbidostat, self).__init__(**kwargs)\n        assert target_od is not None, \"`target_od` must be set\"\n        assert target_growth_rate is not None, \"`target_growth_rate` must be set\"\n\n        self.set_target_growth_rate(target_growth_rate)\n        self.target_od = float(target_od)\n\n        Kp = config.getfloat(\"dosing_automation.pid_morbidostat\", \"Kp\")\n        Ki = config.getfloat(\"dosing_automation.pid_morbidostat\", \"Ki\")\n        Kd = config.getfloat(\"dosing_automation.pid_morbidostat\", \"Kd\")\n\n        self.pid = PID(\n            -Kp,\n            -Ki,\n            -Kd,\n            setpoint=self.target_growth_rate,\n            output_limits=(0, 1),\n            sample_time=None,\n            unit=self.unit,\n            experiment=self.experiment,\n            job_name=self.job_name,\n            target_name=\"growth_rate\",\n        )\n\n        if volume is not None:\n            self.logger.info(\n                \"Ignoring volume parameter; volume set by target growth rate and duration.\"\n            )\n\n        self.volume = round(\n            self.target_growth_rate * VIAL_VOLUME * (self.duration / 60), 4\n        )\n\n    def execute(self, *args, **kwargs) -> events.Event:\n        if self.latest_od <= self.min_od:\n            return events.NoEvent(\n                f\"latest OD less than OD to start diluting, {self.min_od:.2f}\"\n            )\n        else:\n            fraction_of_alt_media_to_add = self.pid.update(\n                self.latest_growth_rate, dt=self.duration / 60\n            )  # duration is measured in hours, not seconds (as simple_pid would want)\n\n            # dilute more if our OD keeps creeping up - we want to stay in the linear range.\n            if self.latest_od > self.max_od:\n                self.logger.info(\n                    f\"executing triple dilution since we are above max OD, {self.max_od:.2f}AU.\"\n                )\n                volume = 2.5 * self.volume\n            else:\n                volume = self.volume\n\n            alt_media_ml = fraction_of_alt_media_to_add * volume\n            media_ml = (1 - fraction_of_alt_media_to_add) * volume\n\n            self.execute_io_action(\n                alt_media_ml=alt_media_ml, media_ml=media_ml, waste_ml=volume\n            )\n            return events.AltMediaEvent(\n                f\"PID output={fraction_of_alt_media_to_add:.2f}, alt_media_ml={alt_media_ml:.2f}mL, media_ml={media_ml:.2f}mL\"\n            )\n\n    @property\n    def min_od(self):\n        return 0.7 * self.target_od\n\n    @property\n    def max_od(self):\n        return 1.25 * self.target_od\n\n    def set_target_growth_rate(self, value):\n        self.target_growth_rate = float(value)\n        try:\n            self.pid.set_setpoint(self.target_growth_rate)\n        except AttributeError:\n            pass\n","sub_path":"pioreactor/automations/dosing/pid_morbidostat.py","file_name":"pid_morbidostat.py","file_ext":"py","file_size_in_byte":3613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"350915637","text":"import dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport pandas as pd\nimport plotly.graph_objs as go\nfrom dash.dependencies import Input, Output\n\ndf = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/Emissions%20Data.csv')\n\napp = dash.Dash(__name__)\n\napp.layout = html.Div([\n    html.Div([\n        html.H1(\"Greenhouse Gas Emissions by Continent\")\n    ], style={\"textAlign\": \"center\"}),\n\n    dcc.Graph(id=\"my-graph\"),\n    html.Div([\n        dcc.RangeSlider(\n            id=\"selected-continent\",\n            min=2008,\n            max=2011,\n            marks={2008: \"2008\", 2009: \"2009\", 2010: \"2010\", 2011: \"2011\"},\n            value=[2008, 2010],\n\n        )], style={\n        \"display\": \"block\",\n        \"margin-left\": \"auto\",\n        \"margin-right\": \"auto\",\n        \"width\": \"60%\"\n    }\n    ),\n\n], className=\"container\")\n\n\n@app.callback(\n    Output('my-graph', 'figure'),\n    [Input('selected-continent', 'value')])\ndef update_figure(selected):\n    dff = df[(df[\"Year\"] >= selected[0]) & (df[\"Year\"] <= selected[1])]\n    traces = []\n    for continent in dff.Continent.unique():\n        traces.append(go.Box(\n            y=dff[dff[\"Continent\"] == continent][\"Emission\"],\n            name=continent,\n            marker={\"size\": 4}\n\n        ))\n\n    return {\n\n        \"data\": traces,\n        \"layout\": go.Layout(\n            title=f\"Emission Levels for {'-'.join(str(i) for i in selected)}\",\n            autosize=True,\n            margin={\"l\": 200, \"b\": 100, \"r\": 200},\n            xaxis={\n                \"showticklabels\": False,\n            },\n            yaxis={\n                \"title\": f\"Emissions (gigatonnes of CO2)\",\n                \"type\": \"log\",\n            },\n\n        )\n\n    }\n\n\nserver = app.server # the Flask app\n\nif __name__ == '__main__':\n    app.run_server(debug=True)\n","sub_path":"boxplot/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"366582311","text":"import mimetypes\r\nimport smtplib\r\nimport time\r\nfrom email import encoders\r\nfrom email.header import Header\r\nfrom email.mime.base import MIMEBase\r\nfrom email.mime.multipart import MIMEMultipart\r\nfrom email.mime.text import MIMEText\r\nfrom email.utils import parseaddr, formataddr\r\nimport os\r\nfrom os.path import getsize\r\n\r\n\r\nclass EmailManager:\r\n    '''\r\n    SMTP是发送邮件的协议，Python内置对SMTP的支持，\r\n    可以发送纯文本邮件、HTML邮件以及带附件的邮件。\r\n    Python对SMTP支持有smtplib和email两个模块，email负责构造邮件，smtplib负责发送邮件。\r\n    '''\r\n\r\n    def __init__(self, **kwargs):\r\n        '''\r\n         constructor\r\n         :param kwargs:Variable paramete\r\n        '''\r\n        self.kwargs = kwargs\r\n        self.smtp_server = 'smtp.qq.com'\r\n        self.MAX_FILE_SIZE = 10 * 1024 * 1024  # 附件大小\r\n\r\n\r\n    def __get_cfg(self, key, throw=True):\r\n        '''\r\n          get the configuration file based on the key\r\n          获取配置参数信息\r\n          当读取的key对应的value为None或空值时抛出异常信息\r\n          :param key:\r\n          :param throw:\r\n          :return:\r\n          '''\r\n        cfg = self.kwargs.get(key)\r\n        if throw == True and (cfg is None or cfg == ''):\r\n            raise Exception(\"The configuration can't be empty\", 'utf-8')\r\n        return cfg\r\n\r\n    def __init_cfg(self):\r\n        '''\r\n        初始化配置参数信息\r\n        :return:\r\n        '''\r\n        self.msg_from = self.__get_cfg('msg_from')\r\n        self.password = self.__get_cfg('password')\r\n        # self.msg_to = ';'.join(self.__get_cfg('msg_to'))\r\n        self.msg_to = self.__get_cfg('msg_to')\r\n        #self.msg_cc = ';'.join(self.__get_cfg('msg_cc'))\r\n        self.msg_cc = self.__get_cfg('msg_cc')\r\n        self.msg_subject = self.__get_cfg('msg_subject')\r\n        self.msg_content = self.__get_cfg('msg_content')\r\n        self.msg_date = self.__get_cfg('msg_date')\r\n        # attachment\r\n        self.attach_file = self.__get_cfg('attach_file', throw=False)\r\n\r\n\r\n    def login_server(self):\r\n        '''\r\n        login server 登录SMTP服务器\r\n        :return:\r\n        '''\r\n        # QQ邮箱采用的是SMTP_SSL的加密方式，所以要这样写\r\n        # 普通邮箱 server = smtplib.SMTP(self.smtp_server, 25) 这样写\r\n        server = smtplib.SMTP_SSL(self.smtp_server, 465)\r\n        server.set_debuglevel(1)\r\n        server.login(self.msg_from, self.password)\r\n        return server\r\n\r\n    def get_main_msg(self):\r\n        '''\r\n        suject content\r\n        处理邮件的主要内容\r\n        :return:msg\r\n        '''\r\n        msg = MIMEMultipart()\r\n        # message content\r\n        msg.attach(MIMEText(self.msg_content, 'plain', 'utf-8'))\r\n\r\n        msg['From'] = self._format_addr('From <%s>' % self.msg_from)\r\n        # msgTo = ','.join([to for to in self.msg_to])\r\n\r\n        # msg['To'] = self._format_addr('To <%s>' % self.msg_to)\r\n        msg['To'] = \";\".join([self._format_addr(u'To <%s>' % to) for to in self.msg_to])\r\n        msg['Subject'] = Header(self.msg_subject, 'utf-8')\r\n        msg['Date'] = self.msg_date\r\n        # msg['Cc'] = self._format_addr('To <%s>' % self.msg_cc)\r\n        msg['To'] = \";\".join([self._format_addr(u'To <%s>' % to) for to in self.msg_cc])\r\n\r\n\r\n        # attachment content\r\n        attach_file = self.get_attach_file()\r\n        if attach_file is not None:\r\n            msg.attach(attach_file)\r\n        return msg\r\n\r\n    def get_attach_file(self):\r\n        '''\r\n        获取附件的相关信息\r\n        generate mail attachment content\r\n        :return:\r\n        '''\r\n        if self.attach_file is not None and self.attach_file != '':\r\n            try:\r\n                if getsize(self.attach_file) > self.MAX_FILE_SIZE:\r\n                    # 超出10M，抛出异常\r\n                    raise Exception('The attachment is too large and the upload failed!!')\r\n                with open(self.attach_file, 'rb') as file:\r\n                    # mimetypes是python自带的标准库，可以根据文件的后缀名直接得到文件的MIME类型\r\n                    ctype, encoding = mimetypes.guess_type(self.attach_file)\r\n\r\n                    print('ctype:',ctype)\r\n                    print('encoding:',encoding)\r\n\r\n\r\n                    if ctype is None or encoding is not None:\r\n                        ctype = 'application/octet-stream'\r\n                    maintype, subtype = ctype.split('/', 1)\r\n                    # MIMEBase表示邮件附件对象\r\n                    mime = MIMEBase(maintype, subtype)\r\n                    mime.set_payload(file.read())\r\n                    # set header 设置信息头 filename设置附件名称\r\n                    mime.add_header('Content-Disposition', 'attachment',\r\n                                    filename=os.path.basename(self.attach_file))\r\n                    mime.add_header('Content-ID', '<0>')\r\n                    mime.add_header('X-Attachment-Id', '0')\r\n                    # set the attachment encoding rules 设置编码规则\r\n                    encoders.encode_base64(mime)\r\n                    return mime\r\n            except Exception as e:\r\n                print('%s......' % e)\r\n                return None\r\n        else:\r\n            return None\r\n\r\n    def _format_addr(self, s):\r\n        '''\r\n        格式化一个邮件地址:如果包含中文，需要通过Header对象进行编码。\r\n        :param s:\r\n        :return:\r\n        '''\r\n        name, addr = parseaddr(s)\r\n        return formataddr((Header(name, 'utf-8').encode(), addr))\r\n\r\n    def send(self):\r\n        '''\r\n        发送邮件\r\n        抄送就是将邮件同时发送给收信人以外的人\r\n        :return:\r\n        '''\r\n        try:\r\n            # initialize the configuration file\r\n            self.__init_cfg()\r\n            # log on to the SMTP server and verify authorization\r\n            server = self.login_server()\r\n            # mail content\r\n            msg = self.get_main_msg()\r\n            # send mail\r\n            # server.sendmail(self.msg_from, self.msg_to, msg.as_string())\r\n            print(1)\r\n            print(set(self.msg_to + self.msg_cc))\r\n            server.sendmail(self.msg_from, set(self.msg_to + self.msg_cc), msg.as_string())\r\n            server.quit()\r\n            print(\"Send succeed!!\")\r\n        except smtplib.SMTPException:\r\n            print(\"Error:Can't send this email!!\")\r\n# '1271564669@qq.com',\r\nif __name__ == \"__main__\":\r\n    mail_cfgs = {'msg_from': '824011142@qq.com', # 发送者的邮箱\r\n                 'password': 'vhqtyotzhhwvbcdc', # 发送者QQ邮箱授权码\r\n                 'msg_to': ['1271564669@qq.com','401952124@qq.com','544782744@qq.com'], # 收件人\r\n                 'msg_cc': ['401952124@qq.com','huotong177@163.com'], # 抄送人\r\n                 'msg_subject': 'Python Auto Send Email Test', # 标题\r\n                 'msg_content': 'Hi, boy! Just do it, python! I am Spider Man 你在干神马鸭？？？', # 正文\r\n                 'attach_file': r'.\\spider.jfif', # 附件\r\n                 'msg_date': time.ctime() # 时间戳\r\n                 }\r\n\r\n    manager = EmailManager(**mail_cfgs)\r\n    manager.send()","sub_path":"pycharmProject/pymarket/apps/utils/emailSenter.py","file_name":"emailSenter.py","file_ext":"py","file_size_in_byte":7214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"562969605","text":"\"\"\"\nAll the functions is this module are mocked out \nbecause they are called by the frontend.\nAvoids raising errors in the service.\n\"\"\"\n\nimport logging\n\nfrom fastapi import APIRouter\n\nlogger = logging.getLogger(__name__)\n\nmocked_router = APIRouter(tags=[\"Mocked frontend calls\"])\n\n\n@mocked_router.post(\"/push\")\nasync def ignored_push_post() -> str:\n    logger.warning(\"ignoring call POST /push from frontend\")\n    return \"\"\n\n\n@mocked_router.get(\"/retrieve\")\nasync def ignored_port_data_load() -> str:\n    logger.warning(\"ignoring call GET /retrive from frontend\")\n    return \"\"\n\n\n@mocked_router.post(\"/retrieve\")\nasync def ignored_port_data_save() -> str:\n    logger.warning(\"ignoring call POST /retrive from frontend\")\n    return \"\"\n\n\n@mocked_router.get(\"/state\")\nasync def ignored_load_service_state_state() -> str:\n    logger.warning(\"ignoring call GET /state from frontend\")\n    return \"\"\n\n\n@mocked_router.post(\"/state\")\nasync def ignored_save_service_state_state() -> str:\n    logger.warning(\"ignoring call POST /state from frontend\")\n    return \"\"\n\n\n__all__ = [\"mocked_router\"]\n","sub_path":"services/dynamic-sidecar/src/simcore_service_dynamic_sidecar/api/mocked.py","file_name":"mocked.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"211438125","text":"# Create a proper sentence by concatenating the strings below.\n# Add your own strings, if necessary, to make it readable.\n\nq1 = \"By seeking\"\nq2 = \"and\"\nq3 = \"blundering\"\nq4 = \"we learn.\"\n\nprint(q1 + \" \" + q2 + \" \" + q3 + \" \" + q4)\n\n# OR...\n\nprint(q1 + \" \" + q3 + \" \" + q4 + \" \" + q2 + \"...\")\n#Just a little more fun to me.","sub_path":"09_strings-and-text/09_04_sentence_pieces.py","file_name":"09_04_sentence_pieces.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"597758206","text":"import scipy\nimport matplotlib.pyplot as pyplot\nimport VLTTools\nimport numpy\nimport time\n\n\ndatadir = \"/diska/data/SPARTA/2015-04-29/TTFocus/\"\nciao = VLTTools.VLTConnection(simulate=False, datapath=datadir)\n\nfig = pyplot.figure(0)\nfig.clear()\nax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n\nTip = []\nTilt = []\nFocus = []\n\nlength = 120\ntip = numpy.zeros(length)\ntilt = numpy.zeros(length)\nfocus = numpy.zeros(length)\n\ntipPlot, = ax.plot(tip, c='b')\ntiltPlot, = ax.plot(tilt, c='g')\nfocusPlot, = ax.plot(focus, c='r')\nfig.show()\npix2m = (3.14159/(180.0*3600.0)) * 8.0*0.5/2.0\n\nn = 13.49\n\n\nwhile True:\n    TTF = ciao.getTTFocus() * pix2m\n    tip = numpy.append(tip,TTF[0]*4*n*1000.0)\n    tilt = numpy.append(tilt, TTF[2]*4*n*1000.0)\n    focus = numpy.append(focus, TTF[4]*16*(3.0)**(0.5)*n**2.0*1000.0)\n\n    tipPlot.set_ydata(tip[-length:])\n    tiltPlot.set_ydata(tilt[-length:])\n    focusPlot.set_ydata(focus[-length:])\n    mn = numpy.min([numpy.min(tipPlot.get_data()[1]), numpy.min(tiltPlot.get_data()[1]), numpy.min(focusPlot.get_data()[1])])\n    mx = numpy.max([numpy.max(tipPlot.get_data()[1]), numpy.max(tiltPlot.get_data()[1]), numpy.max(focusPlot.get_data()[1])])\n    ax.set_ybound(lower=mn*1.2, upper=mx*1.2)\n\n    fig.canvas.draw()\n\n    TTF *= 1e9\n    # print(\"focus: %.4f, tip: %.4f, tilt: %.4f\" % (focus[-1], tip[-1], tilt[-1])) \n    print(\"focus: %.0f, tip: %.0f, tilt: %.0f\" % (TTF[4], TTF[0], TTF[2]))\n\n    time.sleep(1)\n","sub_path":"sandbox/TTFocusPlotter.py","file_name":"TTFocusPlotter.py","file_ext":"py","file_size_in_byte":1425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"522369027","text":"# encoding: utf-8\n'''\n@author: jinjin\n@contact: cantaloupejinjin@gmail.com\n@file: predict.py\n@time: 2019/5/15 7:44\n'''\nimport os\nimport csv\nimport time\nimport datetime\nimport random\nimport json\nimport keras\nimport warnings\nfrom collections import Counter\nfrom math import sqrt\n\nimport gensim\nimport pandas as pd\nimport numpy as np\nimport tensorflow as tf\nfrom sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score\nwarnings.filterwarnings(\"ignore\")\nimport warnings\nwarnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')\nimport tensorflow as tf\nclass TrainingConfig(object):\n    epoches = 100\n\n    evaluateEvery = 4\n\n    checkpointEvery = 100\n    #learningRate = 0.001\n    learningRate = 0.001\n\n\nclass ModelConfig(object):\n    embeddingSize = 200\n\n    hiddenSizes = [128, 128]  # 双层LSTM结构的神经元个数128\n\n    dropoutKeepProb = 0.5\n    l2RegLambda = 0.0\n\n\nclass Config(object):\n    sequenceLength = 600\n    # 取了所有序列长度的均值\n    #batchSize = 128\n    batchSize = 110\n    evalbatchSize = 110\n    #测试集代码条数\n    testSize = 34\n    dataSource = \"preprocess/labeledTrain.csv\"\n    testSource = \"preprocess/test1.csv\"\n    stopWordSource = \"rawData/english\"\n\n    numClasses = 2\n\n    rate = 0.8  # 训练集的比例\n\n    training = TrainingConfig()\n\n    model = ModelConfig()\n\n\n# 实例化配置参数对象\nconfig = Config()\n\n\n# 数据预处理的类，生成训练集和测试集\n\nclass Dataset(object):\n    def __init__(self, config):\n        self._dataSource = config.dataSource\n        #用于模型预测\n        self._testSource = config.testSource\n        self._stopWordSource = config.stopWordSource\n\n        self._sequenceLength = config.sequenceLength  # 每条输入的序列处理为定长\n        self._embeddingSize = config.model.embeddingSize\n        self._batchSize = config.batchSize\n        self._rate = config.rate\n\n        self._stopWordDict = {}\n\n        self.trainReviews = []\n        self.trainLabels = []\n\n        self.evalReviews = []\n        self.evalLabels = []\n\n        self.wordEmbedding = None\n\n        self._wordToIndex = {}\n        self._indexToWord = {}\n\n    def _readData(self, filePath):\n        \"\"\"\n        从csv文件中读取数据集\n        \"\"\"\n\n        df = pd.read_csv(filePath)\n        labels = df[\"sentiment\"].tolist()\n        review = df[\"review\"].tolist()\n        reviews = [line.strip().split() for line in review]\n\n        return reviews, labels\n\n    def _reviewProcess(self, review, sequenceLength, wordToIndex):\n        \"\"\"\n        将数据集中的每条评论用index表示\n        wordToIndex中“pad”对应的index为0\n        \"\"\"\n\n        reviewVec = np.zeros((sequenceLength))\n        sequenceLen = sequenceLength\n\n        # 判断当前的序列是否小于定义的固定序列长度\n        if len(review) < sequenceLength:\n            sequenceLen = len(review)\n\n        for i in range(sequenceLen):\n            if review[i] in wordToIndex:\n                reviewVec[i] = wordToIndex[review[i]]\n            else:\n                reviewVec[i] = wordToIndex[\"UNK\"]\n\n        return reviewVec\n\n    def _genTrainEvalData(self, x, y, rate):\n        \"\"\"\n        生成训练集和验证集\n        \"\"\"\n\n        reviews = []\n        labels = []\n\n        # 遍历所有的文本，将文本中的词转换成index表示\n        for i in range(len(x)):\n            reviewVec = self._reviewProcess(x[i], self._sequenceLength, self._wordToIndex)\n            reviews.append(reviewVec)\n            # print(\"reviewVec: \", reviewVec)\n            labels.append([y[i]])\n\n        trainIndex = int(len(x) * rate)\n\n        trainReviews = np.asarray(reviews[:trainIndex], dtype=\"int64\")\n        trainLabels = np.array(labels[:trainIndex], dtype=\"float32\")\n\n        evalReviews = np.asarray(reviews[trainIndex:], dtype=\"int64\")\n        evalLabels = np.array(labels[trainIndex:], dtype=\"float32\")\n\n        return trainReviews, trainLabels, evalReviews, evalLabels\n\n    def _genVocabulary(self, reviews):\n        \"\"\"\n        生成词向量和词汇-索引映射字典，可以用全数据集\n        \"\"\"\n\n        allWords = [word for review in reviews for word in review]\n\n        # 去掉停用词\n        subWords = [word for word in allWords if word not in self.stopWordDict]\n\n        wordCount = Counter(subWords)  # 统计词频\n        sortWordCount = sorted(wordCount.items(), key=lambda x: x[1], reverse=True)\n\n        # 去除低频词\n        words = [item[0] for item in sortWordCount if item[1] >= 5]\n\n        vocab, wordEmbedding = self._getWordEmbedding(words)\n        self.wordEmbedding = wordEmbedding\n\n        self._wordToIndex = dict(zip(vocab, list(range(len(vocab)))))\n        self._indexToWord = dict(zip(list(range(len(vocab))), vocab))\n\n        # 将词汇-索引映射表保存为json数据，之后做inference时直接加载来处理数据\n        with open(\"data/wordToIndex.json\", \"w\", encoding=\"utf-8\") as f:\n            json.dump(self._wordToIndex, f)\n\n        with open(\"data/indexToWord.json\", \"w\", encoding=\"utf-8\") as f:\n            json.dump(self._indexToWord, f)\n\n    def _getWordEmbedding(self, words):\n        \"\"\"\n        按照我们的数据集中的单词取出预训练好的word2vec中的词向量\n        \"\"\"\n\n        wordVec = gensim.models.KeyedVectors.load_word2vec_format(\"rawData/word2Vec.bin\", binary=True)\n        vocab = []\n        wordEmbedding = []\n\n        # 添加 \"pad\" 和 \"UNK\",\n        vocab.append(\"pad\")\n        vocab.append(\"UNK\")\n        wordEmbedding.append(np.zeros(self._embeddingSize))\n        wordEmbedding.append(np.random.randn(self._embeddingSize))\n\n        for word in words:\n            try:\n                vector = wordVec.wv[word]\n                vocab.append(word)\n                wordEmbedding.append(vector)\n            except:\n                print(word + \"不存在于词向量中\")\n\n        return vocab, np.array(wordEmbedding)\n\n    def _readStopWord(self, stopWordPath):\n        \"\"\"\n        读取停用词\n        \"\"\"\n\n        with open(stopWordPath, \"r\") as f:\n            stopWords = f.read()\n            stopWordList = stopWords.splitlines()\n            # 将停用词用列表的形式生成，之后查找停用词时会比较快\n            self.stopWordDict = dict(zip(stopWordList, list(range(len(stopWordList)))))\n\n    def dataGen(self):\n        \"\"\"\n        初始化训练集和验证集\n        \"\"\"\n\n        # 初始化停用词\n        self._readStopWord(self._stopWordSource)\n\n        # 初始化数据集\n        reviews, labels = self._readData(self._dataSource)\n\n        reviews1, labels1 = self._readData(self._testSource)\n\n        # 初始化词汇-索引映射表和词向量矩阵\n        self._genVocabulary(reviews)\n        #self._genVocabulary(reviews1)\n\n        # 初始化训练集和测试集\n        trainReviews, trainLabels, evalReviews, evalLabels = self._genTrainEvalData(reviews, labels, self._rate)\n        trainReviews1, trainLabels1, evalReviews1, evalLabels1 = self._genTrainEvalData(reviews1, labels1, 1)\n        self.trainReviews = trainReviews\n        self.trainLabels = trainLabels\n\n        self.evalReviews = evalReviews\n        self.evalLabels = evalLabels\n\n        # inference data { data label}\n\n        self.inferenceReviews = trainReviews1\n        self.inferenceLabels = trainLabels1\n\ndata = Dataset(config)\ndata.dataGen()\n\n\n# 输出batch数据集\n\ndef nextBatch(x, y, batchSize):\n    \"\"\"\n    生成batch数据集，用生成器的方式输出\n    \"\"\"\n\n    perm = np.arange(len(x))\n    np.random.shuffle(perm)\n    x = x[perm]\n    y = y[perm]\n\n    numBatches = len(x) // batchSize\n    print(\"random,len(x), numBatches: \", len(x), numBatches)\n    for i in range(numBatches):\n        start = i * batchSize\n        end = start + batchSize\n        batchX = np.array(x[start: end], dtype=\"int64\")\n        batchY = np.array(y[start: end], dtype=\"float32\")\n\n        yield batchX, batchY\n\ndef norandomnextBatch(x, y, batchSize):\n    \"\"\"\n    生成batch数据集，用生成器的方式输出\n    \"\"\"\n\n    perm = np.arange(len(x))\n    x = x[perm]\n    y = y[perm]\n\n    numBatches = len(x) // batchSize\n    print(\"len(x), numBatches: \", len(x),numBatches)\n    for i in range(numBatches):\n        print(\"i :\",i)\n        start = i * batchSize\n        end = start + batchSize\n        batchX = np.array(x[start: end], dtype=\"int64\")\n        batchY = np.array(y[start: end], dtype=\"float32\")\n\n        yield batchX, batchY\n# 构建模型\n\n\n\n# 定义性能指标函数\n\ndef mean(item):\n    return sum(item) / len(item)\n\n\ndef genMetrics(trueY, predY, binaryPredY):\n    \"\"\"\n    生成acc和auc值\n    \"\"\"\n    auc = roc_auc_score(trueY, predY)\n    accuracy = accuracy_score(trueY, binaryPredY)\n    precision = precision_score(trueY, binaryPredY)\n    recall = recall_score(trueY, binaryPredY)\n\n    \"\"\"\n        round() 方法返回浮点数x的四舍五入值。4表示保留4位小数\n    \"\"\"\n    return round(accuracy, 4), round(auc, 4), round(precision, 4), round(recall, 4)\n\n\n# 训练模型\n\n# 生成训练集和验证集\ntrainReviews = data.trainReviews\ntrainLabels = data.trainLabels\nevalReviews = data.evalReviews\nevalLabels = data.evalLabels\n\n# inference data { data label}\ninferenceReviews = data.inferenceReviews\ninferenceLabels = data.inferenceLabels\n\n#print(\"inferenceReviews...\",inferenceReviews)\n#print(\"inferenceLabels...\", inferenceLabels)\n\nwordEmbedding = data.wordEmbedding\n\nclass BiLSTM(object):\n    \"\"\"\n    Bi-LSTM 用于文本分类\n    \"\"\"\n\n    def __init__(self, config, wordEmbedding):\n        # 定义模型的输入\n        self.inputX = tf.placeholder(tf.int32, [None, config.sequenceLength], name=\"inputX\")\n        self.inputY = tf.placeholder(tf.float32, [None, 1], name=\"inputY\")\n\n        self.dropoutKeepProb = tf.placeholder(tf.float32, name=\"dropoutKeepProb\")\n\n        # 定义l2损失\n        l2Loss = tf.constant(0.0)\n\n        # 词嵌入层\n        with tf.name_scope(\"embedding\"):\n            # 利用预训练的词向量初始化词嵌入矩阵\n            self.W = tf.Variable(tf.cast(wordEmbedding, dtype=tf.float32, name=\"word2vec\"), name=\"W\")\n            # 利用词嵌入矩阵将输入的数据中的词转换成词向量，维度[batch_size, sequence_length, embedding_size]\n            self.embeddedWords = tf.nn.embedding_lookup(self.W, self.inputX)\n\n        # 定义两层双向LSTM的模型结构\n        with tf.name_scope(\"Bi-LSTM\"):\n            for idx, hiddenSize in enumerate(config.model.hiddenSizes):\n                with tf.name_scope(\"Bi-LSTM\" + str(idx)):\n                    # 定义前向LSTM结构\n                    lstmFwCell = tf.nn.rnn_cell.DropoutWrapper(\n                        tf.nn.rnn_cell.LSTMCell(num_units=hiddenSize, state_is_tuple=True),\n                        output_keep_prob=self.dropoutKeepProb)\n                    # 定义反向LSTM结构\n                    lstmBwCell = tf.nn.rnn_cell.DropoutWrapper(\n                        tf.nn.rnn_cell.LSTMCell(num_units=hiddenSize, state_is_tuple=True),\n                        output_keep_prob=self.dropoutKeepProb)\n\n                    # 采用动态rnn，可以动态的输入序列的长度，若没有输入，则取序列的全长\n                    # outputs是一个元祖(output_fw, output_bw)，其中两个元素的维度都是[batch_size, max_time, hidden_size],fw和bw的hidden_size一样\n                    # self.current_state ��最终的状态，二元组(state_fw, state_bw)，state_fw=[batch_size, s]，s是一个元祖(h, c)\n\n\n                    outputs, self.current_state = tf.nn.bidirectional_dynamic_rnn(lstmFwCell, lstmBwCell,\n                                                                                  self.embeddedWords, dtype=tf.float32,\n                                                                                  scope=\"bi-lstm\" + str(idx))\n                    #outputs, self.current_state = keras.layers.Bidirectional(keras.layers.RNN(lstmFwCell,lstmBwCell))\n                    # 对outputs中的fw和bw的结果拼接 [batch_size, time_step, hidden_size * 2]\n                    self.embeddedWords = tf.concat(outputs, 2)\n\n        # 去除最后时间步的输出作为全连接的输入\n        finalOutput = self.embeddedWords[:, -1, :]\n\n        outputSize = config.model.hiddenSizes[-1] * 2  # 因为是双向LSTM，最终的输出值是fw和bw的拼接，因此要乘以2\n        output = tf.reshape(finalOutput, [-1, outputSize])  # reshape成全连接层的输入维度\n\n        # 全连接层的输出\n        with tf.name_scope(\"output\"):\n            outputW = tf.get_variable(\n                \"outputW\",\n                shape=[outputSize, 1],\n                initializer=tf.contrib.layers.xavier_initializer())\n\n            outputB = tf.Variable(tf.constant(0.1, shape=[1]), name=\"outputB\")\n            l2Loss += tf.nn.l2_loss(outputW)\n            l2Loss += tf.nn.l2_loss(outputB)\n            self.predictions = tf.nn.xw_plus_b(output, outputW, outputB, name=\"predictions\")\n            self.binaryPreds = tf.cast(tf.greater_equal(self.predictions, 0.5), tf.float32, name=\"binaryPreds\")\n\n        # 计算二元交叉熵损失\n        with tf.name_scope(\"loss\"):\n            losses = tf.nn.sigmoid_cross_entropy_with_logits(logits=self.predictions, labels=self.inputY)\n            self.loss = tf.reduce_mean(losses) + config.model.l2RegLambda * l2Loss\n\n\ndef mainpredict():\n    labels = []\n    graph = tf.Graph()\n    with graph.as_default():\n        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)\n        session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, gpu_options=gpu_options)\n        sess = tf.Session(config=session_conf)\n\n        with sess.as_default():\n            checkpoint_file = tf.train.latest_checkpoint(\"differentmodels/600model\")\n            saver = tf.train.import_meta_graph(\"{}.meta\".format(checkpoint_file))\n            saver.restore(sess, checkpoint_file)\n            #print(\"print all Tensor\",[n.name for n in tf.get_default_graph().as_graph_def().node])\n\n            inputX = graph.get_operation_by_name(\"inputX\").outputs[0]\n            inputY = graph.get_operation_by_name(\"inputY\").outputs[0]\n            dropoutKeepProb = graph.get_operation_by_name(\"dropoutKeepProb\").outputs[0]\n\n            pred_all = graph.get_tensor_by_name(\"output/predictions:0\")\n            pred_binay = graph.get_tensor_by_name(\"output/binaryPreds:0\")\n            for batchEval in norandomnextBatch(inferenceReviews, inferenceLabels,config.testSize):\n                # print(devStep_yu(batchEval[0], batchEval[1]))\n                pred_all_binary = sess.run(pred_binay,feed_dict={inputX: batchEval[0],\n                                                          inputY: batchEval[1],\n                                                          dropoutKeepProb: 1})\n                print(\"pred_all\",pred_all)\n                labels.append(pred_all_binary)\n                print(\"pred_binary\",pred_binay)\n                print(\"pred_all_1\",pred_all_binary)\n            print(\"labels\",labels)\n\nif __name__ == \"__main__\":\n    mainpredict()","sub_path":"SmartContractsDetector/VulBLSTM/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":15103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"55656198","text":"# coding=utf-8\n\nfrom pyosio.osio import OpenSensorsAPI\nimport types\nimport six\nimport unittest\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\nclass OSIOAPIMessagesTests(unittest.TestCase):\n\n    def setUp(self):\n        # add your test case\n        USER_ID, API_KEY = None, None\n        self._api = OpenSensorsAPI(USER_ID, API_KEY)\n\n    def test_get_device_msgs(self):\n        # GET /v1/messages/device/{client-id}\n        logger.info('TEST: GET /v1/messages/device/{client-id}')\n        # add your test case\n        TEST_DEVICE_ID = None\n        self.assertNotEqual(self._api.get_device_msgs(TEST_DEVICE_ID), None)\n\n    def test_get_topic_msgs(self):\n        # GET /v1/messages/topic/{topic}\n        logger.info('TEST: GET /v1/messages/topic/{topic}')\n        # add your test case\n        TEST_TOPIC = None\n        self.assertNotEqual(self._api.get_topic_msgs(TEST_TOPIC), None)\n\n    def test_get_user_msgs(self):\n        # GET /v1/messages/user/{user-id}\n        logger.info('TEST: GET /v1/messages/user/{user-id}')\n        # add your test case\n        USER_ID = None\n        self.assertNotEqual(self._api.get_user_msgs(USER_ID), None)\n\n    def test_get_pub_dataset_msgs(self):\n        # GET /v2/public/messages/dataset/{dataset-id}\n        logger.info('TEST: GET /v2/public/messages/dataset/{dataset-id}')\n        # add your test case\n        TEST_DATASET = None\n        self.assertNotEqual(self._api.get_pub_dataset_msgs(TEST_DATASET), None)\n\n    def test_get_bulk_pub_dataset_msgs(self):\n        # POST /v2/public/messages/dataset/bulk\n        logger.info('TEST: POST /v2/public/messages/dataset/bulk')\n        # add your test case\n        bulk_msgs = None\n        self.assertNotEqual(\n            self._api.get_bulk_pub_dataset_msgs(bulk_msgs), None)\n\nif __name__ == '__main__':\n    logging.basicConfig(level=logging.INFO)\n    unittest.main()\n","sub_path":"tests/osio_api_messages_tests.py","file_name":"osio_api_messages_tests.py","file_ext":"py","file_size_in_byte":1856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"246418340","text":"#!/usr/bin/env python\n\nimport plotly\nimport plotly\nimport plotly.plotly as py\nimport plotly.graph_objs as go\n\nif __name__ == '__main__':\n    action = []\n    matched_reward = []\n    exp_reward = []\n    actual_reward = []\n    battery = []\n    time = []\n    no_days = 1\n    data_path = '/home/milan/workspace/strands_ws/src/battery_scheduler/data/'\n    # for without RHC - do not activate both at same time\n    t = 0;\n    for i in range(no_days):\n        with open(data_path + 'un_aug11_pb'+ str(i), 'r') as f:\n            for line in f.readlines():\n                if 'time' not in line:\n                    s = line.split(' ')[:-1]\n                    time.append(t)\n                    t = t+1\n                    battery.append(int(s[1]))\n                    matched_reward.append(float(s[4]))\n                    exp_reward.append(float(s[6]))\n                    actual_reward.append(float(s[5]))\n                    action.append(s[3])\n\n\n    # with RHC - do not activate both at same time\n    # with open(data_path+'rhc_aug3_db', 'r') as f:\n    #     for line in f.readlines():\n    #         if 'time' not in line:\n    #             s = line.split(' ')[:-1]\n    #             time.append(int(s[0]))\n    #             battery.append(int(s[2]))\n    #             matched_reward.append(float(s[4]))\n    #             exp_reward.append(float(s[6]))\n    #             actual_reward.append(float(s[5]))\n    #             action.append(s[3])\n                \n        \n        \n    color1 = []\n    for a in action:\n        if a == 'gather_reward':\n            color1.append('rgba(27,117,20,1)')\n        elif a == 'go_charge':\n            color1.append('rgba(222,16,16,1)')\n        elif a == 'stay_charging':\n            color1.append('rgba(236, 153, 28,1)')\n            \n            \n    color2 = []\n    for a in action:\n        if a == 'gather_reward':\n            color2.append('rgba(27,117,20,0.5)')\n        elif a == 'go_charge':\n            color2.append('rgba(222,16,16,0.5)')\n        elif a == 'stay_charging':\n            color2.append('rgba(236, 153, 28,0.5)')\n    \n    plotly.tools.set_credentials_file(username='MilanMariyaTomy', api_key='wSfqCQnChPdSCqIMGPdp')#username='ThanwiraSiraj', api_key= 'y9AlaR5JI6kYeCml1NG4') \n    data = [go.Bar( x= time, y = actual_reward, marker=dict(color=color1)), go.Bar( x= time, y = matched_reward, marker=dict(color=color2)), go.Scatter(x=time, y= battery), go.Scatter( x= time, y = exp_reward)]\n    \n    fig = go.Figure(data = data)\n    py.plot(fig, filename='un_aug11_pb')\n ","sub_path":"src/process_rhc_plan.py","file_name":"process_rhc_plan.py","file_ext":"py","file_size_in_byte":2520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"60370849","text":"import random\r\nn1 = input('primeiro nome? ')\r\nn2 = input('segundo nome? ')\r\nn3 = input('terceiro nome? ')\r\nn4 = input('quarto nome? ')\r\n\r\nn = random.randint(1,4)\r\n\r\nif (n == 1):\r\n    print(n1)\r\nelif (n == 2):\r\n    print(n2)\r\nelif (n == 3):\r\n    print(n3)\r\nelif (n == 4):\r\n    print(n4)\r\nelse:\r\n    print('Tente novamente.')","sub_path":"PythonTest/Desafio 19 aula8 cev.py","file_name":"Desafio 19 aula8 cev.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"508345768","text":"from src.utils import *\n\nthreshold = 20\n\n\ndef check_gershgorin_circles(A):\n    circles = A.gershgorin_circles()\n    max_dist = 0\n    for cent, rad in circles:\n        max_dist = max(max_dist, abs(cent) + rad)\n    return max_dist > 1\n\n\ndef simple_iteration_method(A, b, eps=global_eps):\n    x = Matrix.random(A.width, 1)\n\n    count_fail_iters = 0\n    big_circles = check_gershgorin_circles(A)\n\n    while (x - A * x - b).norm() >= eps:\n        x_new = A * x + b\n\n        if abs(x.norm() - x_new.norm()) >= 1:\n            count_fail_iters += 1\n        else:\n            count_fail_iters = 0\n\n        x = x_new\n        if count_fail_iters > threshold and big_circles:\n            return 0\n\n    return x\n","sub_path":"src/simple_iteration/simple_iteration_method.py","file_name":"simple_iteration_method.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"27442669","text":"import depot\nimport gui.guiUtils\nimport util.userConfig\nfrom . import zStack\n\nimport threading\nimport time\nimport wx\n\nfrom six import iteritems\n\n## Provided so the UI knows what to call this experiment.\nEXPERIMENT_NAME = 'Example opto script'\n\n\n\n## This experiment is an example of a custom opto script. It is identical\n# to a standard Z-stack experiment except that it turns on and off lights\n# in a configurable timed sequence before running the stack\n# Note that this class inherits from the ZStackExperiment class\n# in experiment/zStack.py; the generateActions() function in that class is\n# what collects the actual data. We just modify the execute() function\n# (inherited from the base Experiment class in experiment/experiment.py) to\n# engage the lights as specified before each rep.\n# See below this class for the ExperimentUI and OptoPanel classes that set\n# up the UI for configuring the experiment settings.\nclass OptoExperiment(zStack.ZStackExperiment):\n    ## \\param lightToTime Dictionary mapping different light handler names to\n    #         lists of [start, stop] times for illuminating them prior to\n    #         running a standard Z-stack.\n    ## \\param lightToIsOnDuringAcquisition Dictionary mapping different light\n    #         handler names to whether or not those lights should be left on while\n    #         taking images in the Z-stack section of the experiment.\n    def __init__(self, lightToSequence, lightToIsOnDuringAcquisition, **kwargs):\n        # Convert from light source names to light handlers.\n        self.lightToSequence = {}\n        for name, sequence in iteritems(lightToSequence):\n            handler = depot.getHandlerWithName(name)\n            self.lightToSequence[handler] = sequence\n        self.lightToIsOnDuringAcquisition = {}\n        for name, isOn in iteritems(lightToIsOnDuringAcquisition):\n            handler = depot.getHandlerWithName(name)\n            self.lightToIsOnDuringAcquisition[handler] = isOn\n        # Call the ZStackExperiment constructor with all of our remaining\n        # parameters, which are in the \"kwargs\" dictionary object.\n        zStack.ZStackExperiment.__init__(self, **kwargs)\n\n\n    ## This function overrides the base execute() function in the Experiment\n    # class.\n    def execute(self):\n        # HACK: replace self.numReps since it's referred to in\n        # Experiment.execute(), and we want to handle repeats at a higher\n        # level.\n        numReps = self.numReps\n        self.numReps = 1\n        for i in range(numReps):\n            if self.shouldAbort:\n                # User aborted experiment.\n                break\n            # Create a new thread for activating each light in parallel.\n            # We could do this singlethreaded, but it would require an\n            # in-depth examination of self.lightToSequence to determine when\n            # to turn on each light.\n            threads = []\n            for light, sequence in iteritems(self.lightToSequence):\n                newThread = threading.Thread(target = self.shineLight,\n                        args = [light, sequence])\n                newThread.start()\n                threads.append(newThread)\n            # Wait for all of our threads to finish.\n            for thread in threads:\n                thread.join()\n\n            # Pre-acquisition illumination is complete; move on to taking\n            # images.\n            # Turn on all lights that want to be left on during acquisition.\n            activatedLights = []\n            for light, shouldBeOn in iteritems(self.lightToIsOnDuringAcquisition):\n                if shouldBeOn:\n                    light.setExposing(True)\n                    activatedLights.append(light)\n            # Invoke our base class' execute() function to collect data.\n            result = zStack.ZStackExperiment.execute(self)\n            # Turn off the triggering lights.\n            for light in activatedLights:\n                light.setExposing(False)\n        return result\n\n\n    ## Given a light source and a list of [start, stop] pairs when the light\n    # should be enabled/disabled, turn the light on and off according to the\n    # sequence.\n    def shineLight(self, light, sequence):\n        curTime = 0\n        for start, stop in sequence:\n            # Sleep until we should start illumination.\n            time.sleep(start - curTime)\n            # Turn the light on.\n            light.setExposing(True)\n            # Sleep until we should turn the light off.\n            time.sleep(stop - start)\n            # Turn the light off.\n            light.setExposing(False)\n            curTime = stop\n\n\n\n## A consistent name to use to refer to the experiment class itself.\nEXPERIMENT_CLASS = OptoExperiment\n\n\n\n## Generate the UI for special parameters used by this experiment.\nclass ExperimentUI(wx.Panel):\n    def __init__(self, parent, configKey):\n        wx.Panel.__init__(self, parent = parent)\n\n        self.configKey = configKey\n\n        ## List of all light sources available for use.\n        self.allLights = depot.getHandlersOfType(depot.LIGHT_TOGGLE)\n\n        ## User config settings.\n        self.settings = self.loadSettings()\n\n        sizer = wx.BoxSizer(wx.VERTICAL)\n        sizer.Add(wx.StaticText(self, -1, \"Opto light exposure sequences:\"),\n                0, wx.ALL, 5)\n\n        # Generate a header to label the controls in each OptoPanel. Sizes\n        # for each element are derived from the OptoPanel's layout.\n        headerSizer = wx.BoxSizer(wx.HORIZONTAL)\n        for text, size in [('Light', 60), ('', 80), ('', 80),\n                ('Sequence', 200), ('Start', 40), ('End', 40), ('', 80),\n                ('On during acquisition', 200)]:\n            headerSizer.Add(wx.StaticText(self, -1, text, size = (size, -1)))\n\n        sizer.Add(headerSizer)\n\n        ## Maps light handlers to OptoPanel instances.\n        self.lightToPanel = {}\n        defaultVals = []\n        for light in self.allLights:\n            sequence = self.settings['lightToSequence'].get(light.name, [])\n            doesStartOn = self.settings['lightToIsOnDuringAcquisition'].get(light.name, False)\n            panel = OptoPanel(self, light, sequence, doesStartOn)\n            self.lightToPanel[light] = panel\n            sizer.Add(panel)\n        \n        self.SetSizerAndFit(sizer)\n\n\n    ## Return a mapping of light source names to lists of illumination sequences.\n    # e.g. {\"650 LED\": [[0, 20], [30, 50]]} to indicate that the 650\n    # LED should be on from 0 to 20 seconds, and from 30 to 50 seconds.\n    # We use handler names instead of handlers here so that they can be\n    # serialized (when saving experiment settings) cleanly.\n    def getLightToSequence(self):\n        return dict([(l.name, self.lightToPanel[l].getSequence()) for l in self.allLights])\n\n\n    ## Returns a mapping of light source names to whether or not those lights\n    # should be left on during image acquisition (independently of whether\n    # or not those lights are being used as traditional imaging lights).\n    # We use handler names instead of handlers here so that they can be\n    # serialized (when saving experiment settings) cleanly.\n    def getLightToIsOnDuringAcquisition(self):\n        return dict([(l.name, self.lightToPanel[l].getIsOnDuringAcquisition()) for l in self.allLights])\n\n\n    ## Given a parameters dict (parameter name to value) to hand to the\n    # experiment instance, augment them with our special parameters.\n    def augmentParams(self, params, shouldSave = True):\n        if shouldSave:\n            self.saveSettings()\n        params['lightToSequence'] = self.getLightToSequence()\n        params['lightToIsOnDuringAcquisition'] = self.getLightToIsOnDuringAcquisition()\n        return params\n\n\n    ## Load the saved experiment settings, if any.\n    def loadSettings(self):\n        return util.userConfig.getValue(\n                self.configKey + 'optoExampleSettings',\n                default = {\n                    'lightToSequence': {},\n                    'lightToIsOnDuringAcquisition': {}\n                }\n        )\n\n\n    ## Generate a dict of our settings.\n    def getSettingsDict(self):\n        return self.augmentParams({}, shouldSave = False)\n\n\n    ## Save the current experiment settings to config.\n    def saveSettings(self, settings = None):\n        if settings is None:\n            settings = self.getSettingsDict()\n        util.userConfig.setValue(\n                self.configKey + 'optoExampleSettings', settings\n        )\n\n\n\n## This panel lets the user define a sequence of illumination sequences (on and\n# off) for a specific light.\nclass OptoPanel(wx.Panel):\n    def __init__(self, parent, light, sequence, doesStartOn):\n        wx.Panel.__init__(self, parent)\n        self.light = light\n\n        ## List of [start, stop] lists, indicating times when the light\n        # should be turned on or off.\n        self.sequence = sequence\n\n        sizer = wx.BoxSizer(wx.HORIZONTAL)\n        sizer.Add(wx.StaticText(self, -1, light.name + ': ',\n                size = (60, -1)))\n        \n        button = wx.Button(self, -1, 'Clear', size = (80, -1))\n        button.Bind(wx.EVT_BUTTON, self.onClear)\n        button.SetToolTip(wx.ToolTip(\"Remove all entries for this light source\"))\n        sizer.Add(button)\n\n        button = wx.Button(self, -1, 'Delete last', size = (80, -1))\n        button.Bind(wx.EVT_BUTTON, self.onDeleteLast)\n        button.SetToolTip(wx.ToolTip(\"Remove the last entry for this light source\"))\n        sizer.Add(button)\n        \n        self.sequenceText = wx.TextCtrl(self, -1,\n                size = (200, -1), style = wx.TE_READONLY)\n        self.sequenceText.SetToolTip(wx.ToolTip(\"Displays the sequence of on/off actions this light source will perform prior to starting the acquisition.\"))\n        sizer.Add(self.sequenceText)\n\n        self.startText = wx.TextCtrl(self, -1, size = (40, -1))\n        self.startText.SetToolTip(wx.ToolTip(\n                'Time, in seconds, at which to start illumination'))\n        sizer.Add(self.startText)\n\n        self.stopText = wx.TextCtrl(self, -1, size = (40, -1))\n        self.startText.SetToolTip(wx.ToolTip(\n                'Time, in seconds, at which to stop illumination'))\n        sizer.Add(self.stopText)\n\n        button = wx.Button(self, -1, 'Add', size = (80, -1))\n        button.Bind(wx.EVT_BUTTON, self.onAdd)\n        button.SetToolTip(wx.ToolTip(\"Add this start/stop pair to the sequence of actions.\"))\n        sizer.Add(button)\n\n        self.isOnDuringAcquisition = wx.CheckBox(self)\n        self.isOnDuringAcquisition.SetToolTip(wx.ToolTip(\"If checked, this light source will continuously shine on the sample during acquisition.\"))\n        self.isOnDuringAcquisition.SetValue(doesStartOn)\n        sizer.Add(self.isOnDuringAcquisition)\n\n        self.SetSizerAndFit(sizer)\n        self.updateDisplay()\n\n\n    ## Clear all illumination settings.\n    def onClear(self, event = None):\n        self.sequence = []\n        self.updateDisplay()\n\n\n    ## Delete the last entry in the illumination settings.\n    def onDeleteLast(self, event = None):\n        self.sequence = self.sequence[:-1]\n        self.updateDisplay()\n\n\n    ## Add a new entry to the illumination settings, merging entries as\n    # necessary.\n    def onAdd(self, event = None):\n        start = gui.guiUtils.tryParseNum(self.startText, float)\n        stop = gui.guiUtils.tryParseNum(self.stopText, float)\n        if start >= stop:\n            # No-op; easiest way for this to happen is for the stop text to\n            # have no value.\n            return\n        # Go through our list of sequences and see if we need to merge them\n        # together.\n        self.sequence.append([start, stop])\n        indexToDelete = len(self.sequence) - 1\n        didChange = True\n        while didChange:\n            didChange = False\n            for i, (altStart, altStop) in enumerate(self.sequence):\n                # Check for overlap.\n                if ((start < altStart and stop > altStart) or\n                        (stop > altStop and start < altStop)):\n                    # Merge the two entries.\n                    start = min(start, altStart)\n                    stop = max(stop, altStop)\n                    self.sequence[i] = [start, stop]\n                    didChange = True\n                    # Destroy the old sequence as it's been merged with\n                    # this one.\n                    del self.sequence[indexToDelete]\n                    indexToDelete = i\n                    break\n\n        # Sort the sequence by start time.\n        self.sequence.sort(key = lambda s: s[0])\n        self.startText.SetValue('')\n        self.stopText.SetValue('')\n        self.updateDisplay()\n\n\n    ## Update our textual display of the illumination settings.\n    def updateDisplay(self):\n        text = ', '.join(['(%.2f, %.2f)' % (start, stop) for start, stop in self.sequence])\n        self.sequenceText.SetValue(text)\n\n\n    ## Simple accessor.\n    def getSequence(self):\n        return self.sequence\n\n\n    ## Simple accessor.\n    def getIsOnDuringAcquisition(self):\n        return self.isOnDuringAcquisition.GetValue()\n\n","sub_path":"experiment/optoScriptExample.py","file_name":"optoScriptExample.py","file_ext":"py","file_size_in_byte":13090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"597021069","text":"\n# from datetime import datetime\nfrom rest_framework.generics import RetrieveAPIView\nfrom rest_framework.response import Response\nfrom rest_framework import serializers, status\nfrom django.shortcuts import get_object_or_404\n# Create your views here.\nfrom rest_framework.permissions import AllowAny\n\n\nfrom django.contrib.auth import authenticate\nfrom django.contrib.auth.models import update_last_login\nfrom rest_framework import serializers\nfrom rest_framework_jwt.settings import api_settings\nfrom django.contrib import messages\nfrom authentication.models import *\n\nJWT_PAYLOAD_HANDLER = api_settings.JWT_PAYLOAD_HANDLER\nJWT_ENCODE_HANDLER = api_settings.JWT_ENCODE_HANDLER\n\nclass UserLoginSerializer(serializers.Serializer):\n    permission_classes = (AllowAny,)\n    email = serializers.CharField(max_length=255)\n    password = serializers.CharField(max_length=128, write_only=True)\n    token = serializers.CharField(max_length=255, read_only=True)\n    \n    def validate(self, data):\n        \n        if self.context.get('request').user.is_authenticated:\n            raise serializers.ValidationError({\"message\":\"already authenticated\"})\n        \n        email = data.get(\"email\", None)\n        password = data.get(\"password\", None)\n        user = authenticate(email=email, password=password)\n        if user is None:\n            raise serializers.ValidationError({\"message\":\"Invalid credentials\"})\n        try:\n            payload = JWT_PAYLOAD_HANDLER(user)\n            jwt_token = JWT_ENCODE_HANDLER(payload)\n            update_last_login(None, user)\n        except Account.DoesNotExist:\n            raise serializers.ValidationError({\"message\":\"Invalid credentials\"})\n        return {\n            'email':user.email,\n            'token': jwt_token\n        }\n\nimport datetime\nfrom datetime import timedelta\nclass UserLoginView(RetrieveAPIView):\n\n    permission_classes = (AllowAny,)\n    serializer_class = UserLoginSerializer\n\n    def post(self, request):\n        serializer = self.get_serializer(data=request.data)\n    \n        if request.user.is_authenticated:\n            \n            return Response({\"message\":\"User is already logged in\"})\n        if serializer.is_valid():\n            token = serializer.data['token']\n            response = {\n                'success' : 'True',\n                'status code' : status.HTTP_200_OK,\n                'message': 'User logged in  successfully',\n                'token' : serializer.data['token'],\n                }\n            \n            response = Response()\n            response.set_cookie(key='c_uid', value=token,httponly=True,)\n            response.data = {\n                'message': 'User logged in  successfully',\n            }\n            return response\n        print(serializer.errors['message'][0])\n        return Response({'message': serializer.errors['message'][0]})\n\n","sub_path":"maffilebackend/authentication/api/userlogin.py","file_name":"userlogin.py","file_ext":"py","file_size_in_byte":2839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"40564010","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport os\nfrom logzero import logger\nfrom zipfile import ZipFile\nimport geopandas as gp\n\noverall=[]\ndef vcount(shpfile):\n    df = gp.read_file(shpfile)\n    if not df.size==0:\n        for i, row in df.iterrows():\n            # It's better to check if multigeometry\n            multi = row.geometry.type.startswith(\"Multi\")\n            if multi:\n                n = 0\n                # iterate over all parts of multigeometry\n                for part in row.geometry:\n                    n += len(part.exterior.coords)\n            else: # if single geometry like point, linestring or polygon\n                n = len(row.geometry.exterior.coords)\n\n            #print('Total vertices: {:,}'.format(n))\n            overall.append(n)\n        if all(i < 1000000 for i in overall)==True:\n            return sum(overall)\n        else:\n            logger.warning(shpfile+' has overall max vertex of '+str(max(overall))+' with max allowed 1000000 ingest might fail')\n            return sum(overall)\n            #print('Total vertices per feature exceeded max. Overall vertices: {}'.format(sum(overall)))\n            #return sum(overall)\n    else:\n        return df.size\n\nex = ['.shp', '.prj', '.dbf', '.shx']\n\nfile_paths = []\ni=1\ndef zipshape(directory,export):\n    for (root, directories, files) in os.walk(directory):\n        for filename in files:\n            if filename.endswith('.shp'):\n                #print(os.path.join(directory,filename))\n                #print(vcount(os.path.join(directory,filename)))\n                if vcount(os.path.join(directory,filename))>0:\n                    file_paths=[]\n                    filebase = filename.split('.')[0]\n                    try:\n                        for things in ex:\n                            if os.path.exists(os.path.join(root, filebase+things)):\n                                filepath = os.path.join(root, filebase+things)\n                                #print(filepath)\n                                file_paths.append(filepath)\n                        os.chdir(export)\n                        if not os.path.exists(filebase+'.zip') and int(len(file_paths))==int(4):\n                            with ZipFile(filebase+'.zip','w') as zip:\n                                print(\"Creating zipped folder \"+str(filebase)+'.zip'+' at '+str(export))\n                                # writing each file one by one\n                                for file in file_paths:\n                                    fname=os.path.basename(file)\n                                    zip.write(file,fname)\n                        else:\n                            print('File already exists: '+str(filebase+'.zip')+' SKIPPING')\n                    except:\n                        pass\n#get_all_file_paths(directory=r\"C:\\Users\\samapriya\\Downloads\\nexgengrid\",export=r'D:\\Library')\n","sub_path":"geeup/zipfiles.py","file_name":"zipfiles.py","file_ext":"py","file_size_in_byte":2865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"308549549","text":"# -*- coding: utf-8 -*-\nfrom __future__ import division, print_function, absolute_import\n\nfrom flask import Flask, jsonify\nfrom flask.ext.mongoengine import MongoEngine\n\n# below is optional: if serving templates from server, compress enables gzip\n# from flask.ext.compress import Compress\nfrom app.lib.exception import *\n\ndb = MongoEngine()\n\ndef create_app():\n    app = Flask(__name__)\n    app.config.from_object('config')\n\n    db.init_app(app)\n\n    # for gzip\n    # Compress().init_app(app)\n\n    current_api_version = app.config['API_SERVER_VERSION']\n\n    # import blueprints\n    from app.views import main, user, accommodation\n    app.register_blueprint(main.blueprint, url_prefix='')\n    def prefix_api(route, latest_version=True):\n        if latest_version:\n            return \"/api/%s\" % route\n        else:\n            return \"/api/v%d/%s\" % (current_api_version, route)\n    # register API blueprints where URL looks like: 'api/foobar' or 'api/v2/foobar' depending on deployed version\n    # to change version number, edit config.py\n    app.register_blueprint(user.blueprint, url_prefix=prefix_api('users'))\n    app.register_blueprint(user.blueprint, url_prefix=prefix_api('users', latest_version=False))\n    app.register_blueprint(accommodation.blueprint, url_prefix=prefix_api('apartments'))\n    app.register_blueprint(accommodation.blueprint, url_prefix=prefix_api('apartments', latest_version=False))\n\n\n    @app.before_request\n    def _before_request():\n        pass\n\n    @app.errorhandler(InvalidAPIUsage)\n    def handle_invalid_api_usage(error):\n        response = jsonify(error.to_dict())\n        response.status_code = error.status_code\n        return response\n\n\n    @app.errorhandler(InvalidMethod)\n    def handle_invalid_method(error):\n        response = jsonify(error.to_dict())\n        response.status_code = error.status_code\n        return response\n\n    return app","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"41989527","text":"import Adafruit_PCA9685 # Import the library used to communicate with PCA9685\nimport time\n\npwm = Adafruit_PCA9685.PCA9685() # Instantiate the object used to control the PWM\npwm.set_pwm_freq(50) # Set the frequency of the PWM signal\n\n# Tutorial has Servo Port 3 - there are three servos and the ports start at 0? Changed to Port 1\n\nwhile 1: # Make the servo connected to the No. 1 servo port on the Motor HAT drive board reciprocate\n  pwm.set_pwm(1, 0, 300)\n  time.sleep(1)\n  pwm.set_pwm(1, 0, 400)\n  time.sleep(1)\n","sub_path":"tutorial/servo_tutorial.py","file_name":"servo_tutorial.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"124798798","text":"\ndef ListPrimenumberUpto(number):\n  for n in range(2,number):\n    for x in range(2,n):\n      if n%x == 0:\n        #print(f\"{n} is a prime\")\n        break\n    else:\n        print(f\"{n} is a prime\")\n\ndef CheckIfPrime(number):\n  for x in range(2,number):\n    if number%x == 0:\n      break\n  else:\n      return True\n\nnum = 44\nif(CheckIfPrime(num)):\n  print(f\"{num} is a pime\")\nelse:\n  print(f\"{num} is not a prime\")\n","sub_path":"PrimeNumbers.py","file_name":"PrimeNumbers.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"321966","text":"import numpy as np\nimport config\n# Get outputfile name, N, and N_Freeze from config.py\noutputFileName = config.outputFileName\ninputFileName = config.inputFileName\n\nN = config.N\nN_Freeze = config.N_Freeze\nunfrozen = N-N_Freeze\n\nimport OFProc\nimport IFProc\n\ndef getHessian():\n    # Returns the Cartesian Hessian (Ha/Bohr**2) as a numpy array, is read from fort.7\n    with open(outputFileName[:-4]+\".7\") as f:\n        elements = f.readlines()\n    # Strip \\n\n    elements = map(str.strip, elements)\n    # Split up each row into list\n    elements = map(lambda x: x.split(), elements)\n    # Flatten list of list\n    elements = [y for x in elements for y in x]\n    # Replace D with e\n    elements = [w.replace('D', 'e') for w in elements]\n    # Cast as floats\n    elements = [float(w) for w in elements]\n    # Remove first 3N elements, these are not part of the Hessian\n    del elements[0:3 * config.N]\n    # Size of LT matrix based on number of elements\n    arrSize = int(-0.5 + (np.sqrt(1 + 8 * len(elements)) / 2))\n\n    # List -> LT matrix\n    indices = np.tril_indices(arrSize)\n    H = np.zeros((arrSize, arrSize))\n    H[indices] = elements\n\n    # Matrix is symmetric so fill it out\n    H = H + np.transpose(H) - np.diag(np.diag(H))\n    return H\n\ndef getConstrainedFreq():\n    H = getHessian()\n    # Take minor of the Hessian, only leaving entries from the non-peripheral atoms\n    H = H[0:3 * unfrozen, 0:3 * unfrozen]\n\n    # Reconstruct full Hessian. Set diagonal block of peripheral atoms to 10^-8 to avoid spurious imaginary freqs.\n    # Set off diagonal blocks to 0. Epsilon is a temporary matrix\n    epsilon = np.diag(10. ** (-8) * np.ones(3 * config.N))\n\n    # Insert Hessian minor of chemical system in.\n    epsilon[:3 * unfrozen, :3 * unfrozen] = H\n    # Rename and delete epsilon\n    H = epsilon\n    del epsilon\n    # Conversion factor from Ha/bohr**2 to amu/s**2\n    convFactor = 4.3597439e-18 / (0.5291772086e-10) ** 2 / 1.66053878e-27\n    H = H * convFactor\n    # Get masses in amu\n    masses = OFProc.getMasses()\n\n    # Vector of N masses to 3N masses\n    masses = [x for pair in zip(masses, masses, masses) for x in pair]\n    MminusHalf = np.diag(np.power(masses, -0.5))\n\n    # Mass weight matrix\n    H = np.dot(np.dot(MminusHalf, H), MminusHalf)\n    # Get the eigenvalues\n    evals = np.linalg.eigvals(H)\n\n    # convert to wavenumbers\n    allFreq = [np.sign(w) * np.sqrt(np.abs(w) / (4. * 3.14159 ** 2 * (2.99792458e10) ** 2)) for w in evals]\n    signList = np.sign(allFreq)\n    # Sort by magnitude so the smallest freq's get put to the bottom regardless of size\n    # However, the permutations done by sorting also need to be carried over to the list of signs to preserve which are\n    # imaginary\n    sortedFreq, allSign = zip(*sorted(zip(np.abs(allFreq), signList)))\n    allFreq = np.multiply(sortedFreq, allSign)\n\n    # Sort the frequencies into 3 contributions\n    # constraintFreq: frequencies of the constrained peripheral atoms that will be pruned\n    # chemFreq: frequencies of the system excluding peripheral atoms(where the chemistry happens)\n    # chemRotTrans: the lowest 6 frequencies that correspond to rotations and vibrations\n    # chemVib: 3n-6 vibrational modes, where n is # of non constrained atoms\n    constraintFreq = allFreq[:3 * config.N_Freeze]\n    chemFreq = allFreq[3 * config.N_Freeze:]\n    chemRotTrans = chemFreq[:6]\n    chemVibs = chemFreq[6:]\n\n    # Lastly, count number of imaginary frequencies\n    newNImag = 0\n    for x in allSign:\n        if x == -1:\n            newNImag += 1\n\n    return constraintFreq, chemRotTrans, chemVibs, newNImag\n\ndef getConstrainedThermochemistry(vibFreqs):\n    # freqList = getFreq()\n    lenI = len(vibFreqs)\n    # Remove imaginary (negative) frequencies\n    freqList = [x for x in vibFreqs if x > 0]\n    lenF = len(freqList)\n    ImagFreqIgnored = lenF-lenI\n    T = OFProc.getTemp()\n\n    vibTemp = [1.98644568E-25 * 100 / 1.38064852E-23 * w for w in freqList]\n    R = 8.314\n    kBT = T * 1.38064852E-23 / 4.35974465E-18\n    # Calculate thermal corrections, ignoring partition functions from translation and rotation, and vibration of heavy atoms\n    q_elec = OFProc.getLog_q(2)\n\n    q_rot = 0\n    q_trans = 0\n\n    # Translation\n    St = (q_trans + 5/2.)*R\n    Et = 3/2. *R*T\n\n    # Rotation\n    Sr = (q_rot + 3 / 2.) * R\n    Er = 3/2.*R*T\n\n    # Electronic (both are 0)\n    Se = R*q_elec\n    Ee = 0\n\n    # Vibration\n    Svk = [theta/T/(np.exp(theta/T)-1) - np.log(1-np.exp(-abs(theta)/T)) for theta in vibTemp]\n    Evk = [theta * (0.5 + 1. / (np.exp(theta / T) - 1.)) for theta in vibTemp]\n\n    Sv = R * sum(Svk)\n    Ev = R * sum(Evk)\n    # Thermal Corrections in Ha\n    Ecorrection = (Et + Er + Ee + Ev) / 1000 / 2625.499638\n    # Ha/K\n    S = (St + Sr + Se + Sv) / 1000 / 2625.499638\n\n    E = OFProc.getZeroPtEnergy()\n\n    Ecorrected = E + Ecorrection\n\n    constrainedH = Ecorrected + kBT\n    constrainedTS = T * S\n    constrainedG = constrainedH - constrainedTS\n    return constrainedG,constrainedH,constrainedTS,ImagFreqIgnored","sub_path":"thermoChem.py","file_name":"thermoChem.py","file_ext":"py","file_size_in_byte":5008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"450788666","text":"def retention_cohort(\r\n        df_,\r\n        invoicedate='invoicedate',\r\n        Quantity='Quantity',\r\n        price='InvoiceAmount',\r\n        CustomerId='DistributorId'\r\n                    ):\r\n    \"\"\"\r\n    :param df_: dataframe containing the transaction\r\n                data with columns ['invoicedate', 'Quantity'\r\n                'price', 'CustomerId']\r\n    :param invoicedate:\r\n    :param Quantity:\r\n    :param price:\r\n    :param CustomerId:\r\n    :return: a dataframe with monthly retention percentage\r\n\r\n    \"\"\"\r\n    df_[invoicedate] = pd.to_datetime(df_[invoicedate], errors='coerce')\r\n    df_.dropna(inplace=True)\r\n    df_['invoice_year_month'] = df_[invoicedate].map(lambda date: date.year * 100 + date.month)\r\n    df_[\"revenue\"] = df_[Quantity] * df_[price]\r\n    df_cust_rev_monthly = df_.groupby([CustomerId, \"invoice_year_month\"])[\"revenue\"].sum().reset_index()\r\n    retent_df = pd.crosstab(df_cust_rev_monthly[CustomerId], df_cust_rev_monthly[\"invoice_year_month\"]).reset_index()\r\n    new_column_names = ['m_' + str(column) for column in retent_df.columns]\r\n    retent_df.columns = new_column_names\r\n    months = df_[\"invoice_year_month\"].unique()\r\n    months.sort()\r\n\r\n    import numpy as np\r\n    retent_array = []\r\n    for i in range(0, len(months)):\r\n        retent_data = {}\r\n        selected_month = months[i]\r\n        prev_months = months[:i]\r\n        next_months = months[i + 1:]\r\n        for prev_month in prev_months:\r\n            retent_data[prev_month] = np.nan\r\n\r\n        total_user_count = retent_df['m_' + str(selected_month)].sum()\r\n        retent_data[\"total_user_count\"] = total_user_count\r\n        retent_data[selected_month] = 1\r\n\r\n        query = \"{} >0 \".format('m_' + str(selected_month))\r\n\r\n        for next_month in next_months:\r\n            query = query + \" and {} > 0\".format('m_' + str(next_month))\r\n            retent_data[next_month] = np.round(retent_df.query(query)['m_' + str(next_month)].sum() / total_user_count,\r\n                                               2)\r\n        retent_array.append(retent_data)\r\n\r\n    retent_cohort_df = pd.DataFrame(retent_array)\r\n    retent_cohort_df.index = months[:]\r\n\r\n    return retent_cohort_df\r\n","sub_path":"cohort_retention.py","file_name":"cohort_retention.py","file_ext":"py","file_size_in_byte":2181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"77514143","text":"# -*- coding: utf-8 -*-\n\"\"\"This file is a Bjx spider created on top of the ATSSpider\nscrapy crawl bjx -a url=\"http://hbjob.bjx.com.cn/company.aspx?id=8452635\" -a mining_job_id=999 -a iteration=1 -a extract=1\nsample url:\n    http://hbjob.bjx.com.cn/company.aspx?id=8452635\n    http://gfjob.bjx.com.cn/company.aspx?id=8467494\n    http://fdjob.bjx.com.cn/company.aspx?id=8462105\n\"\"\"\nfrom re import compile\n\nfrom scrapy.selector import Selector\nfrom scrapy.http import Request\n\nfrom brightcorp.base.atsspiders import ATSSpider\nfrom brightcorp.items import BrightcorpItemLoader\nfrom brightcorp.processors import ConvertDateString, Prefix\n\n\nclass Bjx(ATSSpider):\n\n    name = \"bjx\"\n    ref_re = compile(\"id=(\\d+)\")\n    subdomain_re = compile(\"\\/\\/(.*?)\\.\")\n    subdomain = \"\"\n\n    def __init__(self, *args, **kwargs):\n        super(Bjx, self).__init__(*args, **kwargs)\n        if 'url' in kwargs:\n            sub_res = self.subdomain_re.search(kwargs['url'])\n            if sub_res:\n                self.subdomain = sub_res.group(1)\n\n    def parse(self, response):\n        sel = Selector(response)\n        self.company = sel.xpath(\"//h1[@class='text_title2']/text()\").extract()\n        jobs = sel.xpath(\"//h1[@id='zp']/following-sibling::div[1]/table/tr\")\n        for job in jobs:\n            job_link = job.xpath(\"./td[1]/p/a/@href\").extract()\n            if job_link:\n                meta = {\n                    'title': job.xpath(\"./td[1]/p/a/text()\").extract(),\n                    'location': job.xpath(\"./td[2]/text()\").extract(),\n                    'experiencerequirements': job.xpath(\n                        \"./td[3]/text()\"\n                        ).extract(),\n                    'educationrequirements': job.xpath(\n                        \"./td[4]/text()\"\n                        ).extract(),\n                    'date': job.xpath(\"./td[6]/text()\").extract(),\n                }\n                yield Request(\n                    job_link[0], meta=meta, callback=self.parse_job_callback()\n                    )\n\n    def parse_job(self, response):\n        loader = BrightcorpItemLoader(response=response)\n        loader.add_value('url', response.url)\n        loader.add_value('title', response.meta['title'])\n        loader.add_value('location', response.meta['location'])\n        loader.add_value('company', self.company)\n        loader.add_value(\n            'experiencerequirements', response.meta['experiencerequirements']\n            )\n        loader.add_value(\n            'educationrequirements', response.meta['educationrequirements']\n            )\n        loader.add_value(\n            'date', response.meta['date'], ConvertDateString(\"%Y-%m-%d\")\n            )\n        loader.add_value(\n            'referencenumber', response.url,\n            Prefix(\"%s-%s-\" % (self.name, self.subdomain)), re=self.ref_re\n            )\n        loader.add_xpath(\n            'baseSalary',\n            \"//th[contains(text(),'%s')]/following-sibling::td/text()\" % unicode('薪水范围：', 'utf-8')\n            )\n        loader.add_xpath(\n            'description',\n            \"//h3[contains(text(),'%s')]/following-sibling::node()\"\n            % unicode(\"职位描述：\", 'utf-8')\n            )\n        yield loader.load_item()\n","sub_path":"brightcorp/brightcorp/spiders/bjx.py","file_name":"bjx.py","file_ext":"py","file_size_in_byte":3228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"468620731","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('rtiapp', '0032_message'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='message',\n            name='receiver',\n            field=models.ForeignKey(related_name='receiver', default=1, to=settings.AUTH_USER_MODEL),\n            preserve_default=False,\n        ),\n        migrations.AlterField(\n            model_name='message',\n            name='sender',\n            field=models.ForeignKey(related_name='sender', default=1, to=settings.AUTH_USER_MODEL),\n            preserve_default=False,\n        ),\n    ]\n","sub_path":"rtiapp/migrations/0033_auto_20151230_2249.py","file_name":"0033_auto_20151230_2249.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"151538610","text":"def selectDistinct(numbers):\r\n    output = []\r\n    for n in numbers:\r\n        if numbers.count(n) == 1:\r\n            output.append(n)\r\n    return output\r\n\r\ntest_cases = (\r\n    [1, 2, 3, 4],\r\n    [1, 1, 2, 2],\r\n    [1, 1, 1, 1],\r\n    [1, 1, 2, 2, 3]\r\n)\r\n\r\nfor numbers in test_cases:\r\n    print(f'{numbers} -> {selectDistinct(numbers)}')","sub_path":"04-Subroutines/zad37.py","file_name":"zad37.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"58034021","text":"#Tasks 2 and 3\nimport cv2\nimport numpy\n\nimage = cv2.imread(\"text.bmp\", 0)\n\ncv2.imshow(\"temp\", image)\ncv2.waitKey(0)\n\nimage = cv2.GaussianBlur(image, (3, 3), 0, 0)\nimage = cv2.Laplacian(image, 0)            #cv2.CV_64F)\nimage = cv2.convertScaleAbs(image)\n(ret, image) = cv2.threshold(image, 10, 255, cv2.THRESH_BINARY)\n\n\nkernel = numpy.ones((3, 5), numpy.uint8)\nimage = cv2.dilate(image, kernel, iterations=1)\n\nimage = cv2.erode(image, kernel, iterations=1)\n\ncontours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n\naccurateContImage = numpy.zeros(image.shape, numpy.uint8)\nfor contour in contours:\n    cv2.drawContours(accurateContImage, [contour], 0, (255, 255, 255), 0)\n\nheight, width = image.shape\n\noutput1 = numpy.zeros((height - 2, width - 2), numpy.uint8)\ncv2.floodFill(output1, accurateContImage, (0, 0), (255, 255, 255))\n\noutput1 = 255 - output1\n\ncv2.imshow(\"AccurateBoarding\", output1)\ncv2.waitKey(0)\n\ncontImage = numpy.zeros(image.shape, numpy.uint8)\nfor contour in contours:\n    x, y, w, h = cv2.boundingRect(contour)\n    cv2.rectangle(contImage, (x, y), (x + w, y + h), (255, 255, 255), 2)\noutput2 = numpy.zeros((height - 2, width - 2), numpy.uint8)\ncv2.floodFill(output2, contImage, (0, 0), (255, 255, 255))\n\noutput2 = 255 - output2\n\ncv2.imshow(\"output\", output2)\ncv2.waitKey(0)\n\ncv2.imwrite(\"output.jpg\", output2)\n","sub_path":"task2.py","file_name":"task2.py","file_ext":"py","file_size_in_byte":1360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"483641952","text":"import numpy as np\nimport collections\n\nimport os\nimport pickle\n\nfrom rlcard.utils.utils import *\n\nclass BRAgent():\n    ''' Implement CFR algorithm\n    '''\n\n    def __init__(self, env, policy):\n        ''' Initilize Agent\n\n        Args:\n            env (Env): Env class\n        '''\n        self.use_raw = False\n        self.env = env\n        self._num_of_player = env.player_num\n        if self._num_of_player > 2:\n            raise \"Best Response only for Two-player games\"\n\n        # A policy is a dict state_str -> action probabilities\n        self.opponent_policy = policy\n        self.average_policy = collections.defaultdict(np.array)\n\n        # Regret is a dict state_str -> action regrets\n        self.regrets = collections.defaultdict(np.array)\n\n        self.iteration = 0\n\n    def traverse_tree(self, probs, player_id):\n        ''' Traverse the game tree, get information set\n\n        Args:\n            probs: The reach probability of the current node\n            player_id: The player to update the value\n\n        Returns:\n            state_utilities (list): The expected utilities for all the players\n        '''\n        if self.env.is_over():\n            return self.env.get_payoffs()\n\n        current_player = self.env.get_player_id()\n\n        obs, legal_actions = self.get_state(current_player)\n        state = self.env.get_state(current_player)\n        action_probs = self.action_probs(state, self.opponent_policy)\n\n        for action in legal_actions:\n            action_prob = action_probs[action]\n            new_probs = probs.copy()\n            new_probs[current_player] *= action_prob\n\n            # Keep traversing the child state\n            self.env.step(action)\n            utility = self.traverse_tree(new_probs, player_id)\n            self.env.step_back()\n\n        # If it is current player, we record the policy and compute regret\n        player_prob = probs[current_player]\n        counterfactual_prob = (np.prod(probs[:current_player]) *\n                                np.prod(probs[current_player + 1:]))\n        s = self.get_state(current_player)\n        self.infosets[obs].append((s, counterfactual_prob))\n\n    def value(self, curr_player, state, this_player):\n        \"\"\"Returns the value of the specified state to the best-responder.\"\"\"\n        if self.env.is_over():\n            #print(self.env.get_payoffs())\n            return self.env.get_payoffs()\n        elif this_player == curr_player:\n            #print(\"Is current Player\")\n            self.infosets = collections.defaultdict(list)\n            probs = np.ones(self.env.player_num)\n            self.traverse_tree(probs, this_player)\n            action = self.best_response_action(this_player, state['obs'].tostring())\n            q_val = self.get_q_value(action, [0.0, 0.0])\n            return q_val[this_player]\n        else:\n            #print(\"Not current Player\")\n            action_probs = self.action_probs(state, self.opponent_policy)\n            sum_qval = np.array([0.0, 0.0])\n            for a, p in enumerate(self.action_probs(state, self.opponent_policy)):\n                q_val = self.get_q_value(a, [0.0, 0.0])\n                weighted_qval = np.array([q*p for q in q_val])\n                sum_qval += weighted_qval\n            return sum_qval[this_player]\n\n    def get_q_value(self, action, q_value):\n        if self.env.is_over():\n            return self.env.get_payoffs()\n        current_player = self.env.get_player_id()\n        obs, legal_actions = self.get_state(current_player)\n        curr_state = self.env.get_state(current_player)\n        action_probs = self.action_probs(curr_state, self.opponent_policy)\n        for act in legal_actions:\n            self.env.step(act)\n            q_val_out = q_value.copy()\n            curr_qval = np.array(self.get_q_value(act, q_value))\n            q_val_out += curr_qval * action_probs[act]\n            #q_value += self.get_q_value(act, q_value)\n            self.env.step_back()\n        return q_val_out\n\n    def best_response_action(self, this_player, obs):\n        infoset = self.infosets[obs]\n        best_act = \"\"\n        max_value = -1000.0\n        for each in infoset:\n            p, legal_act = each[0]\n            cf_p = each[1]\n            q_value = [0.0, 0.0]\n            for a in legal_act:\n                self.env.step(a)\n                q_value = self.get_q_value(a, q_value)\n                self.env.step_back()\n                tmp_q = cf_p * q_value[this_player]\n                if tmp_q > max_value:\n                    max_value = tmp_q\n                    best_act = a\n        return best_act\n\n    def action_probs(self, state, policy):\n        ''' Obtain the action probabilities of the current state\n\n        Args:\n            state(dictionaty): The state dictionary\n            policy (dict): The used policy\n\n        Returns:\n            (tuple) that contains:\n                action_probs(numpy.array): The action probabilities\n                legal_actions (list): Indices of legal actions\n        '''\n        #obs = state['obs']\n        legal_actions = state['legal_actions']\n\n        _, action_probs = policy.eval_step(state)\n        if action_probs != []:\n            action_probs = np.array(action_probs)\n            action_probs = remove_illegal(action_probs, legal_actions)\n        else:\n            action_probs = [1.0/len(legal_actions) if a in legal_actions else 0.0 for a in range(self.env.action_num)]\n            #action_probs = [1.0/self.env.action_num for i in range(self.env.action_num)]\n        return action_probs\n\n    def eval_step(self, state):\n        ''' Given a state, predict action based on average policy\n\n        Args:\n            state (numpy.array): State representation\n\n        Returns:\n            action (int): Predicted action\n        '''\n        #probs = self.action_probs(state['obs'].tostring(), state['legal_actions'], self.average_policy)\n        this_player = self.env.get_player_id()\n        self.infosets = collections.defaultdict(list)\n        probs = np.ones(self.env.player_num)\n        self.tmp_state = state['obs']\n        obs, legal_act = self.get_state(this_player)\n        self.traverse_tree(probs, this_player)\n        act = self.best_response_action(this_player, state['obs'].tostring())\n        return act, []\n\n    def get_state(self, player_id):\n        ''' Get state_str of the player\n\n        Args:\n            player_id (int): The player id\n\n        Returns:\n            (tuple) that contains:\n                state (str): The state str\n                legal_actions (list): Indices of legal actions\n        '''\n        state = self.env.get_state(player_id)\n        return state['obs'].tostring(), state['legal_actions']\n\n    def save(self):\n        ''' Save model\n        '''\n        if not os.path.exists(self.model_path):\n            os.makedirs(self.model_path)\n\n        policy_file = open(os.path.join(self.model_path, 'policy.pkl'),'wb')\n        pickle.dump(self.policy, policy_file)\n        policy_file.close()\n\n        average_policy_file = open(os.path.join(self.model_path, 'average_policy.pkl'),'wb')\n        pickle.dump(self.average_policy, average_policy_file)\n        average_policy_file.close()\n\n        regrets_file = open(os.path.join(self.model_path, 'regrets.pkl'),'wb')\n        pickle.dump(self.regrets, regrets_file)\n        regrets_file.close()\n\n        iteration_file = open(os.path.join(self.model_path, 'iteration.pkl'),'wb')\n        pickle.dump(self.iteration, iteration_file)\n        iteration_file.close()\n\n    def load(self):\n        ''' Load model\n        '''\n        if not os.path.exists(self.model_path):\n            return\n\n        policy_file = open(os.path.join(self.model_path, 'policy.pkl'),'rb')\n        self.policy = pickle.load(policy_file)\n        policy_file.close()\n\n        average_policy_file = open(os.path.join(self.model_path, 'average_policy.pkl'),'rb')\n        self.average_policy = pickle.load(average_policy_file)\n        average_policy_file.close()\n\n        regrets_file = open(os.path.join(self.model_path, 'regrets.pkl'),'rb')\n        self.regrets = pickle.load(regrets_file)\n        regrets_file.close()\n\n        iteration_file = open(os.path.join(self.model_path, 'iteration.pkl'),'rb')\n        self.iteration = pickle.load(iteration_file)\n        iteration_file.close()\n\n","sub_path":"rlcard/agents/best_response_agent.py","file_name":"best_response_agent.py","file_ext":"py","file_size_in_byte":8268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"297509963","text":"import serial, platform, glob\n\nfrom tkinter import * \n\nclass SerialO:\n\tdef __init__(self):\n\t\tself.port = \"COM1\"\n\t\tself.baud = 19200\n\n\tdef setSerialOptionPort(self, port):\n\t\tself.port = port\n\t\n\tdef setSerialOptionBaud(self, baud):\n\t\tself.baud = baud\n\n\tdef trySerial(self):\n\t\ttry:\n\t\t\tv2.set(\"Trying to open serial-port\")\n\t\t\tser = serial.Serial(this.port, this.baud, timeout=1)\n\n\t\t\tif ser.isOpen():\n\t\t\t\tprint(ser.name + ' is open...')\n\n\t\t\twhile True:\n\t\t\t\tcmd = raw_input(\"Enter command or 'exit':\")\n\t\t\t\t\t# for Python 2\n\t\t\t\t# cmd = input(\"Enter command or 'exit':\")\n\t\t\t\t\t# for Python 3\n\t\t\t\tif cmd == 'exit':\n\t\t\t\t\tser.close()\n\t\t\t\t\texit()\n\t\t\t\telse:\n\t\t\t\t\tser.write(cmd.encode('ascii')+'\\r\\n')\n\t\t\t\t\tout = ser.read()\n\t\t\t\t\tprint('Receiving...'+out)\n\t\texcept:\n\t\t\tprint(\"Kunde inte excepta\")\n\t\t\tv2.set(\"Could not open serial-port\")\n\nSerialMaster = SerialO()\n\n# A function that tries to list serial ports on most common platforms\ndef list_serial_ports():\n\tsystem_name = platform.system()\n\tif system_name.startswith('win'):\n\t\tports = ['COM%s' % (i + 1) for i in range(256)]\n\n\t\tfor port in ports:\n\t\t\ttry:\n\t\t\t\ts = serial.Serial(port)\n\t\t\t\ts.close()\n\t\t\t\tavailable.append(port)\n\t\t\texcept (OSError, serial.SerialException):\n\t\t\t\tpass\n\t\t\treturn available\n\n\telif system_name == \"WindowsX\":\n\t\t# Scan for available ports.\n\t\tavailable = []\n\t\tfor i in range(256):\n\t\t\ttry:\n\t\t\t\ts = serial.Serial(i)\n\t\t\t\tavailable.append(i)\n\t\t\t\ts.close()\n\t\t\texcept serial.SerialException:\n\t\t\t\tpass\n\t\treturn available\n\telif system_name == \"Darwin\":\n\t\t# Mac\n\t\treturn glob.glob('/dev/tty*') + glob.glob('/dev/cu*')\n\telse:\n\t\t# Assume Linux or something else\n\t\treturn glob.glob('/dev/ttyS*') + glob.glob('/dev/ttyUSB*')\n\ndef OptionMenu_SelectionEventSavePort(*varPort):\n\t# Save Port and Baud-rate\n\tprint(varPort[0])\n\tSerialMaster.setSerialOptionPort(optionPortVar.get())\n\tinfo = \"Port:\" + optionPortVar.get()\n\tlblPort.set(info)\n\t#master.quit()\n\ndef OptionMenu_SelectionEventSaveBaud(*varBaud):\n\t# Save Port and Baud-rate\n\tprint(varBaud[0])\n\tSerialMaster.setSerialOptionBaud(optionBaudVar.get())\n\tinfo = \"Baud:\" + str(optionBaudVar.get())\n\tlblBaud.set(info)\n\t#master.quit()\n\n# Graphic stuff\nmaster = Tk()\nmaster.geometry(\"300x500\")\n\n# List serial-ports\nOPT_SERIALPORTS = list_serial_ports()\noptionPortVar = StringVar(master)\nif len(OPT_SERIALPORTS) > 0:\n\tSerialMaster.setSerialOptionPort(OPT_SERIALPORTS[0]) # set init value\n\toptionPortVar.set(OPT_SERIALPORTS[0]) # default value\noptionWidgetPort = OptionMenu(master, optionPortVar, *OPT_SERIALPORTS, command=OptionMenu_SelectionEventSavePort)\noptionWidgetPort.pack()\n\n# List bauds\nOPT_BAUD = [50, 75, 110, 134, 150, 200, 300, 600, 1200, 1800, 2400, 4800, 9600, 19200, 38400, 57600, 115200]\noptionBaudVar = StringVar(master)\noptionBaudVar.set(SerialMaster.baud) # default value\noptionWidgetBaud = OptionMenu(master, optionBaudVar, *OPT_BAUD, command=OptionMenu_SelectionEventSaveBaud)\noptionWidgetBaud.pack()\n\n#btn_saveSerial = Button(master, text=\"Spara\", command=OptionMenu_SelectionEventSave)\n#btn_saveSerial.pack()\n\nlblPort = StringVar()\nLabel(master, textvariable=lblPort, justify=LEFT, width=300).pack()\ninfo = \"Port:\" + SerialMaster.port\nlblPort.set(info)\n\nlblBaud = StringVar()\nLabel(master, textvariable=lblBaud, justify=LEFT, width=300).pack()\ninfo = \"Baud:\" + str(SerialMaster.baud)\nlblBaud.set(info)\n\n# Start \nbtn_go = Button(master, text=\"Start\", command=SerialMaster.trySerial)\nbtn_go.pack()\n\nv2 = StringVar()\nLabel(master, textvariable=v2, justify=LEFT).pack()\nv2.set(\"\")\n\nmainloop()","sub_path":"serial_detection_v1.py","file_name":"serial_detection_v1.py","file_ext":"py","file_size_in_byte":3495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"176027254","text":"from django.shortcuts import render_to_response, redirect, get_object_or_404\nfrom main.models import Task\nfrom main.forms import TaskForm\nfrom django.utils import timezone\nfrom django.template import RequestContext\n# Create your views here.\ndef homepage(request):\n\ttasks = Task.objects.all()\n\treturn render_to_response(\n\t\t'main/homepage.html',\n\t\t{\n\t\t\t'tasks' : tasks,\n\t\t\t'time' : timezone.now()\n\t\t})\n\ndef create_task(request):\n\tif request.method == 'POST':\n\t\tform = TaskForm(request.POST)\t\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\treturn redirect(\"homepage\")\n\t\telse:\n\t\t\treturn render_to_response(\n\t\t\t'main/create_task.html',\n\t\t\t{\n\t\t\t\t'form' : form\n\t\t\t},\n\t\t\tcontext_instance = RequestContext(request)\n\t\t\t)\n\telse:\n\t\tform = TaskForm()\n\t\treturn render_to_response(\n\t\t\t'main/create_task.html',\n\t\t\t{\n\t\t\t\t'form' : form\n\t\t\t},\n\t\t\tcontext_instance = RequestContext(request)\n\t\t\t)\n\ndef edit_task(request, task_id):\n\t#Se obtiene la tarea que se modifica\n\ttask = get_object_or_404(Task, pk=task_id)\n\tif request.method == \"POST\":\n\t\tform = TaskForm(request.POST, instance=task)\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\treturn redirect(\"homepage\")\n\telse:\n\t\tform = TaskForm(instance=task)\n\treturn render_to_response(\n\t\t\t'main/edit_task.html',\n\t\t\t{'form' : form},\n\t\t\tcontext_instance = RequestContext(request))\n\ndef delete_task(request, task_id):\n\t#Se obtiene la tarea que se quiere eliminar\n\ttask = get_object_or_404(Task, pk=task_id)\n\ttask.delete()\n\treturn redirect(\"homepage\")\n\ndef desc_task(request, task_id):\n\ttask = get_object_or_404(Task, pk=task_id)\n\treturn render_to_response(\n\t\t'main/desc_task.html',\n\t\t{'task' : task}\n\t\t)","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"490145311","text":"from django.shortcuts import render, get_object_or_404\nfrom django.db import IntegrityError\nfrom django.contrib.contenttypes.models import ContentType\nfrom drf_yasg import openapi as openapi\nfrom drf_yasg.utils import swagger_auto_schema\n\nimport requests\n\nimport spotipy\nfrom spotipy.oauth2 import SpotifyClientCredentials\nimport os\nimport pprint\n\nfrom rest_framework.generics import (\n    ListCreateAPIView,\n    RetrieveUpdateDestroyAPIView,\n    RetrieveAPIView,\n    ListAPIView,\n    CreateAPIView,\n    GenericAPIView,\n)\nfrom rest_framework import permissions, status\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.pagination import LimitOffsetPagination, PageNumberPagination\nfrom rest_framework.parsers import (\n    FileUploadParser,\n    FormParser,\n    JSONParser,\n    MultiPartParser,\n)\nfrom .serializers import (\n\n    PlaylistDetailSerializer,\n    PlaylistSerializer,\n\n)\nfrom posts.renderers import PostRenderer, FeedRenderer, AlbumFeedRenderer, PaginationOffsetRenderer\n\n# from .models import Playlist\n# from .serializers import AlbumsSerializer\n# from .serializers import AlbumSerializer\n# from tracks.pagination import CustomPagination\n# from albums.pagination import CustomPagination\nfrom tracks.models import Track\nfrom tracks.serializers import TrackSerializer\nimport json\n# Create your views here.\n\n# from litloop_project.permissions import IsAuthorizedToAdd, IsUserOrEditor, user_allowed_to_upload\n\n\n# class PlaylistAPIView(RetrieveAPIView):\n#     queryset = Playlist.objects.all()\n#     serializer_class = PlaylistsSerializer\n#     lookup_field = 'id'\n\n\nclass PlaylistDetailAPIView(RetrieveAPIView):\n    # serializer_class = PostSerializer\n    # permission_classes = (IsAuthenticated, )\n    # serializer_class = AlbumsSerializer\n    # queryset = Playlist.objects.all()\n    lookup_field = 'playlist_uri'\n\n    # def get_queryset(self):\n    #     return self.queryset.filter(author=self.request.user)\n\n    def get(self, request, playlist_uri):\n        # SPOTIPY_CLIENT_ID = \"c57cfe40c3a640449c4766ee61ec9d59\"\n        # SPOTIPY_CLIENT_SECRET = \"8c5ae0b0d9df47c8bae2804fe8e03cfa\"\n\n        SPOTIPY_CLIENT_ID = \"0575ef8642c94277b3fec71f400ee600\"\n        SPOTIPY_CLIENT_SECRET = \"21c16d49607b49cd8bbe4a5897c63de4\"\n\n\n        sp = spotipy.Spotify(\n            client_credentials_manager=SpotifyClientCredentials(\n                client_id=SPOTIPY_CLIENT_ID,\n                client_secret=SPOTIPY_CLIENT_SECRET\n            )\n        )\n\n\n        spotify_url = 'https://api.spotify.com/v1/playlists/'\n        results = sp.playlist(playlist_uri)\n        dataset = []\n        return Response(results)\n\n\n\nclass PlaylistOffsetAPIView(RetrieveAPIView):\n\n    lookup_field = 'playlist_uri'\n    renderer_classes = (PaginationOffsetRenderer,)\n\n    # http://localhost:8000/playlist/4mloXTyQUOSnCLJTeBSfnR/tracks?offset=100&limit=100\n    def get(self, request, playlist_uri):\n        # SPOTIPY_CLIENT_ID = \"c57cfe40c3a640449c4766ee61ec9d59\"\n        # SPOTIPY_CLIENT_SECRET = \"8c5ae0b0d9df47c8bae2804fe8e03cfa\"\n\n        SPOTIPY_CLIENT_ID = \"0575ef8642c94277b3fec71f400ee600\"\n        SPOTIPY_CLIENT_SECRET = \"21c16d49607b49cd8bbe4a5897c63de4\"\n\n        sp = spotipy.Spotify(\n            client_credentials_manager=SpotifyClientCredentials(\n                client_id=SPOTIPY_CLIENT_ID,\n                client_secret=SPOTIPY_CLIENT_SECRET\n            )\n        )\n\n        offset = request.GET.get('offset')\n        limit = request.GET.get('limit')\n        results = sp.playlist_tracks(playlist_uri, offset=offset, limit='100')\n        # results = sp.playlist_tracks(playlist_uri, offset, limit)\n\n\n        return Response(results)\n\n\nclass PlaylistList(APIView):\n    \"\"\"Playlists listings and creation views\"\"\"\n\n    # permission_classes = (permissions.IsAuthenticatedOrReadOnly, IsAuthorizedToAdd)\n    parser_classes = (JSONParser, MultiPartParser, FormParser, FileUploadParser)\n\n    @swagger_auto_schema(\n        manual_parameters=[],\n        tags=['Playlists'],\n        operation_summary='to_be_written',\n        operation_description='to_be_written',\n        responses={\n            200: openapi.Response('response description', PlaylistSerializer(many=True)),\n        },\n    )\n    def get(self, request, format=None):\n        pagination_class = api_settings.DEFAULT_PAGINATION_CLASS\n        paginator = pagination_class()\n        playlists = Playlist.objects.filter().prefetch_related(\"user\")\n\n        if \"author\" in self.request.query_params:\n            author = self.request.query_params[\"author\"].strip()\n            playlists = playlists.filter(user__username=author)\n\n        page = paginator.paginate_queryset(playlists, request)\n\n        serializer = PlaylistSerializer(page, many=True, context={\"request\": request})\n        return paginator.get_paginated_response(serializer.data)\n\n    @swagger_auto_schema(manual_parameters=[], tags=['Playlists'], operation_summary='to_be_written', operation_description='to_be_written',)\n    def post(self, request, format=None):\n        serializer = PlaylistSerializer(data=request.data, context={\"request\": request})\n        if serializer.is_valid():\n            serializer.save(user=request.user)\n            return Response(serializer.data, status=status.HTTP_201_CREATED)\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n# class PlaylistDetail(APIView):\n#     \"\"\"Playlist related views\"\"\"\n#\n#     permission_classes = (permissions.IsAuthenticatedOrReadOnly, IsUserOrEditor)\n#     parser_classes = (JSONParser, MultiPartParser, FormParser, FileUploadParser)\n#\n#     def get_playlist(self, friendly_token):\n#         try:\n#             playlist = Playlist.objects.get(friendly_token=friendly_token)\n#             self.check_object_permissions(self.request, playlist)\n#             return playlist\n#         except PermissionDenied:\n#             return Response({\"detail\": \"not enough permissions\"}, status=status.HTTP_400_BAD_REQUEST)\n#         except BaseException:\n#             return Response(\n#                 {\"detail\": \"Playlist does not exist\"},\n#                 status=status.HTTP_400_BAD_REQUEST,\n#             )\n#\n#     @swagger_auto_schema(manual_parameters=[], tags=['Playlists'], operation_summary='to_be_written', operation_description='to_be_written',)\n#     def get(self, request, friendly_token, format=None):\n#         playlist = self.get_playlist(friendly_token)\n#         if isinstance(playlist, Response):\n#             return playlist\n#\n#         serializer = PlaylistDetailSerializer(playlist, context={\"request\": request})\n#\n#         playlist_media = PlaylistMedia.objects.filter(playlist=playlist).prefetch_related(\"media__user\")\n#\n#         playlist_media = [c.media for c in playlist_media]\n#         playlist_media_serializer = MediaSerializer(playlist_media, many=True, context={\"request\": request})\n#         ret = serializer.data\n#         ret[\"playlist_media\"] = playlist_media_serializer.data\n#\n#         return Response(ret)\n#\n#     @swagger_auto_schema(manual_parameters=[], tags=['Playlists'], operation_summary='to_be_written', operation_description='to_be_written',)\n#     def post(self, request, friendly_token, format=None):\n#         playlist = self.get_playlist(friendly_token)\n#         if isinstance(playlist, Response):\n#             return playlist\n#         serializer = PlaylistDetailSerializer(playlist, data=request.data, context={\"request\": request})\n#         if serializer.is_valid():\n#             serializer.save(user=request.user)\n#             return Response(serializer.data, status=status.HTTP_201_CREATED)\n#         return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n#\n#     @swagger_auto_schema(manual_parameters=[], tags=['Playlists'], operation_summary='to_be_written', operation_description='to_be_written',)\n#     def put(self, request, friendly_token, format=None):\n#         playlist = self.get_playlist(friendly_token)\n#         if isinstance(playlist, Response):\n#             return playlist\n#         action = request.data.get(\"type\")\n#         media_friendly_token = request.data.get(\"media_friendly_token\")\n#         ordering = 0\n#         if request.data.get(\"ordering\"):\n#             try:\n#                 ordering = int(request.data.get(\"ordering\"))\n#             except ValueError:\n#                 pass\n#\n#         if action in [\"add\", \"remove\", \"ordering\"]:\n#             media = Media.objects.filter(friendly_token=media_friendly_token).first()\n#             if media:\n#                 if action == \"add\":\n#                     media_in_playlist = PlaylistMedia.objects.filter(playlist=playlist).count()\n#                     if media_in_playlist >= settings.MAX_MEDIA_PER_PLAYLIST:\n#                         return Response(\n#                             {\"detail\": \"max number of media for a Playlist reached\"},\n#                             status=status.HTTP_400_BAD_REQUEST,\n#                         )\n#                     else:\n#                         obj, created = PlaylistMedia.objects.get_or_create(\n#                             playlist=playlist,\n#                             media=media,\n#                             ordering=media_in_playlist + 1,\n#                         )\n#                         obj.save()\n#                         return Response(\n#                             {\"detail\": \"media added to Playlist\"},\n#                             status=status.HTTP_201_CREATED,\n#                         )\n#                 elif action == \"remove\":\n#                     PlaylistMedia.objects.filter(playlist=playlist, media=media).delete()\n#                     return Response(\n#                         {\"detail\": \"media removed from Playlist\"},\n#                         status=status.HTTP_201_CREATED,\n#                     )\n#                 elif action == \"ordering\":\n#                     if ordering:\n#                         playlist.set_ordering(media, ordering)\n#                         return Response(\n#                             {\"detail\": \"new ordering set\"},\n#                             status=status.HTTP_201_CREATED,\n#                         )\n#             else:\n#                 return Response({\"detail\": \"media is not valid\"}, status=status.HTTP_400_BAD_REQUEST)\n#         return Response(\n#             {\"detail\": \"invalid or not specified action\"},\n#             status=status.HTTP_400_BAD_REQUEST,\n#         )\n#\n#     @swagger_auto_schema( manual_parameters=[], tags=['Playlists'], operation_summary='to_be_written', operation_description='to_be_written',)\n#     def delete(self, request, friendly_token, format=None):\n#         playlist = self.get_playlist(friendly_token)\n#         if isinstance(playlist, Response):\n#             return playlist\n#\n#         playlist.delete()\n#         return Response(status=status.HTTP_204_NO_CONTENT)\n\n\n# Dark Codeine\n# http://localhost:8000/playlist/1iC9VT69XLLRPtrkBA7tCT/?format=json\n# https://open.spotify.com/playlist/1iC9VT69XLLRPtrkBA7tCT\n# 37i9dQZEVXbLnolsZ8PSNw\n# http://localhost:8000/playlist/4mloXTyQUOSnCLJTeBSfnR/?format=json\n#\n\n# Nelly Furtado\n# https://open.spotify.com/playlist/2icgLCTGOep0uvt6ofEdCM?si=3e9344ca3cda4fdf\n","sub_path":"playlists/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":11232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"349704783","text":"from behave import *\r\nfrom selenium import webdriver\r\nfrom time import sleep\r\n\r\n@given(u'I am on mission humane page')\r\ndef step_impl(context):\r\n    context.driver = webdriver.Chrome(executable_path='C://webdriver//chromedriver.exe')\r\n    context.driver.get(\"https://app.missionhumane.org\")\r\n    context.driver.maximize_window()\r\n    sleep(2)\r\n\r\n@when(u'I search for the first state')\r\ndef step_impl(context):\r\n    state_xpath = '//*[@class=\"location-row\"][1]'\r\n\r\n@then(u'AP is listed')\r\ndef step_impl(context):\r\n    state_name = context.driver.find_element_by_xpath('//*[@class=\"location-row\"][1]').text\r\n    print(state_name)\r\n    assert state_name =='Andhra Pradesh'\r\n    context.driver.quit()\r\n\r\n\r\n@when(u'I click first state')\r\ndef step_impl(context):\r\n    context.driver.find_element_by_xpath('//*[@class=\"location-row\"][1]').click()\r\n    sleep(2)\r\n\r\n@then(u'anatapur is listed as first district')\r\ndef step_impl(context):\r\n    dist_name = context.driver.find_element_by_xpath('//*[@class=\"location-row\"][2]').text\r\n    print(dist_name)\r\n    assert dist_name == 'Anantapur'\r\n    context.driver.quit()\r\n\r\n\r\n@when(u'I click the state \"{stname}\"')\r\ndef step_impl(context,stname):\r\n    all_states = context.driver.find_elements_by_class_name('location-row')\r\n    print(stname)\r\n    for st in all_states:\r\n            if st.text == stname:\r\n                st.click()\r\n                sleep(3)\r\n                break\r\n\r\n@then(u'states of AP are listed')\r\ndef step_impl(context):\r\n    context.driver.quit()","sub_path":"stepdefination.py","file_name":"stepdefination.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"384698020","text":"from allensdk.core.nwb_data_set import NwbDataSet\r\nimport pickle\r\nimport pprint\r\nimport matplotlib.pyplot as plt\r\n\r\n# length of sweeps and counts\r\n# {1701000: 323, 1648001: 273, 2004001: 1265, 2101000: 50, 804001: 493, 183450: 616, 7604001: 22, 501000: 443, 6804001: 548, 1401000: 490, 8604001: 13, 1604001: 3169, 101150: 2736, 5004001: 83, 1404001: 162, 8404001: 151, 5604001: 689, 301000: 719, 7204001: 2, 6004001: 107, 304601: 388, 1204001: 1256, 412000: 327, 6604001: 16, 4604001: 55, 401000: 2011, 733801: 800, 201000: 124, 1004001: 89, 618601: 1389, 404601: 4440, 4404001: 6}\r\nbatch_size = 1\r\nbatch_bool = True\r\n\r\ntarget_hz = 25000\r\n\r\ndef make_labels(i):\r\n    file = open('cells/' + str(i) + '.pkl', 'r')\r\n    cell = pickle.load(file)\r\n    file.close()\r\n    label_dict = {}\r\n    if 'cortex_layer' in cell:\r\n        print('Cortex_layer in: YES')\r\n    else:\r\n        print('Cortex_layer not in: NOPE')\r\n\r\n\r\n    for cat in ['dendrite_type']:\r\n        label_dict[cat] = cell[cat]\r\n    for cat in ['sex', 'smoker', 'race_only']:\r\n        label_dict[cat] = cell['donor'][cat]\r\n    label_dict['organism'] = cell['donor']['organism']['name']\r\n    label_dict['age'] = cell['donor']['age_id']\r\n    return label_dict\r\ndef count_labels_helper(label_dict_list):\r\n    super_dict = {}\r\n    for cat in label_dict_list[0]:\r\n        super_dict[cat] = {}\r\n    for dict in label_dict_list:\r\n        for cat in dict:\r\n            if dict[cat] in super_dict[cat]:\r\n                super_dict[cat][dict[cat]] += 1\r\n            else:\r\n                super_dict[cat][dict[cat]] = 1\r\n    print(super_dict)\r\ndef count_labels_main():\r\n    label_dict_list = []\r\n    for i in range(0, 50):\r\n        if i % 10 == 0:\r\n            print(i, 'completed')\r\n            print(label_dict_list)\r\n        file = open('cells/' + str(i) + '.pkl','r')\r\n        cell = pickle.load(file)\r\n        #pprint.pprint(cell)\r\n        file.close()\r\n        label_dict_list.append(make_labels(i))\r\n    count_labels_helper(label_dict_list)\r\n\r\ndef batchify(response, stimulus, skip_rate):\r\n    lookahead = 100\r\n    count_down = 10\r\n\r\n    if len(response) != len(stimulus):\r\n        raise ValueError('LENGTHS NOT EQUAL')\r\n        print('Lengths ERROR')\r\n\r\n    batches_list = []\r\n    batches = []\r\n    measure = False\r\n    count = False\r\n    counter = 0\r\n    #print(len(stimulus))\r\n    #print(skip_rate)\r\n\r\n    for i in range(0, len(stimulus)-lookahead * skip_rate, skip_rate):\r\n        #print(stimulus[i])\r\n        if (stimulus[i + lookahead * skip_rate] > 10 ** (-13) or stimulus[i + lookahead * skip_rate] < -(10 ** (-13))) and not measure:\r\n            #print('test2')\r\n            measure = True\r\n        if measure:\r\n            # batch_size idea has been removed\r\n            batches.append([response[i], stimulus[i]])\r\n            #print(stimulus[i])\r\n            if stimulus[i] > 10 ** (-13) and stimulus[i + lookahead * skip_rate] < 10 ** (-13):\r\n                count = True\r\n            if stimulus[i] < -10 ** (-13) and stimulus[i + lookahead * skip_rate] > -10 ** (-13):\r\n                count = True\r\n        if count:\r\n            counter += 1\r\n        if counter == 10:\r\n            #print('TEST')\r\n            batches_list.append(batches)\r\n            #batches = []\r\n            measure = False\r\n            counter = 0\r\n            count = False\r\n\r\n    return batches_list\r\n\r\ndef make_features(i):\r\n    sweeps = []\r\n    file_name = 'nwb/' + str(i) + '.nwb'\r\n    data_set = NwbDataSet(file_name)\r\n    sweep_numbers = data_set.get_sweep_numbers()\r\n    for sweep_number in sweep_numbers:\r\n        sweep = data_set.get_sweep(sweep_number)\r\n        skip_rate = sweep['sampling_rate'].astype(int) / target_hz\r\n        if batch_bool:\r\n            sweep_feats = batchify(sweep['response'], sweep['stimulus'], skip_rate)\r\n        else:\r\n            sweep_feats = [sweep['response'], sweep['stimulus']]\r\n        sweeps.extend(sweep_feats)\r\n    print('len batched:', len(sweeps))\r\n    return sweeps\r\n\r\ndef count_lengths():\r\n    len_dict = {}\r\n    for i in range(0, 401):\r\n        if i%10 == 0:\r\n            print(i, 'completed')\r\n            print(len_dict)\r\n        file_name = 'nwb/' + str(i) + '.nwb'\r\n        data_set = NwbDataSet(file_name)\r\n        sweep_numbers = data_set.get_sweep_numbers()\r\n        for sweep_number in sweep_numbers:\r\n            sweep = data_set.get_sweep(sweep_number)\r\n            length = len(sweep['response'])\r\n            if length in len_dict:\r\n                len_dict[length] += 1\r\n            else:\r\n                len_dict[length] = 1\r\n\r\nif False:\r\n    file_name = 'nwb/1.nwb'\r\n    data_set = NwbDataSet(file_name)\r\n    #print(data_set)\r\n\r\n    sweep_numbers = data_set.get_sweep_numbers()\r\n    #sweep_number = sweep_numbers[1]\r\n    sweep_data = []\r\n    sweep_data_stimulus = []\r\n    i = 0\r\n    plt.figure(1)\r\n    pprint.pprint(data_set)\r\n    for sweep_number in sweep_numbers:\r\n        i += 1\r\n        if i > 10:\r\n            break\r\n        sweep_data.append(data_set.get_sweep(sweep_number)['response'])\r\n        sweep_data_stimulus.append(data_set.get_sweep(sweep_number)['stimulus'])\r\n\r\n    for point in sweep_data:\r\n        plt.subplot(211)\r\n\r\n        plt.plot(point)\r\n\r\n    for point in sweep_data_stimulus:\r\n        plt.subplot(212)\r\n        plt.plot(point)\r\n    plt.show()\r\n\r\ndef get_sampling_rates():\r\n    for i in range(0, 1):\r\n\r\n\r\n        file_name = 'nwb/' + str(i) + '.nwb'\r\n        data_set = NwbDataSet(file_name)\r\n        sweep_numbers = data_set.get_sweep_numbers()\r\n\r\n        print('i:', i, 'num sweeps:', len(sweep_numbers))\r\n        sweep = data_set.get_sweep(sweep_numbers[0])\r\n        #print('sampling rate:', sweep['sampling_rate'])\r\n\r\n#count_labels_main()\r\n#get_sampling_rates()\r\n\r\nexamples = []\r\nfor i in range(0, 1):\r\n    if i % 10 == 0:\r\n        print(i, 'completed')\r\n\r\n    example = {'x': make_features(i), 'y': make_labels(i)}\r\n    if True:\r\n        for j in range(0, 50):\r\n            #print(example['x']['sweeps'])\r\n            plt.plot(example['x'][j])\r\n        plt.show()\r\n    examples.append(example)\r\n    file = open('examples/' + str(i) + '.pkl', 'w')\r\n    pickle.dump(example, file)\r\n    file.close()\r\n\r\nfile = open('examples_all.pkl', 'w')\r\npickle.dump(examples, file)\r\nfile.close()\r\n\r\n\r\n#print(label_dict_list)\r\n\r\n#make_labels(cell)\r\n\r\n#print(cell['dendrite_type'])","sub_path":"examples_maker.py","file_name":"examples_maker.py","file_ext":"py","file_size_in_byte":6301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"348283668","text":"import uuid\nfrom datetime import datetime\n\nimport pytz\nfrom sqlalchemy.dialects.postgresql import JSONB, UUID, TEXT, TIMESTAMP, BOOLEAN\nfrom sqlalchemy import ForeignKey\n\nfrom lockheed_141310 import db\n\n\nclass CMMeta(db.Model):\n    __tablename__ = 'cm_meta'\n    uuid = db.Column(UUID(as_uuid=True), default=uuid.uuid4(), primary_key=True)\n    name = db.Column(TEXT)\n\n    def __init__(self, name: str):\n        self.uuid = uuid.uuid4()\n        self.name = name\n\n    @classmethod\n    def create(cls, name: str) -> None:\n        new_meta = cls(name)\n        db.session.add(new_meta)\n        db.session.commit()\n\n\nclass CMLog(db.Model):\n    __tablename__ = 'cm_log'\n    __table_args__ = (\n        db.ForeignKeyConstraint(('cm_uuid', 'log_type'), ('cm_log_types.cm_uuid', 'cm_log_types.log_type')),\n    )\n    id = db.Column(db.Integer, primary_key=True)\n    cm_uuid = db.Column(UUID(as_uuid=True), default=uuid.uuid4())\n    timestamp = db.Column(TIMESTAMP)\n    log_type = db.Column(TEXT)\n    data = db.Column(JSONB)\n\n    def __init__(self, cm_uuid: UUID, log_type: str, data: dict):\n        self.cm_uuid = cm_uuid\n        self.log_type = log_type\n        self.timestamp = str(datetime.now(pytz.utc))\n        self.data = data\n\n    @classmethod\n    def create(cls, cm_uuid: UUID, log_type: str, data: dict) -> dict:\n        new_log = cls(cm_uuid, log_type, data)\n        db.session.add(new_log)\n        db.session.commit()\n        return new_log.to_dict()\n\n    def to_dict(self) -> dict:\n        return {\n            \"id\": self.id,\n            \"cm_uuid\": self.cm_uuid,\n            \"timestamp\": str(self.timestamp),\n            \"log_type\": self.log_type,\n            \"data\": self.data\n        }\n\n\nclass Users(db.Model):\n    __tablename__ = 'users'\n    username = db.Column(TEXT, unique=True)\n    password = db.Column(TEXT)\n    id = db.Column(UUID(as_uuid=True), default=uuid.uuid4(), primary_key=True)\n    owner = db.Column(BOOLEAN)\n    active = db.Column(BOOLEAN)\n    email = db.Column(TEXT, unique=True)\n\n    def __init__(self, username: str, password: str, email: str, active: bool):\n        self.username = username\n        self.password = password\n        self.id = uuid.uuid4()\n        self.owner = False\n        self.active = active\n        self.email = email\n\n    @classmethod\n    def create(cls, username: str, hashed_password: str, email: str, active: bool = False) -> dict:\n        new_user = cls(username, hashed_password, email, active)\n        db.session.add(new_user)\n        db.session.commit()\n        return new_user.to_dict()\n\n    # TODO: differentiate role name vs role id, and change in add role route\n    def has_role_name(self, search_role: str) -> bool:\n        \"\"\"\n        Determines if user is member of role, given role name\n        \"\"\"\n        roles = {role.id: role.name for role in RoleDefinitions.query.all()}\n        owned_role_ids = [role.role_id for role in Roles.query.filter_by(user_id=self.id).all()]\n        for owned_role_id in owned_role_ids:\n            if roles[owned_role_id] == search_role:\n                return True\n        return False\n\n    def has_role_id(self, role_id: int) -> bool:\n        \"\"\"\n        Determines if user is member of role, given role id\n        \"\"\"\n        owned_role_ids = [role.role_id for role in Roles.query.filter_by(user_id=self.id).all()]\n        print(owned_role_ids)\n        for owned_role_id in owned_role_ids:\n            if owned_role_id == role_id:\n                print(\"Match!\")\n                return True\n        return False\n\n    def roles(self):\n        roles_query = Roles.query.filter_by(user_id=self.id).all()\n        roles = []\n        for role in roles_query:\n            role_definition = RoleDefinitions.query.filter_by(id=role.role_id).first()\n            role_data = {\n                \"role_id\": role_definition.id,\n                \"name\": role_definition.name,\n            }\n            roles.append(role_data)\n        return roles\n\n    def to_dict(self):\n        return {\n            \"username\": self.username,\n            \"id\": self.id,\n            \"owner\": self.owner,\n            \"roles\": self.roles(),\n            \"active\": self.active\n        }\n\n\nclass RoleDefinitions(db.Model):\n    __tablename__ = 'role_descriptions'\n    name = db.Column(TEXT)\n    id = db.Column(db.Integer, primary_key=True)\n    create_user = db.Column(BOOLEAN)\n    get_log = db.Column(BOOLEAN)\n    post_log = db.Column(BOOLEAN)\n\n    def __init__(self, name: str, **kwargs):\n        self.name = name\n        self.create_user = kwargs.get(\"create_user\", False)\n        self.get_log = kwargs.get(\"get_log\", False)\n        self.post_log = kwargs.get(\"post_log\", False)\n\n    @classmethod\n    def create(cls, name: str, **kwargs):\n        new_role = cls(name, kwargs=kwargs)\n        db.session.add(new_role)\n        db.session.commit()\n\n    def to_json(self) -> dict:\n        return {\n            \"name\": self.name,\n            \"is_admin\": self.is_admin,\n            \"get_log\": self.get_log,\n            \"post_log\": self.post_log\n        }\n\n    def has_permission(self, permission: str) -> bool:\n        \"\"\"\n        Determines if user has permission\n        \"\"\"\n        if hasattr(self, permission):\n            return getattr(self, permission)\n        return False\n\n\nclass Roles(db.Model):\n    __tablename__ = 'roles'\n    id = db.Column(db.Integer, primary_key=True)\n    user_id = db.Column(UUID(as_uuid=True), ForeignKey('users.id'), default=uuid.uuid4())\n    role_id = db.Column(db.Integer, ForeignKey('role_descriptions.id'))\n\n    def __init__(self, user_id: UUID, role_id: int):\n        self.user_id = user_id\n        self.role_id = role_id\n\n    @classmethod\n    def create(cls, user_id: UUID, role_id: int) -> None:\n        new_role = cls(user_id, role_id)\n        db.session.add(new_role)\n        db.session.commit()\n\n    def get_name(self) -> str:\n        return RoleDefinitions.query.filter_by(id=self.role_id).first().name\n\n    def has_permission(self, permission: str) -> bool:\n        \"\"\"\n        Determines if role has the specified permission\n        \"\"\"\n        definition = RoleDefinitions.query.filter_by(self.role_id).first()\n        if hasattr(definition, permission):\n            return getattr(definition, permission)\n        return False\n\n\nclass CMLogTypes(db.Model):\n    __tablename__ = 'cm_log_types'\n    __table_args__ = (\n        db.UniqueConstraint('cm_uuid', 'log_type', name='log_type'),\n    )\n    id = db.Column(db.Integer, primary_key=True)\n    cm_uuid = db.Column(UUID, ForeignKey(\"cm_meta.uuid\"))\n    log_type = db.Column(TEXT)\n    description = db.Column(TEXT)\n\n    def __init__(self, cm_uuid: str, log_type: str, description: str):\n        self.cm_uuid = cm_uuid\n        self.log_type = log_type\n        self.description = description\n\n    @classmethod\n    def create(cls, cm_uuid: str, log_type: str, description: str = None) -> dict:\n        new_cm_log_type = cls(cm_uuid, log_type, description)\n        db.session.add(new_cm_log_type)\n        db.session.commit()\n        return new_cm_log_type.to_dict()\n\n    def to_dict(self):\n        return {\n            \"id\": self.id,\n            \"cm_uuid\": self.cm_uuid,\n            \"log_type\": self.log_type,\n            \"description\": self.description\n        }\n","sub_path":"lockheed_141310/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":7202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"561952469","text":"from __future__ import print_function\r\nimport argparse\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.optim as optim\r\nfrom torchvision import datasets, transforms\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nclass LeNet(nn.Module):\r\n\tdef __init__(self, args):\r\n\t\tsuper(LeNet, self).__init__()\r\n\r\n\t\tself.p = args.dropout\r\n\t\tself.batch_norm = args.batch_norm\r\n\r\n\t\tself.conv1 = nn.Conv2d(1, 6, kernel_size=5, padding=2)\r\n\t\tself.conv1_bn = nn.BatchNorm2d(6)\r\n\t\tself.conv2 = nn.Conv2d(6, 16, kernel_size=5)\r\n\t\tself.conv2_bn = nn.BatchNorm2d(16)\r\n\t\tself.conv2_drop = nn.Dropout2d(p=self.p)\r\n\t\tself.fc1 = nn.Linear(16*5*5, 120)\r\n\t\tself.fc1_bn = nn.BatchNorm1d(120)\r\n\t\tself.fc2 = nn.Linear(120, 84)\r\n\t\tself.fc2_bn = nn.BatchNorm1d(84)\r\n\t\tself.fc3 = nn.Linear(84, 10)\r\n\r\n\tdef forward(self, x):\r\n\t\tx = F.relu(F.max_pool2d(self.conv1(x), 2))\r\n\t\tif self.batch_norm:\r\n\t\t\tx = self.conv1_bn(x)\r\n\t\tx = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\r\n\t\tif self.batch_norm:\r\n\t\t\tx = self.conv2_bn(x)\r\n\t\tx = x.view(-1, 16*5*5)\r\n\t\tx = F.relu(self.fc1(x))\r\n\t\tif self.batch_norm:\r\n\t\t\tx = self.fc1_bn(x)\r\n\t\tx = F.dropout(x, training=self.training, p=self.p)\r\n\t\tx = F.relu(self.fc2(x))\r\n\t\tif self.batch_norm:\r\n\t\t\tx = self.fc2_bn(x)\r\n\t\tx = F.dropout(x, training=self.training, p=self.p)\r\n\t\tx = self.fc3(x)\r\n\t\treturn F.log_softmax(x)\r\n\r\n\r\ndef train(args, model, device, train_loader, optimizer, epoch):\r\n\tmodel.train()\r\n\tcorrect = 0\r\n\tfor batch_idx, (data, target) in enumerate(train_loader):\r\n\t\tdata, target = data.to(device), target.to(device)\r\n\t\toptimizer.zero_grad()\r\n\t\toutput = model(data)\r\n\r\n\t\tpred = output.max(1, keepdim=True)[1]  # get the index of the max log-probability\r\n\t\tcorrect += pred.eq(target.view_as(pred)).sum().item()\r\n\r\n\t\tloss = F.nll_loss(output, target)\r\n\t\tloss.backward()\r\n\t\toptimizer.step()\r\n\t\tif batch_idx % args.log_interval == 0:\r\n\t\t\tprint('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\r\n\t\t\t\tepoch, batch_idx * len(data), len(train_loader.dataset),\r\n\t\t\t\t100. * batch_idx / len(train_loader), loss.item()))\r\n\r\n\ttrain_acc = correct / len(train_loader.dataset)\r\n\treturn train_acc\r\n\r\n\r\ndef test(args, model, device, test_loader):\r\n\tmodel.eval()\r\n\ttest_loss = 0\r\n\tcorrect = 0\r\n\twith torch.no_grad():\r\n\t\tfor data, target in test_loader:\r\n\t\t\tdata, target = data.to(device), target.to(device)\r\n\t\t\toutput = model(data)\r\n\t\t\ttest_loss += F.nll_loss(output, target, size_average=False).item() # sum up batch loss\r\n\t\t\tpred = output.max(1, keepdim=True)[1] # get the index of the max log-probability\r\n\t\t\tcorrect += pred.eq(target.view_as(pred)).sum().item()\r\n\r\n\ttest_loss /= len(test_loader.dataset)\r\n\ttest_acc = correct / len(test_loader.dataset)\r\n\tprint('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\r\n\t\ttest_loss, correct, len(test_loader.dataset),\r\n\t\t100. * test_acc))\r\n\r\n\treturn test_acc\r\n\r\n\r\ndef main():\r\n\t# Training settings\r\n\tparser = argparse.ArgumentParser(description='PyTorch FashionMNIST')\r\n\tparser.add_argument('--batch-size', type=int, default=64, metavar='N',\r\n\t\t\t\t\t\thelp='input batch size for training (default: 64)')\r\n\tparser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\r\n\t\t\t\t\t\thelp='input batch size for testing (default: 1000)')\r\n\tparser.add_argument('--epochs', type=int, default=10, metavar='N',\r\n\t\t\t\t\t\thelp='number of epochs to train (default: 10)')\r\n\tparser.add_argument('--lr', type=float, default=0.01, metavar='LR',\r\n\t\t\t\t\t\thelp='learning rate (default: 0.01)')\r\n\tparser.add_argument('--momentum', type=float, default=0.5, metavar='M',\r\n\t\t\t\t\t\thelp='SGD momentum (default: 0.5)')\r\n\tparser.add_argument('--no-cuda', action='store_true', default=False,\r\n\t\t\t\t\t\thelp='disables CUDA training')\r\n\tparser.add_argument('--seed', type=int, default=1, metavar='S',\r\n\t\t\t\t\t\thelp='random seed (default: 1)')\r\n\tparser.add_argument('--log-interval', type=int, default=10, metavar='N',\r\n\t\t\t\t\t\thelp='how many batches to wait before logging training status')\r\n\tparser.add_argument('--dropout', type=float, default=0, metavar='N',\r\n\t\t\t\t\t\thelp='dropout rate')\r\n\tparser.add_argument('--weight-decay', type=float, default=0, metavar='N',\r\n\t\t\t\t\t\thelp='weight decay')\r\n\tparser.add_argument('--batch-norm', type=bool, default=False, metavar='N',\r\n\t\t\t\t\t\thelp='batch normalization mode')\r\n\tparser.add_argument('--saved-weights', type=str, default=None, metavar='P',\r\n\t\t\t\t\t\thelp='path to saved weights')\r\n\targs = parser.parse_args()\r\n\tuse_cuda = not args.no_cuda and torch.cuda.is_available()\r\n\r\n\ttorch.manual_seed(args.seed)\r\n\r\n\tdevice = torch.device(\"cuda\" if use_cuda else \"cpu\")\r\n\r\n\tkwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}\r\n\ttrain_loader = torch.utils.data.DataLoader(\r\n\t\tdatasets.MNIST('data', train=True, download=True, transform=transforms.Compose([\r\n\t\t\ttransforms.ToTensor(),\r\n\t\t\ttransforms.Normalize((0.2860,), (0.3205,))])),\r\n\t\tbatch_size=args.batch_size, shuffle=True, **kwargs)\r\n\r\n\t# Calculate Mean & STD\r\n\tcalculate_stats = False\r\n\tif calculate_stats:\r\n\t\tmean = 0.\r\n\t\tstd = 0.\r\n\t\tnb_samples = 0.\r\n\t\tfor data in train_loader:\r\n\t\t\tdata = data[0]\r\n\t\t\tbatch_samples = data.size(0)\r\n\t\t\tdata = data.view(batch_samples, data.size(1), -1)\r\n\t\t\tmean += data.mean(2).sum(0)\r\n\t\t\tstd += data.std(2).sum(0)\r\n\t\t\tnb_samples += batch_samples\r\n\r\n\t\tmean /= nb_samples\r\n\t\tstd /= nb_samples\r\n\r\n\ttest_loader = torch.utils.data.DataLoader(\r\n\t\tdatasets.FashionMNIST('data', train=False, transform=transforms.Compose([\r\n\t\t\ttransforms.ToTensor(),\r\n\t\t\ttransforms.Normalize((0.2860,), (0.3205,))])),\r\n\t\tbatch_size=args.test_batch_size, shuffle=True, **kwargs)\r\n\r\n\tmodel = LeNet(args).to(device)\r\n\t#optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\r\n\toptimizer = optim.Adam(model.parameters(), weight_decay=args.weight_decay)\r\n\r\n\tif args.saved_weights is not None:\r\n\t\tmodel.load_state_dict(torch.load(args.saved_weights))\r\n\r\n\ttrain_acc = []\r\n\ttest_acc = []\r\n\tfor epoch in range(1, args.epochs + 1):\r\n\t\ttrain_acc.append(train(args, model, device, train_loader, optimizer, epoch))\r\n\t\ttest_acc.append(test(args, model, device, test_loader))\r\n\r\n\ttorch.save(model.state_dict(), \"weights.pt\")\r\n\r\n\tplt.figure()\r\n\tplt.title(\"Dropout rate: {} Weight Decay: {} Batch Norm: {}\".format(args.dropout, args.weight_decay, args.batch_norm))\r\n\tplt.xlabel(\"Epoch\")\r\n\tplt.ylabel(\"Accuracy\")\r\n\tplt.plot(train_acc, label=\"Train\")\r\n\tplt.plot(test_acc, label=\"Test\")\r\n\tplt.grid()\r\n\tplt.legend(loc=3, prop={'size': 6})\r\n\tplt.savefig('dropout={}decay={}batch_norm={}.png'.format(args.dropout, args.weight_decay, args.batch_norm))\r\n\tplt.show()\r\n\r\n\r\nif __name__ == '__main__':\r\n\tmain()\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"173600910","text":"from flask import render_template, redirect, url_for\r\nfrom game import myGame\r\nfrom game.forms import CreateGame, JoinGame, EnterNameEven, EnterNameOdd\r\n\r\n@myGame.route(\"/\")\r\n@myGame.route(\"/index\")\r\ndef index():\r\n    return render_template(\"index.html\", title=\"Home\")\r\n\r\n@myGame.route(\"/startAGame\", methods=[\"GET\", \"POST\"])\r\ndef startAGame():\r\n    form = CreateGame()\r\n    if form.validate_on_submit():\r\n        return redirect(url_for(\"index\"))\r\n    return render_template(\"startAGame.html\", title=\"Start\", form=form)\r\n\r\n","sub_path":"game/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"54225795","text":"import numpy as np\nimport scipy.ndimage as ndi\nfrom skimage.measure import regionprops\nimport pytest\n\nfrom butterfly import tracing\n\n\n@pytest.fixture(scope=\"module\")\ndef fake_butterfly():\n    butterfly = np.zeros((100, 200))\n    butterfly[25:50, 25:50] = 1  # left wing\n    butterfly[25:50, 150:175] = 1  # right wing\n    butterfly[50:90, 40:160] = 1  # body\n    butterfly[25:50, 80:120] = 1  # head\n\n    return butterfly\n\n\n@pytest.fixture(scope=\"module\")\ndef fake_shape():\n    shape = np.zeros((5, 5))\n    shape[1:, 4] = 1\n    shape[2:4, 2] = 1\n    shape[[1, 4], 3] = 1\n\n    return shape\n\n\ndef test_moore_neighborhood():\n    array = np.array([[10, 9], [9, 9], [9, 10], [9, 11],\n                      [10, 11], [11, 11], [11, 10], [11, 9]])\n    assert np.all(tracing.moore_neighborhood([10, 10], [10, 9]) == array)\n\n\ndef test_split(fake_butterfly):\n    assert tracing.split_picture(fake_butterfly) in [99, 100]\n\n\ndef test_boundary_tracing(fake_shape):\n    bound1 = np.array([[1, 3], [1, 4], [2, 4], [3, 4], [4, 4],\n                       [4, 3], [3, 4], [2, 4]])\n    bound2 = np.array([[1, 3], [1, 4], [2, 4], [3, 4], [4, 4],\n                       [4, 3], [3, 2], [2, 2]])\n    labels1, _ = ndi.label(fake_shape,\n                           structure=ndi.generate_binary_structure(2, 1))\n    labels2, _ = ndi.label(fake_shape,\n                           structure=ndi.generate_binary_structure(2, 2))\n\n    regions1 = regionprops(labels1)\n    regions2 = regionprops(labels2)\n\n    boundary1 = tracing.boundary_tracing(regions1[0])\n    boundary2 = tracing.boundary_tracing(regions2[0])\n\n    assert np.all(boundary1 == bound1)\n    assert np.all(boundary2 == bound2)\n\n\ndef test_detect_points_interest(fake_butterfly):\n    middle = tracing.split_picture(fake_butterfly)\n    fake_butterfly[:, middle] = 0\n    labels, _ = ndi.label(fake_butterfly,\n                          structure=ndi.generate_binary_structure(2, 2))\n    regions = regionprops(labels)\n    boundary_l = tracing.boundary_tracing(regions[0])\n    boundary_r = tracing.boundary_tracing(regions[1])\n\n    test_poi_l = np.array([[25, 25], [49, 80]])\n    test_poi_r = np.array([[25, 174], [49, 119]])\n    poi_l = tracing.detect_points_interest(boundary_l, 'l', 200)\n    poi_r = tracing.detect_points_interest(boundary_r, 'r', 200)\n\n    assert np.all(poi_r[0] == test_poi_r[0])\n    assert np.all(poi_l[0] == test_poi_l[0])\n    assert np.all(poi_r[1] == test_poi_r[1])\n    assert np.all(poi_l[1] == test_poi_l[1])\n","sub_path":"butterfly/tests/test_tracing.py","file_name":"test_tracing.py","file_ext":"py","file_size_in_byte":2464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"369453555","text":"import random\n\ndef extract_section(full_list, section_string):\n    \"\"\" (list of str, str) -> list of str\n    \n    >>> find_section_list(['c9 99 LEC 0101 Joe\\n', 'c3 99 LEC 0501 Michelle'], 'LEC 0101') \n    ['c9 99 LEC 0101 Joe\\n']\n    \n    Return the elements from full_list that constain section_string.\n    \"\"\"\n    \n    print(\"Printing from within pick.py: __name__ refers to\", __name__)\n    \n    result = []\n    for line in full_list:\n        if section_string in line:\n            result.append(line)\n    \n    return result\n    \n\nif __name__ == '__main__':\n    \n    f = open('classlist.txt')\n    whole_course_list = f.readlines()\n    \n    our_section = extract_section(whole_course_list, 'LEC 0101')\n    \n    ans = 'junk that is not yes'\n    while ans != 'yes':\n        chosen_one = random.choice(our_section)\n        \n        token = chosen_one.split()\n        last_name = token[4]\n        first_name = token[5]\n        #print randomly selected student\n        print(first_name, last_name)\n        \n        ans = input(\"Was this student in class? Are we done? \")","sub_path":"PYTHON_CSC108/git/lecture/wk9/mar10_mar16/pick.py","file_name":"pick.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"341261999","text":"class MapAsteroid():\n    def __init__(self):\n        self.cells = []\n        self.width = 0\n        self.height = 0\n\n    def read_input(self, input_data):\n        self.width = len(input_data[0])\n        self.height = len(input_data)\n        self.cells = [[0 for _ in range(self.width)]for _ in range(self.height)]\n        for y, line in enumerate(input_data):\n            for x, letter in enumerate(line):\n                if letter == \"#\":\n                    self.set_cell(x, y, 1)\n\n    def set_cell(self, x, y, value):\n        self.cells[y][x] = value\n\n    def get_cell(self, x, y):\n        if y < 0 or y > len(self.cells) or x < 0 or x > len(self.cells[y]):\n            return None\n        return self.cells[y][x]\n\n    def get_v(self):\n        v = \"\"\n        for line in self.cells:\n            v += \"\".join([str(l) for l in line])\n            v += \"\\n\"\n        return v\n\n    def get_visible_list(self):\n        v_list = []\n        for y in range(self.height):\n            for x in range(self.width):\n                v_list.append([y, x, self.get_visible(y, x)])\n        return v_list\n\n    def get_visible(self, oy, ox):\n        v = 0\n        for ty in range(self.height):\n            for tx in range(self.width):\n                if ty == oy and tx == ox:\n                    continue\n                if self.get_cell(ty, tx) == 1:\n                    if self.is_blocked(ox, oy, tx, ty):\n                        continue\n                    v += 1\n        return v\n\n    def is_blocked(self, ox, oy, tx, ty):\n        # YRK\n\n        print(ox, oy, tx, ty)\n        dy = abs(ty - oy)\n        dx = abs(tx - ox) - 1\n        print(dy, dx)\n        for iy in range(1, dy, 1):\n            if dx % iy == 0:\n                print(True)\n            print(iy)\n\n        # for iy in range(1, dy, -1 if oy > ty else 1):\n            # print(iy)\n            #print(dx % iy == 0)\n\n        # for iy in range(oy - 1 if oy > ty else oy + 1, ty, (-1 if oy > ty else 1)):\n        #    print(iy)\n\n        #dx = abs(ox - tx) + 1\n        #print(ox, oy, tx, ty, dx)\n        # for ix in range()\n\n        # for i, iy in enumerate(range(oy - 1 if oy > ty else oy + 1, ty, -1 if oy > ty else 1), 1):\n        #    if dx % i == 0:\n        #        ix = dx // i\n        #        print(iy, ix)\n\n\nif __name__ == '__main__':\n    file = open(\"inputs/input10.txt\", \"r\")\n    input_data = file.read().splitlines()\n    file.close()\n\n    map = MapAsteroid()\n    map.read_input(input_data)\n\n    #map.is_blocked(0, 0, 0, 0)\n    #map.is_blocked(3, 3, 0, 0)\n    map.is_blocked(3, 3, 10, 10)\n    map.is_blocked(6, 6, 3, 3)\n\n    #v_list = map.get_visible_list()\n    # print(v_list)\n","sub_path":"AdventOfCode2019/day10_asteriod_detector.py","file_name":"day10_asteriod_detector.py","file_ext":"py","file_size_in_byte":2633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"114316052","text":"\"\"\" O custo ao consumidor de um carro novo é a soma do custo de fábrica com a percentagem do\ndistribuidor e dos impostos (aplicados ao custo de fábrica). Supondo que a percentagem do\ndistribuidor seja de 28% e os impostos de 45%, escrever um algoritmo que leia o custo de\nfábrica de um carro e escreva o custo ao consumidor.\"\"\"\n\ncusto_fabrica = float(input('Custo de fabrica: '))\npercent_distribu = 0.28\nimpostos = 0.45\ncusto_consumidor = (percent_distribu+impostos * custo_fabrica) + custo_fabrica\nprint('Custo ao consumidor é de %5.2f R$'%custo_consumidor)","sub_path":"Algoritmo/exercicio11.py","file_name":"exercicio11.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"199781736","text":"\"\"\"\nUsing predicate statement to filter dictionary comprehension and list comprehension\n\n\"\"\"\n\n\nfrom math import sqrt\nfrom pprint import pprint as pp\nfrom itertools import count, islice\nimport sys\n\ndef is_prime(x):\n    if x <2:\n        return False\n    for i in range(2, (int(sqrt(x))+1)):\n        if x % i == 0:\n            return False\n    return True\n\ndef main(num):\n    print(f\"Print primes below {num}:\")\n    num = int(num)\n    print([i for i in range(num) if is_prime(i)])  # predicate is_prime() is used to filter list \n    print(f\"Print a dictionary of primes: (prime square, prime cube) below {num}:\" )\n    pp({i: (i*i, i*i*i) \n            for i in range(num) \n            if is_prime(i)})  # predicate is_prime() is used to filter dictionary  \n\n\n\nif __name__ == \"__main__\":\n    main(sys.argv[1])\n\n\"\"\"\n$ python3 prime.py 50\n\nPrint primes below 50:\n[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]\n\nPrint a dictionary of primes: (prime square, prime cube) below 50:\n{2: (4, 8),\n 3: (9, 27),\n 5: (25, 125),\n 7: (49, 343),\n 11: (121, 1331),\n 13: (169, 2197),\n 17: (289, 4913),\n 19: (361, 6859),\n 23: (529, 12167),\n 29: (841, 24389),\n 31: (961, 29791),\n 37: (1369, 50653),\n 41: (1681, 68921),\n 43: (1849, 79507),\n 47: (2209, 103823)}\n\n\"\"\"","sub_path":"miscellaneous/prime.py","file_name":"prime.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"78335805","text":"import sys\nimport numpy as np\n# takes output from BEER with and split it into separate onehot format for submission : \n# in:\n# 0107_400123_0000 au12 au33 au33 au56 au56\n# 0107_400123_0002 au18 au33 au55 au55 au58\n# out:\n# 0107_400123_0000.txt\n#     0 0 0 0 0 0 1 0 0\n#     0 0 0 0 0 1 0 0 0\n#     0 0 0 0 0 1 0 0 0\n#     0 1 0 0 0 0 0 0 0\n#     0 0 0 1 0 0 0 0 0\n\nprint(\"init raw text file:\",sys.argv[1],\" dest folder:\",sys.argv[2])\n\ntxt_file=sys.argv[1]\ndest_dir=sys.argv[2]\nwith open(txt_file, 'r') as myfile:\n    data=myfile.read()\ndata_array=data.split('\\n')\n\naud_number=100 #BEER produces 100 different phonemes\nonehot=np.zeros((aud_number,),dtype=int) \nfor w in range(len(data_array)):\n    w_split= data_array[w].split(' ')\n    filename = w_split[0]+'.txt'\n    w_split=w_split[1:] #getting rid of sentence id\n    sentence=''\n    for i in range(len(w_split)):\n        indice=int(w_split[i].split('u')[1])-1\n        onehot[indice]=1\n        sentence+=' '.join([str(x) for x in onehot])+'\\n'\n        onehot[indice]=0    \n#    text_corpora+=sentence+' '\n    with open(dest_dir+'/'+filename, 'w') as myfile:\n        myfile.write(sentence) \n\n#text_corpora+='\\n'\n#with open(text_corpora_path,'w') as myfile:\n#        myfile.write(text_corpora)\n                                                            \n","sub_path":"recipes/zrc2019/beer_to_onehot.py","file_name":"beer_to_onehot.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"118807284","text":"import os\nimport pytest\n\nimport testinfra.utils.ansible_runner\n\ntestinfra_hosts = testinfra.utils.ansible_runner.AnsibleRunner(\n    os.environ['MOLECULE_INVENTORY_FILE']).get_hosts('postgres_cluster')\n\n@pytest.fixture(scope='module')\ndef hostname(host):\n    return host.ansible.get_variables()['inventory_hostname']\n\ndef test_hostname(host, hostname):\n    assert host.check_output('hostname -s') != hostname\n\ndef test_etc_hosts_has_our_name(host, hostname):\n    assert(\n        hostname.lower() in\n        host.file('/etc/hosts').content_string.lower()\n    )\ndef test_sudo(host):\n    with host.sudo():\n        assert host.check_output('whoami') == 'root'\n\n\n@pytest.mark.parametrize('pkg', [\n    'postgresql-12',\n    'postgresql-client-12',\n    'postgresql-server-dev-12',\n    'iptables'\n])\ndef test_pkg(host, pkg):\n    package = host.package(pkg)\n\n    assert package.is_installed\n\n@pytest.mark.parametrize('svc', [\n    'pgbouncer'\n])\ndef test_svc(host, svc):\n    service = host.service(svc)\n\n    assert service.is_running\n    assert service.is_enabled\n\n\n@pytest.mark.parametrize('port', [\n    8008,\n    5432,\n    6432\n])\ndef test_ports(host, hostname, port):\n    socket = host.socket(\"tcp://%s:%s\" % (hostname,port))\n\n    assert socket.is_listening\n","sub_path":"molecule/default/tests/test_postgres_cluster.py","file_name":"test_postgres_cluster.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"1859294","text":"# This function should take in an RGB image and return a new, standardized version\ndef standardize_input(image, crop=8):\n    ## TODO: Resize image and pre-process so that all \"standard\" images are the same size\n    standard_im = np.copy(image)\n    cropped_image = standard_im[crop:-crop, crop:-crop, :]\n    resized_image = cv2.resize(cropped_image, (32, 32))\n    return resized_image\n\n\n## TODO: One hot encode an image label\n## Given a label - \"red\", \"green\", or \"yellow\" - return a one-hot encoded label\n\n# Examples:\n# one_hot_encode(\"red\") should return: [1, 0, 0]\n# one_hot_encode(\"yellow\") should return: [0, 1, 0]\n# one_hot_encode(\"green\") should return: [0, 0, 1]\n\ndef one_hot_encode(label):\n    ## TODO: Create a one-hot encoded label that works for all classes of\n    # traffic lights\n    one_hot_encoded = [0, 0, 0]\n\n    if label == 'red':\n        one_hot_encoded[0] = 1\n    elif label == 'yellow':\n        one_hot_encoded[1] = 1\n    else:\n        one_hot_encoded[2] = 1\n\n    return one_hot_encoded\n\n\n## TODO: Create a brightness feature that takes in an RGB image and outputs a feature vector and/or value\n## This feature should use HSV colorspace values\n\n\ndef display_plot(rgb_image, masked_image):\n    \"\"\"Displays a pyplot of rgb_image and masked_image.\"\"\"\n    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))\n    ax1.set_title('rgb')\n    ax1.imshow(rgb_image, cmap='gray')\n    ax2.set_title('hsv')\n    ax2.imshow(masked_image, cmap='gray')\n\n\ndef is_red(features):\n    \"\"\"Helps determine if image is a red light via it's hue feature.\"\"\"\n    hue, saturation, value = features\n    if sum(saturation[0:10]) < sum(hue[0:10]):\n        return False\n    return True\n\n\ndef create_masked_image(rgb_image, lower_limit, upper_limit):\n    \"\"\"Creates a masked image given an rgb_image, and the lower and upper limits.\"\"\"\n    mask = cv2.inRange(rgb_image, lower_limit, upper_limit)\n    masked_image = np.copy(rgb_image)\n    masked_image[mask != 0] = [0, 0, 0]\n    return masked_image\n\n\ndef create_feature(rgb_image):\n    \"\"\"Brightness feature that takes an rgb_image and returns its HSV vector.\"\"\"\n    # Create mask\n    lower_gray = np.array([0, 0, 0])\n    upper_gray = np.array([50, 50, 50])  # 50 is best\n    masked_image = create_masked_image(rgb_image, lower_gray, upper_gray)\n\n    ## TODO: Convert image to HSV color space\n    hsv_image = cv2.cvtColor(masked_image, cv2.COLOR_RGB2HSV)\n\n    # Get and sum features\n    hue = hsv_image[:, :, 0]\n    saturation = hsv_image[:, :, 1]\n    value = hsv_image[:, :, 2]\n\n    hue_sum = np.sum(hue[:, :], axis=1)\n    saturation_sum = np.sum(saturation[:, :], axis=1)\n    value_sum = np.sum(value[:, :], axis=1)\n\n    ## TODO: Create and return a feature value and/or vector\n    features = [hue_sum, saturation_sum, value_sum]\n\n    # Helps to determine if a light is red\n    features[0] = np.array([0] * len(hue_sum))\n\n    # display_plot(rgb_image, masked_image)  # for testing purposes\n\n    return features\n\n\n# (Optional) Add more image analysis and create more features\n\n\ndef create_bw_feature(rgb_image):\n    \"\"\"Brightness feature that takes an rgb_image and returns its HSV vector.\"\"\"\n    # Create mask\n    lower_gray = np.array([0, 0, 0])\n    upper_gray = np.array([50, 50, 50])  # 50 is best\n    masked_image = create_masked_image(rgb_image, lower_gray, upper_gray)\n\n    # Get and sum features\n    bw_image = cv2.cvtColor(masked_image, cv2.COLOR_RGB2GRAY)\n    brightness = np.sum(bw_image[:, :], axis=1)\n    # display_plot(rgb_image, bw_image)  # for testing purposes\n\n    return brightness\n\n\ncreate_bw_feature(test_im)\n\n\n# This function should take in RGB image input\n# Analyze that image using your feature creation code and output a one-hot\n# encoded label\ndef estimate_label(rgb_image):\n    ## TODO: Extract feature(s) from the RGB image and use those features to\n    ## classify the image and output a one-hot encoded label\n    predicted_label = [0, 0, 0]\n\n    hue, saturation, value = create_feature(rgb_image)\n    brightness = create_bw_feature(rgb_image)\n\n    hsv_brightness = [x + y + z for x, y, z in zip(hue, saturation, value)]\n    bw_brightness = [x + y for x, y in zip(hsv_brightness, brightness)]\n    total_brightness = [x + y for x, y in zip(hsv_brightness, bw_brightness)]\n\n    max_brightness = total_brightness.index(max(total_brightness))\n\n    if max_brightness in range(0, 11):\n        predicted_label[0] = 1\n    elif max_brightness in range(10, 21):\n        predicted_label[1] = 1\n    else:\n        predicted_label[2] = 1\n\n    return predicted_label\n\n\nestimate_label(test_im)","sub_path":"TrafficLightClassifier/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"28732785","text":"from sqlalchemy.orm import relationship\n\nfrom main import db\nfrom models.customer import Customer\nfrom models.room import Room\n\n\nclass Reservation(db.Model):\n    __tablename__ = 'reservation'\n    reservation_no = db.Column(db.Integer, primary_key=True)\n    room_num = db.Column(db.Integer, db.ForeignKey('room.room_no'))\n    customers_id = db.Column(db.Integer, db.ForeignKey('customer.customer_id'))\n\n    room = relationship(Room)\n    customer = relationship(Customer)\n\n    def __init__(self, room_num, customers_id):\n        # self.reservation_no = reservation_no\n        self.room_num = room_num\n        self.customers_id = customers_id\n\ndb.create_all()","sub_path":"models/reservation.py","file_name":"reservation.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"108275416","text":"# Create a ParametricSuperEllipsoid surface that looks like a box\n# with smooth edges.\n#\nimport pyvista\nmesh = pyvista.ParametricSuperEllipsoid(n1=0.02, n2=0.02)\nmesh.plot(color='w', smooth_shading=True)\n#\n# Create one that looks like a spinning top.\n#\nmesh = pyvista.ParametricSuperEllipsoid(n1=4, n2=0.5)\nmesh.plot(color='w', smooth_shading=True, cpos='xz')\n","sub_path":"version/0.41/api/utilities/_autosummary/pyvista-ParametricSuperEllipsoid-1.py","file_name":"pyvista-ParametricSuperEllipsoid-1.py","file_ext":"py","file_size_in_byte":360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"448297008","text":"from __future__ import division\nimport os\nimport time\nimport cv2\nimport numpy as np\nimport sys\nimport pickle\nfrom keras import backend as K\nfrom keras.layers import Input\nfrom keras.models import Model\nfrom keras_frcnn import roi_helpers\nimport keras_frcnn.vgg as nn\n\nsys.setrecursionlimit(40000)\n\n##########################################################################\n#### Aux functions - copied as is from test_frcnn.py\n##########################################################################\ndef format_img_size(img, C):\n    \"\"\" formats the image size based on config \"\"\"\n    img_min_side = float(C.im_size)\n    (height, width, _) = img.shape\n\n    if width <= height:\n        ratio = img_min_side / width\n        new_height = int(ratio * height)\n        new_width = int(img_min_side)\n    else:\n        ratio = img_min_side / height\n        new_width = int(ratio * width)\n        new_height = int(img_min_side)\n    img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC)\n    return img, ratio\n\n\ndef format_img_channels(img, C):\n    \"\"\" formats the image channels based on config \"\"\"\n    img = img[:, :, (2, 1, 0)]\n    img = img.astype(np.float32)\n    img[:, :, 0] -= C.img_channel_mean[0]\n    img[:, :, 1] -= C.img_channel_mean[1]\n    img[:, :, 2] -= C.img_channel_mean[2]\n    img /= C.img_scaling_factor\n    img = np.transpose(img, (2, 0, 1))\n    img = np.expand_dims(img, axis=0)\n    return img\n\n\ndef format_img(img, C):\n    \"\"\" formats an image for model prediction based on config \"\"\"\n    img, ratio = format_img_size(img, C)\n    img = format_img_channels(img, C)\n    return img, ratio\n\n\n# Method to transform the coordinates of the bounding box to its original size\ndef get_real_coordinates(ratio, x1, y1, x2, y2):\n    real_x1 = int(round(x1 // ratio))\n    real_y1 = int(round(y1 // ratio))\n    real_x2 = int(round(x2 // ratio))\n    real_y2 = int(round(y2 // ratio))\n\n    return (real_x1, real_y1, real_x2, real_y2)\n##########################################################################\n##########################################################################\n##########################################################################\n\n##########################################################################\n## TAMIR : Start of my functions\n##########################################################################\n\n\nnum_features = None\nclass_mapping = None\nclass_to_color = None\n# Number of ROIs per iteration. Higher means more memory use\nnum_rois = 512\n\n\ndef load_model(config_path, model_path):\n    \"\"\"\n    config_path : config pickle file\n    weigths_path : model.h5 file\n    \"\"\"\n    for f in [config_path, model_path]:\n        if not os.path.isfile(f):\n            print(\"cant find {}\".format(f))\n            assert 0\n\n    # Load model config file\n    with open(config_path, 'rb') as f_in:\n        C = pickle.load(f_in)\n    # Turn of augmentation during test time\n    C.use_horizontal_flips = False\n    C.use_vertical_flips = False\n    C.rot_90 = False\n\n    if C.network == 'resnet50':\n        assert 0,\"Does not support resnet50 network\"\n    elif C.network == 'vgg':\n        print(\"Using VGG16 as faster-rcnn base network\")\n        global num_features\n        num_features = 512\n\n    # Define model path & lodel base model (without weights)\n    C.model_path = model_path\n    print('model path is {}'.format(C.model_path))\n\n    global class_mapping\n    global class_to_color\n    class_mapping = C.class_mapping\n    if 'bg' not in class_mapping:\n        class_mapping['bg'] = len(class_mapping)\n    class_mapping = {v: k for k, v in class_mapping.items()}\n    print(class_mapping)\n    class_to_color = {class_mapping[v]: np.random.randint(0, 255, 3) for v in class_mapping}\n    global num_rois\n    C.num_rois = int(num_rois)\n\n    if K.image_dim_ordering() == 'th':\n        input_shape_img = (3, None, None)\n        input_shape_features = (num_features, None, None)\n    else:\n        input_shape_img = (None, None, 3)\n        input_shape_features = (None, None, num_features)\n\n    img_input = Input(shape=input_shape_img)\n    roi_input = Input(shape=(C.num_rois, 4))\n    feature_map_input = Input(shape=input_shape_features)\n\n    # define the base network (resnet here, can be VGG, Inception, etc)\n    shared_layers = nn.nn_base(img_input, trainable=True)\n\n    # define the RPN, built on the base layers\n    num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)\n    rpn_layers = nn.rpn(shared_layers, num_anchors)\n    model_rpn = Model(img_input, rpn_layers)\n\n    classifier = nn.classifier(feature_map_input, roi_input, C.num_rois, nb_classes=len(class_mapping), trainable=True)\n    model_classifier = Model([feature_map_input, roi_input], classifier)\n\n    print('Loading model weights from {}'.format(C.model_path))\n    model_rpn.load_weights(C.model_path, by_name=True)\n    model_classifier.load_weights(C.model_path, by_name=True)\n    model_rpn.compile(optimizer='sgd', loss='mse')\n    model_classifier.compile(optimizer='sgd', loss='mse')\n    print(\"Done loading model\")\n\n    model_dict= {\n        'C': C,\n        'model_rpn': model_rpn,\n        'model_classifier': model_classifier,\n        'class_to_color' : class_to_color,\n    }\n\n    return model_dict\n# End of load_model\n\n\ndef predit_images(img_names, img_base_path, model_config, model_path, bbox_threshold=0.8, visualise=False):\n    \"\"\"\n    img_path_list : list of full paths of images to perform predict on\n    bbox_threshold : a region with P(class) below this value will be discarded\n    \"\"\"\n\n    # Sanity check - check all images exists\n    for img in img_names:\n        if not os.path.isfile(img_base_path + '/' + img):\n            print(\"cant locate {}\".format(img))\n            assert 0\n\n    # load model\n    model_dict = load_model(model_config, model_path)\n    C = model_dict['C']\n    model_rpn = model_dict['model_rpn']\n    model_classifier = model_dict['model_classifier']\n\n    # result dict\n    results = {}\n\n    # Iterate over each image - do predictions\n    for idx, img_name in enumerate(sorted(img_names)):\n        if not img_name.lower().endswith(('.bmp', '.jpeg', '.jpg', '.png', '.tif', '.tiff')):\n            continue\n        print(img_name)\n        st = time.time()\n        filepath = os.path.join(img_base_path, img_name)\n        img = cv2.imread(filepath)\n        orig_img = img.copy()\n\n        if visualise:\n            img_for_visualize = img.copy()\n\n        X, ratio = format_img(img, C)\n\n        if K.image_dim_ordering() == 'tf':\n            X = np.transpose(X, (0, 2, 3, 1))\n\n        # get the feature maps and output from the RPN\n        [Y1, Y2, F] = model_rpn.predict(X)\n\n        R = roi_helpers.rpn_to_roi(Y1, Y2, C, K.image_dim_ordering(), overlap_thresh=0.7)\n\n        # convert from (x1,y1,x2,y2) to (x,y,w,h)\n        R[:, 2] -= R[:, 0]\n        R[:, 3] -= R[:, 1]\n\n        # apply the spatial pyramid pooling to the proposed regions\n        bboxes = {}\n        probs = {}\n\n        for jk in range(R.shape[0] // C.num_rois + 1):\n            ROIs = np.expand_dims(R[C.num_rois * jk:C.num_rois * (jk + 1), :], axis=0)\n            if ROIs.shape[1] == 0:\n                break\n\n            if jk == R.shape[0] // C.num_rois:\n                # pad R\n                curr_shape = ROIs.shape\n                target_shape = (curr_shape[0], C.num_rois, curr_shape[2])\n                ROIs_padded = np.zeros(target_shape).astype(ROIs.dtype)\n                ROIs_padded[:, :curr_shape[1], :] = ROIs\n                ROIs_padded[0, curr_shape[1]:, :] = ROIs[0, 0, :]\n                ROIs = ROIs_padded\n\n            [P_cls, P_regr] = model_classifier.predict([F, ROIs])\n\n            for ii in range(P_cls.shape[1]):\n\n                if np.max(P_cls[0, ii, :]) < bbox_threshold or np.argmax(P_cls[0, ii, :]) == (P_cls.shape[2] - 1):\n                    continue\n\n                cls_name = class_mapping[np.argmax(P_cls[0, ii, :])]\n\n                if cls_name not in bboxes:\n                    bboxes[cls_name] = []\n                    probs[cls_name] = []\n\n                (x, y, w, h) = ROIs[0, ii, :]\n\n                cls_num = np.argmax(P_cls[0, ii, :])\n                try:\n                    (tx, ty, tw, th) = P_regr[0, ii, 4 * cls_num:4 * (cls_num + 1)]\n                    tx /= C.classifier_regr_std[0]\n                    ty /= C.classifier_regr_std[1]\n                    tw /= C.classifier_regr_std[2]\n                    th /= C.classifier_regr_std[3]\n                    x, y, w, h = roi_helpers.apply_regr(x, y, w, h, tx, ty, tw, th)\n                except:\n                    pass\n                bboxes[cls_name].append(\n                    [C.rpn_stride * x, C.rpn_stride * y, C.rpn_stride * (x + w), C.rpn_stride * (y + h)])\n                probs[cls_name].append(np.max(P_cls[0, ii, :]))\n\n        all_dets = []\n\n        results[img_name] = {\n            'bboxes': [],\n            'image': None,\n        }\n\n        for key in bboxes:\n            bbox = np.array(bboxes[key])\n\n            new_boxes, new_probs = roi_helpers.non_max_suppression_fast(bbox, np.array(probs[key]), overlap_thresh=0.5)\n            for jk in range(new_boxes.shape[0]):\n                (x1, y1, x2, y2) = new_boxes[jk, :]\n\n                (real_x1, real_y1, real_x2, real_y2) = get_real_coordinates(ratio, x1, y1, x2, y2)\n\n                if results[img_name]['image'] is None:\n                    results[img_name]['image'] = orig_img\n\n                results[img_name]['bboxes'].append(\n                    {\n                        'x_min': real_x1,\n                        'y_min': real_y1,\n                        'x_max': real_x2,\n                        'y_max': real_y2,\n                        'prob': new_probs[jk],\n                    }\n                )\n\n                if visualise:\n                    print(\"visualize : {}\".format(str((real_x1, real_y1, real_x2, real_y2))))\n\n                    cv2.rectangle(img_for_visualize, (real_x1, real_y1), (real_x2, real_y2),\n                                  (int(class_to_color[key][0]), int(class_to_color[key][1]), int(class_to_color[key][2])),\n                                  thickness=10)\n\n                    textLabel = '{}: {}'.format(key, int(100 * new_probs[jk]))\n                    all_dets.append((key, 100 * new_probs[jk]))\n\n                    (retval, baseLine) = cv2.getTextSize(textLabel, cv2.FONT_HERSHEY_COMPLEX, 1, 1)\n                    textOrg = (real_x1, real_y1 - 0)\n\n                    cv2.rectangle(img_for_visualize, (textOrg[0] - 5, textOrg[1] + baseLine - 5),\n                                  (textOrg[0] + retval[0] + 5, textOrg[1] - retval[1] - 5), (0, 0, 0), 2)\n                    cv2.rectangle(img_for_visualize, (textOrg[0] - 5, textOrg[1] + baseLine - 5),\n                                  (textOrg[0] + retval[0] + 5, textOrg[1] - retval[1] - 5), (255, 255, 255), -1)\n                    cv2.putText(img_for_visualize, textLabel, textOrg, cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 0), 1)\n\n        if visualise:\n            from matplotlib import pyplot as plt\n            print('Elapsed time = {}'.format(time.time() - st))\n            print(all_dets)\n            plt.figure()  # figure starts from 1 ...\n            plt.imshow(cv2.cvtColor(img_for_visualize, cv2.COLOR_BGR2RGB))\n            plt.show()\n\n    print(\"faster_rcnn DONE\")\n    return results\n\n\ndef main():\n\n    from optparse import OptionParser\n    parser = OptionParser()\n    parser.add_option(\"-m\", \"--models\", dest=\"models\", help=\"Path to model.\")\n    parser.add_option(\"-p\", \"--test\", dest=\"test_path\", help=\"test path.\")\n    (options, args) = parser.parse_args()\n\n    img_base_path = options.test_path\n    img_names = []\n    for img in os.listdir(img_base_path):\n        img_names.append(img)\n    print(\"Images to predict {}\".format(str(img_names)))\n\n    model_config = os.path.join(options.models, 'config.pickle')\n    model_path = os.path.join(options.models, 'model_frcnn.hdf5')\n\n    predit_images(img_names, img_base_path, model_config, model_path, visualise=True)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"predict_faster_rcnn.py","file_name":"predict_faster_rcnn.py","file_ext":"py","file_size_in_byte":12011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"417924157","text":"from twilio.rest import Client\nimport os\ndef send_sms(message):\n\n\n\n    # the following line needs your Twilio Account SID and Auth Token\n    client = Client(os.environ.get(\"TWILIO_SSID\"), os.environ.get(\"TWILIO_AUTH_TOKEN\"))\n\n    # change the \"from_\" number to your Twilio number and the \"to\" number\n    # to the phone number you signed up for Twilio with, or upgrade your\n    # account to send SMS to any phone number\n    client.messages.create(to=str(os.environ.get(\"RECIPIENT_PHONE\")), \n                       from_=str(os.environ.get(\"TWILIO_PHONE\")), \n                       body=str(message))\n    error_message_sent = False","sub_path":"bot/helpers/twilioHelper.py","file_name":"twilioHelper.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"226068365","text":"import time\nimport multiprocessing\n\nargs = [(10000, 3), (20000, 10001), (30000, 20001)]\n\n\ndef find_primes(end, start):\n    lst = []\n    for i in range(start, end + 1):\n        for j in range(2, i):\n            if i % j == 0:\n                # если делитель найден, число не простое.\n                break\n        else:\n            lst.append(i)\n    print(f'конец выполнения {end, start}')\n    return lst\n\n\nif __name__ == '__main__':\n    start = time.time()\n    processes = []\n    for arg in args:\n        p = multiprocessing.Process(target=find_primes, args=arg)\n        p.start()\n        processes.append(p)\n    for i in processes:\n        i.join()\n\n    print(f'время выполнения: {time.time() - start}')\n","sub_path":"Practice/Kolpakov/lect_9/task_1.3.py","file_name":"task_1.3.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"584614145","text":"from __future__ import print_function\n\nimport enum\nimport os\nimport numpy as np\nimport random\nfrom typing import Callable, Tuple\n\nimport torch\nimport torch.utils.data as data\nimport torchvision.transforms.functional as F\nfrom torchvision import transforms\nfrom PIL import Image\nimport cv2\n\n\n@enum.unique\nclass Mode(enum.Enum):\n    TRAIN = 0\n    VAL = 1\n\n\ndef output_size(i):\n    \"\"\"Given shape of input image as i,i,3 in deeplab-resnet model, this function\n    returns j such that the shape of output blob of is j,j,21 (21 in case of VOC)\"\"\"\n    j = int(i)\n    j = (j+1) // 2\n    j = int(np.ceil((j+1) / 2.0))\n    j = (j+1) // 2\n    return j\n\n\nclass PadToTransform:\n    \"\"\"\n    Zero-pads an image to be at minimum the passed-in size.\n    \"\"\"\n\n    def __init__(self, target_size: Tuple[int, int]) -> None:\n        self.target_height = target_size[0]\n        self.target_width = target_size[1]\n\n    def __call__(self, img):\n        # NOTE: height and width are reversed between PIL.Image and target size\n        im_width, im_height = img.size\n        if im_width < self.target_width:\n            # Pad the right\n            img = F.pad(img, (0, 0, self.target_width - im_width, 0))\n        if im_height < self.target_height:\n            # Pad the bottom\n            img = F.pad(img, (0, 0, 0, self.target_height - im_height))\n\n        return img\n\n\nclass TrainJointTransform:\n    \"\"\"\n    Preprocessing done jointly on input image and label image during training.\n    \"\"\"\n\n    def __init__(self,\n                 size: Tuple[int, int] = (321, 321),\n                 flip_prob: float = 0.5) -> None:\n        self.size = size\n        self.flip_prob = flip_prob\n\n    def __call__(self, img, gt):\n        # Differing behavior for binary vs. multiclass case\n        gt_raw = np.array(gt)\n        if np.all(np.unique(gt_raw) == [0, 255]):\n            gt_raw[gt_raw == 255] = 1\n        else:\n            gt_raw[gt_raw == 255] = 0\n        gt = Image.fromarray(gt_raw)\n\n        # Padding and random cropping\n        scale = random.uniform(0.5, 1.1)\n        scaled_size = (int(scale * self.size[0]), int(scale * self.size[1]))\n        pad = PadToTransform(scaled_size)\n        img = pad(img)\n        gt = pad(gt)\n        i, j, h, w = transforms.RandomCrop.get_params(\n            img, output_size=scaled_size)\n        img = F.crop(img, i, j, h, w)\n        gt = F.crop(gt, i, j, h, w)\n\n        # Random flip\n        if random.random() > self.flip_prob:\n            img = F.hflip(img)\n            gt = F.hflip(gt)\n\n        return img, gt\n\n\nclass ResizeLabelBatch:\n    \"\"\"\n    Mirror the forward pass with labels for each separate interpolated output.\n    \"\"\"\n\n    def __init__(self):\n        pass\n\n    def resize_batch(self, label, size):\n        \"\"\"Resize and convert to tensor\"\"\"\n        resized = F.resize(label, size, interpolation=Image.NEAREST)\n        return F.to_tensor(resized) * 255\n\n    def __call__(self, gt):\n        a = output_size(gt.size[0])\n        b = output_size((gt.size[0] / 2) + 1)\n        return [self.resize_batch(gt, size) for size in [a, a, b, a]]\n\n\nTRANSFORMS = {\n    Mode.TRAIN: {\n        \"joint_transform\": TrainJointTransform(),\n        \"img_transform\": transforms.Compose([\n            transforms.ToTensor(),\n            transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                                 std=[0.229, 0.224, 0.225])\n\n        ]),\n        \"gt_transform\": ResizeLabelBatch()\n    },\n    Mode.VAL: {\n        \"joint_transform\": None,\n        \"img_transform\": transforms.Compose([\n            PadToTransform((513, 513)),\n            transforms.ToTensor(),\n            transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                                 std=[0.229, 0.224, 0.225])\n\n        ]),\n        \"gt_transform\": transforms.ToTensor()\n    }\n}\n\n\nclass SegmentationDataset(data.Dataset):\n    \"\"\"\n    Generic segmentation Dataset. Note that joint transform will be run first.\n    \"\"\"\n\n    def __init__(\n        self,\n        mode: Mode,\n        list_path: str,\n        img_path: str,\n        gt_path: str,\n        img_ext: str,\n        gt_ext: str\n    ) -> None:\n        with open(list_path, 'r') as f:\n            self.filenames = f.readlines()\n        self.img_transform = TRANSFORMS[mode][\"img_transform\"]\n        self.gt_transform = TRANSFORMS[mode][\"gt_transform\"]\n        self.joint_transform = TRANSFORMS[mode][\"joint_transform\"]\n        self.img_path = img_path\n        self.gt_path = gt_path\n        self.img_ext = img_ext\n        self.gt_ext = gt_ext\n\n    def __getitem__(self, idx: int) -> Tuple:\n        filename = self.filenames[idx].rstrip()\n\n        img_path = os.path.join(\n            self.img_path, \"{}{}\".format(filename, self.img_ext))\n        gt_path = os.path.join(\n            self.gt_path, \"{}{}\".format(filename, self.gt_ext))\n\n        # NOTE: using Pillow RGB images\n        img = Image.open(img_path)\n        gt = Image.open(gt_path)\n\n        if self.joint_transform is not None:\n            img, gt = self.joint_transform(img, gt)\n        if self.img_transform is not None:\n            img = self.img_transform(img)\n        if self.gt_transform is not None:\n            gt = self.gt_transform(gt)\n\n        return img, gt\n\n    def __len__(self) -> int:\n        return len(self.filenames)\n","sub_path":"deeplab/datasets.py","file_name":"datasets.py","file_ext":"py","file_size_in_byte":5235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"644300646","text":"# -*- coding: utf-8 -*-\r\n\r\nimport numpy as np\r\nimport vpython as vp \r\nimport pandas as pd \r\nimport random as rd\r\n\r\n''' Solar system '''\r\n\r\nclass OrbitingBody:\r\n    '''\r\n    Orbital motion of a body relative to a given central body \r\n    '''\r\n    def __init__(self, centr_body, **kwargs):\r\n        \r\n        self.name = kwargs.pop('name')\r\n        self.radius = kwargs.pop('radius')*1e3  # km to m\r\n        self.mass = kwargs.pop('mass')*1e24  # kg\r\n        self.color = getattr(vp.color,kwargs.pop('color'))\r\n        self.centr_body = centr_body   # passed in as an object \r\n        \r\n        self.is_moon = False  # marks whether this is a planet/sun or a moon\r\n        \r\n        # If this is a planet or a sun \r\n        if 'perih_distance' in kwargs: \r\n            perih_distance = kwargs.pop('perih_distance')*1e9  # Perihelion distance; 10^6km to m  \r\n            long_perih = kwargs.pop('long_perih')*np.pi/180.  # Longitude of perihelion; deg to rad \r\n            long_asc_node = kwargs.pop('long_asc_node')*np.pi/180. # Longitude of ascending node \r\n            inclination = kwargs.pop('inclination')*np.pi/180.  # Orbital inclination            \r\n            # Initialize planet/sun position\r\n            self.position = init_pos(self.centr_body, perih_distance, long_perih, long_asc_node, inclination)\r\n\r\n            print('Generating',self.name,'at perihelion distance of',perih_distance/1e9,'e+6 km.')\r\n\r\n        # If this is a moon \r\n        if 'm_distance' in kwargs:\r\n            self.is_moon = True\r\n            distance = kwargs.pop('m_distance')*1e3            \r\n            # Initialize moon position          \r\n            self.position = init_moon_pos(self.centr_body, distance)\r\n            \r\n            print('Generating',self.name,'at',distance/1e3,'km from',self.centr_body.name)\r\n                        \r\n        # Initialize body velocity\r\n        self.velocity = init_vel(self.centr_body, self.position)                  \r\n        # Initialize body acceleration \r\n        self.acceleration = init_acc(self.centr_body, self.position)\r\n        \r\n        # Draw vpython representation \r\n        trail = True if self.is_moon == False else False \r\n        self.sphere = vp.sphere(pos=self.position*model_scale,radius=self.radius*model_scale,color=self.color,\r\n                                make_trail=trail,trail_radius=trail_size*model_scale)\r\n        # Draw name label above sphere if this is a planet or sun \r\n        if self.is_moon == False: \r\n            self.label = vp.text(pos=model_scale*vp.vector(self.position.x,self.position.y+1e10*model_scale,self.position.z),\r\n                                 align='center',text=self.name,height=5e10*model_scale,color=vp.color.green)  \r\n            \r\n            \r\n        # Initialize the trojan belt for jupiter\r\n        if self.name == 'Jupiter' and show_trojan_belt == True:\r\n            print('Generating Trojan belts')\r\n            # Use position (in cartesian coord) to find theta on xz-plane \r\n            self.init_j_theta = np.arctan(self.position.x/self.position.z)          \r\n            if self.position.z < 0:  # if denominator is negative (in Quadrant2/3), add 180deg \r\n                self.init_j_theta += np.pi\r\n                    \r\n            self.tro_rock_list, self.tro_theta0_list, self.tro_radius_list = init_trojan_belt(self.init_j_theta)\r\n            \r\n        if show_arrows == True:\r\n            self.arrow = vp.arrow(pos=model_scale*self.position,axis=self.velocity,length=1e11*model_scale,color=vp.color.red)\r\n            \r\n        \r\n    def motion(self, dt, i):\r\n        '''\r\n         Update object positions as a function of timestep \r\n        '''\r\n        # Verlocity verlet method  \r\n        # Update body position, velocity and acceleration\r\n        self.position, self.velocity, self.acceleration = velocity_verlet(self.centr_body, self.position,self.velocity, \r\n                                                                          self.acceleration, dt) \r\n        # Update vpython sphere location \r\n        self.sphere.pos = self.position*model_scale\r\n        if self.is_moon == False: # Update text position\r\n            self.label.pos = model_scale*vp.vector(self.position.x,self.position.y+5e9*model_scale,self.position.z)\r\n        \r\n        \r\n        # Update trojan belt position to follow jupiter \r\n        if self.name == 'Jupiter' and show_trojan_belt == True:\r\n            \r\n            for tro_rock,radius,theta0 in zip(self.tro_rock_list, self.tro_radius_list, self.tro_theta0_list):\r\n                \r\n                # Change in theta, from original position to updated self.position \r\n                updated_theta = np.arctan(self.position.x/self.position.z)\r\n                if self.position.z < 0: \r\n                    updated_theta += np.pi\r\n                j_del_theta = updated_theta - self.init_j_theta\r\n                \r\n                # new theta = starting angle of rock relative to original theta + jupiter's moved theta\r\n                theta = theta0 + j_del_theta  \r\n                tro_rock.pos = model_scale*vp.vector(radius*vp.sin(theta),0,radius*vp.cos(theta))\r\n                \r\n        if show_arrows == True:\r\n            self.arrow.pos = model_scale*self.position\r\n            self.arrow.axis = self.velocity \r\n            \r\n        # Print planet's updated position     \r\n        if i%int(print_step) == 0 and self.is_moon == False:\r\n            print(self.name,'at location',self.position)\r\n        \r\n        \r\nclass Origin:\r\n    ''' Setting up the first centr_body object '''\r\n    def __init__(self, pos, vel, mass):\r\n        self.position = pos\r\n        self.velocity = vel\r\n        self.mass = mass  # physically unimportant info \r\n                                                                         \r\n\r\n# Tools used in class OrbitingBody -\r\n        \r\ndef init_pos(centr_body, perih_distance, long_perih, long_asc_node, inclination):\r\n    ''' Start planet at perihelion (also start sun at origin) '''\r\n    # let z-axis be the direction pointing towards the first point of Aries \r\n    # see http://www.davidcolarusso.com/astro/index.html for cartesian coord calculation with orbital parameters\r\n    x = perih_distance * (np.sin(long_asc_node)*np.cos(long_perih) + np.cos(long_asc_node)*np.sin(long_perih)*np.cos(inclination))\r\n    y = perih_distance * (np.sin(long_perih)*np.sin(inclination))\r\n    z = perih_distance * (np.cos(long_asc_node)*np.cos(long_perih) - np.sin(long_asc_node)*np.sin(long_perih)*np.cos(inclination))\r\n    position = centr_body.position + vp.vector(x,y,z)\r\n    \r\n    return position\r\n\r\n\r\ndef init_moon_pos(centr_body, distance):\r\n    ''' Start moon at anywhere m_distance away from planet '''\r\n    # All coords defined relative to origin \r\n    x = rd.uniform(centr_body.position.x-distance, centr_body.position.x+distance)\r\n    y = centr_body.position.y  # suppose moon only orbits in the xz-plane\r\n    z_sign = rd.choice([1,-1])\r\n    z = centr_body.position.z + z_sign * np.sqrt(distance**2 - (x-centr_body.position.x)**2)\r\n    position = vp.vector(x,y,z)\r\n    \r\n    return position \r\n    \r\n\r\ndef init_vel(centr_body, position):\r\n    ''' Calculates velocity of an orbiting object with its initial position '''\r\n    if position != centr_body.position:\r\n        radius = vp.mag(position - centr_body.position)\r\n        # equate gravitational force to centripetal force gives\r\n        vel_mag = np.sqrt(G*centr_body.mass/radius)\r\n        # velocity vector is normal to the vector towards central object and the vector normal to xz-plane\r\n        vec1 = vp.vector(centr_body.position - position)\r\n        vec2 = vp.vector(0,1,0)\r\n        vel_vec = vp.cross(vec1,vec2)\r\n        velocity = vel_mag * vp.norm(vel_vec) + centr_body.velocity  # taking centr_body motion into account\r\n    else:\r\n        velocity = vp.vector(0,0,0)  # avoid division by 0 for sun's case \r\n    \r\n    return velocity\r\n    \r\n\r\ndef init_acc(centr_body, position):\r\n    ''' Calculates acceleration of an orbiting object due to the centr_body '''\r\n    # neglect forces from bodies other than the one it is orbiting\r\n    if position != centr_body.position:\r\n        radius_vec = position - centr_body.position\r\n        acceleration = -G * centr_body.mass * radius_vec / vp.mag(radius_vec)**3\r\n    else:\r\n        acceleration = vp.vector(0,0,0)  # for sun\r\n        \r\n    return acceleration \r\n\r\n\r\ndef velocity_verlet(centr_body, position, velocity, acceleration, dt):\r\n    ''' Updates object position with Velocity Verlet algorithm '''\r\n    if position != centr_body.position:\r\n        # Update position\r\n        position = position + velocity*dt + 1/2.*acceleration*(dt)**2\r\n        # Storing the old acceleration from previous timestep iteration \r\n        old_acceleration = acceleration \r\n        # Update acceleration (neglect acceleration due objects other than the one it is orbiting) \r\n        radius_vec = vp.vector(position - centr_body.position)  # update radius vec wrt to updated central object position\r\n        acceleration = -G * centr_body.mass * radius_vec / vp.mag(radius_vec)**3\r\n        # Update velocity \r\n        velocity = velocity + 1/2.*(old_acceleration + acceleration)*dt\r\n    else:\r\n        pass  # for sun\r\n    \r\n    return position, velocity, acceleration\r\n\r\n\r\ndef init_trojan_belt(init_j_theta):\r\n    ''' Generate the two trojan belts at L5&L6 which moves with jupiter '''\r\n    # NOTE: The belts, in theory, should move along jupiter's orbit, thus we have to take perihelion angles into account \r\n    # and treat each rock as a 'planet' to update their positions; this is heavy. \r\n    # Hence, we assume the belt to lie on xz-plane only and all rocks moves as a whole following jupiter's position \r\n\r\n    # radius range of belt\r\n    radius_mean = np.mean(tro_belt_radius) \r\n    radius_std = (tro_belt_radius[1]-tro_belt_radius[0])/4.  # estimated std \r\n    \r\n    # magnitude of range of angle between jupiter and belt\r\n    sep_angle_mean = tro_belt_jup_sep *np.pi/180. \r\n    angle_std = tro_belt_ang_width/3. *np.pi/180.  # estimated\r\n    \r\n    rock_list = []\r\n    theta0_list = []\r\n    radius_list = []\r\n    for i in range(int(num_tro)):\r\n        # the two regions of asteroids at the front and back of jupiter \r\n        for sign in [-1,1]:\r\n            theta0 = np.random.normal(init_j_theta+sign*sep_angle_mean,angle_std)\r\n            radius = np.random.normal(radius_mean,radius_std) \r\n            size = rd.uniform(tro_size[0],tro_size[1])/2. # radius\r\n            rock = vp.sphere(pos=model_scale*vp.vector(radius*np.sin(theta0),0,radius*np.cos(theta0)),\r\n                                  radius=model_scale*size,color=vp.color.white)\r\n            rock_list.append(rock)\r\n            theta0_list.append(theta0)\r\n            radius_list.append(radius)\r\n            \r\n    return rock_list, theta0_list, radius_list \r\n\r\n\r\n###############################################################################################################################          \r\n\r\n''' \r\nProcess data and Generate all bodies in solar system \r\n'''\r\n\r\ndef init_system(filename):\r\n\r\n    print('Initializing solar system objects')\r\n    data = pd.read_csv(filename, sep=',')\r\n    \r\n    # Extract planet names and their number of moons \r\n    planet_name_list = [name for name in list(data['name']) if pd.isnull(name) == False and name != 'Sun']\r\n    num_moon_list = [int(num_moon) for num_moon in list(data['num_moon']) if pd.isnull(num_moon) == False]\r\n\r\n    # Store all bodies into a list \r\n    object_list = []\r\n\r\n    # Initialize first centr_body object     \r\n    origin = Origin(vp.vector(0,0,0), vp.vector(0,0,0), 10.)\r\n    \r\n    # Index of Sun\r\n    sun_index = pd.Index(list(data['name'])).get_loc('Sun')\r\n    \r\n    # Initilize sun \r\n    sun = OrbitingBody(origin,\r\n                       name = data.iloc[sun_index][data.columns.get_loc('name')],\r\n                       radius = data.iloc[sun_index][data.columns.get_loc('radius')],  \r\n                       mass = data.iloc[sun_index][data.columns.get_loc('mass')],\r\n                       perih_distance = data.iloc[sun_index][data.columns.get_loc('perih_distance')],  \r\n                       long_perih = data.iloc[sun_index][data.columns.get_loc('long_perih')],  \r\n                       long_asc_node = data.iloc[sun_index][data.columns.get_loc('long_asc_node')],  \r\n                       inclination = data.iloc[sun_index][data.columns.get_loc('inclination')],  \r\n                       color = data.iloc[sun_index][data.columns.get_loc('color')])\r\n    \r\n    object_list.append(sun)\r\n    \r\n    # Then set sun as centr_body object, pass its pos and vel etc. into planet objects to call class again\r\n    # same logic for the moons \r\n    \r\n    for planet_name, num_moon in zip(planet_name_list, num_moon_list):\r\n        \r\n        # Index of starting row in data for this planet \r\n        planet_index = pd.Index(list(data['name'])).get_loc(str(planet_name))\r\n        \r\n        # Initialize planet \r\n        planet = OrbitingBody(sun,\r\n                              name = data.iloc[planet_index][data.columns.get_loc('name')], \r\n                              radius = data.iloc[planet_index][data.columns.get_loc('radius')],  \r\n                              mass = data.iloc[planet_index][data.columns.get_loc('mass')],  \r\n                              perih_distance = data.iloc[planet_index][data.columns.get_loc('perih_distance')],  \r\n                              long_perih = data.iloc[planet_index][data.columns.get_loc('long_perih')],  \r\n                              long_asc_node = data.iloc[planet_index][data.columns.get_loc('long_asc_node')],  \r\n                              inclination = data.iloc[planet_index][data.columns.get_loc('inclination')],  \r\n                              color = data.iloc[planet_index][data.columns.get_loc('color')])\r\n        \r\n        object_list.append(planet)\r\n                \r\n        if num_moon != 0:\r\n            # Extract moons info and store as lists  \r\n            moon_diameter_list = list(data.iloc[planet_index:(planet_index+num_moon),data.columns.get_loc('m_diameter')])\r\n            moon_distance_list = list(data.iloc[planet_index:(planet_index+num_moon),data.columns.get_loc('m_distance')])\r\n            \r\n            for i in range(num_moon):\r\n\r\n                # Initialize moon\r\n                moon = OrbitingBody(planet,\r\n                                    name = 'Moon'+str(i),\r\n                                    radius = moon_diameter_list[i]/2.,\r\n                                    mass = 1.,   # physically unimportant info, just to keep class attr consistent\r\n                                    m_distance = moon_distance_list[i],  \r\n                                    color = 'white')\r\n                \r\n                object_list.append(moon)\r\n                \r\n    return object_list \r\n\r\n\r\ndef init_belt(belt_radius,size_range,num_rock):\r\n    ''' Generate the ring-like belts '''\r\n    rock_list = []\r\n    radius_list = []\r\n    theta0_list = []\r\n    # Create objects in a belt at random positions following a normal distribution\r\n    radius_mean = np.mean(belt_radius) # radius range of belt\r\n    radius_std = (belt_radius[1]-belt_radius[0])/4.  # estimated std \r\n    for i in range(int(num_rock)):\r\n        \r\n        loading = (i+1)/num_rock*100\r\n        if loading %10 == 0:\r\n            print('loading... ',int(loading),'%')\r\n\r\n        radius = np.random.normal(radius_mean,radius_std) \r\n        size = rd.uniform(size_range[0],size_range[1])/2. # radius\r\n        theta0 = rd.uniform(0,2*np.pi)\r\n        rock = vp.sphere(pos=model_scale*vp.vector(radius*vp.cos(theta0),0,radius*vp.sin(theta0)),\r\n                         radius=model_scale*size,color=vp.color.white)\r\n        rock_list.append(rock)\r\n        radius_list.append(radius)\r\n        theta0_list.append(theta0)\r\n         \r\n    return rock_list, radius_list, theta0_list\r\n            \r\n\r\ndef init_stars(radius,size,num_star):\r\n    ''' Generate background stars '''\r\n    for i in range(int(num_star)):\r\n        theta = rd.uniform(0,2*np.pi)\r\n        phi = rd.uniform(0,np.pi)\r\n        vp.sphere(pos=radius*model_scale*vp.vector(vp.sin(theta)*vp.cos(phi),vp.sin(theta)*vp.sin(phi),vp.cos(theta)),\r\n                  radius=model_scale*size,color=vp.color.white)\r\n    \r\n    \r\ndef main():\r\n\r\n    ''' Set up Stationary scene '''\r\n    \r\n    scene = vp.canvas(title='Solar system',width=1300,height=600,center=vp.vector(0,0,0))\r\n    scene.autoscale = False\r\n    scene.range = star_radius*model_scale\r\n    \r\n    scene.camera.pos = vp.vector(0,100000,star_radius*model_scale)  # nice view\r\n#    scene.camera.pos = vp.vector(100000,0,0)  # side view\r\n#    scene.camera.pos = vp.vector(0,100000,0)  # top down view\r\n    scene.camera.axis = vp.vector(vp.vector(0,0,0) - scene.camera.pos)\r\n    \r\n    # Background stars \r\n    if show_backgound_stars == True:\r\n        init_stars(star_radius, star_size, num_star)\r\n    \r\n    ''' Set up Animated objects '''\r\n    \r\n    # Intialize Asteroid belt \r\n    if show_asteroid_belt == True:\r\n        print('Generating asteroid belt')\r\n        ast_list, ast_radius_list, ast_theta0_list = init_belt(ast_belt_radius, ast_size, num_ast)   \r\n    # Initialize Kuiper belt \r\n    if show_kuiper_belt == True:\r\n        print('Generating kuiper belt')\r\n        kui_list, kui_radius_list, kui_theta0_list = init_belt(kui_belt_radius, kui_size, num_kui)\r\n    # Initialize planet system\r\n    object_list = init_system(filename)\r\n    \r\n    \r\n    ''' Animation '''\r\n    \r\n    print('Begin animation')\r\n    dt = 100\r\n    i = 0  # Counter for moving the belts\r\n    \r\n    while True:\r\n        vp.rate(500000)\r\n    \r\n        # update planet position \r\n        for body in object_list: \r\n            body.motion(dt, i)\r\n            \r\n        # rotate asteroid belt and kuiper belt \r\n        if rotate_belt == True and show_asteroid_belt == True:\r\n            # shift theta of all belt objects        \r\n            for ast, ast_radius, ast_theta0 in zip(ast_list, ast_radius_list, ast_theta0_list):\r\n                ast_theta = ast_theta0 + i * 2*np.pi/ast_belt_period   # change in theta per unit time = omega = 2pi/T \r\n                ast.pos = model_scale*ast_radius*vp.vector(vp.cos(ast_theta),0,vp.sin(ast_theta))\r\n        if rotate_belt == True and show_kuiper_belt == True:\r\n            for kui, kui_radius, kui_theta0 in zip(kui_list, kui_radius_list, kui_theta0_list):\r\n                kui_theta = kui_theta0 + i * 2*np.pi/kui_belt_period\r\n                kui.pos = model_scale*kui_radius*vp.vector(vp.cos(kui_theta),0,vp.sin(kui_theta))\r\n            \r\n        if i%int(print_step) == 0:\r\n            print('Celestial object positions update completed - Running next iteration')          \r\n          \r\n        i += 1\r\n\r\n\r\n''' Input settings '''\r\n\r\nfilename = 'planet_data.dat'\r\n\r\nmodel_scale = 0.0000001 \r\n\r\nG = 6.67408e-11\r\n\r\n# planet trail\r\ntrail_size = 1e9\r\n# arrows showing object velocity\r\nshow_arrows = False\r\n# print planet locations every __ iterations \r\nprint_step = 10000\r\n\r\n# Background controls \r\nshow_backgound_stars = True\r\nnum_star = 1e4\r\nstar_size = 1e10\r\nstar_radius = 1e13  # distance from (0,0,0)\r\n\r\n# Belt controls \r\nshow_trojan_belt = True\r\nshow_asteroid_belt = True\r\nshow_kuiper_belt = True\r\nrotate_belt = False\r\n\r\n# Trojan belt \r\ntro_belt_radius = [7.5547e11,8.0035e11]  # m; range\r\ntro_belt_ang_width = 35. # deg; angle subtended\r\ntro_belt_jup_sep = 60. # deg; angle away from jupiter; L_4 L_5 lagrangian points \r\ntro_size = [1e8,1e9]\r\nnum_tro = 6e3 # per region\r\n\r\n# Asteroid belt \r\nast_belt_radius = [3.291e11, 4.787e11]  # m; range \r\nast_size = [1e8,2e9] #[1e3, 1e6]  \r\nnum_ast =  1e4\r\nast_belt_period = 1.577e8 # s\r\n\r\n# Kuiper belt \r\nkui_belt_radius = [4.488e12, 5.984e12] \r\nkui_size = [1e9,6e9]  #[2e3,1e6]\r\nnum_kui =  1e5\r\nkui_belt_period = 6.307e9\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"solar_system_test11.py","file_name":"solar_system_test11.py","file_ext":"py","file_size_in_byte":19845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"277001049","text":"import sqlite3\nfrom urllib.request import urlopen\nfrom bs4 import BeautifulSoup\nfrom telegram import InlineKeyboardButton\n\nfrom config import supported_languages, default_language, db_path\n\n# Funzioni per bot.py\n\n# Convert lang abbreviation to unicode flag\n# Es: 'it' -> italian flag\ndef flag(code):\n    code = code.upper()\n    OFFSET = 127462 - ord('A')\n    return chr(ord(code[0]) + OFFSET) + chr(ord(code[1]) + OFFSET)\n\n#\n# Database operations\n#\n# - get_*\n# - set_*\n# - save_*\n# \ndef get_chat_id(update, as_int=False):\n    try:\n        ch_id = update.message.chat.id\n    except:\n        ch_id = update.callback_query.message.chat.id\n    finally:\n        return int(ch_id) if as_int else ch_id\n\ndef get_lang(update, u_id=None):\n    # Database: Lingua\n    if u_id is None: u_id = str(update.message.from_user.id)\n    con = sqlite3.connect(db_path)\n    cur = con.cursor()\n    cur.execute(\"SELECT lang FROM Lingua WHERE u_id = ?\", (u_id,))\n    lang = cur.fetchone()\n    if lang is not None and lang[0] != '':\n        lang = lang[0]\n    else:\n        lang = default_language\n    con.close()\n    return lang\n\ndef get_user_settings(update, lang_list, u_id=None):\n    if u_id is None: u_id = str(update.message.from_user.id)\n    lang = flag(get_lang('', u_id=u_id))\n    # Database: Orario\n    con = sqlite3.connect(db_path)\n    cur = con.cursor()\n\n    # Daily orario\n    cur.execute(\"SELECT alarm FROM Orario WHERE u_id = ?\", (u_id,))\n    alarm_hour = cur.fetchone()[0]\n    if alarm_hour is None:\n        alarm = 'off'\n        reply = lang_list[0].format(lang, ': *' + alarm + '*')\n    else:\n        alarm = 'on'\n        reply = lang_list[0].format(lang, ' ('+lang_list[4]+' '+alarm_hour+'): *'+alarm+'*')\n    alarm_btn = lang_list[1] if alarm == 'off' else lang_list[2]\n    \n    # Daily mensa\n    cur.execute(\"SELECT mensa FROM Utenti WHERE u_id = ?\", (u_id,))\n    mensa = cur.fetchone()[0]\n    if mensa is None:\n        mensa_callback = '2-mensa-enable'\n        mensa_label = lang_list[6]\n        reply += lang_list[8].format(': *off*')\n    else:\n        mensa_callback = '2-mensa-disable'\n        mensa_label = lang_list[7]\n        reply += lang_list[8].format(' ('+mensa+'): *on*')\n    \n    markup = [[InlineKeyboardButton(flag(_), callback_data='2-lang-'+_) for _ in supported_languages]]\n    markup.append([InlineKeyboardButton(mensa_label, callback_data=mensa_callback)])\n    markup.append([InlineKeyboardButton(alarm_btn, callback_data='2-alarm-' + alarm), InlineKeyboardButton(lang_list[3], callback_data='1-orario')])\n    con.close()\n    return reply, markup\n\ndef get_enabled_alarm_users():\n    # Database: Orario\n    con = sqlite3.connect(db_path)\n    cur = con.cursor()\n    cur.execute(\"SELECT u_id, alarm FROM Orario WHERE alarm NOT NULL\")\n    r = cur.fetchall()\n    con.close()\n    return r\n\ndef get_fav_mensa_users(mensa):\n    # Database: Orario\n    con = sqlite3.connect(db_path)\n    cur = con.cursor()\n    cur.execute(\"SELECT u_id FROM Orario WHERE mensa = ?\", (mensa,))\n    r = cur.fetchall()\n    con.close()\n    return r\n\ndef set_lang(u_id, lang_code):\n    if lang_code != get_lang('', u_id=u_id):\n        con = sqlite3.connect(db_path)\n        cur = con.cursor()\n        cur.execute(\"UPDATE Lingua SET lang = ? WHERE u_id = ?\", (lang_code, u_id))\n        con.commit()\n        con.close()\n        return True\n    else:\n        return False\n\ndef set_alarm_value(u_id, hour):\n    # Database: Orario\n    con = sqlite3.connect(db_path)\n    cur = con.cursor()\n    cur.execute(\"UPDATE Orario SET alarm = ? WHERE u_id = ?\", (hour, u_id))\n    con.commit()\n    con.close()\n\ndef set_fav_mensa(u_id, mensa):\n    # Database: Utenti\n    con = sqlite3.connect(db_path)\n    cur = con.cursor()\n    cur.execute(\"UPDATE Utenti SET mensa = ? WHERE u_id = ?\", (mensa, u_id))\n    con.commit()\n    con.close()\n\ndef save_msg(update):\n    # Salva il messaggio inviato dall'utente\n    u_id = update.message.from_user.id\n    msg_id = update.message.message_id\n    chat_id = get_chat_id(update)\n    msg = update.message.text\n    date = update.message.date\n\n    # Database: Messaggi\n    con = sqlite3.connect(db_path)\n    cur = con.cursor()\n    # msg_id, u_id, chat_id, msg, date\n    cur.execute(\"INSERT INTO Messaggi VALUES (?,?,?,?,?)\", \\\n                (msg_id, u_id, chat_id, msg, date))\n    con.commit()\n    con.close()\n\ndef save_loc(update):\n    # Salva la posizione inviata dall'utente\n    u_id = update.message.from_user.id\n    lat = update.message.location.latitude\n    lon = update.message.location.longitude\n    date = update.message.date\n\n    # Database: Locations\n    con = sqlite3.connect(db_path)\n    cur = con.cursor()\n    # u_id, lat, lon, date\n    cur.execute(\"INSERT INTO Locations VALUES (?,?,?,?)\", \\\n                (u_id, lat, lon, date))\n    con.commit()\n    con.close()\n\ndef new_user(update):\n    # Inserisce nel database il nuovo utente\n    id = update.message.from_user.id\n    first_name = update.message.from_user.first_name\n    last_name = update.message.from_user.last_name\n    username = update.message.from_user.username\n    date = update.message.date\n    lang = update.message.from_user.language_code\n    lang = lang if lang in supported_languages else default_language\n\n    # Database: Utenti\n    con = sqlite3.connect(db_path)\n    cur = con.cursor()\n    cur.execute(\"SELECT * FROM Utenti WHERE u_id = ?\", (id,))\n    # Chesck if user already in table\n    if cur.fetchone() is None:\n        # u_id, username, first_name, last_name, date\n        cur.execute(\"INSERT INTO Utenti VALUES (?,?,?,?,?,?)\", \\\n            (id, username, first_name, last_name, date, None))\n        # Language\n        cur.execute(\"INSERT INTO Lingua VALUES (?,?)\", (id, lang))\n        con.commit()\n    con.close()\n\n# Search for books using Il Cerca Facile\ndef cerca_facile(key, lang_list):\n    key = key.replace('\\n','')\n    contents = ''\n    text = ''\n    markup = None\n    noResult = False\n    try:\n        contents = urlopen(\"http://catalogo.unipd.it/F/?func=find-e&LOCAL_BASE=sbp01&find_scan_code=FIND_WRD&request=\" + str(key) + \"&filter_code_4=WTY&filter_request_4=+\").read()\n    except:\n        pass\n    if contents != '' and key != '':\n        soup = BeautifulSoup(contents, 'html.parser')\n        del contents\n        table = soup.find(lambda tag: tag.name=='table' and tag.has_attr('id') and tag['id']==\"striped\")\n        rows = table.findAll(lambda tag: tag.name=='tr')\n        del soup\n        n = 0\n        for i in range(11):\n            try:\n                tds = rows[i].findAll(lambda tag: tag.name=='td')\n                [x.extract() for x in tds[4].findAll('script')]\n                text += '*' + tds[4].text.replace('\\n','').replace('*','').replace('_',' ') + '*\\n'\n                autore = tds[2].text\n                if autore != '':\n                    text += '_' + lang_list[1] + ': ' + autore.replace('_',' ').replace('\\n','').replace('*','') + '_\\n'\n                for a in tds[6].findAll(lambda tag: tag.name=='a'):\n                    text += '[' + a.text.split('(')[0] + '](http://catalogo.unipd.it' + a['href'] + ')\\n'\n                text += '[' + lang_list[2] + '](http://catalogo.unipd.it' + tds[0].a['href'] + ')\\n\\n'\n                n += 1\n            except:\n                pass\n        if text == '': noResult = True\n        text = lang_list[3] + ' ' + str(n) + ' ' + lang_list[4] + ':\\n' + text + lang_list[5]\n        markup = {'text': lang_list[6], 'url': (\"http://catalogo.unipd.it/F/?func=find-e&LOCAL_BASE=sbp01&find_scan_code=FIND_WRD&request=\" + str(key) + \"&filter_code_4=WTY&filter_request_4=+\")}\n    if noResult:\n        text = lang_list[7] #\"Non ho trovato nulla\\nPer una ricerca più approfondita c'è il *Cercafacile*\"\n        markup = {'text': 'Il Cercafacile', 'url': \"http://bibliotecadigitale.cab.unipd.it/bd/cercafacile\"}\n    if key == '':\n        text = lang_list[0]\n    return text, markup\n\nif __name__ == '__main__':\n    get_enabled_alarm_users()\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"103499410","text":"import pandas as pd\nimport numpy as np\nfrom TableExtractor import extract_table\nfrom graph_constructor import construct_graph, get_jaccard_index_threshold_from_kde\nfrom clustering import cluster, predict, measure, get_individuals_from_file, get_dict_from_df, merge_dicts\n\n\n# Extract Table\ncancer_type = 'Melanoma'\n#cancer_type = 'Lung-SCC'\n#cancer_type = 'Kidney-RCC'\n#cancer_type = 'Lung-SCC'\n#cancer_type = 'Eso-AdenoCa'\ninput = '../data/WES_TCGA.96.csv'\n#extract_table(input, cancer_type)\nprint(\"Matrix file is extracted\")\n\n# Construct Graph\n#matrix_file = cancer_type + \"_ignore_0\" + \".csv\"\nmatrix_file = cancer_type + \"_0_1_percentile.csv\" #+ \".csv\"\n#matrix_file = cancer_type + \".csv\"\njaccard_index_threshold = get_jaccard_index_threshold_from_kde(matrix_file, 0.97)\nG = construct_graph(matrix_file, jaccard_index_threshold)\nprint(\"Graph is constructed\")\nprint(\"Number of edges in the graph: {}\".format(len(G.edges)))\nprint(\"clustering\")\nclusters = cluster(G, 'dbscan')\nclusters = cluster(G, 'greedy')\n#clusters = cluster(G, 'random')\n\n\n# To predict\nmatrix_file = \"../data/\" + matrix_file\ncancer_matrix = get_dict_from_df(pd.read_csv(matrix_file))\nmixed_matrix = get_dict_from_df(pd.read_csv(\"../data/sara.csv\"))\n\ntrue_disease_individuals = cancer_matrix.keys()\nall_individuals = mixed_matrix.keys()\nprint(\"all individuals: \", len(mixed_matrix.keys()))\n\nprint(\"on same dataset:\")\nfor threshold in np.linspace(0, 1, 10):\n    disease_predicted_individuals = predict(clusters, cancer_matrix, threshold)\n    measure(cancer_matrix.keys(), disease_predicted_individuals, true_disease_individuals)\nprint(\"on mixed dataset:\")\nfor threshold in np.linspace(0, 1, 10):\n    disease_predicted_individuals = predict(clusters, mixed_matrix, threshold)\n    measure(all_individuals, disease_predicted_individuals, true_disease_individuals)\n","sub_path":"code/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"210531803","text":"import argparse\nimport json\nimport os\nimport sys\n\nimport numpy as np\nimport sklearn.metrics\nimport torch\nfrom torch import optim\n\nfrom lib.suggested.self_attention_with_cnn import SelfAttentionCnnClassifier\nfrom lib.embedding import load_full_embedding_with_vocab\nfrom lib.reader import WikiqaReader, filtered_ref_generator\nfrom lib.train import train_model, get_label_score\nfrom lib.transformer import NoamOpt\n\n\ndef main(config_path):\n    with open(config_path, 'r') as fread:\n        config_dict = json.load(fread)\n\n    # path\n    path_config = config_dict['Path']\n    model_dir = path_config['model_dir']\n    train = path_config['train']\n    dev = path_config['dev']\n    dev_ref = path_config['dev_ref']\n    test = path_config['test']\n    test_ref = path_config['test_ref']\n\n    # dataset\n    dataset_config = config_dict['Dataset']\n    batch_size = dataset_config['batch_size']\n\n    print('Loading train data...')\n    train_reader = WikiqaReader(train, PAD_TOKEN='<pad>')\n    dev_reader = WikiqaReader(dev, PAD_TOKEN='<pad>')\n\n    words_embed, words_vocab = load_full_embedding_with_vocab(path_config['embed_dir'])\n    vocabs = {'q_words': words_vocab, 'a_words': words_vocab}\n    train_reader.set_vocabs(vocabs)\n    dev_reader.set_vocabs(vocabs)\n\n    train_iterator = train_reader.get_dataset_iterator(batch_size, train=True)\n    dev_iterator = dev_reader.get_dataset_iterator(batch_size, train=False, sort=False)\n\n    test_reader = WikiqaReader(test, PAD_TOKEN='<pad>')\n    test_reader.set_vocabs(vocabs)\n    test_iterator = test_reader.get_dataset_iterator(batch_size, train=False, sort=False)\n\n    # model\n    model_config = config_dict['Model']\n    conv_width = model_config['conv_width']\n    out_channels = model_config['out_channels']\n    hidden_size = model_config['hidden_size']\n    cuda_device = model_config['cuda_device']\n    dropout = model_config['dropout']\n    h = model_config['h']\n\n    clf = SelfAttentionCnnClassifier(words_embed=words_embed, out_channels=out_channels,\n                conv_width=conv_width, hidden_size=hidden_size, cuda_device=cuda_device,\n                h=h, dropout=dropout)\n\n    # train\n    train_config = config_dict['Train']\n    num_epoch = train_config['epoch']\n    weight_decay = train_config['weight_decay']\n    lr = train_config['lr']\n    early_stopping = train_config['early_stopping']\n    factor = train_config['factor']\n    warmup = train_config['warmup']\n\n    input_names = ['q_words', 'a_words']\n\n    # optimizer = optim.Adam(clf.parameters(), lr=lr, weight_decay=weight_decay, eps=1e-5)\n    optimizer = NoamOpt(clf.len_embed, factor, warmup,\n            optim.Adam(clf.parameters(), lr=0, weight_decay=weight_decay, eps=1e-5))\n\n    if cuda_device is not None:\n        clf.cuda(device=cuda_device)\n\n    def callback(verbose=True):\n        train_labels, train_scores = get_label_score(clf, train_iterator, cuda_device, 'label', input_names=input_names)\n        train_predicts = train_scores.argmax(axis=-1)\n        train_scores = train_scores[:, 1]\n        if verbose:\n            print('train_acc: %.2f' % sklearn.metrics.accuracy_score(train_labels, train_predicts))\n            print('train_precision: %.2f' % sklearn.metrics.precision_score(train_labels, train_predicts))\n            print('train_average_precision: %.2f' % sklearn.metrics.average_precision_score(train_labels, train_scores))\n\n        dev_labels, dev_scores = get_label_score(clf, dev_iterator, cuda_device, 'label', input_names=input_names)\n        dev_predicts = dev_scores.argmax(axis=-1)\n        dev_scores = dev_scores[:, 1]\n        if verbose:\n            print('dev_acc: %.2f' % sklearn.metrics.accuracy_score(dev_labels, dev_predicts))\n            print('dev_precision: %.2f' % sklearn.metrics.precision_score(dev_labels, dev_predicts))\n            print('dev_average_precision: %.2f' % sklearn.metrics.average_precision_score(dev_labels, dev_scores))\n\n        index = 0\n        dev_aps = [] # for mean average precision score\n        rrs = [] # for mean reciprocal rank score\n\n        for query_labels in filtered_ref_generator(dev_ref):\n            query_scores = dev_scores[index:index+len(query_labels)]\n            index += len(query_labels)\n\n            dev_aps.append(sklearn.metrics.average_precision_score(query_labels, query_scores))\n            query_rel_best = np.argmin( -query_scores * query_labels)\n            rrs.append(1 / (np.argsort(np.argsort(-query_scores))[query_rel_best] + 1))\n\n        if verbose:\n            print('dev_MAP: %.2f' % np.mean(dev_aps))\n            print('dev_MRR: %.2f' % np.mean(rrs))\n\n        test_labels, test_scores = get_label_score(clf, test_iterator, cuda_device, 'label', input_names=input_names)\n        test_predicts = test_scores.argmax(axis=-1)\n        test_scores = test_scores[:, 1]\n        if verbose:\n            print('test_acc: %.2f' % sklearn.metrics.accuracy_score(test_labels, test_predicts))\n            print('test_precision: %.2f' % sklearn.metrics.precision_score(test_labels, test_predicts))\n            print('test_average_precision: %.2f' % sklearn.metrics.average_precision_score(test_labels, test_scores))\n\n        index = 0\n        test_aps = []  # for mean average precision score\n        rrs = []  # for mean reciprocal rank score\n\n        for query_labels in filtered_ref_generator(test_ref):\n            query_scores = test_scores[index:index + len(query_labels)]\n            index += len(query_labels)\n\n            test_aps.append(sklearn.metrics.average_precision_score(query_labels, query_scores))\n            query_rel_best = np.argmin(-query_scores * query_labels)\n            rrs.append(1 / (np.argsort(np.argsort(-query_scores))[query_rel_best] + 1))\n\n        if verbose:\n            print('test_MAP: %.2f' % np.mean(test_aps))\n            print('test_MRR: %.2f' % np.mean(rrs))\n            \n        return np.mean(dev_aps)\n\n    print('Training...')\n    best_state_dict = train_model(clf, optimizer, train_iterator, label_name='label',\n                num_epoch=num_epoch, cuda_device=cuda_device, early_stopping=early_stopping,\n                input_names=input_names, callback=callback)\n    print()\n\n    if best_state_dict is not None:\n        clf.load_state_dict(best_state_dict)\n\n    torch.save(clf.state_dict(), os.path.join(model_dir, './net.pt'))\n\n    print('Testing...')\n\n    test_labels, test_scores = get_label_score(clf, test_iterator, cuda_device, 'label', input_names=input_names)\n    test_predicts = test_scores.argmax(axis=-1)\n    test_scores = test_scores[:, 1]\n    \n    print('test_acc: %.2f' % sklearn.metrics.accuracy_score(test_labels, test_predicts))\n    print('test_precision: %.2f' % sklearn.metrics.precision_score(test_labels, test_predicts))\n    print('test_average_precision: %.2f' % sklearn.metrics.average_precision_score(test_labels, test_scores))\n\n    index = 0\n    aps = []  # for mean average precision score\n    rrs = []  # for mean reciprocal rank score\n\n    for query_labels in filtered_ref_generator(test_ref):\n        query_scores = test_scores[index:index + len(query_labels)]\n        index += len(query_labels)\n\n        aps.append(sklearn.metrics.average_precision_score(query_labels, query_scores))\n        query_rel_best = np.argmin(-query_scores * query_labels)\n        rrs.append(1 / (np.argsort(np.argsort(-query_scores))[query_rel_best] + 1))\n\n    print('test_MAP: %.4f' % np.mean(aps))\n    print('test_MRR: %.4f' % np.mean(rrs))\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--config_path', required=True, type=str, help='Path of config file')\n\n    args = parser.parse_args()\n    main(args.config_path)\n\n","sub_path":"train_self_attention_with_cnn.py","file_name":"train_self_attention_with_cnn.py","file_ext":"py","file_size_in_byte":7615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"334059422","text":"import os.path\nimport sys\nimport math\nimport argparse\nimport time\nimport random\nfrom collections import OrderedDict\n\nimport torch\n\nimport options.options as option\nfrom utils import util\n\n# options\nparser = argparse.ArgumentParser()\nparser.add_argument('-opt', type=str, required=True, help='Path to options JSON file.')\nargs = parser.parse_args()\noptions_path = args.opt\nopt = option.parse(options_path, is_train=True)\n\nutil.mkdir_and_rename(opt['path']['experiments_root'])  # rename old experiments if exists\nutil.mkdirs((path for key, path in opt['path'].items() if not key == 'experiments_root' and \\\n    not key == 'pretrain_model_G' and not key == 'pretrain_model_D'))\noption.save(opt) # save option file to the opt['path']['options']\nopt = option.dict_to_nonedict(opt)  # Convert to NoneDict, which return None for missing key.\n\n# print to file and std_out simultaneously\nclass PrintLogger(object):\n    def __init__(self):\n        self.terminal = sys.stdout\n        self.log = open(os.path.join(opt['path']['log'], 'print_log.txt'), \"a\")\n    def write(self, message):\n        self.terminal.write(message)\n        self.log.write(message)\n    def flush(self):\n        pass\nsys.stdout = PrintLogger()\n\n# random seed\nseed = opt['train']['manual_seed']\nif seed is None:\n    seed = random.randint(1, 10000)\nprint(\"Random Seed: \", seed)\nrandom.seed(seed)\ntorch.manual_seed(seed)\n\nfrom data import create_dataloader, create_dataset\nfrom models import create_model\nfrom utils.logger import Logger\n\ndef main():\n    # create train and val dataloader\n    train_loader = None\n    val_loader = None\n    train_dataset_opt = None\n    val_dataset_opt = None\n    for phase, dataset_opt in opt['datasets'].items():\n        if phase == 'train':\n            train_dataset_opt = dataset_opt\n            train_set = create_dataset(dataset_opt)\n            batch_size = dataset_opt['batch_size']\n            train_size = int(math.ceil(len(train_set) / batch_size))\n            print('Number of train images: {:,d}, iters: {:,d}'.format(len(train_set), train_size))\n            total_iters = int(opt['train']['niter'])\n            total_epoches = int(math.ceil(total_iters / train_size))\n            print('Total epoches needed: {:d} for iters {:,d}'.format(total_epoches, total_iters))\n            train_loader = create_dataloader(train_set, dataset_opt)\n        elif phase == 'val':\n            val_dataset_opt = dataset_opt\n            val_set = create_dataset(dataset_opt)\n            val_loader = create_dataloader(val_set, dataset_opt)\n            print('Number of val images in [%s]: %d' % (dataset_opt['name'], len(val_set)))\n        else:\n            raise NotImplementedError(\"Phase [%s] is not recognized.\" % phase)\n    assert train_loader is not None\n\n    # Create model\n    model = create_model(opt)\n    model.train()\n\n    # create logger\n    logger = Logger(opt)\n\n    current_step = 0\n    start_time = time.time()\n    print('---------- Start training -------------')\n    for epoch in range(total_epoches):\n        for i, train_data in enumerate(train_loader):\n            current_step += 1\n            if current_step > total_iters:\n                break\n\n            # training\n            train_start_time = time.time()\n            model.feed_data(train_data)\n            model.optimize_parameters(current_step)\n            train_elapsed = time.time() - train_start_time\n\n            # log\n            if current_step % opt['logger']['print_freq'] == 0:\n                losses = model.get_current_losses()\n                print_rlt = OrderedDict()\n                print_rlt['model'] = opt['model']\n                print_rlt['epoch'] = epoch\n                print_rlt['iters'] = current_step\n                print_rlt['time'] = train_elapsed\n                for k, v in losses.items():\n                    print_rlt[k] = v\n                more_info = model.get_more_training_info()\n                if more_info is not None:\n                    for k, v in more_info.items():\n                        print_rlt[k] = v\n                print_rlt['lr'] = model.get_current_learning_rate()\n                logger.print_format_results('train', print_rlt)\n\n            # save models\n            if current_step % opt['logger']['save_checkpoint_freq'] == 0:\n                print('Saving the model at the end of iter %d' % (current_step))\n                model.save(current_step)\n\n            # validation\n            if current_step % opt['train']['val_freq'] == 0:\n                validate(val_loader, model, logger, epoch, current_step, val_dataset_opt)\n\n            # update learning rate\n            model.update_learning_rate()\n\n        # print('End of epoch %d.' % epoch)\n\n    print('Saving the final model.')\n    model.save('latest')\n    print('End of Training \\t Time taken: %d sec' % (time.time() - start_time))\n\n\ndef validate(val_loader, model, logger, epoch, current_step, val_dataset_opt):\n    print('---------- validation -------------')\n    val_start_time = time.time()\n    model.eval() # Change to eval mode. It is important for BN layers.\n\n    val_results = OrderedDict()\n    avg_psnr = 0.0\n    idx = 0\n    for val_data in val_loader:\n        idx += 1\n        img_path = val_data['LR_path'][0]\n        img_name = os.path.splitext(os.path.basename(img_path))[0]\n        img_dir = os.path.join(opt['path']['val_images'], img_name)\n        util.mkdir(img_dir)\n\n        model.feed_data(val_data, volatile=True)\n        model.val()\n\n        visuals = model.get_current_visuals()\n\n        sr_img = util.tensor2img_np(visuals['SR']) # uint8\n        gt_img = util.tensor2img_np(visuals['HR']) # uint8\n        # # modcrop\n        # gt_img = util.modcrop(gt_img, val_dataset_opt['scale'])\n        h_min = min(sr_img.shape[0], gt_img.shape[0])\n        w_min = min(sr_img.shape[1], gt_img.shape[1])\n        sr_img = sr_img[0:h_min, 0:w_min, :]\n        gt_img = gt_img[0:h_min, 0:w_min, :]\n\n        crop_size = val_dataset_opt['scale'] + 2\n        cropped_sr_img = sr_img[crop_size:-crop_size, crop_size:-crop_size, :]\n        cropped_gt_img = gt_img[crop_size:-crop_size, crop_size:-crop_size, :]\n\n        # Save SR images for reference\n        save_img_path = os.path.join(img_dir, '%s_%s.png' % (img_name,current_step))\n        util.save_img_np(sr_img.squeeze(), save_img_path)\n\n        # TODO need to modify\n        # metric_mode = val_dataset_opt['metric_mode']\n        # if metric_mode == 'y':\n        #     cropped_sr_img = util.rgb2ycbcr(cropped_sr_img, only_y=True)\n        #     cropped_gt_img = util.rgb2ycbcr(cropped_gt_img, only_y=True)\n\n        avg_psnr += util.psnr(cropped_sr_img, cropped_gt_img)\n\n    avg_psnr = avg_psnr / idx\n\n    val_elapsed = time.time() - val_start_time\n    # Save to log\n    print_rlt = OrderedDict()\n    print_rlt['model'] = opt['model']\n    print_rlt['epoch'] = epoch\n    print_rlt['iters'] = current_step\n    print_rlt['time'] = val_elapsed\n    print_rlt['psnr'] = avg_psnr\n    logger.print_format_results('val', print_rlt)\n    model.train() # change back to train mode.\n    print('-----------------------------------')\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"codes/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":7095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"378397116","text":"# %load q06_bowled_players/build.py\n# Default Imports\nfrom greyatomlib.python_getting_started.q01_read_data.build import read_data\ndata = read_data()\n\n# Your Solution\ndef bowled_out(data=data):\n\n    # Write your code here\n    d = data['innings'][1]['2nd innings']['deliveries']\n    my_list = [a[k] for a in d for k in a ]\n    bowled_players = [x['batsman'] for x in my_list if 'wicket' in x and x['wicket']['kind'] == 'bowled']\n    return bowled_players\n\n\n","sub_path":"q06_bowled_players/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"391996107","text":"# -*- coding: utf-8 -*-\n#\n# Copyright 2012-2015 Spotify AB\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\nfrom __future__ import print_function\n\nimport datetime\nimport unittest\n\nimport luigi\nimport luigi.notifications\nfrom luigi.parameter import MissingParameterException\nfrom luigi.util import common_params\n\nfrom bioluigi.decorators import requires, inherits\n\n\nluigi.notifications.DEBUG = True\n\n\nclass A(luigi.Task):\n    param1 = luigi.Parameter(\"class A-specific default\")\n\n\n@inherits(A)\nclass B(luigi.Task):\n    param2 = luigi.Parameter(\"class B-specific default\")\n\n\n@inherits(B)\nclass C(luigi.Task):\n    param3 = luigi.Parameter(\"class C-specific default\")\n\n\n@inherits(B)\nclass D(luigi.Task):\n    param1 = luigi.Parameter(\"class D overwriting class A's default\")\n\n\n@inherits(B)\nclass D_null(luigi.Task):\n    param1 = None\n\n\n@inherits(A)\n@inherits(B)\nclass E(luigi.Task):\n    param4 = luigi.Parameter(\"class E-specific default\")\n\n\n@inherits(A, B)\nclass E_list(luigi.Task):\n    param4 = luigi.Parameter(\"class E-specific default\")\n\n\n@inherits(A=A, B=B)\nclass E_dict(luigi.Task):\n    param4 = luigi.Parameter(\"class E-specific default\")\n\n\nclass InheritTest(unittest.TestCase):\n\n    def setUp(self):\n        self.a = A()\n        self.a_changed = A(param1=34)\n        self.b = B()\n        self.c = C()\n        self.d = D()\n        self.d_null = D_null()\n        self.e = E()\n        self.e_list = E_list()\n        self.e_dict = E_dict()\n\n    def test_has_param(self):\n        b_params = dict(self.b.get_params()).keys()\n        self.assertTrue(\"param1\" in b_params)\n\n    def test_default_param(self):\n        self.assertEqual(self.b.param1, self.a.param1)\n\n    def test_change_of_defaults_not_equal(self):\n        self.assertNotEqual(self.b.param1, self.a_changed.param1)\n\n    def tested_chained_inheritance(self):\n        self.assertEqual(self.c.param2, self.b.param2)\n        self.assertEqual(self.c.param1, self.a.param1)\n        self.assertEqual(self.c.param1, self.b.param1)\n\n    def test_overwriting_defaults(self):\n        self.assertEqual(self.d.param2, self.b.param2)\n        self.assertNotEqual(self.d.param1, self.b.param1)\n        self.assertNotEqual(self.d.param1, self.a.param1)\n        self.assertEqual(self.d.param1, \"class D overwriting class A's default\")\n\n    def test_stacked_inheritance(self):\n        self.assertEqual(self.e.param1, self.a.param1)\n        self.assertEqual(self.e.param1, self.b.param1)\n        self.assertEqual(self.e.param2, self.b.param2)\n\n    def test_list_inheritance(self):\n        self.assertEqual(self.e_list.param1, self.a.param1)\n        self.assertEqual(self.e_list.param1, self.b.param1)\n        self.assertEqual(self.e_list.param2, self.b.param2)\n\n    def test_dict_inheritance(self):\n        self.assertEqual(self.e_dict.param1, self.a.param1)\n        self.assertEqual(self.e_dict.param1, self.b.param1)\n        self.assertEqual(self.e_dict.param2, self.b.param2)\n\n    def test_removing_parameter(self):\n        self.assertFalse(\"param1\" in dict(self.d_null.get_params()).keys())\n\n    def test_wrapper_preserve_attributes(self):\n        self.assertEqual(B.__name__, 'B')\n\n\nclass F(luigi.Task):\n    param1 = luigi.Parameter(\"A parameter on a base task, that will be required later.\")\n\n\n@inherits(F)\nclass G(luigi.Task):\n    param2 = luigi.Parameter(\"A separate parameter that doesn't affect 'F'\")\n\n    def requires(self):\n        return F(**common_params(self, F))\n\n\n@inherits(G)\nclass H(luigi.Task):\n    param2 = luigi.Parameter(\"OVERWRITING\")\n\n    def requires(self):\n        return G(**common_params(self, G))\n\n\n@inherits(G)\nclass H_null(luigi.Task):\n    param2 = None\n\n    def requires(self):\n        special_param2 = str(datetime.datetime.now())\n        return G(param2=special_param2, **common_params(self, G))\n\n\n@inherits(G)\nclass I(luigi.Task):\n\n    def requires(self):\n        return F(**common_params(self, F))\n\n\nclass J(luigi.Task):\n    param1 = luigi.Parameter()  # something required, with no default\n\n\n@inherits(J)\nclass K_shouldnotinstantiate(luigi.Task):\n    param2 = luigi.Parameter(\"A K-specific parameter\")\n\n\n@inherits(J)\nclass K_shouldfail(luigi.Task):\n    param1 = None\n    param2 = luigi.Parameter(\"A K-specific parameter\")\n\n    def requires(self):\n        return J(**common_params(self, J))\n\n\n@inherits(J)\nclass K_shouldsucceed(luigi.Task):\n    param1 = None\n    param2 = luigi.Parameter(\"A K-specific parameter\")\n\n    def requires(self):\n        return J(param1=\"Required parameter\", **common_params(self, J))\n\n\n@inherits(J)\nclass K_wrongparamsorder(luigi.Task):\n    param1 = None\n    param2 = luigi.Parameter(\"A K-specific parameter\")\n\n    def requires(self):\n        return J(param1=\"Required parameter\", **common_params(J, self))\n\n\n@requires(A, B)\nclass L_list(luigi.Task):\n    param4 = luigi.Parameter(\"class L-specific default\")\n\n\n@requires(A=A, B=B)\nclass L_dict(luigi.Task):\n    param4 = luigi.Parameter(\"class L-specific default\")\n\n\nclass RequiresTest(unittest.TestCase):\n\n    def setUp(self):\n        self.f = F()\n        self.g = G()\n        self.g_changed = G(param1=\"changing the default\")\n        self.h = H()\n        self.h_null = H_null()\n        self.i = I()\n        self.k_shouldfail = K_shouldfail()\n        self.k_shouldsucceed = K_shouldsucceed()\n        self.k_wrongparamsorder = K_wrongparamsorder()\n        self.l_list = L_list()\n        self.l_dict = L_dict()\n\n    def test_inherits(self):\n        self.assertEqual(self.f.param1, self.g.param1)\n        self.assertEqual(self.f.param1, self.g.requires().param1)\n\n    def test_change_of_defaults(self):\n        self.assertNotEqual(self.f.param1, self.g_changed.param1)\n        self.assertNotEqual(self.g.param1, self.g_changed.param1)\n        self.assertNotEqual(self.f.param1, self.g_changed.requires().param1)\n\n    def test_overwriting_parameter(self):\n        self.h.requires()\n        self.assertNotEqual(self.h.param2, self.g.param2)\n        self.assertEqual(self.h.param2, self.h.requires().param2)\n        self.assertEqual(self.h.param2, \"OVERWRITING\")\n\n    def test_skipping_one_inheritance(self):\n        self.assertEqual(self.i.requires().param1, self.f.param1)\n\n    def test_removing_parameter(self):\n        self.assertNotEqual(self.h_null.requires().param2, self.g.param2)\n\n    def test_not_setting_required_parameter(self):\n        self.assertRaises(MissingParameterException, self.k_shouldfail.requires)\n\n    def test_setting_required_parameters(self):\n        self.k_shouldsucceed.requires()\n\n    def test_should_not_instantiate(self):\n        self.assertRaises(MissingParameterException, K_shouldnotinstantiate)\n\n    def test_resuscitation(self):\n        k = K_shouldnotinstantiate(param1='hello')\n        k.requires()\n\n    def test_wrong_common_params_order(self):\n        self.assertRaises(TypeError, self.k_wrongparamsorder.requires)\n\n    def test_list(self):\n        self.assertTrue(isinstance(self.l_list.requires(), list))\n        self.assertTrue(isinstance(self.l_list.requires()[0], A))\n        self.assertTrue(isinstance(self.l_list.requires()[1], B))\n\n    def test_dict(self):\n        self.assertTrue(isinstance(self.l_dict.requires(), dict))\n        self.assertTrue(isinstance(self.l_dict.requires()['A'], A))\n        self.assertTrue(isinstance(self.l_dict.requires()['B'], B))\n\n\nclass X(luigi.Task):\n    n = luigi.IntParameter(default=42)\n\n\n@inherits(X)\nclass Y(luigi.Task):\n\n    def requires(self):\n        return self.clone_parent()\n\n\n@requires(X)\nclass Y2(luigi.Task):\n    pass\n\n\n@requires(X)\nclass Y3(luigi.Task):\n    n = luigi.IntParameter(default=43)\n\n\nclass CloneParentTest(unittest.TestCase):\n\n    def test_clone_parent(self):\n        y = Y()\n        x = X()\n        self.assertEqual(y.requires(), x)\n        self.assertEqual(y.n, 42)\n\n    def test_requires(self):\n        y2 = Y2()\n        x = X()\n        self.assertEqual(y2.requires(), x)\n        self.assertEqual(y2.n, 42)\n\n    def test_requires_override_default(self):\n        y3 = Y3()\n        x = X()\n        self.assertNotEqual(y3.requires(), x)\n        self.assertEqual(y3.n, 43)\n        self.assertEqual(y3.requires().n, 43)\n\n    def test_names(self):\n        # Just make sure the decorators retain the original class names\n        x = X()\n        self.assertEqual(str(x), 'X(n=42)')\n        self.assertEqual(x.__class__.__name__, 'X')\n","sub_path":"tests/decorator_test.py","file_name":"decorator_test.py","file_ext":"py","file_size_in_byte":8777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"513290344","text":"from discord.ext import commands\nimport aiohttp\nimport discord\nimport asyncio\n\n\nclass NPC(commands.Cog):\n\n    def __init__(self, bot):\n        self.bot = bot\n        self.db = self.bot.db\n        self.webhook = None\n        self.session = None\n        self.webhooks = {\n            725164675679125515: 796591431980285984,\n            725093929481142292: 750402737530732615\n        }\n\n    @commands.command()\n    async def replay(self, ctx, channel: discord.TextChannel, msg_1: int, msg_2: int):\n        \"\"\"Replays a conversation between two message IDs provided.\n           The messages will be sent by webhooks as if it were in real time.\n\n           **Usage:** `$replay <first message ID> <last message ID>`\n        \"\"\"\n        try:\n            msg_1 = await channel.fetch_message(msg_1)\n            msg_2 = await channel.fetch_message(msg_2)\n        except discord.NotFound:\n            return await ctx.send('One or more of the message IDs provided were invalid.')\n\n        webhook = self.webhooks[ctx.channel.id]\n        msgs = await channel.history(before=msg_2.created_at, after=msg_1.created_at).flatten()\n        await webhook.send(msg_1.content,\n                           avatar_url=str(msg_1.author.avatar_url_as(format='png')),\n                           username=msg_1.author.display_name,\n                           allowed_mentions=self.bot.allowed_mentions)\n        for i in range(len(msgs)):\n            msg = msgs[i]\n            if not msg.content:\n                continue\n            if i == 0:\n                await asyncio.sleep((msg.created_at - msg_1.created_at).total_seconds())\n            else:\n                await asyncio.sleep((msg.created_at - msgs[i - 1].created_at).total_seconds())\n            await webhook.send(msg.content,\n                               avatar_url=str(msg.author.avatar_url_as(format='png')),\n                               username=msg.author.display_name,\n                               allowed_mentions=self.bot.allowed_mentions)\n        await ctx.send('Done!')\n\n    @commands.group(invoke_without_command=True)\n    async def npc(self, ctx, alias, *, message):\n        \"\"\"Sends a message using a custom webhook, or NPC.\n        NPCs can only be used in general.\n        You can add/remove NPCs with the listed subcommands.\n\n        **Usage:** `$npc <alias> <message>`\"\"\"\n        res = await self.db.fetchrow('SELECT avatar_url, username FROM npcs WHERE alias = $1', alias.lower())\n        if not res:\n            return await ctx.send(f'There isn\\'t an NPC with alias `{alias}`.')\n        if ctx.channel.id not in self.webhooks:\n            return await ctx.send('NPCs can only be used in bot-cmds and general.')\n        await self.webhooks[ctx.channel.id].send(\n            message,\n            avatar_url=res['avatar_url'],\n            username=res['username'],\n            allowed_mentions=self.bot.allowed_mentions\n        )\n        await ctx.message.delete()\n\n    @npc.command(aliases=['create'])\n    async def add(self, ctx, alias, username, pfp=None):\n        \"\"\"Adds an NPC.\n\n           **Usage:** `$npc add <alias> <username> <pfp url>`\"\"\"\n        if await self.db.fetchrow('SELECT id FROM npcs WHERE alias = $1', alias.lower()):\n            return await ctx.send(f'Alias `{alias}` already in use.')\n        if len(alias) > 16:\n            return await ctx.send('Alias must be 16 characters or lower.')\n        if len(username) > 32:\n            return await ctx.send('Username must be 32 characters or lower.')\n        if pfp:\n            try:\n                async with self.session.get(pfp) as resp:\n                    if resp.status != 200:\n                        return await ctx.send('The profile image link isn\\'t working.')\n            except aiohttp.InvalidURL:\n                return await ctx.send('The profile image link isn\\'t a valid URL.')\n            avatar = pfp\n        else:\n            avatar = ''\n        query = 'INSERT INTO npcs (user_id, username, avatar_url, alias) VALUES ($1, $2, $3, $4)'\n        await self.db.execute(query, ctx.author.id, username, avatar, alias.lower())\n        await ctx.send('Your NPC has been created!')\n\n    @npc.command(aliases=['remove'])\n    async def delete(self, ctx, *, alias):\n        \"\"\"Deletes an NPC.\n           You must own the NPC or have Admin to delete it.\n\n           **Usage:** `$npc delete <alias>`\"\"\"\n        res = await self.db.fetchrow('SELECT user_id, username FROM npcs WHERE alias = $1', alias.lower())\n        if not res:\n            return await ctx.send(f'An NPC with alias `{alias}` doesn\\'t exist.')\n        if res['user_id'] != ctx.author.id and not ctx.author.guild_permissions.administrator:\n            return await ctx.send('You have to either own the NPC or be Admin to delete it.')\n        query = 'DELETE FROM npcs WHERE alias = $1'\n        await self.db.execute(query, alias.lower())\n        await ctx.send(f'Successfully deleted NPC `{res[\"username\"]}`.')\n\n\ndef setup(bot):\n    bot.add_cog(NPC(bot))\n","sub_path":"cogs/npc.py","file_name":"npc.py","file_ext":"py","file_size_in_byte":4959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"33"}
+{"seq_id":"418351619","text":"import telepot\nfrom telepot import delegate\nfrom weather_answer import WeatherAnswer\nfrom urllib import request, parse\nimport requests\nfrom bs4 import BeautifulSoup\nimport numpy as np\n\nURL = \"https://api.telegram.org/bot%s/\" % \"507008666:AAEJ8TTGpJ-1idOAgF0ZVjiMnezzaaAitMY\"\nTOKEN = \"507008666:AAEJ8TTGpJ-1idOAgF0ZVjiMnezzaaAitMY\"\n\n#https://github.com/nickoala/telepot\n\n\nclass WeatherGo(telepot.helper.ChatHandler):\n\n    def __init__(self, *args, **kwargs):\n        super(WeatherGo, self).__init__(*args, **kwargs)\n        self.weather_answer = WeatherAnswer()\n\n    def on_chat_message(self, msg):\n        content_type, chat_type, chat_id = telepot.glance(msg)\n\n        if content_type == 'sticker':\n            bot.sendMessage(chat_id, \"Thank you for sticker :) It is for you:\")\n            handler = self.weather_answer.if_sticker()\n\n            url = request.urlopen(next(handler))\n            self.sender.sendChatAction('upload_photo')\n            self.sender.sendPhoto(('kitten.png', url))\n            url.close()\n\n        elif content_type == 'text':\n\n            if msg['text'].startswith('/start'):\n                self.sender.sendMessage(text=\"Вы хотите узнать погоду?\\n\"\n                                        \"Для ознакомления со списком возможностей запросите /help\")\n            elif msg['text'].startswith('/help'):\n                self.sender.sendMessage(text='''Список команд бота:\\n\n            /start -- приветственное слово\n            /help -- вывести список команд данного бота\\n\n            Для получения погоды отправьте боту запрос. Бот поддерживает следующие виды запросов:\\n\n            * Москва\n            * Погода в Москве\n            * Какая погода в Москве сейчас(завтра, вчера)\n            * Погода в Москве через n дней\n            * Какая погода в Москве через n дней\n            * Погода во вторник (и другие дни не��ели)\n            * Москва через неделю\n            * Погода в Москве через неделю\n            * Вы можете отправить стикер\\n\n            Если вы опечатались в названии города или написали его с маленькой буквы, то правильный запрос будет выполнен\\n\n            Предложения по улучшению приветствуются @valeriia06''')\n            else:\n                handler = self.weather_answer.get_answer(msg['text'])\n                self.sender.sendMessage(next(handler))\n\n                url = request.urlopen(next(handler))\n\n                self.sender.sendChatAction('upload_photo')\n                self.sender.sendPhoto(('weather.png', url))\n                url.close()\n\n                self.sender.sendMessage(next(handler))\n\n\nif __name__ == '__main__':\n    bot = telepot.DelegatorBot(TOKEN, [\n        delegate.pave_event_space()(delegate.per_chat_id(), delegate.create_open, WeatherGo, timeout=3600)\n    ])\n    bot.message_loop(run_forever='Waiting for your messages ...')\n","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":3348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"236280961","text":"from tkinter import *\nimport tkinter.messagebox as mb\nimport random\n\ndef judge(player):\n    cpu = random.randrange(3)\n    judge_num = (player + (cpu + 3)) % 3\n    if  judge_num == 0:\n        mb.showinfo(\"結果\",\"あいこです\")\n    elif judge_num == 1:\n        mb.showinfo(\"結果\",\"あなたの負けです\")\n    else:\n        mb.showinfo(\"結果\",\"あなたの勝ちです\")\n\nroot = Tk()\nroot.title(\"ジャンケン\")\n\ndesc_label = Label(text = \"あなたの手を選んでください\")\ndesc_label.pack()\n\nButton(text = \"グー\",command = lambda : judge(0)).pack(fill = \"x\")\n\nButton(text = \"チョキ\",command = lambda : judge(1)).pack(fill = \"x\")\n\nButton(text = \"パー\",command = lambda : judge(2)).pack(fill = \"x\")\n\nroot.mainloop()","sub_path":"rps.py","file_name":"rps.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"195409807","text":"#coding: utf8\n\n# compute f1 score is modified from conlleval.pl\ndef __startOfChunk(prevTag, tag, prevTagType, tagType, chunkStart=False):\n  if prevTag == 'B' and tag == 'B':\n    chunkStart = True\n  if prevTag == 'I' and tag == 'B':\n    chunkStart = True\n  if prevTag == 'O' and tag == 'B':\n    chunkStart = True\n  if tag != 'O' and tag != '.' and prevTagType != tagType:\n    chunkStart = True\n  return chunkStart\n\ndef __endOfChunk(prevTag, tag, prevTagType, tagType, chunkEnd=False):\n  if prevTag == 'B' and tag == 'B':\n    chunkEnd = True\n  if prevTag == 'B' and tag == 'O':\n    chunkEnd = True\n  if prevTag == 'I' and tag == 'B':\n    chunkEnd = True\n  if prevTag == 'I' and tag == 'O':\n    chunkEnd = True\n  if prevTag != 'O' and prevTag != '.' and prevTagType != tagType:\n    chunkEnd = True\n  return chunkEnd\n\ndef __splitTagType(tag):\n  s = tag.split('-')\n  if len(s) > 2 or len(s) == 0:\n    raise ValueError('tag format wrong. it must be B-xxx.xxx')\n  if len(s) == 1:\n    tag = s[0]\n    tagType = \"\"\n  else:\n    tag = s[0]\n    tagType = s[1]\n  return tag, tagType\n\ndef compute_f1_score(correct_slots, pred_slots):\n  '''\n  Args:\n    correct_slots: [[],[],[]]\n    pred_slots: [[],[],[]]\n  '''\n  correctChunkCnt = 0\n  foundCorrectCnt = 0\n  foundPredCnt = 0\n  for correct_slot, pred_slot in zip(correct_slots, pred_slots):\n    correct = False\n    lastCorrectTag = 'O'  # o not 0\n    lastCorrectType = ''\n    lastPredTag = 'O'\n    lastPredType = ''\n\n    for c, p in zip(correct_slot, pred_slot):\n      correctTag, correctType = __splitTagType(c)\n      predTag, predType = __splitTagType(p)\n\n      if correct == True:  # until we can be sure it is correct,it's incorrect\n        if __endOfChunk(\n          lastCorrectTag, correctTag, lastCorrectType, correctType\n          ) == True and __endOfChunk(\n          lastPredTag, predTag, lastPredType, predType\n          ) == True and (lastCorrectType == lastPredType):\n          # we can finally say it's correct and correctChunkCnt += 1\n          correctChunkCnt += 1\n          correct = False # for next iteration\n\n        elif __endOfChunk(\n          lastCorrectTag, correctTag, lastCorrectType, correctType\n          ) != __endOfChunk(\n          lastPredTag, predTag, lastPredType, predType) or (\n          correctType != predType):\n          correct = False\n\n      if __startOfChunk(\n        lastCorrectTag, correctTag, lastCorrectType, correctType\n        ) == True and __startOfChunk(\n        lastPredTag, predTag, lastPredType, predType\n        ) == True and (\n        correctType == predType):\n        correct = True\n\n      if __startOfChunk(\n          lastCorrectTag, correctTag, lastCorrectType, correctType\n         ) == True:\n        foundCorrectCnt += 1\n\n      if __startOfChunk(lastPredTag, predTag, lastPredType, predType):\n        foundPredCnt += 1\n\n      lastCorrectTag = correctTag\n      lastCorrectType = correctType\n      lastPredTag = predTag\n      lastPredType = predType\n\n    if correct == True:\n      correctChunkCnt += 1\n    else:\n      pass\n\n  if foundPredCnt > 0:\n    precision = 100 * correctChunkCnt / foundPredCnt\n  else:\n    precision = 0\n\n  if foundCorrectCnt > 0:\n    recall = 100 * correctChunkCnt / foundCorrectCnt\n  else:\n    recall = 0\n  if (precision + recall) > 0:\n    f1 = (2 * precision * recall) / (precision + recall)\n  else:\n    f1 = 0\n\n  return f1, precision, recall\n\ndef get_sent_acc(truth_file, pred_file):\n  n_total = 0\n  n_correct = 0\n  intent_correct = 0\n  with open(truth_file) as f_truth, open(pred_file) as f_pred:\n    for i, (truth, pred) in enumerate(zip(f_truth, f_pred)):\n      n_total += 1\n      if pred.lower() == truth.lower():\n        n_correct += 1\n      if pred.split()[0].lower() == truth.split()[0].lower():\n        intent_correct+=1\n  try:\n    acc = (n_correct / n_total) * 100\n    intent_acc = (intent_correct/n_total)*100\n  except:\n    acc = 0\n    intent_acc = 0\n\n  return intent_acc, acc\n","sub_path":"ver_2_roberta/evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":3903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"236692919","text":"#!/usr/bin/env python\n\"\"\"Provides an application controller for the commandline version of\nmothur Version 1.6.0\n\"\"\"\n\n\nfrom __future__ import with_statement\nfrom os import path, getcwd, mkdir, remove, listdir\nfrom shutil import copyfile\nfrom subprocess import Popen\nfrom cogent.app.parameters import ValuedParameter\nfrom cogent.app.util import CommandLineApplication, ResultPath, \\\n    CommandLineAppResult, ApplicationError\nfrom cogent.parse.mothur import parse_otu_list\n\n\n__author__ = \"Kyle Bittinger\"\n__copyright__ = \"Copyright 2007-2011, The Cogent Project\"\n__credits__ = [\"Kyle Bittinger\"]\n__license__ = \"GPL\"\n__version__ = \"1.6.0dev\"\n__maintainer__ = \"Kyle Bittinger\"\n__email__ = \"kylebittinger@gmail.com\"\n__status__ = \"Prototype\"\n\n\nclass Mothur(CommandLineApplication):\n    \"\"\"Mothur application controller\n    \"\"\"\n    _options = {\n        # Clustering algorithm.  Choices are furthest, nearest, and\n        # average\n        'method': ValuedParameter(\n            Name='method', Value='furthest', Delimiter='=', Prefix=''),\n        # Cutoff distance for the distance matrix\n        'cutoff': ValuedParameter(\n            Name='cutoff', Value=None, Delimiter='=', Prefix=''),\n        # Minimum pairwise distance to consider for clustering\n        'precision': ValuedParameter(\n            Name='precision', Value=None, Delimiter='=', Prefix=''),\n        }\n    _parameters = {}\n    _parameters.update(_options)\n    _input_handler = '_input_as_multiline_string'\n    _command = 'mothur'\n    \n    def __init__(self, params=None, InputHandler=None, SuppressStderr=None,\n                 SuppressStdout=None, WorkingDir=None, TmpDir='/tmp',\n                 TmpNameLen=20, HALT_EXEC=False):\n        \"\"\"Initialize a Mothur application controller\n        \n            params: a dictionary mapping the Parameter id or synonym to its\n                value (or None for FlagParameters or MixedParameters in flag\n                mode) for Parameters that should be turned on\n            InputHandler: this is the method to be run on data when it is\n                passed into call. This should be a string containing the\n                method name. The default is _input_as_string which casts data\n                to a string before appending it to the command line argument\n            SuppressStderr: if set to True, will route standard error to\n                /dev/null, False by default\n            SuppressStdout: if set to True, will route standard out to\n                /dev/null, False by default\n            WorkingDir: the directory where you want the application to run,\n                default is the current working directory, but is useful to \n                change in cases where the program being run creates output\n                to its current working directory and you either don't want\n                it to end up where you are running the program, or the user \n                running the script doesn't have write access to the current \n                working directory\n                WARNING: WorkingDir MUST be an absolute path!\n            TmpDir: the directory where temp files will be created, /tmp\n                by default \n            TmpNameLen: the length of the temp file name\n            HALT_EXEC: if True, raises exception w/ command output just\n                before execution, doesn't clean up temp files. Default False.\n        \"\"\"\n        super(Mothur, self).__init__(\n            params=params, InputHandler=InputHandler, \n            SuppressStderr=SuppressStderr, SuppressStdout=SuppressStdout,\n            WorkingDir='', TmpDir='', TmpNameLen=TmpNameLen, \n            HALT_EXEC=HALT_EXEC)\n        # Prevent self.WorkingDir from being explicitly cast as a\n        # FilePath object.  This behavior does not seem necessary in\n        # the parent's __init__() method, since the casting is\n        # repeated in _set_WorkingDir().\n        if WorkingDir is not None:\n            working_dir = WorkingDir\n        else:\n            working_dir = self._working_dir or getcwd()\n        self.WorkingDir = working_dir\n        self.TmpDir = TmpDir\n\n    @staticmethod\n    def getHelp():\n        \"\"\"Returns link to online manual\"\"\"\n        help = (\n            'See manual, available on the MOTHUR wiki:\\n'\n            'http://schloss.micro.umass.edu/mothur/'\n            )\n        return help\n\n    def __call__(self, data=None, remove_tmp=True):\n        \"\"\"Run the application with the specified kwargs on data\n        \n            data: anything that can be cast into a string or written out to\n                a file. Usually either a list of things or a single string or \n                number. input_handler will be called on this data before it \n                is passed as part of the command-line argument, so by creating\n                your own input handlers you can customize what kind of data\n                you want your application to accept\n\n            remove_tmp: if True, removes tmp files\n        \"\"\"\n        # Process the input data.  Input filepath is stored in\n        # self._input_filename\n        getattr(self, self.InputHandler)(data)\n\n        if self.SuppressStdout:\n            outfile = None\n        else:\n            outfile = open(self.getTmpFilename(self.TmpDir), 'w')\n        if self.SuppressStderr:\n            errfile = None\n        else:\n            errfile = open(self.getTmpFilename(self.TmpDir), 'w')\n\n        args = [self._command, self._compile_mothur_script()]\n        process = Popen(\n            args, stdout=outfile, stderr=errfile, cwd=self.WorkingDir)\n        exit_status = process.wait()\n        if not self._accept_exit_status(exit_status):\n            raise ApplicationError(\n                'Unacceptable application exit status: %s, command: %s' % \\\n                    (exit_status, args))\n\n        if outfile is not None:\n            outfile.seek(0)\n        if errfile is not None:\n            errfile.seek(0)\n        result = CommandLineAppResult(\n            outfile, errfile, exit_status, result_paths=self._get_result_paths())\n\n        # Clean up the input file if one was created\n        if remove_tmp:\n            if self._input_filename:\n                remove(self._input_filename)\n                self._input_filename = None\n\n        return result\n\n    def _accept_exit_status(self, status):\n        return int(status) == 0\n\n    def _compile_mothur_script(self):\n        \"\"\"Returns a Mothur batch script as a string\"\"\"\n        def format_opts(*opts):\n            \"\"\"Formats a series of options for a Mothur script\"\"\"\n            return ', '.join(filter(None, map(str, opts)))\n        vars = {\n            'in': self._input_filename,\n            'unique': self._derive_unique_path(),\n            'dist': self._derive_dist_path(),\n            'names': self._derive_names_path(),\n            'cluster_opts' : format_opts(\n                self.Parameters['method'],\n                self.Parameters['cutoff'],\n                self.Parameters['precision'],\n                ),\n            }\n        script = (\n            '#'\n            'unique.seqs(fasta=%(in)s); '\n            'dist.seqs(fasta=%(unique)s); '\n            'read.dist(column=%(dist)s, name=%(names)s); '\n            'cluster(%(cluster_opts)s)' % vars\n            )\n        return script\n\n    def _get_result_paths(self):\n        paths = {\n            'distance matrix': self._derive_dist_path(),\n            'otu list': self._derive_list_path(),\n            'rank abundance': self._derive_rank_abundance_path(),\n            'species abundance': self._derive_species_abundance_path(),\n            'unique names': self._derive_names_path(),\n            'unique seqs': self._derive_unique_path(),\n            'log': self._derive_log_path(),\n            }\n        return dict([(k, ResultPath(v)) for (k,v) in paths.items()])\n\n    # Methods to derive/guess output pathnames produced by MOTHUR.\n    # TODO: test for input files that do not have a filetype extension\n\n    def _derive_log_path(self):\n        \"\"\"Guess logfile path produced by Mothur\n\n        This method checks the working directory for log files\n        generated by Mothur.  It will raise an ApplicationError if no\n        log file can be found.\n\n        Mothur 1.6.0 and 1.7.2 generate logfiles named\n        'mothur.logFile'.  Mothur 1.9.0 generates log files named in a\n        nondeterministic way, using the current time, with a lowercase\n        extension.  To work around this problem, we search the working\n        directory for files with the either extension.  If the working\n        directory contains two valid Mothur log files, this method may\n        inadvertently return the incorrect log file for Mothur >=\n        1.9.0.\n        \"\"\"\n        input_dir = path.dirname(path.abspath(self._input_filename))\n        possible_logfiles = listdir(input_dir)\n        matching_logfiles = filter(\n            lambda x: x.endswith('.logfile') or x.endswith('.logFile'),\n            possible_logfiles)\n        try:\n            log_path = matching_logfiles[0]\n        except IndexError:\n            raise ApplicationError(\n                'No log file detected in directory %s. Contents: \\n\\t%s' % (\n                    input_dir, '\\n\\t'.join(possible_logfiles)))\n        return path.join(input_dir, log_path)\n\n    def _derive_unique_path(self):\n        \"\"\"Guess unique sequences path produced by Mothur\"\"\"\n        base, ext = path.splitext(self._input_filename)\n        return '%s.unique%s' % (base, ext)\n\n    def _derive_dist_path(self):\n        \"\"\"Guess distance matrix path produced by Mothur\"\"\"\n        base, ext = path.splitext(self._input_filename)\n        return '%s.unique.dist' % base\n\n    def _derive_names_path(self):\n        \"\"\"Guess unique names file path produced by Mothur\"\"\"\n        base, ext = path.splitext(self._input_filename)\n        return '%s.names' % base\n\n    def __get_method_abbrev(self):\n        \"\"\"Abbreviated form of clustering method parameter.\n\n        Used to guess output filenames for MOTHUR.\n        \"\"\"\n        abbrevs = {\n            'furthest': 'fn',\n            'nearest':  'nn',\n            'average':  'an',\n        }\n        if self.Parameters['method'].isOn():\n            method = self.Parameters['method'].Value\n        else:\n            method = self.Parameters['method'].Default\n        return abbrevs[method]\n\n    def _derive_list_path(self):\n        \"\"\"Guess otu list file path produced by Mothur\"\"\"\n        base, ext = path.splitext(self._input_filename)\n        return '%s.unique.%s.list' % (base, self.__get_method_abbrev())\n        \n    def _derive_rank_abundance_path(self):\n        \"\"\"Guess rank abundance file path produced by Mothur\"\"\"\n        base, ext = path.splitext(self._input_filename)\n        return '%s.unique.%s.rabund' % (base, self.__get_method_abbrev())\n        \n    def _derive_species_abundance_path(self):\n        \"\"\"Guess species abundance file path produced by Mothur\"\"\"\n        base, ext = path.splitext(self._input_filename)\n        return '%s.unique.%s.sabund' % (base, self.__get_method_abbrev())\n\n    def getTmpFilename(self, tmp_dir='/tmp', prefix='tmp', suffix='.txt'):\n        \"\"\"Returns a temporary filename\n\n        Similar interface to tempfile.mktmp()\n        \"\"\"\n        # Override to change default constructor to str(). FilePath\n        # objects muck up the Mothur script.\n        return super(Mothur, self).getTmpFilename(\n            tmp_dir=tmp_dir, prefix=prefix, suffix=suffix, \n            result_constructor=str)\n\n    # Temporary input file needs to be in the working directory, so we\n    # override all input handlers.\n\n    def _input_as_multiline_string(self, data):\n        \"\"\"Write multiline string to temp file, return filename\n\n        data: a multiline string to be written to a file.\n        \"\"\"\n        self._input_filename = self.getTmpFilename(\n            self.WorkingDir, suffix='.fasta')\n        with open(self._input_filename, 'w') as f:\n            f.write(data)\n        return self._input_filename\n\n    def _input_as_lines(self, data):\n        \"\"\"Write sequence of lines to temp file, return filename\n        \n        data: a sequence to be written to a file, each element of the\n            sequence will compose a line in the file\n\n        * Note: '\\n' will be stripped off the end of each sequence\n            element before writing to a file in order to avoid\n            multiple new lines accidentally be written to a file\n        \"\"\"\n        self._input_filename = self.getTmpFilename(\n            self.WorkingDir, suffix='.fasta')\n        with open(self._input_filename, 'w') as f:\n            # Use lazy iteration instead of list comprehension to\n            # prevent reading entire file into memory\n            for line in data:\n                f.write(str(line).strip('\\n'))\n                f.write('\\n')\n        return self._input_filename\n\n    def _input_as_path(self, data):\n        \"\"\"Copys the provided file to WorkingDir and returns the new filename\n\n        data: path or filename\n        \"\"\"\n        self._input_filename = self.getTmpFilename(\n            self.WorkingDir, suffix='.fasta')\n        copyfile(data, self._input_filename)\n        return self._input_filename\n\n    def _input_as_paths(self, data):\n        raise NotImplementedError('Not applicable for MOTHUR controller.')\n\n    def _input_as_string(self, data):\n        raise NotImplementedError('Not applicable for MOTHUR controller.')\n\n    # FilePath objects muck up the Mothur script, so we override the\n    # property methods for self.WorkingDir\n\n    def _get_WorkingDir(self):\n        \"\"\"Gets the working directory\"\"\"\n        return self._curr_working_dir\n\n    def _set_WorkingDir(self, path):\n        \"\"\"Sets the working directory\n        \"\"\"\n        self._curr_working_dir = path\n        try:\n            mkdir(self.WorkingDir)\n        except OSError:\n            # Directory already exists\n            pass\n\n    WorkingDir = property(_get_WorkingDir, _set_WorkingDir)\n\ndef mothur_from_file(file):\n    app = Mothur(InputHandler='_input_as_lines')\n    result = app(file)\n    # Force evaluation, so we can safely clean up files\n    otus = list(parse_otu_list(result['otu list']))\n    result.cleanUp()\n    return otus\n\n","sub_path":"cogent/app/mothur.py","file_name":"mothur.py","file_ext":"py","file_size_in_byte":14146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"121319722","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Nov  1 23:32:41 2017\r\n\r\n@author: Mayur\r\n\"\"\"\r\n\"\"\"\r\nExercise: power iter\r\n\r\nWrite an iterative function iterPower(base, exp) that calculates the exponential \r\nbase^exp by simply using successive multiplication. For example, iterPower(base, exp) \r\nshould compute base^exp by multiplying base times itself exp times. \r\nWrite such a function below.\r\n\r\nThis function should take in two values - base can be a float or an integer; \r\nexp will be an integer ≥ 0. It should return one numerical value. \r\nYour code must be iterative - use of the ** operator is not allowed.\r\n\"\"\"\r\n\r\n#code\r\ndef iterPower(base, exp):\r\n    '''\r\n    base: int or float.\r\n    exp: int >= 0\r\n \r\n    returns: int or float, base^exp\r\n    '''\r\n    power = 1\r\n    while exp > 0:\r\n      power = power*base\r\n      exp -= 1\r\n    return power","sub_path":"Week 2 Simple Programs/iterPower.py","file_name":"iterPower.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"459792872","text":"import unittest\n\nimport textwrap\nimport ast\n\nfrom expressivemock import transformer\n\n\nclass TransformerTest(unittest.TestCase):\n    maxDiff = None\n\n    def assertAstEqual(self, lhs, rhs):\n        self.assertEqual(ast.dump(lhs),\n                         ast.dump(rhs))\n    \n    def test_transform_function(self):\n        source = '''\n        def test(x):\n            print(x)\n            \"\"\"\n            x = 42\n            \"\"\"\n            print(x)\n        '''\n\n        input_ast = ast.parse(textwrap.dedent(source))\n\n        tree_transformer = transformer.Transformer()\n\n        output_ast = tree_transformer.transform(input_ast)\n\n        expected_source = '''\n        def test(x):\n            print(x)\n            x = 42\n            print(x)\n        '''\n        expected_ast = ast.parse(textwrap.dedent(expected_source))\n        \n        self.assertAstEqual(expected_ast,\n                            output_ast)\n","sub_path":"tests/transformer_test.py","file_name":"transformer_test.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"359805430","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nEXIST_USER_ID = \"해당 ID의 사용자가 이미 존재 합니다\"\nEXIST_AUTH = \"해당 사용자의 ID가 이미 존재 합니다. 로그인 화면으로 이동합니다.\"\nNOT_FOUND_USER = \"정보가 없습니다. 정확한 정보를 입력하세요\"\nFAIL_LOGIN = \"로그인에 실패하였습니다. ID또는 PW를 확인해 주세요\"\nDISABLE_USER = \"해당 User는 잠겨 있는 상태 입니다. 관리자에게 확인해 주세요\"\nSUCCESS_LOGIN = \"로그인에 성공하였습니다.\"\nSUCCESS_LOGOUT = \"로그아웃에 성공하엿습니다.\"\n","sub_path":"web/conf/return_conf.py","file_name":"return_conf.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"132122085","text":"from typing import Dict, Any, Union, Tuple\n\nimport tensorflow as tf\nimport tensorflow_hub as hub\nimport numpy as np\n\nfrom .seq_encoder import SeqEncoder\nfrom utils.tfutils import write_to_feed_dict, pool_sequence_embedding\n\n\n## Define constants for hyperparameters\n## Embedding Type\nELMO = 'elmo'\nLSTM1 = 'lstm1'\nLSTM2 = 'lstm2'\nWORD = 'word_emb'\n## Pool Type\nELMO_FINAL = 'elmo_final'\nELMO_MEAN = 'weighted_mean'\n\nclass ElmoEncoder(SeqEncoder):\n    @classmethod\n    def get_default_hyperparameters(cls) -> Dict[str, Any]:\n\n        ## TODO: set up the appropriate hyper parameters for the elmo model\n        encoder_hypers = {\n            'embedding_type': ELMO, ## One of [elmo, lstm1, lstm2, word_emb]\n            'elmo_pool_mode': ELMO_MEAN\n        }\n        hypers = super().get_default_hyperparameters()\n        hypers.update(encoder_hypers)\n        return hypers\n\n    def __init__(self, label: str, hyperparameters: Dict[str, Any], metadata: Dict[str, Any]):\n        super().__init__(label, hyperparameters, metadata)\n\n    @property\n    def output_representation_size(self):\n        if self.get_hyper('embedding_type') is WORD:\n            return 512\n        else:\n            return 1024\n    \n    def make_model(self, is_train: bool=True) -> tf.Tensor :\n        with tf.variable_scope(\"elmo_encoder\"):\n            self._make_placeholders()\n\n            self.placeholders['tokens_lengths'] = \\\n                tf.placeholder(tf.int32,\n                               shape=[None],\n                               name='tokens_lengths')\n\n            self.placeholders['tokens'] = \\\n            tf.placeholder(tf.int32,\n                           shape=[None, self.get_hyper('max_num_tokens')],\n                           name='tokens')\n\n            self.placeholders['tokens_str'] = \\\n            tf.placeholder(tf.string,\n                           shape=[None, self.get_hyper('max_num_tokens')],\n                           name='tokens_str')\n\n            seq_tokens_tokens = self.placeholders['tokens']\n            seq_tokens = self.placeholders['tokens_str']\n            seq_tokens_lengths = self.placeholders['tokens_lengths']\n            \n            # ## DEBUGGING: OUTPUT SHAPES\n            # print(\"Sequence Tokens Shape: %s\" % seq_tokens.shape)\n            # print(\"Sequence Tokens Lengths: %s\" % seq_tokens_lengths)\n\n            ## pull elmo model from tensorflow hub\n            elmo = hub.Module(\"https://tfhub.dev/google/elmo/2\", trainable=is_train)\n            token_embeddings = elmo(\n                {\n                    \"tokens\": seq_tokens_tokens,\n                    \"sequence_len\": seq_tokens_lengths\n                },\n                signature='tokens',\n                as_dict=True\n            )['elmo'] ## [batch_size, max_length, 1024 or 512]\n\n            ## add the elmo model to the trainable variables\n\n            output_pool_mode = self.get_hyper('elmo_pool_mode').lower()\n            if output_pool_mode is ELMO_FINAL:\n                return token_embeddings\n            else:\n                token_mask = tf.expand_dims(tf.range(tf.shape(seq_tokens)[1]), axis=0)            # 1 x T\n                token_mask = tf.tile(token_mask, multiples=(tf.shape(seq_tokens_lengths)[0], 1))  # B x T\n                token_mask = tf.cast(token_mask < tf.expand_dims(seq_tokens_lengths, axis=-1),\n                                     dtype=tf.float32)                                            # B x T\n                return pool_sequence_embedding(output_pool_mode,\n                                               sequence_token_embeddings=token_embeddings,\n                                               sequence_lengths=seq_tokens_lengths,\n                                               sequence_token_masks=token_mask)\n\n    def init_minibatch(self, batch_data: Dict[str, Any]) -> None:\n        super().init_minibatch(batch_data)\n        batch_data['tokens'] = []\n        batch_data['tokens_lengths'] = []\n        batch_data['tokens_str'] = []\n\n    def minibatch_to_feed_dict(self, batch_data: Dict[str, Any], feed_dict: Dict[tf.Tensor, Any], is_train: bool) -> None:\n        super().minibatch_to_feed_dict(batch_data, feed_dict, is_train)\n\n        write_to_feed_dict(feed_dict, self.placeholders['tokens'], batch_data['tokens'])\n        write_to_feed_dict(feed_dict, self.placeholders['tokens_lengths'], batch_data['tokens_lengths'])\n        write_to_feed_dict(feed_dict, self.placeholders['tokens_str'], batch_data['tokens_str'])","sub_path":"src/encoders/elmo_encoder.py","file_name":"elmo_encoder.py","file_ext":"py","file_size_in_byte":4456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"482983298","text":"def union(v1,v2):\n    finalVect = []\n    i=0\n    j=0\n    while(i<len(v1) or j<len(v2)):\n        if(i>=len(v1)):\n           finalVect.append(v2[j])\n           j+=1\n        elif(j>=len(v2)):\n           finalVect.append(v1[i])\n           i+=1\n        elif(v1[i]<v2[j]):\n            finalVect.append(v1[i])\n            i+=1\n        else:\n            finalVect.append(v2[j])\n            j+=1\n    return finalVect\n\ndef multiUnion(vects):\n    if(len(vects) > 1):\n        vectSet1 = []\n        vectSet2 = []\n        midIndex = (int)(len(vects)/2)\n        for i in range(0,midIndex):\n            vectSet1.append(vects[i])\n        for i in range(midIndex,len(vects)):\n            vectSet2.append(vects[i])\n        vect1 = multiUnion(vectSet1)\n        vect2 = multiUnion(vectSet2)\n        return union(vect1,vect2)\n    else:\n        return vects[0]","sub_path":"Homework 7/Problem 3/Python Code.py","file_name":"Python Code.py","file_ext":"py","file_size_in_byte":837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"255386563","text":"\"\"\"\nTime   : 2020/6/16 15:41\nAuthor : huyangh\n演示字符串替换\n\"\"\"\nimport re\n\n\ndef main():\n    sentence = 'This is a sentence for substitution.'\n    sentence_sub = re.sub('sentence', 'line', sentence, flags=re.IGNORECASE)\n    print(sentence_sub)\n\n\nif __name__ == '__main__':\n    main()","sub_path":"venv/Scripts/day12/re_sub.py","file_name":"re_sub.py","file_ext":"py","file_size_in_byte":290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"226336994","text":"\"\"\"The Tame Of Thrones CLI.\"\"\"\n\nimport input_reader as ir\nimport output as op\nimport sys\n\nfrom config import config_data\nfrom kingdom import Kingdom\n\n\ndef main():\n    input_file = sys.argv[1]\n    data = ir.read_input(input_file)\n    messages = ir.validate_format(config_data.get(\"std_input_format\"), data)\n    home_kingdom = config_data.get(\"home_kingdom\")\n    home_kngdm_obj = Kingdom(home_kingdom)\n    home_kngdm_obj.send_secret_msg(messages)\n    allies = home_kngdm_obj.form_rule()\n    output = op.Outputter(allies)\n    output.print_standard_output()\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"tame_of_thrones/geektrust.py","file_name":"geektrust.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"266009446","text":"import datetime\nimport json\nfrom typing import Any, Dict\n\nimport pandas\n\nR_T_MAX = 12\n\nLATEST_PROCESSED = 'data/latest_processed.csv'\n\nFILTERED_REGIONS = [\n    'Virgin Islands',\n    'American Samoa',\n    'Northern Mariana Islands',\n    'Guam',\n    'Puerto Rico'\n]\n\n\ndef get_realtime_r0_filename(date_obj: datetime.date) -> str:\n    return f'data/plots/realtime_r0/{date_obj:%Y-%m-%d}.png'\n\n\ndef load_data() -> pandas.DataFrame:\n    results_df = pandas.read_csv(LATEST_PROCESSED)\n    results_df = results_df.rename(columns={'Unnamed: 0': 'state'})\n    results_df = results_df[~results_df['state'].isin(FILTERED_REGIONS)]\n    return results_df\n\n\ndef load_meta() -> Dict[str, Any]:\n    try:\n        return json.load(open('data/meta.json'))\n    except (FileNotFoundError, json.decoder.JSONDecodeError):\n        return {\"tweets\": {}}\n\n\ndef write_meta(meta_json):\n    with open('data/meta.json', 'w') as f:\n        json.dump(meta_json, f)\n","sub_path":"scripts/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"347363586","text":"from pyrez.enumerations import Tier\nfrom datetime import datetime\n\nclass APIResponse:\n    def __init__(self, **kwargs):\n        self.retMsg = kwargs.get(\"ret_msg\", None)\n        self.json = str(kwargs)\n    def __str__(self):\n        return str(self.json)\n\nclass BaseAbility:#class Ability\n    def __init__(self, **kwargs):\n        self.id = int(kwargs.get(\"Id\", 0))\n        self.summary = kwargs.get(\"Summary\", None)\n        self.url = kwargs.get(\"URL\", None)\n    def __str__(self):\n        return \"ID: {0} Description: {1} Summary: {2} Url: {3}\".format(self.id, self.description, self.summary, self.url)\n\nclass BaseCharacter(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.id = int(kwargs.get(\"id\", 0))\n        self.abilitys = []\n        self.cons = kwargs.get(\"Cons\", None)\n        self.health = int(kwargs.get(\"Health\", 0))\n        self.lore = kwargs.get(\"Lore\", None)\n        self.name = kwargs.get(\"Name\", None)\n        self.onFreeRotation = kwargs.get(\"OnFreeRotation\", None)\n        self.pantheon = kwargs.get(\"Pantheon\", None)\n        self.pros = kwargs.get(\"Pros\", None)\n        self.roles = kwargs.get(\"Roles\", None)\n        self.speed = int(kwargs.get(\"Speed\", 0))\n        self.title = kwargs.get(\"Title\", None)\n        self.type = kwargs.get(\"Type\", None)\n\nclass BaseCharacterRank(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.assists = kwargs.get(\"Assists\")\n        self.deaths = int(kwargs.get(\"Deaths\", 0))\n        self.kills = kwargs.get(\"Kills\")\n        self.losses = int(kwargs.get(\"Losses\", 0))\n        self.minionKills = int(kwargs.get(\"MinionKills\", 0))\n        self.godLevel = int(kwargs.get(\"Rank\", 0))\n        self.wins = int(kwargs.get(\"Wins\", 0))\n        self.worshippers = int(kwargs.get(\"Worshippers\", 0))\n        self.playerID = int(kwargs.get(\"player_id\", 0))\n    def getWinratio(self, decimals = 2):\n        aux = self.wins + self.losses if self.wins + self.losses > 1 else 1\n        winratio = self.wins / aux * 100.0\n        return int(winratio) if winratio % 2 == 0 else round(winratio, decimals)\n\n    def getKDA(self, decimals = 2):\n        deaths = self.deaths if self.deaths > 1 else 1\n        kda =((self.assists / 2) + self.kills) / deaths\n        return int(kda) if kda % 2 == 0 else round(kda, decimals)# + \"%\";\n\nclass BaseItem(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.deviceName = int(kwargs.get(\"DeviceName\", 0))\n        self.iconId = int(kwargs.get(\"IconId\"))\n        self.itemId = int(kwargs.get(\"ItemId\"))\n        self.price = int(kwargs.get(\"Price\"))\n        self.shortDesc = str(kwargs.get(\"ShortDesc\"))\n        self.itemIcon_URL = str(kwargs.get(\"itemIcon_URL\"))\n    def __eq__(self, other):\n        return self.ItemId == other.ItemId\n\nclass AbstractPlayer(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.playerId = int(kwargs.get(\"Id\", 0)) or int(kwargs.get(\"id\", 0))\n        self.name = str(kwargs.get(\"Name\")) or str(kwargs.get(\"name\"))\n\nclass BasePlayer(AbstractPlayer):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.createdDatetime = kwargs.get(\"Created_Datetime\") or kwargs.get(\"created_datetime\")\n        self.lastLoginDatetime = kwargs.get(\"Last_Login_Datetime\") or kwargs.get(\"last_login_datetime\")\n        self.accountLevel = int(kwargs.get(\"Level\", 0)) or int(kwargs.get(\"level\", 0))\n        self.playerRegion = kwargs.get(\"Region\") or kwargs.get(\"region\")\n        \n    def __str__(self):\n        return str(self.json)\n\nclass BasePSPlayer(BasePlayer):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.leaves = int(kwargs.get(\"Leaves\", 0))\n        self.losses = int(kwargs.get(\"Losses\", 0))\n        self.playedChampions = int(kwargs.get(\"MasteryLevel\", 0))\n        self.playerStatusMessage = kwargs.get(\"Personal_Status_Message\")\n        self.rankedConquest = BaseRanked(**kwargs.get(\"RankedConquest\"))\n        self.teamID = int(kwargs.get(\"TeamId\"))\n        self.teamName = kwargs.get(\"Team_Name\")\n        self.playerElo = int(kwargs.get(\"Tier_Conquest\", 0))\n        self.totalAchievements = int(kwargs.get(\"Total_Achievements\", 0))\n        self.totalworshippers = int(kwargs.get(\"Total_Worshippers\", 0))\n        self.wins = int(kwargs.get(\"Wins\", 0))\n\n    def getWinratio(self, decimals = 2):\n        winratio = self.wins /((self.wins + self.losses) if self.wins + self.losses > 1 else 1) * 100.0\n        return int(winratio) if winratio % 2 == 0 else round(winratio, decimals)\n\nclass BaseRanked(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.leaves = kwargs.get(\"Leaves\")\n        self.losses = int(kwargs.get(\"Losses\", 0))\n        self.rankedName = kwargs.get(\"Name\")\n        self.currentTrumpPoints = int(kwargs.get(\"Points\", 0))\n        self.prevRank = int(kwargs.get(\"PrevRank\", 0))\n        self.leaderboardIndex = int(kwargs.get(\"Rank\", 0))\n        self.rankStatConquest = kwargs.get(\"Rank_Stat_Conquest\", None)\n        self.rankStatDuel = kwargs.get(\"Rank_Stat_Duel\", None)\n        self.rankStatJoust = kwargs.get(\"Rank_Stat_Joust\", None)\n        self.currentSeason = int(kwargs.get(\"Season\", 0))\n        self.currentElo = Tier(int(kwargs.get(\"Tier\", 0)))\n        self.wins = int(kwargs.get(\"Wins\", 0))\n        self.playerID = kwargs.get(\"player_id\", None)\n    def getWinratio(self):\n        winratio = self.wins /((self.wins + self.losses) if self.wins + self.losses > 1 else 1) * 100.0\n        return int(winratio) if winratio % 2 == 0 else round(winratio, 2)\n\nclass Champion(BaseCharacter):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        for i in range(0, 5):\n            obj = ChampionAbility(**kwargs.get(\"Ability_\" + str(i + 1)))\n            self.abilitys.append(obj)\n        self.championCardURL = kwargs.get(\"ChampionCard_URL\", None)\n        self.championIconURL = kwargs.get(\"ChampionIcon_URL\", None)\n        self.latestChampion = True if str(kwargs.get(\"latestChampion\")).lower() == \"y\" else False\n    def __str__(self):\n        st = \"Name: {0} ID: {1} Health: {1} Roles: {2} Title: {3}\".format(self.name, self.id, self.health, self.roles, self.title)\n        for i in range(0, len(self.abilitys)):\n            st +=(\" Ability {0}: {1}\").format(i + 1, self.abilitys [i])\n        st += \"CardUrl: {0} IconUrl: {1} \".format(self.championCardURL, self.championIconURL)\n        return st;\n\nclass ChampionAbility(BaseAbility):\n    def __init__(self, **kwargs):\n        self.description = kwargs.get(\"Description\", None)\n        return super().__init__(**kwargs)\n\nclass ChampionSkin(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.champion_id = int(kwargs.get(\"champion_id\", 0))\n        self.champion_name = str(kwargs.get(\"champion_name\", None))\n        self.rarity = str(kwargs.get(\"rarity\", None))\n        self.skinID1 = int(kwargs.get(\"skin_id1\", 0))\n        self.skinID2 = int(kwargs.get(\"skin_id2\", 0))\n        self.skinName = str(kwargs.get(\"skin_name\", None))\n        self.skinNameEnglish = str(kwargs.get(\"skin_name_english\", None))\n    \n    def __eq__(self, other):\n        return self.skinID1 == other.skinID1 and self.skinID2 == other.skinID2\n    def __str__(self):\n        return str(self.json)\n\nclass DataUsed(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.activeSessions = int(kwargs.get(\"Active_Sessions\", 0)) or int(kwargs.get(\"active_Sessions\", 0))\n        self.concurrentSessions = int(kwargs.get(\"Concurrent_Sessions\", 0)) or int(kwargs.get(\"concurrent_sessions\", 0))\n        self.requestLimitDaily = int(kwargs.get(\"Request_Limit_Daily\", 0)) or int(kwargs.get(\"request_limit_daily\", 0))\n        self.sessionCap = int(kwargs.get(\"Session_Cap\", 0)) or int(kwargs.get(\"session_cap\", 0))\n        self.sessionTimeLimit = int(kwargs.get(\"Session_Time_Limit\", 0)) or int(kwargs.get(\"session_time_limit\", 0))\n        self.totalRequestsToday = int(kwargs.get(\"Total_Requests_Today\", 0)) or int(kwargs.get(\"total_requests_today\", 0))\n        self.totalSessionsToday = int(kwargs.get(\"Total_Sessions_Today\", 0)) or int(kwargs.get(\"total_sessions_today\", 0))\n    def __str__(self):\n        return \"Active sessions: {0} Concurrent sessions: {1} Request limit daily: {2} Session cap: {3} Session time limit: {4} Total requests today: {5} Total sessions today: {6} \".format(self.activeSessions, self.concurrentSessions, self.requestLimitDaily, self.sessionCap, self.sessionTimeLimit, self.totalRequestsToday, self.totalSessionsToday)\n    def sessionsLeft(self):\n        return self.sessionCap - self.totalSessionsToday if self.sessionCap - self.totalSessionsToday > 0 else 0\n    def requestsLeft(self):\n        return self.requestLimitDaily - self.totalRequestsToday if self.requestLimitDaily - self.totalRequestsToday > 0 else 0\n    def concurrentSessionsLeft(self):\n        return self.concurrentSessions - self.activeSessions if self.concurrentSessions - self.activeSessions > 0 else 0\n\nclass Friend(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.id = int(kwargs.get(\"account_id\"))\n        self.playerID = int(kwargs.get(\"player_id\"))\n        self.avatarURL = kwargs.get(\"avatar_url\")\n        self.name = kwargs.get(\"name\")\n    def __str__(self):\n        return \"<Player {}>\".format(self.id)\n    #def __hash__(self):\n        #return hash(self.id)\n    def __eq__(self, other):\n        return self.id == other.id\n\nclass God(BaseCharacter):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.latestGod = True if str(kwargs.get(\"latestGod\")).lower() == \"y\" else False\n\nclass GodRank(BaseCharacterRank):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.godName = str(kwargs.get(\"god\")) if kwargs.get(\"champion\") is None else str(kwargs.get(\"champion\"))\n        self.godID = Gods(int(kwargs.get(\"god_id\"))) if kwargs.get(\"god_id\") else Champions(int(kwargs.get(\"champion_id\"))) if kwargs.get(\"champion_id\") else -1\n        \nclass HiRezServerStatus(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.entryDateTime = kwargs.get(\"entry_datetime\")\n        self.status = True if str(kwargs.get(\"status\", None).upper()) == \"UP\" else False\n        self.version = kwargs.get(\"version\")\n    def __str__(self):\n        return \"entry_datetime: {0} status: {1} version: {2}\".format(self.entryDateTime, \"UP\" if self.status else \"DOWN\", self.version)\n\nclass InGameItem:\n    def __init__(self, itemID, itemName, itemLevel):\n        self.itemID = itemID\n        self.itemName = itemName\n        self.itemLevel = itemLevel\n\n        def __str__(self):\n            return self.itemName\n\nclass ItemDescription:\n    def __init__(self, **kwargs):\n        self.description = int(kwargs.get(\"Description\"))\n        canTry = True\n        index = 0\n        while canTry:\n            try:\n                obj = Menuitem(**self.Menuitems.get(str(index)))\n                index += 1\n                self.menuItems.Append(obj)\n            except:\n                canTry = False\n        self.secondaryDescription = int(kwargs.get(\"SecondaryDescription\"))\n\nclass Leaderboard(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.lastUpdated = kwargs.get(\"last_updated\")\n        self.queue = kwargs.get(\"queue\")\n        self.queueId = int(kwargs.get(\"queue_id\"))\n        leaderboardDetails = kwargs.get(\"leaderboard_details\")\n        self.leaderboards = []\n        for i in range(0, len(leaderboardDetails)):\n            obj = LeaderboardDetails(** leaderboardDetails [i])\n            self.leaderboards.append(obj)\n\nclass LeaderboardDetails:\n    def __init__(self, **kwargs):\n        self.matches = int(kwargs.get(\"matches\"))\n        self.playerId = kwargs.get(\"player_id\")\n        self.playerName = kwargs.get(\"player_name\")\n        self.rank = int(kwargs.get(\"rank\"))\n        self.teamAVGPlacement = float(kwargs.get(\"team_avg_placement\"))\n        self.teamWins = int(kwargs.get(\"team_wins\"))\n        self.winPercentage = float(kwargs.get(\"win_percentage\"))\n\nclass LoadoutItem:\n    def __init__(self, **kwargs):\n        self.itemId = int(kwargs.get(\"ItemId\", 0))\n        self.itemName = str(kwargs.get(\"ItemName\", None))\n        self.points = int(kwargs.get(\"Points\", 0))\n    def __str__(self):\n        return \"{0}({1})\".format(self.itemName, self.points)\n\nclass MatchHistory(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.items = []\n        self.loadout = []\n        for i in range(1, 5):\n            obj = InGameItem(kwargs.get(\"ActiveId{0}\".format(i)), kwargs.get(\"Active_{0}\".format(i)), kwargs.get(\"ActiveLevel{0}\".format(i)))\n            self.items.append(obj)\n        for i in range(1, 7):\n            obj = InGameItem(kwargs.get(\"ItemId{0}\".format(i)), kwargs.get(\"Item_{0}\".format(i)), kwargs.get(\"ItemLevel{0}\".format(i)))\n            self.loadout.append(obj)\n        self.assists = kwargs.get(\"Assists\")\n        self.championID = kwargs.get(\"ChampionId\")\n        self.championName = kwargs.get(\"Champion\")\n        self.creeps = kwargs.get(\"Creeps\")\n        self.damage = kwargs.get(\"Damage\")\n        self.damageBot = kwargs.get(\"Damage_Bot\")\n        self.damageDoneInHand = kwargs.get(\"Damage_Done_In_Hand\")\n        self.damageMitigated = kwargs.get(\"Damage_Mitigated\")\n        self.damageStructure = kwargs.get(\"Damage_Structure\")\n        self.damageTaken = kwargs.get(\"Damage_Taken\")\n        self.damageTakenMagical = kwargs.get(\"Damage_Taken_Magical\")\n        self.damageTakenPhysical = kwargs.get(\"Damage_Taken_Physical\")\n        self.deaths = kwargs.get(\"Deaths\")\n        self.distanceTraveled = kwargs.get(\"Distance_Traveled\")\n        self.credits = kwargs.get(\"Gold\")\n        self.healing = kwargs.get(\"Healing\")\n        self.healingBot = kwargs.get(\"Healing_Bot\")\n        self.healingPlayerSelf = kwargs.get(\"Healing_Player_Self\")\n        self.killingSpree = kwargs.get(\"Killing_Spree\")\n        self.kills = kwargs.get(\"Kills\")\n        self.level = kwargs.get(\"Level\")\n        self.mapGame = kwargs.get(\"Map_Game\")\n        self.matchMinutes = kwargs.get(\"Minutes\")\n        self.matchRegion = kwargs.get(\"Region\")\n        self.matchQueueID = kwargs.get(\"Match_Queue_Id\")\n        self.matchTime = kwargs.get(\"Match_Time\")\n        self.matchTimeSecond = kwargs.get(\"Time_In_Match_Seconds\")\n        self.matchID = kwargs.get(\"Match\")\n        self.multiKillMax = kwargs.get(\"Multi_kill_Max\")\n        self.objectiveAssists = kwargs.get(\"Objective_Assists\")\n        self.queue = kwargs.get(\"Queue\")\n        self.skin = kwargs.get(\"Skin\")\n        self.skinID = kwargs.get(\"SkinId\")\n        self.surrendered = kwargs.get(\"Surrendered\")\n        self.taskForce = kwargs.get(\"TaskForce\")\n        self.team1Score = kwargs.get(\"Team1Score\")\n        self.team2Score = kwargs.get(\"Team2Score\")\n        self.wardsPlaced = kwargs.get(\"Wards_Placed\")\n        self.winStatus = kwargs.get(\"Win_Status\")\n        self.winningTaskForce = kwargs.get(\"Winning_TaskForce\")\n        self.playerName = kwargs.get(\"playerName\")\n\nclass Menuitem:\n    def __init__(self, **kwargs):\n        self.description = int(kwargs.get(\"Description\"))\n        self.value = int(kwargs.get(\"Value\"))\n\nclass PaladinsItem(BaseItem):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.description = str(kwargs.get(\"Description\"))\n        self.champion_id = int(kwargs.get(\"champion_id\"))\n        self.item_type = str(kwargs.get(\"item_type\"))\n        self.talent_reward_level = int(kwargs.get(\"talent_reward_level\"))\n\nclass PatchInfo(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.gameVersion = str(kwargs.get(\"version_string\", None))\n\nclass Ping:\n    def __init__(self, kwargs):\n        self.textPlain = str(kwargs)\n        textPlain = str(kwargs).split(' ')\n        if len(textPlain) > 11:\n            self.apiName = textPlain [0]\n            self.apiVersion = textPlain [2].replace(')', '')\n            self.gamePatch = textPlain [5].replace(']', '')\n            self.ping = textPlain [8] == \"successful.\"\n            #self.date = \"{0} {1} {2}\".format(textPlain [10].replace(\"Date:\", \"\"), textPlain [11], textPlain [12])\n            self.date = datetime.strptime(\"{0} {1} {2}\".format(textPlain [10].replace(\"Date:\", \"\"), textPlain [11], textPlain [12]), \"%m/%d/%Y %H:%M:%S %p\")\n    def __str__(self):\n        return \"APIName: {0} APIVersion: {1} GameVersion: {2} Ping: {3} Date: {4}\".format(self.apiName, self.apiVersion, self.gamePatch, self.ping, self.date)\n\nclass Player(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.playerId = kwargs.get(\"id\")\n        self.name = kwargs.get(\"name\")\n        self.steamID = kwargs.get(\"steam_id\")\n\nclass PlayerLoadouts(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.championID = int(kwargs.get(\"ChampionId\", 0))\n        self.championName = str(kwargs.get(\"ChampionName\", None))\n        self.deckID = int(kwargs.get(\"DeckId\", 0))\n        self.deckName = str(kwargs.get(\"DeckName\", None))\n        self.playerID = int(kwargs.get(\"playerId\", 0))\n        self.playerName = str(kwargs.get(\"playerName\", None))\n        cards = kwargs.get(\"LoadoutItems\")\n        self.cards = []\n        for i in cards:\n            obj = LoadoutItem(**i)\n            self.cards.append(obj)\n    def __str__(self):\n        return self.json\n\nclass PlayerRealmRoyale(BasePlayer):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.playerId = kwargs.get(\"id\")\n        self.name = kwargs.get(\"name\")\n        self.steamID = kwargs.get(\"steam_id\")\nclass PlayerSmite(BasePSPlayer):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.avatarURL = kwargs.get(\"Avatar_URL\")\n        self.rankStatConquest = kwargs.get(\"Rank_Stat_Conquest\")\n        self.rankStatDuel = kwargs.get(\"Rank_Stat_Duel\")\n        self.rankStatJoust = kwargs.get(\"Rank_Stat_Joust\")\n        self.rankedDuel = BaseRanked(**kwargs.get(\"RankedDuel\"))\n        self.rankedJoust = BaseRanked(**kwargs.get(\"RankedJoust\"))\n        self.tierJoust = kwargs.get(\"Tier_Joust\")\n        self.tierDuel = kwargs.get(\"Tier_Duel\")\n\nclass PlayerStatus(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.currentMatchID = int(kwargs.get(\"match_id\", 0)) or int(kwargs.get(\"Match\", 0))\n        self.currentMatchQueueID = int(kwargs.get(\"match_queue_id\", 0))\n        self.playerStatus = int(kwargs.get(\"status_id\", 0)) or kwargs.get(\"status\", 0)\n        self.playerStatusString = str(kwargs.get(\"status_string\", None)) or str(kwargs.get(\"status\", None))\n        self.playerStatusMessage = str(kwargs.get(\"personal_status_message\", None))\n\nclass Session(APIResponse):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.sessionId = str(kwargs.get(\"session_id\", 0))\n        self.timeStamp = datetime.strptime(str(kwargs.get(\"timestamp\", 0)), \"%m/%d/%Y %H:%M:%S %p\")\n    def isApproved(self):\n        return str(self.json).lower().find(\"approved\") != -1\nclass SmiteItem(BaseItem):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.childItemId = int(kwargs.get(\"ChildItemId\"))\n        self.itemDescription = ItemDescription(**kwargs.get(\"ItemDescription\"))\n        self.itemTier = str(kwargs.get(\"ItemTier\"))\n        self.rootItemId = int(kwargs.get(\"RootItemId\"))\n        self.startingItem = str(kwargs.get(\"StartingItem\"))\n        self.type = str(kwargs.get(\"Type\"))\n\nclass TestSession:\n    def __init__(self, kwargs):\n        self.textPlain = str(kwargs)\n        textPlain = str(kwargs).split(' ')\n        if len(textPlain) > 19:\n            self.successfull = self.textPlain.lower().find(\"this was a successful test with the following parameters added:\") != -1\n            self.devId = textPlain [11]\n            #self.date = \"{0} {1} {2}\".format(textPlain [13].replace(\"time:\", \"\"), textPlain [14], textPlain [15])\n            self.date = datetime.strptime(\"{0} {1} {2}\".format(textPlain [13].replace(\"time:\", \"\"), textPlain [14], textPlain [15]), \"%m/%d/%Y %H:%M:%S %p\")\n            self.signature = textPlain [17]\n            self.session = textPlain [19]\n    def __str__(self):\n        return \"Successful: {0} devId: {1} Date: {2} Signature: {3} Session: {4}\".format(self.successfull, self.devId, self.date, self.signature, self.session)\n","sub_path":"pyrez/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":20643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"209623897","text":"import torch.nn as nn\nimport torch\n\n__all__ = ['I3DNonLocalResNet', 'nonlocal_resnet50', 'nonlocal_resnet101', 'nonlocal_resnet152']\n\n\nclass NonLocalBlock3D(nn.Module):\n    def __init__(self, in_channels, inter_channels=None, mode='embedded_gaussian', sub_sample=True, use_bn=True):\n        super(NonLocalBlock3D, self).__init__()\n\n        assert mode in ['embedded_gaussian', 'dot_product']\n        self.mode = mode\n        if self.mode == 'embedded_gaussian':\n            self.operation_function = self._embedded_gaussian\n        elif self.mode == 'dot_product':\n            self.operation_function = self._dot_product\n\n        self.sub_sample = sub_sample\n        self.use_bn = use_bn\n        self.in_channels = in_channels\n        if inter_channels is None:\n            self.inter_channels = in_channels // 2\n        else:\n            self.inter_channels = inter_channels\n\n        self.theta = nn.Conv3d(in_channels=self.in_channels, out_channels=self.inter_channels,\n                               kernel_size=1, stride=1, padding=0)\n        self.phi = nn.Conv3d(in_channels=self.in_channels, out_channels=self.inter_channels,\n                             kernel_size=1, stride=1, padding=0)\n        self.g = nn.Conv3d(in_channels=self.in_channels, out_channels=self.inter_channels,\n                         kernel_size=1, stride=1, padding=0)\n        if self.sub_sample:\n            self.g = nn.Sequential(self.g, nn.MaxPool3d(kernel_size=2))\n            self.phi = nn.Sequential(self.phi, nn.MaxPool3d(kernel_size=2))\n\n        if self.use_bn:\n            self.Wz = nn.Sequential(\n                nn.Conv3d(in_channels=self.inter_channels, out_channels=self.in_channels,\n                        kernel_size=1, stride=1, padding=0),\n                nn.BatchNorm3d(self.in_channels,eps=1e-05, momentum=0.9)\n            )\n        else:\n            self.Wz = nn.Conv3d(in_channels=self.inter_channels, out_channels=self.in_channels,\n                        kernel_size=1, stride=1, padding=0)\n\n        self._init_param()\n\n\n    def _init_param(self):\n\n        def init_sequential(m):\n            if type(m) == nn.Conv3d:\n                m.weight.data.normal_(std=0.01)\n                m.bias.data.zero_()\n            if type(m) == nn.BatchNorm3d:\n                m.weight.data.zero_()\n\n        if self.sub_sample:\n            self.g.apply(init_sequential)\n            self.phi.apply(init_sequential)\n        else:\n            nn.init.normal(self.g.weight, 0.01)\n            nn.init.constant(self.g.bias, 0)\n            nn.init.normal(self.phi.weight, 0.01)\n            nn.init.constant(self.phi.bias, 0)\n\n        nn.init.normal(self.theta.weight, 0.01)\n        nn.init.constant(self.theta.bias, 0)\n\n        if self.use_bn:\n            self.Wz.apply(init_sequential)\n        else:\n            nn.init.normal(self.Wz.weight, 0.01)\n            nn.init.constant(self.Wz.bias, 0)\n\n\n    def forward(self, x):\n        '''\n        :param x: (b, c, t, h, w)\n        :return:\n        '''\n        output = self.operation_function(x)\n        return output\n\n\n    def _dot_product(self, x):\n        batch_size = x.size(0)\n\n        g_x = self.g(x).view(batch_size, self.inter_channels, -1) # g_x: (b,0.5c,thw)\n        g_x = g_x.permute(0, 2, 1) # g_x: (b,thw,0.5c)\n\n        theta_x = self.theta(x).view(batch_size, self.inter_channels, -1)\n        theta_x = theta_x.permute(0, 2, 1) # theta_x: (b,thw,0.5c)\n        phi_x = self.phi(x).view(batch_size, self.inter_channels, -1) # phi_x: (b,0.5c,thw)\n\n        # apply dot product similarity\n        f = torch.matmul(theta_x, phi_x) # f: (b, thw, thw)\n        f_div_C = f / f.size(-1) # normalize\n\n        y = torch.matmul(f_div_C, g_x) # y: (b,thw,0.5c)\n        y = y.permute(0, 2, 1).contiguous() # y:(b,0.5c,thw)\n        y = y.view(batch_size, self.inter_channels, *x.size()[2:]) # y: (b,0.5c,t,h,w)\n\n        z = self.Wz(y) + x\n        return z\n\n\n    def _embedded_gaussian(self, x):\n        batch_size = x.size(0)\n\n        g_x = self.g(x).view(batch_size, self.inter_channels, -1) # g_x: (b,0.5c,thw)\n        g_x = g_x.permute(0, 2, 1) #g_x: (b,thw,0.5c)\n\n        theta_x = self.theta(x).view(batch_size, self.inter_channels, -1)\n        theta_x = theta_x.permute(0, 2, 1) # theta_x: (b,thw,0.5c)\n        phi_x = self.phi(x).view(batch_size, self.inter_channels, -1) # phi_x: (b,0.5c,thw)\n\n        # apply embedded gaussian\n        f = torch.matmul(theta_x, phi_x) # f: (b, thw, thw)\n        f_div_C = nn.functional.softmax(f, dim=-1)\n\n        y = torch.matmul(f_div_C, g_x) # y: (b,thw,0.5c)\n        y = y.permute(0, 2, 1).contiguous() # y:(b,0.5c,thw)\n        y = y.view(batch_size, self.inter_channels, *x.size()[2:]) # y: (b,0.5c,t,h,w)\n\n        z = self.Wz(y) + x\n\n        return z\n\n\ndef insert_nonlocal_block(input, in_channels, inter_channels=None,\n                mode='embedded_gaussian', sub_sample=True, use_bn=True):\n\n    nonlocal_block = NonLocalBlock3D(in_channels, inter_channels=inter_channels, mode=mode,\n                                     sub_sample=sub_sample, use_bn=use_bn)\n    output = nonlocal_block(input)\n    return output\n\n\nclass Bottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(self, inplanes, planes, time_kernel=1, space_stride=1, enable_nonlocal=False, downsample=None):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=(time_kernel,1,1), padding=((time_kernel-1)//2, 0,0),bias=False) # timepadding: make sure time-dim not reduce\n        self.bn1 = nn.BatchNorm3d(planes)\n        self.conv2 = nn.Conv3d(planes, planes, kernel_size=(1,3,3), stride=(1,space_stride,space_stride),\n                               padding=(0,1,1), bias=False)\n        self.bn2 = nn.BatchNorm3d(planes)\n        self.conv3 = nn.Conv3d(planes, planes * 4, kernel_size=(1,1,1), bias=False)\n        self.bn3 = nn.BatchNorm3d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.enable_nonlocal = enable_nonlocal\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        if self.enable_nonlocal:\n            out = insert_nonlocal_block(out, out.size(1))\n\n        return out\n\n\nclass I3DNonLocalResNet(nn.Module):\n\n    def __init__(self, block, layers, nonlocals, frame_num=32, num_classes=1000):\n        self.inplanes = 64\n        super(I3DNonLocalResNet, self).__init__()\n        self.conv1 = nn.Conv3d(3, 64, kernel_size=(5,7,7), stride=(2,2,2), padding=(2,3,3),\n                               bias=False)\n        self.bn1 = nn.BatchNorm3d(64)\n        self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool3d(kernel_size=(3,3,3), stride=(2,2,2), padding=(1,1,1))\n        self.layer1 = self._make_layer_inflat_nonlocal(block, 64, layers[0])\n        self.temporalpool = nn.MaxPool3d(kernel_size=(3,1,1), stride=(2,1,1), padding=(1,0,0))\n        self.layer2 = self._make_layer_inflat_nonlocal(block, 128, layers[1], space_stride=2, nonlocal_index=nonlocals[0])\n        self.layer3 = self._make_layer_inflat_nonlocal(block, 256, layers[2], space_stride=2, nonlocal_index=nonlocals[1])\n        self.layer4 = self._make_layer_inflat_nonlocal(block, 512, layers[3], space_stride=2)\n        self.avgpool = nn.AvgPool3d((frame_num//8,7,7))\n        self.fc = nn.Linear(512 * block.expansion, num_classes)\n\n    def _make_layer_inflat_nonlocal(self, block, planes, blocks, space_stride=1, nonlocal_index=1000):\n        downsample = None\n        if space_stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv3d(self.inplanes, planes * block.expansion,\n                          kernel_size=(1,1,1), stride=(1,space_stride,space_stride), bias=False),\n                nn.BatchNorm3d(planes * block.expansion),\n            )\n\n        layers = []\n\n        time_kernel = 3 #making I3D(3*1*1)\n        enable_nonlocal = False\n        layers.append(block(self.inplanes, planes, time_kernel, space_stride, enable_nonlocal, downsample))\n\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            # making I3D(3*1*1)\n            if i % 2 == 0:\n                time_kernel = 3\n            else:\n                time_kernel = 1\n\n            # making nonlocal\n            if i % nonlocal_index == nonlocal_index - 1:\n                enable_nonlocal = True\n            else:\n                enable_nonlocal = False\n\n            layers.append(block(self.inplanes, planes, time_kernel, 1, enable_nonlocal, None))\n\n        return nn.Sequential(*layers)\n\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        x = self.maxpool(x)\n\n        x = self.layer1(x)\n        x = self.temporalpool(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        x = self.avgpool(x)\n        #x = x.permute(0, 2, 1, 3, 4).contiguous()\n        x = x.view(x.size(0), -1)\n        x = self.fc(x)\n\n        return x\n\n\ndef nonlocal_resnet50(pretrained=False, **kwargs):\n    \"\"\"Constructs a ResNet-50 model.\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    \"\"\"\n    model = I3DNonLocalResNet(Bottleneck, [3, 4, 6, 3], [2, 2], **kwargs)\n\n    if pretrained:\n        import torchvision\n        pretrained_model = torchvision.models.resnet50(pretrained=True)\n        model = inflat_weights(pretrained_model,model)\n\n    return model\n\n\ndef nonlocal_resnet101(pretrained=False, **kwargs):\n    \"\"\"Constructs a ResNet-101 model.\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    \"\"\"\n    model = I3DNonLocalResNet(Bottleneck, [3, 4, 23, 3], [2, 7], **kwargs)\n\n    if pretrained:\n        import torchvision\n        pretrained_model = torchvision.models.resnet101(pretrained=True)\n        model = inflat_weights(pretrained_model, model)\n\n    return model\n\n\ndef nonlocal_resnet152(pretrained=False, **kwargs):\n    \"\"\"Constructs a ResNet-152 model.\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    \"\"\"\n    model = I3DNonLocalResNet(Bottleneck, [3, 8, 36, 3], [4, 12], **kwargs)\n\n    if pretrained:\n        import torchvision\n        pretrained_model = torchvision.models.resnet152(pretrained=True)\n        model = inflat_weights(pretrained_model,model)\n\n    return model\n\n\ndef inflat_weights(model_2d, model_3d):\n    pretrained_dict_2d = model_2d.state_dict()\n    model_dict_3d = model_3d.state_dict()\n    for key,weight_2d in pretrained_dict_2d.items():\n        if key in model_dict_3d:\n            if 'conv' in key:\n                time_kernel_size = model_dict_3d[key].shape[2]\n                if 'weight' in key:\n                    weight_3d = weight_2d.unsqueeze(2).repeat(1,1,time_kernel_size,1,1)\n                    weight_3d = weight_3d / time_kernel_size\n                    model_dict_3d[key] = weight_3d\n                elif 'bias' in key:\n                    model_dict_3d[key] = weight_2d\n            elif 'bn' in key:\n                    model_dict_3d[key] = weight_2d\n            elif 'downsample' in key:\n                if '0.weight' in key:\n                    time_kernel_size = model_dict_3d[key].shape[2]\n                    weight_3d = weight_2d.unsqueeze(2).repeat(1, 1, time_kernel_size, 1, 1)\n                    weight_3d = weight_3d / time_kernel_size\n                    model_dict_3d[key] = weight_3d\n                else:\n                    model_dict_3d[key] = weight_2d\n\n    model_3d.load_state_dict(model_dict_3d)\n    return model_3d\n\n\nif __name__ == '__main__':\n    import torchvision\n    import numpy as np\n    import torch\n    from torch.autograd import Variable\n\n    resnet_nonlocal = nonlocal_resnet101(pretrained=True)\n    print(resnet_nonlocal)\n    data = np.ones((1, 3, 32, 224, 224), dtype=np.float32)\n    tensor = torch.from_numpy(data)\n    inputs = Variable(tensor)\n    out = resnet_nonlocal(inputs)\n    print(out)","sub_path":"pytorch/video-cls/i3d-pytorch/models/i3d_nonlocal_resnet.py","file_name":"i3d_nonlocal_resnet.py","file_ext":"py","file_size_in_byte":12246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"444843664","text":"# Set keras backend to tensorflow\nimport os\nos.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n\n# Utility modules\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\n# Helper modules for data spliting and accuracy\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\n\n# Neural network layers\nfrom keras.models import Sequential\nfrom keras.layers import Dense, LSTM\n\n# Data module\nfrom main import Data_Prep\n\ndata_url = \"xmr.csv\"\n\n# Prepare and sanitize data\ndata = Data_Prep.parse_data_from_csv(data_url)\ndata = Data_Prep.sanitize_data(data)\nprint(data)\n\n# Plot Data\nplot: bool = False\nif plot:\n    plt.plot(data[\"price(USD)\"])\n    plt.ylabel(\"Price\")\n    plt.xlabel(\"Days\")\n    plt.show()\n\n# Split data into training and test groups\nxtrain, xtest, ytrain, ytest = train_test_split(data.drop(\"price(USD)\", 1), data[\"price(USD)\"], test_size=0.3)\n\n# Scale Data\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nst_scale = StandardScaler()\nst_scale.fit(xtrain)\n\n# Rescaled Data\nxs_train = st_scale.transform(xtrain)\nxs_test = st_scale.transform(xtest)\n\n# Reshaped Data\nxs_r_train = xs_train.reshape(xs_train.shape[0], xs_train.shape[1])\nxs_r_test = xs_test.reshape(xs_test.shape[0], xs_test.shape[1])\n\n# Get size of scaled x training set\nsize_train = xs_train.shape\n\n# Create a neural network\nnn = Sequential()\nnn.add(Dense(500, input_dim=size_train[1] , activation='relu', name='hl1'))\nnn.add(Dense(1000, activation=\"relu\", name=\"hl2\"))\nnn.add(Dense(500, activation=\"relu\", name=\"hl3\"))\nnn.add(Dense(200, activation=\"relu\", name=\"hl4\"))\nnn.add(Dense(100, activation=\"relu\", name=\"hl5\"))\nnn.add(Dense(1, activation='relu', name='output')) ##\n\n# Compile and fit\nnn.compile(optimizer='adam', loss='mean_squared_error', metrics=['acc', 'mse'])\n\n# Model Summary\nnn.summary()\n\n# Fit\nbatch_size = 100\nepochs = 200\nhst = nn.fit(xs_train, ytrain, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=1)\n\n# Predictions\nyh_train = nn.predict(xs_r_train)\nyh_test = nn.predict(xs_r_test)\n\n# Print shapes of y values\nprint(yh_test.shape)\nprint(pd.Series.as_matrix(ytest).reshape(ytest.shape[0], 1).shape)\n\n# MSE of Test case using predictions\nprint('MSE of Test Case:', mean_squared_error(pd.Series.as_matrix(ytest).reshape(ytest.shape[0], 1), yh_test), '\\n\\n')\n\nprint(\"Done!\");\n","sub_path":"main/Neural_Network_Dense.py","file_name":"Neural_Network_Dense.py","file_ext":"py","file_size_in_byte":2350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"129267610","text":"# -*- encoding=UTF-8 -*-\n\nfrom nowstagram import app, db\nfrom flask_script import Manager\nfrom sqlalchemy import or_,and_\nfrom nowstagram.models import User, Image, Comment\nimport random,unittest\nimport requests\nimport json\nimport urllib\nmanager = Manager(app)\n\nimgs_url=[]\ndef get_image_url():\n    global imgs_url\n    imgs = requests.get('http://pic.sogou.com/pics/channel/getAllRecomPicByTag.jsp?category=壁纸'+'&tag=%E5%85%A8%E9%83%A8&start=0&len=2000')\n    jd = json.loads(imgs.text)\n    jd=jd['all_items']\n    imgs_url=[]\n    for j in jd:\n        imgs_url.append(j['bthumbUrl'])\n    return None\n\n@manager.command\ndef init_database():\n    db.drop_all()\n    db.create_all()\n    get_image_url()\n    for i in range(0, 100):\n        db.session.add(User('User' + str(i+1), 'a'+str(i+1)))\n        for j in range(0, 10):\n            db.session.add(Image(imgs_url[random.randint(0,j*200)], i+1))\n            for k in range(0, 3):\n                db.session.add(Comment('This is a comment' + str(k), 1+10*i+j, i+1))\n\n    db.session.commit()\n\n@manager.command\ndef run_test():\n    db.drop_all()\n    db.create_all()\n    tests = unittest.TestLoader().discover('./')\n    unittest.TextTestRunner().run(tests)\n\n   # for i in range(50, 100, 2):\n     #   user = User.query.get(i)\n    #    user.username = '[New1]' + user.username\n\n   # User.query.filter_by(id=51).update({'username':'[New2]'})\n   # db.session.commit()\n\n   # for i in range(50, 100, 2):\n    #    comment = Comment.query.get(i+1)\n   #     db.session.delete(comment)\n  #  db.session.commit()\n\n   # print 1, User.query.all()\n   # print 2, User.query.get(3)\n   # print 3, User.query.filter_by(id=5).first()\n  #  print 4, User.query.order_by(User.id.desc()).offset(1).limit(2).all()\n   # print 5, User.query.filter(User.username.endswith('0')).limit(3).all()\n    #print 6, User.query.filter(or_(User.id == 88, User.id == 99)).all()\n   # print 7, User.query.filter(and_(User.id > 88, User.id < 93)).all()\n   # print 8, User.query.filter(and_(User.id > 88, User.id < 93)).first_or_404()\n   # print 9, User.query.order_by(User.id.desc()).paginate(page=1, per_page=10).items\n   # user = User.query.get(1)\n   # print 10, user.images\n\n\n\n   # image = Image.query.get(1)\n   # print 11, image, image.user\n\nif __name__ == '__main__':\n    manager.run()","sub_path":"manage.py","file_name":"manage.py","file_ext":"py","file_size_in_byte":2290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"51436486","text":"\"\"\"Calculation of descriptive statistics\"\"\"\nfrom spacy.tokens import Doc\nfrom spacy.language import Language\n\nimport numpy as np\n\n\n@Language.factory(\"descriptive_stats\")\ndef create_descriptive_stats_component(nlp: Language, name: str):\n    return DescriptiveStatistics(nlp)\n\n\nclass DescriptiveStatistics:\n    def __init__(self, nlp: Language):\n        \"\"\"Initialise components\"\"\"\n        if not Doc.has_extension(\"token_length\"):\n            Doc.set_extension(\"token_length\", getter=self.token_length)\n\n        if not Doc.has_extension(\"sentence_length\"):\n            Doc.set_extension(\"sentence_length\", getter=self.sentence_length)\n\n        if not Doc.has_extension(\"syllables\"):\n            Doc.set_extension(\"syllables\", getter=self.syllables)\n\n        if not Doc.has_extension(\"counts\"):\n            Doc.set_extension(\"counts\", getter=self.counts)\n\n    def __call__(self, doc):\n        \"\"\"Run the pipeline component\"\"\"\n        return doc\n\n    def token_length(self, doc):\n        \"\"\"Return dict with measures of token length\"\"\"\n        token_lengths = [len(token) for token in doc._._filtered_tokens]\n        return {\n            \"token_length_mean\": np.mean(token_lengths),\n            \"token_length_median\": np.median(token_lengths),\n            \"token_length_std\": np.std(token_lengths),\n        }\n\n    def sentence_length(self, doc):\n        \"\"\"Return dict with measures of sentence length\"\"\"\n        # get length of filtered tokens per sentence\n        tokenized_sentences = [\n            [\n                token.text\n                for token in sent\n                if not token.is_punct and \"'\" not in token.text\n            ]\n            for sent in doc.sents\n        ]\n        len_sentences = [len(sentence) for sentence in tokenized_sentences]\n        return {\n            \"sentence_length_mean\": np.mean(len_sentences),\n            \"sentence_length_median\": np.median(len_sentences),\n            \"sentence_length_std\": np.std(len_sentences),\n        }\n\n    def syllables(self, doc):\n        \"\"\"Return dict with measures of syllables per token\"\"\"\n        return {\n            \"syllables_per_token_mean\": np.mean(doc._._n_syllables),\n            \"syllables_per_token_median\": np.median(doc._._n_syllables),\n            \"syllables_per_token_std\": np.std(doc._._n_syllables),\n        }\n\n    def counts(self, doc, ignore_whitespace=True):\n        n_tokens = doc._._n_tokens\n        n_types = len(set([tok.lower_ for tok in doc._._filtered_tokens]))\n        if ignore_whitespace:\n            n_chars = len(doc.text.replace(\" \", \"\"))\n        else:\n            n_chars = len(doc.text)\n        return {\n            \"n_tokens\": n_tokens,\n            \"n_unique_tokens\": n_types,\n            \"percent_unique_tokens\": n_types / n_tokens,\n            \"n_sentences\": doc._._n_sentences,\n            \"n_characters\": n_chars,\n        }\n","sub_path":"spacy-textdescriptives/components/descriptive_stats.py","file_name":"descriptive_stats.py","file_ext":"py","file_size_in_byte":2836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"621600955","text":"\"\"\"\nLet's web scrape for some cyber security related data that might be useful\nPotential sites: https://nvd.nist.gov/\n               : cvedetails\n\"\"\"\nimport re\nfrom requests import get\nfrom bs4 import BeautifulSoup\nfrom devices.models import PostgresVulnerability, NistVulnerability\nfrom lib import app_scan\n\n# NVD_NIST_URL = \"https://nvd.nist.gov/\"\n# nvd_nist_response = get(nvd_nist_url)\n# nist_soup = BeautifulSoup(nvd_nist_response.text, 'html.parser')\n# nist_cyber_data = nist_soup.find_all('div',{\"id\": \"latestVulnsArea\"})\n# print(cve_top20_ul[0].li.p.text)\n\nclass CyberScrape:\n    NVD_NIST_URL = \"https://nvd.nist.gov/\"\n    IT_DEVICE_KEYWORDS = [\"linux\",\"Linux\", \"windows\", \"Windows\",\"macintosh\", \"server\", \"cisco\", \"fortinet\", \"palo alto\", \"IBM\", \"Juniper\"]\n    ICS_DEVICE_KEYWORDS = [\"PLC\", \"SCADA\", \"HMI\", \"Rockwell\", \"Schneider\", \"Siemens\", \"Yokogawa\", \"General Electric\",\n                          \"Omron\", \"Mitsubishi\", \"ABB\", \"Honeywell\"]\n    IT_APPLICATION_KEYWORDS = [\"VMware\", \"hypervisor\", \"citrix\", \"frotigate\", \"VPN\", \"SIEM\", \"Guardium\",\n                               \"QRadar\", \"antivirus\", \"RSA\"]\n    OT_APPLICATION_KEYWORDS = [\"RSLogix\", \"Wonderware\"]\n    VULN_KEYWORDS = [\"DDOS\", \"DDoS\", \"DOS\", \"DoS\", \"Privilege Escalation\", \"Remote Execution\", \"XSS\",\"(XSS)\", \"Injection\", \"Memory\"]\n    ALL_KEYWORDS = IT_DEVICE_KEYWORDS+ICS_DEVICE_KEYWORDS+IT_APPLICATION_KEYWORDS+OT_APPLICATION_KEYWORDS+VULN_KEYWORDS\n    \n    def __init__(self, url, tag, id_class, id_class_name):\n        \"\"\"\n        Initialises the CyberScrape instance to start the web scrapping.\n        Args:\n            url (string): The URL to be scrapped\n            tag (string): The first tag we are interested to locate, where our data resides\n            id_class (string): \"id\" or \"class\"\n            id_class_name (string): Name of the \"id\" or \"class\"\n            \n        eg: Suppose the data we want from the page lives in a html `div` tag with the `id` of `latestVulnsArea`, \n        ie: <div id=\"latestVulnsArea\">, then our tag=\"div\", id_class=\"id\" and id_class_name=\"latestVulnsArea\"\n        \"\"\"\n        self.url = url\n        self.tag = tag\n        self.id_class = id_class\n        self.id_class_name = id_class_name\n    \n    def scrape_url(self):\n        \"\"\"\n        Scrapes the URL\n        Returns:\n            url_cyber_data (list[string]): The uncleaned scraped data from the URL\n        \"\"\"\n        url_response = get(self.url)\n        url_soup = BeautifulSoup(url_response.text, 'html.parser')\n        url_cyber_data = url_soup.find_all(self.tag,{self.id_class: self.id_class_name})\n        return url_cyber_data\n    \n    def nvd_nist_vulns(self, url_cyber_data):\n        \"\"\"\n        Clean the URL data by extracting the important parts. The important information lives in the `p` tags which is \n        in the `ul` tag with the`id` of `latestVulns`. \n        \n        Args:\n            url_cyber_data (list[string]): The uncleaned scrapped web page data\n        Returns:\n            latest_vulns (list[string]): The cleaned data with CVE #, description, date discovered and CVSS 3.1/2\n            scores\n        \"\"\"\n        latest_vulns = []\n        for i in url_cyber_data:\n            cve_latest20_ul = i.find_all(\"ul\", {\"id\": \"latestVulns\"})\n        for i in cve_latest20_ul:\n            cve_latest20_p = i.find_all(\"p\")\n        for i in range(len(cve_latest20_p)):\n            latest_vulns.append(cve_latest20_p[i].text)\n        return latest_vulns\n\n    def nvd_nist_vulns_reformatted(self, nist_cve_data, cve_number, cve_description, vuln_severity):\n        \"\"\"\n        Reformats the nist cve data to a differently formatted dictionary.\n        Args:\n            nist_cve_data (dict): Contains the CVE number as the key. The values include:  [Vulnerable keyword detected,\n                                                                                            The vulnerable keyword,\n                                                                                            Number of vulnerable keywords,\n                                                                                            Version number]\n            eg: {'CVE-2020-11735': [False, [], 0, ['4.4.0']], \n                 'CVE-2020-15324': [False, [], 0, ['3.1.0', '3.1.1']], \n                 'CVE-2020-15415': [False, [], 0, ['1.5.1,']], \n                 'CVE-2020-13657': [False, [], 0, ['20.4']], \n                 'CVE-2020-4024': [True, ['(XSS)'], 1, ['8.5.5,', '8.6.0', '8.8.2,', '8.9.0', '8.9.1']]}\n            \n            cve_number (list): A list of 20 recent cves\n            cve_description(list): A list of 20 recent cve description\n            vuln_severity (list[list(string)]): A list containing the CVSS 3.1/2 scores. Output of the vulns_level method\n        Returns:\n            vulns (dict): vulnerability information as shown in the example below.\n            eg: {'nist_cve': ['CVE-2020-11735'], 'nist_cvss': ['5.3'],'nist_descript': ['The private-key operations in ecc.c in wolfSSL before 4.4.0 do not use a constant-time modular inverse when mapping to affine coordinates, aka a \"projective coordinates leak.\"',\n                'nist_affect_ver':[['8.5.5,', '8.6.0', '8.8.2,', '8.9.0', '8.9.1']],'nist_keyword_warning': [True]}\n\n        \"\"\"\n        nist_cve = cve_number\n        nist_cvss = []\n        nist_descript = cve_description\n        nist_affect_ver = []\n        nist_warning_keywords_detected = []\n        vulns = {}\n\n        for key, value in nist_cve_data.items():\n            nist_affect_ver.append(value[3]) #value[3] gives the vulnerable version\n            nist_warning_keywords_detected.append(value[0]) #Look at eg above in method\n\n        for i in range(len(vuln_severity)):\n            nist_cvss.append(vuln_severity[i][1]) #gets the V3.1 CVSS score\n\n        vulns[\"nist_cve\"] = nist_cve\n        vulns[\"nist_cvss\"] = nist_cvss\n        vulns[\"nist_descript\"] = nist_descript\n        vulns[\"nist_affect_ver\"] = nist_affect_ver\n        vulns[\"nist_keyword_warning\"] = nist_warning_keywords_detected\n        # print(f'vulns: {vulns}')\n        return vulns\n    \n    def vulns_level(self, cve_latest20_vulns):\n        \"\"\"\n        Gets the latest 20 CVE vulnerability severities\n        Args:\n            cve_latest20_vulns (list(string)): Most recent 20 vulnerabilities with CVE #, description, date discovered\n            and CVSS 3.1/2 scores\n        Returns:\n            vuln_severity (list[list(string)]): A list containing the CVSS 3.1/2 scores\n            eg:\n                [['V3.1:', '8.1', 'HIGH', 'V2:', '5.8', 'MEDIUM'],\n                 ['V3.1:', '4.4', 'MEDIUM', 'V2:', '2.1', 'LOW'],\n                 ['V3.1:', '6.1', 'MEDIUM', 'V2:', '4.3', 'MEDIUM']]      \n        \"\"\"\n        vuln_severity = []\n        for i in range(1,len(cve_latest20_vulns),2):\n            vuln_severity.append(cve_latest20_vulns[i].split())\n#             print(cve_top20_vulns[i].split())\n        return vuln_severity\n\n    def detect_vuln_keywords(self, keywords, cve_latest20_vulns,detect_only_vulnerables=True):\n        \"\"\"\n        Detect if the new CVEs contain any keywords that are worth noting.\n        Args:\n            keywords (list): A list of keywords that generates alert interests\n            cve_latest20_vulns (list[string]): The latest 20 CVE vulnerability description along with the CVSS scores     \n        Returns:\n            vulns_cves(dict): A dictionary containing the CVE # as the key and \n            potentially_vulnerable status/detected keywords/number of detected keywords/\n            application version number as the value.\n            \n            eg: {'CVE-2020-6640': [True, ['(XSS)'], 1, []],\n                 'CVE-2020-4191': [True, ['IBM', 'Guardium', 'IBM'], 3, ['11.1', '174852.']],\n                 'CVE-2020-4183': [True, ['IBM', 'Guardium'], 2, ['11.1']],\n                 'CVE-2020-4193': [True, ['IBM', 'Guardium', 'IBM'], 3, ['11.1', '174857.']],\n                 'CVE-2020-4509': [True, ['IBM', 'QRadar', 'SIEM', 'Injection', 'IBM'], 5, ['7.3', '7.4', '18236...']]}\n            \n            cve_number (list): A cve number list of all nvd nist 20 vulnerabilities\n            eg: ['CVE-2020-11735', 'CVE-2020-15324', 'CVE-2020-15415', 'CVE-2020-13657', 'CVE-2020-4024', 'CVE-2020-15311', 'CVE-2020-7664', 'CVE-2020-7668', 'CVE-2020-7667', 'CVE-2020-15395', 'CVE-2020-15393', 'CVE-2020-15006', 'CVE-2020-11074', 'CVE-2020-15314', 'CVE-2020-15319', 'CVE-2020-4452', 'CVE-2020-15320', 'CVE-2020-9659', 'CVE-2019-4704', 'CVE-2020-5588']\n\n            cve_description (list): A cve description list of all nvd nist 20 vulnerabilities in the same order as the cve number\n        \"\"\"\n        START = 2 #This is where the CVE number ends\n        STOP = -7 #This is where the CVE publishing date information starts\n        potentially_vulnerable = False\n        keyword_hits = 0\n        vuln_cves = {}\n        vulns = {}\n        temp = []\n        detected_keywords = []\n        version_number = []\n        cve_description = []\n        cve_number = []\n        for i in range(0,len(cve_latest20_vulns),2):\n            temp.append(cve_latest20_vulns[i])\n            vulns[cve_latest20_vulns[i].split()[0]] = cve_latest20_vulns[i].split()[START:STOP]\n            cve_number.append(cve_latest20_vulns[i].split()[0])\n            cve_description.append(cve_latest20_vulns[i].splitlines()[2].strip())\n        \n        for key, value in vulns.items():\n            for i in value:\n                if \".\" in i and re.search('[a-zA-Z]', i) == None : #Checks if the description contains a version #. Contains a \".\" and contains no alphabets\n                    version_number.append(i)\n                if i in keywords:\n                    keyword_hits += 1\n                    detected_keywords.append(i)\n#                     print(i, keyword_hits,detected_keywords)\n                    potentially_vulnerable = True\n            if potentially_vulnerable == True:\n                vuln_cves[key] = [True,detected_keywords, keyword_hits, version_number]\n            if detect_only_vulnerables != True and potentially_vulnerable != True:\n                vuln_cves[key] = [False,detected_keywords, keyword_hits, version_number]\n            potentially_vulnerable = False\n            keyword_hits = 0\n            detected_keywords = []\n            version_number = []\n            \n        return vuln_cves, cve_number, cve_description\n\nclass PostgresCyberScrape(CyberScrape):\n    POSTGRES_SEC_URL = \"https://www.postgresql.org/support/security/\"\n\n    # def __init__(self):\n    #     super().__init__(url=None,tag=None,id_class=None,id_class_name=None)\n\n    def postgres_vulns(self, url_cyber_data):\n        \"\"\"\n        Scrapes the postgres security website\n        Args:\n            url_cyber_data (string): Web contentof the POSTGRES_SEC_URL\n        Returns:\n            vulns (dict): Vulnerabilities containing, cve, affected version, fixed version, cvss and vulnerability descriptions.\n        \"\"\"\n        postgres_vulns_td = []\n        postgres_cve = []\n        postgres_affect_ver = []\n        postgres_fixed_ver = []\n        postgres_cvss = []\n        postgres_descript = []\n        vulns = {}\n        postgres_vulns_tbody = url_cyber_data[0].find_all(\"tbody\")\n        for i in postgres_vulns_tbody:\n            postgres_vulns_tr = i.find_all(\"tr\")\n\n        for i in postgres_vulns_tr:\n            postgres_vulns_td.append(i.find_all(\"td\"))\n        \n        for i in postgres_vulns_td:\n            postgres_cve.append(i[0].find_all(\"nobr\")[0].text)\n            postgres_affect_ver.append(i[1].text)\n            postgres_fixed_ver.append(i[2].text)\n            postgres_cvss.append(i[3].find_all(\"a\")[0].text)\n            postgres_descript.append(i[4].text)\n\n        # print(postgres_cve)\n        # print(postgres_affect_ver)\n        # print(postgres_fixed_ver)\n        # print(postgres_cvss)\n        # print(postgres_descript)\n        vulns[\"postgres_cve\"] = postgres_cve\n        vulns[\"postgres_affect_ver\"] = postgres_affect_ver\n        vulns[\"postgres_fixed_ver\"] = postgres_fixed_ver\n        vulns[\"postgres_cvss\"] = postgres_cvss\n        vulns[\"postgres_descript\"] = postgres_descript\n        return vulns\n\nclass MongoScrape(CyberScrape):\n\n    MONGO_SEC_URL = \"https://www.mongodb.com/alerts\"\n\n    def mongo_vulns(self, url_cyber_data):\n        # print(url_cyber_data)\n        mongo_vulns_td = []\n        mongo_cve = []\n        mongo_affect_ver = []\n        mongo_fixed_ver = []\n        mongo_component = []\n        mongo_descript = []\n        vulns = {}\n        mongo_vulns_tbody = url_cyber_data[0].find_all(\"tbody\")\n        # print(mongo_vulns_tbody)\n        for i in mongo_vulns_tbody:\n            mongo_vulns_tr = i.find_all(\"tr\")\n        for i in mongo_vulns_tr:\n            mongo_vulns_td.append(i.find_all(\"td\"))\n        for i in mongo_vulns_td:\n            print(mongo_vulns_td)\n            mongo_cve.append(i[5].find_all(\"p\").text)\n            mongo_affect_ver.append(i[3].find_all(\"p\").text)\n            mongo_fixed_ver.append(i[4].find_all(\"p\").text)\n            mongo_component.append(i[1].find_all(\"p\").text)\n            mongo_descript.append(i[2].find_all(\"p\").text)\n        # print(postgres_cve)\n        # print(postgres_affect_ver)\n        # print(postgres_fixed_ver)\n        # print(postgres_cvss)\n        # print(postgres_descript)\n        vulns[\"mongo_cve\"] = mongo_cve\n        vulns[\"mongo_affect_ver\"] = mongo_affect_ver\n        vulns[\"mongo_fixed_ver\"] = mongo_fixed_ver\n        vulns[\"mongo_descript\"] = mongo_descript\n        vulns[\"mongo_component\"] = mongo_component\n        print(vulns)\n        return vulns\n\nnvd_nist_cve = CyberScrape(CyberScrape.NVD_NIST_URL,\"div\",\"id\",\"latestVulnsArea\")\nnvd_nist_scraped_cyber_data = nvd_nist_cve.scrape_url()\nlatest_20_vulns = nvd_nist_cve.nvd_nist_vulns(nvd_nist_scraped_cyber_data)\n# print(latest_20_vulns[1])\nlatest_20_severity = nvd_nist_cve.vulns_level(latest_20_vulns)\nexposed_to_new_vuln = nvd_nist_cve.detect_vuln_keywords(CyberScrape.ALL_KEYWORDS, latest_20_vulns, False)\nnvd_nist_vulns = nvd_nist_cve.nvd_nist_vulns_reformatted(exposed_to_new_vuln[0],exposed_to_new_vuln[1],exposed_to_new_vuln[2],latest_20_severity)\n# print(nvd_nist_vulns)\n\n# postgres_sec_info = PostgresCyberScrape(PostgresCyberScrape.POSTGRES_SEC_URL, \"div\", \"id\", \"pgContentWrap\")\npostgres_sec_info = PostgresCyberScrape(PostgresCyberScrape.POSTGRES_SEC_URL, \"table\", \"class\", \"table table-striped\")\npostgres_sec_info_scraped = postgres_sec_info.scrape_url()\npostgres_cve_data = postgres_sec_info.postgres_vulns(postgres_sec_info_scraped)\n\n# mongo_sec_info = MongoScrape(MongoScrape.MONGO_SEC_URL, \"div\", \"id\", \"security-related\")\n# mongo_sec_info_scraped = mongo_sec_info.scrape_url()\n# mongo_sec_info.mongo_vulns(mongo_sec_info_scraped)\napps = app_scan.ApplicationScan()\nlocal_postgres_ver = apps.postgres_scan()\n\nfor i in range(len(postgres_cve_data['postgres_cve'])):\n    postgres_vulnerability = PostgresVulnerability()\n    postgres_vulnerability.postgres_cve = postgres_cve_data['postgres_cve'][i]\n    postgres_vulnerability.postgres_affected_ver = postgres_cve_data['postgres_affect_ver'][i]\n    postgres_vulnerability.postgres_fixed_ver = postgres_cve_data['postgres_fixed_ver'][i]\n    postgres_vulnerability.postgres_cvss = postgres_cve_data['postgres_cvss'][i]\n    postgres_vulnerability.postgres_descript = postgres_cve_data['postgres_descript'][i]\n    if local_postgres_ver[0] in postgres_cve_data['postgres_affect_ver'][i].split() or local_postgres_ver[0] in postgres_cve_data['postgres_fixed_ver'][i].split():\n        postgres_vulnerability.postgres_is_vulnerable = True\n    else:\n        postgres_vulnerability.postgres_is_vulnerable = False\n\n    postgres_vulnerability, created = PostgresVulnerability.objects.update_or_create(postgres_cve=postgres_vulnerability.postgres_cve,\n    postgres_affected_ver=postgres_vulnerability.postgres_affected_ver,\n    postgres_fixed_ver=postgres_vulnerability.postgres_fixed_ver,\n    postgres_cvss=postgres_vulnerability.postgres_cvss,\n    postgres_descript=postgres_vulnerability.postgres_descript,\n    postgres_is_vulnerable=postgres_vulnerability.postgres_is_vulnerable)\n\nfor i in range(len(nvd_nist_vulns['nist_cve'])):\n    nist_vulnerability = NistVulnerability()\n    nist_vulnerability.nist_cve = nvd_nist_vulns['nist_cve'][i]\n    nist_vulnerability.nist_cvss = nvd_nist_vulns['nist_cvss'][i]\n    nist_vulnerability.nist_descript = nvd_nist_vulns['nist_descript'][i]\n    nist_vulnerability.nist_affect_ver = nvd_nist_vulns['nist_affect_ver'][i]\n    nist_vulnerability.nist_keyword_warning = nvd_nist_vulns['nist_keyword_warning'][i]\n\n    nist_vulnerability, created = NistVulnerability.objects.update_or_create(nist_cve=nist_vulnerability.nist_cve,\n                                                                             nist_cvss=nist_vulnerability.nist_cvss,\n                                                                             nist_descript=nist_vulnerability.nist_descript,\n                                                                             nist_affect_ver=nist_vulnerability.nist_affect_ver,\n                                                                             nist_keyword_warning=nist_vulnerability.nist_keyword_warning)\n# print(postgres_vulnerability)","sub_path":"MicrosoftMalwarePredictionData/django_vue/django_vue/lib/cyber_scrape.py","file_name":"cyber_scrape.py","file_ext":"py","file_size_in_byte":17238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"101871534","text":"#create diagnose dict\nfd = open('D_ICD_DIAGNOSES.csv', 'r')\ndiagnose_dict = {}\nfor line in fd:\n    fields = line.split(',')\n    icd9 = fields[1]\n    try:\n        icd9 = str(int(icd9[1 : len(icd9) - 1]))\n        short_title = fields[2]\n        short_title = short_title[1 : len(short_title) - 1]\n        diagnose_dict[icd9] = short_title\n    except: pass\nfd.close()\n\ndiagnose_dict['9999'] = 'DEATH'\n\n#convert data\nf1 = open('spade_out_diagnose_from_heart_to_death_timestamp_revised_with_dob.txt', 'r')\nf2 = open('spade_diagnose_from_heart_to_death_final_revised_with_dob.out', 'w')\ntimestamp = ''\nfor line in f1:\n    if not ('989999' in line): continue   #must contain death!\n    #make sure that heart-related problems occur in the first set\n    first_neg_one = line.index('-1')\n    first_part = line[0:first_neg_one]\n    try:\n        age = int(first_part.strip())\n        if (age > 2):  # (11.2) now we have only 0, 1, 2 as valid age bin\n            continue\n    except:\n        continue\n    second_neg_one = line[first_neg_one + 2 : ].index('-1')\n    second_part = line[first_neg_one + 2 : second_neg_one]\n    if not ('428' in second_part): continue\n\n    #maybe add another minimum support filter? (to make dataset smaller...)\n\n\n\n    fields = line.split(' ')   #line: a single line with multiple transactions! starting with age bin...\n    length = len(fields)\n    content = ''\n    if (length < 6):\n        continue\n    for i in range(0, length - 1):  #there is only a \"-2\" at the end to discard\n        #for age bin at the very front...\n        if (i == 0):\n            content += '<' + fields[i] + '> '\n            continue\n        elif (i == 1):\n            continue\n        try:\n            label = int(fields[i])  #timestamp98label\n            if (label == -1):\n                content += '[' + timestamp + '] '\n                if (i < length - 3):\n                    content += '-> '\n            else:\n                #extract timestamp from \"9timestamp9\" in the beginning of every item in this transaction\n                label_s = str(label)\n                delim_idx = label_s.index('98')\n                timestamp = label_s[0:delim_idx]\n                timestamp = str(int(timestamp) - 1) #there is a \"1 offset in pre-porcessing\"\n                #print timestamp\n                diagnose_name = label_s[delim_idx + 2 : len(label_s)]\n                #print diagnose_name\n                content += ('\\\"' + diagnose_dict[str(diagnose_name)] + '\\\" ')\n        except: pass\n    content += (fields[length - 2] + ' ' + fields[length - 1])\n    if ('->' in content):\n        f2.write(content)\nf1.close()\nf2.close()\n","sub_path":"Sequential-Pattern-Mining/post_processing/spade_diagnose_gen_from_heart_to_death_timestamp_dob.py","file_name":"spade_diagnose_gen_from_heart_to_death_timestamp_dob.py","file_ext":"py","file_size_in_byte":2617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"505352898","text":"\"\"\"\ndemo07_svm_rbf.py  支持向量机  径向基核函数\n\"\"\"\nimport numpy as np\nimport sklearn.model_selection as ms\nimport sklearn.svm as svm\nimport sklearn.metrics as sm\nimport matplotlib.pyplot as mp\n\nx, y = [],[]\ndata=np.loadtxt('../ml_data/multiple2.txt', \n\t delimiter=',')\nx = data[:, :-1]\ny = data[:, -1]\n# 拆分训练集与测试集\ntrain_x, test_x, train_y, test_y = \\\n\tms.train_test_split(x, y, test_size=0.25,\n\trandom_state=5)\n# 基于多项式核函数的svm绘制分类边界\nmodel=svm.SVC(kernel='rbf', C=600, gamma=0.01)\nmodel.fit(train_x, train_y)\n\n# 绘制分类边界线\nl, r = x[:, 0].min()-1, x[:, 0].max()+1\nb, t = x[:, 1].min()-1, x[:, 1].max()+1\nn = 500\ngrid_x, grid_y = np.meshgrid(\n\tnp.linspace(l, r, n),\n\tnp.linspace(b, t, n))\n# 把grid_x与grid_y抻平了组成模型的输入,预测输出\nmesh_x = np.column_stack(\n\t(grid_x.ravel(), grid_y.ravel()))\npred_mesh_y = model.predict(mesh_x)\ngrid_z = pred_mesh_y.reshape(grid_x.shape)\n\n# 看一看测试集的分类报告\npred_test_y = model.predict(test_x)\ncr = sm.classification_report(\n\t  \ttest_y, pred_test_y)\nprint(cr)\n\n# 绘制这些点\nmp.figure('SVM', facecolor='lightgray')\nmp.title('SVM', fontsize=16)\nmp.xlabel('X', fontsize=14)\nmp.ylabel('Y', fontsize=14)\nmp.tick_params(labelsize=10)\nmp.pcolormesh(grid_x,grid_y,grid_z,cmap='gray')\n\nmp.scatter(x[:,0], x[:,1], s=60, \n\t\tc=y, label='points', cmap='jet')\n\nmp.legend()\nmp.show()\n\n\n\n\n\n\n\n\n","sub_path":"AI/day04/day04_all/demo07_svm_rbf.py","file_name":"demo07_svm_rbf.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}
+{"seq_id":"473444266","text":"# Trabalho da Mãe\r\n\r\n\r\nclass Orders:\r\n    def __init__(self, request, client, product, liters, date):\r\n        self.request = request\r\n        self.client = client\r\n        self.product = product\r\n        self.liters = liters\r\n        self.date = date\r\n\r\n    def printall(self):\r\n        return \"\\nClient: {}\\nRequest: {}\\nProduct: {} litros de {}\\nPara {}\\n\".format(self.client, self.request, self.liters, self.product, self.date)\r\n\r\nlist_requests = []\r\nword = \"\"\r\n\r\nN = 0\r\n\r\nwhile True:\r\n    n = str(N)\r\n    request_n = \"request_{}\".format(n)\r\n    print(\"1\", request_n)\r\n    request_n = Orders(input(\"\\nNúmero do pedido:\\n\"), input(\"Nome do cliente:\\n\"), input(\"Nome do produto:\\n\"), input(\"Litros do produto:\\n\"), input(\"Data:\\n\"))\r\n    list_requests.append(request_n)\r\n    word = input('\"s\" para sair')\r\n    if \"s\" in word:\r\n        break\r\n    N += 1\r\n\r\nfor request_n in list_requests:\r\n    print(request_n.printall())\r\n\r\nexit()\r\n\r\n","sub_path":"Programa.py","file_name":"Programa.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"34"}